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.

thres200		88.92 123	87.65 130	92.73 98	96.30 100	85.62 43	97.85 50	98.86 1	84.38 137	84.82 141	93.99 166	75.12 148	98.01 135	70.86 257	86.67 174	94.56 193

thres100view900		88.30 141	86.95 150	92.33 112	96.10 106	84.90 62	97.14 107	98.85 2	82.69 177	83.41 159	93.66 172	75.43 139	97.93 137	69.04 263	86.24 180	94.17 195

tfpn200view9		88.48 136	87.15 145	92.47 106	96.21 102	85.30 49	97.44 82	98.85 2	83.37 163	83.99 151	93.82 169	75.36 142	97.93 137	69.04 263	86.24 180	94.17 195

thres600view7		88.06 144	86.70 153	92.15 119	96.10 106	85.17 55	97.14 107	98.85 2	82.70 176	83.41 159	93.66 172	75.43 139	97.82 144	67.13 272	85.88 184	93.45 208

thres400		88.42 139	87.15 145	92.23 116	96.21 102	85.30 49	97.44 82	98.85 2	83.37 163	83.99 151	93.82 169	75.36 142	97.93 137	69.04 263	86.24 180	93.45 208

MVS_111021_HR		93.41 40	93.39 39	93.47 70	97.34 88	82.83 98	97.56 74	98.27 6	89.16 42	89.71 95	97.14 93	79.77 72	99.56 51	93.65 47	97.94 61	98.02 80

sss		90.87 90	89.96 96	93.60 60	94.15 158	83.84 78	97.14 107	98.13 7	85.93 97	89.68 96	96.09 119	71.67 177	99.30 72	87.69 115	89.16 152	97.66 107

MG-MVS		94.25 24	93.72 34	95.85 9	99.38 3	89.35 9	97.98 44	98.09 8	89.99 32	92.34 62	96.97 99	81.30 58	98.99 100	88.54 107	98.88 19	99.20 18

VNet		92.11 66	91.22 75	94.79 21	96.91 94	86.98 24	97.91 47	97.96 9	86.38 88	93.65 48	95.74 124	70.16 193	98.95 105	93.39 51	88.87 156	98.43 49

test_yl		91.46 78	90.53 84	94.24 36	97.41 82	85.18 51	98.08 38	97.72 10	80.94 198	89.85 92	96.14 117	75.61 132	98.81 112	90.42 88	88.56 161	98.74 31

DCV-MVSNet		91.46 78	90.53 84	94.24 36	97.41 82	85.18 51	98.08 38	97.72 10	80.94 198	89.85 92	96.14 117	75.61 132	98.81 112	90.42 88	88.56 161	98.74 31

WTY-MVS		92.65 58	91.68 70	95.56 11	96.00 108	88.90 10	98.23 31	97.65 12	88.57 52	89.82 94	97.22 91	79.29 75	99.06 96	89.57 98	88.73 158	98.73 35

EPNet		94.06 30	94.15 28	93.76 50	97.27 90	84.35 67	98.29 29	97.64 13	94.57 4	95.36 23	96.88 102	79.96 71	99.12 93	91.30 72	96.11 94	97.82 98
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HY-MVS	84.06 6	91.63 74	90.37 87	95.39 14	96.12 105	88.25 12	90.22 288	97.58 14	88.33 58	90.50 86	91.96 188	79.26 77	99.06 96	90.29 90	89.07 153	98.88 27

baseline2		90.39 100	90.21 90	90.93 151	90.86 237	80.99 138	95.20 208	97.41 15	86.03 95	80.07 199	94.61 154	90.58 4	97.47 163	87.29 119	89.86 149	94.35 194

PVSNet	82.34 9	89.02 120	87.79 128	92.71 99	95.49 119	81.50 129	97.70 64	97.29 16	87.76 69	85.47 137	95.12 145	56.90 270	98.90 109	80.33 172	94.02 114	97.71 104

PGM-MVS		91.93 68	91.80 68	92.32 113	98.27 50	79.74 171	95.28 203	97.27 17	83.83 154	90.89 84	97.78 62	76.12 126	99.56 51	88.82 105	97.93 63	97.66 107

IB-MVS	85.34 4	88.67 131	87.14 147	93.26 73	93.12 181	84.32 68	98.76 17	97.27 17	87.19 82	79.36 203	90.45 211	83.92 38	98.53 121	84.41 137	69.79 280	96.93 139
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
MVS		90.60 95	88.64 116	96.50 3	94.25 156	90.53 6	93.33 252	97.21 19	77.59 256	78.88 206	97.31 85	71.52 180	99.69 36	89.60 97	98.03 59	99.27 16

