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.

MVS_111021_HR		96.69 37	96.69 34	96.72 90	98.58 102	91.00 126	99.14 89	99.45 1	93.86 36	95.15 108	98.73 90	88.48 71	99.76 72	97.23 53	99.56 59	99.40 93

thres100view900		93.34 131	92.15 141	96.90 77	97.62 124	94.84 39	99.06 98	99.36 2	87.96 192	90.47 176	96.78 179	83.29 161	98.75 152	84.11 233	90.69 208	97.12 204

tfpn200view9		93.43 127	92.27 137	96.90 77	97.68 122	94.84 39	99.18 77	99.36 2	88.45 174	90.79 168	96.90 174	83.31 159	98.75 152	84.11 233	90.69 208	97.12 204

thres600view7		93.18 136	92.00 144	96.75 86	97.62 124	94.92 34	99.07 96	99.36 2	87.96 192	90.47 176	96.78 179	83.29 161	98.71 156	82.93 247	90.47 212	96.61 213

thres400		93.39 129	92.27 137	96.73 88	97.68 122	94.84 39	99.18 77	99.36 2	88.45 174	90.79 168	96.90 174	83.31 159	98.75 152	84.11 233	90.69 208	96.61 213

thres200		93.69 119	92.59 132	96.97 72	97.76 119	94.74 44	99.35 66	99.36 2	89.23 149	91.21 164	96.97 171	83.42 158	98.77 150	85.08 218	90.96 206	97.39 198

MVS_111021_LR		95.78 69	95.94 56	95.28 144	98.19 111	87.69 198	98.80 124	99.26 7	93.39 46	95.04 110	98.69 96	84.09 150	99.76 72	96.96 60	99.06 85	98.38 170

sss		94.85 89	93.94 106	97.58 43	96.43 163	94.09 60	98.93 111	99.16 8	89.50 145	95.27 105	97.85 128	81.50 191	99.65 88	92.79 139	94.02 168	98.99 126

MG-MVS		97.24 19	96.83 30	98.47 14	99.79 5	95.71 17	99.07 96	99.06 9	94.45 23	96.42 83	98.70 95	88.81 66	99.74 74	95.35 94	99.86 12	99.97 7

test2506		94.80 90	94.21 93	96.58 97	96.41 164	92.18 98	98.01 211	98.96 10	90.82 105	93.46 134	97.28 152	85.92 125	98.45 161	89.82 166	97.19 130	99.12 117

PVSNet	87.13 12	93.69 119	92.83 127	96.28 111	97.99 116	90.22 143	99.38 61	98.93 11	91.42 92	93.66 132	97.68 138	71.29 266	99.64 90	87.94 191	97.20 129	98.98 127

PGM-MVS		95.85 66	95.65 69	96.45 104	99.50 47	89.77 158	98.22 191	98.90 12	89.19 150	96.74 76	98.95 73	85.91 127	99.92 40	93.94 118	99.46 64	99.66 69

EPNet		96.82 35	96.68 35	97.25 58	98.65 98	93.10 79	99.48 42	98.76 13	96.54 5	97.84 49	98.22 122	87.49 89	99.66 84	95.35 94	97.78 119	99.00 125
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WTY-MVS		95.97 60	95.11 79	98.54 12	97.62 124	96.65 8	99.44 52	98.74 14	92.25 72	95.21 106	98.46 113	86.56 114	99.46 115	95.00 102	92.69 180	99.50 86

HY-MVS	88.56 7	95.29 78	94.23 92	98.48 13	97.72 120	96.41 12	94.03 315	98.74 14	92.42 68	95.65 100	94.76 215	86.52 115	99.49 108	95.29 96	92.97 176	99.53 82

VNet		95.08 85	94.26 91	97.55 46	98.07 114	93.88 62	98.68 138	98.73 16	90.33 118	97.16 62	97.43 149	79.19 207	99.53 101	96.91 61	91.85 195	99.24 107

test_yl		95.27 79	94.60 85	97.28 56	98.53 103	92.98 83	99.05 99	98.70 17	86.76 220	94.65 116	97.74 135	87.78 82	99.44 116	95.57 90	92.61 181	99.44 91

DCV-MVSNet		95.27 79	94.60 85	97.28 56	98.53 103	92.98 83	99.05 99	98.70 17	86.76 220	94.65 116	97.74 135	87.78 82	99.44 116	95.57 90	92.61 181	99.44 91

PVSNet_0	83.28 16	87.31 240	85.16 254	93.74 196	94.78 232	84.59 270	98.91 114	98.69 19	89.81 133	78.59 303	93.23 245	61.95 315	99.34 130	94.75 106	55.72 360	97.30 200

ACMMP		94.67 97	94.30 90	95.79 128	99.25 69	88.13 191	98.41 173	98.67 20	90.38 116	91.43 158	98.72 92	82.22 183	99.95 34	93.83 122	95.76 154	99.29 101
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
D2MVS		87.96 229	87.39 221	89.70 280	91.84 289	83.40 283	98.31 186	98.49 21	88.04 190	78.23 307	90.26 305	73.57 243	96.79 243	84.21 230	83.53 249	88.90 332

HyFIR lowres test		93.68 121	93.29 117	94.87 156	97.57 128	88.04 193	98.18 195	98.47 22	87.57 205	91.24 163	95.05 211	85.49 133	97.46 218	93.22 132	92.82 177	99.10 119

UniMVSNet (Re)		89.50 204	88.32 210	93.03 206	92.21 282	90.96 127	98.90 115	98.39 23	89.13 152	83.22 237	92.03 261	81.69 189	96.34 271	86.79 203	72.53 321	91.81 261

CHOSEN 280x420		96.80 36	96.85 28	96.66 94	97.85 118	94.42 53	94.76 307	98.36 24	92.50 63	95.62 101	97.52 145	97.92 1	97.38 223	98.31 37	98.80 98	98.20 181

VPA-MVSNet		89.10 206	87.66 218	93.45 200	92.56 277	91.02 125	97.97 214	98.32 25	86.92 216	86.03 217	92.01 263	68.84 277	97.10 231	90.92 153	75.34 291	92.23 248

CHOSEN 1792x2688		94.35 105	93.82 109	95.95 124	97.40 131	88.74 180	98.41 173	98.27 26	92.18 74	91.43 158	96.40 189	78.88 208	99.81 66	93.59 126	97.81 116	99.30 100

FIs		90.70 182	89.87 181	93.18 204	92.29 280	91.12 119	98.17 197	98.25 27	89.11 153	83.44 236	94.82 214	82.26 182	96.17 280	87.76 192	82.76 255	92.25 246

UGNet		91.91 161	90.85 167	95.10 147	97.06 145	88.69 181	98.01 211	98.24 28	92.41 69	92.39 147	93.61 236	60.52 320	99.68 82	88.14 188	97.25 128	96.92 211
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
FC-MVSNet-test		90.22 190	89.40 188	92.67 218	91.78 290	89.86 156	97.89 216	98.22 29	88.81 164	82.96 243	94.66 216	81.90 188	95.96 288	85.89 213	82.52 258	92.20 250

MVS_0304		84.13 287	82.66 286	88.52 301	93.07 273	80.15 317	95.81 298	98.21 30	79.27 315	86.85 212	86.40 340	41.33 362	94.69 324	76.36 294	86.69 225	90.73 301

WR-MVS_H		86.53 253	85.49 251	89.66 283	91.04 299	83.31 285	97.53 235	98.20 31	84.95 249	79.64 290	90.90 284	78.01 216	95.33 310	76.29 295	72.81 317	90.35 309

MVS		93.92 112	92.28 136	98.83 6	95.69 191	96.82 7	96.22 284	98.17 32	84.89 250	84.34 229	98.61 101	79.32 206	99.83 61	93.88 120	99.43 68	99.86 32

PAPM		96.35 48	95.94 56	97.58 43	94.10 244	95.25 23	98.93 111	98.17 32	94.26 24	93.94 127	98.72 92	89.68 57	97.88 188	96.36 72	99.29 78	99.62 75

baseline2		94.04 109	93.80 110	94.74 162	93.07 273	90.25 141	98.12 200	98.16 34	89.86 130	86.53 215	96.95 172	95.56 5	98.05 180	91.44 147	94.53 163	95.93 224

UniMVSNet_NR-MVSNet		89.60 201	88.55 207	92.75 215	92.17 283	90.07 148	98.74 130	98.15 35	88.37 180	83.21 238	93.98 226	82.86 169	95.93 290	86.95 200	72.47 322	92.25 246

CSCG		94.87 88	94.71 83	95.36 141	99.54 40	86.49 227	99.34 68	98.15 35	82.71 284	90.15 181	99.25 27	89.48 59	99.86 56	94.97 103	98.82 97	99.72 58

MSLP-MVS++		97.50 15	97.45 13	97.63 41	99.65 19	93.21 75	99.70 17	98.13 37	94.61 19	97.78 50	99.46 11	89.85 53	99.81 66	97.97 42	99.91 6	99.88 28

h-mvs33		92.47 150	91.95 146	94.05 186	97.13 141	85.01 265	98.36 181	98.08 38	93.85 37	96.27 84	96.73 181	83.19 164	99.43 118	95.81 82	68.09 338	97.70 190

IB-MVS	89.43 6	92.12 156	90.83 170	95.98 122	95.40 204	90.78 130	99.81 5	98.06 39	91.23 97	85.63 219	93.66 235	90.63 40	98.78 149	91.22 149	71.85 328	98.36 173
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
PHI-MVS		96.65 39	96.46 39	97.21 59	99.34 58	91.77 100	99.70 17	98.05 40	86.48 226	98.05 40	99.20 33	89.33 60	99.96 30	98.38 32	99.62 51	99.90 24

PVSNet_BlendedMVS		93.36 130	93.20 119	93.84 193	98.77 95	91.61 106	99.47 44	98.04 41	91.44 89	94.21 122	92.63 257	83.50 155	99.87 51	97.41 49	83.37 251	90.05 317

PVSNet_Blended		95.94 62	95.66 67	96.75 86	98.77 95	91.61 106	99.88 1	98.04 41	93.64 43	94.21 122	97.76 133	83.50 155	99.87 51	97.41 49	97.75 120	98.79 148

EPMVS		92.59 147	91.59 153	95.59 135	97.22 137	90.03 152	91.78 333	98.04 41	90.42 115	91.66 153	90.65 293	86.49 117	97.46 218	81.78 258	96.31 143	99.28 103

CNVR-MVS		98.46 1	98.38 1	98.72 8	99.80 4	96.19 14	99.80 7	97.99 44	97.05 3	99.41 2	99.59 2	92.89 24	100.00 1	98.99 17	99.90 7	99.96 10

MCST-MVS		98.18 2	97.95 8	98.86 5	99.85 3	96.60 9	99.70 17	97.98 45	97.18 2	95.96 90	99.33 23	92.62 25	100.00 1	98.99 17	99.93 1	99.98 6

Regformer-1		96.97 29	96.80 31	97.47 47	99.46 52	93.11 78	98.89 116	97.94 46	92.89 57	96.90 65	99.02 60	89.78 54	99.53 101	97.06 54	99.26 80	99.75 52

Regformer-2		96.94 32	96.78 32	97.42 49	99.46 52	92.97 85	98.89 116	97.93 47	92.86 59	96.88 66	99.02 60	89.74 56	99.53 101	97.03 55	99.26 80	99.75 52

