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	90.79 104	73.65 10	92.66 28	91.17 155	86.57 1	87.39 60	94.97 26	71.70 67	97.68 1	92.19 1	95.63 32	95.57 2

UA-Net		85.08 87	84.96 87	85.45 91	92.07 81	68.07 147	89.78 91	90.86 167	82.48 2	84.60 96	93.20 89	69.35 102	95.22 90	71.39 244	90.88 119	93.07 149

MGCNet		87.69 24	87.55 29	88.12 13	89.45 141	71.76 54	91.47 57	89.54 213	82.14 3	86.65 69	94.28 47	68.28 125	97.46 6	90.81 6	95.31 38	95.15 9

CANet		86.45 48	86.10 62	87.51 42	90.09 117	70.94 78	89.70 94	92.59 82	81.78 4	81.32 165	91.43 150	70.34 84	97.23 16	84.26 77	93.36 74	94.37 65

NCCC		88.06 18	88.01 22	88.24 11	94.41 27	73.62 11	91.22 62	92.83 67	81.50 5	85.79 75	93.47 82	73.02 48	97.00 22	84.90 66	94.94 44	94.10 80

EPNet		83.72 115	82.92 130	86.14 74	84.22 340	69.48 103	91.05 64	85.27 343	81.30 6	76.83 263	91.65 138	66.09 156	95.56 70	76.00 190	93.85 68	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 12	73.82 8	90.51 70	93.00 53	80.90 7	88.06 46	94.06 60	76.43 21	96.84 26	88.48 37	95.99 20	94.34 67

3Dnovator+	77.84 4	85.48 75	84.47 95	88.51 7	91.08 95	73.49 16	93.18 16	93.78 24	80.79 8	76.66 268	93.37 85	60.40 248	96.75 31	77.20 171	93.73 70	95.29 7

TranMVSNet+NR-MVSNet		80.84 186	80.31 183	82.42 249	87.85 218	62.33 320	87.74 185	91.33 150	80.55 9	77.99 236	89.86 200	65.23 166	92.62 233	67.05 292	75.24 392	92.30 188

Casviewmamba		86.09 56	86.04 64	86.24 67	88.17 200	68.05 149	89.44 104	92.79 71	80.30 10	84.71 89	92.78 104	72.83 52	95.05 101	82.81 95	90.57 123	95.62 1

MSP-MVS		89.51 5	89.91 6	88.30 10	94.28 35	73.46 17	92.90 21	94.11 11	80.27 11	91.35 17	94.16 55	78.35 15	96.77 29	89.59 17	94.22 66	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 9	76.03 1	92.38 32	92.85 66	80.26 12	87.78 51	94.27 48	75.89 24	96.81 28	87.45 48	96.44 9	93.05 152

fmvsm_s_conf0.5_n_9		87.39 33	87.95 23	85.70 83	89.48 140	67.88 156	88.59 148	89.05 243	80.19 13	90.70 21	95.40 18	74.56 30	93.92 155	91.54 2	92.07 94	95.31 6

UniMVSNet_NR-MVSNet		81.88 160	81.54 159	82.92 227	88.46 188	63.46 291	87.13 208	92.37 90	80.19 13	78.38 225	89.14 225	71.66 69	93.05 217	70.05 260	76.46 365	92.25 190

SteuartSystems-ACMMP		88.72 14	88.86 14	88.32 9	92.14 80	72.96 25	93.73 5	93.67 26	80.19 13	88.10 45	94.80 28	73.76 40	97.11 18	87.51 47	95.82 25	94.90 18
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		84.19 99	83.81 108	85.31 96	88.18 199	67.85 157	87.66 186	89.73 207	80.05 16	82.95 135	89.59 214	70.74 81	94.82 112	80.66 122	84.72 245	93.28 132

ETV-MVS		84.90 91	84.67 91	85.59 88	89.39 145	68.66 129	88.74 141	92.64 80	79.97 17	84.10 108	85.71 327	69.32 103	95.38 84	80.82 117	91.37 108	92.72 166

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 125	87.76 227	65.62 218	89.20 115	92.21 106	79.94 18	89.74 29	94.86 27	68.63 119	94.20 140	90.83 5	91.39 107	94.38 64

EI-MVSNet-UG-set		83.81 109	83.38 120	85.09 106	87.87 217	67.53 169	87.44 199	89.66 208	79.74 19	82.23 148	89.41 223	70.24 87	94.74 118	79.95 130	83.92 260	92.99 158

fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 216	87.08 268	65.21 232	89.09 124	90.21 190	79.67 20	89.98 26	95.02 25	73.17 45	91.71 279	91.30 3	91.60 102	92.34 185

CS-MVS		86.69 44	86.95 42	85.90 80	90.76 105	67.57 167	92.83 22	93.30 39	79.67 20	84.57 97	92.27 111	71.47 70	95.02 103	84.24 79	93.46 73	95.13 11

casdiffmvs_mvg		85.99 60	86.09 63	85.70 83	87.65 235	67.22 183	88.69 144	93.04 48	79.64 22	85.33 79	92.54 107	73.30 42	94.50 128	83.49 85	91.14 112	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 39	87.90 22	94.18 40	74.25 5	86.58 234	92.02 115	79.45 23	85.88 73	94.80 28	68.07 127	96.21 52	86.69 54	95.34 36	93.23 134

EC-MVSNet		86.01 59	86.38 52	84.91 116	89.31 150	66.27 199	92.32 35	93.63 27	79.37 24	84.17 107	91.88 127	69.04 114	95.43 79	83.93 83	93.77 69	93.01 156

NormalMVS		86.29 54	85.88 67	87.52 41	93.26 57	72.47 38	91.65 47	92.19 109	79.31 25	84.39 100	92.18 117	64.64 174	95.53 73	80.70 120	94.65 52	94.56 55

SymmetryMVS		85.38 80	84.81 89	87.07 51	91.47 89	72.47 38	91.65 47	88.06 279	79.31 25	84.39 100	92.18 117	64.64 174	95.53 73	80.70 120	90.91 118	93.21 137

XVS		87.18 37	86.91 44	88.00 17	94.42 25	73.33 19	92.78 23	92.99 56	79.14 27	83.67 118	94.17 54	67.45 133	96.60 39	83.06 89	94.50 57	94.07 82

X-MVStestdata		80.37 209	77.83 249	88.00 17	94.42 25	73.33 19	92.78 23	92.99 56	79.14 27	83.67 118	12.47 534	67.45 133	96.60 39	83.06 89	94.50 57	94.07 82

HQP_MVS		83.64 118	83.14 123	85.14 101	90.08 118	68.71 125	91.25 60	92.44 85	79.12 29	78.92 212	91.00 168	60.42 246	95.38 84	78.71 152	86.32 212	91.33 224

plane_prior2							91.25 60		79.12 29

IS-MVSNet		83.15 134	82.81 131	84.18 159	89.94 125	63.30 295	91.59 51	88.46 272	79.04 31	79.49 201	92.16 119	65.10 168	94.28 134	67.71 283	91.86 100	94.95 15

DU-MVS		81.12 181	80.52 178	82.90 228	87.80 221	63.46 291	87.02 213	91.87 125	79.01 32	78.38 225	89.07 227	65.02 169	93.05 217	70.05 260	76.46 365	92.20 193

NR-MVSNet		80.23 213	79.38 211	82.78 238	87.80 221	63.34 294	86.31 246	91.09 159	79.01 32	72.17 358	89.07 227	67.20 136	92.81 230	66.08 299	75.65 378	92.20 193

SPE-MVS-test		86.29 54	86.48 51	85.71 82	91.02 97	67.21 184	92.36 34	93.78 24	78.97 34	83.51 125	91.20 158	70.65 83	95.15 93	81.96 105	94.89 46	94.77 30

DELS-MVS		85.41 78	85.30 82	85.77 81	88.49 186	67.93 155	85.52 275	93.44 33	78.70 35	83.63 120	89.03 229	74.57 29	95.71 68	80.26 128	94.04 67	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		79.49 227	79.22 218	80.27 306	88.79 175	58.35 378	85.06 285	88.61 270	78.56 36	77.65 243	88.34 253	63.81 183	90.66 334	64.98 308	77.22 353	91.80 207

plane_prior3						68.60 130			78.44 37	78.92 212

UniMVSNet (Re)		81.60 168	81.11 165	83.09 216	88.38 192	64.41 263	87.60 187	93.02 52	78.42 38	78.56 220	88.16 259	69.78 96	93.26 199	69.58 267	76.49 364	91.60 214

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 32	71.25 66	95.06 1	94.23 6	78.38 39	92.78 5	95.74 9	82.45 3	97.49 4	89.42 19	96.68 2	94.95 15

test_0728_THIRD									78.38 39	92.12 12	95.78 7	81.46 8	97.40 9	89.42 19	96.57 7	94.67 42

testing3-2		75.12 329	75.19 311	74.91 405	90.40 111	45.09 491	80.29 392	78.42 442	78.37 41	76.54 273	87.75 269	44.36 418	87.28 392	57.04 398	83.49 272	92.37 184

test_one_0601					95.07 7	71.46 60		94.14 10	78.27 42	92.05 14	95.74 9	80.83 12

hybridcas		85.11 85	85.18 84	84.90 117	87.47 247	65.68 216	88.53 152	92.38 89	77.91 43	84.27 104	92.48 108	72.19 59	93.88 160	80.37 123	90.97 115	95.15 9

SD-MVS		88.06 18	88.50 18	86.71 61	92.60 77	72.71 29	91.81 46	93.19 42	77.87 44	90.32 25	94.00 64	74.83 28	93.78 163	87.63 46	94.27 65	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 85	85.01 109	87.20 259	65.77 215	87.75 184	92.83 67	77.84 45	84.36 103	92.38 110	72.15 60	93.93 154	81.27 113	90.48 125	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	82.61 137	85.39 94	87.08 268	67.56 168	88.06 172	91.65 138	77.80 46	82.21 149	91.79 130	57.27 273	94.07 146	77.77 164	89.89 138	94.56 55

BP-MVS1		84.32 94	83.71 111	86.17 70	87.84 219	67.85 157	89.38 110	89.64 210	77.73 47	83.98 111	92.12 122	56.89 278	95.43 79	84.03 82	91.75 101	95.24 8

CP-MVSNet		78.22 262	78.34 236	77.84 368	87.83 220	54.54 437	87.94 177	91.17 155	77.65 48	73.48 339	88.49 249	62.24 209	88.43 377	62.19 341	74.07 401	90.55 255

plane_prior						68.71 125	90.38 78		77.62 49						86.16 217

baseline		84.93 89	84.98 86	84.80 122	87.30 257	65.39 225	87.30 205	92.88 64	77.62 49	84.04 110	92.26 112	71.81 64	93.96 148	81.31 111	90.30 128	95.03 13

VDD-MVS		83.01 140	82.36 142	84.96 111	91.02 97	66.40 196	88.91 129	88.11 275	77.57 51	84.39 100	93.29 87	52.19 321	93.91 156	77.05 174	88.70 161	94.57 53

MP-MVS		87.71 23	87.64 26	87.93 21	94.36 31	73.88 6	92.71 27	92.65 78	77.57 51	83.84 114	94.40 42	72.24 58	96.28 49	85.65 61	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		77.73 277	77.69 257	77.84 368	87.07 270	53.91 442	87.91 179	91.18 154	77.56 53	73.14 343	88.82 239	61.23 230	89.17 362	59.95 366	72.37 416	90.43 260

OPM-MVS		83.50 124	82.95 129	85.14 101	88.79 175	70.95 77	89.13 122	91.52 144	77.55 54	80.96 175	91.75 133	60.71 238	94.50 128	79.67 139	86.51 209	89.97 287
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 17	86.73 60	92.24 79	69.03 112	89.57 99	93.39 36	77.53 55	89.79 27	94.12 57	78.98 13	96.58 41	85.66 60	95.72 28	94.58 51

PS-CasMVS		78.01 271	78.09 241	77.77 370	87.71 230	54.39 439	88.02 173	91.22 152	77.50 56	73.26 341	88.64 244	60.73 237	88.41 378	61.88 347	73.88 405	90.53 256

MSLP-MVS++		85.43 77	85.76 71	84.45 137	91.93 83	70.24 87	90.71 67	92.86 65	77.46 57	84.22 105	92.81 101	67.16 137	92.94 221	80.36 124	94.35 63	90.16 271

RRT-MVS		82.60 148	82.10 149	84.10 161	87.98 213	62.94 309	87.45 194	91.27 151	77.42 58	79.85 196	90.28 192	56.62 281	94.70 121	79.87 136	88.15 174	94.67 42

fmvsm_s_conf0.5_n_11		86.06 57	86.75 47	84.00 178	87.78 224	66.09 201	89.96 86	90.80 169	77.37 59	86.72 68	94.20 53	72.51 55	92.78 231	89.08 22	92.33 89	93.13 146

DVP-MVS		89.60 4	90.35 4	87.33 45	95.27 5	71.25 66	93.49 10	92.73 72	77.33 60	92.12 12	95.78 7	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 5	71.25 66	93.60 7	94.11 11	77.33 60	92.81 4	95.79 6	80.98 10

MED-MVS		89.78 3	90.41 3	87.89 24	94.57 18	71.43 61	93.28 12	94.36 3	77.30 62	92.25 10	95.87 4	81.59 7	97.39 11	88.15 40	96.28 16	94.85 24

TestfortrainingZip a		88.83 13	89.21 11	87.68 37	94.57 18	71.25 66	93.28 12	93.91 20	77.30 62	91.13 19	95.87 4	77.62 17	96.95 23	86.12 59	93.07 76	94.85 24

BridgeMVS		86.78 42	86.99 40	86.15 72	91.24 92	67.61 165	90.51 70	92.90 63	77.26 64	87.44 59	91.63 140	71.27 74	96.06 56	85.62 62	95.01 41	94.78 29

SED-MVS		90.08 2	90.85 2	87.77 28	95.30 2	70.98 74	93.57 8	94.06 15	77.24 65	93.10 1	95.72 11	82.99 1	97.44 7	89.07 25	96.63 4	94.88 19

test_241102_TWO								94.06 15	77.24 65	92.78 5	95.72 11	81.26 9	97.44 7	89.07 25	96.58 6	94.26 73

3Dnovator	76.31 5	83.38 128	82.31 143	86.59 62	87.94 214	72.94 28	90.64 68	92.14 114	77.21 67	75.47 294	92.83 99	58.56 260	94.72 119	73.24 222	92.71 83	92.13 200

test_241102_ONE					95.30 2	70.98 74		94.06 15	77.17 68	93.10 1	95.39 19	82.99 1	97.27 14

WR-MVS_H		78.51 257	78.49 231	78.56 352	88.02 210	56.38 413	88.43 154	92.67 75	77.14 69	73.89 333	87.55 277	66.25 151	89.24 360	58.92 378	73.55 408	90.06 281

lecture		88.09 17	88.59 16	86.58 63	93.26 57	69.77 98	93.70 6	94.16 8	77.13 70	89.76 28	95.52 17	72.26 57	96.27 50	86.87 52	94.65 52	93.70 106

DeepC-MVS	79.81 2	87.08 40	86.88 45	87.69 36	91.16 93	72.32 45	90.31 79	93.94 19	77.12 71	82.82 140	94.23 51	72.13 61	97.09 19	84.83 69	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	82.02 152	80.03 313	88.42 191	55.97 419	87.95 176	93.42 35	77.10 72	77.38 249	90.98 170	69.96 93	91.79 274	68.46 279	84.50 248	92.33 186

DTE-MVSNet		76.99 294	76.80 277	77.54 377	86.24 290	53.06 452	87.52 189	90.66 172	77.08 73	72.50 352	88.67 243	60.48 245	89.52 354	57.33 395	70.74 428	90.05 282

LFMVS		81.82 162	81.23 162	83.57 194	91.89 84	63.43 293	89.84 87	81.85 400	77.04 74	83.21 128	93.10 90	52.26 320	93.43 192	71.98 239	89.95 136	93.85 94

casdiffseed414692147		83.62 120	83.02 126	85.40 93	87.31 256	67.50 170	88.70 143	91.72 134	76.97 75	82.77 142	91.72 134	66.85 141	93.71 170	73.06 224	88.12 175	94.98 14

UGNet		80.83 187	79.59 206	84.54 129	88.04 209	68.09 146	89.42 107	88.16 274	76.95 76	76.22 280	89.46 219	49.30 372	93.94 151	68.48 278	90.31 127	91.60 214
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	82.42 139	81.04 287	88.80 174	58.34 379	88.26 165	93.49 32	76.93 77	78.47 224	91.04 165	69.92 94	92.34 252	69.87 264	84.97 239	92.44 183