CSCG		92.02 67	91.65 71	93.12 79	98.53 36	80.59 149	97.47 80	97.18 20	77.06 264	84.64 145	97.98 52	83.98 37	99.52 53	90.72 81	97.33 75	99.23 17

PHI-MVS		93.59 38	93.63 35	93.48 67	98.05 61	81.76 122	98.64 21	97.13 21	82.60 179	94.09 45	98.49 23	80.35 64	99.85 10	94.74 38	98.62 31	98.83 28

CNVR-MVS		96.30 1	96.54 1	95.55 12	99.31 5	87.69 19	99.06 9	97.12 22	94.66 3	96.79 9	98.78 11	86.42 24	99.95 3	97.59 9	99.18 5	99.00 23

MCST-MVS		96.17 3	96.12 5	96.32 5	99.42 2	89.36 8	98.94 15	97.10 23	95.17 2	92.11 63	98.46 24	87.33 20	99.97 2	97.21 12	99.31 2	99.63 5

VPA-MVSNet		85.32 182	83.83 186	89.77 184	90.25 244	82.63 100	96.36 161	97.07 24	83.03 170	81.21 185	89.02 227	61.58 241	96.31 210	85.02 135	70.95 268	90.36 224

Regformer-1		94.00 32	94.04 31	93.87 47	98.41 42	84.29 69	97.43 86	97.04 25	89.50 37	92.75 58	98.13 38	82.60 53	99.26 75	93.55 49	96.99 80	98.06 77

DELS-MVS		94.98 11	94.49 19	96.44 4	96.42 99	90.59 5	99.21 2	97.02 26	94.40 5	91.46 71	97.08 96	83.32 43	99.69 36	92.83 59	98.70 29	99.04 21
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
GG-mvs-BLEND				93.49 66	94.94 137	86.26 29	81.62 324	97.00 27		88.32 116	94.30 160	91.23 3	96.21 214	88.49 109	97.43 72	98.00 85

Regformer-2		93.92 33	94.01 32	93.67 56	98.41 42	83.75 79	97.43 86	97.00 27	89.43 39	92.69 59	98.13 38	82.48 54	99.22 78	93.51 50	96.99 80	98.04 78

DPM-MVS		96.21 2	95.53 9	98.26 1	96.26 101	95.09 1	99.15 4	96.98 29	93.39 9	96.45 14	98.79 10	90.17 7	99.99 1	89.33 102	99.25 4	99.70 3

Regformer-3		93.19 41	93.19 42	93.19 77	98.10 58	83.01 96	97.08 116	96.98 29	88.98 43	91.35 76	97.89 57	80.80 60	99.23 76	92.30 65	95.20 105	97.32 127

gg-mvs-nofinetune		85.48 181	82.90 200	93.24 74	94.51 151	85.82 37	79.22 328	96.97 31	61.19 327	87.33 124	53.01 339	90.58 4	96.07 215	86.07 126	97.23 77	97.81 99

NCCC		95.63 5	95.94 6	94.69 24	99.21 7	85.15 56	99.16 3	96.96 32	94.11 6	95.59 21	98.64 19	85.07 28	99.91 4	95.61 27	99.10 7	99.00 23

FIs		86.73 166	86.10 157	88.61 202	90.05 249	80.21 160	96.14 173	96.95 33	85.56 106	78.37 211	92.30 183	76.73 115	95.28 259	79.51 181	79.27 226	90.35 225

PVSNet_0	77.72 15	81.70 236	78.95 249	89.94 178	90.77 238	76.72 242	95.96 179	96.95 33	85.01 119	70.24 286	88.53 235	52.32 290	98.20 132	86.68 125	44.08 340	94.89 186

HPM-MVS++		95.32 9	95.48 10	94.85 20	98.62 33	86.04 32	97.81 54	96.93 35	92.45 11	95.69 20	98.50 22	85.38 27	99.85 10	94.75 37	99.18 5	98.65 38

MSLP-MVS++		94.28 22	94.39 23	93.97 44	98.30 49	84.06 74	98.64 21	96.93 35	90.71 25	93.08 54	98.70 16	79.98 70	99.21 80	94.12 44	99.07 9	98.63 39

Regformer-4		93.06 44	93.12 43	92.89 91	98.10 58	82.20 109	97.08 116	96.92 37	88.87 45	91.23 78	97.89 57	80.57 63	99.19 85	92.21 67	95.20 105	97.29 131

