High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

MM		89.16 7	89.23 9	88.97 4	90.79 105	73.65 10	92.66 29	91.17 159	86.57 1	87.39 63	94.97 27	71.70 71	97.68 1	92.19 1	95.63 32	95.57 2

UA-Net		85.08 87	84.96 88	85.45 91	92.07 82	68.07 148	89.78 92	90.86 171	82.48 2	84.60 99	93.20 90	69.35 106	95.22 91	71.39 245	90.88 121	93.07 149

MGCNet		87.69 24	87.55 30	88.12 13	89.45 142	71.76 55	91.47 58	89.54 217	82.14 3	86.65 72	94.28 48	68.28 129	97.46 6	90.81 6	95.31 38	95.15 9

CANet		86.45 48	86.10 63	87.51 42	90.09 118	70.94 79	89.70 95	92.59 86	81.78 4	81.32 168	91.43 151	70.34 88	97.23 17	84.26 78	93.36 75	94.37 65

NCCC		88.06 18	88.01 23	88.24 11	94.41 28	73.62 11	91.22 63	92.83 71	81.50 5	85.79 78	93.47 83	73.02 52	97.00 23	84.90 67	94.94 44	94.10 80

EPNet		83.72 115	82.92 131	86.14 74	84.22 342	69.48 104	91.05 65	85.27 347	81.30 6	76.83 267	91.65 139	66.09 160	95.56 71	76.00 191	93.85 69	93.38 126
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		88.93 12	89.13 13	88.33 8	94.77 13	73.82 8	90.51 71	93.00 57	80.90 7	88.06 49	94.06 61	76.43 24	96.84 27	88.48 36	95.99 20	94.34 67

3Dnovator+	77.84 4	85.48 75	84.47 96	88.51 7	91.08 96	73.49 17	93.18 17	93.78 23	80.79 8	76.66 272	93.37 86	60.40 253	96.75 32	77.20 172	93.73 71	95.29 7

TranMVSNet+NR-MVSNet		80.84 186	80.31 184	82.42 249	87.85 220	62.33 321	87.74 186	91.33 154	80.55 9	77.99 240	89.86 201	65.23 170	92.62 234	67.05 293	75.24 395	92.30 189

Casviewmamba		86.09 56	86.04 65	86.24 67	88.17 202	68.05 150	89.44 105	92.79 75	80.30 10	84.71 92	92.78 105	72.83 56	95.05 102	82.81 96	90.57 125	95.62 1

MSP-MVS		89.51 5	89.91 6	88.30 10	94.28 36	73.46 18	92.90 22	94.11 10	80.27 11	91.35 19	94.16 56	78.35 17	96.77 30	89.59 17	94.22 67	94.67 42
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
HPM-MVS++		89.02 10	89.15 12	88.63 5	95.01 10	76.03 1	92.38 33	92.85 70	80.26 12	87.78 54	94.27 49	75.89 27	96.81 29	87.45 49	96.44 9	93.05 152

fmvsm_s_conf0.5_n_9		87.39 33	87.95 24	85.70 83	89.48 141	67.88 157	88.59 149	89.05 247	80.19 13	90.70 24	95.40 19	74.56 34	93.92 156	91.54 2	92.07 95	95.31 6

UniMVSNet_NR-MVSNet		81.88 160	81.54 160	82.92 227	88.46 190	63.46 292	87.13 209	92.37 94	80.19 13	78.38 229	89.14 227	71.66 73	93.05 218	70.05 261	76.46 368	92.25 191

SteuartSystems-ACMMP		88.72 14	88.86 14	88.32 9	92.14 81	72.96 26	93.73 6	93.67 26	80.19 13	88.10 48	94.80 29	73.76 44	97.11 19	87.51 48	95.82 25	94.90 18
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		84.19 99	83.81 109	85.31 96	88.18 201	67.85 158	87.66 187	89.73 211	80.05 16	82.95 138	89.59 215	70.74 85	94.82 113	80.66 123	84.72 248	93.28 132

ETV-MVS		84.90 91	84.67 92	85.59 88	89.39 146	68.66 130	88.74 142	92.64 84	79.97 17	84.10 111	85.71 329	69.32 107	95.38 85	80.82 118	91.37 110	92.72 167

fmvsm_s_conf0.5_n_8		86.56 47	87.17 39	84.73 125	87.76 229	65.62 219	89.20 116	92.21 110	79.94 18	89.74 32	94.86 28	68.63 123	94.20 141	90.83 5	91.39 109	94.38 64

EI-MVSNet-UG-set		83.81 109	83.38 121	85.09 106	87.87 219	67.53 170	87.44 200	89.66 212	79.74 19	82.23 151	89.41 224	70.24 91	94.74 119	79.95 131	83.92 263	92.99 159

fmvsm_s_conf0.5_n_3		86.36 53	87.46 33	83.09 216	87.08 270	65.21 233	89.09 125	90.21 194	79.67 20	89.98 29	95.02 26	73.17 49	91.71 280	91.30 3	91.60 104	92.34 186

CS-MVS		86.69 44	86.95 43	85.90 80	90.76 106	67.57 168	92.83 23	93.30 42	79.67 20	84.57 100	92.27 112	71.47 74	95.02 104	84.24 80	93.46 74	95.13 11

casdiffmvs_mvg		85.99 60	86.09 64	85.70 83	87.65 237	67.22 184	88.69 145	93.04 52	79.64 22	85.33 82	92.54 108	73.30 46	94.50 129	83.49 86	91.14 114	95.37 3
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MTAPA		87.23 36	87.00 40	87.90 22	94.18 41	74.25 5	86.58 236	92.02 119	79.45 23	85.88 76	94.80 29	68.07 131	96.21 53	86.69 55	95.34 36	93.23 134

EC-MVSNet		86.01 59	86.38 53	84.91 116	89.31 151	66.27 200	92.32 36	93.63 27	79.37 24	84.17 110	91.88 128	69.04 118	95.43 80	83.93 84	93.77 70	93.01 156

NormalMVS		86.29 54	85.88 68	87.52 41	93.26 58	72.47 39	91.65 48	92.19 113	79.31 25	84.39 103	92.18 118	64.64 178	95.53 74	80.70 121	94.65 53	94.56 55

SymmetryMVS		85.38 80	84.81 90	87.07 51	91.47 90	72.47 39	91.65 48	88.06 283	79.31 25	84.39 103	92.18 118	64.64 178	95.53 74	80.70 121	90.91 120	93.21 137

XVS		87.18 37	86.91 45	88.00 17	94.42 26	73.33 20	92.78 24	92.99 60	79.14 27	83.67 121	94.17 55	67.45 137	96.60 40	83.06 90	94.50 58	94.07 82

X-MVStestdata		80.37 209	77.83 251	88.00 17	94.42 26	73.33 20	92.78 24	92.99 60	79.14 27	83.67 121	12.47 536	67.45 137	96.60 40	83.06 90	94.50 58	94.07 82

HQP_MVS		83.64 118	83.14 124	85.14 101	90.08 119	68.71 126	91.25 61	92.44 89	79.12 29	78.92 216	91.00 169	60.42 251	95.38 85	78.71 153	86.32 215	91.33 225

plane_prior2							91.25 61		79.12 29

IS-MVSNet		83.15 134	82.81 132	84.18 159	89.94 126	63.30 296	91.59 52	88.46 276	79.04 31	79.49 205	92.16 120	65.10 172	94.28 135	67.71 284	91.86 102	94.95 15

DU-MVS		81.12 181	80.52 179	82.90 228	87.80 223	63.46 292	87.02 215	91.87 129	79.01 32	78.38 229	89.07 229	65.02 173	93.05 218	70.05 261	76.46 368	92.20 194

NR-MVSNet		80.23 213	79.38 213	82.78 238	87.80 223	63.34 295	86.31 248	91.09 163	79.01 32	72.17 362	89.07 229	67.20 140	92.81 231	66.08 300	75.65 381	92.20 194

SPE-MVS-test		86.29 54	86.48 52	85.71 82	91.02 98	67.21 185	92.36 35	93.78 23	78.97 34	83.51 128	91.20 159	70.65 87	95.15 94	81.96 106	94.89 47	94.77 30

DELS-MVS		85.41 78	85.30 83	85.77 81	88.49 188	67.93 156	85.52 277	93.44 34	78.70 35	83.63 123	89.03 231	74.57 33	95.71 69	80.26 129	94.04 68	93.66 108
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
WR-MVS		79.49 228	79.22 220	80.27 306	88.79 177	58.35 380	85.06 287	88.61 274	78.56 36	77.65 247	88.34 255	63.81 188	90.66 335	64.98 310	77.22 356	91.80 208

plane_prior3						68.60 131			78.44 37	78.92 216

UniMVSNet (Re)		81.60 168	81.11 166	83.09 216	88.38 194	64.41 264	87.60 188	93.02 56	78.42 38	78.56 224	88.16 261	69.78 100	93.26 200	69.58 268	76.49 367	91.60 215

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 33	71.25 67	95.06 1	94.23 5	78.38 39	92.78 5	95.74 10	82.45 3	97.49 4	89.42 19	96.68 2	94.95 15

test_0728_THIRD									78.38 39	92.12 14	95.78 8	81.46 8	97.40 9	89.42 19	96.57 7	94.67 42

testing3-2		75.12 330	75.19 313	74.91 406	90.40 112	45.09 493	80.29 394	78.42 447	78.37 41	76.54 277	87.75 271	44.36 423	87.28 394	57.04 400	83.49 275	92.37 185

test_one_0601					95.07 8	71.46 61		94.14 9	78.27 42	92.05 16	95.74 10	80.83 12

hybridcas		85.11 85	85.18 85	84.90 117	87.47 249	65.68 217	88.53 153	92.38 93	77.91 43	84.27 107	92.48 109	72.19 63	93.88 161	80.37 124	90.97 117	95.15 9

SD-MVS		88.06 18	88.50 19	86.71 61	92.60 78	72.71 30	91.81 47	93.19 45	77.87 44	90.32 28	94.00 65	74.83 32	93.78 164	87.63 47	94.27 66	93.65 112
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
casdiffmvs		85.11 85	85.14 86	85.01 109	87.20 261	65.77 216	87.75 185	92.83 71	77.84 45	84.36 106	92.38 111	72.15 64	93.93 155	81.27 114	90.48 127	95.33 5
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
KinetiMVS		83.31 132	82.61 138	85.39 94	87.08 270	67.56 169	88.06 173	91.65 142	77.80 46	82.21 152	91.79 131	57.27 278	94.07 147	77.77 165	89.89 140	94.56 55

BP-MVS1		84.32 94	83.71 112	86.17 70	87.84 221	67.85 158	89.38 111	89.64 214	77.73 47	83.98 114	92.12 123	56.89 283	95.43 80	84.03 83	91.75 103	95.24 8

CP-MVSNet		78.22 263	78.34 238	77.84 369	87.83 222	54.54 439	87.94 178	91.17 159	77.65 48	73.48 343	88.49 251	62.24 214	88.43 379	62.19 343	74.07 404	90.55 256

plane_prior						68.71 126	90.38 79		77.62 49						86.16 220

baseline		84.93 89	84.98 87	84.80 122	87.30 259	65.39 226	87.30 206	92.88 68	77.62 49	84.04 113	92.26 113	71.81 68	93.96 149	81.31 112	90.30 130	95.03 13

VDD-MVS		83.01 140	82.36 143	84.96 111	91.02 98	66.40 197	88.91 130	88.11 279	77.57 51	84.39 103	93.29 88	52.19 326	93.91 157	77.05 175	88.70 164	94.57 53

MP-MVS		87.71 23	87.64 27	87.93 21	94.36 32	73.88 6	92.71 28	92.65 82	77.57 51	83.84 117	94.40 43	72.24 62	96.28 50	85.65 62	95.30 39	93.62 115
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PEN-MVS		77.73 278	77.69 259	77.84 369	87.07 272	53.91 444	87.91 180	91.18 158	77.56 53	73.14 347	88.82 241	61.23 235	89.17 364	59.95 368	72.37 419	90.43 262

OPM-MVS		83.50 124	82.95 130	85.14 101	88.79 177	70.95 78	89.13 123	91.52 148	77.55 54	80.96 178	91.75 134	60.71 243	94.50 129	79.67 140	86.51 212	89.97 289
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DeepPCF-MVS	80.84 1	88.10 16	88.56 18	86.73 60	92.24 80	69.03 113	89.57 100	93.39 37	77.53 55	89.79 30	94.12 58	78.98 16	96.58 42	85.66 61	95.72 28	94.58 51

PS-CasMVS		78.01 272	78.09 243	77.77 371	87.71 232	54.39 441	88.02 174	91.22 156	77.50 56	73.26 345	88.64 246	60.73 242	88.41 380	61.88 349	73.88 408	90.53 257

MSLP-MVS++		85.43 77	85.76 72	84.45 137	91.93 84	70.24 88	90.71 68	92.86 69	77.46 57	84.22 108	92.81 102	67.16 141	92.94 222	80.36 125	94.35 64	90.16 273

RRT-MVS		82.60 148	82.10 150	84.10 161	87.98 215	62.94 310	87.45 195	91.27 155	77.42 58	79.85 200	90.28 193	56.62 286	94.70 122	79.87 137	88.15 177	94.67 42

fmvsm_s_conf0.5_n_11		86.06 57	86.75 48	84.00 178	87.78 226	66.09 202	89.96 87	90.80 173	77.37 59	86.72 71	94.20 54	72.51 59	92.78 232	89.08 22	92.33 90	93.13 146

DVP-MVS		89.60 4	90.35 4	87.33 45	95.27 6	71.25 67	93.49 11	92.73 76	77.33 60	92.12 14	95.78 8	80.98 10	97.40 9	89.08 22	96.41 12	93.33 130
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					95.27 6	71.25 67	93.60 8	94.11 10	77.33 60	92.81 4	95.79 7	80.98 10

MED-MVS		89.78 3	90.41 3	87.89 24	94.57 19	71.43 62	93.28 13	94.36 3	77.30 62	92.25 12	95.87 4	81.59 7	97.39 11	88.15 39	96.28 16	94.85 24

TestfortrainingZip a		88.83 13	89.21 11	87.68 37	94.57 19	71.25 67	93.28 13	93.91 19	77.30 62	91.13 21	95.87 4	77.62 19	96.95 24	86.12 60	93.07 77	94.85 24

BridgeMVS		86.78 42	86.99 41	86.15 72	91.24 93	67.61 166	90.51 71	92.90 67	77.26 64	87.44 62	91.63 141	71.27 78	96.06 57	85.62 63	95.01 41	94.78 29

SED-MVS		90.08 2	90.85 2	87.77 28	95.30 3	70.98 75	93.57 9	94.06 14	77.24 65	93.10 1	95.72 12	82.99 1	97.44 7	89.07 25	96.63 4	94.88 19

test_241102_TWO								94.06 14	77.24 65	92.78 5	95.72 12	81.26 9	97.44 7	89.07 25	96.58 6	94.26 73

3Dnovator	76.31 5	83.38 128	82.31 144	86.59 62	87.94 216	72.94 29	90.64 69	92.14 118	77.21 67	75.47 298	92.83 100	58.56 265	94.72 120	73.24 223	92.71 84	92.13 201

test_241102_ONE					95.30 3	70.98 75		94.06 14	77.17 68	93.10 1	95.39 20	82.99 1	97.27 14

WR-MVS_H		78.51 258	78.49 233	78.56 353	88.02 212	56.38 415	88.43 155	92.67 79	77.14 69	73.89 337	87.55 279	66.25 155	89.24 362	58.92 380	73.55 411	90.06 283

lecture		88.09 17	88.59 17	86.58 63	93.26 58	69.77 99	93.70 7	94.16 7	77.13 70	89.76 31	95.52 18	72.26 61	96.27 51	86.87 53	94.65 53	93.70 106

DeepC-MVS	79.81 2	87.08 40	86.88 46	87.69 36	91.16 94	72.32 46	90.31 80	93.94 18	77.12 71	82.82 143	94.23 52	72.13 65	97.09 20	84.83 70	95.37 35	93.65 112
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FC-MVSNet-test		81.52 172	82.02 153	80.03 313	88.42 193	55.97 421	87.95 177	93.42 36	77.10 72	77.38 253	90.98 171	69.96 97	91.79 275	68.46 280	84.50 251	92.33 187

DTE-MVSNet		76.99 295	76.80 279	77.54 378	86.24 292	53.06 454	87.52 190	90.66 176	77.08 73	72.50 356	88.67 245	60.48 250	89.52 356	57.33 397	70.74 431	90.05 284

LFMVS		81.82 162	81.23 163	83.57 194	91.89 85	63.43 294	89.84 88	81.85 405	77.04 74	83.21 131	93.10 91	52.26 325	93.43 193	71.98 240	89.95 138	93.85 94

casdiffseed414692147		83.62 120	83.02 127	85.40 93	87.31 258	67.50 171	88.70 144	91.72 138	76.97 75	82.77 145	91.72 135	66.85 145	93.71 171	73.06 225	88.12 178	94.98 14