1314		93.44 126	91.98 145	97.84 33	95.24 206	94.38 54	96.22 284	97.92 48	90.18 122	82.28 254	97.71 137	77.63 218	99.80 68	91.94 144	98.67 103	99.34 97

Regformer-3		96.50 43	96.36 42	96.91 76	99.34 58	91.72 103	98.71 131	97.90 49	92.48 64	96.00 87	98.95 73	88.60 68	99.52 104	96.44 70	98.83 95	99.49 87

Regformer-4		96.45 46	96.33 44	96.81 83	99.34 58	91.44 110	98.71 131	97.88 50	92.43 65	95.97 89	98.95 73	88.42 72	99.51 105	96.40 71	98.83 95	99.49 87

NCCC		98.12 5	98.11 3	98.13 23	99.76 6	94.46 50	99.81 5	97.88 50	96.54 5	98.84 17	99.46 11	92.55 26	99.98 10	98.25 38	99.93 1	99.94 18

tfpnnormal		83.65 290	81.35 296	90.56 258	91.37 296	88.06 192	97.29 242	97.87 52	78.51 321	76.20 312	90.91 283	64.78 305	96.47 258	61.71 351	73.50 312	87.13 346

3Dnovator	87.35 11	93.17 137	91.77 150	97.37 54	95.41 203	93.07 80	98.82 122	97.85 53	91.53 85	82.56 248	97.58 144	71.97 258	99.82 64	91.01 152	99.23 82	99.22 110

WR-MVS		88.54 222	87.22 226	92.52 219	91.93 288	89.50 163	98.56 156	97.84 54	86.99 212	81.87 266	93.81 230	74.25 240	95.92 292	85.29 216	74.43 301	92.12 252

DELS-MVS		97.12 25	96.60 36	98.68 10	98.03 115	96.57 10	99.84 3	97.84 54	96.36 8	95.20 107	98.24 121	88.17 76	99.83 61	96.11 77	99.60 56	99.64 71
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
EI-MVSNet-Vis-set		95.76 71	95.63 71	96.17 115	99.14 76	90.33 139	98.49 165	97.82 56	91.92 78	94.75 113	98.88 81	87.06 100	99.48 113	95.40 93	97.17 132	98.70 155

无先验							98.52 158	97.82 56	87.20 211				99.90 44	87.64 194		99.85 33

EPNet_dtu		92.28 153	92.15 141	92.70 216	97.29 135	84.84 267	98.64 144	97.82 56	92.91 56	93.02 141	97.02 169	85.48 135	95.70 301	72.25 321	94.89 161	97.55 196
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HFP-MVS		96.42 47	96.26 45	96.90 77	99.69 9	90.96 127	99.47 44	97.81 59	90.54 112	96.88 66	99.05 57	87.57 86	99.96 30	95.65 85	99.72 34	99.78 42

#test#		96.48 44	96.34 43	96.90 77	99.69 9	90.96 127	99.53 39	97.81 59	90.94 102	96.88 66	99.05 57	87.57 86	99.96 30	95.87 81	99.72 34	99.78 42

EI-MVSNet-UG-set		95.43 74	95.29 73	95.86 126	99.07 82	89.87 155	98.43 170	97.80 61	91.78 81	94.11 124	98.77 86	86.25 122	99.48 113	94.95 104	96.45 139	98.22 179

ACMMPR		96.28 52	96.14 52	96.73 88	99.68 12	90.47 138	99.47 44	97.80 61	90.54 112	96.83 74	99.03 59	86.51 116	99.95 34	95.65 85	99.72 34	99.75 52

MAR-MVS		94.43 103	94.09 98	95.45 138	99.10 80	87.47 205	98.39 179	97.79 63	88.37 180	94.02 126	99.17 39	78.64 213	99.91 42	92.48 140	98.85 94	98.96 129
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
DPM-MVS		97.86 8	97.25 18	99.68 1	98.25 107	99.10 1	99.76 12	97.78 64	96.61 4	98.15 34	99.53 7	93.62 16	100.00 1	91.79 145	99.80 27	99.94 18

API-MVS		94.78 91	94.18 96	96.59 96	99.21 73	90.06 151	98.80 124	97.78 64	83.59 269	93.85 129	99.21 32	83.79 152	99.97 23	92.37 141	99.00 88	99.74 55

新几何1				97.40 51	98.92 89	92.51 95		97.77 66	85.52 236	96.69 78	99.06 56	88.08 79	99.89 47	84.88 222	99.62 51	99.79 38

HPM-MVS++		97.72 10	97.59 10	98.14 22	99.53 45	94.76 43	99.19 75	97.75 67	95.66 13	98.21 33	99.29 24	91.10 31	99.99 5	97.68 46	99.87 9	99.68 65

1121		95.19 82	94.45 88	97.42 49	98.88 91	92.58 93	96.22 284	97.75 67	85.50 238	96.86 69	99.01 64	88.59 70	99.90 44	87.64 194	99.60 56	99.79 38

testtj		97.23 21	97.05 21	97.75 38	99.75 7	93.34 73	99.16 80	97.74 69	91.28 95	98.40 29	99.29 24	89.95 52	99.98 10	98.20 39	99.70 39	99.94 18

GG-mvs-BLEND				96.98 71	96.53 160	94.81 42	87.20 346	97.74 69		93.91 128	96.40 189	96.56 2	96.94 237	95.08 99	98.95 92	99.20 111

gg-mvs-nofinetune		90.00 196	87.71 217	96.89 82	96.15 178	94.69 47	85.15 352	97.74 69	68.32 354	92.97 142	60.16 364	96.10 3	96.84 239	93.89 119	98.87 93	99.14 114

旧先验1					98.97 85	92.90 87		97.74 69			99.15 43	91.05 32			99.33 74	99.60 77

IU-MVS					99.63 21	95.38 21		97.73 73	95.54 15	99.54 1				99.69 5	99.81 23	99.99 1

ETH3 D test6400		97.67 11	97.33 17	98.69 9	99.69 9	96.43 11	99.63 25	97.73 73	91.05 98	98.66 22	99.53 7	90.59 41	99.71 77	99.32 11	99.80 27	99.91 22

SED-MVS		98.18 2	98.10 4	98.41 17	99.63 21	95.24 24	99.77 9	97.72 75	94.17 25	99.30 5	99.54 3	93.32 18	99.98 10	99.70 3	99.81 23	99.99 1

test_241102_TWO								97.72 75	94.17 25	99.23 7	99.54 3	93.14 23	99.98 10	99.70 3	99.82 19	99.99 1

test_241102_ONE					99.63 21	95.24 24		97.72 75	94.16 27	99.30 5	99.49 10	93.32 18	99.98 10

DPE-MVS		98.11 6	98.00 6	98.44 15	99.50 47	95.39 20	99.29 71	97.72 75	94.50 21	98.64 23	99.54 3	93.32 18	99.97 23	99.58 8	99.90 7	99.95 15
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DeepPCF-MVS	93.56 1	96.55 42	97.84 9	92.68 217	98.71 97	78.11 331	99.70 17	97.71 79	98.18 1	97.36 58	99.76 1	90.37 48	99.94 37	99.27 12	99.54 61	99.99 1

test0726					99.66 15	95.20 29	99.77 9	97.70 80	93.95 30	99.35 4	99.54 3	93.18 21

MSP-MVS		97.77 9	98.18 2	96.53 101	99.54 40	90.14 144	99.41 58	97.70 80	95.46 17	98.60 24	99.19 34	95.71 4	99.49 108	98.15 40	99.85 13	99.95 15
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
MSC_two_6792asdad				99.51 2	99.61 27	98.60 2		97.69 82					99.98 10	99.55 9	99.83 15	99.96 10

No_MVS				99.51 2	99.61 27	98.60 2		97.69 82					99.98 10	99.55 9	99.83 15	99.96 10

DVP-MVS++		98.18 2	98.09 5	98.44 15	99.61 27	95.38 21	99.55 34	97.68 84	93.01 51	99.23 7	99.45 16	95.12 7	99.98 10	99.25 14	99.92 3	99.97 7

test_0728_SECOND				98.77 7	99.66 15	96.37 13	99.72 14	97.68 84					99.98 10	99.64 6	99.82 19	99.96 10

test11								97.68 84

TEST9					99.57 37	93.17 76	99.38 61	97.66 87	89.57 142	98.39 30	99.18 37	90.88 35	99.66 84

train_agg		97.20 23	97.08 20	97.57 45	99.57 37	93.17 76	99.38 61	97.66 87	90.18 122	98.39 30	99.18 37	90.94 33	99.66 84	98.58 26	99.85 13	99.88 28

region2R		96.30 51	96.17 48	96.70 91	99.70 8	90.31 140	99.46 49	97.66 87	90.55 111	97.07 63	99.07 54	86.85 104	99.97 23	95.43 92	99.74 32	99.81 35

SteuartSystems-ACMMP		97.25 18	97.34 16	97.01 65	97.38 132	91.46 109	99.75 13	97.66 87	94.14 29	98.13 35	99.26 26	92.16 27	99.66 84	97.91 44	99.64 47	99.90 24
Skip Steuart: Steuart Systems R&D Blog.
EPP-MVSNet		93.75 118	93.67 111	94.01 188	95.86 186	85.70 252	98.67 140	97.66 87	84.46 255	91.36 161	97.18 162	91.16 29	97.79 194	92.93 136	93.75 169	98.53 162

SMA-MVS		97.24 19	96.99 24	98.00 30	99.30 65	94.20 57	99.16 80	97.65 92	89.55 144	99.22 9	99.52 9	90.34 49	99.99 5	98.32 36	99.83 15	99.82 34
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
test_one_0601					99.59 31	94.89 35		97.64 93	93.14 50	98.93 15	99.45 16	93.45 17

test_8					99.55 39	93.07 80	99.37 64	97.64 93	90.18 122	98.36 32	99.19 34	90.94 33	99.64 90

agg_prior1		97.12 25	97.03 22	97.38 53	99.54 40	92.66 88	99.35 66	97.64 93	90.38 116	97.98 44	99.17 39	90.84 37	99.61 93	98.57 27	99.78 31	99.87 31

agg_prior					99.54 40	92.66 88		97.64 93		97.98 44			99.61 93

DeepC-MVS_fast	93.52 2	97.16 24	96.84 29	98.13 23	99.61 27	94.45 51	98.85 119	97.64 93	96.51 7	95.88 93	99.39 21	87.35 96	99.99 5	96.61 65	99.69 41	99.96 10
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
save fliter					99.34 58	93.85 63	99.65 23	97.63 98	95.69 11

原ACMM1				96.18 113	99.03 83	90.08 147		97.63 98	88.98 157	97.00 64	98.97 66	88.14 78	99.71 77	88.23 187	99.62 51	98.76 152

DU-MVS		88.83 215	87.51 219	92.79 212	91.46 294	90.07 148	98.71 131	97.62 100	88.87 163	83.21 238	93.68 233	74.63 229	95.93 290	86.95 200	72.47 322	92.36 243