UniMVSNet (Re)		85.31 183	84.23 182	88.55 203	89.75 252	80.55 151	96.72 140	96.89 38	85.42 107	78.40 210	88.93 228	75.38 141	95.52 249	78.58 190	68.02 296	89.57 240

FC-MVSNet-test		85.96 172	85.39 163	87.66 221	89.38 261	78.02 215	95.65 195	96.87 39	85.12 117	77.34 217	91.94 190	76.28 124	94.74 278	77.09 203	78.82 230	90.21 228

EI-MVSNet-Vis-set		91.84 70	91.77 69	92.04 123	97.60 73	81.17 133	96.61 146	96.87 39	88.20 60	89.19 104	97.55 75	78.69 86	99.14 91	90.29 90	90.94 144	95.80 170

IU-MVS					99.03 12	85.34 47		96.86 41	92.05 15	98.74 1				98.15 2	98.97 15	99.42 9

EI-MVSNet-UG-set		91.35 82	91.22 75	91.73 132	97.39 84	80.68 147	96.47 152	96.83 42	87.92 64	88.30 117	97.36 84	77.84 97	99.13 92	89.43 101	89.45 151	95.37 180

ETH3 D test6400		95.56 8	95.41 11	96.00 7	99.02 15	89.42 7	98.75 18	96.80 43	87.28 77	95.88 19	98.95 2	85.92 26	99.41 62	97.15 13	98.95 18	99.18 20

SED-MVS		95.88 4	96.22 3	94.87 19	99.03 12	85.03 58	99.12 6	96.78 44	88.72 49	97.79 3	98.91 3	88.48 14	99.82 16	98.15 2	98.97 15	99.74 1

test_241102_TWO								96.78 44	88.72 49	97.70 5	98.91 3	87.86 17	99.82 16	98.15 2	99.00 13	99.47 7

test_241102_ONE					99.03 12	85.03 58		96.78 44	88.72 49	97.79 3	98.90 6	88.48 14	99.82 16

test0726					99.05 9	85.18 51	99.11 8	96.78 44	88.75 47	97.65 6	98.91 3	87.69 18

DVP-MVS		95.62 6	96.54 1	92.86 92	98.31 48	80.10 163	97.42 88	96.78 44	92.20 13	97.11 8	98.29 28	93.46 1	99.10 94	96.01 20	99.30 3	99.38 10

无先验							96.87 131	96.78 44	77.39 258				99.52 53	79.95 177		98.43 49

test_0728_SECOND				95.14 15	99.04 11	86.14 31	99.06 9	96.77 50					99.84 12	97.90 5	98.85 20	99.45 8

SMA-MVS		94.70 15	94.68 15	94.76 22	98.02 62	85.94 35	97.47 80	96.77 50	85.32 109	97.92 2	98.70 16	83.09 47	99.84 12	95.79 24	99.08 8	98.49 45

test_part1				0.00 337		0.00 357	0.00 348	96.77 50				0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

MVS_111021_LR		91.60 76	91.64 72	91.47 139	95.74 113	78.79 194	96.15 172	96.77 50	88.49 54	88.64 111	97.07 97	72.33 173	99.19 85	93.13 57	96.48 91	96.43 156

3Dnovator	82.32 10	89.33 116	87.64 131	94.42 30	93.73 169	85.70 41	97.73 62	96.75 54	86.73 87	76.21 237	95.93 121	62.17 233	99.68 38	81.67 165	97.81 64	97.88 92

DPE-MVS		95.32 9	95.55 8	94.64 25	98.79 20	84.87 63	97.77 56	96.74 55	86.11 91	96.54 13	98.89 7	88.39 16	99.74 28	97.67 8	99.05 10	99.31 14

PVSNet_BlendedMVS		90.05 105	89.96 96	90.33 164	97.47 78	83.86 76	98.02 43	96.73 56	87.98 63	89.53 100	89.61 222	76.42 120	99.57 49	94.29 42	79.59 223	87.57 289

PVSNet_Blended		93.13 42	92.98 46	93.57 61	97.47 78	83.86 76	99.32 1	96.73 56	91.02 23	89.53 100	96.21 116	76.42 120	99.57 49	94.29 42	95.81 101	97.29 131