UGNet		80.83 187	79.59 208	84.54 129	88.04 211	68.09 147	89.42 108	88.16 278	76.95 76	76.22 284	89.46 220	49.30 377	93.94 152	68.48 279	90.31 129	91.60 215
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
FIs		82.07 156	82.42 140	81.04 287	88.80 176	58.34 381	88.26 166	93.49 33	76.93 77	78.47 228	91.04 166	69.92 98	92.34 253	69.87 265	84.97 242	92.44 184

GST-MVS		87.42 31	87.26 35	87.89 24	94.12 42	72.97 25	92.39 32	93.43 35	76.89 78	84.68 93	93.99 67	70.67 86	96.82 28	84.18 82	95.01 41	93.90 92

mPP-MVS		86.67 46	86.32 55	87.72 33	94.41 28	73.55 14	92.74 26	92.22 108	76.87 79	82.81 144	94.25 51	66.44 152	96.24 52	82.88 95	94.28 65	93.38 126

ZNCC-MVS		87.94 22	87.85 25	88.20 12	94.39 30	73.33 20	93.03 20	93.81 22	76.81 80	85.24 83	94.32 46	71.76 69	96.93 25	85.53 64	95.79 26	94.32 69

VPNet		78.69 253	78.66 230	78.76 348	88.31 196	55.72 425	84.45 307	86.63 328	76.79 81	78.26 232	90.55 185	59.30 259	89.70 354	66.63 295	77.05 358	90.88 241

HFP-MVS		87.58 26	87.47 32	87.94 19	94.58 17	73.54 16	93.04 18	93.24 43	76.78 82	84.91 88	94.44 41	70.78 84	96.61 39	84.53 75	94.89 47	93.66 108

ACMMPR		87.44 29	87.23 37	88.08 15	94.64 14	73.59 12	93.04 18	93.20 44	76.78 82	84.66 96	94.52 34	68.81 120	96.65 37	84.53 75	94.90 46	94.00 86

ACMMP		85.89 66	85.39 79	87.38 44	93.59 51	72.63 34	92.74 26	93.18 49	76.78 82	80.73 184	93.82 74	64.33 181	96.29 49	82.67 103	90.69 123	93.23 134
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
region2R		87.42 31	87.20 38	88.09 14	94.63 15	73.55 14	93.03 20	93.12 51	76.73 85	84.45 101	94.52 34	69.09 114	96.70 33	84.37 77	94.83 50	94.03 84

sasdasda		85.91 64	85.87 70	86.04 76	89.84 128	69.44 108	90.45 77	93.00 57	76.70 86	88.01 51	91.23 155	73.28 47	93.91 157	81.50 109	88.80 160	94.77 30

canonicalmvs		85.91 64	85.87 70	86.04 76	89.84 128	69.44 108	90.45 77	93.00 57	76.70 86	88.01 51	91.23 155	73.28 47	93.91 157	81.50 109	88.80 160	94.77 30

CP-MVS		87.11 38	86.92 44	87.68 37	94.20 40	73.86 7	93.98 4	92.82 74	76.62 88	83.68 120	94.46 38	67.93 132	95.95 65	84.20 81	94.39 62	93.23 134

DeepC-MVS_fast	79.65 3	86.91 41	86.62 51	87.76 29	93.52 52	72.37 44	91.26 60	93.04 52	76.62 88	84.22 108	93.36 87	71.44 75	96.76 31	80.82 118	95.33 37	94.16 76
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TestfortrainingZip				87.28 46	92.85 70	72.05 51	93.28 13	93.32 40	76.52 90	88.91 37	93.52 79	77.30 20	96.67 35		91.98 97	93.13 146

E5new		84.22 95	84.12 99	84.51 132	87.60 239	65.36 228	87.45 195	92.31 97	76.51 91	83.53 124	92.26 113	69.25 111	93.50 184	79.88 133	88.26 171	94.69 37

E6new		84.22 95	84.12 99	84.52 130	87.60 239	65.36 228	87.45 195	92.30 99	76.51 91	83.53 124	92.26 113	69.26 109	93.49 186	79.88 133	88.26 171	94.69 37

E6		84.22 95	84.12 99	84.52 130	87.60 239	65.36 228	87.45 195	92.30 99	76.51 91	83.53 124	92.26 113	69.26 109	93.49 186	79.88 133	88.26 171	94.69 37

E5		84.22 95	84.12 99	84.51 132	87.60 239	65.36 228	87.45 195	92.31 97	76.51 91	83.53 124	92.26 113	69.25 111	93.50 184	79.88 133	88.26 171	94.69 37

fmvsm_l_mol_unc0.5_1		85.55 73	86.37 54	83.10 215	86.42 289	62.98 309	85.89 262	84.85 355	76.48 95	92.88 3	96.67 1	74.16 41	92.46 245	87.11 51	92.90 80	93.85 94

MGCFI-Net		85.06 88	85.51 77	83.70 189	89.42 143	63.01 303	89.43 106	92.62 85	76.43 96	87.53 59	91.34 153	72.82 57	93.42 194	81.28 113	88.74 163	94.66 45

TSAR-MVS + GP.		85.71 70	85.33 81	86.84 57	91.34 91	72.50 37	89.07 126	87.28 305	76.41 97	85.80 77	90.22 197	74.15 42	95.37 88	81.82 107	91.88 99	92.65 172

HQP-NCC					89.33 148		89.17 118		76.41 97	77.23 258

ACMP_Plane					89.33 148		89.17 118		76.41 97	77.23 258

HQP-MVS		82.61 146	82.02 153	84.37 142	89.33 148	66.98 188	89.17 118	92.19 113	76.41 97	77.23 258	90.23 196	60.17 254	95.11 97	77.47 169	85.99 226	91.03 235

E4		84.10 101	83.99 105	84.45 137	87.58 247	64.99 242	86.54 238	92.25 104	76.38 101	83.37 129	92.09 124	69.88 99	93.58 173	79.78 138	88.03 182	94.77 30

CANet_DTU		80.61 198	79.87 198	82.83 231	85.60 308	63.17 301	87.36 203	88.65 272	76.37 102	75.88 291	88.44 253	53.51 314	93.07 216	73.30 221	89.74 142	92.25 191

VNet		82.21 153	82.41 141	81.62 268	90.82 103	60.93 348	84.47 304	89.78 206	76.36 103	84.07 112	91.88 128	64.71 177	90.26 341	70.68 253	88.89 157	93.66 108

Vis-MVSNet		83.46 125	82.80 133	85.43 92	90.25 115	68.74 124	90.30 81	90.13 197	76.33 104	80.87 181	92.89 98	61.00 240	94.20 141	72.45 237	90.97 117	93.35 129
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
aaEdge-Enhanced		88.98 11	89.39 8	87.75 30	94.54 22	71.43 62	91.61 50	94.25 4	76.30 105	90.62 26	95.03 24	78.06 18	97.07 21	88.15 39	95.96 21	94.75 35

ACMMP_NAP		88.05 20	88.08 22	87.94 19	93.70 47	73.05 23	90.86 66	93.59 29	76.27 106	88.14 47	95.09 23	71.06 81	96.67 35	87.67 46	96.37 14	94.09 81

fmvsm_s_conf0.5_n_10		86.38 52	86.76 47	85.24 98	87.33 255	67.30 179	89.50 102	90.98 164	76.25 107	90.56 27	94.75 31	68.38 126	94.24 140	90.80 7	92.32 92	94.19 75

alignmvs		85.48 75	85.32 82	85.96 79	89.51 138	69.47 105	89.74 93	92.47 88	76.17 108	87.73 58	91.46 150	70.32 89	93.78 164	81.51 108	88.95 156	94.63 48

MVS_111021_HR		85.14 84	84.75 91	86.32 66	91.65 88	72.70 31	85.98 258	90.33 189	76.11 109	82.08 154	91.61 144	71.36 77	94.17 144	81.02 115	92.58 85	92.08 202

HPM-MVS		87.11 38	86.98 42	87.50 43	93.88 45	72.16 48	92.19 39	93.33 39	76.07 110	83.81 118	93.95 70	69.77 101	96.01 61	85.15 65	94.66 52	94.32 69
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
h-mvs33		83.15 134	82.19 147	86.02 78	90.56 108	70.85 82	88.15 171	89.16 241	76.02 111	84.67 94	91.39 152	61.54 226	95.50 76	82.71 100	75.48 385	91.72 213

hse-mvs2		81.72 163	80.94 170	84.07 167	88.72 181	67.68 164	85.87 263	87.26 310	76.02 111	84.67 94	88.22 260	61.54 226	93.48 189	82.71 100	73.44 413	91.06 233

DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 12	72.69 32	91.59 52	94.10 12	75.90 113	92.29 10	95.66 14	81.67 6	97.38 13	87.44 50	96.34 15	93.95 89
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
E2		84.00 104	83.87 106	84.39 140	87.70 234	64.95 243	86.40 245	92.23 105	75.85 114	83.21 131	91.78 132	70.09 94	93.55 178	79.52 142	88.05 180	94.66 45

E3		84.00 104	83.87 106	84.39 140	87.70 234	64.95 243	86.40 245	92.23 105	75.85 114	83.21 131	91.78 132	70.09 94	93.55 178	79.52 142	88.05 180	94.66 45

CLD-MVS		82.31 151	81.65 159	84.29 150	88.47 189	67.73 162	85.81 267	92.35 95	75.78 116	78.33 231	86.58 310	64.01 185	94.35 133	76.05 190	87.48 193	90.79 244
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
viewmacassd2359aftdt		83.76 113	83.66 114	84.07 167	86.59 285	64.56 256	86.88 222	91.82 132	75.72 117	83.34 130	92.15 122	68.24 130	92.88 225	79.05 145	89.15 154	94.77 30

SF-MVS		88.46 15	88.74 16	87.64 39	92.78 73	71.95 53	92.40 30	94.74 2	75.71 118	89.16 34	95.10 22	75.65 29	96.19 54	87.07 52	96.01 19	94.79 28

testdata1							84.14 319		75.71 118

APDe-MVS		89.15 8	89.63 7	87.73 31	94.49 24	71.69 56	93.83 5	93.96 17	75.70 120	91.06 22	96.03 2	76.84 22	97.03 22	89.09 21	95.65 31	94.47 60
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
VPA-MVSNet		80.60 200	80.55 178	80.76 294	88.07 210	60.80 351	86.86 223	91.58 147	75.67 121	80.24 195	89.45 222	63.34 189	90.25 342	70.51 255	79.22 335	91.23 228

fmvsm_l_conf0.5_n_9		85.84 67	86.63 50	83.46 196	87.12 269	66.01 205	88.56 151	89.43 221	75.59 122	89.32 33	94.32 46	72.89 53	91.21 309	90.11 11	92.33 90	93.16 142

PGM-MVS		86.68 45	86.27 57	87.90 22	94.22 39	73.38 19	90.22 82	93.04 52	75.53 123	83.86 116	94.42 42	67.87 134	96.64 38	82.70 102	94.57 57	93.66 108

Effi-MVS+		83.62 120	83.08 125	85.24 98	88.38 194	67.45 172	88.89 131	89.15 243	75.50 124	82.27 150	88.28 257	69.61 103	94.45 132	77.81 164	87.84 185	93.84 97

viewcassd2359sk11		83.89 107	83.74 111	84.34 145	87.76 229	64.91 250	86.30 249	92.22 108	75.47 125	83.04 137	91.52 146	70.15 92	93.53 181	79.26 144	87.96 183	94.57 53

fmvsm_s_conf0.5_n_4		85.39 79	85.75 73	84.30 149	86.70 281	65.83 212	88.77 138	89.78 206	75.46 126	88.35 42	93.73 76	69.19 113	93.06 217	91.30 3	88.44 169	94.02 85

fmvsm_s_conf0.5_n_6		85.55 73	86.20 58	83.60 191	87.32 257	65.13 236	88.86 132	91.63 143	75.41 127	88.23 46	93.45 84	68.56 124	92.47 244	89.52 18	92.78 82	93.20 139

test_prior2							88.85 134		75.41 127	84.91 88	93.54 78	74.28 39		83.31 88	95.86 24

LPG-MVS_test		82.08 155	81.27 162	84.50 134	89.23 156	68.76 122	90.22 82	91.94 125	75.37 129	76.64 273	91.51 147	54.29 305	94.91 106	78.44 155	83.78 264	89.83 294

LGP-MVS_train				84.50 134	89.23 156	68.76 122		91.94 125	75.37 129	76.64 273	91.51 147	54.29 305	94.91 106	78.44 155	83.78 264	89.83 294

fmvsm_l_conf0.5_n_3		86.02 58	86.32 55	85.14 101	87.20 261	68.54 133	89.57 100	90.44 183	75.31 131	87.49 60	94.39 44	72.86 54	92.72 233	89.04 27	90.56 126	94.16 76

viewmamba		82.38 149	82.11 148	83.19 210	83.30 365	64.26 267	84.62 300	89.16 241	75.24 132	80.97 177	91.10 162	67.12 142	91.63 281	81.36 111	86.13 221	93.67 107

aaatest				87.86 27	94.57 19	71.43 62	93.28 13	93.59 29	75.24 132	92.25 12	95.03 24		97.39 11	88.15 39	95.96 21	94.75 35

viewdifsd2359ckpt07		82.83 143	82.78 135	82.99 223	86.51 287	62.58 314	85.09 286	90.83 172	75.22 134	82.28 149	91.63 141	69.43 105	92.03 263	77.71 166	86.32 215	94.34 67

MG-MVS		83.41 126	83.45 119	83.28 204	92.74 74	62.28 323	88.17 169	89.50 219	75.22 134	81.49 165	92.74 107	66.75 146	95.11 97	72.85 227	91.58 106	92.45 183

SSC-MVS3.2		73.35 354	73.39 337	73.23 424	85.30 317	49.01 478	74.58 458	81.57 407	75.21 136	73.68 340	85.58 335	52.53 319	82.05 443	54.33 419	77.69 352	88.63 337

LCM-MVSNet-Re		77.05 294	76.94 276	77.36 379	87.20 261	51.60 463	80.06 397	80.46 423	75.20 137	67.69 417	86.72 300	62.48 208	88.98 368	63.44 320	89.25 150	91.51 219

SDMVSNet		80.38 207	80.18 188	80.99 288	89.03 165	64.94 246	80.45 391	89.40 222	75.19 138	76.61 275	89.98 199	60.61 248	87.69 389	76.83 180	83.55 273	90.33 267

sd_testset		77.70 281	77.40 266	78.60 351	89.03 165	60.02 365	79.00 413	85.83 342	75.19 138	76.61 275	89.98 199	54.81 297	85.46 414	62.63 336	83.55 273	90.33 267

MP-MVS-pluss		87.67 25	87.72 26	87.54 40	93.64 50	72.04 52	89.80 91	93.50 32	75.17 140	86.34 74	95.29 21	70.86 83	96.00 62	88.78 31	96.04 18	94.58 51
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
E3new		83.78 112	83.60 116	84.31 147	87.76 229	64.89 251	86.24 252	92.20 111	75.15 141	82.87 140	91.23 155	70.11 93	93.52 183	79.05 145	87.79 186	94.51 58

test1111		79.43 231	79.18 221	80.15 311	89.99 124	53.31 450	87.33 205	77.05 459	75.04 142	80.23 196	92.77 106	48.97 382	92.33 254	68.87 275	92.40 89	94.81 27

Effi-MVS+-dtu		80.03 219	78.57 232	84.42 139	85.13 323	68.74 124	88.77 138	88.10 280	74.99 143	74.97 322	83.49 387	57.27 278	93.36 195	73.53 217	80.88 310	91.18 229

reproduce-ours		87.47 27	87.61 28	87.07 51	93.27 56	71.60 57	91.56 55	93.19 45	74.98 144	88.96 35	95.54 16	71.20 79	96.54 43	86.28 57	93.49 72	93.06 150

our_new_method		87.47 27	87.61 28	87.07 51	93.27 56	71.60 57	91.56 55	93.19 45	74.98 144	88.96 35	95.54 16	71.20 79	96.54 43	86.28 57	93.49 72	93.06 150

fmvsm_s_conf0.5_n_7		83.34 129	84.03 104	81.28 279	85.73 304	65.13 236	85.40 278	89.90 204	74.96 146	82.13 153	93.89 71	66.65 147	87.92 385	86.56 56	91.05 115	90.80 243

testtestaaa								93.78 23	74.95 147	92.47 7		79.30 15

OMC-MVS		82.69 144	81.97 155	84.85 119	88.75 180	67.42 173	87.98 175	90.87 170	74.92 148	79.72 202	91.65 139	62.19 215	93.96 149	75.26 202	86.42 213	93.16 142

viewmanbaseed2359cas		83.66 116	83.55 117	84.00 178	86.81 277	64.53 257	86.65 232	91.75 137	74.89 149	83.15 136	91.68 137	68.74 122	92.83 230	79.02 147	89.24 151	94.63 48

test2506		77.30 291	76.49 287	79.74 326	90.08 119	52.02 456	87.86 183	63.10 504	74.88 150	80.16 197	92.79 103	38.29 464	92.35 252	68.74 277	92.50 87	94.86 22

ECVR-MVS		79.61 224	79.26 218	80.67 296	90.08 119	54.69 437	87.89 181	77.44 455	74.88 150	80.27 194	92.79 103	48.96 383	92.45 246	68.55 278	92.50 87	94.86 22