ZD-MVS					99.67 13	93.28 74		97.61 101	87.78 197	97.41 56	99.16 41	90.15 50	99.56 97	98.35 33	99.70 39

CP-MVS		96.22 53	96.15 51	96.42 106	99.67 13	89.62 162	99.70 17	97.61 101	90.07 128	96.00 87	99.16 41	87.43 90	99.92 40	96.03 79	99.72 34	99.70 61

thisisatest0530		94.00 110	93.52 113	95.43 139	95.76 189	90.02 153	98.99 106	97.60 103	86.58 223	91.74 151	97.36 151	94.78 10	98.34 164	86.37 206	92.48 184	97.94 187

tttt0517		93.30 132	93.01 124	94.17 181	95.57 196	86.47 228	98.51 161	97.60 103	85.99 231	90.55 173	97.19 161	94.80 9	98.31 165	85.06 219	91.86 194	97.74 189

thisisatest0515		94.75 92	94.19 94	96.43 105	96.13 183	92.64 92	99.47 44	97.60 103	87.55 206	93.17 137	97.59 143	94.71 11	98.42 162	88.28 186	93.20 173	98.24 178

testdata				95.26 145	98.20 109	87.28 212		97.60 103	85.21 241	98.48 28	99.15 43	88.15 77	98.72 155	90.29 161	99.45 66	99.78 42

ACMMP_NAP		96.59 40	96.18 46	97.81 35	98.82 94	93.55 68	98.88 118	97.59 107	90.66 107	97.98 44	99.14 45	86.59 112	100.00 1	96.47 69	99.46 64	99.89 27

CVMVSNet		90.30 188	90.91 166	88.46 303	94.32 240	73.58 345	97.61 233	97.59 107	90.16 125	88.43 197	97.10 165	76.83 222	92.86 338	82.64 249	93.54 172	98.93 135

XVS		96.47 45	96.37 41	96.77 84	99.62 25	90.66 135	99.43 55	97.58 109	92.41 69	96.86 69	98.96 71	87.37 92	99.87 51	95.65 85	99.43 68	99.78 42

X-MVStestdata		90.69 183	88.66 203	96.77 84	99.62 25	90.66 135	99.43 55	97.58 109	92.41 69	96.86 69	29.59 375	87.37 92	99.87 51	95.65 85	99.43 68	99.78 42

test222					98.32 106	91.21 114	98.08 207	97.58 109	83.74 265	95.87 94	99.02 60	86.74 107			99.64 47	99.81 35

test_prior3		97.07 27	97.09 19	97.01 65	99.58 33	91.77 100	99.57 31	97.57 112	91.43 90	98.12 37	98.97 66	90.43 43	99.49 108	98.33 34	99.81 23	99.79 38

test_prior				97.01 65	99.58 33	91.77 100		97.57 112					99.49 108			99.79 38

CP-MVSNet		86.54 252	85.45 252	89.79 278	91.02 300	82.78 294	97.38 239	97.56 114	85.37 239	79.53 293	93.03 251	71.86 260	95.25 312	79.92 269	73.43 315	91.34 281

test12				97.83 34	99.33 64	94.45 51		97.55 115		97.56 51		88.60 68	99.50 107		99.71 38	99.55 81

PAPR		96.35 48	95.82 60	97.94 32	99.63 21	94.19 58	99.42 57	97.55 115	92.43 65	93.82 131	99.12 48	87.30 97	99.91 42	94.02 117	99.06 85	99.74 55

AdaColmap		93.82 116	93.06 121	96.10 117	99.88 1	89.07 168	98.33 183	97.55 115	86.81 219	90.39 178	98.65 97	75.09 227	99.98 10	93.32 131	97.53 124	99.26 105

TESTMET0.1,1		93.82 116	93.26 118	95.49 136	95.21 208	90.25 141	99.15 86	97.54 118	89.18 151	91.79 150	94.87 213	89.13 61	97.63 207	86.21 207	96.29 145	98.60 160

hse-mvs2		91.67 164	91.51 155	92.15 226	96.22 172	82.61 297	97.74 227	97.53 119	93.85 37	96.27 84	96.15 194	83.19 164	97.44 220	95.81 82	66.86 343	96.40 220

AUN-MVS		90.17 192	89.50 185	92.19 224	96.21 173	82.67 295	97.76 226	97.53 119	88.05 189	91.67 152	96.15 194	83.10 166	97.47 217	88.11 189	66.91 342	96.43 219

ZNCC-MVS		96.09 56	95.81 62	96.95 75	99.42 54	91.19 115	99.55 34	97.53 119	89.72 135	95.86 95	98.94 78	86.59 112	99.97 23	95.13 98	99.56 59	99.68 65

ETH3D-3000-0.1		97.29 17	97.01 23	98.12 25	99.18 74	94.97 33	99.47 44	97.52 122	89.85 131	98.79 19	99.46 11	90.41 47	99.69 79	98.78 19	99.67 42	99.70 61

CANet		97.00 28	96.49 38	98.55 11	98.86 93	96.10 15	99.83 4	97.52 122	95.90 9	97.21 60	98.90 79	82.66 175	99.93 39	98.71 20	98.80 98	99.63 73

APDe-MVS		97.53 12	97.47 11	97.70 39	99.58 33	93.63 66	99.56 33	97.52 122	93.59 44	98.01 43	99.12 48	90.80 38	99.55 98	99.26 13	99.79 29	99.93 21

MDTV_nov1_ep13			90.47 177		96.14 180	88.55 183	91.34 336	97.51 125	89.58 141	92.24 148	90.50 303	86.99 103	97.61 209	77.64 284	92.34 186

QAPM		91.41 169	89.49 186	97.17 61	95.66 194	93.42 72	98.60 151	97.51 125	80.92 308	81.39 273	97.41 150	72.89 251	99.87 51	82.33 252	98.68 102	98.21 180

PAPM_NR		95.43 74	95.05 80	96.57 99	99.42 54	90.14 144	98.58 155	97.51 125	90.65 109	92.44 146	98.90 79	87.77 84	99.90 44	90.88 154	99.32 75	99.68 65

TSAR-MVS + MP.		97.44 16	97.46 12	97.39 52	99.12 77	93.49 71	98.52 158	97.50 128	94.46 22	98.99 12	98.64 98	91.58 28	99.08 143	98.49 28	99.83 15	99.60 77
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
alignmvs		95.77 70	95.00 81	98.06 28	97.35 133	95.68 18	99.71 16	97.50 128	91.50 87	96.16 86	98.61 101	86.28 121	99.00 145	96.19 75	91.74 197	99.51 85

9.14			96.87 27		99.34 58		99.50 41	97.49 130	89.41 147	98.59 25	99.43 18	89.78 54	99.69 79	98.69 21	99.62 51

GST-MVS		95.97 60	95.66 67	96.90 77	99.49 50	91.22 113	99.45 51	97.48 131	89.69 136	95.89 92	98.72 92	86.37 120	99.95 34	94.62 111	99.22 83	99.52 83

DP-MVS Recon		95.85 66	95.15 78	97.95 31	99.87 2	94.38 54	99.60 28	97.48 131	86.58 223	94.42 118	99.13 47	87.36 95	99.98 10	93.64 125	98.33 111	99.48 89

FOURS1					99.50 47	88.94 173	99.55 34	97.47 133	91.32 94	98.12 37

DVP-MVS		98.07 7	98.00 6	98.29 18	99.66 15	95.20 29	99.72 14	97.47 133	93.95 30	99.07 10	99.46 11	93.18 21	99.97 23	99.64 6	99.82 19	99.69 64
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
CPTT-MVS		94.60 100	94.43 89	95.09 148	99.66 15	86.85 222	99.44 52	97.47 133	83.22 274	94.34 121	98.96 71	82.50 176	99.55 98	94.81 105	99.50 62	98.88 138

SF-MVS		97.22 22	96.92 25	98.12 25	99.11 78	94.88 36	99.44 52	97.45 136	89.60 140	98.70 20	99.42 19	90.42 45	99.72 75	98.47 29	99.65 44	99.77 48

zzz-MVS		96.21 54	95.96 55	96.96 73	99.29 66	91.19 115	98.69 136	97.45 136	92.58 60	94.39 119	99.24 29	86.43 118	99.99 5	96.22 73	99.40 72	99.71 59

MTGPA								97.45 136

MTAPA		96.09 56	95.80 64	96.96 73	99.29 66	91.19 115	97.23 247	97.45 136	92.58 60	94.39 119	99.24 29	86.43 118	99.99 5	96.22 73	99.40 72	99.71 59

CDPH-MVS		96.56 41	96.18 46	97.70 39	99.59 31	93.92 61	99.13 92	97.44 140	89.02 156	97.90 48	99.22 31	88.90 65	99.49 108	94.63 110	99.79 29	99.68 65

APD-MVS		96.95 30	96.72 33	97.63 41	99.51 46	93.58 67	99.16 80	97.44 140	90.08 127	98.59 25	99.07 54	89.06 62	99.42 119	97.92 43	99.66 43	99.88 28
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PVSNet_Blended_VisFu		94.67 97	94.11 97	96.34 110	97.14 140	91.10 121	99.32 70	97.43 142	92.10 77	91.53 157	96.38 192	83.29 161	99.68 82	93.42 130	96.37 141	98.25 177

NR-MVSNet		87.74 235	86.00 243	92.96 208	91.46 294	90.68 134	96.65 270	97.42 143	88.02 191	73.42 329	93.68 233	77.31 219	95.83 297	84.26 229	71.82 329	92.36 243

MP-MVS		96.00 58	95.82 60	96.54 100	99.47 51	90.13 146	99.36 65	97.41 144	90.64 110	95.49 102	98.95 73	85.51 132	99.98 10	96.00 80	99.59 58	99.52 83
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		95.90 64	95.75 65	96.38 108	99.58 33	89.41 166	99.26 72	97.41 144	90.66 107	94.82 112	98.95 73	86.15 123	99.98 10	95.24 97	99.64 47	99.74 55

OpenMVS	85.28 14	90.75 181	88.84 198	96.48 102	93.58 261	93.51 70	98.80 124	97.41 144	82.59 285	78.62 301	97.49 147	68.00 284	99.82 64	84.52 227	98.55 107	96.11 223

ETH3D cwj APD-0.16		96.94 32	96.58 37	98.01 29	98.62 100	94.73 45	99.13 92	97.38 147	88.44 177	98.53 27	99.39 21	89.66 58	99.69 79	98.43 31	99.61 55	99.61 76

SD-MVS		97.51 13	97.40 15	97.81 35	99.01 84	93.79 65	99.33 69	97.38 147	93.73 41	98.83 18	99.02 60	90.87 36	99.88 48	98.69 21	99.74 32	99.77 48
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
DWT-MVSNet_test		94.36 104	93.95 105	95.62 132	96.99 148	89.47 164	96.62 271	97.38 147	90.96 101	93.07 140	97.27 154	93.73 15	98.09 175	85.86 214	93.65 171	99.29 101

tpmvs		89.16 205	87.76 215	93.35 201	97.19 138	84.75 269	90.58 343	97.36 150	81.99 295	84.56 226	89.31 321	83.98 151	98.17 170	74.85 305	90.00 214	97.12 204

PS-CasMVS		85.81 264	84.58 266	89.49 288	90.77 302	82.11 300	97.20 249	97.36 150	84.83 251	79.12 298	92.84 254	67.42 289	95.16 314	78.39 281	73.25 316	91.21 286

SR-MVS		96.13 55	96.16 50	96.07 118	99.42 54	89.04 169	98.59 153	97.33 152	90.44 114	96.84 72	99.12 48	86.75 106	99.41 121	97.47 48	99.44 67	99.76 51