ACMMP		90.39 100	89.97 95	91.64 134	97.58 75	78.21 211	96.78 137	96.72 58	84.73 125	84.72 143	97.23 90	71.22 182	99.63 44	88.37 112	92.41 133	97.08 136
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
新几何1				93.12 79	97.44 80	81.60 128		96.71 59	74.54 277	91.22 79	97.57 71	79.13 80	99.51 56	77.40 202	98.46 38	98.26 62

HFP-MVS		92.89 47	92.86 49	92.98 86	98.71 22	81.12 134	97.58 72	96.70 60	85.20 115	91.75 67	97.97 54	78.47 87	99.71 32	90.95 75	98.41 43	98.12 73

#test#		92.99 45	92.99 45	92.98 86	98.71 22	81.12 134	97.77 56	96.70 60	85.75 100	91.75 67	97.97 54	78.47 87	99.71 32	91.36 71	98.41 43	98.12 73

ACMMPR		92.69 56	92.67 53	92.75 96	98.66 27	80.57 150	97.58 72	96.69 62	85.20 115	91.57 70	97.92 56	77.01 110	99.67 40	90.95 75	98.41 43	98.00 85

DeepPCF-MVS	89.82 1	94.61 16	96.17 4	89.91 179	97.09 93	70.21 298	98.99 14	96.69 62	95.57 1	95.08 28	99.23 1	86.40 25	99.87 8	97.84 7	98.66 30	99.65 4

thisisatest0530		89.65 111	89.02 111	91.53 137	93.46 174	80.78 144	96.52 149	96.67 64	81.69 191	83.79 156	94.90 150	88.85 12	97.68 148	77.80 193	87.49 171	96.14 164

tttt0517		88.57 135	88.19 121	89.71 185	93.00 183	75.99 251	95.67 193	96.67 64	80.78 201	81.82 181	94.40 158	88.97 11	97.58 153	76.05 217	86.31 177	95.57 176

thisisatest0515		90.95 88	90.26 88	93.01 85	94.03 164	84.27 71	97.91 47	96.67 64	83.18 166	86.87 129	95.51 133	88.66 13	97.85 143	80.46 171	89.01 154	96.92 141

1121		90.66 93	89.82 101	93.16 78	97.39 84	81.71 125	93.33 252	96.66 67	74.45 278	91.38 72	97.55 75	79.27 76	99.52 53	79.95 177	98.43 40	98.26 62

ACMMP_NAP		93.46 39	93.23 41	94.17 39	97.16 91	84.28 70	96.82 134	96.65 68	86.24 89	94.27 40	97.99 50	77.94 95	99.83 15	93.39 51	98.57 32	98.39 51

TEST9					98.64 30	83.71 80	97.82 52	96.65 68	84.29 141	95.16 25	98.09 43	84.39 31	99.36 70

train_agg		94.28 22	94.45 20	93.74 51	98.64 30	83.71 80	97.82 52	96.65 68	84.50 133	95.16 25	98.09 43	84.33 32	99.36 70	95.91 23	98.96 17	98.16 68

1314		88.94 122	87.20 143	94.17 39	93.21 177	85.73 40	93.33 252	96.64 71	82.89 173	75.98 240	96.36 114	66.83 209	99.39 63	83.52 152	96.02 97	97.39 125

DeepC-MVS_fast	89.06 2	94.48 18	94.30 26	95.02 17	98.86 18	85.68 42	98.06 40	96.64 71	93.64 8	91.74 69	98.54 20	80.17 69	99.90 5	92.28 66	98.75 26	99.49 6
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_8					98.63 32	83.64 83	97.81 54	96.63 73	84.50 133	95.10 27	98.11 42	84.33 32	99.23 76

testtj		94.09 29	94.08 29	94.09 42	99.28 6	83.32 90	97.59 71	96.61 74	83.60 161	94.77 36	98.46 24	82.72 51	99.64 42	95.29 32	98.42 41	99.32 13

原ACMM1				91.22 145	97.77 70	78.10 214		96.61 74	81.05 197	91.28 77	97.42 82	77.92 96	98.98 101	79.85 180	98.51 34	96.59 152