MonoMVSNet		76.49 306	75.80 296	78.58 352	81.55 411	58.45 378	86.36 247	86.22 335	74.87 152	74.73 326	83.73 380	51.79 340	88.73 373	70.78 250	72.15 422	88.55 340

nrg030		83.88 108	83.53 118	84.96 111	86.77 279	69.28 112	90.46 76	92.67 79	74.79 153	82.95 138	91.33 154	72.70 58	93.09 215	80.79 120	79.28 334	92.50 179

SMA-MVS		89.08 9	89.23 9	88.61 6	94.25 37	73.73 9	92.40 30	93.63 27	74.77 154	92.29 10	95.97 3	74.28 39	97.24 16	88.58 33	96.91 1	94.87 21
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
reproduce_model		87.28 35	87.39 34	86.95 55	93.10 64	71.24 72	91.60 51	93.19 45	74.69 155	88.80 39	95.61 15	70.29 90	96.44 46	86.20 59	93.08 76	93.16 142

MVS_111021_LR		82.61 146	82.11 148	84.11 160	88.82 171	71.58 59	85.15 283	86.16 337	74.69 155	80.47 192	91.04 166	62.29 212	90.55 336	80.33 127	90.08 135	90.20 272

EIA-MVS		83.31 132	82.80 133	84.82 120	89.59 134	65.59 220	88.21 167	92.68 78	74.66 157	78.96 214	86.42 315	69.06 116	95.26 90	75.54 198	90.09 134	93.62 115

TSAR-MVS + MP.		88.02 21	88.11 21	87.72 33	93.68 49	72.13 49	91.41 59	92.35 95	74.62 158	88.90 38	93.85 73	75.75 28	96.00 62	87.80 45	94.63 55	95.04 12
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SR-MVS		86.73 43	86.67 49	86.91 56	94.11 43	72.11 50	92.37 34	92.56 87	74.50 159	86.84 70	94.65 33	67.31 139	95.77 67	84.80 71	92.85 81	92.84 166

FOURS1					95.00 11	72.39 42	95.06 1	93.84 20	74.49 160	91.30 20

ACMP	74.13 6	81.51 174	80.57 177	84.36 143	89.42 143	68.69 129	89.97 86	91.50 152	74.46 161	75.04 320	90.41 188	53.82 311	94.54 126	77.56 168	82.91 284	89.86 293
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPP-MVSNet		83.40 127	83.02 127	84.57 128	90.13 117	64.47 262	92.32 36	90.73 175	74.45 162	79.35 210	91.10 162	69.05 117	95.12 95	72.78 228	87.22 197	94.13 78

balanced_ft_v1		83.98 106	83.64 115	85.03 107	89.76 131	65.86 211	88.31 164	91.71 139	74.41 163	80.41 193	90.82 174	62.90 203	94.90 108	83.04 92	91.37 110	94.32 69

fmvsm_s_conf0.5_n_2		84.04 102	84.11 103	83.81 187	86.17 295	65.00 241	86.96 217	87.28 305	74.35 164	88.25 45	94.23 52	61.82 221	92.60 236	89.85 12	88.09 179	93.84 97

fmvsm_s_conf0.1_n_2		83.80 110	83.79 110	83.83 185	85.62 307	64.94 246	87.03 214	86.62 329	74.32 165	87.97 53	94.33 45	60.67 245	92.60 236	89.72 14	87.79 186	93.96 87

save fliter					93.80 46	72.35 45	90.47 75	91.17 159	74.31 166

MVS_Test		83.15 134	83.06 126	83.41 201	86.86 274	63.21 298	86.11 256	92.00 121	74.31 166	82.87 140	89.44 223	70.03 96	93.21 204	77.39 171	88.50 168	93.81 99

myMVS_eth3d28		73.62 345	73.53 336	73.90 420	88.20 199	47.41 483	78.06 428	79.37 439	74.29 168	73.98 336	84.29 364	44.67 419	83.54 431	51.47 433	87.39 194	90.74 248

UniMVSNet_ETH3D		79.10 242	78.24 241	81.70 267	86.85 275	60.24 363	87.28 207	88.79 259	74.25 169	76.84 266	90.53 186	49.48 372	91.56 287	67.98 282	82.15 293	93.29 131

IterMVS-LS		80.06 217	79.38 213	82.11 258	85.89 300	63.20 299	86.79 226	89.34 224	74.19 170	75.45 301	86.72 300	66.62 148	92.39 249	72.58 230	76.86 361	90.75 247
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 204	79.98 194	82.12 256	84.28 340	63.19 300	86.41 242	88.95 254	74.18 171	78.69 219	87.54 280	66.62 148	92.43 247	72.57 231	80.57 316	90.74 248

Vis-MVSNet (Re-imp)		78.36 261	78.45 234	78.07 365	88.64 184	51.78 462	86.70 230	79.63 437	74.14 172	75.11 317	90.83 173	61.29 234	89.75 352	58.10 390	91.60 104	92.69 170

v8		79.97 221	79.02 224	82.80 234	84.09 345	64.50 261	87.96 176	90.29 192	74.13 173	75.24 313	86.81 297	62.88 204	93.89 160	74.39 210	75.40 390	90.00 285

testing915		80.13 216	80.30 185	79.64 331	89.00 167	58.38 379	87.08 212	84.16 367	74.04 174	80.14 198	89.37 226	64.04 184	90.08 345	66.04 301	88.82 159	90.45 261

guyue		81.13 180	80.64 176	82.60 245	86.52 286	63.92 275	86.69 231	87.73 295	73.97 175	80.83 183	89.69 209	56.70 284	91.33 303	78.26 163	85.40 239	92.54 175

onestephybrid01		82.22 152	81.81 158	83.46 196	83.16 373	64.93 249	84.64 299	89.19 240	73.95 176	81.48 166	90.63 180	66.00 164	91.92 271	80.33 127	86.93 203	93.53 122

CSCG		86.41 51	86.19 60	87.07 51	92.91 69	72.48 38	90.81 67	93.56 31	73.95 176	83.16 135	91.07 165	75.94 26	95.19 92	79.94 132	94.38 63	93.55 120

thres100view900		76.50 303	75.55 303	79.33 338	89.52 137	56.99 404	85.83 266	83.23 381	73.94 178	76.32 282	87.12 292	51.89 337	91.95 268	48.33 453	83.75 267	89.07 312

9.14			88.26 20		92.84 72		91.52 57	94.75 1	73.93 179	88.57 41	94.67 32	75.57 30	95.79 66	86.77 54	95.76 27

HPM-MVS_fast		85.35 81	84.95 89	86.57 64	93.69 48	70.58 87	92.15 41	91.62 144	73.89 180	82.67 147	94.09 59	62.60 205	95.54 73	80.93 116	92.93 79	93.57 118

PAPM_NR		83.02 139	82.41 141	84.82 120	92.47 79	66.37 198	87.93 179	91.80 133	73.82 181	77.32 255	90.66 179	67.90 133	94.90 108	70.37 256	89.48 147	93.19 140

thres600view7		76.50 303	75.44 304	79.68 329	89.40 145	57.16 401	85.53 275	83.23 381	73.79 182	76.26 283	87.09 293	51.89 337	91.89 272	48.05 458	83.72 270	90.00 285

testing91		76.54 301	75.66 301	79.18 342	88.43 192	55.89 422	81.08 378	83.00 388	73.76 183	75.34 306	84.29 364	46.20 407	90.07 346	64.33 314	84.50 251	91.58 217

AstraMVS		80.81 188	80.14 191	82.80 234	86.05 299	63.96 272	86.46 241	85.90 341	73.71 184	80.85 182	90.56 184	54.06 309	91.57 286	79.72 139	83.97 262	92.86 164

v7n		78.97 246	77.58 262	83.14 213	83.45 362	65.51 221	88.32 163	91.21 157	73.69 185	72.41 358	86.32 318	57.93 269	93.81 163	69.18 271	75.65 381	90.11 277

dcpmvs_2		85.63 71	86.15 62	84.06 170	91.71 87	64.94 246	86.47 240	91.87 129	73.63 186	86.60 73	93.02 96	76.57 23	91.87 274	83.36 87	92.15 93	95.35 4

v2v482		80.23 213	79.29 217	83.05 220	83.62 358	64.14 269	87.04 213	89.97 201	73.61 187	78.18 235	87.22 288	61.10 238	93.82 162	76.11 188	76.78 364	91.18 229

Baseline_NR-MVSNet		78.15 267	78.33 239	77.61 375	85.79 302	56.21 419	86.78 227	85.76 343	73.60 188	77.93 241	87.57 277	65.02 173	88.99 367	67.14 292	75.33 392	87.63 360

BH-RMVSNet		79.61 224	78.44 235	83.14 213	89.38 147	65.93 208	84.95 290	87.15 313	73.56 189	78.19 234	89.79 207	56.67 285	93.36 195	59.53 373	86.74 208	90.13 275

PRO-TEST		83.03 138	82.63 137	84.23 157	88.20 199	66.81 193	87.41 201	90.93 166	73.55 190	80.73 184	88.90 237	66.17 158	92.85 226	78.39 158	89.36 149	93.02 154

APD-MVS_3200maxsize		85.97 62	85.88 68	86.22 69	92.69 75	69.53 102	91.93 43	92.99 60	73.54 191	85.94 75	94.51 37	65.80 166	95.61 70	83.04 92	92.51 86	93.53 122

SR-MVS-dyc-post		85.77 68	85.61 75	86.23 68	93.06 66	70.63 85	91.88 44	92.27 101	73.53 192	85.69 79	94.45 39	65.00 175	95.56 71	82.75 98	91.87 100	92.50 179

RE-MVS-def			85.48 78		93.06 66	70.63 85	91.88 44	92.27 101	73.53 192	85.69 79	94.45 39	63.87 186		82.75 98	91.87 100	92.50 179

reproduce_monomvs		75.40 326	74.38 325	78.46 358	83.92 350	57.80 392	83.78 324	86.94 319	73.47 194	72.25 361	84.47 358	38.74 460	89.27 361	75.32 201	70.53 432	88.31 344

test_fmvsmconf_n		85.92 63	86.04 65	85.57 89	85.03 326	69.51 103	89.62 99	90.58 178	73.42 195	87.75 56	94.02 63	72.85 55	93.24 201	90.37 8	90.75 122	93.96 87

tfpn200view9		76.42 309	75.37 308	79.55 335	89.13 160	57.65 395	85.17 281	83.60 373	73.41 196	76.45 278	86.39 316	52.12 327	91.95 268	48.33 453	83.75 267	89.07 312

thres400		76.50 303	75.37 308	79.86 319	89.13 160	57.65 395	85.17 281	83.60 373	73.41 196	76.45 278	86.39 316	52.12 327	91.95 268	48.33 453	83.75 267	90.00 285

diffmvs_AUTHOR		82.38 149	82.27 146	82.73 242	83.26 367	63.80 277	83.89 322	89.76 208	73.35 198	82.37 148	90.84 172	66.25 155	90.79 328	82.77 97	87.93 184	93.59 117

test_fmvsmconf0.1_n		85.61 72	85.65 74	85.50 90	82.99 383	69.39 110	89.65 96	90.29 192	73.31 199	87.77 55	94.15 57	71.72 70	93.23 202	90.31 9	90.67 124	93.89 93

testing99		76.09 315	75.12 315	79.00 343	88.16 203	55.50 428	80.79 382	81.40 410	73.30 200	75.17 314	84.27 367	44.48 422	90.02 347	64.28 315	84.22 260	91.48 222

v148		78.72 252	77.80 253	81.47 272	82.73 389	61.96 329	86.30 249	88.08 281	73.26 201	76.18 286	85.47 338	62.46 209	92.36 251	71.92 241	73.82 409	90.09 279

FA-MVS(test-final)		80.96 184	79.91 196	84.10 161	88.30 197	65.01 240	84.55 303	90.01 200	73.25 202	79.61 203	87.57 277	58.35 267	94.72 120	71.29 246	86.25 218	92.56 174

test_fmvsmconf0.01_n		84.73 92	84.52 95	85.34 95	80.25 428	69.03 113	89.47 103	89.65 213	73.24 203	86.98 68	94.27 49	66.62 148	93.23 202	90.26 10	89.95 138	93.78 103

viewdifsd2359ckpt11		80.37 209	79.73 202	82.30 253	83.70 356	62.39 318	84.20 316	86.67 325	73.22 204	80.90 179	90.62 181	63.00 201	91.56 287	76.81 181	78.44 341	92.95 161

viewmsd2359difaftdt		80.37 209	79.73 202	82.30 253	83.70 356	62.39 318	84.20 316	86.67 325	73.22 204	80.90 179	90.62 181	63.00 201	91.56 287	76.81 181	78.44 341	92.95 161

v10		79.74 223	78.67 229	82.97 226	84.06 346	64.95 243	87.88 182	90.62 177	73.11 206	75.11 317	86.56 311	61.46 229	94.05 148	73.68 215	75.55 383	89.90 291

MCST-MVS		87.37 34	87.25 36	87.73 31	94.53 23	72.46 41	89.82 89	93.82 21	73.07 207	84.86 91	92.89 98	76.22 25	96.33 48	84.89 69	95.13 40	94.40 63

baseline1		76.98 296	76.75 283	77.66 373	88.13 206	55.66 426	85.12 284	81.89 403	73.04 208	76.79 268	88.90 237	62.43 210	87.78 388	63.30 322	71.18 429	89.55 303

APD-MVS		87.44 29	87.52 31	87.19 48	94.24 38	72.39 42	91.86 46	92.83 71	73.01 209	88.58 40	94.52 34	73.36 45	96.49 45	84.26 78	95.01 41	92.70 168
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
diffmvs		82.10 154	81.88 156	82.76 240	83.00 379	63.78 279	83.68 327	89.76 208	72.94 210	82.02 155	89.85 202	65.96 165	90.79 328	82.38 104	87.30 196	93.71 105
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
K. test v3		71.19 381	68.51 395	79.21 341	83.04 378	57.78 393	84.35 313	76.91 460	72.90 211	62.99 464	82.86 399	39.27 456	91.09 315	61.65 353	52.66 493	88.75 332

MVSMamba_PlusPlus		85.99 60	85.96 67	86.05 75	91.09 95	67.64 165	89.63 98	92.65 82	72.89 212	84.64 97	91.71 136	71.85 67	96.03 58	84.77 72	94.45 61	94.49 59

GDP-MVS		83.52 123	82.64 136	86.16 71	88.14 205	68.45 135	89.13 123	92.69 77	72.82 213	83.71 119	91.86 130	55.69 292	95.35 89	80.03 130	89.74 142	94.69 37

hybridnocas07		81.44 175	81.13 165	82.37 251	82.13 401	63.11 302	83.45 336	88.74 266	72.54 214	80.71 186	90.73 175	65.14 171	90.74 333	80.35 126	86.41 214	93.27 133

fmvsm_s_conf0.5_n_5		85.22 83	85.55 76	84.25 156	86.26 291	67.40 175	89.18 117	89.31 230	72.50 215	88.31 43	93.86 72	69.66 102	91.96 267	89.81 13	91.05 115	93.38 126

Fast-Effi-MVS+-dtu		78.02 271	76.49 287	82.62 244	83.16 373	66.96 190	86.94 219	87.45 302	72.45 216	71.49 371	84.17 371	54.79 301	91.58 284	67.61 285	80.31 319	89.30 310

PHI-MVS		86.43 49	86.17 61	87.24 47	90.88 102	70.96 77	92.27 38	94.07 13	72.45 216	85.22 84	91.90 127	69.47 104	96.42 47	83.28 89	95.94 23	94.35 66

thres200		75.55 321	74.47 323	78.82 347	87.78 226	57.85 390	83.07 349	83.51 376	72.44 218	75.84 292	84.42 359	52.08 330	91.75 277	47.41 460	83.64 272	86.86 391

test_yl		81.17 178	80.47 181	83.24 207	89.13 160	63.62 281	86.21 253	89.95 202	72.43 219	81.78 160	89.61 213	57.50 275	93.58 173	70.75 251	86.90 204	92.52 177

DCV-MVSNet		81.17 178	80.47 181	83.24 207	89.13 160	63.62 281	86.21 253	89.95 202	72.43 219	81.78 160	89.61 213	57.50 275	93.58 173	70.75 251	86.90 204	92.52 177

aa-1031								93.32 40	72.21 221			77.30 20			91.98 97

viewdifsd2359ckpt13		82.91 141	82.29 145	84.77 123	86.96 273	66.90 192	87.47 192	91.62 144	72.19 222	81.68 162	90.71 177	66.92 144	93.28 197	75.90 192	87.15 199	94.12 79

BH-untuned		79.47 229	78.60 231	82.05 259	89.19 158	65.91 209	86.07 257	88.52 275	72.18 223	75.42 302	87.69 274	61.15 237	93.54 180	60.38 364	86.83 207	86.70 396

TransMVSNet (Re)		75.39 327	74.56 321	77.86 368	85.50 312	57.10 403	86.78 227	86.09 339	72.17 224	71.53 370	87.34 283	63.01 200	89.31 360	56.84 403	61.83 474	87.17 381

GA-MVS		76.87 298	75.17 314	81.97 262	82.75 388	62.58 314	81.44 373	86.35 334	72.16 225	74.74 325	82.89 398	46.20 407	92.02 265	68.85 276	81.09 307	91.30 227