PatchmatchNet		92.05 159	91.04 163	95.06 150	96.17 177	89.04 169	91.26 337	97.26 153	89.56 143	90.64 172	90.56 299	88.35 74	97.11 229	79.53 270	96.07 150	99.03 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test-LLR		93.11 138	92.68 129	94.40 173	94.94 227	87.27 213	99.15 86	97.25 154	90.21 120	91.57 154	94.04 221	84.89 142	97.58 210	85.94 211	96.13 146	98.36 173

test-mter		93.27 134	92.89 126	94.40 173	94.94 227	87.27 213	99.15 86	97.25 154	88.95 159	91.57 154	94.04 221	88.03 80	97.58 210	85.94 211	96.13 146	98.36 173

test1172		95.92 63	96.07 53	95.46 137	99.42 54	87.24 217	98.51 161	97.24 156	90.29 119	96.56 82	99.12 48	86.73 108	99.36 125	97.33 51	99.42 71	99.78 42

PEN-MVS		85.21 272	83.93 274	89.07 295	89.89 311	81.31 308	97.09 252	97.24 156	84.45 256	78.66 300	92.68 256	68.44 280	94.87 319	75.98 297	70.92 333	91.04 290

ab-mvs		91.05 175	89.17 192	96.69 92	95.96 184	91.72 103	92.62 328	97.23 158	85.61 235	89.74 186	93.89 229	68.55 278	99.42 119	91.09 150	87.84 220	98.92 136

APD-MVS_3200maxsize		95.64 73	95.65 69	95.62 132	99.24 70	87.80 197	98.42 171	97.22 159	88.93 161	96.64 81	98.98 65	85.49 133	99.36 125	96.68 62	99.27 79	99.70 61

SR-MVS-dyc-post		95.75 72	95.86 59	95.41 140	99.22 71	87.26 215	98.40 176	97.21 160	89.63 138	96.67 79	98.97 66	86.73 108	99.36 125	96.62 63	99.31 76	99.60 77

RE-MVS-def			95.70 66		99.22 71	87.26 215	98.40 176	97.21 160	89.63 138	96.67 79	98.97 66	85.24 138		96.62 63	99.31 76	99.60 77

SCA		90.64 184	89.25 191	94.83 159	94.95 226	88.83 176	96.26 281	97.21 160	90.06 129	90.03 182	90.62 295	66.61 294	96.81 241	83.16 243	94.36 165	98.84 141

RPMNet		85.07 273	81.88 290	94.64 166	93.47 263	86.24 235	84.97 354	97.21 160	64.85 360	90.76 170	78.80 358	80.95 196	99.27 133	53.76 361	92.17 191	98.41 167

VPNet		88.30 225	86.57 234	93.49 199	91.95 286	91.35 111	98.18 195	97.20 164	88.61 167	84.52 228	94.89 212	62.21 314	96.76 244	89.34 175	72.26 325	92.36 243

TranMVSNet+NR-MVSNet		87.75 233	86.31 238	92.07 228	90.81 301	88.56 182	98.33 183	97.18 165	87.76 198	81.87 266	93.90 228	72.45 253	95.43 307	83.13 245	71.30 332	92.23 248

cdsmvs_eth3d_5k		22.52 340	30.03 343	0.00 359	0.00 382	0.00 383	0.00 370	97.17 166	0.00 377	0.00 378	98.77 86	74.35 237	0.00 378	0.00 376	0.00 376	0.00 374

tpm2		91.77 162	91.09 161	93.82 194	94.83 231	85.56 255	92.51 329	97.16 167	84.00 261	93.83 130	90.66 292	87.54 88	97.17 227	87.73 193	91.55 201	98.72 153

MP-MVS-pluss		95.80 68	95.30 72	97.29 55	98.95 88	92.66 88	98.59 153	97.14 168	88.95 159	93.12 138	99.25 27	85.62 129	99.94 37	96.56 67	99.48 63	99.28 103
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PatchMatch-RL		91.47 167	90.54 175	94.26 178	98.20 109	86.36 233	96.94 257	97.14 168	87.75 199	88.98 192	95.75 201	71.80 261	99.40 122	80.92 263	97.39 127	97.02 210

Anonymous20240529		87.66 236	85.58 249	93.92 190	97.59 127	85.01 265	98.13 198	97.13 170	66.69 358	88.47 196	96.01 199	55.09 337	99.51 105	87.00 199	84.12 243	97.23 203

JIA-IIPM		85.97 260	84.85 260	89.33 290	93.23 270	73.68 344	85.05 353	97.13 170	69.62 350	91.56 156	68.03 362	88.03 80	96.96 235	77.89 283	93.12 174	97.34 199

PS-MVSNAJ		96.87 34	96.40 40	98.29 18	97.35 133	97.29 5	99.03 101	97.11 172	95.83 10	98.97 13	99.14 45	82.48 178	99.60 95	98.60 23	99.08 84	98.00 185

HPM-MVS_fast		94.89 87	94.62 84	95.70 131	99.11 78	88.44 186	99.14 89	97.11 172	85.82 233	95.69 99	98.47 111	83.46 157	99.32 131	93.16 133	99.63 50	99.35 95

DeepC-MVS	91.02 4	94.56 102	93.92 107	96.46 103	97.16 139	90.76 131	98.39 179	97.11 172	93.92 32	88.66 194	98.33 116	78.14 215	99.85 58	95.02 101	98.57 106	98.78 150
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tpmrst		92.78 141	92.16 140	94.65 165	96.27 170	87.45 206	91.83 332	97.10 175	89.10 154	94.68 115	90.69 290	88.22 75	97.73 203	89.78 167	91.80 196	98.77 151

HPM-MVS		95.41 76	95.22 76	95.99 121	99.29 66	89.14 167	99.17 79	97.09 176	87.28 210	95.40 103	98.48 110	84.93 141	99.38 123	95.64 89	99.65 44	99.47 90
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
tpm cat1		88.89 211	87.27 224	93.76 195	95.79 187	85.32 259	90.76 341	97.09 176	76.14 332	85.72 218	88.59 324	82.92 168	98.04 181	76.96 288	91.43 202	97.90 188

dp		90.16 193	88.83 199	94.14 182	96.38 167	86.42 229	91.57 334	97.06 178	84.76 252	88.81 193	90.19 311	84.29 148	97.43 221	75.05 302	91.35 205	98.56 161

xiu_mvs_v2_base		96.66 38	96.17 48	98.11 27	97.11 143	96.96 6	99.01 104	97.04 179	95.51 16	98.86 16	99.11 53	82.19 184	99.36 125	98.59 25	98.14 113	98.00 185

3Dnovator+	87.72 8	93.43 127	91.84 148	98.17 21	95.73 190	95.08 32	98.92 113	97.04 179	91.42 92	81.48 272	97.60 142	74.60 231	99.79 69	90.84 155	98.97 89	99.64 71

CDS-MVSNet		93.47 125	93.04 123	94.76 160	94.75 233	89.45 165	98.82 122	97.03 181	87.91 194	90.97 166	96.48 187	89.06 62	96.36 265	89.50 170	92.81 179	98.49 164
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test0.0.03 1		88.96 209	88.61 204	90.03 273	91.09 298	84.43 272	98.97 108	97.02 182	90.21 120	80.29 282	96.31 193	84.89 142	91.93 352	72.98 318	85.70 234	93.73 231

114514_t		94.06 108	93.05 122	97.06 63	99.08 81	92.26 97	98.97 108	97.01 183	82.58 286	92.57 144	98.22 122	80.68 197	99.30 132	89.34 175	99.02 87	99.63 73

CostFormer		92.89 140	92.48 134	94.12 183	94.99 224	85.89 247	92.89 324	97.00 184	86.98 214	95.00 111	90.78 286	90.05 51	97.51 215	92.92 137	91.73 198	98.96 129

ET-MVSNet_ETH3D		92.56 148	91.45 156	95.88 125	96.39 166	94.13 59	99.46 49	96.97 185	92.18 74	66.94 351	98.29 120	94.65 13	94.28 329	94.34 115	83.82 247	99.24 107

UA-Net		93.30 132	92.62 131	95.34 142	96.27 170	88.53 185	95.88 294	96.97 185	90.90 103	95.37 104	97.07 167	82.38 181	99.10 142	83.91 237	94.86 162	98.38 170

abl_6		94.63 99	94.48 87	95.09 148	98.61 101	86.96 220	98.06 209	96.97 185	89.31 148	95.86 95	98.56 103	79.82 200	99.64 90	94.53 113	98.65 104	98.66 159

TAMVS		92.62 145	92.09 143	94.20 180	94.10 244	87.68 199	98.41 173	96.97 185	87.53 207	89.74 186	96.04 198	84.77 145	96.49 257	88.97 182	92.31 187	98.42 166

Vis-MVSNet		92.64 144	91.85 147	95.03 152	95.12 215	88.23 187	98.48 166	96.81 189	91.61 83	92.16 149	97.22 159	71.58 264	98.00 184	85.85 215	97.81 116	98.88 138
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PMMVS		93.62 124	93.90 108	92.79 212	96.79 153	81.40 305	98.85 119	96.81 189	91.25 96	96.82 75	98.15 126	77.02 221	98.13 172	93.15 134	96.30 144	98.83 144

ADS-MVSNet		88.99 208	87.30 223	94.07 184	96.21 173	87.56 203	87.15 347	96.78 191	83.01 277	89.91 184	87.27 333	78.87 209	97.01 234	74.20 309	92.27 188	97.64 191

Vis-MVSNet (Re-imp)		93.26 135	93.00 125	94.06 185	96.14 180	86.71 225	98.68 138	96.70 192	88.30 182	89.71 188	97.64 141	85.43 136	96.39 263	88.06 190	96.32 142	99.08 121

Anonymous20231211		84.72 276	82.65 287	90.91 249	97.71 121	84.55 271	97.28 243	96.67 193	66.88 357	79.18 297	90.87 285	58.47 324	96.60 249	82.61 250	74.20 305	91.59 271

EIA-MVS		95.11 83	95.27 74	94.64 166	96.34 168	86.51 226	99.59 29	96.62 194	92.51 62	94.08 125	98.64 98	86.05 124	98.24 169	95.07 100	98.50 108	99.18 112

ETV-MVS		96.00 58	96.00 54	96.00 120	96.56 159	91.05 124	99.63 25	96.61 195	93.26 49	97.39 57	98.30 118	86.62 111	98.13 172	98.07 41	97.57 121	98.82 145

LS3D		90.19 191	88.72 201	94.59 168	98.97 85	86.33 234	96.90 259	96.60 196	74.96 335	84.06 232	98.74 89	75.78 224	99.83 61	74.93 303	97.57 121	97.62 194

EI-MVSNet		89.87 198	89.38 189	91.36 241	94.32 240	85.87 248	97.61 233	96.59 197	85.10 243	85.51 220	97.10 165	81.30 195	96.56 251	83.85 239	83.03 253	91.64 264

MVSTER		92.71 142	92.32 135	93.86 192	97.29 135	92.95 86	99.01 104	96.59 197	90.09 126	85.51 220	94.00 225	94.61 14	96.56 251	90.77 157	83.03 253	92.08 254

cascas		90.93 178	89.33 190	95.76 129	95.69 191	93.03 82	98.99 106	96.59 197	80.49 310	86.79 214	94.45 218	65.23 304	98.60 159	93.52 127	92.18 190	95.66 226

TAPA-MVS	87.50 9	90.35 186	89.05 194	94.25 179	98.48 105	85.17 262	98.42 171	96.58 200	82.44 290	87.24 206	98.53 104	82.77 171	98.84 148	59.09 356	97.88 115	98.72 153
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OMC-MVS		93.90 114	93.62 112	94.73 163	98.63 99	87.00 219	98.04 210	96.56 201	92.19 73	92.46 145	98.73 90	79.49 205	99.14 140	92.16 143	94.34 166	98.03 184

test_part1		88.43 223	86.68 233	93.67 198	97.56 129	92.40 96	98.12 200	96.55 202	82.26 292	80.31 281	93.16 248	74.59 233	96.62 248	85.00 221	72.61 320	91.99 258