GST-MVS		87.42 31	87.26 34	87.89 24	94.12 41	72.97 24	92.39 31	93.43 34	76.89 78	84.68 90	93.99 66	70.67 82	96.82 27	84.18 81	95.01 41	93.90 92

mPP-MVS		86.67 46	86.32 54	87.72 33	94.41 27	73.55 13	92.74 25	92.22 104	76.87 79	82.81 141	94.25 50	66.44 148	96.24 51	82.88 94	94.28 64	93.38 126

ZNCC-MVS		87.94 22	87.85 24	88.20 12	94.39 29	73.33 19	93.03 19	93.81 23	76.81 80	85.24 80	94.32 45	71.76 65	96.93 24	85.53 63	95.79 26	94.32 69

VPNet		78.69 252	78.66 228	78.76 347	88.31 194	55.72 423	84.45 305	86.63 324	76.79 81	78.26 228	90.55 184	59.30 254	89.70 352	66.63 294	77.05 355	90.88 240

HFP-MVS		87.58 26	87.47 31	87.94 19	94.58 16	73.54 15	93.04 17	93.24 40	76.78 82	84.91 85	94.44 40	70.78 80	96.61 38	84.53 74	94.89 46	93.66 108

ACMMPR		87.44 29	87.23 36	88.08 15	94.64 13	73.59 12	93.04 17	93.20 41	76.78 82	84.66 93	94.52 33	68.81 116	96.65 36	84.53 74	94.90 45	94.00 86

ACMMP		85.89 66	85.39 78	87.38 44	93.59 50	72.63 33	92.74 25	93.18 46	76.78 82	80.73 181	93.82 73	64.33 177	96.29 48	82.67 102	90.69 121	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 37	88.09 14	94.63 14	73.55 13	93.03 19	93.12 47	76.73 85	84.45 98	94.52 33	69.09 110	96.70 32	84.37 76	94.83 49	94.03 84

sasdasda		85.91 64	85.87 69	86.04 76	89.84 127	69.44 107	90.45 76	93.00 53	76.70 86	88.01 48	91.23 154	73.28 43	93.91 156	81.50 108	88.80 157	94.77 30

canonicalmvs		85.91 64	85.87 69	86.04 76	89.84 127	69.44 107	90.45 76	93.00 53	76.70 86	88.01 48	91.23 154	73.28 43	93.91 156	81.50 108	88.80 157	94.77 30

CP-MVS		87.11 38	86.92 43	87.68 37	94.20 39	73.86 7	93.98 3	92.82 70	76.62 88	83.68 117	94.46 37	67.93 128	95.95 64	84.20 80	94.39 61	93.23 134

DeepC-MVS_fast	79.65 3	86.91 41	86.62 50	87.76 29	93.52 51	72.37 43	91.26 59	93.04 48	76.62 88	84.22 105	93.36 86	71.44 71	96.76 30	80.82 117	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 69	72.05 50	93.28 12	93.32 38	76.52 90	88.91 34	93.52 78	77.30 18	96.67 34		91.98 96	93.13 146

E5new		84.22 95	84.12 98	84.51 132	87.60 237	65.36 227	87.45 194	92.31 93	76.51 91	83.53 121	92.26 112	69.25 107	93.50 183	79.88 132	88.26 168	94.69 37

E6new		84.22 95	84.12 98	84.52 130	87.60 237	65.36 227	87.45 194	92.30 95	76.51 91	83.53 121	92.26 112	69.26 105	93.49 185	79.88 132	88.26 168	94.69 37

E6		84.22 95	84.12 98	84.52 130	87.60 237	65.36 227	87.45 194	92.30 95	76.51 91	83.53 121	92.26 112	69.26 105	93.49 185	79.88 132	88.26 168	94.69 37

E5		84.22 95	84.12 98	84.51 132	87.60 237	65.36 227	87.45 194	92.31 93	76.51 91	83.53 121	92.26 112	69.25 107	93.50 183	79.88 132	88.26 168	94.69 37

fmvsm_l_mol_unc0.5_1		85.55 73	86.37 53	83.10 215	86.42 287	62.98 308	85.89 260	84.85 351	76.48 95	92.88 3	96.67 1	74.16 37	92.46 244	87.11 50	92.90 79	93.85 94

MGCFI-Net		85.06 88	85.51 76	83.70 189	89.42 142	63.01 302	89.43 105	92.62 81	76.43 96	87.53 56	91.34 152	72.82 53	93.42 193	81.28 112	88.74 160	94.66 45

TSAR-MVS + GP.		85.71 70	85.33 80	86.84 57	91.34 90	72.50 36	89.07 125	87.28 301	76.41 97	85.80 74	90.22 196	74.15 38	95.37 87	81.82 106	91.88 97	92.65 171

HQP-NCC					89.33 147		89.17 117		76.41 97	77.23 254

ACMP_Plane					89.33 147		89.17 117		76.41 97	77.23 254

HQP-MVS		82.61 146	82.02 152	84.37 142	89.33 147	66.98 187	89.17 117	92.19 109	76.41 97	77.23 254	90.23 195	60.17 249	95.11 96	77.47 168	85.99 223	91.03 234

E4		84.10 101	83.99 104	84.45 137	87.58 245	64.99 241	86.54 236	92.25 100	76.38 101	83.37 126	92.09 123	69.88 95	93.58 172	79.78 137	88.03 179	94.77 30

CANet_DTU		80.61 198	79.87 196	82.83 231	85.60 306	63.17 300	87.36 202	88.65 268	76.37 102	75.88 287	88.44 251	53.51 309	93.07 215	73.30 220	89.74 140	92.25 190

VNet		82.21 153	82.41 140	81.62 268	90.82 102	60.93 347	84.47 302	89.78 202	76.36 103	84.07 109	91.88 127	64.71 173	90.26 340	70.68 252	88.89 155	93.66 108

Vis-MVSNet		83.46 125	82.80 132	85.43 92	90.25 114	68.74 123	90.30 80	90.13 193	76.33 104	80.87 178	92.89 97	61.00 235	94.20 140	72.45 236	90.97 115	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 21	71.43 61	91.61 49	94.25 5	76.30 105	90.62 23	95.03 23	78.06 16	97.07 20	88.15 40	95.96 21	94.75 35

ACMMP_NAP		88.05 20	88.08 21	87.94 19	93.70 46	73.05 22	90.86 65	93.59 29	76.27 106	88.14 44	95.09 22	71.06 77	96.67 34	87.67 45	96.37 14	94.09 81

fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 98	87.33 253	67.30 178	89.50 101	90.98 160	76.25 107	90.56 24	94.75 30	68.38 122	94.24 139	90.80 7	92.32 91	94.19 75

alignmvs		85.48 75	85.32 81	85.96 79	89.51 137	69.47 104	89.74 92	92.47 84	76.17 108	87.73 55	91.46 149	70.32 85	93.78 163	81.51 107	88.95 154	94.63 48

MVS_111021_HR		85.14 84	84.75 90	86.32 66	91.65 87	72.70 30	85.98 256	90.33 185	76.11 109	82.08 151	91.61 143	71.36 73	94.17 143	81.02 114	92.58 84	92.08 201

HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 44	72.16 47	92.19 38	93.33 37	76.07 110	83.81 115	93.95 69	69.77 97	96.01 60	85.15 64	94.66 51	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	82.19 146	86.02 78	90.56 107	70.85 81	88.15 170	89.16 237	76.02 111	84.67 91	91.39 151	61.54 221	95.50 75	82.71 99	75.48 382	91.72 212

hse-mvs2		81.72 163	80.94 169	84.07 167	88.72 179	67.68 163	85.87 261	87.26 306	76.02 111	84.67 91	88.22 258	61.54 221	93.48 188	82.71 99	73.44 410	91.06 232

DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 13	75.90 113	92.29 8	95.66 13	81.67 6	97.38 13	87.44 49	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	83.87 105	84.39 140	87.70 232	64.95 242	86.40 243	92.23 101	75.85 114	83.21 128	91.78 131	70.09 90	93.55 177	79.52 141	88.05 177	94.66 45

E3		84.00 104	83.87 105	84.39 140	87.70 232	64.95 242	86.40 243	92.23 101	75.85 114	83.21 128	91.78 131	70.09 90	93.55 177	79.52 141	88.05 177	94.66 45

CLD-MVS		82.31 151	81.65 158	84.29 150	88.47 187	67.73 161	85.81 265	92.35 91	75.78 116	78.33 227	86.58 308	64.01 180	94.35 132	76.05 189	87.48 190	90.79 243
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	83.66 113	84.07 167	86.59 283	64.56 255	86.88 220	91.82 128	75.72 117	83.34 127	92.15 121	68.24 126	92.88 224	79.05 144	89.15 152	94.77 30

SF-MVS		88.46 15	88.74 15	87.64 39	92.78 72	71.95 52	92.40 29	94.74 2	75.71 118	89.16 31	95.10 21	75.65 26	96.19 53	87.07 51	96.01 19	94.79 28

testdata1							84.14 317		75.71 118

APDe-MVS		89.15 8	89.63 7	87.73 31	94.49 23	71.69 55	93.83 4	93.96 18	75.70 120	91.06 20	96.03 2	76.84 19	97.03 21	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	80.55 177	80.76 294	88.07 208	60.80 350	86.86 221	91.58 143	75.67 121	80.24 192	89.45 221	63.34 184	90.25 341	70.51 254	79.22 332	91.23 227

fmvsm_l_conf0.5_n_9		85.84 67	86.63 49	83.46 196	87.12 267	66.01 204	88.56 150	89.43 217	75.59 122	89.32 30	94.32 45	72.89 49	91.21 308	90.11 11	92.33 89	93.16 142

PGM-MVS		86.68 45	86.27 56	87.90 22	94.22 38	73.38 18	90.22 81	93.04 48	75.53 123	83.86 113	94.42 41	67.87 130	96.64 37	82.70 101	94.57 56	93.66 108

Effi-MVS+		83.62 120	83.08 124	85.24 98	88.38 192	67.45 171	88.89 130	89.15 239	75.50 124	82.27 147	88.28 255	69.61 99	94.45 131	77.81 163	87.84 182	93.84 97

viewcassd2359sk11		83.89 107	83.74 110	84.34 145	87.76 227	64.91 249	86.30 247	92.22 104	75.47 125	83.04 134	91.52 145	70.15 88	93.53 180	79.26 143	87.96 180	94.57 53

fmvsm_s_conf0.5_n_4		85.39 79	85.75 72	84.30 149	86.70 279	65.83 211	88.77 137	89.78 202	75.46 126	88.35 39	93.73 75	69.19 109	93.06 216	91.30 3	88.44 166	94.02 85

fmvsm_s_conf0.5_n_6		85.55 73	86.20 57	83.60 191	87.32 255	65.13 235	88.86 131	91.63 139	75.41 127	88.23 43	93.45 83	68.56 120	92.47 243	89.52 18	92.78 81	93.20 139

test_prior2							88.85 133		75.41 127	84.91 85	93.54 77	74.28 35		83.31 87	95.86 24

LPG-MVS_test		82.08 155	81.27 161	84.50 134	89.23 155	68.76 121	90.22 81	91.94 121	75.37 129	76.64 269	91.51 146	54.29 300	94.91 105	78.44 154	83.78 261	89.83 292

LGP-MVS_train				84.50 134	89.23 155	68.76 121		91.94 121	75.37 129	76.64 269	91.51 146	54.29 300	94.91 105	78.44 154	83.78 261	89.83 292

fmvsm_l_conf0.5_n_3		86.02 58	86.32 54	85.14 101	87.20 259	68.54 132	89.57 99	90.44 179	75.31 131	87.49 57	94.39 43	72.86 50	92.72 232	89.04 27	90.56 124	94.16 76

viewmamba		82.38 149	82.11 147	83.19 210	83.30 363	64.26 266	84.62 298	89.16 237	75.24 132	80.97 174	91.10 161	67.12 138	91.63 280	81.36 110	86.13 218	93.67 107

aaatest				87.86 27	94.57 18	71.43 61	93.28 12	94.36 3	75.24 132	92.25 10	95.03 23		97.39 11	88.15 40	95.96 21	94.75 35

viewdifsd2359ckpt07		82.83 143	82.78 134	82.99 223	86.51 285	62.58 313	85.09 284	90.83 168	75.22 134	82.28 146	91.63 140	69.43 101	92.03 262	77.71 165	86.32 212	94.34 67

MG-MVS		83.41 126	83.45 118	83.28 204	92.74 73	62.28 322	88.17 168	89.50 215	75.22 134	81.49 162	92.74 106	66.75 142	95.11 96	72.85 226	91.58 104	92.45 182

SSC-MVS3.2		73.35 353	73.39 335	73.23 423	85.30 315	49.01 476	74.58 456	81.57 402	75.21 136	73.68 336	85.58 333	52.53 314	82.05 441	54.33 417	77.69 349	88.63 335

LCM-MVSNet-Re		77.05 293	76.94 274	77.36 378	87.20 259	51.60 461	80.06 395	80.46 418	75.20 137	67.69 413	86.72 298	62.48 203	88.98 366	63.44 318	89.25 148	91.51 218

SDMVSNet		80.38 207	80.18 186	80.99 288	89.03 164	64.94 245	80.45 389	89.40 218	75.19 138	76.61 271	89.98 198	60.61 243	87.69 387	76.83 179	83.55 270	90.33 265

sd_testset		77.70 280	77.40 264	78.60 350	89.03 164	60.02 364	79.00 411	85.83 338	75.19 138	76.61 271	89.98 198	54.81 292	85.46 412	62.63 334	83.55 270	90.33 265

MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 49	72.04 51	89.80 90	93.50 31	75.17 140	86.34 71	95.29 20	70.86 79	96.00 61	88.78 31	96.04 18	94.58 51
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
E3new		83.78 112	83.60 115	84.31 147	87.76 227	64.89 250	86.24 250	92.20 107	75.15 141	82.87 137	91.23 154	70.11 89	93.52 182	79.05 144	87.79 183	94.51 58

test1111		79.43 230	79.18 219	80.15 311	89.99 123	53.31 448	87.33 204	77.05 454	75.04 142	80.23 193	92.77 105	48.97 377	92.33 253	68.87 274	92.40 88	94.81 27

Effi-MVS+-dtu		80.03 218	78.57 230	84.42 139	85.13 321	68.74 123	88.77 137	88.10 276	74.99 143	74.97 318	83.49 385	57.27 273	93.36 194	73.53 216	80.88 307	91.18 228

reproduce-ours		87.47 27	87.61 27	87.07 51	93.27 55	71.60 56	91.56 54	93.19 42	74.98 144	88.96 32	95.54 15	71.20 75	96.54 42	86.28 56	93.49 71	93.06 150

our_new_method		87.47 27	87.61 27	87.07 51	93.27 55	71.60 56	91.56 54	93.19 42	74.98 144	88.96 32	95.54 15	71.20 75	96.54 42	86.28 56	93.49 71	93.06 150

fmvsm_s_conf0.5_n_7		83.34 129	84.03 103	81.28 279	85.73 302	65.13 235	85.40 276	89.90 200	74.96 146	82.13 150	93.89 70	66.65 143	87.92 383	86.56 55	91.05 113	90.80 242

OMC-MVS		82.69 144	81.97 154	84.85 119	88.75 178	67.42 172	87.98 174	90.87 166	74.92 147	79.72 198	91.65 138	62.19 210	93.96 148	75.26 201	86.42 210	93.16 142

viewmanbaseed2359cas		83.66 116	83.55 116	84.00 178	86.81 275	64.53 256	86.65 230	91.75 133	74.89 148	83.15 133	91.68 136	68.74 118	92.83 229	79.02 146	89.24 149	94.63 48

test2506		77.30 290	76.49 285	79.74 326	90.08 118	52.02 454	87.86 182	63.10 499	74.88 149	80.16 194	92.79 102	38.29 459	92.35 251	68.74 276	92.50 86	94.86 22

ECVR-MVS		79.61 223	79.26 216	80.67 296	90.08 118	54.69 435	87.89 180	77.44 450	74.88 149	80.27 191	92.79 102	48.96 378	92.45 245	68.55 277	92.50 86	94.86 22

MonoMVSNet		76.49 305	75.80 294	78.58 351	81.55 409	58.45 377	86.36 245	86.22 331	74.87 151	74.73 322	83.73 378	51.79 335	88.73 371	70.78 249	72.15 419	88.55 338

nrg030		83.88 108	83.53 117	84.96 111	86.77 277	69.28 111	90.46 75	92.67 75	74.79 152	82.95 135	91.33 153	72.70 54	93.09 214	80.79 119	79.28 331	92.50 178