VortexMVS		78.57 257	77.89 249	80.59 297	85.89 300	62.76 312	85.61 268	89.62 215	72.06 226	74.99 321	85.38 340	55.94 291	90.77 331	74.99 203	76.58 365	88.23 347

mmtdpeth		74.16 338	73.01 343	77.60 377	83.72 355	61.13 341	85.10 285	85.10 350	72.06 226	77.21 262	80.33 430	43.84 427	85.75 408	77.14 174	52.61 494	85.91 412

v1144		80.03 219	79.03 223	83.01 222	83.78 353	64.51 259	87.11 211	90.57 180	71.96 228	78.08 238	86.20 321	61.41 230	93.94 152	74.93 204	77.23 355	90.60 254

viewdifsd2359ckpt09		83.34 129	82.55 139	85.70 83	87.64 238	67.72 163	88.43 155	91.68 141	71.91 229	81.65 163	90.68 178	67.10 143	94.75 118	76.17 187	87.70 189	94.62 50

PS-MVSNAJss		82.07 156	81.31 161	84.34 145	86.51 287	67.27 181	89.27 114	91.51 149	71.75 230	79.37 209	90.22 197	63.15 196	94.27 136	77.69 167	82.36 292	91.49 221

EPNet_dtu		75.46 323	74.86 316	77.23 382	82.57 394	54.60 438	86.89 221	83.09 385	71.64 231	66.25 440	85.86 327	55.99 290	88.04 384	54.92 415	86.55 211	89.05 317
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		78.40 259	77.40 266	81.40 275	87.60 239	63.01 303	88.39 158	89.28 231	71.63 232	75.34 306	87.28 284	54.80 298	91.11 310	62.72 332	79.57 326	90.09 279

test1		78.40 259	77.40 266	81.40 275	87.60 239	63.01 303	88.39 158	89.28 231	71.63 232	75.34 306	87.28 284	54.80 298	91.11 310	62.72 332	79.57 326	90.09 279

FMVSNet2		78.20 265	77.21 270	81.20 282	87.60 239	62.89 311	87.47 192	89.02 249	71.63 232	75.29 312	87.28 284	54.80 298	91.10 313	62.38 340	79.38 332	89.61 301

patch_mono-2		83.65 117	84.54 93	80.99 288	90.06 123	65.83 212	84.21 315	88.74 266	71.60 235	85.01 85	92.44 110	74.51 35	83.50 432	82.15 105	92.15 93	93.64 114

V42		79.38 235	78.24 241	82.83 231	81.10 420	65.50 222	85.55 273	89.82 205	71.57 236	78.21 233	86.12 323	60.66 246	93.18 210	75.64 195	75.46 387	89.81 296

API-MVS		81.99 158	81.23 163	84.26 155	90.94 100	70.18 94	91.10 64	89.32 229	71.51 237	78.66 221	88.28 257	65.26 169	95.10 100	64.74 312	91.23 113	87.51 366

tttt0517		79.40 233	77.91 247	83.90 184	88.10 208	63.84 276	88.37 161	84.05 368	71.45 238	76.78 269	89.12 228	49.93 368	94.89 110	70.18 260	83.18 282	92.96 160

pm-mvs1		77.25 292	76.68 285	78.93 345	84.22 342	58.62 377	86.41 242	88.36 277	71.37 239	73.31 344	88.01 267	61.22 236	89.15 365	64.24 316	73.01 416	89.03 318

Elysia		81.53 170	80.16 189	85.62 86	85.51 310	68.25 142	88.84 135	92.19 113	71.31 240	80.50 190	89.83 203	46.89 395	94.82 113	76.85 177	89.57 144	93.80 101

StellarMVS		81.53 170	80.16 189	85.62 86	85.51 310	68.25 142	88.84 135	92.19 113	71.31 240	80.50 190	89.83 203	46.89 395	94.82 113	76.85 177	89.57 144	93.80 101

testing222		74.04 340	72.66 347	78.19 361	87.89 218	55.36 429	81.06 379	79.20 442	71.30 242	74.65 328	83.57 386	39.11 459	88.67 375	51.43 435	85.75 233	90.53 257

GeoE		81.71 164	81.01 169	83.80 188	89.51 138	64.45 263	88.97 128	88.73 268	71.27 243	78.63 222	89.76 208	66.32 154	93.20 207	69.89 264	86.02 225	93.74 104

tt0805		78.73 251	77.83 251	81.43 273	85.17 319	60.30 362	89.41 109	90.90 168	71.21 244	77.17 263	88.73 242	46.38 402	93.21 204	72.57 231	78.96 336	90.79 244

FMVSNet3		77.88 275	76.85 278	80.97 290	86.84 276	62.36 320	86.52 239	88.77 260	71.13 245	75.34 306	86.66 306	54.07 308	91.10 313	62.72 332	79.57 326	89.45 305

VDDNet		81.52 172	80.67 174	84.05 173	90.44 111	64.13 270	89.73 94	85.91 340	71.11 246	83.18 134	93.48 81	50.54 358	93.49 186	73.40 220	88.25 175	94.54 57

fmvsm_s_conf0.5_n		83.80 110	83.71 112	84.07 167	86.69 282	67.31 178	89.46 104	83.07 386	71.09 247	86.96 69	93.70 77	69.02 119	91.47 297	88.79 30	84.62 250	93.44 125

XVG-OURS		80.41 205	79.23 219	83.97 181	85.64 306	69.02 115	83.03 351	90.39 184	71.09 247	77.63 248	91.49 149	54.62 304	91.35 301	75.71 194	83.47 276	91.54 218

SSM_0407		81.58 169	80.48 180	84.87 118	88.81 172	67.96 153	87.37 202	89.25 235	71.06 249	79.48 206	90.39 190	59.57 256	94.48 131	72.45 237	85.93 228	92.18 196

SSM_0404		81.91 159	80.84 172	85.13 104	89.24 155	68.26 140	87.84 184	89.25 235	71.06 249	80.62 187	90.39 190	59.57 256	94.65 124	72.45 237	87.19 198	92.47 182

SixPastTwentyTwo		73.37 351	71.26 366	79.70 328	85.08 324	57.89 389	85.57 269	83.56 375	71.03 251	65.66 444	85.88 326	42.10 439	92.57 238	59.11 378	63.34 468	88.65 336

ZD-MVS					94.38 31	72.22 47		92.67 79	70.98 252	87.75 56	94.07 60	74.01 43	96.70 33	84.66 73	94.84 49

mamba_0408		79.37 236	77.52 263	84.93 114	88.81 172	67.96 153	65.03 497	88.66 270	70.96 253	79.48 206	89.80 205	58.69 262	94.65 124	70.35 257	85.93 228	92.18 196

SSM_04072		77.67 283	77.52 263	78.12 363	88.81 172	67.96 153	65.03 497	88.66 270	70.96 253	79.48 206	89.80 205	58.69 262	74.23 490	70.35 257	85.93 228	92.18 196

FBQ-MVS		77.66 284	76.04 295	82.50 247	88.78 179	63.76 280	86.60 235	84.86 354	70.85 255	77.63 248	82.83 400	47.83 388	92.10 261	60.18 367	84.82 246	91.65 214

v1192		79.59 226	78.43 236	83.07 219	83.55 360	64.52 258	86.93 220	90.58 178	70.83 256	77.78 245	85.90 325	59.15 260	93.94 152	73.96 214	77.19 357	90.76 246

RA-MVS			88.76 15		95.68 1	73.57 13	94.07 3	93.39 37	70.82 257	90.99 23	95.81 6	75.13 31	97.22 18	88.08 42	94.91 45

Fast-Effi-MVS+		80.81 188	79.92 195	83.47 195	88.85 168	64.51 259	85.53 275	89.39 223	70.79 258	78.49 226	85.06 349	67.54 136	93.58 173	67.03 294	86.58 210	92.32 188

PS-MVSNAJ		81.69 165	81.02 168	83.70 189	89.51 138	68.21 145	84.28 314	90.09 198	70.79 258	81.26 172	85.62 334	63.15 196	94.29 134	75.62 196	88.87 158	88.59 338

hybrid		81.05 182	80.66 175	82.22 255	81.97 403	62.99 307	83.42 337	88.68 269	70.76 260	80.56 189	90.40 189	64.49 180	90.48 337	79.57 141	86.06 223	93.19 140

LTVRE_ROB	69.57 13	76.25 312	74.54 322	81.41 274	88.60 185	64.38 265	79.24 408	89.12 246	70.76 260	69.79 392	87.86 270	49.09 380	93.20 207	56.21 409	80.16 320	86.65 398
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
testing11		75.14 329	74.01 328	78.53 355	88.16 203	56.38 415	80.74 385	80.42 425	70.67 262	72.69 355	83.72 381	43.61 429	89.86 349	62.29 342	83.76 266	89.36 308

fmvsm_s_conf0.1_n		83.56 122	83.38 121	84.10 161	84.86 328	67.28 180	89.40 110	83.01 387	70.67 262	87.08 66	93.96 69	68.38 126	91.45 298	88.56 34	84.50 251	93.56 119

xiu_mvs_v2_base		81.69 165	81.05 167	83.60 191	89.15 159	68.03 151	84.46 306	90.02 199	70.67 262	81.30 171	86.53 313	63.17 195	94.19 143	75.60 197	88.54 166	88.57 339

XVG-OURS-SEG-HR		80.81 188	79.76 201	83.96 182	85.60 308	68.78 121	83.54 335	90.50 181	70.66 265	76.71 271	91.66 138	60.69 244	91.26 304	76.94 176	81.58 302	91.83 206

Anonymous202405211		78.25 262	77.01 273	81.99 261	91.03 97	60.67 355	84.77 293	83.90 370	70.65 266	80.00 199	91.20 159	41.08 446	91.43 299	65.21 307	85.26 240	93.85 94

DP-MVS Recon		83.11 137	82.09 151	86.15 72	94.44 25	70.92 80	88.79 137	92.20 111	70.53 267	79.17 212	91.03 168	64.12 183	96.03 58	68.39 281	90.14 133	91.50 220

icg_test_0407_2		78.92 248	78.93 226	78.90 346	87.13 264	63.59 285	76.58 441	89.33 225	70.51 268	77.82 242	89.03 231	61.84 219	81.38 449	72.56 233	85.56 235	91.74 209

IMVS_0407		80.61 198	79.90 197	82.75 241	87.13 264	63.59 285	85.33 279	89.33 225	70.51 268	77.82 242	89.03 231	61.84 219	92.91 223	72.56 233	85.56 235	91.74 209

IMVS_0404		77.16 293	76.42 290	79.37 337	87.13 264	63.59 285	77.12 438	89.33 225	70.51 268	66.22 441	89.03 231	50.36 360	82.78 437	72.56 233	85.56 235	91.74 209

IMVS_0403		80.80 191	80.12 192	82.87 230	87.13 264	63.59 285	85.19 280	89.33 225	70.51 268	78.49 226	89.03 231	63.26 192	93.27 199	72.56 233	85.56 235	91.74 209

FMVSNet1		77.44 287	76.12 294	81.40 275	86.81 277	63.01 303	88.39 158	89.28 231	70.49 272	74.39 332	87.28 284	49.06 381	91.11 310	60.91 360	78.52 339	90.09 279

LuminaMVS		80.68 196	79.62 207	83.83 185	85.07 325	68.01 152	86.99 216	88.83 257	70.36 273	81.38 167	87.99 268	50.11 363	92.51 243	79.02 147	86.89 206	90.97 238

testing3		68.56 414	67.67 412	71.22 445	87.33 255	42.87 498	83.06 350	71.54 481	70.36 273	69.08 399	84.38 361	30.33 487	85.69 410	37.50 493	75.45 388	85.09 429

ab-mvs		79.51 227	78.97 225	81.14 284	88.46 190	60.91 349	83.84 323	89.24 237	70.36 273	79.03 213	88.87 240	63.23 194	90.21 343	65.12 308	82.57 290	92.28 190

tfpnnormal		74.39 334	73.16 341	78.08 364	86.10 298	58.05 384	84.65 298	87.53 299	70.32 276	71.22 374	85.63 333	54.97 296	89.86 349	43.03 479	75.02 397	86.32 401

ACMM	73.20 8	80.78 195	79.84 199	83.58 193	89.31 151	68.37 137	89.99 85	91.60 146	70.28 277	77.25 256	89.66 211	53.37 316	93.53 181	74.24 212	82.85 285	88.85 327
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n_a		83.63 119	83.41 120	84.28 151	86.14 296	68.12 146	89.43 106	82.87 391	70.27 278	87.27 65	93.80 75	69.09 114	91.58 284	88.21 38	83.65 271	93.14 145

ACMH+	68.96 14	76.01 316	74.01 328	82.03 260	88.60 185	65.31 232	88.86 132	87.55 298	70.25 279	67.75 416	87.47 282	41.27 444	93.19 209	58.37 387	75.94 378	87.60 361

IB-MVS	68.01 15	75.85 318	73.36 339	83.31 203	84.76 331	66.03 203	83.38 339	85.06 351	70.21 280	69.40 394	81.05 420	45.76 412	94.66 123	65.10 309	75.49 384	89.25 311
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
thisisatest0530		79.40 233	77.76 256	84.31 147	87.69 236	65.10 239	87.36 203	84.26 365	70.04 281	77.42 252	88.26 259	49.94 366	94.79 117	70.20 259	84.70 249	93.03 153

mvsmamba		80.60 200	79.38 213	84.27 153	89.74 132	67.24 183	87.47 192	86.95 318	70.02 282	75.38 304	88.93 236	51.24 348	92.56 239	75.47 200	89.22 152	93.00 157

test_fmvsmvis_n_1920		84.02 103	83.87 106	84.49 136	84.12 344	69.37 111	88.15 171	87.96 287	70.01 283	83.95 115	93.23 89	68.80 121	91.51 294	88.61 32	89.96 137	92.57 173

v144192		79.47 229	78.37 237	82.78 238	83.35 363	63.96 272	86.96 217	90.36 188	69.99 284	77.50 250	85.67 332	60.66 246	93.77 166	74.27 211	76.58 365	90.62 252

test_fmvsm_n_1920		85.29 82	85.34 80	85.13 104	86.12 297	69.93 95	88.65 147	90.78 174	69.97 285	88.27 44	93.98 68	71.39 76	91.54 291	88.49 35	90.45 128	93.91 90

c3_l		78.75 250	77.91 247	81.26 280	82.89 386	61.56 335	84.09 320	89.13 245	69.97 285	75.56 296	84.29 364	66.36 153	92.09 262	73.47 219	75.48 385	90.12 276

v1921920		79.22 238	78.03 244	82.80 234	83.30 365	63.94 274	86.80 225	90.33 189	69.91 287	77.48 251	85.53 336	58.44 266	93.75 168	73.60 216	76.85 362	90.71 250

ACMH	67.68 16	75.89 317	73.93 330	81.77 266	88.71 182	66.61 195	88.62 148	89.01 250	69.81 288	66.78 431	86.70 304	41.95 441	91.51 294	55.64 410	78.14 347	87.17 381
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.1_n_a		83.32 131	82.99 129	84.28 151	83.79 352	68.07 148	89.34 113	82.85 392	69.80 289	87.36 64	94.06 61	68.34 128	91.56 287	87.95 44	83.46 277	93.21 137

DPM-MVS		84.93 89	84.29 97	86.84 57	90.20 116	73.04 24	87.12 210	93.04 52	69.80 289	82.85 142	91.22 158	73.06 51	96.02 60	76.72 184	94.63 55	91.46 224

MAR-MVS		81.84 161	80.70 173	85.27 97	91.32 92	71.53 60	89.82 89	90.92 167	69.77 291	78.50 225	86.21 320	62.36 211	94.52 128	65.36 306	92.05 96	89.77 297
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
XVG-ACMP-BASELINE		76.11 314	74.27 327	81.62 268	83.20 370	64.67 255	83.60 332	89.75 210	69.75 292	71.85 366	87.09 293	32.78 480	92.11 260	69.99 263	80.43 318	88.09 351

BH-w/o		78.21 264	77.33 269	80.84 292	88.81 172	65.13 236	84.87 291	87.85 292	69.75 292	74.52 330	84.74 356	61.34 232	93.11 214	58.24 389	85.84 231	84.27 438

v1240		78.99 245	77.78 254	82.64 243	83.21 369	63.54 289	86.62 234	90.30 191	69.74 294	77.33 254	85.68 331	57.04 281	93.76 167	73.13 224	76.92 359	90.62 252

FE-MVSNET2		72.88 367	71.28 364	77.67 372	78.30 453	57.78 393	84.43 309	88.92 256	69.56 295	64.61 453	81.67 415	46.73 399	88.54 378	59.33 374	67.99 446	86.69 397

ET-MVSNet_ETH3D		78.63 254	76.63 286	84.64 127	86.73 280	69.47 105	85.01 288	84.61 358	69.54 296	66.51 438	86.59 308	50.16 362	91.75 277	76.26 186	84.24 259	92.69 170

eth_miper_zixun_eth		77.92 274	76.69 284	81.61 270	83.00 379	61.98 328	83.15 344	89.20 239	69.52 297	74.86 324	84.35 363	61.76 222	92.56 239	71.50 244	72.89 417	90.28 270