PLC	91.07 3	94.23 107	94.01 100	94.87 156	99.17 75	87.49 204	99.25 73	96.55 202	88.43 178	91.26 162	98.21 124	85.92 125	99.86 56	89.77 168	97.57 121	97.24 202
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TSAR-MVS + GP.		96.95 30	96.91 26	97.07 62	98.88 91	91.62 105	99.58 30	96.54 204	95.09 18	96.84 72	98.63 100	91.16 29	99.77 71	99.04 16	96.42 140	99.81 35

cl22		89.57 202	88.79 200	91.91 229	97.94 117	87.62 201	97.98 213	96.51 205	85.03 246	82.37 253	91.79 267	83.65 153	96.50 255	85.96 210	77.89 278	91.61 269

xiu_mvs_v1_base_debu		94.73 93	93.98 101	96.99 68	95.19 209	95.24 24	98.62 147	96.50 206	92.99 53	97.52 52	98.83 83	72.37 254	99.15 137	97.03 55	96.74 135	96.58 215

xiu_mvs_v1_base		94.73 93	93.98 101	96.99 68	95.19 209	95.24 24	98.62 147	96.50 206	92.99 53	97.52 52	98.83 83	72.37 254	99.15 137	97.03 55	96.74 135	96.58 215

xiu_mvs_v1_base_debi		94.73 93	93.98 101	96.99 68	95.19 209	95.24 24	98.62 147	96.50 206	92.99 53	97.52 52	98.83 83	72.37 254	99.15 137	97.03 55	96.74 135	96.58 215

lupinMVS		96.32 50	95.94 56	97.44 48	95.05 222	94.87 37	99.86 2	96.50 206	93.82 39	98.04 41	98.77 86	85.52 130	98.09 175	96.98 59	98.97 89	99.37 94

mvs_anonymous		92.50 149	91.65 152	95.06 150	96.60 158	89.64 161	97.06 253	96.44 210	86.64 222	84.14 230	93.93 227	82.49 177	96.17 280	91.47 146	96.08 149	99.35 95

VDDNet		90.08 195	88.54 208	94.69 164	94.41 239	87.68 199	98.21 193	96.40 211	76.21 331	93.33 136	97.75 134	54.93 338	98.77 150	94.71 109	90.96 206	97.61 195

RRT_test8_iter05		91.04 176	90.40 178	92.95 209	96.20 176	89.75 159	98.97 108	96.38 212	88.52 170	82.00 262	93.51 240	90.69 39	96.73 245	90.43 159	76.91 286	92.38 242

HQP3-MVS								96.37 213							86.29 226

PatchT		85.44 270	83.19 277	92.22 222	93.13 272	83.00 287	83.80 360	96.37 213	70.62 345	90.55 173	79.63 357	84.81 144	94.87 319	58.18 358	91.59 200	98.79 148

HQP-MVS		91.50 166	91.23 159	92.29 221	93.95 248	86.39 231	99.16 80	96.37 213	93.92 32	87.57 201	96.67 183	73.34 245	97.77 196	93.82 123	86.29 226	92.72 236

UnsupCasMVSNet_eth		78.90 312	76.67 316	85.58 322	82.81 358	74.94 339	91.98 331	96.31 216	84.64 253	65.84 355	87.71 327	51.33 347	92.23 348	72.89 319	56.50 359	89.56 325

HQP_MVS		91.26 170	90.95 165	92.16 225	93.84 255	86.07 243	99.02 102	96.30 217	93.38 47	86.99 208	96.52 185	72.92 249	97.75 201	93.46 128	86.17 229	92.67 238

plane_prior5								96.30 217					97.75 201	93.46 128	86.17 229	92.67 238

jason		95.40 77	94.86 82	97.03 64	92.91 275	94.23 56	99.70 17	96.30 217	93.56 45	96.73 77	98.52 105	81.46 193	97.91 185	96.08 78	98.47 109	98.96 129
jason: jason.
CLD-MVS		91.06 174	90.71 172	92.10 227	94.05 247	86.10 241	99.55 34	96.29 220	94.16 27	84.70 225	97.17 163	69.62 273	97.82 192	94.74 107	86.08 231	92.39 241
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
GA-MVS		90.10 194	88.69 202	94.33 175	92.44 279	87.97 195	99.08 95	96.26 221	89.65 137	86.92 210	93.11 250	68.09 282	96.96 235	82.54 251	90.15 213	98.05 183

DTE-MVSNet		84.14 286	82.80 281	88.14 304	88.95 324	79.87 320	96.81 262	96.24 222	83.50 270	77.60 309	92.52 258	67.89 286	94.24 330	72.64 320	69.05 336	90.32 310

RRT_MVS		91.95 160	91.09 161	94.53 169	96.71 157	95.12 31	98.64 144	96.23 223	89.04 155	85.24 222	95.06 210	87.71 85	96.43 261	89.10 181	82.06 260	92.05 256

LFMVS		92.23 155	90.84 168	96.42 106	98.24 108	91.08 123	98.24 190	96.22 224	83.39 272	94.74 114	98.31 117	61.12 319	98.85 147	94.45 114	92.82 177	99.32 98

baseline1		92.61 146	91.28 158	96.58 97	97.05 146	94.63 48	97.72 228	96.20 225	89.82 132	88.56 195	96.85 177	86.85 104	97.82 192	88.42 184	80.10 268	97.30 200

FMVSNet3		88.81 217	87.08 227	93.99 189	96.52 161	94.59 49	98.08 207	96.20 225	85.85 232	82.12 257	91.60 271	74.05 241	95.40 309	79.04 274	80.24 265	91.99 258

canonicalmvs		95.02 86	93.96 104	98.20 20	97.53 130	95.92 16	98.71 131	96.19 227	91.78 81	95.86 95	98.49 109	79.53 204	99.03 144	96.12 76	91.42 203	99.66 69

MVSFormer		94.71 96	94.08 99	96.61 95	95.05 222	94.87 37	97.77 224	96.17 228	86.84 217	98.04 41	98.52 105	85.52 130	95.99 286	89.83 164	98.97 89	98.96 129

test_djsdf		88.26 227	87.73 216	89.84 276	88.05 334	82.21 299	97.77 224	96.17 228	86.84 217	82.41 252	91.95 266	72.07 257	95.99 286	89.83 164	84.50 240	91.32 282

MS-PatchMatch		86.75 247	85.92 244	89.22 291	91.97 285	82.47 298	96.91 258	96.14 230	83.74 265	77.73 308	93.53 239	58.19 325	97.37 225	76.75 291	98.35 110	87.84 338

CS-MVS		95.86 65	95.81 62	95.98 122	95.62 195	91.26 112	99.80 7	96.12 231	92.15 76	97.93 47	98.45 114	85.88 128	97.55 214	97.56 47	98.80 98	99.14 114

VDD-MVS		91.24 173	90.18 179	94.45 172	97.08 144	85.84 250	98.40 176	96.10 232	86.99 212	93.36 135	98.16 125	54.27 340	99.20 134	96.59 66	90.63 211	98.31 176

PCF-MVS	89.78 5	91.26 170	89.63 183	96.16 116	95.44 201	91.58 108	95.29 303	96.10 232	85.07 245	82.75 244	97.45 148	78.28 214	99.78 70	80.60 266	95.65 157	97.12 204
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_Test		93.67 122	92.67 130	96.69 92	96.72 155	92.66 88	97.22 248	96.03 234	87.69 203	95.12 109	94.03 223	81.55 190	98.28 168	89.17 179	96.46 138	99.14 114

jajsoiax		87.35 239	86.51 236	89.87 274	87.75 339	81.74 302	97.03 254	95.98 235	88.47 171	80.15 284	93.80 231	61.47 316	96.36 265	89.44 173	84.47 241	91.50 273

PS-MVSNAJss		89.54 203	89.05 194	91.00 247	88.77 325	84.36 273	97.39 237	95.97 236	88.47 171	81.88 265	93.80 231	82.48 178	96.50 255	89.34 175	83.34 252	92.15 251

F-COLMAP		92.07 158	91.75 151	93.02 207	98.16 112	82.89 291	98.79 128	95.97 236	86.54 225	87.92 199	97.80 131	78.69 212	99.65 88	85.97 209	95.93 152	96.53 218

miper_enhance_ethall		90.33 187	89.70 182	92.22 222	97.12 142	88.93 174	98.35 182	95.96 238	88.60 168	83.14 242	92.33 259	87.38 91	96.18 279	86.49 205	77.89 278	91.55 272

TR-MVS		90.77 180	89.44 187	94.76 160	96.31 169	88.02 194	97.92 215	95.96 238	85.52 236	88.22 198	97.23 158	66.80 293	98.09 175	84.58 226	92.38 185	98.17 182

CMPMVS	58.40 21	80.48 304	80.11 304	81.59 339	85.10 350	59.56 365	94.14 314	95.95 240	68.54 353	60.71 359	93.31 242	55.35 336	97.87 189	83.06 246	84.85 238	87.33 343
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LPG-MVS_test		88.86 212	88.47 209	90.06 270	93.35 268	80.95 314	98.22 191	95.94 241	87.73 201	83.17 240	96.11 196	66.28 297	97.77 196	90.19 162	85.19 235	91.46 275

LGP-MVS_train				90.06 270	93.35 268	80.95 314		95.94 241	87.73 201	83.17 240	96.11 196	66.28 297	97.77 196	90.19 162	85.19 235	91.46 275

OPM-MVS		89.76 199	89.15 193	91.57 238	90.53 304	85.58 254	98.11 203	95.93 243	92.88 58	86.05 216	96.47 188	67.06 292	97.87 189	89.29 178	86.08 231	91.26 285
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
XVG-OURS-SEG-HR		90.95 177	90.66 174	91.83 231	95.18 212	81.14 312	95.92 291	95.92 244	88.40 179	90.33 179	97.85 128	70.66 269	99.38 123	92.83 138	88.83 217	94.98 227

XVG-OURS		90.83 179	90.49 176	91.86 230	95.23 207	81.25 309	95.79 299	95.92 244	88.96 158	90.02 183	98.03 127	71.60 263	99.35 129	91.06 151	87.78 221	94.98 227

tpm		89.67 200	88.95 196	91.82 232	92.54 278	81.43 304	92.95 323	95.92 244	87.81 196	90.50 175	89.44 318	84.99 140	95.65 302	83.67 240	82.71 256	98.38 170

DROMVSNet		95.09 84	95.17 77	94.84 158	95.42 202	88.17 188	99.48 42	95.92 244	91.47 88	97.34 59	98.36 115	82.77 171	97.41 222	97.24 52	98.58 105	98.94 134

ACMM	86.95 13	88.77 218	88.22 212	90.43 261	93.61 260	81.34 307	98.50 163	95.92 244	87.88 195	83.85 234	95.20 209	67.20 290	97.89 187	86.90 202	84.90 237	92.06 255
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline		93.91 113	93.30 116	95.72 130	95.10 219	90.07 148	97.48 236	95.91 249	91.03 99	93.54 133	97.68 138	79.58 202	98.02 182	94.27 116	95.14 159	99.08 121

mvs_tets		87.09 242	86.22 239	89.71 279	87.87 335	81.39 306	96.73 268	95.90 250	88.19 186	79.99 286	93.61 236	59.96 322	96.31 273	89.40 174	84.34 242	91.43 277