SMA-MVS		89.08 9	89.23 9	88.61 6	94.25 36	73.73 9	92.40 29	93.63 27	74.77 153	92.29 8	95.97 3	74.28 35	97.24 15	88.58 34	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 33	86.95 55	93.10 63	71.24 71	91.60 50	93.19 42	74.69 154	88.80 36	95.61 14	70.29 86	96.44 45	86.20 58	93.08 75	93.16 142

MVS_111021_LR		82.61 146	82.11 147	84.11 160	88.82 169	71.58 58	85.15 281	86.16 333	74.69 154	80.47 189	91.04 165	62.29 207	90.55 335	80.33 126	90.08 133	90.20 270

EIA-MVS		83.31 132	82.80 132	84.82 120	89.59 133	65.59 219	88.21 166	92.68 74	74.66 156	78.96 210	86.42 313	69.06 112	95.26 89	75.54 197	90.09 132	93.62 115

TSAR-MVS + MP.		88.02 21	88.11 20	87.72 33	93.68 48	72.13 48	91.41 58	92.35 91	74.62 157	88.90 35	93.85 72	75.75 25	96.00 61	87.80 44	94.63 54	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 48	86.91 56	94.11 42	72.11 49	92.37 33	92.56 83	74.50 158	86.84 67	94.65 32	67.31 135	95.77 66	84.80 70	92.85 80	92.84 165

FOURS1					95.00 10	72.39 41	95.06 1	93.84 21	74.49 159	91.30 18

ACMP	74.13 6	81.51 174	80.57 176	84.36 143	89.42 142	68.69 128	89.97 85	91.50 148	74.46 160	75.04 316	90.41 187	53.82 306	94.54 125	77.56 167	82.91 281	89.86 291
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPP-MVSNet		83.40 127	83.02 126	84.57 128	90.13 116	64.47 261	92.32 35	90.73 171	74.45 161	79.35 206	91.10 161	69.05 113	95.12 94	72.78 227	87.22 194	94.13 78

balanced_ft_v1		83.98 106	83.64 114	85.03 107	89.76 130	65.86 210	88.31 163	91.71 135	74.41 162	80.41 190	90.82 173	62.90 198	94.90 107	83.04 91	91.37 108	94.32 69

fmvsm_s_conf0.5_n_2		84.04 102	84.11 102	83.81 187	86.17 293	65.00 240	86.96 215	87.28 301	74.35 163	88.25 42	94.23 51	61.82 216	92.60 235	89.85 12	88.09 176	93.84 97

fmvsm_s_conf0.1_n_2		83.80 110	83.79 109	83.83 185	85.62 305	64.94 245	87.03 212	86.62 325	74.32 164	87.97 50	94.33 44	60.67 240	92.60 235	89.72 14	87.79 183	93.96 87

save fliter					93.80 45	72.35 44	90.47 74	91.17 155	74.31 165

MVS_Test		83.15 134	83.06 125	83.41 201	86.86 272	63.21 297	86.11 254	92.00 117	74.31 165	82.87 137	89.44 222	70.03 92	93.21 203	77.39 170	88.50 165	93.81 99

myMVS_eth3d28		73.62 344	73.53 334	73.90 419	88.20 197	47.41 481	78.06 426	79.37 434	74.29 167	73.98 332	84.29 362	44.67 414	83.54 429	51.47 431	87.39 191	90.74 247

UniMVSNet_ETH3D		79.10 241	78.24 239	81.70 267	86.85 273	60.24 362	87.28 206	88.79 255	74.25 168	76.84 262	90.53 185	49.48 367	91.56 286	67.98 281	82.15 290	93.29 131

IterMVS-LS		80.06 216	79.38 211	82.11 258	85.89 298	63.20 298	86.79 224	89.34 220	74.19 169	75.45 297	86.72 298	66.62 144	92.39 248	72.58 229	76.86 358	90.75 246
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 204	79.98 192	82.12 256	84.28 338	63.19 299	86.41 240	88.95 250	74.18 170	78.69 215	87.54 278	66.62 144	92.43 246	72.57 230	80.57 313	90.74 247

Vis-MVSNet (Re-imp)		78.36 260	78.45 232	78.07 364	88.64 182	51.78 460	86.70 228	79.63 432	74.14 171	75.11 313	90.83 172	61.29 229	89.75 350	58.10 388	91.60 102	92.69 169

v8		79.97 220	79.02 222	82.80 234	84.09 343	64.50 260	87.96 175	90.29 188	74.13 172	75.24 309	86.81 295	62.88 199	93.89 159	74.39 209	75.40 387	90.00 283

guyue		81.13 180	80.64 175	82.60 245	86.52 284	63.92 274	86.69 229	87.73 291	73.97 173	80.83 180	89.69 208	56.70 279	91.33 302	78.26 162	85.40 236	92.54 174

onestephybrid01		82.22 152	81.81 157	83.46 196	83.16 371	64.93 248	84.64 297	89.19 236	73.95 174	81.48 163	90.63 179	66.00 160	91.92 270	80.33 126	86.93 200	93.53 122

CSCG		86.41 51	86.19 59	87.07 51	92.91 68	72.48 37	90.81 66	93.56 30	73.95 174	83.16 132	91.07 164	75.94 23	95.19 91	79.94 131	94.38 62	93.55 120

thres100view900		76.50 302	75.55 301	79.33 337	89.52 136	56.99 402	85.83 264	83.23 376	73.94 176	76.32 278	87.12 290	51.89 332	91.95 267	48.33 451	83.75 264	89.07 310

9.14			88.26 19		92.84 71		91.52 56	94.75 1	73.93 177	88.57 38	94.67 31	75.57 27	95.79 65	86.77 53	95.76 27

HPM-MVS_fast		85.35 81	84.95 88	86.57 64	93.69 47	70.58 86	92.15 40	91.62 140	73.89 178	82.67 144	94.09 58	62.60 200	95.54 72	80.93 115	92.93 78	93.57 118

PAPM_NR		83.02 139	82.41 140	84.82 120	92.47 78	66.37 197	87.93 178	91.80 129	73.82 179	77.32 251	90.66 178	67.90 129	94.90 107	70.37 255	89.48 145	93.19 140

thres600view7		76.50 302	75.44 302	79.68 329	89.40 144	57.16 399	85.53 273	83.23 376	73.79 180	76.26 279	87.09 291	51.89 332	91.89 271	48.05 456	83.72 267	90.00 283

testing91		76.54 300	75.66 299	79.18 341	88.43 190	55.89 420	81.08 376	83.00 383	73.76 181	75.34 302	84.29 362	46.20 402	90.07 344	64.33 312	84.50 248	91.58 216

AstraMVS		80.81 188	80.14 189	82.80 234	86.05 297	63.96 271	86.46 239	85.90 337	73.71 182	80.85 179	90.56 183	54.06 304	91.57 285	79.72 138	83.97 259	92.86 163

v7n		78.97 245	77.58 260	83.14 213	83.45 360	65.51 220	88.32 162	91.21 153	73.69 183	72.41 354	86.32 316	57.93 264	93.81 162	69.18 270	75.65 378	90.11 275

dcpmvs_2		85.63 71	86.15 61	84.06 170	91.71 86	64.94 245	86.47 238	91.87 125	73.63 184	86.60 70	93.02 95	76.57 20	91.87 273	83.36 86	92.15 92	95.35 4

v2v482		80.23 213	79.29 215	83.05 220	83.62 356	64.14 268	87.04 211	89.97 197	73.61 185	78.18 231	87.22 286	61.10 233	93.82 161	76.11 187	76.78 361	91.18 228

Baseline_NR-MVSNet		78.15 266	78.33 237	77.61 374	85.79 300	56.21 417	86.78 225	85.76 339	73.60 186	77.93 237	87.57 275	65.02 169	88.99 365	67.14 291	75.33 389	87.63 358

BH-RMVSNet		79.61 223	78.44 233	83.14 213	89.38 146	65.93 207	84.95 288	87.15 309	73.56 187	78.19 230	89.79 206	56.67 280	93.36 194	59.53 371	86.74 205	90.13 273

PRO-TEST		83.03 138	82.63 136	84.23 157	88.20 197	66.81 192	87.41 200	90.93 162	73.55 188	80.73 181	88.90 235	66.17 154	92.85 225	78.39 157	89.36 147	93.02 154

APD-MVS_3200maxsize		85.97 62	85.88 67	86.22 69	92.69 74	69.53 101	91.93 42	92.99 56	73.54 189	85.94 72	94.51 36	65.80 162	95.61 69	83.04 91	92.51 85	93.53 122

SR-MVS-dyc-post		85.77 68	85.61 74	86.23 68	93.06 65	70.63 84	91.88 43	92.27 97	73.53 190	85.69 76	94.45 38	65.00 171	95.56 70	82.75 97	91.87 98	92.50 178

RE-MVS-def			85.48 77		93.06 65	70.63 84	91.88 43	92.27 97	73.53 190	85.69 76	94.45 38	63.87 181		82.75 97	91.87 98	92.50 178

reproduce_monomvs		75.40 325	74.38 323	78.46 357	83.92 348	57.80 390	83.78 322	86.94 315	73.47 192	72.25 357	84.47 356	38.74 455	89.27 359	75.32 200	70.53 429	88.31 342

test_fmvsmconf_n		85.92 63	86.04 64	85.57 89	85.03 324	69.51 102	89.62 98	90.58 174	73.42 193	87.75 53	94.02 62	72.85 51	93.24 200	90.37 8	90.75 120	93.96 87

tfpn200view9		76.42 308	75.37 306	79.55 334	89.13 159	57.65 393	85.17 279	83.60 368	73.41 194	76.45 274	86.39 314	52.12 322	91.95 267	48.33 451	83.75 264	89.07 310

thres400		76.50 302	75.37 306	79.86 319	89.13 159	57.65 393	85.17 279	83.60 368	73.41 194	76.45 274	86.39 314	52.12 322	91.95 267	48.33 451	83.75 264	90.00 283

diffmvs_AUTHOR		82.38 149	82.27 145	82.73 242	83.26 365	63.80 276	83.89 320	89.76 204	73.35 196	82.37 145	90.84 171	66.25 151	90.79 327	82.77 96	87.93 181	93.59 117

test_fmvsmconf0.1_n		85.61 72	85.65 73	85.50 90	82.99 381	69.39 109	89.65 95	90.29 188	73.31 197	87.77 52	94.15 56	71.72 66	93.23 201	90.31 9	90.67 122	93.89 93

testing99		76.09 314	75.12 313	79.00 342	88.16 201	55.50 426	80.79 380	81.40 405	73.30 198	75.17 310	84.27 365	44.48 417	90.02 345	64.28 313	84.22 257	91.48 221

v148		78.72 251	77.80 251	81.47 272	82.73 387	61.96 328	86.30 247	88.08 277	73.26 199	76.18 282	85.47 336	62.46 204	92.36 250	71.92 240	73.82 406	90.09 277

FA-MVS(test-final)		80.96 184	79.91 194	84.10 161	88.30 195	65.01 239	84.55 301	90.01 196	73.25 200	79.61 199	87.57 275	58.35 262	94.72 119	71.29 245	86.25 215	92.56 173

test_fmvsmconf0.01_n		84.73 92	84.52 94	85.34 95	80.25 426	69.03 112	89.47 102	89.65 209	73.24 201	86.98 65	94.27 48	66.62 144	93.23 201	90.26 10	89.95 136	93.78 103

viewdifsd2359ckpt11		80.37 209	79.73 200	82.30 253	83.70 354	62.39 317	84.20 314	86.67 321	73.22 202	80.90 176	90.62 180	63.00 196	91.56 286	76.81 180	78.44 338	92.95 160

viewmsd2359difaftdt		80.37 209	79.73 200	82.30 253	83.70 354	62.39 317	84.20 314	86.67 321	73.22 202	80.90 176	90.62 180	63.00 196	91.56 286	76.81 180	78.44 338	92.95 160

v10		79.74 222	78.67 227	82.97 226	84.06 344	64.95 242	87.88 181	90.62 173	73.11 204	75.11 313	86.56 309	61.46 224	94.05 147	73.68 214	75.55 380	89.90 289

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 22	72.46 40	89.82 88	93.82 22	73.07 205	84.86 88	92.89 97	76.22 22	96.33 47	84.89 68	95.13 40	94.40 63

baseline1		76.98 295	76.75 281	77.66 372	88.13 204	55.66 424	85.12 282	81.89 398	73.04 206	76.79 264	88.90 235	62.43 205	87.78 386	63.30 320	71.18 426	89.55 301

APD-MVS		87.44 29	87.52 30	87.19 48	94.24 37	72.39 41	91.86 45	92.83 67	73.01 207	88.58 37	94.52 33	73.36 41	96.49 44	84.26 77	95.01 41	92.70 167
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
diffmvs		82.10 154	81.88 155	82.76 240	83.00 377	63.78 278	83.68 325	89.76 204	72.94 208	82.02 152	89.85 201	65.96 161	90.79 327	82.38 103	87.30 193	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		71.19 380	68.51 393	79.21 340	83.04 376	57.78 391	84.35 311	76.91 455	72.90 209	62.99 460	82.86 397	39.27 451	91.09 314	61.65 351	52.66 490	88.75 330

MVSMamba_PlusPlus		85.99 60	85.96 66	86.05 75	91.09 94	67.64 164	89.63 97	92.65 78	72.89 210	84.64 94	91.71 135	71.85 63	96.03 57	84.77 71	94.45 60	94.49 59

GDP-MVS		83.52 123	82.64 135	86.16 71	88.14 203	68.45 134	89.13 122	92.69 73	72.82 211	83.71 116	91.86 129	55.69 287	95.35 88	80.03 129	89.74 140	94.69 37

hybridnocas07		81.44 175	81.13 164	82.37 251	82.13 399	63.11 301	83.45 334	88.74 262	72.54 212	80.71 183	90.73 174	65.14 167	90.74 332	80.35 125	86.41 211	93.27 133

fmvsm_s_conf0.5_n_5		85.22 83	85.55 75	84.25 156	86.26 289	67.40 174	89.18 116	89.31 226	72.50 213	88.31 40	93.86 71	69.66 98	91.96 266	89.81 13	91.05 113	93.38 126

Fast-Effi-MVS+-dtu		78.02 270	76.49 285	82.62 244	83.16 371	66.96 189	86.94 217	87.45 298	72.45 214	71.49 367	84.17 369	54.79 296	91.58 283	67.61 284	80.31 316	89.30 308

PHI-MVS		86.43 49	86.17 60	87.24 47	90.88 101	70.96 76	92.27 37	94.07 14	72.45 214	85.22 81	91.90 126	69.47 100	96.42 46	83.28 88	95.94 23	94.35 66

thres200		75.55 320	74.47 321	78.82 346	87.78 224	57.85 388	83.07 347	83.51 371	72.44 216	75.84 288	84.42 357	52.08 325	91.75 276	47.41 458	83.64 269	86.86 389

test_yl		81.17 178	80.47 180	83.24 207	89.13 159	63.62 280	86.21 251	89.95 198	72.43 217	81.78 157	89.61 212	57.50 270	93.58 172	70.75 250	86.90 201	92.52 176

DCV-MVSNet		81.17 178	80.47 180	83.24 207	89.13 159	63.62 280	86.21 251	89.95 198	72.43 217	81.78 157	89.61 212	57.50 270	93.58 172	70.75 250	86.90 201	92.52 176

viewdifsd2359ckpt13		82.91 141	82.29 144	84.77 123	86.96 271	66.90 191	87.47 191	91.62 140	72.19 219	81.68 159	90.71 176	66.92 140	93.28 196	75.90 191	87.15 196	94.12 79

BH-untuned		79.47 228	78.60 229	82.05 259	89.19 157	65.91 208	86.07 255	88.52 271	72.18 220	75.42 298	87.69 272	61.15 232	93.54 179	60.38 362	86.83 204	86.70 394

TransMVSNet (Re)		75.39 326	74.56 319	77.86 367	85.50 310	57.10 401	86.78 225	86.09 335	72.17 221	71.53 366	87.34 281	63.01 195	89.31 358	56.84 401	61.83 471	87.17 379

GA-MVS		76.87 297	75.17 312	81.97 262	82.75 386	62.58 313	81.44 371	86.35 330	72.16 222	74.74 321	82.89 396	46.20 402	92.02 264	68.85 275	81.09 304	91.30 226

VortexMVS		78.57 256	77.89 247	80.59 297	85.89 298	62.76 311	85.61 266	89.62 211	72.06 223	74.99 317	85.38 338	55.94 286	90.77 330	74.99 202	76.58 362	88.23 345

mmtdpeth		74.16 337	73.01 341	77.60 376	83.72 353	61.13 340	85.10 283	85.10 346	72.06 223	77.21 258	80.33 428	43.84 422	85.75 406	77.14 173	52.61 491	85.91 410

v1144		80.03 218	79.03 221	83.01 222	83.78 351	64.51 258	87.11 210	90.57 176	71.96 225	78.08 234	86.20 319	61.41 225	93.94 151	74.93 203	77.23 352	90.60 253

viewdifsd2359ckpt09		83.34 129	82.55 138	85.70 83	87.64 236	67.72 162	88.43 154	91.68 137	71.91 226	81.65 160	90.68 177	67.10 139	94.75 117	76.17 186	87.70 186	94.62 50