PVSNet_Blended_VisFu		82.62 145	81.83 157	84.96 111	90.80 104	69.76 100	88.74 142	91.70 140	69.39 298	78.96 214	88.46 252	65.47 168	94.87 112	74.42 209	88.57 165	90.24 271

mvs_tets		79.13 241	77.77 255	83.22 209	84.70 332	66.37 198	89.17 118	90.19 195	69.38 299	75.40 303	89.46 220	44.17 425	93.15 211	76.78 183	80.70 314	90.14 274

PVSNet_BlendedMVS		80.60 200	80.02 193	82.36 252	88.85 168	65.40 224	86.16 255	92.00 121	69.34 300	78.11 236	86.09 324	66.02 162	94.27 136	71.52 242	82.06 295	87.39 369

SD_0403		74.65 333	74.77 318	74.29 414	86.20 294	47.42 482	83.71 326	85.12 349	69.30 301	68.50 407	87.95 269	59.40 258	86.05 405	49.38 447	83.35 278	89.40 306

AdaColmap		80.58 203	79.42 211	84.06 170	93.09 65	68.91 118	89.36 112	88.97 253	69.27 302	75.70 294	89.69 209	57.20 280	95.77 67	63.06 327	88.41 170	87.50 367

ETVMVS		72.25 374	71.05 369	75.84 392	87.77 228	51.91 459	79.39 406	74.98 469	69.26 303	73.71 339	82.95 396	40.82 448	86.14 404	46.17 466	84.43 256	89.47 304

ITE_SJBPF				78.22 360	81.77 407	60.57 357		83.30 379	69.25 304	67.54 418	87.20 289	36.33 473	87.28 394	54.34 418	74.62 401	86.80 393

cl____		77.72 279	76.76 281	80.58 298	82.49 396	60.48 359	83.09 347	87.87 290	69.22 305	74.38 333	85.22 345	62.10 216	91.53 292	71.09 248	75.41 389	89.73 299

DIV-MVS_self_test		77.72 279	76.76 281	80.58 298	82.48 397	60.48 359	83.09 347	87.86 291	69.22 305	74.38 333	85.24 343	62.10 216	91.53 292	71.09 248	75.40 390	89.74 298

jajsoiax		79.29 237	77.96 245	83.27 205	84.68 333	66.57 196	89.25 115	90.16 196	69.20 307	75.46 300	89.49 217	45.75 413	93.13 213	76.84 179	80.80 312	90.11 277

IterMVS-SCA-FT		75.43 324	73.87 332	80.11 312	82.69 390	64.85 252	81.57 370	83.47 377	69.16 308	70.49 378	84.15 372	51.95 333	88.15 382	69.23 270	72.14 423	87.34 374

CL-MVSNet_self_test		72.37 371	71.46 360	75.09 404	79.49 442	53.53 446	80.76 384	85.01 353	69.12 309	70.51 377	82.05 412	57.92 270	84.13 425	52.27 429	66.00 454	87.60 361

AUN-MVS		79.21 239	77.60 261	84.05 173	88.71 182	67.61 166	85.84 265	87.26 310	69.08 310	77.23 258	88.14 265	53.20 318	93.47 190	75.50 199	73.45 412	91.06 233

xiu_mvs_v1_base_debu		80.80 191	79.72 204	84.03 175	87.35 250	70.19 91	85.56 270	88.77 260	69.06 311	81.83 156	88.16 261	50.91 351	92.85 226	78.29 160	87.56 190	89.06 314

xiu_mvs_v1_base		80.80 191	79.72 204	84.03 175	87.35 250	70.19 91	85.56 270	88.77 260	69.06 311	81.83 156	88.16 261	50.91 351	92.85 226	78.29 160	87.56 190	89.06 314

xiu_mvs_v1_base_debi		80.80 191	79.72 204	84.03 175	87.35 250	70.19 91	85.56 270	88.77 260	69.06 311	81.83 156	88.16 261	50.91 351	92.85 226	78.29 160	87.56 190	89.06 314

MVSTER		79.01 244	77.88 250	82.38 250	83.07 376	64.80 253	84.08 321	88.95 254	69.01 314	78.69 219	87.17 291	54.70 302	92.43 247	74.69 205	80.57 316	89.89 292

usedtu_dtu_shiyan1		76.43 307	75.32 310	79.76 324	83.00 379	60.72 352	81.74 365	88.76 264	68.99 315	72.98 349	84.19 369	56.41 288	90.27 339	62.39 338	79.40 330	88.31 344

FE-MVSNET3		76.43 307	75.32 310	79.76 324	83.00 379	60.72 352	81.74 365	88.76 264	68.99 315	72.98 349	84.19 369	56.41 288	90.27 339	62.39 338	79.40 330	88.31 344

cl22		78.07 269	77.01 273	81.23 281	82.37 399	61.83 331	83.55 333	87.98 285	68.96 317	75.06 319	83.87 374	61.40 231	91.88 273	73.53 217	76.39 370	89.98 288

miper_ehance_all_eth		78.59 256	77.76 256	81.08 286	82.66 391	61.56 335	83.65 328	89.15 243	68.87 318	75.55 297	83.79 378	66.49 151	92.03 263	73.25 222	76.39 370	89.64 300

PAPR		81.66 167	80.89 171	83.99 180	90.27 114	64.00 271	86.76 229	91.77 136	68.84 319	77.13 265	89.50 216	67.63 135	94.88 111	67.55 286	88.52 167	93.09 148

CPTT-MVS		83.73 114	83.33 123	84.92 115	93.28 55	70.86 81	92.09 42	90.38 185	68.75 320	79.57 204	92.83 100	60.60 249	93.04 220	80.92 117	91.56 107	90.86 242

train_agg		86.43 49	86.20 58	87.13 50	93.26 58	72.96 26	88.75 140	91.89 127	68.69 321	85.00 86	93.10 91	74.43 36	95.41 83	84.97 66	95.71 29	93.02 154

test_8					93.13 62	72.57 36	88.68 146	91.84 131	68.69 321	84.87 90	93.10 91	74.43 36	95.16 93

dmvs_re		71.14 382	70.58 377	72.80 431	81.96 404	59.68 368	75.60 449	79.34 440	68.55 323	69.27 398	80.72 426	49.42 373	76.54 471	52.56 428	77.79 349	82.19 462

MVSFormer		82.85 142	82.05 152	85.24 98	87.35 250	70.21 89	90.50 73	90.38 185	68.55 323	81.32 168	89.47 218	61.68 223	93.46 191	78.98 150	90.26 131	92.05 203

test_djsdf		80.30 212	79.32 216	83.27 205	83.98 348	65.37 227	90.50 73	90.38 185	68.55 323	76.19 285	88.70 243	56.44 287	93.46 191	78.98 150	80.14 322	90.97 238

TEST9					93.26 58	72.96 26	88.75 140	91.89 127	68.44 326	85.00 86	93.10 91	74.36 38	95.41 83

FE-MVS		77.78 277	75.68 299	84.08 166	88.09 209	66.00 206	83.13 345	87.79 293	68.42 327	78.01 239	85.23 344	45.50 416	95.12 95	59.11 378	85.83 232	91.11 231

CDPH-MVS		85.76 69	85.29 84	87.17 49	93.49 53	71.08 73	88.58 150	92.42 92	68.32 328	84.61 98	93.48 81	72.32 60	96.15 56	79.00 149	95.43 34	94.28 72

PC_three_1452									68.21 329	92.02 17	94.00 65	82.09 5	95.98 64	84.58 74	96.68 2	94.95 15

fmvsm_l_conf0.5_n		84.47 93	84.54 93	84.27 153	85.42 313	68.81 119	88.49 154	87.26 310	68.08 330	88.03 50	93.49 80	72.04 66	91.77 276	88.90 29	89.14 155	92.24 193

IterMVS		74.29 335	72.94 344	78.35 359	81.53 412	63.49 291	81.58 369	82.49 395	68.06 331	69.99 387	83.69 382	51.66 342	85.54 412	65.85 303	71.64 426	86.01 409
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dmvs_testset		62.63 445	64.11 436	58.19 477	78.55 449	24.76 521	75.28 450	65.94 498	67.91 332	60.34 474	76.01 471	53.56 313	73.94 493	31.79 499	67.65 447	75.88 486

TAMVS		78.89 249	77.51 265	83.03 221	87.80 223	67.79 161	84.72 294	85.05 352	67.63 333	76.75 270	87.70 273	62.25 213	90.82 327	58.53 385	87.13 200	90.49 259

PVSNet_Blended		80.98 183	80.34 183	82.90 228	88.85 168	65.40 224	84.43 309	92.00 121	67.62 334	78.11 236	85.05 350	66.02 162	94.27 136	71.52 242	89.50 146	89.01 319

TR-MVS		77.44 287	76.18 293	81.20 282	88.24 198	63.24 297	84.61 301	86.40 332	67.55 335	77.81 244	86.48 314	54.10 307	93.15 211	57.75 393	82.72 288	87.20 379

CDS-MVSNet		79.07 243	77.70 258	83.17 212	87.60 239	68.23 144	84.40 312	86.20 336	67.49 336	76.36 281	86.54 312	61.54 226	90.79 328	61.86 350	87.33 195	90.49 259
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
fmvsm_l_conf0.5_n_a		84.13 100	84.16 98	84.06 170	85.38 314	68.40 136	88.34 162	86.85 322	67.48 337	87.48 61	93.40 85	70.89 82	91.61 282	88.38 37	89.22 152	92.16 200

dtuplus		80.04 218	79.40 212	81.97 262	83.08 375	62.61 313	83.63 331	87.98 285	67.47 338	81.02 175	90.50 187	64.86 176	90.77 331	71.28 247	84.76 247	92.53 176

mvs_anonymous		79.42 232	79.11 222	80.34 304	84.45 339	57.97 387	82.59 353	87.62 297	67.40 339	76.17 288	88.56 250	68.47 125	89.59 355	70.65 254	86.05 224	93.47 124

viewmambaseed2359dif		80.41 205	79.84 199	82.12 256	82.95 385	62.50 317	83.39 338	88.06 283	67.11 340	80.98 176	90.31 192	66.20 157	91.01 318	74.62 206	84.90 243	92.86 164

mvs5depth		69.45 406	67.45 416	75.46 400	73.93 478	55.83 423	79.19 410	83.23 381	66.89 341	71.63 369	83.32 389	33.69 479	85.09 417	59.81 370	55.34 490	85.46 420

IU-MVS					95.30 3	71.25 67		92.95 66	66.81 342	92.39 9				88.94 28	96.63 4	94.85 24

baseline2		75.70 319	73.83 333	81.30 278	83.26 367	61.79 332	82.57 354	80.65 418	66.81 342	66.88 429	83.42 388	57.86 271	92.19 258	63.47 319	79.57 326	89.91 290

miper_lstm_enhance		74.11 339	73.11 342	77.13 383	80.11 431	59.62 369	72.23 467	86.92 321	66.76 344	70.40 379	82.92 397	56.93 282	82.92 436	69.06 273	72.63 418	88.87 326

OpenMVS	72.83 10	79.77 222	78.33 239	84.09 165	85.17 319	69.91 96	90.57 70	90.97 165	66.70 345	72.17 362	91.91 126	54.70 302	93.96 149	61.81 351	90.95 119	88.41 343

test-LLR		72.94 364	72.43 349	74.48 411	81.35 416	58.04 385	78.38 422	77.46 453	66.66 346	69.95 388	79.00 445	48.06 386	79.24 457	66.13 297	84.83 244	86.15 405

test20.03		67.45 422	66.95 422	68.94 455	75.48 473	44.84 494	77.50 434	77.67 451	66.66 346	63.01 463	83.80 377	47.02 393	78.40 461	42.53 483	68.86 441	83.58 447

test0.0.03 1		68.00 420	67.69 411	68.90 456	77.55 462	47.43 481	75.70 448	72.95 480	66.66 346	66.56 434	82.29 409	48.06 386	75.87 480	44.97 475	74.51 402	83.41 448

Syy-MVS		68.05 419	67.85 406	68.67 459	84.68 333	40.97 504	78.62 419	73.08 478	66.65 349	66.74 432	79.46 440	52.11 329	82.30 441	32.89 498	76.38 373	82.75 457

myMVS_eth3d		67.02 426	66.29 427	69.21 454	84.68 333	42.58 499	78.62 419	73.08 478	66.65 349	66.74 432	79.46 440	31.53 484	82.30 441	39.43 489	76.38 373	82.75 457

QAPM		80.88 185	79.50 210	85.03 107	88.01 214	68.97 117	91.59 52	92.00 121	66.63 351	75.15 316	92.16 120	57.70 272	95.45 78	63.52 318	88.76 162	90.66 251

XXY-MVS		75.41 325	75.56 302	74.96 405	83.59 359	57.82 391	80.59 388	83.87 371	66.54 352	74.93 323	88.31 256	63.24 193	80.09 455	62.16 344	76.85 362	86.97 389

OurMVSNet-221017-0		74.26 336	72.42 350	79.80 321	83.76 354	59.59 370	85.92 261	86.64 327	66.39 353	66.96 428	87.58 276	39.46 455	91.60 283	65.76 304	69.27 437	88.22 348

SCA		74.22 337	72.33 351	79.91 317	84.05 347	62.17 324	79.96 400	79.29 441	66.30 354	72.38 359	80.13 433	51.95 333	88.60 376	59.25 376	77.67 353	88.96 323

nomal-1		73.10 360	71.76 356	77.13 383	82.58 393	65.50 222	73.53 464	79.64 436	66.14 355	72.17 362	81.27 417	46.45 400	81.47 448	62.08 347	81.93 298	84.42 437

testgi		66.67 429	66.53 426	67.08 466	75.62 472	41.69 503	75.93 444	76.50 462	66.11 356	65.20 451	86.59 308	35.72 475	74.71 487	43.71 476	73.38 414	84.84 432

HY-MVS	69.67 12	77.95 273	77.15 271	80.36 303	87.57 248	60.21 364	83.37 340	87.78 294	66.11 356	75.37 305	87.06 295	63.27 191	90.48 337	61.38 357	82.43 291	90.40 264

EG-PatchMatch MVS		74.04 340	71.82 355	80.71 295	84.92 327	67.42 173	85.86 264	88.08 281	66.04 358	64.22 456	83.85 375	35.10 476	92.56 239	57.44 395	80.83 311	82.16 463

CNLPA		78.08 268	76.79 280	81.97 262	90.40 112	71.07 74	87.59 189	84.55 359	66.03 359	72.38 359	89.64 212	57.56 274	86.04 406	59.61 372	83.35 278	88.79 330

gbinet_0.2-2-1-0.02		73.24 357	70.86 374	80.39 301	78.03 456	61.62 334	83.10 346	86.69 324	65.98 360	69.29 397	76.15 470	49.77 369	91.51 294	62.75 331	66.00 454	88.03 352

Anonymous20240529		80.19 215	78.89 227	84.10 161	90.60 107	64.75 254	88.95 129	90.90 168	65.97 361	80.59 188	91.17 161	49.97 365	93.73 170	69.16 272	82.70 289	93.81 99

TAPA-MVS	73.13 9	79.15 240	77.94 246	82.79 237	89.59 134	62.99 307	88.16 170	91.51 149	65.77 362	77.14 264	91.09 164	60.91 241	93.21 204	50.26 443	87.05 201	92.17 199
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
100007								93.18 49	65.76 363	92.41 8		79.65 13				93.00 157

MSDG		73.36 353	70.99 370	80.49 300	84.51 338	65.80 214	80.71 386	86.13 338	65.70 364	65.46 446	83.74 379	44.60 420	90.91 324	51.13 436	76.89 360	84.74 433

anonymousdsp		78.60 255	77.15 271	82.98 225	80.51 426	67.08 186	87.24 208	89.53 218	65.66 365	75.16 315	87.19 290	52.52 320	92.25 256	77.17 173	79.34 333	89.61 301

test_0402		72.79 368	70.44 380	79.84 320	88.13 206	65.99 207	85.93 260	84.29 363	65.57 366	67.40 424	85.49 337	46.92 394	92.61 235	35.88 495	74.38 403	80.94 470

UBG		73.08 361	72.27 352	75.51 398	88.02 212	51.29 467	78.35 425	77.38 456	65.52 367	73.87 338	82.36 406	45.55 414	86.48 401	55.02 414	84.39 257	88.75 332

miper_enhance_ethall		77.87 276	76.86 277	80.92 291	81.65 408	61.38 339	82.68 352	88.98 251	65.52 367	75.47 298	82.30 408	65.76 167	92.00 266	72.95 226	76.39 370	89.39 307

WBMVS		73.43 348	72.81 345	75.28 402	87.91 217	50.99 469	78.59 421	81.31 412	65.51 369	74.47 331	84.83 353	46.39 401	86.68 398	58.41 386	77.86 348	88.17 350

blend_shiyan4		72.29 373	69.65 387	80.21 309	78.24 454	62.16 325	82.29 358	87.27 308	65.41 370	68.43 409	76.42 466	39.91 453	91.23 306	63.21 325	65.66 461	87.22 378

blended_shiyan8		73.38 349	71.17 367	80.02 314	78.36 451	61.51 337	82.43 355	87.28 305	65.40 371	68.61 403	77.53 458	51.91 336	91.00 321	63.28 323	65.76 456	87.53 365

blended_shiyan6		73.38 349	71.17 367	80.01 315	78.36 451	61.48 338	82.43 355	87.27 308	65.40 371	68.56 405	77.55 457	51.94 335	91.01 318	63.27 324	65.76 456	87.55 364