XXY-MVS		87.75 233	86.02 242	92.95 209	90.46 305	89.70 160	97.71 230	95.90 250	84.02 260	80.95 274	94.05 220	67.51 288	97.10 231	85.16 217	78.41 275	92.04 257

nrg030		90.23 189	88.87 197	94.32 176	91.53 293	93.54 69	98.79 128	95.89 252	88.12 188	84.55 227	94.61 217	78.80 211	96.88 238	92.35 142	75.21 292	92.53 240

CNLPA		93.64 123	92.74 128	96.36 109	98.96 87	90.01 154	99.19 75	95.89 252	86.22 229	89.40 189	98.85 82	80.66 198	99.84 59	88.57 183	96.92 134	99.24 107

KD-MVS_2432*1600		82.98 293	80.52 301	90.38 263	94.32 240	88.98 171	92.87 325	95.87 254	80.46 311	73.79 327	87.49 330	82.76 173	93.29 335	70.56 326	46.53 365	88.87 333

miper_refine_blended		82.98 293	80.52 301	90.38 263	94.32 240	88.98 171	92.87 325	95.87 254	80.46 311	73.79 327	87.49 330	82.76 173	93.29 335	70.56 326	46.53 365	88.87 333

FMVSNet2		86.90 244	84.79 262	93.24 203	95.11 216	92.54 94	97.67 231	95.86 256	82.94 279	80.55 278	91.17 280	62.89 311	95.29 311	77.23 285	79.71 271	91.90 260

CS-MVS-test		95.20 81	95.27 74	94.98 154	95.67 193	88.17 188	99.62 27	95.84 257	91.52 86	97.42 55	98.30 118	85.07 139	97.51 215	95.81 82	98.20 112	99.26 105

casdiffmvs		93.98 111	93.43 114	95.61 134	95.07 221	89.86 156	98.80 124	95.84 257	90.98 100	92.74 143	97.66 140	79.71 201	98.10 174	94.72 108	95.37 158	98.87 140

UniMVSNet_ETH3D		85.65 269	83.79 275	91.21 242	90.41 306	80.75 316	95.36 302	95.78 259	78.76 320	81.83 269	94.33 219	49.86 351	96.66 246	84.30 228	83.52 250	96.22 222

Effi-MVS+		93.87 115	93.15 120	96.02 119	95.79 187	90.76 131	96.70 269	95.78 259	86.98 214	95.71 98	97.17 163	79.58 202	98.01 183	94.57 112	96.09 148	99.31 99

EU-MVSNet		84.19 285	84.42 269	83.52 332	88.64 328	67.37 361	96.04 290	95.76 261	85.29 240	78.44 304	93.18 246	70.67 268	91.48 354	75.79 299	75.98 288	91.70 263

BH-w/o		92.32 151	91.79 149	93.91 191	96.85 150	86.18 238	99.11 94	95.74 262	88.13 187	84.81 224	97.00 170	77.26 220	97.91 185	89.16 180	98.03 114	97.64 191

anonymousdsp		86.69 248	85.75 247	89.53 285	86.46 345	82.94 288	96.39 275	95.71 263	83.97 262	79.63 291	90.70 289	68.85 276	95.94 289	86.01 208	84.02 244	89.72 322

Fast-Effi-MVS+		91.72 163	90.79 171	94.49 170	95.89 185	87.40 208	99.54 38	95.70 264	85.01 248	89.28 191	95.68 202	77.75 217	97.57 213	83.22 242	95.06 160	98.51 163

IS-MVSNet		93.00 139	92.51 133	94.49 170	96.14 180	87.36 209	98.31 186	95.70 264	88.58 169	90.17 180	97.50 146	83.02 167	97.22 226	87.06 197	96.07 150	98.90 137

diffmvs		94.59 101	94.19 94	95.81 127	95.54 198	90.69 133	98.70 135	95.68 266	91.61 83	95.96 90	97.81 130	80.11 199	98.06 179	96.52 68	95.76 154	98.67 156

v7n		84.42 283	82.75 284	89.43 289	88.15 332	81.86 301	96.75 266	95.67 267	80.53 309	78.38 305	89.43 319	69.89 270	96.35 270	73.83 313	72.13 326	90.07 315

ACMP	87.39 10	88.71 220	88.24 211	90.12 269	93.91 253	81.06 313	98.50 163	95.67 267	89.43 146	80.37 280	95.55 203	65.67 299	97.83 191	90.55 158	84.51 239	91.47 274
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CL-MVSNet_self_test		79.89 308	78.34 308	84.54 328	81.56 360	75.01 338	96.88 260	95.62 269	81.10 304	75.86 317	85.81 343	68.49 279	90.26 356	63.21 347	56.51 358	88.35 335

bset_n11_16_dypcd		89.07 207	87.85 214	92.76 214	86.16 348	90.66 135	97.30 241	95.62 269	89.78 134	83.94 233	93.15 249	74.85 228	95.89 295	91.34 148	78.48 274	91.74 262

V42		87.00 243	85.68 248	90.98 248	89.91 309	86.08 242	98.32 185	95.61 271	83.67 268	82.72 245	90.67 291	74.00 242	96.53 253	81.94 257	74.28 304	90.32 310

XVG-ACMP-BASELINE		85.86 262	84.95 258	88.57 300	89.90 310	77.12 334	94.30 311	95.60 272	87.40 209	82.12 257	92.99 253	53.42 343	97.66 205	85.02 220	83.83 245	90.92 293

Anonymous202405211		88.84 213	87.03 228	94.27 177	98.14 113	84.18 275	98.44 169	95.58 273	76.79 330	89.34 190	96.88 176	53.42 343	99.54 100	87.53 196	87.12 224	99.09 120

miper_ehance_all_eth		88.94 210	88.12 213	91.40 239	95.32 205	86.93 221	97.85 220	95.55 274	84.19 258	81.97 263	91.50 273	84.16 149	95.91 293	84.69 224	77.89 278	91.36 280

CANet_DTU		94.31 106	93.35 115	97.20 60	97.03 147	94.71 46	98.62 147	95.54 275	95.61 14	97.21 60	98.47 111	71.88 259	99.84 59	88.38 185	97.46 126	97.04 209

v2v482		87.27 241	85.76 246	91.78 237	89.59 314	87.58 202	98.56 156	95.54 275	84.53 254	82.51 249	91.78 268	73.11 248	96.47 258	82.07 254	74.14 307	91.30 283

BH-untuned		91.46 168	90.84 168	93.33 202	96.51 162	84.83 268	98.84 121	95.50 277	86.44 228	83.50 235	96.70 182	75.49 226	97.77 196	86.78 204	97.81 116	97.40 197

v148		86.38 255	85.06 255	90.37 265	89.47 319	84.10 276	98.52 158	95.48 278	83.80 264	80.93 275	90.22 309	74.60 231	96.31 273	80.92 263	71.55 330	90.69 303

IterMVS-LS		88.34 224	87.44 220	91.04 246	94.10 244	85.85 249	98.10 204	95.48 278	85.12 242	82.03 261	91.21 279	81.35 194	95.63 303	83.86 238	75.73 290	91.63 265
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
xxxxxxxxxxxxxcwj		97.51 13	97.42 14	97.78 37	99.34 58	93.85 63	99.65 23	95.45 280	95.69 11	98.70 20	99.42 19	90.42 45	99.72 75	98.47 29	99.65 44	99.77 48

v1144		86.83 246	85.31 253	91.40 239	89.75 312	87.21 218	98.31 186	95.45 280	83.22 274	82.70 246	90.78 286	73.36 244	96.36 265	79.49 271	74.69 298	90.63 305

v1192		86.32 256	84.71 263	91.17 243	89.53 317	86.40 230	98.13 198	95.44 282	82.52 288	82.42 251	90.62 295	71.58 264	96.33 272	77.23 285	74.88 295	90.79 297

v144192		86.40 254	84.89 259	90.91 249	89.48 318	85.59 253	98.21 193	95.43 283	82.45 289	82.62 247	90.58 298	72.79 252	96.36 265	78.45 280	74.04 308	90.79 297

Effi-MVS+-dtu		89.97 197	90.68 173	87.81 307	95.15 213	71.98 351	97.87 219	95.40 284	91.92 78	87.57 201	91.44 274	74.27 238	96.84 239	89.45 171	93.10 175	94.60 229

mvs-test1		91.57 165	92.20 139	89.70 280	95.15 213	74.34 341	99.51 40	95.40 284	91.92 78	91.02 165	97.25 156	74.27 238	98.08 178	89.45 171	95.83 153	96.67 212

c3_l		88.19 228	87.23 225	91.06 245	94.97 225	86.17 239	97.72 228	95.38 286	83.43 271	81.68 270	91.37 275	82.81 170	95.72 300	84.04 236	73.70 309	91.29 284

eth_miper_zixun_eth		87.76 232	87.00 229	90.06 270	94.67 235	82.65 296	97.02 256	95.37 287	84.19 258	81.86 268	91.58 272	81.47 192	95.90 294	83.24 241	73.61 310	91.61 269

v8		86.11 258	84.45 267	91.10 244	89.99 308	86.85 222	97.24 246	95.36 288	81.99 295	79.89 288	89.86 314	74.53 234	96.39 263	78.83 278	72.32 324	90.05 317

v1921920		86.02 259	84.44 268	90.77 254	89.32 320	85.20 260	98.10 204	95.35 289	82.19 293	82.25 255	90.71 288	70.73 267	96.30 276	76.85 290	74.49 300	90.80 296

pmmvs4		87.58 238	86.17 241	91.80 233	89.58 315	88.92 175	97.25 245	95.28 290	82.54 287	80.49 279	93.17 247	75.62 225	96.05 285	82.75 248	78.90 272	90.42 308

GBi-Net		86.67 249	84.96 256	91.80 233	95.11 216	88.81 177	96.77 263	95.25 291	82.94 279	82.12 257	90.25 306	62.89 311	94.97 316	79.04 274	80.24 265	91.62 266

test1		86.67 249	84.96 256	91.80 233	95.11 216	88.81 177	96.77 263	95.25 291	82.94 279	82.12 257	90.25 306	62.89 311	94.97 316	79.04 274	80.24 265	91.62 266

FMVSNet1		83.94 289	81.32 297	91.80 233	91.94 287	88.81 177	96.77 263	95.25 291	77.98 322	78.25 306	90.25 306	50.37 350	94.97 316	73.27 316	77.81 282	91.62 266

cl____		87.82 230	86.79 232	90.89 251	94.88 229	85.43 256	97.81 221	95.24 294	82.91 283	80.71 277	91.22 278	81.97 187	95.84 296	81.34 260	75.06 293	91.40 279

miper_lstm_enhance		86.90 244	86.20 240	89.00 296	94.53 237	81.19 310	96.74 267	95.24 294	82.33 291	80.15 284	90.51 302	81.99 185	94.68 325	80.71 265	73.58 311	91.12 288

UnsupCasMVSNet_bld		73.85 325	70.14 328	84.99 324	79.44 364	75.73 336	88.53 345	95.24 294	70.12 349	61.94 358	74.81 359	41.41 361	93.62 332	68.65 332	51.13 364	85.62 352

v1240		85.77 266	84.11 271	90.73 255	89.26 321	85.15 263	97.88 218	95.23 297	81.89 298	82.16 256	90.55 300	69.60 274	96.31 273	75.59 300	74.87 296	90.72 302