PS-MVSNAJss		82.07 156	81.31 160	84.34 145	86.51 285	67.27 180	89.27 113	91.51 145	71.75 227	79.37 205	90.22 196	63.15 191	94.27 135	77.69 166	82.36 289	91.49 220

EPNet_dtu		75.46 322	74.86 314	77.23 381	82.57 392	54.60 436	86.89 219	83.09 380	71.64 228	66.25 436	85.86 325	55.99 285	88.04 382	54.92 413	86.55 208	89.05 315
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		78.40 258	77.40 264	81.40 275	87.60 237	63.01 302	88.39 157	89.28 227	71.63 229	75.34 302	87.28 282	54.80 293	91.11 309	62.72 330	79.57 323	90.09 277

test1		78.40 258	77.40 264	81.40 275	87.60 237	63.01 302	88.39 157	89.28 227	71.63 229	75.34 302	87.28 282	54.80 293	91.11 309	62.72 330	79.57 323	90.09 277

FMVSNet2		78.20 264	77.21 268	81.20 282	87.60 237	62.89 310	87.47 191	89.02 245	71.63 229	75.29 308	87.28 282	54.80 293	91.10 312	62.38 338	79.38 329	89.61 299

patch_mono-2		83.65 117	84.54 92	80.99 288	90.06 122	65.83 211	84.21 313	88.74 262	71.60 232	85.01 82	92.44 109	74.51 31	83.50 430	82.15 104	92.15 92	93.64 114

V42		79.38 234	78.24 239	82.83 231	81.10 418	65.50 221	85.55 271	89.82 201	71.57 233	78.21 229	86.12 321	60.66 241	93.18 209	75.64 194	75.46 384	89.81 294

API-MVS		81.99 158	81.23 162	84.26 155	90.94 99	70.18 93	91.10 63	89.32 225	71.51 234	78.66 217	88.28 255	65.26 165	95.10 99	64.74 310	91.23 111	87.51 364

tttt0517		79.40 232	77.91 245	83.90 184	88.10 206	63.84 275	88.37 160	84.05 363	71.45 235	76.78 265	89.12 226	49.93 363	94.89 109	70.18 259	83.18 279	92.96 159

pm-mvs1		77.25 291	76.68 283	78.93 344	84.22 340	58.62 376	86.41 240	88.36 273	71.37 236	73.31 340	88.01 265	61.22 231	89.15 363	64.24 314	73.01 413	89.03 316

Elysia		81.53 170	80.16 187	85.62 86	85.51 308	68.25 141	88.84 134	92.19 109	71.31 237	80.50 187	89.83 202	46.89 390	94.82 112	76.85 176	89.57 142	93.80 101

StellarMVS		81.53 170	80.16 187	85.62 86	85.51 308	68.25 141	88.84 134	92.19 109	71.31 237	80.50 187	89.83 202	46.89 390	94.82 112	76.85 176	89.57 142	93.80 101

testing222		74.04 339	72.66 345	78.19 360	87.89 216	55.36 427	81.06 377	79.20 437	71.30 239	74.65 324	83.57 384	39.11 454	88.67 373	51.43 433	85.75 230	90.53 256

GeoE		81.71 164	81.01 168	83.80 188	89.51 137	64.45 262	88.97 127	88.73 264	71.27 240	78.63 218	89.76 207	66.32 150	93.20 206	69.89 263	86.02 222	93.74 104

tt0805		78.73 250	77.83 249	81.43 273	85.17 317	60.30 361	89.41 108	90.90 164	71.21 241	77.17 259	88.73 240	46.38 397	93.21 203	72.57 230	78.96 333	90.79 243

FMVSNet3		77.88 274	76.85 276	80.97 290	86.84 274	62.36 319	86.52 237	88.77 256	71.13 242	75.34 302	86.66 304	54.07 303	91.10 312	62.72 330	79.57 323	89.45 303

VDDNet		81.52 172	80.67 173	84.05 173	90.44 110	64.13 269	89.73 93	85.91 336	71.11 243	83.18 131	93.48 80	50.54 353	93.49 185	73.40 219	88.25 172	94.54 57

fmvsm_s_conf0.5_n		83.80 110	83.71 111	84.07 167	86.69 280	67.31 177	89.46 103	83.07 381	71.09 244	86.96 66	93.70 76	69.02 115	91.47 296	88.79 30	84.62 247	93.44 125

XVG-OURS		80.41 205	79.23 217	83.97 181	85.64 304	69.02 114	83.03 349	90.39 180	71.09 244	77.63 244	91.49 148	54.62 299	91.35 300	75.71 193	83.47 273	91.54 217

SSM_0407		81.58 169	80.48 179	84.87 118	88.81 170	67.96 152	87.37 201	89.25 231	71.06 246	79.48 202	90.39 189	59.57 251	94.48 130	72.45 236	85.93 225	92.18 195

SSM_0404		81.91 159	80.84 171	85.13 104	89.24 154	68.26 139	87.84 183	89.25 231	71.06 246	80.62 184	90.39 189	59.57 251	94.65 123	72.45 236	87.19 195	92.47 181

SixPastTwentyTwo		73.37 350	71.26 364	79.70 328	85.08 322	57.89 387	85.57 267	83.56 370	71.03 248	65.66 440	85.88 324	42.10 434	92.57 237	59.11 376	63.34 465	88.65 334

ZD-MVS					94.38 30	72.22 46		92.67 75	70.98 249	87.75 53	94.07 59	74.01 39	96.70 32	84.66 72	94.84 48

mamba_0408		79.37 235	77.52 261	84.93 114	88.81 170	67.96 152	65.03 495	88.66 266	70.96 250	79.48 202	89.80 204	58.69 257	94.65 123	70.35 256	85.93 225	92.18 195

SSM_04072		77.67 282	77.52 261	78.12 362	88.81 170	67.96 152	65.03 495	88.66 266	70.96 250	79.48 202	89.80 204	58.69 257	74.23 488	70.35 256	85.93 225	92.18 195

FBQ-MVS		77.66 283	76.04 293	82.50 247	88.78 177	63.76 279	86.60 233	84.86 350	70.85 252	77.63 244	82.83 398	47.83 383	92.10 260	60.18 365	84.82 243	91.65 213

v1192		79.59 225	78.43 234	83.07 219	83.55 358	64.52 257	86.93 218	90.58 174	70.83 253	77.78 241	85.90 323	59.15 255	93.94 151	73.96 213	77.19 354	90.76 245

Fast-Effi-MVS+		80.81 188	79.92 193	83.47 195	88.85 166	64.51 258	85.53 273	89.39 219	70.79 254	78.49 222	85.06 347	67.54 132	93.58 172	67.03 293	86.58 207	92.32 187

PS-MVSNAJ		81.69 165	81.02 167	83.70 189	89.51 137	68.21 144	84.28 312	90.09 194	70.79 254	81.26 169	85.62 332	63.15 191	94.29 133	75.62 195	88.87 156	88.59 336

hybrid		81.05 182	80.66 174	82.22 255	81.97 401	62.99 306	83.42 335	88.68 265	70.76 256	80.56 186	90.40 188	64.49 176	90.48 336	79.57 140	86.06 220	93.19 140

LTVRE_ROB	69.57 13	76.25 311	74.54 320	81.41 274	88.60 183	64.38 264	79.24 406	89.12 242	70.76 256	69.79 388	87.86 268	49.09 375	93.20 206	56.21 407	80.16 317	86.65 396
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		75.14 328	74.01 326	78.53 354	88.16 201	56.38 413	80.74 383	80.42 420	70.67 258	72.69 351	83.72 379	43.61 424	89.86 347	62.29 340	83.76 263	89.36 306

fmvsm_s_conf0.1_n		83.56 122	83.38 120	84.10 161	84.86 326	67.28 179	89.40 109	83.01 382	70.67 258	87.08 63	93.96 68	68.38 122	91.45 297	88.56 35	84.50 248	93.56 119

xiu_mvs_v2_base		81.69 165	81.05 166	83.60 191	89.15 158	68.03 150	84.46 304	90.02 195	70.67 258	81.30 168	86.53 311	63.17 190	94.19 142	75.60 196	88.54 163	88.57 337

XVG-OURS-SEG-HR		80.81 188	79.76 199	83.96 182	85.60 306	68.78 120	83.54 333	90.50 177	70.66 261	76.71 267	91.66 137	60.69 239	91.26 303	76.94 175	81.58 299	91.83 205

Anonymous202405211		78.25 261	77.01 271	81.99 261	91.03 96	60.67 354	84.77 291	83.90 365	70.65 262	80.00 195	91.20 158	41.08 441	91.43 298	65.21 305	85.26 237	93.85 94

DP-MVS Recon		83.11 137	82.09 150	86.15 72	94.44 24	70.92 79	88.79 136	92.20 107	70.53 263	79.17 208	91.03 167	64.12 179	96.03 57	68.39 280	90.14 131	91.50 219

icg_test_0407_2		78.92 247	78.93 224	78.90 345	87.13 262	63.59 284	76.58 439	89.33 221	70.51 264	77.82 238	89.03 229	61.84 214	81.38 447	72.56 232	85.56 232	91.74 208

IMVS_0407		80.61 198	79.90 195	82.75 241	87.13 262	63.59 284	85.33 277	89.33 221	70.51 264	77.82 238	89.03 229	61.84 214	92.91 222	72.56 232	85.56 232	91.74 208

IMVS_0404		77.16 292	76.42 288	79.37 336	87.13 262	63.59 284	77.12 436	89.33 221	70.51 264	66.22 437	89.03 229	50.36 355	82.78 435	72.56 232	85.56 232	91.74 208

IMVS_0403		80.80 191	80.12 190	82.87 230	87.13 262	63.59 284	85.19 278	89.33 221	70.51 264	78.49 222	89.03 229	63.26 187	93.27 198	72.56 232	85.56 232	91.74 208

FMVSNet1		77.44 286	76.12 292	81.40 275	86.81 275	63.01 302	88.39 157	89.28 227	70.49 268	74.39 328	87.28 282	49.06 376	91.11 309	60.91 358	78.52 336	90.09 277

LuminaMVS		80.68 196	79.62 205	83.83 185	85.07 323	68.01 151	86.99 214	88.83 253	70.36 269	81.38 164	87.99 266	50.11 358	92.51 242	79.02 146	86.89 203	90.97 237

testing3		68.56 413	67.67 410	71.22 444	87.33 253	42.87 496	83.06 348	71.54 476	70.36 269	69.08 395	84.38 359	30.33 482	85.69 408	37.50 491	75.45 385	85.09 427

ab-mvs		79.51 226	78.97 223	81.14 284	88.46 188	60.91 348	83.84 321	89.24 233	70.36 269	79.03 209	88.87 238	63.23 189	90.21 342	65.12 306	82.57 287	92.28 189

tfpnnormal		74.39 333	73.16 339	78.08 363	86.10 296	58.05 382	84.65 296	87.53 295	70.32 272	71.22 370	85.63 331	54.97 291	89.86 347	43.03 477	75.02 394	86.32 399

ACMM	73.20 8	80.78 195	79.84 197	83.58 193	89.31 150	68.37 136	89.99 84	91.60 142	70.28 273	77.25 252	89.66 210	53.37 311	93.53 180	74.24 211	82.85 282	88.85 325
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n_a		83.63 119	83.41 119	84.28 151	86.14 294	68.12 145	89.43 105	82.87 386	70.27 274	87.27 62	93.80 74	69.09 110	91.58 283	88.21 39	83.65 268	93.14 145

ACMH+	68.96 14	76.01 315	74.01 326	82.03 260	88.60 183	65.31 231	88.86 131	87.55 294	70.25 275	67.75 412	87.47 280	41.27 439	93.19 208	58.37 385	75.94 375	87.60 359

IB-MVS	68.01 15	75.85 317	73.36 337	83.31 203	84.76 329	66.03 202	83.38 337	85.06 347	70.21 276	69.40 390	81.05 418	45.76 407	94.66 122	65.10 307	75.49 381	89.25 309
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		79.40 232	77.76 254	84.31 147	87.69 234	65.10 238	87.36 202	84.26 361	70.04 277	77.42 248	88.26 257	49.94 361	94.79 116	70.20 258	84.70 246	93.03 153

mvsmamba		80.60 200	79.38 211	84.27 153	89.74 131	67.24 182	87.47 191	86.95 314	70.02 278	75.38 300	88.93 234	51.24 343	92.56 238	75.47 199	89.22 150	93.00 157

test_fmvsmvis_n_1920		84.02 103	83.87 105	84.49 136	84.12 342	69.37 110	88.15 170	87.96 283	70.01 279	83.95 112	93.23 88	68.80 117	91.51 293	88.61 32	89.96 135	92.57 172

v144192		79.47 228	78.37 235	82.78 238	83.35 361	63.96 271	86.96 215	90.36 184	69.99 280	77.50 246	85.67 330	60.66 241	93.77 165	74.27 210	76.58 362	90.62 251

test_fmvsm_n_1920		85.29 82	85.34 79	85.13 104	86.12 295	69.93 94	88.65 146	90.78 170	69.97 281	88.27 41	93.98 67	71.39 72	91.54 290	88.49 36	90.45 126	93.91 90

c3_l		78.75 249	77.91 245	81.26 280	82.89 384	61.56 334	84.09 318	89.13 241	69.97 281	75.56 292	84.29 362	66.36 149	92.09 261	73.47 218	75.48 382	90.12 274

v1921920		79.22 237	78.03 242	82.80 234	83.30 363	63.94 273	86.80 223	90.33 185	69.91 283	77.48 247	85.53 334	58.44 261	93.75 167	73.60 215	76.85 359	90.71 249

ACMH	67.68 16	75.89 316	73.93 328	81.77 266	88.71 180	66.61 194	88.62 147	89.01 246	69.81 284	66.78 427	86.70 302	41.95 436	91.51 293	55.64 408	78.14 344	87.17 379
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.1_n_a		83.32 131	82.99 128	84.28 151	83.79 350	68.07 147	89.34 112	82.85 387	69.80 285	87.36 61	94.06 60	68.34 124	91.56 286	87.95 43	83.46 274	93.21 137

DPM-MVS		84.93 89	84.29 96	86.84 57	90.20 115	73.04 23	87.12 209	93.04 48	69.80 285	82.85 139	91.22 157	73.06 47	96.02 59	76.72 183	94.63 54	91.46 223

MAR-MVS		81.84 161	80.70 172	85.27 97	91.32 91	71.53 59	89.82 88	90.92 163	69.77 287	78.50 221	86.21 318	62.36 206	94.52 127	65.36 304	92.05 95	89.77 295
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		76.11 313	74.27 325	81.62 268	83.20 368	64.67 254	83.60 330	89.75 206	69.75 288	71.85 362	87.09 291	32.78 475	92.11 259	69.99 262	80.43 315	88.09 349

BH-w/o		78.21 263	77.33 267	80.84 292	88.81 170	65.13 235	84.87 289	87.85 288	69.75 288	74.52 326	84.74 354	61.34 227	93.11 213	58.24 387	85.84 228	84.27 436

v1240		78.99 244	77.78 252	82.64 243	83.21 367	63.54 288	86.62 232	90.30 187	69.74 290	77.33 250	85.68 329	57.04 276	93.76 166	73.13 223	76.92 356	90.62 251

FE-MVSNET2		72.88 366	71.28 362	77.67 371	78.30 451	57.78 391	84.43 307	88.92 252	69.56 291	64.61 449	81.67 413	46.73 394	88.54 376	59.33 372	67.99 443	86.69 395

ET-MVSNet_ETH3D		78.63 253	76.63 284	84.64 127	86.73 278	69.47 104	85.01 286	84.61 354	69.54 292	66.51 434	86.59 306	50.16 357	91.75 276	76.26 185	84.24 256	92.69 169

eth_miper_zixun_eth		77.92 273	76.69 282	81.61 270	83.00 377	61.98 327	83.15 342	89.20 235	69.52 293	74.86 320	84.35 361	61.76 217	92.56 238	71.50 243	72.89 414	90.28 268

PVSNet_Blended_VisFu		82.62 145	81.83 156	84.96 111	90.80 103	69.76 99	88.74 141	91.70 136	69.39 294	78.96 210	88.46 250	65.47 164	94.87 111	74.42 208	88.57 162	90.24 269

mvs_tets		79.13 240	77.77 253	83.22 209	84.70 330	66.37 197	89.17 117	90.19 191	69.38 295	75.40 299	89.46 219	44.17 420	93.15 210	76.78 182	80.70 311	90.14 272