UnsupCasMVSNet_eth		67.33 423	65.99 428	71.37 441	73.48 483	51.47 465	75.16 452	85.19 348	65.20 373	60.78 472	80.93 425	42.35 435	77.20 467	57.12 398	53.69 492	85.44 421

wanda-best-256-512		72.94 364	70.66 375	79.79 322	77.80 458	61.03 346	81.31 375	87.15 313	65.18 374	68.09 410	76.28 467	51.32 344	90.97 322	63.06 327	65.76 456	87.35 371

FE-blended-shiyan7		72.94 364	70.66 375	79.79 322	77.80 458	61.03 346	81.31 375	87.15 313	65.18 374	68.09 410	76.28 467	51.32 344	90.97 322	63.06 327	65.76 456	87.35 371

WTY-MVS		75.65 320	75.68 299	75.57 396	86.40 290	56.82 406	77.92 431	82.40 396	65.10 376	76.18 286	87.72 272	63.13 199	80.90 452	60.31 365	81.96 296	89.00 321

thisisatest0515		77.33 290	75.38 307	83.18 211	85.27 318	63.80 277	82.11 361	83.27 380	65.06 377	75.91 290	83.84 376	49.54 371	94.27 136	67.24 290	86.19 219	91.48 222

MVP-Stereo		76.12 313	74.46 324	81.13 285	85.37 315	69.79 98	84.42 311	87.95 288	65.03 378	67.46 421	85.33 341	53.28 317	91.73 279	58.01 391	83.27 280	81.85 465
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20231211		78.97 246	77.69 259	82.81 233	90.54 109	64.29 266	90.11 84	91.51 149	65.01 379	76.16 289	88.13 266	50.56 357	93.03 221	69.68 267	77.56 354	91.11 231

pmmvs6		74.69 332	73.39 337	78.61 350	81.38 415	57.48 398	86.64 233	87.95 288	64.99 380	70.18 382	86.61 307	50.43 359	89.52 356	62.12 345	70.18 434	88.83 328

PAPM		77.68 282	76.40 291	81.51 271	87.29 260	61.85 330	83.78 324	89.59 216	64.74 381	71.23 373	88.70 243	62.59 206	93.66 172	52.66 427	87.03 202	89.01 319

MIMVSNet		70.69 389	69.30 389	74.88 407	84.52 337	56.35 417	75.87 447	79.42 438	64.59 382	67.76 415	82.41 405	41.10 445	81.54 446	46.64 464	81.34 303	86.75 395

tpm		72.37 371	71.71 357	74.35 413	82.19 400	52.00 457	79.22 409	77.29 457	64.56 383	72.95 351	83.68 383	51.35 343	83.26 435	58.33 388	75.80 379	87.81 357

MDA-MVSNet-bldmvs		66.68 428	63.66 439	75.75 393	79.28 445	60.56 358	73.92 462	78.35 448	64.43 384	50.13 498	79.87 437	44.02 426	83.67 428	46.10 467	56.86 484	83.03 454

usedtu_blend_shiyan5		73.29 355	70.96 371	80.25 307	77.80 458	62.16 325	84.44 308	87.38 303	64.41 385	68.09 410	76.28 467	51.32 344	91.23 306	63.21 325	65.76 456	87.35 371

MIMVSNet1		68.58 413	66.78 425	73.98 419	80.07 432	51.82 461	80.77 383	84.37 360	64.40 386	59.75 478	82.16 411	36.47 472	83.63 429	42.73 480	70.33 433	86.48 400

D2MVS		74.82 331	73.21 340	79.64 331	79.81 436	62.56 316	80.34 393	87.35 304	64.37 387	68.86 400	82.66 403	46.37 403	90.10 344	67.91 283	81.24 305	86.25 402

PLC	70.83 11	78.05 270	76.37 292	83.08 218	91.88 86	67.80 160	88.19 168	89.46 220	64.33 388	69.87 390	88.38 254	53.66 312	93.58 173	58.86 381	82.73 287	87.86 356
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchmatchNet		73.12 359	71.33 363	78.49 357	83.18 371	60.85 350	79.63 403	78.57 446	64.13 389	71.73 367	79.81 438	51.20 349	85.97 407	57.40 396	76.36 375	88.66 335
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_2432*1600		66.22 433	63.89 437	73.21 425	75.47 474	53.42 448	70.76 474	84.35 361	64.10 390	66.52 436	78.52 449	34.55 477	84.98 418	50.40 439	50.33 497	81.23 468

miper_refine_blended		66.22 433	63.89 437	73.21 425	75.47 474	53.42 448	70.76 474	84.35 361	64.10 390	66.52 436	78.52 449	34.55 477	84.98 418	50.40 439	50.33 497	81.23 468

tpmvs		71.09 383	69.29 390	76.49 388	82.04 402	56.04 420	78.92 416	81.37 411	64.05 392	67.18 426	78.28 451	49.74 370	89.77 351	49.67 446	72.37 419	83.67 446

F-COLMAP		76.38 311	74.33 326	82.50 247	89.28 153	66.95 191	88.41 157	89.03 248	64.05 392	66.83 430	88.61 247	46.78 397	92.89 224	57.48 394	78.55 338	87.67 359

DP-MVS		76.78 299	74.57 320	83.42 199	93.29 54	69.46 107	88.55 152	83.70 372	63.98 394	70.20 381	88.89 239	54.01 310	94.80 116	46.66 462	81.88 299	86.01 409

原ACMM1				84.35 144	93.01 68	68.79 120		92.44 89	63.96 395	81.09 173	91.57 145	66.06 161	95.45 78	67.19 291	94.82 51	88.81 329

PM-MVS		66.41 431	64.14 435	73.20 427	73.92 479	56.45 412	78.97 414	64.96 501	63.88 396	64.72 452	80.24 432	19.84 504	83.44 433	66.24 296	64.52 466	79.71 477

FE-MVSNET		67.25 425	65.33 430	73.02 429	75.86 469	52.54 455	80.26 396	80.56 420	63.80 397	60.39 473	79.70 439	41.41 443	84.66 423	43.34 478	62.62 472	81.86 464

UWE-MVS		72.13 376	71.49 359	74.03 418	86.66 283	47.70 480	81.40 374	76.89 461	63.60 398	75.59 295	84.22 368	39.94 452	85.62 411	48.98 450	86.13 221	88.77 331

0.4-1-1-0.1		70.93 385	67.94 405	79.91 317	79.35 444	61.27 340	78.95 415	82.19 400	63.36 399	67.50 419	69.40 491	39.83 454	91.04 317	62.44 337	68.40 443	87.40 368

jason		81.39 176	80.29 186	84.70 126	86.63 284	69.90 97	85.95 259	86.77 323	63.24 400	81.07 174	89.47 218	61.08 239	92.15 259	78.33 159	90.07 136	92.05 203
jason: jason.
KD-MVS_self_test		68.81 410	67.59 414	72.46 434	74.29 477	45.45 488	77.93 430	87.00 317	63.12 401	63.99 459	78.99 447	42.32 436	84.77 421	56.55 407	64.09 467	87.16 383

gg-mvs-nofinetune		69.95 401	67.96 403	75.94 391	83.07 376	54.51 440	77.23 437	70.29 484	63.11 402	70.32 380	62.33 496	43.62 428	88.69 374	53.88 421	87.76 188	84.62 435

tpmrst		72.39 369	72.13 353	73.18 428	80.54 425	49.91 474	79.91 401	79.08 443	63.11 402	71.69 368	79.95 435	55.32 294	82.77 438	65.66 305	73.89 407	86.87 390

PCF-MVS	73.52 7	80.38 207	78.84 228	85.01 109	87.71 232	68.99 116	83.65 328	91.46 153	63.00 404	77.77 246	90.28 193	66.10 159	95.09 101	61.40 356	88.22 176	90.94 240
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
COLMAP_ROB	66.92 17	73.01 362	70.41 381	80.81 293	87.13 264	65.63 218	88.30 165	84.19 366	62.96 405	63.80 461	87.69 274	38.04 465	92.56 239	46.66 462	74.91 398	84.24 439
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-RL test		70.24 395	67.78 410	77.61 375	77.43 463	59.57 371	71.16 471	70.33 483	62.94 406	68.65 402	72.77 482	50.62 356	85.49 413	69.58 268	66.58 451	87.77 358

lupinMVS		81.39 176	80.27 187	84.76 124	87.35 250	70.21 89	85.55 273	86.41 331	62.85 407	81.32 168	88.61 247	61.68 223	92.24 257	78.41 157	90.26 131	91.83 206

test_vis1_n_1920		75.52 322	75.78 297	74.75 410	79.84 435	57.44 399	83.26 342	85.52 345	62.83 408	79.34 211	86.17 322	45.10 418	79.71 456	78.75 152	81.21 306	87.10 387

EPMVS		69.02 409	68.16 399	71.59 439	79.61 440	49.80 476	77.40 435	66.93 495	62.82 409	70.01 385	79.05 443	45.79 411	77.86 465	56.58 406	75.26 394	87.13 384

PatchMatch-RL		72.38 370	70.90 372	76.80 387	88.60 185	67.38 176	79.53 404	76.17 466	62.75 410	69.36 395	82.00 414	45.51 415	84.89 420	53.62 422	80.58 315	78.12 481

gm-plane-assit					81.40 414	53.83 445			62.72 411		80.94 423		92.39 249	63.40 321

0.3-1-1-0.015		70.03 399	66.80 424	79.72 327	78.18 455	61.07 344	77.63 433	82.32 399	62.65 412	65.50 445	67.29 492	37.62 468	90.91 324	61.99 348	68.04 445	87.19 380

FMVSNet5		69.50 405	67.96 403	74.15 416	82.97 384	55.35 430	80.01 399	82.12 402	62.56 413	63.02 462	81.53 416	36.92 469	81.92 444	48.42 452	74.06 405	85.17 427

sss		73.60 346	73.64 335	73.51 423	82.80 387	55.01 434	76.12 443	81.69 406	62.47 414	74.68 327	85.85 328	57.32 277	78.11 463	60.86 361	80.93 308	87.39 369

0.4-1-1-0.2		70.01 400	66.86 423	79.44 336	77.61 461	60.64 356	76.77 440	82.34 398	62.40 415	65.91 443	66.65 493	40.05 451	90.83 326	61.77 352	68.24 444	86.86 391

WB-MVSnew		71.96 378	71.65 358	72.89 430	84.67 336	51.88 460	82.29 358	77.57 452	62.31 416	73.67 341	83.00 395	53.49 315	81.10 451	45.75 470	82.13 294	85.70 416

AllTest		70.96 384	68.09 401	79.58 333	85.15 321	63.62 281	84.58 302	79.83 433	62.31 416	60.32 475	86.73 298	32.02 481	88.96 370	50.28 441	71.57 427	86.15 405

TestCases				79.58 333	85.15 321	63.62 281		79.83 433	62.31 416	60.32 475	86.73 298	32.02 481	88.96 370	50.28 441	71.57 427	86.15 405

1112_ss		77.40 289	76.43 289	80.32 305	89.11 164	60.41 361	83.65 328	87.72 296	62.13 419	73.05 348	86.72 300	62.58 207	89.97 348	62.11 346	80.80 312	90.59 255

PVSNet	64.34 18	72.08 377	70.87 373	75.69 394	86.21 293	56.44 413	74.37 460	80.73 417	62.06 420	70.17 383	82.23 410	42.86 433	83.31 434	54.77 416	84.45 255	87.32 375

UWE-MVS-28		65.32 436	64.93 431	66.49 467	78.70 448	38.55 506	77.86 432	64.39 502	62.00 421	64.13 457	83.60 384	41.44 442	76.00 478	31.39 500	80.89 309	84.92 430

LS3D		76.95 297	74.82 317	83.37 202	90.45 110	67.36 177	89.15 122	86.94 319	61.87 422	69.52 393	90.61 183	51.71 341	94.53 127	46.38 465	86.71 209	88.21 349

CostFormer		75.24 328	73.90 331	79.27 339	82.65 392	58.27 382	80.80 381	82.73 394	61.57 423	75.33 310	83.13 393	55.52 293	91.07 316	64.98 310	78.34 346	88.45 341

new-patchmatchnet		61.73 447	61.73 448	61.70 473	72.74 490	24.50 522	69.16 481	78.03 449	61.40 424	56.72 487	75.53 475	38.42 462	76.48 473	45.95 468	57.67 483	84.13 441

ANet_high		50.57 465	46.10 470	63.99 470	48.67 519	39.13 505	70.99 473	80.85 415	61.39 425	31.18 510	57.70 505	17.02 507	73.65 494	31.22 501	15.89 521	79.18 478

MS-PatchMatch		73.83 343	72.67 346	77.30 381	83.87 351	66.02 204	81.82 363	84.66 357	61.37 426	68.61 403	82.82 401	47.29 390	88.21 381	59.27 375	84.32 258	77.68 482

USDC		70.33 394	68.37 396	76.21 390	80.60 424	56.23 418	79.19 410	86.49 330	60.89 427	61.29 470	85.47 338	31.78 483	89.47 358	53.37 424	76.21 376	82.94 456

cascas		76.72 300	74.64 319	82.99 223	85.78 303	65.88 210	82.33 357	89.21 238	60.85 428	72.74 352	81.02 421	47.28 391	93.75 168	67.48 287	85.02 241	89.34 309

sc_t1		72.19 375	69.51 388	80.23 308	84.81 329	61.09 343	84.68 295	80.22 430	60.70 429	71.27 372	83.58 385	36.59 471	89.24 362	60.41 363	63.31 469	90.37 265

MDTV_nov1_ep13			69.97 386		83.18 371	53.48 447	77.10 439	80.18 432	60.45 430	69.33 396	80.44 427	48.89 384	86.90 396	51.60 432	78.51 340

TinyColmap		67.30 424	64.81 432	74.76 409	81.92 406	56.68 410	80.29 394	81.49 409	60.33 431	56.27 490	83.22 390	24.77 496	87.66 390	45.52 471	69.47 436	79.95 476

test-mter		71.41 380	70.39 382	74.48 411	81.35 416	58.04 385	78.38 422	77.46 453	60.32 432	69.95 388	79.00 445	36.08 474	79.24 457	66.13 297	84.83 244	86.15 405

1314		76.53 302	75.30 312	80.21 309	83.93 349	62.32 322	84.66 296	88.81 258	60.23 433	70.16 384	84.07 373	55.30 295	90.73 334	67.37 288	83.21 281	87.59 363

PatchT		68.46 416	67.85 406	70.29 449	80.70 423	43.93 496	72.47 466	74.88 470	60.15 434	70.55 376	76.57 462	49.94 366	81.59 445	50.58 437	74.83 399	85.34 422

dtuonlycased		68.45 417	67.29 419	71.92 436	80.18 430	54.90 435	79.76 402	80.38 427	60.11 435	62.57 467	76.44 465	49.34 375	82.31 440	55.05 413	61.77 475	78.53 480

无先验							87.48 191	88.98 251	60.00 436				94.12 145	67.28 289		88.97 322

CR-MVSNet		73.37 351	71.27 365	79.67 330	81.32 418	65.19 234	75.92 445	80.30 428	59.92 437	72.73 353	81.19 418	52.50 321	86.69 397	59.84 369	77.71 350	87.11 385

TDRefinement		67.49 421	64.34 434	76.92 385	73.47 484	61.07 344	84.86 292	82.98 389	59.77 438	58.30 482	85.13 347	26.06 492	87.89 386	47.92 459	60.59 480	81.81 466

dp		66.80 427	65.43 429	70.90 448	79.74 439	48.82 479	75.12 454	74.77 471	59.61 439	64.08 458	77.23 459	42.89 432	80.72 453	48.86 451	66.58 451	83.16 451

our_test_3		69.14 408	67.00 421	75.57 396	79.80 437	58.80 375	77.96 429	77.81 450	59.55 440	62.90 465	78.25 452	47.43 389	83.97 426	51.71 431	67.58 448	83.93 444

Test_1112_low_res		76.40 310	75.44 304	79.27 339	89.28 153	58.09 383	81.69 368	87.07 316	59.53 441	72.48 357	86.67 305	61.30 233	89.33 359	60.81 362	80.15 321	90.41 263

pmmvs4		74.03 342	71.91 354	80.39 301	81.96 404	68.32 138	81.45 372	82.14 401	59.32 442	69.87 390	85.13 347	52.40 323	88.13 383	60.21 366	74.74 400	84.73 434

testdata				79.97 316	90.90 101	64.21 268		84.71 356	59.27 443	85.40 81	92.91 97	62.02 218	89.08 366	68.95 274	91.37 110	86.63 399