DIV-MVS_self_test		87.82 230	86.81 231	90.87 252	94.87 230	85.39 258	97.81 221	95.22 298	82.92 282	80.76 276	91.31 277	81.99 185	95.81 298	81.36 259	75.04 294	91.42 278

v10		85.73 267	84.01 273	90.87 252	90.03 307	86.73 224	97.20 249	95.22 298	81.25 303	79.85 289	89.75 315	73.30 247	96.28 277	76.87 289	72.64 319	89.61 324

BH-RMVSNet		91.25 172	89.99 180	95.03 152	96.75 154	88.55 183	98.65 142	94.95 300	87.74 200	87.74 200	97.80 131	68.27 281	98.14 171	80.53 267	97.49 125	98.41 167

GeoE		90.60 185	89.56 184	93.72 197	95.10 219	85.43 256	99.41 58	94.94 301	83.96 263	87.21 207	96.83 178	74.37 236	97.05 233	80.50 268	93.73 170	98.67 156

ACMH	83.09 17	84.60 278	82.61 288	90.57 257	93.18 271	82.94 288	96.27 279	94.92 302	81.01 306	72.61 337	93.61 236	56.54 329	97.79 194	74.31 308	81.07 264	90.99 291
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test1111		92.12 156	91.19 160	94.94 155	96.15 178	87.36 209	98.12 200	94.84 303	90.85 104	90.97 166	97.26 155	65.60 302	98.37 163	89.74 169	97.14 133	99.07 123

ECVR-MVS		92.29 152	91.33 157	95.15 146	96.41 164	87.84 196	98.10 204	94.84 303	90.82 105	91.42 160	97.28 152	65.61 301	98.49 160	90.33 160	97.19 130	99.12 117

IterMVS		85.81 264	84.67 264	89.22 291	93.51 262	83.67 281	96.32 278	94.80 305	85.09 244	78.69 299	90.17 312	66.57 296	93.17 337	79.48 272	77.42 284	90.81 295
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
LTVRE_ROB	81.71 19	84.59 279	82.72 285	90.18 267	92.89 276	83.18 286	93.15 322	94.74 306	78.99 317	75.14 322	92.69 255	65.64 300	97.63 207	69.46 329	81.82 262	89.74 321
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
pm-mvs1		84.68 277	82.78 283	90.40 262	89.58 315	85.18 261	97.31 240	94.73 307	81.93 297	76.05 314	92.01 263	65.48 303	96.11 283	78.75 279	69.14 335	89.91 320

IterMVS-SCA-FT		85.73 267	84.64 265	89.00 296	93.46 265	82.90 290	96.27 279	94.70 308	85.02 247	78.62 301	90.35 304	66.61 294	93.33 334	79.38 273	77.36 285	90.76 299

1112_ss		92.71 142	91.55 154	96.20 112	95.56 197	91.12 119	98.48 166	94.69 309	88.29 183	86.89 211	98.50 107	87.02 101	98.66 157	84.75 223	89.77 215	98.81 146

Test_1112_low_res		92.27 154	90.97 164	96.18 113	95.53 199	91.10 121	98.47 168	94.66 310	88.28 184	86.83 213	93.50 241	87.00 102	98.65 158	84.69 224	89.74 216	98.80 147

Fast-Effi-MVS+-dtu		88.84 213	88.59 206	89.58 284	93.44 266	78.18 329	98.65 142	94.62 311	88.46 173	84.12 231	95.37 208	68.91 275	96.52 254	82.06 255	91.70 199	94.06 230

our_test_3		84.47 282	82.80 281	89.50 286	89.01 322	83.90 279	97.03 254	94.56 312	81.33 302	75.36 321	90.52 301	71.69 262	94.54 327	68.81 331	76.84 287	90.07 315

ppachtmachnet_test		83.63 291	81.57 294	89.80 277	89.01 322	85.09 264	97.13 251	94.50 313	78.84 318	76.14 313	91.00 282	69.78 271	94.61 326	63.40 346	74.36 302	89.71 323

YYNet1		79.64 310	77.04 314	87.43 311	87.80 337	79.98 319	96.23 283	94.44 314	73.83 340	51.83 361	87.53 329	67.96 285	92.07 351	66.00 341	67.75 341	90.23 312

MDA-MVSNet_test_wron		79.65 309	77.05 313	87.45 310	87.79 338	80.13 318	96.25 282	94.44 314	73.87 339	51.80 362	87.47 332	68.04 283	92.12 350	66.02 340	67.79 340	90.09 313

MIMVSNet		84.48 281	81.83 291	92.42 220	91.73 291	87.36 209	85.52 350	94.42 316	81.40 301	81.91 264	87.58 328	51.92 346	92.81 340	73.84 312	88.15 219	97.08 208

MVP-Stereo		86.61 251	85.83 245	88.93 298	88.70 327	83.85 280	96.07 289	94.41 317	82.15 294	75.64 319	91.96 265	67.65 287	96.45 260	77.20 287	98.72 101	86.51 349
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MSDG		88.29 226	86.37 237	94.04 187	96.90 149	86.15 240	96.52 273	94.36 318	77.89 326	79.22 296	96.95 172	69.72 272	99.59 96	73.20 317	92.58 183	96.37 221

ACMH+	83.78 15	84.21 284	82.56 289	89.15 293	93.73 259	79.16 321	96.43 274	94.28 319	81.09 305	74.00 326	94.03 223	54.58 339	97.67 204	76.10 296	78.81 273	90.63 305

Patchmatch-test		86.25 257	84.06 272	92.82 211	94.42 238	82.88 292	82.88 361	94.23 320	71.58 343	79.39 294	90.62 295	89.00 64	96.42 262	63.03 348	91.37 204	99.16 113

CR-MVSNet		88.83 215	87.38 222	93.16 205	93.47 263	86.24 235	84.97 354	94.20 321	88.92 162	90.76 170	86.88 337	84.43 146	94.82 321	70.64 325	92.17 191	98.41 167

Patchmtry		83.61 292	81.64 292	89.50 286	93.36 267	82.84 293	84.10 357	94.20 321	69.47 351	79.57 292	86.88 337	84.43 146	94.78 322	68.48 333	74.30 303	90.88 294

EG-PatchMatch MVS		79.92 306	77.59 310	86.90 314	87.06 343	77.90 333	96.20 287	94.06 323	74.61 336	66.53 353	88.76 323	40.40 364	96.20 278	67.02 337	83.66 248	86.61 347

KD-MVS_self_test		77.47 320	75.88 319	82.24 334	81.59 359	68.93 359	92.83 327	94.02 324	77.03 328	73.14 331	83.39 348	55.44 335	90.42 355	67.95 334	57.53 357	87.38 341

K. test v3		81.04 302	79.77 305	84.83 325	87.41 340	70.23 356	95.60 301	93.93 325	83.70 267	67.51 349	89.35 320	55.76 331	93.58 333	76.67 292	68.03 339	90.67 304

RPSCF		85.33 271	85.55 250	84.67 327	94.63 236	62.28 363	93.73 317	93.76 326	74.38 338	85.23 223	97.06 168	64.09 307	98.31 165	80.98 261	86.08 231	93.41 235

MVS-HIRNet		79.01 311	75.13 321	90.66 256	93.82 257	81.69 303	85.16 351	93.75 327	54.54 362	74.17 325	59.15 366	57.46 327	96.58 250	63.74 345	94.38 164	93.72 232

pmmvs5		85.87 261	84.40 270	90.30 266	88.53 329	84.23 274	98.60 151	93.71 328	81.53 300	80.29 282	92.02 262	64.51 306	95.52 305	82.04 256	78.34 276	91.15 287

pmmvs6		79.90 307	77.31 312	87.67 308	84.17 353	78.13 330	95.86 296	93.68 329	67.94 355	72.67 336	89.62 317	50.98 349	95.75 299	74.80 306	66.04 344	89.14 330

OurMVSNet-221017-0		84.13 287	83.59 276	85.77 321	87.81 336	70.24 355	94.89 306	93.65 330	86.08 230	76.53 311	93.28 244	61.41 317	96.14 282	80.95 262	77.69 283	90.93 292

Anonymous20240521		78.63 315	76.90 315	83.82 330	82.82 357	72.86 347	95.72 300	93.57 331	73.55 341	72.17 338	84.79 345	49.69 352	92.51 345	65.29 343	74.50 299	86.09 351

DP-MVS		88.75 219	86.56 235	95.34 142	98.92 89	87.45 206	97.64 232	93.52 332	70.55 346	81.49 271	97.25 156	74.43 235	99.88 48	71.14 324	94.09 167	98.67 156

ITE_SJBPF				87.93 305	92.26 281	76.44 335		93.47 333	87.67 204	79.95 287	95.49 206	56.50 330	97.38 223	75.24 301	82.33 259	89.98 319

USDC		84.74 275	82.93 279	90.16 268	91.73 291	83.54 282	95.00 305	93.30 334	88.77 165	73.19 330	93.30 243	53.62 342	97.65 206	75.88 298	81.54 263	89.30 327

ADS-MVSNet2		87.62 237	86.88 230	89.86 275	96.21 173	79.14 322	87.15 347	92.99 335	83.01 277	89.91 184	87.27 333	78.87 209	92.80 341	74.20 309	92.27 188	97.64 191

Anonymous20231206		80.76 303	79.42 307	84.79 326	84.78 351	72.98 346	96.53 272	92.97 336	79.56 314	74.33 323	88.83 322	61.27 318	92.15 349	60.59 353	75.92 289	89.24 329

MDA-MVSNet-bldmvs		77.82 319	74.75 323	87.03 313	88.33 330	78.52 327	96.34 277	92.85 337	75.57 333	48.87 364	87.89 326	57.32 328	92.49 346	60.79 352	64.80 347	90.08 314

test20.03		78.51 316	77.48 311	81.62 338	83.07 356	71.03 353	96.11 288	92.83 338	81.66 299	69.31 342	89.68 316	57.53 326	87.29 363	58.65 357	68.47 337	86.53 348

COLMAP_ROB	82.69 18	84.54 280	82.82 280	89.70 280	96.72 155	78.85 323	95.89 292	92.83 338	71.55 344	77.54 310	95.89 200	59.40 323	99.14 140	67.26 336	88.26 218	91.11 289
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SixPastTwentyTwo		82.63 295	81.58 293	85.79 320	88.12 333	71.01 354	95.17 304	92.54 340	84.33 257	72.93 335	92.08 260	60.41 321	95.61 304	74.47 307	74.15 306	90.75 300

FMVSNet5		82.29 296	80.54 300	87.52 309	93.79 258	84.01 277	93.73 317	92.47 341	76.92 329	74.27 324	86.15 342	63.69 310	89.24 358	69.07 330	74.79 297	89.29 328

new-patchmatchnet		74.80 324	72.40 327	81.99 337	78.36 366	72.20 350	94.44 309	92.36 342	77.06 327	63.47 356	79.98 356	51.04 348	88.85 359	60.53 354	54.35 361	84.92 356

new_pmnet		76.02 321	73.71 324	82.95 333	83.88 354	72.85 348	91.26 337	92.26 343	70.44 347	62.60 357	81.37 352	47.64 355	92.32 347	61.85 350	72.10 327	83.68 358

AllTest		84.97 274	83.12 278	90.52 259	96.82 151	78.84 324	95.89 292	92.17 344	77.96 324	75.94 315	95.50 204	55.48 333	99.18 135	71.15 322	87.14 222	93.55 233