PVSNet_BlendedMVS		80.60 200	80.02 191	82.36 252	88.85 166	65.40 223	86.16 253	92.00 117	69.34 296	78.11 232	86.09 322	66.02 158	94.27 135	71.52 241	82.06 292	87.39 367

SD_0403		74.65 332	74.77 316	74.29 413	86.20 292	47.42 480	83.71 324	85.12 345	69.30 297	68.50 403	87.95 267	59.40 253	86.05 403	49.38 445	83.35 275	89.40 304

AdaColmap		80.58 203	79.42 209	84.06 170	93.09 64	68.91 117	89.36 111	88.97 249	69.27 298	75.70 290	89.69 208	57.20 275	95.77 66	63.06 325	88.41 167	87.50 365

ETVMVS		72.25 373	71.05 367	75.84 391	87.77 226	51.91 457	79.39 404	74.98 464	69.26 299	73.71 335	82.95 394	40.82 443	86.14 402	46.17 464	84.43 253	89.47 302

ITE_SJBPF				78.22 359	81.77 405	60.57 356		83.30 374	69.25 300	67.54 414	87.20 287	36.33 468	87.28 392	54.34 416	74.62 398	86.80 391

cl____		77.72 278	76.76 279	80.58 298	82.49 394	60.48 358	83.09 345	87.87 286	69.22 301	74.38 329	85.22 343	62.10 211	91.53 291	71.09 247	75.41 386	89.73 297

DIV-MVS_self_test		77.72 278	76.76 279	80.58 298	82.48 395	60.48 358	83.09 345	87.86 287	69.22 301	74.38 329	85.24 341	62.10 211	91.53 291	71.09 247	75.40 387	89.74 296

jajsoiax		79.29 236	77.96 243	83.27 205	84.68 331	66.57 195	89.25 114	90.16 192	69.20 303	75.46 296	89.49 216	45.75 408	93.13 212	76.84 178	80.80 309	90.11 275

IterMVS-SCA-FT		75.43 323	73.87 330	80.11 312	82.69 388	64.85 251	81.57 368	83.47 372	69.16 304	70.49 374	84.15 370	51.95 328	88.15 380	69.23 269	72.14 420	87.34 372

CL-MVSNet_self_test		72.37 370	71.46 358	75.09 403	79.49 440	53.53 444	80.76 382	85.01 349	69.12 305	70.51 373	82.05 410	57.92 265	84.13 423	52.27 427	66.00 451	87.60 359

AUN-MVS		79.21 238	77.60 259	84.05 173	88.71 180	67.61 165	85.84 263	87.26 306	69.08 306	77.23 254	88.14 263	53.20 313	93.47 189	75.50 198	73.45 409	91.06 232

xiu_mvs_v1_base_debu		80.80 191	79.72 202	84.03 175	87.35 248	70.19 90	85.56 268	88.77 256	69.06 307	81.83 153	88.16 259	50.91 346	92.85 225	78.29 159	87.56 187	89.06 312

xiu_mvs_v1_base		80.80 191	79.72 202	84.03 175	87.35 248	70.19 90	85.56 268	88.77 256	69.06 307	81.83 153	88.16 259	50.91 346	92.85 225	78.29 159	87.56 187	89.06 312

xiu_mvs_v1_base_debi		80.80 191	79.72 202	84.03 175	87.35 248	70.19 90	85.56 268	88.77 256	69.06 307	81.83 153	88.16 259	50.91 346	92.85 225	78.29 159	87.56 187	89.06 312

MVSTER		79.01 243	77.88 248	82.38 250	83.07 374	64.80 252	84.08 319	88.95 250	69.01 310	78.69 215	87.17 289	54.70 297	92.43 246	74.69 204	80.57 313	89.89 290

usedtu_dtu_shiyan1		76.43 306	75.32 308	79.76 324	83.00 377	60.72 351	81.74 363	88.76 260	68.99 311	72.98 345	84.19 367	56.41 283	90.27 338	62.39 336	79.40 327	88.31 342

FE-MVSNET3		76.43 306	75.32 308	79.76 324	83.00 377	60.72 351	81.74 363	88.76 260	68.99 311	72.98 345	84.19 367	56.41 283	90.27 338	62.39 336	79.40 327	88.31 342

cl22		78.07 268	77.01 271	81.23 281	82.37 397	61.83 330	83.55 331	87.98 281	68.96 313	75.06 315	83.87 372	61.40 226	91.88 272	73.53 216	76.39 367	89.98 286

miper_ehance_all_eth		78.59 255	77.76 254	81.08 286	82.66 389	61.56 334	83.65 326	89.15 239	68.87 314	75.55 293	83.79 376	66.49 147	92.03 262	73.25 221	76.39 367	89.64 298

PAPR		81.66 167	80.89 170	83.99 180	90.27 113	64.00 270	86.76 227	91.77 132	68.84 315	77.13 261	89.50 215	67.63 131	94.88 110	67.55 285	88.52 164	93.09 148

CPTT-MVS		83.73 114	83.33 122	84.92 115	93.28 54	70.86 80	92.09 41	90.38 181	68.75 316	79.57 200	92.83 99	60.60 244	93.04 219	80.92 116	91.56 105	90.86 241

train_agg		86.43 49	86.20 57	87.13 50	93.26 57	72.96 25	88.75 139	91.89 123	68.69 317	85.00 83	93.10 90	74.43 32	95.41 82	84.97 65	95.71 29	93.02 154

test_8					93.13 61	72.57 35	88.68 145	91.84 127	68.69 317	84.87 87	93.10 90	74.43 32	95.16 92

dmvs_re		71.14 381	70.58 375	72.80 430	81.96 402	59.68 367	75.60 447	79.34 435	68.55 319	69.27 394	80.72 424	49.42 368	76.54 469	52.56 426	77.79 346	82.19 460

MVSFormer		82.85 142	82.05 151	85.24 98	87.35 248	70.21 88	90.50 72	90.38 181	68.55 319	81.32 165	89.47 217	61.68 218	93.46 190	78.98 149	90.26 129	92.05 202

test_djsdf		80.30 212	79.32 214	83.27 205	83.98 346	65.37 226	90.50 72	90.38 181	68.55 319	76.19 281	88.70 241	56.44 282	93.46 190	78.98 149	80.14 319	90.97 237

TEST9					93.26 57	72.96 25	88.75 139	91.89 123	68.44 322	85.00 83	93.10 90	74.36 34	95.41 82

FE-MVS		77.78 276	75.68 297	84.08 166	88.09 207	66.00 205	83.13 343	87.79 289	68.42 323	78.01 235	85.23 342	45.50 411	95.12 94	59.11 376	85.83 229	91.11 230

CDPH-MVS		85.76 69	85.29 83	87.17 49	93.49 52	71.08 72	88.58 149	92.42 88	68.32 324	84.61 95	93.48 80	72.32 56	96.15 55	79.00 148	95.43 34	94.28 72

PC_three_1452									68.21 325	92.02 15	94.00 64	82.09 5	95.98 63	84.58 73	96.68 2	94.95 15

fmvsm_l_conf0.5_n		84.47 93	84.54 92	84.27 153	85.42 311	68.81 118	88.49 153	87.26 306	68.08 326	88.03 47	93.49 79	72.04 62	91.77 275	88.90 29	89.14 153	92.24 192

IterMVS		74.29 334	72.94 342	78.35 358	81.53 410	63.49 290	81.58 367	82.49 390	68.06 327	69.99 383	83.69 380	51.66 337	85.54 410	65.85 301	71.64 423	86.01 407
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dmvs_testset		62.63 444	64.11 434	58.19 476	78.55 447	24.76 519	75.28 448	65.94 493	67.91 328	60.34 470	76.01 469	53.56 308	73.94 491	31.79 497	67.65 444	75.88 484

TAMVS		78.89 248	77.51 263	83.03 221	87.80 221	67.79 160	84.72 292	85.05 348	67.63 329	76.75 266	87.70 271	62.25 208	90.82 326	58.53 383	87.13 197	90.49 258

PVSNet_Blended		80.98 183	80.34 182	82.90 228	88.85 166	65.40 223	84.43 307	92.00 117	67.62 330	78.11 232	85.05 348	66.02 158	94.27 135	71.52 241	89.50 144	89.01 317

TR-MVS		77.44 286	76.18 291	81.20 282	88.24 196	63.24 296	84.61 299	86.40 328	67.55 331	77.81 240	86.48 312	54.10 302	93.15 210	57.75 391	82.72 285	87.20 377

CDS-MVSNet		79.07 242	77.70 256	83.17 212	87.60 237	68.23 143	84.40 310	86.20 332	67.49 332	76.36 277	86.54 310	61.54 221	90.79 327	61.86 348	87.33 192	90.49 258
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 97	84.06 170	85.38 312	68.40 135	88.34 161	86.85 318	67.48 333	87.48 58	93.40 84	70.89 78	91.61 281	88.38 38	89.22 150	92.16 199

dtuplus		80.04 217	79.40 210	81.97 262	83.08 373	62.61 312	83.63 329	87.98 281	67.47 334	81.02 172	90.50 186	64.86 172	90.77 330	71.28 246	84.76 244	92.53 175

mvs_anonymous		79.42 231	79.11 220	80.34 304	84.45 337	57.97 385	82.59 351	87.62 293	67.40 335	76.17 284	88.56 248	68.47 121	89.59 353	70.65 253	86.05 221	93.47 124

viewmambaseed2359dif		80.41 205	79.84 197	82.12 256	82.95 383	62.50 316	83.39 336	88.06 279	67.11 336	80.98 173	90.31 191	66.20 153	91.01 317	74.62 205	84.90 240	92.86 163

mvs5depth		69.45 405	67.45 414	75.46 399	73.93 476	55.83 421	79.19 408	83.23 376	66.89 337	71.63 365	83.32 387	33.69 474	85.09 415	59.81 368	55.34 487	85.46 418

IU-MVS					95.30 2	71.25 66		92.95 62	66.81 338	92.39 7				88.94 28	96.63 4	94.85 24

baseline2		75.70 318	73.83 331	81.30 278	83.26 365	61.79 331	82.57 352	80.65 413	66.81 338	66.88 425	83.42 386	57.86 266	92.19 257	63.47 317	79.57 323	89.91 288

miper_lstm_enhance		74.11 338	73.11 340	77.13 382	80.11 429	59.62 368	72.23 465	86.92 317	66.76 340	70.40 375	82.92 395	56.93 277	82.92 434	69.06 272	72.63 415	88.87 324

OpenMVS	72.83 10	79.77 221	78.33 237	84.09 165	85.17 317	69.91 95	90.57 69	90.97 161	66.70 341	72.17 358	91.91 125	54.70 297	93.96 148	61.81 349	90.95 117	88.41 341

test-LLR		72.94 363	72.43 347	74.48 410	81.35 414	58.04 383	78.38 420	77.46 448	66.66 342	69.95 384	79.00 443	48.06 381	79.24 455	66.13 296	84.83 241	86.15 403

test20.03		67.45 421	66.95 420	68.94 454	75.48 471	44.84 492	77.50 432	77.67 446	66.66 342	63.01 459	83.80 375	47.02 388	78.40 459	42.53 481	68.86 438	83.58 445

test0.0.03 1		68.00 419	67.69 409	68.90 455	77.55 460	47.43 479	75.70 446	72.95 475	66.66 342	66.56 430	82.29 407	48.06 381	75.87 478	44.97 473	74.51 399	83.41 446

Syy-MVS		68.05 418	67.85 404	68.67 458	84.68 331	40.97 502	78.62 417	73.08 473	66.65 345	66.74 428	79.46 438	52.11 324	82.30 439	32.89 496	76.38 370	82.75 455

myMVS_eth3d		67.02 425	66.29 425	69.21 453	84.68 331	42.58 497	78.62 417	73.08 473	66.65 345	66.74 428	79.46 438	31.53 479	82.30 439	39.43 487	76.38 370	82.75 455

QAPM		80.88 185	79.50 208	85.03 107	88.01 212	68.97 116	91.59 51	92.00 117	66.63 347	75.15 312	92.16 119	57.70 267	95.45 77	63.52 316	88.76 159	90.66 250

XXY-MVS		75.41 324	75.56 300	74.96 404	83.59 357	57.82 389	80.59 386	83.87 366	66.54 348	74.93 319	88.31 254	63.24 188	80.09 453	62.16 342	76.85 359	86.97 387

OurMVSNet-221017-0		74.26 335	72.42 348	79.80 321	83.76 352	59.59 369	85.92 259	86.64 323	66.39 349	66.96 424	87.58 274	39.46 450	91.60 282	65.76 302	69.27 434	88.22 346

SCA		74.22 336	72.33 349	79.91 317	84.05 345	62.17 323	79.96 398	79.29 436	66.30 350	72.38 355	80.13 431	51.95 328	88.60 374	59.25 374	77.67 350	88.96 321

nomal-1		73.10 359	71.76 354	77.13 382	82.58 391	65.50 221	73.53 462	79.64 431	66.14 351	72.17 358	81.27 415	46.45 395	81.47 446	62.08 345	81.93 295	84.42 435

testgi		66.67 428	66.53 424	67.08 465	75.62 470	41.69 501	75.93 442	76.50 457	66.11 352	65.20 447	86.59 306	35.72 470	74.71 485	43.71 474	73.38 411	84.84 430

HY-MVS	69.67 12	77.95 272	77.15 269	80.36 303	87.57 246	60.21 363	83.37 338	87.78 290	66.11 352	75.37 301	87.06 293	63.27 186	90.48 336	61.38 355	82.43 288	90.40 262

EG-PatchMatch MVS		74.04 339	71.82 353	80.71 295	84.92 325	67.42 172	85.86 262	88.08 277	66.04 354	64.22 452	83.85 373	35.10 471	92.56 238	57.44 393	80.83 308	82.16 461

CNLPA		78.08 267	76.79 278	81.97 262	90.40 111	71.07 73	87.59 188	84.55 355	66.03 355	72.38 355	89.64 211	57.56 269	86.04 404	59.61 370	83.35 275	88.79 328

gbinet_0.2-2-1-0.02		73.24 356	70.86 372	80.39 301	78.03 454	61.62 333	83.10 344	86.69 320	65.98 356	69.29 393	76.15 468	49.77 364	91.51 293	62.75 329	66.00 451	88.03 350

Anonymous20240529		80.19 215	78.89 225	84.10 161	90.60 106	64.75 253	88.95 128	90.90 164	65.97 357	80.59 185	91.17 160	49.97 360	93.73 169	69.16 271	82.70 286	93.81 99

TAPA-MVS	73.13 9	79.15 239	77.94 244	82.79 237	89.59 133	62.99 306	88.16 169	91.51 145	65.77 358	77.14 260	91.09 163	60.91 236	93.21 203	50.26 441	87.05 198	92.17 198
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MSDG		73.36 352	70.99 368	80.49 300	84.51 336	65.80 213	80.71 384	86.13 334	65.70 359	65.46 442	83.74 377	44.60 415	90.91 323	51.13 434	76.89 357	84.74 431

anonymousdsp		78.60 254	77.15 269	82.98 225	80.51 424	67.08 185	87.24 207	89.53 214	65.66 360	75.16 311	87.19 288	52.52 315	92.25 255	77.17 172	79.34 330	89.61 299

test_0402		72.79 367	70.44 378	79.84 320	88.13 204	65.99 206	85.93 258	84.29 359	65.57 361	67.40 420	85.49 335	46.92 389	92.61 234	35.88 493	74.38 400	80.94 468

UBG		73.08 360	72.27 350	75.51 397	88.02 210	51.29 465	78.35 423	77.38 451	65.52 362	73.87 334	82.36 404	45.55 409	86.48 399	55.02 412	84.39 254	88.75 330

miper_enhance_ethall		77.87 275	76.86 275	80.92 291	81.65 406	61.38 338	82.68 350	88.98 247	65.52 362	75.47 294	82.30 406	65.76 163	92.00 265	72.95 225	76.39 367	89.39 305

WBMVS		73.43 347	72.81 343	75.28 401	87.91 215	50.99 467	78.59 419	81.31 407	65.51 364	74.47 327	84.83 351	46.39 396	86.68 396	58.41 384	77.86 345	88.17 348

blend_shiyan4		72.29 372	69.65 385	80.21 309	78.24 452	62.16 324	82.29 356	87.27 304	65.41 365	68.43 405	76.42 464	39.91 448	91.23 305	63.21 323	65.66 458	87.22 376

blended_shiyan8		73.38 348	71.17 365	80.02 314	78.36 449	61.51 336	82.43 353	87.28 301	65.40 366	68.61 399	77.53 456	51.91 331	91.00 320	63.28 321	65.76 453	87.53 363

blended_shiyan6		73.38 348	71.17 365	80.01 315	78.36 449	61.48 337	82.43 353	87.27 304	65.40 366	68.56 401	77.55 455	51.94 330	91.01 317	63.27 322	65.76 453	87.55 362