WB-MVS		54.94 455	54.72 457	55.60 484	73.50 482	20.90 524	74.27 461	61.19 506	59.16 444	50.61 496	74.15 478	47.19 392	75.78 481	17.31 518	35.07 506	70.12 494

ppachtmachnet_test		70.04 398	67.34 418	78.14 362	79.80 437	61.13 341	79.19 410	80.59 419	59.16 444	65.27 448	79.29 442	46.75 398	87.29 393	49.33 448	66.72 449	86.00 411

RPSCF		73.23 358	71.46 360	78.54 354	82.50 395	59.85 366	82.18 360	82.84 393	58.96 446	71.15 375	89.41 224	45.48 417	84.77 421	58.82 382	71.83 425	91.02 237

pmmvs-eth3d		70.50 392	67.83 408	78.52 356	77.37 464	66.18 201	81.82 363	81.51 408	58.90 447	63.90 460	80.42 428	42.69 434	86.28 403	58.56 384	65.30 463	83.11 452

tt0320-xc		70.11 397	67.45 416	78.07 365	85.33 316	59.51 372	83.28 341	78.96 444	58.77 448	67.10 427	80.28 431	36.73 470	87.42 392	56.83 404	59.77 482	87.29 376

OpenMVS_ROB	64.09 19	70.56 391	68.19 398	77.65 374	80.26 427	59.41 373	85.01 288	82.96 390	58.76 449	65.43 447	82.33 407	37.63 467	91.23 306	45.34 474	76.03 377	82.32 460

114514_t		80.68 196	79.51 209	84.20 158	94.09 44	67.27 181	89.64 97	91.11 162	58.75 450	74.08 335	90.72 176	58.10 268	95.04 103	69.70 266	89.42 148	90.30 269

Patchmtry		70.74 388	69.16 392	75.49 399	80.72 422	54.07 443	74.94 456	80.30 428	58.34 451	70.01 385	81.19 418	52.50 321	86.54 399	53.37 424	71.09 430	85.87 414

test_cas_vis1_n_1920		73.76 344	73.74 334	73.81 421	75.90 468	59.77 367	80.51 389	82.40 396	58.30 452	81.62 164	85.69 330	44.35 424	76.41 474	76.29 185	78.61 337	85.23 424

Anonymous20240521		68.80 411	67.22 420	73.55 422	74.33 476	54.11 442	83.18 343	85.61 344	58.15 453	61.68 469	80.94 423	30.71 486	81.27 450	57.00 401	73.34 415	85.28 423

tt0320		70.49 393	68.03 402	77.89 367	84.78 330	59.12 374	83.55 333	80.44 424	58.13 454	67.43 423	80.41 429	39.26 457	87.54 391	55.12 412	63.18 470	86.99 388

旧先验2							86.56 237		58.10 455	87.04 67			88.98 368	74.07 213

JIA-IIPM		66.32 432	62.82 445	76.82 386	77.09 465	61.72 333	65.34 495	75.38 467	58.04 456	64.51 454	62.32 497	42.05 440	86.51 400	51.45 434	69.22 438	82.21 461

pmmvs5		71.55 379	70.20 384	75.61 395	77.83 457	56.39 414	81.74 365	80.89 414	57.76 457	67.46 421	84.49 357	49.26 378	85.32 416	57.08 399	75.29 393	85.11 428

TESTMET0.1,1		69.89 403	69.00 393	72.55 433	79.27 446	56.85 405	78.38 422	74.71 473	57.64 458	68.09 410	77.19 460	37.75 466	76.70 470	63.92 317	84.09 261	84.10 442

RPMNet		73.51 347	70.49 379	82.58 246	81.32 418	65.19 234	75.92 445	92.27 101	57.60 459	72.73 353	76.45 463	52.30 324	95.43 80	48.14 457	77.71 350	87.11 385

SSC-MVS		53.88 458	53.59 459	54.75 487	72.87 489	19.59 525	73.84 463	60.53 508	57.58 460	49.18 500	73.45 481	46.34 405	75.47 484	16.20 521	32.28 508	69.20 495

dtuonly		69.95 401	69.98 385	69.85 451	73.09 488	49.46 477	74.55 459	76.40 463	57.56 461	67.82 414	86.31 319	50.89 355	74.23 490	61.46 355	81.71 301	85.86 415

新几何1				83.42 199	93.13 62	70.71 83		85.48 346	57.43 462	81.80 159	91.98 125	63.28 190	92.27 255	64.60 313	92.99 78	87.27 377

YYNet1		65.03 437	62.91 443	71.38 440	75.85 470	56.60 411	69.12 482	74.66 474	57.28 463	54.12 492	77.87 454	45.85 410	74.48 488	49.95 444	61.52 477	83.05 453

MDA-MVSNet_test_wron		65.03 437	62.92 442	71.37 441	75.93 467	56.73 407	69.09 483	74.73 472	57.28 463	54.03 493	77.89 453	45.88 409	74.39 489	49.89 445	61.55 476	82.99 455

Anonymous20231206		68.60 412	67.80 409	71.02 446	80.23 429	50.75 471	78.30 426	80.47 422	56.79 465	66.11 442	82.63 404	46.35 404	78.95 459	43.62 477	75.70 380	83.36 449

tpm2		73.26 356	71.46 360	78.63 349	83.34 364	56.71 409	80.65 387	80.40 426	56.63 466	73.55 342	82.02 413	51.80 339	91.24 305	56.35 408	78.42 344	87.95 353

CHOSEN 1792x2688		77.63 285	75.69 298	83.44 198	89.98 125	68.58 132	78.70 418	87.50 300	56.38 467	75.80 293	86.84 296	58.67 264	91.40 300	61.58 354	85.75 233	90.34 266

HyFIR lowres test		77.53 286	75.40 306	83.94 183	89.59 134	66.62 194	80.36 392	88.64 273	56.29 468	76.45 278	85.17 346	57.64 273	93.28 197	61.34 358	83.10 283	91.91 205

usedtu_dtu_shiyan2		64.75 440	61.63 449	74.10 417	70.64 494	53.18 453	82.10 362	81.27 413	56.22 469	56.39 489	74.67 477	27.94 490	83.56 430	42.71 481	62.73 471	85.57 418

PVSNet_0	57.27 20	61.67 448	59.27 452	68.85 457	79.61 440	57.44 399	68.01 484	73.44 477	55.93 470	58.54 481	70.41 488	44.58 421	77.55 466	47.01 461	35.91 505	71.55 493

UnsupCasMVSNet_bld		63.70 443	61.53 450	70.21 450	73.69 481	51.39 466	72.82 465	81.89 403	55.63 471	57.81 484	71.80 484	38.67 461	78.61 460	49.26 449	52.21 495	80.63 472

MDTV_nov1_ep13_2view						37.79 507	75.16 452		55.10 472	66.53 435		49.34 375		53.98 420		87.94 354

MVS		78.19 266	76.99 275	81.78 265	85.66 305	66.99 187	84.66 296	90.47 182	55.08 473	72.02 365	85.27 342	63.83 187	94.11 146	66.10 299	89.80 141	84.24 439

test222					91.50 89	68.26 140	84.16 318	83.20 384	54.63 474	79.74 201	91.63 141	58.97 261			91.42 108	86.77 394

dongtai		45.42 469	45.38 471	45.55 491	73.36 485	26.85 519	67.72 485	34.19 521	54.15 475	49.65 499	56.41 508	25.43 493	62.94 508	19.45 516	28.09 510	46.86 513

CHOSEN 280x420		66.51 430	64.71 433	71.90 437	81.45 413	63.52 290	57.98 506	68.95 490	53.57 476	62.59 466	76.70 461	46.22 406	75.29 486	55.25 411	79.68 325	76.88 484

ADS-MVSNet2		66.20 435	63.33 440	74.82 408	79.92 433	58.75 376	67.55 486	75.19 468	53.37 477	65.25 449	75.86 472	42.32 436	80.53 454	41.57 484	68.91 439	85.18 425

ADS-MVSNet		64.36 441	62.88 444	68.78 458	79.92 433	47.17 484	67.55 486	71.18 482	53.37 477	65.25 449	75.86 472	42.32 436	73.99 492	41.57 484	68.91 439	85.18 425

LF4IMVS		64.02 442	62.19 446	69.50 453	70.90 493	53.29 451	76.13 442	77.18 458	52.65 479	58.59 480	80.98 422	23.55 499	76.52 472	53.06 426	66.66 450	78.68 479

tpm cat1		70.57 390	68.31 397	77.35 380	82.41 398	57.95 388	78.08 427	80.22 430	52.04 480	68.54 406	77.66 456	52.00 332	87.84 387	51.77 430	72.07 424	86.25 402

test_vis1_n		69.85 404	69.21 391	71.77 438	72.66 491	55.27 432	81.48 371	76.21 465	52.03 481	75.30 311	83.20 392	28.97 488	76.22 476	74.60 207	78.41 345	83.81 445

Patchmatch-test		64.82 439	63.24 441	69.57 452	79.42 443	49.82 475	63.49 501	69.05 489	51.98 482	59.95 477	80.13 433	50.91 351	70.98 496	40.66 486	73.57 410	87.90 355

N_pmnet		52.79 461	53.26 460	51.40 489	78.99 447	7.68 538	69.52 478	3.89 541	51.63 483	57.01 486	74.98 476	40.83 447	65.96 504	37.78 491	64.67 465	80.56 475

test_fmvs1_n		70.86 387	70.24 383	72.73 432	72.51 492	55.28 431	81.27 377	79.71 435	51.49 484	78.73 218	84.87 352	27.54 491	77.02 468	76.06 189	79.97 324	85.88 413

test_fmvs1		70.93 385	70.52 378	72.16 435	73.71 480	55.05 433	80.82 380	78.77 445	51.21 485	78.58 223	84.41 360	31.20 485	76.94 469	75.88 193	80.12 323	84.47 436

PatchmatchNet2					0.00 568	30.51 514	67.30 488	67.46 493	50.92 486
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PMMVS		69.34 407	68.67 394	71.35 443	75.67 471	62.03 327	75.17 451	73.46 476	50.00 487	68.68 401	79.05 443	52.07 331	78.13 462	61.16 359	82.77 286	73.90 489

test_fmvs2		68.35 418	67.48 415	70.98 447	69.50 496	51.95 458	80.05 398	76.38 464	49.33 488	74.65 328	84.38 361	23.30 500	75.40 485	74.51 208	75.17 396	85.60 417

ttmdpeth		59.91 450	57.10 455	68.34 461	67.13 500	46.65 487	74.64 457	67.41 494	48.30 489	62.52 468	85.04 351	20.40 502	75.93 479	42.55 482	45.90 503	82.44 459

CMPMVS	51.72 21	70.19 396	68.16 399	76.28 389	73.15 487	57.55 397	79.47 405	83.92 369	48.02 490	56.48 488	84.81 354	43.13 431	86.42 402	62.67 335	81.81 300	84.89 431
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
mvsany_test1		62.30 446	61.26 451	65.41 469	69.52 495	54.86 436	66.86 489	49.78 515	46.65 491	68.50 407	83.21 391	49.15 379	66.28 503	56.93 402	60.77 478	75.11 487

kuosan		39.70 475	40.40 475	37.58 496	64.52 503	26.98 517	65.62 494	33.02 522	46.12 492	42.79 504	48.99 515	24.10 498	46.56 518	12.16 526	26.30 511	39.20 517

test_fmvs3		63.36 444	61.82 447	67.98 463	62.51 505	46.96 486	77.37 436	74.03 475	45.24 493	67.50 419	78.79 448	12.16 512	72.98 495	72.77 229	66.02 453	83.99 443

CVMVSNet		72.99 363	72.58 348	74.25 415	84.28 340	50.85 470	86.41 242	83.45 378	44.56 494	73.23 346	87.54 280	49.38 374	85.70 409	65.90 302	78.44 341	86.19 404

test_vis1_rt		60.28 449	58.42 453	65.84 468	67.25 499	55.60 427	70.44 476	60.94 507	44.33 495	59.00 479	66.64 494	24.91 495	68.67 501	62.80 330	69.48 435	73.25 490

mvsany_test3		53.99 457	51.45 463	61.61 474	55.51 510	44.74 495	63.52 500	45.41 519	43.69 496	58.11 483	76.45 463	17.99 505	63.76 507	54.77 416	47.59 499	76.34 485

EU-MVSNet		68.53 415	67.61 413	71.31 444	78.51 450	47.01 485	84.47 304	84.27 364	42.27 497	66.44 439	84.79 355	40.44 449	83.76 427	58.76 383	68.54 442	83.17 450

FPMVS		53.68 459	51.64 462	59.81 476	65.08 502	51.03 468	69.48 479	69.58 487	41.46 498	40.67 506	72.32 483	16.46 508	70.00 500	24.24 511	65.42 462	58.40 504

pmmvs3		57.79 452	54.26 458	68.37 460	64.02 504	56.72 408	75.12 454	65.17 499	40.20 499	52.93 494	69.86 490	20.36 503	75.48 483	45.45 472	55.25 491	72.90 491

new_pmnet		50.91 464	50.29 465	52.78 488	68.58 497	34.94 512	63.71 499	56.63 512	39.73 500	44.95 501	65.47 495	21.93 501	58.48 510	34.98 496	56.62 485	64.92 498

MVS-HIRNet		59.14 451	57.67 454	63.57 471	81.65 408	43.50 497	71.73 468	65.06 500	39.59 501	51.43 495	57.73 504	38.34 463	82.58 439	39.53 487	73.95 406	64.62 499

MVStest1		56.63 454	52.76 461	68.25 462	61.67 506	53.25 452	71.67 469	68.90 491	38.59 502	50.59 497	83.05 394	25.08 494	70.66 497	36.76 494	38.56 504	80.83 471

PMMVS2		40.82 474	38.86 479	46.69 490	53.84 512	16.45 529	48.61 509	49.92 514	37.49 503	31.67 509	60.97 499	8.14 518	56.42 512	28.42 503	30.72 509	67.19 497

test_vis3_rt		49.26 466	47.02 469	56.00 481	54.30 511	45.27 492	66.76 491	48.08 516	36.83 504	44.38 502	53.20 511	7.17 519	64.07 506	56.77 405	55.66 487	58.65 503

test_f		52.09 462	50.82 464	55.90 482	53.82 513	42.31 502	59.42 505	58.31 511	36.45 505	56.12 491	70.96 487	12.18 511	57.79 511	53.51 423	56.57 486	67.60 496

LCM-MVSNet		54.25 456	49.68 467	67.97 464	53.73 514	45.28 491	66.85 490	80.78 416	35.96 506	39.45 508	62.23 498	8.70 516	78.06 464	48.24 456	51.20 496	80.57 474

ArgMatch-Sym		43.72 473	39.92 477	55.10 486	52.36 516	37.56 508	61.93 503	23.00 527	35.80 507	43.62 503	70.22 489	3.22 525	55.93 513	45.35 473	23.80 514	71.81 492

APD_test1		53.31 460	49.93 466	63.42 472	65.68 501	50.13 473	71.59 470	66.90 496	34.43 508	40.58 507	71.56 485	8.65 517	76.27 475	34.64 497	55.36 489	63.86 500

PMVS	37.38 22	44.16 471	40.28 476	55.82 483	40.82 522	42.54 501	65.12 496	63.99 503	34.43 508	24.48 516	57.12 506	3.92 524	76.17 477	17.10 519	55.52 488	48.75 510
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ArgMatch-SfM		44.04 472	39.87 478	56.58 480	50.92 518	36.22 509	59.86 504	27.68 525	33.67 510	42.15 505	71.07 486	3.10 527	59.10 509	45.79 469	24.54 512	74.41 488

Gipuma		45.18 470	41.86 474	55.16 485	77.03 466	51.52 464	32.50 516	80.52 421	32.46 511	27.12 514	35.02 524	9.52 515	75.50 482	22.31 513	60.21 481	38.45 518
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DSMNet-mixed		57.77 453	56.90 456	60.38 475	67.70 498	35.61 510	69.18 480	53.97 513	32.30 512	57.49 485	79.88 436	40.39 450	68.57 502	38.78 490	72.37 419	76.97 483

testf1		45.72 467	41.96 472	57.00 478	56.90 508	45.32 489	66.14 492	59.26 509	26.19 513	30.89 511	60.96 500	4.14 522	70.64 498	26.39 509	46.73 501	55.04 506

APD_test2		45.72 467	41.96 472	57.00 478	56.90 508	45.32 489	66.14 492	59.26 509	26.19 513	30.89 511	60.96 500	4.14 522	70.64 498	26.39 509	46.73 501	55.04 506

DenseAffine		31.97 476	28.22 483	43.21 493	43.10 521	27.10 516	46.21 510	11.36 531	24.92 515	27.70 513	58.81 503	1.09 531	46.50 519	26.95 506	13.85 525	56.02 505

E-PMN		31.77 477	30.64 480	35.15 498	52.87 515	27.67 515	57.09 507	47.86 517	24.64 516	16.40 530	33.05 525	11.23 513	54.90 514	14.46 522	18.15 519	22.87 525

EMVS		30.81 479	29.65 481	34.27 499	50.96 517	25.95 520	56.58 508	46.80 518	24.01 517	15.53 531	30.68 528	12.47 510	54.43 515	12.81 525	17.05 520	22.43 526

RoMa-SfM		28.67 481	25.38 486	38.54 494	32.61 526	22.48 523	40.24 511	7.23 535	21.81 518	26.66 515	60.46 502	0.96 532	41.72 520	26.47 508	11.95 526	51.40 509