TestCases				90.52 259	96.82 151	78.84 324		92.17 344	77.96 324	75.94 315	95.50 204	55.48 333	99.18 135	71.15 322	87.14 222	93.55 233

pmmvs-eth3d		78.71 314	76.16 318	86.38 316	80.25 363	81.19 310	94.17 313	92.13 346	77.97 323	66.90 352	82.31 350	55.76 331	92.56 344	73.63 315	62.31 351	85.38 353

MIMVSNet1		75.92 322	73.30 325	83.81 331	81.29 361	75.57 337	92.26 330	92.05 347	73.09 342	67.48 350	86.18 341	40.87 363	87.64 362	55.78 359	70.68 334	88.21 336

ambc				79.60 341	72.76 368	56.61 367	76.20 363	92.01 348		68.25 345	80.23 355	23.34 369	94.73 323	73.78 314	60.81 352	87.48 340

LF4IMVS		81.94 299	81.17 298	84.25 329	87.23 342	68.87 360	93.35 321	91.93 349	83.35 273	75.40 320	93.00 252	49.25 354	96.65 247	78.88 277	78.11 277	87.22 345

TransMVSNet (Re)		81.97 298	79.61 306	89.08 294	89.70 313	84.01 277	97.26 244	91.85 350	78.84 318	73.07 334	91.62 270	67.17 291	95.21 313	67.50 335	59.46 355	88.02 337

Baseline_NR-MVSNet		85.83 263	84.82 261	88.87 299	88.73 326	83.34 284	98.63 146	91.66 351	80.41 313	82.44 250	91.35 276	74.63 229	95.42 308	84.13 232	71.39 331	87.84 338

testgi		82.29 296	81.00 299	86.17 318	87.24 341	74.84 340	97.39 237	91.62 352	88.63 166	75.85 318	95.42 207	46.07 357	91.55 353	66.87 339	79.94 269	92.12 252

TDRefinement		78.01 317	75.31 320	86.10 319	70.06 369	73.84 343	93.59 320	91.58 353	74.51 337	73.08 333	91.04 281	49.63 353	97.12 228	74.88 304	59.47 354	87.33 343

OpenMVS_ROB	73.86 20	77.99 318	75.06 322	86.77 315	83.81 355	77.94 332	96.38 276	91.53 354	67.54 356	68.38 344	87.13 336	43.94 358	96.08 284	55.03 360	81.83 261	86.29 350

test_0402		78.81 313	76.33 317	86.26 317	91.18 297	78.44 328	95.88 294	91.34 355	68.55 352	70.51 340	89.91 313	52.65 345	94.99 315	47.14 364	79.78 270	85.34 355

MTMP							99.21 74	91.09 356

DeepMVS_CX				76.08 343	90.74 303	51.65 371		90.84 357	86.47 227	57.89 360	87.98 325	35.88 366	92.60 342	65.77 342	65.06 346	83.97 357

lessismore_v0				85.08 323	85.59 349	69.28 358		90.56 358		67.68 348	90.21 310	54.21 341	95.46 306	73.88 311	62.64 349	90.50 307

Gipuma		54.77 333	52.22 337	62.40 350	86.50 344	59.37 366	50.20 368	90.35 359	36.52 366	41.20 367	49.49 368	18.33 372	81.29 365	32.10 368	65.34 345	46.54 368
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
TinyColmap		80.42 305	77.94 309	87.85 306	92.09 284	78.58 326	93.74 316	89.94 360	74.99 334	69.77 341	91.78 268	46.09 356	97.58 210	65.17 344	77.89 278	87.38 341

test_method		70.10 328	68.66 331	74.41 344	86.30 347	55.84 368	94.47 308	89.82 361	35.18 367	66.15 354	84.75 346	30.54 367	77.96 368	70.40 328	60.33 353	89.44 326

FPMVS		61.57 329	60.32 332	65.34 348	60.14 373	42.44 374	91.02 339	89.72 362	44.15 364	42.63 366	80.93 353	19.02 370	80.59 367	42.50 365	72.76 318	73.00 362

LCM-MVSNet		60.07 331	56.37 333	71.18 345	54.81 375	48.67 372	82.17 362	89.48 363	37.95 365	49.13 363	69.12 360	13.75 376	81.76 364	59.28 355	51.63 363	83.10 360

pmmvs3		72.86 326	69.76 330	82.17 335	73.86 367	74.19 342	94.20 312	89.01 364	64.23 361	67.72 347	80.91 354	41.48 360	88.65 360	62.40 349	54.02 362	83.68 358

LCM-MVSNet-Re		88.59 221	88.61 204	88.51 302	95.53 199	72.68 349	96.85 261	88.43 365	88.45 174	73.14 331	90.63 294	75.82 223	94.38 328	92.95 135	95.71 156	98.48 165

Patchmatch-RL test		81.90 300	80.13 303	87.23 312	80.71 362	70.12 357	84.07 358	88.19 366	83.16 276	70.57 339	82.18 351	87.18 98	92.59 343	82.28 253	62.78 348	98.98 127

DSMNet-mixed		81.60 301	81.43 295	82.10 336	84.36 352	60.79 364	93.63 319	86.74 367	79.00 316	79.32 295	87.15 335	63.87 309	89.78 357	66.89 338	91.92 193	95.73 225

PM-MVS		74.88 323	72.85 326	80.98 340	78.98 365	64.75 362	90.81 340	85.77 368	80.95 307	68.23 346	82.81 349	29.08 368	92.84 339	76.54 293	62.46 350	85.36 354

door								85.30 369

door-mid								84.90 370

EGC-MVSNET		60.70 330	55.37 334	76.72 342	86.35 346	71.08 352	89.96 344	84.44 371	0.38 376	1.50 377	84.09 347	37.30 365	88.10 361	40.85 366	73.44 314	70.97 364

PMMVS2		58.97 332	55.07 335	70.69 347	62.72 370	55.37 369	85.97 349	80.52 372	49.48 363	45.94 365	68.31 361	15.73 374	80.78 366	49.79 363	37.12 367	75.91 361

ANet_high		50.71 335	46.17 338	64.33 349	44.27 377	52.30 370	76.13 364	78.73 373	64.95 359	27.37 370	55.23 367	14.61 375	67.74 370	36.01 367	18.23 370	72.95 363

PMVS	41.42 23	45.67 336	42.50 339	55.17 352	34.28 378	32.37 377	66.24 366	78.71 374	30.72 368	22.04 373	59.59 365	4.59 377	77.85 369	27.49 369	58.84 356	55.29 366
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		53.66 334	52.86 336	56.05 351	32.75 379	41.97 375	73.42 365	76.12 375	21.91 372	39.68 368	96.39 191	42.59 359	65.10 371	78.00 282	14.92 372	61.08 365

MVE	44.00 22	41.70 337	37.64 342	53.90 353	49.46 376	43.37 373	65.09 367	66.66 376	26.19 371	25.77 372	48.53 369	3.58 379	63.35 372	26.15 370	27.28 368	54.97 367
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		41.02 338	40.93 340	41.29 354	61.97 371	33.83 376	84.00 359	65.17 377	27.17 369	27.56 369	46.72 370	17.63 373	60.41 373	19.32 371	18.82 369	29.61 369

EMVS		39.96 339	39.88 341	40.18 355	59.57 374	32.12 378	84.79 356	64.57 378	26.27 370	26.14 371	44.18 373	18.73 371	59.29 374	17.03 372	17.67 371	29.12 370

N_pmnet		70.19 327	69.87 329	71.12 346	88.24 331	30.63 379	95.85 297	28.70 379	70.18 348	68.73 343	86.55 339	64.04 308	93.81 331	53.12 362	73.46 313	88.94 331

wuyk23d		16.71 342	16.73 346	16.65 356	60.15 372	25.22 380	41.24 369	5.17 380	6.56 373	5.48 376	3.61 376	3.64 378	22.72 375	15.20 373	9.52 373	1.99 373

testmvs		18.81 341	23.05 344	6.10 358	4.48 380	2.29 382	97.78 223	3.00 381	3.27 374	18.60 374	62.71 363	1.53 381	2.49 377	14.26 374	1.80 374	13.50 372

test123		16.58 343	19.47 345	7.91 357	3.59 381	5.37 381	94.32 310	1.39 382	2.49 375	13.98 375	44.60 372	2.91 380	2.65 376	11.35 375	0.57 375	15.70 371

test_blank		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uanet_test		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

pcd_1.5k_mvsjas		6.87 345	9.16 348	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	82.48 178	0.00 378	0.00 376	0.00 376	0.00 374

sosnet-low-res		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

sosnet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uncertanet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

Regformer		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

n2								0.00 383

nn								0.00 383

ab-mvs-re		8.21 344	10.94 347	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	98.50 107	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

uanet		0.00 346	0.00 349	0.00 359	0.00 382	0.00 383	0.00 370	0.00 383	0.00 377	0.00 378	0.00 377	0.00 382	0.00 378	0.00 376	0.00 376	0.00 374

PC_three_1452									94.60 20	99.41 2	99.12 48	95.50 6	99.96 30	99.84 2	99.92 3	99.97 7

eth-test2					0.00 382

eth-test					0.00 382

OPU-MVS				99.49 4	99.64 20	98.51 4	99.77 9				99.19 34	95.12 7	99.97 23	99.90 1	99.92 3	99.99 1

test_0728_THIRD									93.01 51	99.07 10	99.46 11	94.66 12	99.97 23	99.25 14	99.82 19	99.95 15

GSMVS																98.84 141

test_part2					99.54 40	95.42 19				98.13 35

sam_mvs1												88.39 73				98.84 141

sam_mvs												87.08 99

test_post1							90.74 342				41.37 374	85.38 137	96.36 265	83.16 243

test_post											46.00 371	87.37 92	97.11 229

patchmatchnet-post											84.86 344	88.73 67	96.81 241

gm-plane-assit					94.69 234	88.14 190			88.22 185		97.20 160		98.29 167	90.79 156

test9_res														98.60 23	99.87 9	99.90 24

agg_prior2														97.84 45	99.87 9	99.91 22

test_prior4						92.00 99	99.41 58

test_prior2							99.57 31		91.43 90	98.12 37	98.97 66	90.43 43		98.33 34	99.81 23

旧先验2							98.67 140		85.75 234	98.96 14			98.97 146	93.84 121

新几何2							98.26 189

原ACMM2							98.69 136

testdata2													99.88 48	84.16 231

segment_acmp												90.56 42

testdata1							97.89 216		92.43 65

plane_prior7					93.84 255	85.73 251

plane_prior6					93.92 252	86.02 245						72.92 249

plane_prior4											96.52 185

plane_prior3						85.91 246			93.65 42	86.99 208

plane_prior2							99.02 102		93.38 47

plane_prior1					93.90 254

plane_prior						86.07 243	99.14 89		93.81 40						86.26 228

HQP5-MVS						86.39 231

HQP-NCC					93.95 248		99.16 80		93.92 32	87.57 201

ACMP_Plane					93.95 248		99.16 80		93.92 32	87.57 201

BP-MVS														93.82 123

HQP4-MVS										87.57 201			97.77 196			92.72 236

HQP2-MVS												73.34 245

NP-MVS					93.94 251	86.22 237					96.67 183

MDTV_nov1_ep13_2view						91.17 118	91.38 335		87.45 208	93.08 139		86.67 110		87.02 198		98.95 133

ACMMP++_ref															82.64 257

ACMMP++															83.83 245

Test By Simon												83.62 154

High-resolution multi-view benchmark