UnsupCasMVSNet_eth		67.33 422	65.99 426	71.37 440	73.48 481	51.47 463	75.16 450	85.19 344	65.20 368	60.78 468	80.93 423	42.35 430	77.20 465	57.12 396	53.69 489	85.44 419

wanda-best-256-512		72.94 363	70.66 373	79.79 322	77.80 456	61.03 345	81.31 373	87.15 309	65.18 369	68.09 406	76.28 465	51.32 339	90.97 321	63.06 325	65.76 453	87.35 369

FE-blended-shiyan7		72.94 363	70.66 373	79.79 322	77.80 456	61.03 345	81.31 373	87.15 309	65.18 369	68.09 406	76.28 465	51.32 339	90.97 321	63.06 325	65.76 453	87.35 369

WTY-MVS		75.65 319	75.68 297	75.57 395	86.40 288	56.82 404	77.92 429	82.40 391	65.10 371	76.18 282	87.72 270	63.13 194	80.90 450	60.31 363	81.96 293	89.00 319

thisisatest0515		77.33 289	75.38 305	83.18 211	85.27 316	63.80 276	82.11 359	83.27 375	65.06 372	75.91 286	83.84 374	49.54 366	94.27 135	67.24 289	86.19 216	91.48 221

MVP-Stereo		76.12 312	74.46 322	81.13 285	85.37 313	69.79 97	84.42 309	87.95 284	65.03 373	67.46 417	85.33 339	53.28 312	91.73 278	58.01 389	83.27 277	81.85 463
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20231211		78.97 245	77.69 257	82.81 233	90.54 108	64.29 265	90.11 83	91.51 145	65.01 374	76.16 285	88.13 264	50.56 352	93.03 220	69.68 266	77.56 351	91.11 230

pmmvs6		74.69 331	73.39 335	78.61 349	81.38 413	57.48 396	86.64 231	87.95 284	64.99 375	70.18 378	86.61 305	50.43 354	89.52 354	62.12 343	70.18 431	88.83 326

PAPM		77.68 281	76.40 289	81.51 271	87.29 258	61.85 329	83.78 322	89.59 212	64.74 376	71.23 369	88.70 241	62.59 201	93.66 171	52.66 425	87.03 199	89.01 317

MIMVSNet		70.69 388	69.30 387	74.88 406	84.52 335	56.35 415	75.87 445	79.42 433	64.59 377	67.76 411	82.41 403	41.10 440	81.54 444	46.64 462	81.34 300	86.75 393

tpm		72.37 370	71.71 355	74.35 412	82.19 398	52.00 455	79.22 407	77.29 452	64.56 378	72.95 347	83.68 381	51.35 338	83.26 433	58.33 386	75.80 376	87.81 355

MDA-MVSNet-bldmvs		66.68 427	63.66 437	75.75 392	79.28 443	60.56 357	73.92 460	78.35 443	64.43 379	50.13 494	79.87 435	44.02 421	83.67 426	46.10 465	56.86 481	83.03 452

usedtu_blend_shiyan5		73.29 354	70.96 369	80.25 307	77.80 456	62.16 324	84.44 306	87.38 299	64.41 380	68.09 406	76.28 465	51.32 339	91.23 305	63.21 323	65.76 453	87.35 369

MIMVSNet1		68.58 412	66.78 423	73.98 418	80.07 430	51.82 459	80.77 381	84.37 356	64.40 381	59.75 474	82.16 409	36.47 467	83.63 427	42.73 478	70.33 430	86.48 398

D2MVS		74.82 330	73.21 338	79.64 331	79.81 434	62.56 315	80.34 391	87.35 300	64.37 382	68.86 396	82.66 401	46.37 398	90.10 343	67.91 282	81.24 302	86.25 400

PLC	70.83 11	78.05 269	76.37 290	83.08 218	91.88 85	67.80 159	88.19 167	89.46 216	64.33 383	69.87 386	88.38 252	53.66 307	93.58 172	58.86 379	82.73 284	87.86 354
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchmatchNet		73.12 358	71.33 361	78.49 356	83.18 369	60.85 349	79.63 401	78.57 441	64.13 384	71.73 363	79.81 436	51.20 344	85.97 405	57.40 394	76.36 372	88.66 333
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_2432*1600		66.22 432	63.89 435	73.21 424	75.47 472	53.42 446	70.76 472	84.35 357	64.10 385	66.52 432	78.52 447	34.55 472	84.98 416	50.40 437	50.33 494	81.23 466

miper_refine_blended		66.22 432	63.89 435	73.21 424	75.47 472	53.42 446	70.76 472	84.35 357	64.10 385	66.52 432	78.52 447	34.55 472	84.98 416	50.40 437	50.33 494	81.23 466

tpmvs		71.09 382	69.29 388	76.49 387	82.04 400	56.04 418	78.92 414	81.37 406	64.05 387	67.18 422	78.28 449	49.74 365	89.77 349	49.67 444	72.37 416	83.67 444

F-COLMAP		76.38 310	74.33 324	82.50 247	89.28 152	66.95 190	88.41 156	89.03 244	64.05 387	66.83 426	88.61 245	46.78 392	92.89 223	57.48 392	78.55 335	87.67 357

DP-MVS		76.78 298	74.57 318	83.42 199	93.29 53	69.46 106	88.55 151	83.70 367	63.98 389	70.20 377	88.89 237	54.01 305	94.80 115	46.66 460	81.88 296	86.01 407

原ACMM1				84.35 144	93.01 67	68.79 119		92.44 85	63.96 390	81.09 170	91.57 144	66.06 157	95.45 77	67.19 290	94.82 50	88.81 327

PM-MVS		66.41 430	64.14 433	73.20 426	73.92 477	56.45 410	78.97 412	64.96 496	63.88 391	64.72 448	80.24 430	19.84 499	83.44 431	66.24 295	64.52 463	79.71 475

FE-MVSNET		67.25 424	65.33 428	73.02 428	75.86 467	52.54 453	80.26 394	80.56 415	63.80 392	60.39 469	79.70 437	41.41 438	84.66 421	43.34 476	62.62 469	81.86 462

UWE-MVS		72.13 375	71.49 357	74.03 417	86.66 281	47.70 478	81.40 372	76.89 456	63.60 393	75.59 291	84.22 366	39.94 447	85.62 409	48.98 448	86.13 218	88.77 329

0.4-1-1-0.1		70.93 384	67.94 403	79.91 317	79.35 442	61.27 339	78.95 413	82.19 395	63.36 394	67.50 415	69.40 489	39.83 449	91.04 316	62.44 335	68.40 440	87.40 366

jason		81.39 176	80.29 184	84.70 126	86.63 282	69.90 96	85.95 257	86.77 319	63.24 395	81.07 171	89.47 217	61.08 234	92.15 258	78.33 158	90.07 134	92.05 202
jason: jason.
KD-MVS_self_test		68.81 409	67.59 412	72.46 433	74.29 475	45.45 486	77.93 428	87.00 313	63.12 396	63.99 455	78.99 445	42.32 431	84.77 419	56.55 405	64.09 464	87.16 381

gg-mvs-nofinetune		69.95 400	67.96 401	75.94 390	83.07 374	54.51 438	77.23 435	70.29 479	63.11 397	70.32 376	62.33 494	43.62 423	88.69 372	53.88 419	87.76 185	84.62 433

tpmrst		72.39 368	72.13 351	73.18 427	80.54 423	49.91 472	79.91 399	79.08 438	63.11 397	71.69 364	79.95 433	55.32 289	82.77 436	65.66 303	73.89 404	86.87 388

PCF-MVS	73.52 7	80.38 207	78.84 226	85.01 109	87.71 230	68.99 115	83.65 326	91.46 149	63.00 399	77.77 242	90.28 192	66.10 155	95.09 100	61.40 354	88.22 173	90.94 239
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
COLMAP_ROB	66.92 17	73.01 361	70.41 379	80.81 293	87.13 262	65.63 217	88.30 164	84.19 362	62.96 400	63.80 457	87.69 272	38.04 460	92.56 238	46.66 460	74.91 395	84.24 437
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-RL test		70.24 394	67.78 408	77.61 374	77.43 461	59.57 370	71.16 469	70.33 478	62.94 401	68.65 398	72.77 480	50.62 351	85.49 411	69.58 267	66.58 448	87.77 356

lupinMVS		81.39 176	80.27 185	84.76 124	87.35 248	70.21 88	85.55 271	86.41 327	62.85 402	81.32 165	88.61 245	61.68 218	92.24 256	78.41 156	90.26 129	91.83 205

test_vis1_n_1920		75.52 321	75.78 295	74.75 409	79.84 433	57.44 397	83.26 340	85.52 341	62.83 403	79.34 207	86.17 320	45.10 413	79.71 454	78.75 151	81.21 303	87.10 385

EPMVS		69.02 408	68.16 397	71.59 438	79.61 438	49.80 474	77.40 433	66.93 490	62.82 404	70.01 381	79.05 441	45.79 406	77.86 463	56.58 404	75.26 391	87.13 382

PatchMatch-RL		72.38 369	70.90 370	76.80 386	88.60 183	67.38 175	79.53 402	76.17 461	62.75 405	69.36 391	82.00 412	45.51 410	84.89 418	53.62 420	80.58 312	78.12 479

gm-plane-assit					81.40 412	53.83 443			62.72 406		80.94 421		92.39 248	63.40 319

0.3-1-1-0.015		70.03 398	66.80 422	79.72 327	78.18 453	61.07 343	77.63 431	82.32 394	62.65 407	65.50 441	67.29 490	37.62 463	90.91 323	61.99 346	68.04 442	87.19 378

FMVSNet5		69.50 404	67.96 401	74.15 415	82.97 382	55.35 428	80.01 397	82.12 397	62.56 408	63.02 458	81.53 414	36.92 464	81.92 442	48.42 450	74.06 402	85.17 425

sss		73.60 345	73.64 333	73.51 422	82.80 385	55.01 432	76.12 441	81.69 401	62.47 409	74.68 323	85.85 326	57.32 272	78.11 461	60.86 359	80.93 305	87.39 367

0.4-1-1-0.2		70.01 399	66.86 421	79.44 335	77.61 459	60.64 355	76.77 438	82.34 393	62.40 410	65.91 439	66.65 491	40.05 446	90.83 325	61.77 350	68.24 441	86.86 389

WB-MVSnew		71.96 377	71.65 356	72.89 429	84.67 334	51.88 458	82.29 356	77.57 447	62.31 411	73.67 337	83.00 393	53.49 310	81.10 449	45.75 468	82.13 291	85.70 414

AllTest		70.96 383	68.09 399	79.58 332	85.15 319	63.62 280	84.58 300	79.83 428	62.31 411	60.32 471	86.73 296	32.02 476	88.96 368	50.28 439	71.57 424	86.15 403

TestCases				79.58 332	85.15 319	63.62 280		79.83 428	62.31 411	60.32 471	86.73 296	32.02 476	88.96 368	50.28 439	71.57 424	86.15 403

1112_ss		77.40 288	76.43 287	80.32 305	89.11 163	60.41 360	83.65 326	87.72 292	62.13 414	73.05 344	86.72 298	62.58 202	89.97 346	62.11 344	80.80 309	90.59 254

PVSNet	64.34 18	72.08 376	70.87 371	75.69 393	86.21 291	56.44 411	74.37 458	80.73 412	62.06 415	70.17 379	82.23 408	42.86 428	83.31 432	54.77 414	84.45 252	87.32 373

UWE-MVS-28		65.32 435	64.93 429	66.49 466	78.70 446	38.55 504	77.86 430	64.39 497	62.00 416	64.13 453	83.60 382	41.44 437	76.00 476	31.39 498	80.89 306	84.92 428

LS3D		76.95 296	74.82 315	83.37 202	90.45 109	67.36 176	89.15 121	86.94 315	61.87 417	69.52 389	90.61 182	51.71 336	94.53 126	46.38 463	86.71 206	88.21 347

CostFormer		75.24 327	73.90 329	79.27 338	82.65 390	58.27 380	80.80 379	82.73 389	61.57 418	75.33 306	83.13 391	55.52 288	91.07 315	64.98 308	78.34 343	88.45 339

new-patchmatchnet		61.73 446	61.73 446	61.70 472	72.74 488	24.50 520	69.16 479	78.03 444	61.40 419	56.72 483	75.53 473	38.42 457	76.48 471	45.95 466	57.67 480	84.13 439

ANet_high		50.57 464	46.10 468	63.99 469	48.67 517	39.13 503	70.99 471	80.85 410	61.39 420	31.18 506	57.70 503	17.02 502	73.65 492	31.22 499	15.89 518	79.18 476

MS-PatchMatch		73.83 342	72.67 344	77.30 380	83.87 349	66.02 203	81.82 361	84.66 353	61.37 421	68.61 399	82.82 399	47.29 385	88.21 379	59.27 373	84.32 255	77.68 480

USDC		70.33 393	68.37 394	76.21 389	80.60 422	56.23 416	79.19 408	86.49 326	60.89 422	61.29 466	85.47 336	31.78 478	89.47 356	53.37 422	76.21 373	82.94 454

cascas		76.72 299	74.64 317	82.99 223	85.78 301	65.88 209	82.33 355	89.21 234	60.85 423	72.74 348	81.02 419	47.28 386	93.75 167	67.48 286	85.02 238	89.34 307

sc_t1		72.19 374	69.51 386	80.23 308	84.81 327	61.09 342	84.68 293	80.22 425	60.70 424	71.27 368	83.58 383	36.59 466	89.24 360	60.41 361	63.31 466	90.37 263

MDTV_nov1_ep13			69.97 384		83.18 369	53.48 445	77.10 437	80.18 427	60.45 425	69.33 392	80.44 425	48.89 379	86.90 394	51.60 430	78.51 337

TinyColmap		67.30 423	64.81 430	74.76 408	81.92 404	56.68 408	80.29 392	81.49 404	60.33 426	56.27 486	83.22 388	24.77 491	87.66 388	45.52 469	69.47 433	79.95 474

test-mter		71.41 379	70.39 380	74.48 410	81.35 414	58.04 383	78.38 420	77.46 448	60.32 427	69.95 384	79.00 443	36.08 469	79.24 455	66.13 296	84.83 241	86.15 403

1314		76.53 301	75.30 310	80.21 309	83.93 347	62.32 321	84.66 294	88.81 254	60.23 428	70.16 380	84.07 371	55.30 290	90.73 333	67.37 287	83.21 278	87.59 361

PatchT		68.46 415	67.85 404	70.29 448	80.70 421	43.93 494	72.47 464	74.88 465	60.15 429	70.55 372	76.57 460	49.94 361	81.59 443	50.58 435	74.83 396	85.34 420

dtuonlycased		68.45 416	67.29 417	71.92 435	80.18 428	54.90 433	79.76 400	80.38 422	60.11 430	62.57 463	76.44 463	49.34 370	82.31 438	55.05 411	61.77 472	78.53 478

无先验							87.48 190	88.98 247	60.00 431				94.12 144	67.28 288		88.97 320

CR-MVSNet		73.37 350	71.27 363	79.67 330	81.32 416	65.19 233	75.92 443	80.30 423	59.92 432	72.73 349	81.19 416	52.50 316	86.69 395	59.84 367	77.71 347	87.11 383

TDRefinement		67.49 420	64.34 432	76.92 384	73.47 482	61.07 343	84.86 290	82.98 384	59.77 433	58.30 478	85.13 345	26.06 487	87.89 384	47.92 457	60.59 477	81.81 464

dp		66.80 426	65.43 427	70.90 447	79.74 437	48.82 477	75.12 452	74.77 466	59.61 434	64.08 454	77.23 457	42.89 427	80.72 451	48.86 449	66.58 448	83.16 449

our_test_3		69.14 407	67.00 419	75.57 395	79.80 435	58.80 374	77.96 427	77.81 445	59.55 435	62.90 461	78.25 450	47.43 384	83.97 424	51.71 429	67.58 445	83.93 442

Test_1112_low_res		76.40 309	75.44 302	79.27 338	89.28 152	58.09 381	81.69 366	87.07 312	59.53 436	72.48 353	86.67 303	61.30 228	89.33 357	60.81 360	80.15 318	90.41 261

pmmvs4		74.03 341	71.91 352	80.39 301	81.96 402	68.32 137	81.45 370	82.14 396	59.32 437	69.87 386	85.13 345	52.40 318	88.13 381	60.21 364	74.74 397	84.73 432

testdata				79.97 316	90.90 100	64.21 267		84.71 352	59.27 438	85.40 78	92.91 96	62.02 213	89.08 364	68.95 273	91.37 108	86.63 397