DKM		25.67 483	23.01 488	33.64 500	32.08 527	19.25 527	37.50 513	5.52 537	18.67 519	23.58 519	55.44 509	0.64 538	34.02 522	23.95 512	9.73 528	47.66 512

PDCNetPlus		24.75 484	22.46 489	31.64 501	35.53 524	17.00 528	32.00 517	9.46 532	18.43 520	18.56 528	51.31 513	1.65 529	33.00 524	26.51 507	8.70 530	44.91 514

MVE	26.22 23	30.37 480	25.89 485	43.81 492	44.55 520	35.46 511	28.87 521	39.07 520	18.20 521	18.58 527	40.18 520	2.68 528	47.37 517	17.07 520	23.78 515	48.60 511
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				27.40 504	40.17 523	26.90 518		24.59 526	17.44 522	23.95 517	48.61 517	9.77 514	26.48 526	18.06 517	24.47 513	28.83 523

RoMa-HiRes		21.63 486	19.64 492	27.59 503	22.40 531	14.25 531	29.71 519	4.10 539	15.42 523	21.09 523	54.77 510	0.72 536	28.87 525	21.01 514	7.52 534	39.65 516

DKM-HiRes		20.87 487	19.15 493	26.02 505	25.34 530	14.13 532	29.63 520	3.62 544	14.53 524	20.13 524	50.55 514	0.47 546	24.22 529	20.96 515	7.15 535	39.70 515

LoFTR		27.52 482	24.27 487	37.29 497	34.75 525	19.27 526	33.78 515	21.60 528	12.42 525	21.61 522	56.59 507	0.91 533	40.37 521	13.94 523	22.80 516	52.22 508

MatchFormer		22.13 485	19.86 491	28.93 502	28.66 528	15.74 530	31.91 518	17.10 530	7.75 526	18.87 526	47.50 518	0.62 540	33.92 523	7.49 533	18.87 518	37.14 519

wuyk23d		16.82 490	15.94 495	19.46 508	58.74 507	31.45 513	39.22 512	3.74 543	6.84 527	6.04 538	2.70 559	1.27 530	24.29 528	10.54 531	14.40 524	2.63 543

PMatch-SfM		14.15 493	12.67 498	18.59 509	12.84 537	7.03 540	17.41 523	2.28 546	6.63 528	12.96 532	43.56 519	0.09 563	16.11 532	13.90 524	4.38 545	32.63 522

ELoFTR		14.23 492	11.56 499	22.24 506	11.02 539	6.56 542	13.59 528	7.57 534	5.55 529	11.96 534	39.09 521	0.21 551	24.93 527	9.43 532	5.66 539	35.22 520

PMatch-Up-SfM		10.76 497	9.99 501	13.09 510	9.50 545	4.83 545	12.94 530	1.40 555	4.65 530	10.16 535	37.54 522	0.07 566	10.94 534	10.71 530	2.92 556	23.50 524

MASt3R-SfM		13.55 494	13.93 496	12.41 511	10.54 542	5.97 544	16.61 524	6.07 536	4.50 531	16.53 529	48.67 516	0.73 535	9.44 535	11.56 529	10.18 527	21.81 527

GLUNet-SfM		12.90 495	10.00 500	21.62 507	13.58 536	8.30 536	10.19 532	9.30 533	4.31 532	12.18 533	30.90 527	0.50 544	22.76 530	4.89 534	4.14 546	33.79 521

test_method		31.52 478	29.28 482	38.23 495	27.03 529	6.50 543	20.94 522	62.21 505	4.05 533	22.35 520	52.50 512	13.33 509	47.58 516	27.04 505	34.04 507	60.62 501

VLMVS_CLIP		15.14 491	16.11 494	12.23 512	12.32 538	7.35 539	15.53 525	20.73 529	4.02 534	22.32 521	31.59 526	4.37 521	21.02 531	11.59 528	22.52 517	8.32 529

ALIKED-LG		8.61 498	8.70 503	8.33 514	20.63 532	8.70 535	15.50 526	4.61 538	2.19 535	5.84 539	18.70 530	0.80 534	8.06 536	1.03 544	8.97 529	8.25 530

MVS_clip		11.37 496	13.03 497	6.40 517	15.78 535	6.79 541	11.98 531	1.47 554	1.89 536	19.38 525	35.95 523	3.13 526	3.09 541	12.10 527	15.54 522	9.34 528

ALIKED-MNN		7.86 499	7.83 506	7.97 515	19.40 533	8.86 534	14.48 527	3.90 540	1.59 537	4.74 544	16.49 531	0.59 541	7.65 537	0.91 545	8.34 532	7.39 533

ALIKED-NN		7.51 500	7.61 507	7.21 516	18.26 534	8.10 537	13.45 529	3.88 542	1.50 538	4.87 542	16.47 532	0.64 538	7.00 538	0.88 546	8.50 531	6.52 538

tmp_tt		18.61 489	21.40 490	10.23 513	4.82 561	10.11 533	34.70 514	30.74 524	1.48 539	23.91 518	26.07 529	28.42 489	13.41 533	27.12 504	15.35 523	7.17 536

SP-DiffGlue		4.29 507	4.46 511	3.77 522	3.68 562	2.12 553	5.97 537	2.22 547	1.10 540	4.89 541	13.93 535	0.66 537	1.95 547	2.47 535	5.24 540	7.22 535

XFeat-MNN		4.39 506	4.49 510	4.10 518	2.88 564	1.91 559	5.86 538	2.57 545	1.06 541	5.04 540	13.99 534	0.43 548	4.47 539	2.00 537	6.55 537	5.92 539

SP-SuperGlue		4.24 509	4.38 513	3.81 521	10.75 541	2.00 555	8.18 534	2.09 548	1.00 542	2.41 545	8.29 539	0.56 542	2.05 546	1.27 540	4.91 542	7.39 533

SP-LightGlue		4.27 508	4.41 512	3.86 519	10.99 540	1.99 556	8.19 533	2.06 549	0.98 543	2.37 546	8.29 539	0.56 542	2.10 544	1.27 540	4.99 541	7.48 532

SP-NN		4.00 511	4.12 515	3.63 523	9.92 544	1.81 561	7.94 536	1.90 552	0.86 544	2.15 548	8.00 542	0.50 544	2.09 545	1.20 542	4.63 544	6.98 537

SP-MNN		4.14 510	4.24 514	3.82 520	10.32 543	1.83 560	8.11 535	1.99 550	0.82 545	2.23 547	8.27 541	0.47 546	2.14 543	1.20 542	4.77 543	7.49 531

XFeat-NN		3.78 512	3.96 517	3.23 525	2.65 565	1.53 564	4.99 539	1.92 551	0.81 546	4.77 543	12.37 537	0.38 549	3.39 540	1.64 538	6.13 538	4.77 541

SIFT-NN		2.77 514	2.92 518	2.34 526	8.70 546	3.08 546	4.46 540	1.01 557	0.68 547	1.46 549	5.49 543	0.16 552	1.65 548	0.26 547	4.04 547	2.27 544

SIFT-MNN		2.63 515	2.75 519	2.25 527	8.10 547	2.84 547	4.08 541	1.02 556	0.68 547	1.28 550	5.34 546	0.15 553	1.64 549	0.26 547	3.88 549	2.27 544

SIFT-NN-UMatch		2.26 519	2.39 523	1.89 532	6.21 555	2.08 554	3.76 543	0.83 560	0.66 549	1.04 554	5.09 547	0.14 554	1.52 552	0.23 550	3.51 551	2.07 548

SIFT-NCM-Cal		2.40 517	2.52 521	2.05 529	7.74 548	2.54 549	3.75 544	0.84 559	0.65 550	0.89 557	4.78 552	0.13 557	1.60 550	0.19 558	3.71 550	2.01 550

SIFT-NN-NCMNet		2.52 516	2.64 520	2.14 528	7.53 549	2.74 548	4.00 542	0.98 558	0.65 550	1.24 552	5.08 549	0.14 554	1.60 550	0.23 550	3.94 548	2.07 548

SIFT-NN-CMatch		2.31 518	2.41 522	2.00 530	6.59 553	2.34 551	3.48 545	0.83 560	0.65 550	1.28 550	5.09 547	0.14 554	1.52 552	0.23 550	3.41 552	2.14 546

SIFT-ConvMatch		2.25 520	2.37 524	1.90 531	7.29 550	2.37 550	3.21 549	0.75 562	0.65 550	1.03 555	4.91 550	0.12 560	1.51 554	0.22 553	3.13 554	1.81 551

SIFT-UMatch		2.16 521	2.30 525	1.72 534	6.99 551	1.97 558	3.32 547	0.70 564	0.64 554	0.91 556	4.86 551	0.12 560	1.49 555	0.22 553	2.97 555	1.72 553

SIFT-CM-Cal		2.02 523	2.13 527	1.67 535	6.79 552	1.99 556	2.79 551	0.64 565	0.63 555	0.87 558	4.48 555	0.13 557	1.41 557	0.19 558	2.70 557	1.61 555

SIFT-UM-Cal		1.97 524	2.12 528	1.52 536	6.57 554	1.67 562	2.93 550	0.57 567	0.62 556	0.83 559	4.55 554	0.11 562	1.37 558	0.20 557	2.69 558	1.53 556

SIFT-NN-PointCN		2.07 522	2.18 526	1.74 533	5.75 556	1.65 563	3.27 548	0.73 563	0.60 557	1.07 553	4.62 553	0.13 557	1.43 556	0.21 555	3.22 553	2.12 547

SIFT-NCMNet		1.44 527	1.56 531	1.08 540	5.14 559	1.07 567	1.97 554	0.32 569	0.56 558	0.64 562	3.23 558	0.07 566	1.01 561	0.14 562	1.95 561	1.15 557

SIFT-PCN-Cal		1.72 525	1.82 530	1.39 537	5.64 557	1.19 566	2.39 553	0.53 568	0.55 559	0.72 560	3.90 556	0.09 563	1.22 560	0.17 560	2.42 560	1.76 552

SIFT-PointCN		1.72 525	1.83 529	1.36 538	5.55 558	1.22 565	2.59 552	0.59 566	0.55 559	0.71 561	3.77 557	0.08 565	1.24 559	0.17 560	2.48 559	1.63 554

VLMVS		4.54 505	4.93 509	3.37 524	4.86 560	2.23 552	3.38 546	1.77 553	0.23 561	7.94 536	11.34 538	4.62 520	2.44 542	2.43 536	7.76 533	5.44 540

EGC-MVSNET		52.07 463	47.05 468	67.14 465	83.51 361	60.71 354	80.50 390	67.75 492	0.07 562	0.43 563	75.85 474	24.26 497	81.54 446	28.82 502	62.25 473	59.16 502

testmvs		6.04 503	8.02 505	0.10 542	0.08 566	0.03 570	69.74 477	0.04 571	0.05 563	0.31 564	1.68 560	0.02 569	0.04 562	0.24 549	0.02 562	0.25 559

MVS_baseline		3.29 513	4.00 516	1.16 539	3.08 563	0.09 568	1.26 555	0.24 570	0.04 564	6.52 537	16.19 533	0.30 550	0.00 564	1.53 539	6.83 536	3.39 542

test123		6.12 502	8.11 504	0.14 541	0.06 567	0.09 568	71.05 472	0.03 572	0.04 564	0.25 565	1.30 561	0.05 568	0.03 563	0.21 555	0.01 563	0.29 558

mmdepth		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 573	0.00 566	0.00 566	0.00 562	0.00 570	0.00 564	0.00 563	0.00 564	0.00 560

monomultidepth		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 573	0.00 566	0.00 566	0.00 562	0.00 570	0.00 564	0.00 563	0.00 564	0.00 560

test_blank		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 573	0.00 566	0.00 566	0.00 562	0.00 570	0.00 564	0.00 563	0.00 564	0.00 560

uanet_test		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 573	0.00 566	0.00 566	0.00 562	0.00 570	0.00 564	0.00 563	0.00 564	0.00 560

DCPMVS		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 573	0.00 566	0.00 566	0.00 562	0.00 570	0.00 564	0.00 563	0.00 564	0.00 560

cdsmvs_eth3d_5k		19.96 488	26.61 484	0.00 543	0.00 568	0.00 571	0.00 556	89.26 234	0.00 566	0.00 566	88.61 247	61.62 225	0.00 564	0.00 563	0.00 564	0.00 560

pcd_1.5k_mvsjas		5.26 504	7.02 508	0.00 543	0.00 568	0.00 571	0.00 556	0.00 573	0.00 566	0.00 566	0.00 562	63.15 196	0.00 564	0.00 563	0.00 564	0.00 560

sosnet-low-res		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 573	0.00 566	0.00 566	0.00 562	0.00 570	0.00 564	0.00 563	0.00 564	0.00 560

sosnet		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 573	0.00 566	0.00 566	0.00 562	0.00 570	0.00 564	0.00 563	0.00 564	0.00 560

uncertanet		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 573	0.00 566	0.00 566	0.00 562	0.00 570	0.00 564	0.00 563	0.00 564	0.00 560

Regformer		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 573	0.00 566	0.00 566	0.00 562	0.00 570	0.00 564	0.00 563	0.00 564	0.00 560

ab-mvs-re		7.23 501	9.64 502	0.00 543	0.00 568	0.00 571	0.00 556	0.00 573	0.00 566	0.00 566	86.72 300	0.00 570	0.00 564	0.00 563	0.00 564	0.00 560

uanet		0.00 528	0.00 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 573	0.00 566	0.00 566	0.00 562	0.00 570	0.00 564	0.00 563	0.00 564	0.00 560

Meshroom													0.00 564
: In preparation.
AliceVision / Meshro													0.00 564
: In preparation.
AliceVision_Meshroom													0.00 564
: In preparation.
PatchmatchNet1														37.67 492	64.79 464	80.58 473
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													65.90 505
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
test-260524					94.58 17	71.43 62		94.16 7		90.64 25		79.61 14	97.25 15	88.08 42	96.28 16

WAC-MVS						42.58 499								39.46 488

MSC_two_6792asdad				89.16 1	94.34 33	75.53 2		92.99 60					97.53 2	89.67 15	96.44 9	94.41 61

No_MVS				89.16 1	94.34 33	75.53 2		92.99 60					97.53 2	89.67 15	96.44 9	94.41 61

eth-test2					0.00 568

eth-test					0.00 568

OPU-MVS				89.06 3	94.62 16	75.42 4	93.57 9				94.02 63	82.45 3	96.87 26	83.77 85	96.48 8	94.88 19

test_0728_SECOND				87.71 35	95.34 2	71.43 62	93.49 11	94.23 5					97.49 4	89.08 22	96.41 12	94.21 74

GSMVS																88.96 323

test_part2					95.06 9	72.65 33				91.80 18

sam_mvs1												51.32 344				88.96 323

sam_mvs												50.01 364

ambc				75.24 403	73.16 486	50.51 472	63.05 502	87.47 301		64.28 455	77.81 455	17.80 506	89.73 353	57.88 392	60.64 479	85.49 419

MTGPA								92.02 119

test_post1							78.90 417				5.43 545	48.81 385	85.44 415	59.25 376

test_post											5.46 544	50.36 360	84.24 424

patchmatchnet-post											74.00 479	51.12 350	88.60 376

GG-mvs-BLEND				75.38 401	81.59 410	55.80 424	79.32 407	69.63 486		67.19 425	73.67 480	43.24 430	88.90 372	50.41 438	84.50 251	81.45 467

MTMP							92.18 40	32.83 523

test9_res														84.90 67	95.70 30	92.87 163

agg_prior2														82.91 94	95.45 33	92.70 168

agg_prior					92.85 70	71.94 54		91.78 135		84.41 102			94.93 105

test_prior4						72.60 35	89.01 127

test_prior				86.33 65	92.61 77	69.59 101		92.97 65					95.48 77			93.91 90

新几何2							86.29 251

旧先验1					91.96 83	65.79 215		86.37 333			93.08 95	69.31 108			92.74 83	88.74 334

原ACMM2							86.86 223

testdata2													91.01 318	62.37 341

segment_acmp												73.08 50

test12				86.80 59	92.63 76	70.70 84		91.79 134		82.71 146		71.67 72	96.16 55		94.50 58	93.54 121

plane_prior7					90.08 119	68.51 134

plane_prior6					89.84 128	68.70 128						60.42 251

plane_prior5								92.44 89					95.38 85	78.71 153	86.32 215	91.33 225

plane_prior4											91.00 169

plane_prior1					89.90 127

n2								0.00 573

nn								0.00 573

door-mid								69.98 485

lessismore_v0				78.97 344	81.01 421	57.15 402		65.99 497		61.16 471	82.82 401	39.12 458	91.34 302	59.67 371	46.92 500	88.43 342

test11								92.23 105

door								69.44 488

HQP5-MVS						66.98 188

BP-MVS														77.47 169

HQP4-MVS										77.24 257			95.11 97			91.03 235

HQP3-MVS								92.19 113							85.99 226

HQP2-MVS												60.17 254

NP-MVS					89.62 133	68.32 138					90.24 195

ACMMP++_ref															81.95 297

ACMMP++															81.25 304

Test By Simon												64.33 181

High-resolution multi-view benchmark