WB-MVS		54.94 454	54.72 455	55.60 483	73.50 480	20.90 522	74.27 459	61.19 501	59.16 439	50.61 492	74.15 476	47.19 387	75.78 479	17.31 516	35.07 503	70.12 492

ppachtmachnet_test		70.04 397	67.34 416	78.14 361	79.80 435	61.13 340	79.19 408	80.59 414	59.16 439	65.27 444	79.29 440	46.75 393	87.29 391	49.33 446	66.72 446	86.00 409

RPSCF		73.23 357	71.46 358	78.54 353	82.50 393	59.85 365	82.18 358	82.84 388	58.96 441	71.15 371	89.41 223	45.48 412	84.77 419	58.82 380	71.83 422	91.02 236

pmmvs-eth3d		70.50 391	67.83 406	78.52 355	77.37 462	66.18 200	81.82 361	81.51 403	58.90 442	63.90 456	80.42 426	42.69 429	86.28 401	58.56 382	65.30 460	83.11 450

tt0320-xc		70.11 396	67.45 414	78.07 364	85.33 314	59.51 371	83.28 339	78.96 439	58.77 443	67.10 423	80.28 429	36.73 465	87.42 390	56.83 402	59.77 479	87.29 374

OpenMVS_ROB	64.09 19	70.56 390	68.19 396	77.65 373	80.26 425	59.41 372	85.01 286	82.96 385	58.76 444	65.43 443	82.33 405	37.63 462	91.23 305	45.34 472	76.03 374	82.32 458

114514_t		80.68 196	79.51 207	84.20 158	94.09 43	67.27 180	89.64 96	91.11 158	58.75 445	74.08 331	90.72 175	58.10 263	95.04 102	69.70 265	89.42 146	90.30 267

Patchmtry		70.74 387	69.16 390	75.49 398	80.72 420	54.07 441	74.94 454	80.30 423	58.34 446	70.01 381	81.19 416	52.50 316	86.54 397	53.37 422	71.09 427	85.87 412

test_cas_vis1_n_1920		73.76 343	73.74 332	73.81 420	75.90 466	59.77 366	80.51 387	82.40 391	58.30 447	81.62 161	85.69 328	44.35 419	76.41 472	76.29 184	78.61 334	85.23 422

Anonymous20240521		68.80 410	67.22 418	73.55 421	74.33 474	54.11 440	83.18 341	85.61 340	58.15 448	61.68 465	80.94 421	30.71 481	81.27 448	57.00 399	73.34 412	85.28 421

tt0320		70.49 392	68.03 400	77.89 366	84.78 328	59.12 373	83.55 331	80.44 419	58.13 449	67.43 419	80.41 427	39.26 452	87.54 389	55.12 410	63.18 467	86.99 386

旧先验2							86.56 235		58.10 450	87.04 64			88.98 366	74.07 212

JIA-IIPM		66.32 431	62.82 443	76.82 385	77.09 463	61.72 332	65.34 493	75.38 462	58.04 451	64.51 450	62.32 495	42.05 435	86.51 398	51.45 432	69.22 435	82.21 459

pmmvs5		71.55 378	70.20 382	75.61 394	77.83 455	56.39 412	81.74 363	80.89 409	57.76 452	67.46 417	84.49 355	49.26 373	85.32 414	57.08 397	75.29 390	85.11 426

TESTMET0.1,1		69.89 402	69.00 391	72.55 432	79.27 444	56.85 403	78.38 420	74.71 468	57.64 453	68.09 406	77.19 458	37.75 461	76.70 468	63.92 315	84.09 258	84.10 440

RPMNet		73.51 346	70.49 377	82.58 246	81.32 416	65.19 233	75.92 443	92.27 97	57.60 454	72.73 349	76.45 461	52.30 319	95.43 79	48.14 455	77.71 347	87.11 383

SSC-MVS		53.88 457	53.59 457	54.75 486	72.87 487	19.59 523	73.84 461	60.53 503	57.58 455	49.18 496	73.45 479	46.34 400	75.47 482	16.20 519	32.28 505	69.20 493

dtuonly		69.95 400	69.98 383	69.85 450	73.09 486	49.46 475	74.55 457	76.40 458	57.56 456	67.82 410	86.31 317	50.89 350	74.23 488	61.46 353	81.71 298	85.86 413

新几何1				83.42 199	93.13 61	70.71 82		85.48 342	57.43 457	81.80 156	91.98 124	63.28 185	92.27 254	64.60 311	92.99 77	87.27 375

YYNet1		65.03 436	62.91 441	71.38 439	75.85 468	56.60 409	69.12 480	74.66 469	57.28 458	54.12 488	77.87 452	45.85 405	74.48 486	49.95 442	61.52 474	83.05 451

MDA-MVSNet_test_wron		65.03 436	62.92 440	71.37 440	75.93 465	56.73 405	69.09 481	74.73 467	57.28 458	54.03 489	77.89 451	45.88 404	74.39 487	49.89 443	61.55 473	82.99 453

Anonymous20231206		68.60 411	67.80 407	71.02 445	80.23 427	50.75 469	78.30 424	80.47 417	56.79 460	66.11 438	82.63 402	46.35 399	78.95 457	43.62 475	75.70 377	83.36 447

tpm2		73.26 355	71.46 358	78.63 348	83.34 362	56.71 407	80.65 385	80.40 421	56.63 461	73.55 338	82.02 411	51.80 334	91.24 304	56.35 406	78.42 341	87.95 351

CHOSEN 1792x2688		77.63 284	75.69 296	83.44 198	89.98 124	68.58 131	78.70 416	87.50 296	56.38 462	75.80 289	86.84 294	58.67 259	91.40 299	61.58 352	85.75 230	90.34 264

HyFIR lowres test		77.53 285	75.40 304	83.94 183	89.59 133	66.62 193	80.36 390	88.64 269	56.29 463	76.45 274	85.17 344	57.64 268	93.28 196	61.34 356	83.10 280	91.91 204

usedtu_dtu_shiyan2		64.75 439	61.63 447	74.10 416	70.64 492	53.18 451	82.10 360	81.27 408	56.22 464	56.39 485	74.67 475	27.94 485	83.56 428	42.71 479	62.73 468	85.57 416

PVSNet_0	57.27 20	61.67 447	59.27 450	68.85 456	79.61 438	57.44 397	68.01 482	73.44 472	55.93 465	58.54 477	70.41 486	44.58 416	77.55 464	47.01 459	35.91 502	71.55 491

UnsupCasMVSNet_bld		63.70 442	61.53 448	70.21 449	73.69 479	51.39 464	72.82 463	81.89 398	55.63 466	57.81 480	71.80 482	38.67 456	78.61 458	49.26 447	52.21 492	80.63 470

MDTV_nov1_ep13_2view						37.79 505	75.16 450		55.10 467	66.53 431		49.34 370		53.98 418		87.94 352

MVS		78.19 265	76.99 273	81.78 265	85.66 303	66.99 186	84.66 294	90.47 178	55.08 468	72.02 361	85.27 340	63.83 182	94.11 145	66.10 298	89.80 139	84.24 437

test222					91.50 88	68.26 139	84.16 316	83.20 379	54.63 469	79.74 197	91.63 140	58.97 256			91.42 106	86.77 392

dongtai		45.42 468	45.38 469	45.55 490	73.36 483	26.85 517	67.72 483	34.19 516	54.15 470	49.65 495	56.41 506	25.43 488	62.94 506	19.45 514	28.09 507	46.86 511

CHOSEN 280x420		66.51 429	64.71 431	71.90 436	81.45 411	63.52 289	57.98 504	68.95 485	53.57 471	62.59 462	76.70 459	46.22 401	75.29 484	55.25 409	79.68 322	76.88 482

ADS-MVSNet2		66.20 434	63.33 438	74.82 407	79.92 431	58.75 375	67.55 484	75.19 463	53.37 472	65.25 445	75.86 470	42.32 431	80.53 452	41.57 482	68.91 436	85.18 423

ADS-MVSNet		64.36 440	62.88 442	68.78 457	79.92 431	47.17 482	67.55 484	71.18 477	53.37 472	65.25 445	75.86 470	42.32 431	73.99 490	41.57 482	68.91 436	85.18 423

LF4IMVS		64.02 441	62.19 444	69.50 452	70.90 491	53.29 449	76.13 440	77.18 453	52.65 474	58.59 476	80.98 420	23.55 494	76.52 470	53.06 424	66.66 447	78.68 477

tpm cat1		70.57 389	68.31 395	77.35 379	82.41 396	57.95 386	78.08 425	80.22 425	52.04 475	68.54 402	77.66 454	52.00 327	87.84 385	51.77 428	72.07 421	86.25 400

test_vis1_n		69.85 403	69.21 389	71.77 437	72.66 489	55.27 430	81.48 369	76.21 460	52.03 476	75.30 307	83.20 390	28.97 483	76.22 474	74.60 206	78.41 342	83.81 443

Patchmatch-test		64.82 438	63.24 439	69.57 451	79.42 441	49.82 473	63.49 499	69.05 484	51.98 477	59.95 473	80.13 431	50.91 346	70.98 494	40.66 484	73.57 407	87.90 353

N_pmnet		52.79 460	53.26 458	51.40 488	78.99 445	7.68 536	69.52 476	3.89 536	51.63 478	57.01 482	74.98 474	40.83 442	65.96 502	37.78 489	64.67 462	80.56 473

test_fmvs1_n		70.86 386	70.24 381	72.73 431	72.51 490	55.28 429	81.27 375	79.71 430	51.49 479	78.73 214	84.87 350	27.54 486	77.02 466	76.06 188	79.97 321	85.88 411

test_fmvs1		70.93 384	70.52 376	72.16 434	73.71 478	55.05 431	80.82 378	78.77 440	51.21 480	78.58 219	84.41 358	31.20 480	76.94 467	75.88 192	80.12 320	84.47 434

PatchmatchNet2					0.00 566	30.51 512	67.30 486	67.46 488	50.92 481
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PMMVS		69.34 406	68.67 392	71.35 442	75.67 469	62.03 326	75.17 449	73.46 471	50.00 482	68.68 397	79.05 441	52.07 326	78.13 460	61.16 357	82.77 283	73.90 487

test_fmvs2		68.35 417	67.48 413	70.98 446	69.50 494	51.95 456	80.05 396	76.38 459	49.33 483	74.65 324	84.38 359	23.30 495	75.40 483	74.51 207	75.17 393	85.60 415

ttmdpeth		59.91 449	57.10 453	68.34 460	67.13 498	46.65 485	74.64 455	67.41 489	48.30 484	62.52 464	85.04 349	20.40 497	75.93 477	42.55 480	45.90 500	82.44 457

CMPMVS	51.72 21	70.19 395	68.16 397	76.28 388	73.15 485	57.55 395	79.47 403	83.92 364	48.02 485	56.48 484	84.81 352	43.13 426	86.42 400	62.67 333	81.81 297	84.89 429
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
mvsany_test1		62.30 445	61.26 449	65.41 468	69.52 493	54.86 434	66.86 487	49.78 510	46.65 486	68.50 403	83.21 389	49.15 374	66.28 501	56.93 400	60.77 475	75.11 485

kuosan		39.70 474	40.40 473	37.58 495	64.52 501	26.98 515	65.62 492	33.02 517	46.12 487	42.79 500	48.99 513	24.10 493	46.56 516	12.16 524	26.30 508	39.20 515

test_fmvs3		63.36 443	61.82 445	67.98 462	62.51 503	46.96 484	77.37 434	74.03 470	45.24 488	67.50 415	78.79 446	12.16 507	72.98 493	72.77 228	66.02 450	83.99 441

CVMVSNet		72.99 362	72.58 346	74.25 414	84.28 338	50.85 468	86.41 240	83.45 373	44.56 489	73.23 342	87.54 278	49.38 369	85.70 407	65.90 300	78.44 338	86.19 402

test_vis1_rt		60.28 448	58.42 451	65.84 467	67.25 497	55.60 425	70.44 474	60.94 502	44.33 490	59.00 475	66.64 492	24.91 490	68.67 499	62.80 328	69.48 432	73.25 488

mvsany_test3		53.99 456	51.45 461	61.61 473	55.51 508	44.74 493	63.52 498	45.41 514	43.69 491	58.11 479	76.45 461	17.99 500	63.76 505	54.77 414	47.59 496	76.34 483

EU-MVSNet		68.53 414	67.61 411	71.31 443	78.51 448	47.01 483	84.47 302	84.27 360	42.27 492	66.44 435	84.79 353	40.44 444	83.76 425	58.76 381	68.54 439	83.17 448

FPMVS		53.68 458	51.64 460	59.81 475	65.08 500	51.03 466	69.48 477	69.58 482	41.46 493	40.67 502	72.32 481	16.46 503	70.00 498	24.24 509	65.42 459	58.40 502

pmmvs3		57.79 451	54.26 456	68.37 459	64.02 502	56.72 406	75.12 452	65.17 494	40.20 494	52.93 490	69.86 488	20.36 498	75.48 481	45.45 470	55.25 488	72.90 489

new_pmnet		50.91 463	50.29 463	52.78 487	68.58 495	34.94 510	63.71 497	56.63 507	39.73 495	44.95 497	65.47 493	21.93 496	58.48 508	34.98 494	56.62 482	64.92 496

MVS-HIRNet		59.14 450	57.67 452	63.57 470	81.65 406	43.50 495	71.73 466	65.06 495	39.59 496	51.43 491	57.73 502	38.34 458	82.58 437	39.53 485	73.95 403	64.62 497

MVStest1		56.63 453	52.76 459	68.25 461	61.67 504	53.25 450	71.67 467	68.90 486	38.59 497	50.59 493	83.05 392	25.08 489	70.66 495	36.76 492	38.56 501	80.83 469

PMMVS2		40.82 473	38.86 477	46.69 489	53.84 510	16.45 527	48.61 507	49.92 509	37.49 498	31.67 505	60.97 497	8.14 513	56.42 510	28.42 501	30.72 506	67.19 495

test_vis3_rt		49.26 465	47.02 467	56.00 480	54.30 509	45.27 490	66.76 489	48.08 511	36.83 499	44.38 498	53.20 509	7.17 514	64.07 504	56.77 403	55.66 484	58.65 501

test_f		52.09 461	50.82 462	55.90 481	53.82 511	42.31 500	59.42 503	58.31 506	36.45 500	56.12 487	70.96 485	12.18 506	57.79 509	53.51 421	56.57 483	67.60 494

LCM-MVSNet		54.25 455	49.68 465	67.97 463	53.73 512	45.28 489	66.85 488	80.78 411	35.96 501	39.45 504	62.23 496	8.70 511	78.06 462	48.24 454	51.20 493	80.57 472

ArgMatch-Sym		43.72 472	39.92 475	55.10 485	52.36 514	37.56 506	61.93 501	23.00 522	35.80 502	43.62 499	70.22 487	3.22 520	55.93 511	45.35 471	23.80 511	71.81 490

APD_test1		53.31 459	49.93 464	63.42 471	65.68 499	50.13 471	71.59 468	66.90 491	34.43 503	40.58 503	71.56 483	8.65 512	76.27 473	34.64 495	55.36 486	63.86 498

PMVS	37.38 22	44.16 470	40.28 474	55.82 482	40.82 520	42.54 499	65.12 494	63.99 498	34.43 503	24.48 512	57.12 504	3.92 519	76.17 475	17.10 517	55.52 485	48.75 508
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ArgMatch-SfM		44.04 471	39.87 476	56.58 479	50.92 516	36.22 507	59.86 502	27.68 520	33.67 505	42.15 501	71.07 484	3.10 522	59.10 507	45.79 467	24.54 509	74.41 486

Gipuma		45.18 469	41.86 472	55.16 484	77.03 464	51.52 462	32.50 514	80.52 416	32.46 506	27.12 510	35.02 522	9.52 510	75.50 480	22.31 511	60.21 478	38.45 516
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DSMNet-mixed		57.77 452	56.90 454	60.38 474	67.70 496	35.61 508	69.18 478	53.97 508	32.30 507	57.49 481	79.88 434	40.39 445	68.57 500	38.78 488	72.37 416	76.97 481

testf1		45.72 466	41.96 470	57.00 477	56.90 506	45.32 487	66.14 490	59.26 504	26.19 508	30.89 507	60.96 498	4.14 517	70.64 496	26.39 507	46.73 498	55.04 504

APD_test2		45.72 466	41.96 470	57.00 477	56.90 506	45.32 487	66.14 490	59.26 504	26.19 508	30.89 507	60.96 498	4.14 517	70.64 496	26.39 507	46.73 498	55.04 504

DenseAffine		31.97 475	28.22 481	43.21 492	43.10 519	27.10 514	46.21 508	11.36 526	24.92 510	27.70 509	58.81 501	1.09 526	46.50 517	26.95 504	13.85 522	56.02 503

E-PMN		31.77 476	30.64 478	35.15 497	52.87 513	27.67 513	57.09 505	47.86 512	24.64 511	16.40 526	33.05 523	11.23 508	54.90 512	14.46 520	18.15 516	22.87 523

EMVS		30.81 478	29.65 479	34.27 498	50.96 515	25.95 518	56.58 506	46.80 513	24.01 512	15.53 527	30.68 526	12.47 505	54.43 513	12.81 523	17.05 517	22.43 524

RoMa-SfM		28.67 480	25.38 484	38.54 493	32.61 524	22.48 521	40.24 509	7.23 530	21.81 513	26.66 511	60.46 500	0.96 527	41.72 518	26.47 506	11.95 523	51.40 507

DKM		25.67 482	23.01 486	33.64 499	32.08 525	19.25 525	37.50 511	5.52 532	18.67 514	23.58 515	55.44 507	0.64 533	34.02 520	23.95 510	9.73 525	47.66 510

PDCNetPlus		24.75 483	22.46 487	31.64 500	35.53 522	17.00 526	32.00 515	9.46 527	18.43 515	18.56 524	51.31 511	1.65 524	33.00 522	26.51 505	8.70 527	44.91 512

MVE	26.22 23	30.37 479	25.89 483	43.81 491	44.55 518	35.46 509	28.87 519	39.07 515	18.20 516	18.58 523	40.18 518	2.68 523	47.37 515	17.07 518	23.78 512	48.60 509
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				27.40 503	40.17 521	26.90 516		24.59 521	17.44 517	23.95 513	48.61 515	9.77 509	26.48 524	18.06 515	24.47 510	28.83 521

RoMa-HiRes		21.63 485	19.64 490	27.59 502	22.40 529	14.25 529	29.71 517	4.10 534	15.42 518	21.09 519	54.77 508	0.72 531	28.87 523	21.01 512	7.52 531	39.65 514

DKM-HiRes		20.87 486	19.15 491	26.02 504	25.34 528	14.13 530	29.63 518	3.62 539	14.53 519	20.13 520	50.55 512	0.47 541	24.22 527	20.96 513	7.15 532	39.70 513

LoFTR		27.52 481	24.27 485	37.29 496	34.75 523	19.27 524	33.78 513	21.60 523	12.42 520	21.61 518	56.59 505	0.91 528	40.37 519	13.94 521	22.80 513	52.22 506

MatchFormer		22.13 484	19.86 489	28.93 501	28.66 526	15.74 528	31.91 516	17.10 525	7.75 521	18.87 522	47.50 516	0.62 535	33.92 521	7.49 531	18.87 515	37.14 517

wuyk23d		16.82 489	15.94 493	19.46 507	58.74 505	31.45 511	39.22 510	3.74 538	6.84 522	6.04 534	2.70 557	1.27 525	24.29 526	10.54 529	14.40 521	2.63 541

PMatch-SfM		14.15 492	12.67 496	18.59 508	12.84 535	7.03 538	17.41 521	2.28 541	6.63 523	12.96 528	43.56 517	0.09 558	16.11 530	13.90 522	4.38 542	32.63 520

ELoFTR		14.23 491	11.56 497	22.24 505	11.02 537	6.56 540	13.59 526	7.57 529	5.55 524	11.96 530	39.09 519	0.21 546	24.93 525	9.43 530	5.66 536	35.22 518

PMatch-Up-SfM		10.76 496	9.99 499	13.09 509	9.50 543	4.83 543	12.94 528	1.40 550	4.65 525	10.16 531	37.54 520	0.07 561	10.94 532	10.71 528	2.92 553	23.50 522

MASt3R-SfM		13.55 493	13.93 494	12.41 510	10.54 540	5.97 542	16.61 522	6.07 531	4.50 526	16.53 525	48.67 514	0.73 530	9.44 533	11.56 527	10.18 524	21.81 525

GLUNet-SfM		12.90 494	10.00 498	21.62 506	13.58 534	8.30 534	10.19 530	9.30 528	4.31 527	12.18 529	30.90 525	0.50 539	22.76 528	4.89 532	4.14 543	33.79 519

test_method		31.52 477	29.28 480	38.23 494	27.03 527	6.50 541	20.94 520	62.21 500	4.05 528	22.35 516	52.50 510	13.33 504	47.58 514	27.04 503	34.04 504	60.62 499

VLMVS_CLIP		15.14 490	16.11 492	12.23 511	12.32 536	7.35 537	15.53 523	20.73 524	4.02 529	22.32 517	31.59 524	4.37 516	21.02 529	11.59 526	22.52 514	8.32 527

ALIKED-LG		8.61 497	8.70 501	8.33 513	20.63 530	8.70 533	15.50 524	4.61 533	2.19 530	5.84 535	18.70 528	0.80 529	8.06 534	1.03 542	8.97 526	8.25 528

MVS_clip		11.37 495	13.03 495	6.40 516	15.78 533	6.79 539	11.98 529	1.47 549	1.89 531	19.38 521	35.95 521	3.13 521	3.09 539	12.10 525	15.54 519	9.34 526

ALIKED-MNN		7.86 498	7.83 504	7.97 514	19.40 531	8.86 532	14.48 525	3.90 535	1.59 532	4.74 540	16.49 529	0.59 536	7.65 535	0.91 543	8.34 529	7.39 531

ALIKED-NN		7.51 499	7.61 505	7.21 515	18.26 532	8.10 535	13.45 527	3.88 537	1.50 533	4.87 538	16.47 530	0.64 533	7.00 536	0.88 544	8.50 528	6.52 536

tmp_tt		18.61 488	21.40 488	10.23 512	4.82 559	10.11 531	34.70 512	30.74 519	1.48 534	23.91 514	26.07 527	28.42 484	13.41 531	27.12 502	15.35 520	7.17 534

SP-DiffGlue		4.29 506	4.46 509	3.77 521	3.68 560	2.12 551	5.97 535	2.22 542	1.10 535	4.89 537	13.93 533	0.66 532	1.95 545	2.47 533	5.24 537	7.22 533

XFeat-MNN		4.39 505	4.49 508	4.10 517	2.88 562	1.91 557	5.86 536	2.57 540	1.06 536	5.04 536	13.99 532	0.43 543	4.47 537	2.00 535	6.55 534	5.92 537

SP-SuperGlue		4.24 508	4.38 511	3.81 520	10.75 539	2.00 553	8.18 532	2.09 543	1.00 537	2.41 541	8.29 537	0.56 537	2.05 544	1.27 538	4.91 539	7.39 531

SP-LightGlue		4.27 507	4.41 510	3.86 518	10.99 538	1.99 554	8.19 531	2.06 544	0.98 538	2.37 542	8.29 537	0.56 537	2.10 542	1.27 538	4.99 538	7.48 530

SP-NN		4.00 510	4.12 513	3.63 522	9.92 542	1.81 559	7.94 534	1.90 547	0.86 539	2.15 544	8.00 540	0.50 539	2.09 543	1.20 540	4.63 541	6.98 535

SP-MNN		4.14 509	4.24 512	3.82 519	10.32 541	1.83 558	8.11 533	1.99 545	0.82 540	2.23 543	8.27 539	0.47 541	2.14 541	1.20 540	4.77 540	7.49 529

XFeat-NN		3.78 511	3.96 515	3.23 524	2.65 563	1.53 562	4.99 537	1.92 546	0.81 541	4.77 539	12.37 535	0.38 544	3.39 538	1.64 536	6.13 535	4.77 539

SIFT-NN		2.77 513	2.92 516	2.34 525	8.70 544	3.08 544	4.46 538	1.01 552	0.68 542	1.46 545	5.49 541	0.16 547	1.65 546	0.26 545	4.04 544	2.27 542

SIFT-MNN		2.63 514	2.75 517	2.25 526	8.10 545	2.84 545	4.08 539	1.02 551	0.68 542	1.28 546	5.34 544	0.15 548	1.64 547	0.26 545	3.88 546	2.27 542

SIFT-NN-UMatch		2.26 518	2.39 521	1.89 531	6.21 553	2.08 552	3.76 541	0.83 555	0.66 544	1.04 550	5.09 545	0.14 549	1.52 550	0.23 548	3.51 548	2.07 546

SIFT-NCM-Cal		2.40 516	2.52 519	2.05 528	7.74 546	2.54 547	3.75 542	0.84 554	0.65 545	0.89 553	4.78 550	0.13 552	1.60 548	0.19 556	3.71 547	2.01 548

SIFT-NN-NCMNet		2.52 515	2.64 518	2.14 527	7.53 547	2.74 546	4.00 540	0.98 553	0.65 545	1.24 548	5.08 547	0.14 549	1.60 548	0.23 548	3.94 545	2.07 546

SIFT-NN-CMatch		2.31 517	2.41 520	2.00 529	6.59 551	2.34 549	3.48 543	0.83 555	0.65 545	1.28 546	5.09 545	0.14 549	1.52 550	0.23 548	3.41 549	2.14 544

SIFT-ConvMatch		2.25 519	2.37 522	1.90 530	7.29 548	2.37 548	3.21 547	0.75 557	0.65 545	1.03 551	4.91 548	0.12 555	1.51 552	0.22 551	3.13 551	1.81 549

SIFT-UMatch		2.16 520	2.30 523	1.72 533	6.99 549	1.97 556	3.32 545	0.70 559	0.64 549	0.91 552	4.86 549	0.12 555	1.49 553	0.22 551	2.97 552	1.72 551

SIFT-CM-Cal		2.02 522	2.13 525	1.67 534	6.79 550	1.99 554	2.79 549	0.64 560	0.63 550	0.87 554	4.48 553	0.13 552	1.41 555	0.19 556	2.70 554	1.61 553

SIFT-UM-Cal		1.97 523	2.12 526	1.52 535	6.57 552	1.67 560	2.93 548	0.57 562	0.62 551	0.83 555	4.55 552	0.11 557	1.37 556	0.20 555	2.69 555	1.53 554

SIFT-NN-PointCN		2.07 521	2.18 524	1.74 532	5.75 554	1.65 561	3.27 546	0.73 558	0.60 552	1.07 549	4.62 551	0.13 552	1.43 554	0.21 553	3.22 550	2.12 545

SIFT-NCMNet		1.44 526	1.56 529	1.08 539	5.14 557	1.07 565	1.97 552	0.32 564	0.56 553	0.64 558	3.23 556	0.07 561	1.01 559	0.14 560	1.95 558	1.15 555

SIFT-PCN-Cal		1.72 524	1.82 528	1.39 536	5.64 555	1.19 564	2.39 551	0.53 563	0.55 554	0.72 556	3.90 554	0.09 558	1.22 558	0.17 558	2.42 557	1.76 550

SIFT-PointCN		1.72 524	1.83 527	1.36 537	5.55 556	1.22 563	2.59 550	0.59 561	0.55 554	0.71 557	3.77 555	0.08 560	1.24 557	0.17 558	2.48 556	1.63 552

VLMVS		4.54 504	4.93 507	3.37 523	4.86 558	2.23 550	3.38 544	1.77 548	0.23 556	7.94 532	11.34 536	4.62 515	2.44 540	2.43 534	7.76 530	5.44 538

EGC-MVSNET		52.07 462	47.05 466	67.14 464	83.51 359	60.71 353	80.50 388	67.75 487	0.07 557	0.43 559	75.85 472	24.26 492	81.54 444	28.82 500	62.25 470	59.16 500

testmvs		6.04 502	8.02 503	0.10 541	0.08 564	0.03 568	69.74 475	0.04 566	0.05 558	0.31 560	1.68 558	0.02 564	0.04 560	0.24 547	0.02 559	0.25 557

MVS_baseline		3.29 512	4.00 514	1.16 538	3.08 561	0.09 566	1.26 553	0.24 565	0.04 559	6.52 533	16.19 531	0.30 545	0.00 562	1.53 537	6.83 533	3.39 540

test123		6.12 501	8.11 502	0.14 540	0.06 565	0.09 566	71.05 470	0.03 567	0.04 559	0.25 561	1.30 559	0.05 563	0.03 561	0.21 553	0.01 560	0.29 556

mmdepth		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

monomultidepth		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

test_blank		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

uanet_test		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

DCPMVS		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

cdsmvs_eth3d_5k		19.96 487	26.61 482	0.00 542	0.00 566	0.00 569	0.00 554	89.26 230	0.00 561	0.00 562	88.61 245	61.62 220	0.00 562	0.00 561	0.00 561	0.00 558

pcd_1.5k_mvsjas		5.26 503	7.02 506	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	63.15 191	0.00 562	0.00 561	0.00 561	0.00 558

sosnet-low-res		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

sosnet		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

uncertanet		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

Regformer		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

ab-mvs-re		7.23 500	9.64 500	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	86.72 298	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

uanet		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

Meshroom													0.00 562
: In preparation.
AliceVision / Meshro													0.00 562
: In preparation.
AliceVision_Meshroom													0.00 562
: In preparation.
PatchmatchNet1														37.67 490	64.79 461	80.58 471
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													65.90 503
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
test-260524					94.58 16	71.43 61		94.16 8		90.64 22		78.62 14	97.13 17	88.60 33	96.28 16

WAC-MVS						42.58 497								39.46 486

MSC_two_6792asdad				89.16 1	94.34 32	75.53 2		92.99 56					97.53 2	89.67 15	96.44 9	94.41 61

No_MVS				89.16 1	94.34 32	75.53 2		92.99 56					97.53 2	89.67 15	96.44 9	94.41 61

eth-test2					0.00 566

eth-test					0.00 566

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 62	82.45 3	96.87 25	83.77 84	96.48 8	94.88 19

test_0728_SECOND				87.71 35	95.34 1	71.43 61	93.49 10	94.23 6					97.49 4	89.08 22	96.41 12	94.21 74

GSMVS																88.96 321

test_part2					95.06 8	72.65 32				91.80 16

sam_mvs1												51.32 339				88.96 321

sam_mvs												50.01 359

ambc				75.24 402	73.16 484	50.51 470	63.05 500	87.47 297		64.28 451	77.81 453	17.80 501	89.73 351	57.88 390	60.64 476	85.49 417

MTGPA								92.02 115

test_post1							78.90 415				5.43 543	48.81 380	85.44 413	59.25 374

test_post											5.46 542	50.36 355	84.24 422

patchmatchnet-post											74.00 477	51.12 345	88.60 374

GG-mvs-BLEND				75.38 400	81.59 408	55.80 422	79.32 405	69.63 481		67.19 421	73.67 478	43.24 425	88.90 370	50.41 436	84.50 248	81.45 465

MTMP							92.18 39	32.83 518

test9_res														84.90 66	95.70 30	92.87 162

agg_prior2														82.91 93	95.45 33	92.70 167

agg_prior					92.85 69	71.94 53		91.78 131		84.41 99			94.93 104

test_prior4						72.60 34	89.01 126

test_prior				86.33 65	92.61 76	69.59 100		92.97 61					95.48 76			93.91 90

新几何2							86.29 249

旧先验1					91.96 82	65.79 214		86.37 329			93.08 94	69.31 104			92.74 82	88.74 332

原ACMM2							86.86 221

testdata2													91.01 317	62.37 339

segment_acmp												73.08 46

test12				86.80 59	92.63 75	70.70 83		91.79 130		82.71 143		71.67 68	96.16 54		94.50 57	93.54 121

plane_prior7					90.08 118	68.51 133

plane_prior6					89.84 127	68.70 127						60.42 246

plane_prior5								92.44 85					95.38 84	78.71 152	86.32 212	91.33 224

plane_prior4											91.00 168

plane_prior1					89.90 126

n2								0.00 568

nn								0.00 568

door-mid								69.98 480

lessismore_v0				78.97 343	81.01 419	57.15 400		65.99 492		61.16 467	82.82 399	39.12 453	91.34 301	59.67 369	46.92 497	88.43 340

test11								92.23 101

door								69.44 483

HQP5-MVS						66.98 187

BP-MVS														77.47 168

HQP4-MVS										77.24 253			95.11 96			91.03 234

HQP3-MVS								92.19 109							85.99 223

HQP2-MVS												60.17 249

NP-MVS					89.62 132	68.32 137					90.24 194

ACMMP++_ref															81.95 294

ACMMP++															81.25 301

Test By Simon												64.33 177

High-resolution multi-view benchmark