MAR-MVS		90.63 94	90.22 89	91.86 128	98.47 41	78.20 212	97.18 101	96.61 74	83.87 153	88.18 118	98.18 32	68.71 198	99.75 26	83.66 148	97.15 78	97.63 110
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
SteuartSystems-ACMMP		94.13 27	94.44 21	93.20 76	95.41 121	81.35 131	99.02 13	96.59 77	89.50 37	94.18 43	98.36 27	83.68 40	99.45 60	94.77 36	98.45 39	98.81 29
Skip Steuart: Steuart Systems R&D Blog.
D2MVS		82.67 223	81.55 218	86.04 253	87.77 276	76.47 243	95.21 207	96.58 78	82.66 178	70.26 285	85.46 282	60.39 244	95.80 231	76.40 212	79.18 227	85.83 311

save fliter					98.24 51	83.34 88	98.61 23	96.57 79	91.32 18

TESTMET0.1,1		89.83 107	89.34 108	91.31 140	92.54 195	80.19 161	97.11 110	96.57 79	86.15 90	86.85 130	91.83 192	79.32 74	96.95 186	81.30 166	92.35 134	96.77 147

agg_prior1		94.10 28	94.31 25	93.48 67	98.59 34	83.13 92	97.77 56	96.56 81	84.38 137	94.19 41	98.13 38	84.66 30	99.16 89	95.74 25	98.74 27	98.15 70

agg_prior					98.59 34	83.13 92		96.56 81		94.19 41			99.16 89

DWT-MVSNet_test		90.52 99	89.80 102	92.70 100	95.73 115	82.20 109	93.69 243	96.55 83	88.34 57	87.04 128	95.34 136	86.53 22	97.55 155	76.32 214	88.66 159	98.34 52

旧先验1					97.39 84	79.58 176		96.54 84			98.08 46	84.00 36			97.42 73	97.62 111

WR-MVS_H		81.02 244	80.09 237	83.79 281	88.08 274	71.26 293	94.46 226	96.54 84	80.08 219	72.81 269	86.82 258	70.36 191	92.65 307	64.18 286	67.50 302	87.46 293

ETH3D-3000-0.1		94.43 19	94.42 22	94.45 28	97.78 69	85.78 38	97.98 44	96.53 86	85.29 112	95.45 22	98.81 8	83.36 42	99.38 64	96.07 19	98.53 33	98.19 65

9.14			94.26 27		98.10 58		98.14 34	96.52 87	84.74 124	94.83 34	98.80 9	82.80 50	99.37 68	95.95 22	98.42 41

region2R		92.72 54	92.70 52	92.79 95	98.68 24	80.53 153	97.53 76	96.51 88	85.22 113	91.94 65	97.98 52	77.26 105	99.67 40	90.83 79	98.37 48	98.18 66

EPP-MVSNet		89.76 109	89.72 103	89.87 180	93.78 166	76.02 250	97.22 95	96.51 88	79.35 231	85.11 138	95.01 148	84.82 29	97.10 182	87.46 118	88.21 165	96.50 154

ZNCC-MVS		92.75 50	92.60 55	93.23 75	98.24 51	81.82 120	97.63 67	96.50 90	85.00 120	91.05 81	97.74 63	78.38 89	99.80 19	90.48 84	98.34 50	98.07 76

test11								96.50 90

EPNet_dtu		87.65 151	87.89 125	86.93 239	94.57 145	71.37 292	96.72 140	96.50 90	88.56 53	87.12 126	95.02 147	75.91 130	94.01 291	66.62 274	90.00 148	95.42 179
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testdata				90.13 169	95.92 110	74.17 267		96.49 93	73.49 286	94.82 35	97.99 50	78.80 84	97.93 137	83.53 151	97.52 68	98.29 59

MSP-MVS		95.58 7	95.91 7	94.57 26	99.05 9	85.18 51	99.06 9	96.46 94	88.75 47	96.69 10	98.76 12	87.69 18	99.76 20	97.90 5	98.85 20	98.77 30

test222					96.15 104	78.41 202	95.87 186	96.46 94	71.97 297	89.66 97	97.45 78	76.33 123			98.24 53	98.30 58

XVS		92.69 56	92.71 50	92.63 102	98.52 37	80.29 156	97.37 91	96.44 96	87.04 84	91.38 72	97.83 60	77.24 107	99.59 47	90.46 85	98.07 57	98.02 80

X-MVStestdata		86.26 170	84.14 184	92.63 102	98.52 37	80.29 156	97.37 91	96.44 96	87.04 84	91.38 72	20.73 349	77.24 107	99.59 47	90.46 85	98.07 57	98.02 80

SF-MVS		94.17 25	94.05 30	94.55 27	97.56 76	85.95 33	97.73 62	96.43 98	84.02 147	95.07 29	98.74 14	82.93 48	99.38 64	95.42 29	98.51 34	98.32 54

TSAR-MVS + MP.		94.79 14	95.17 12	93.64 57	97.66 71	84.10 73	95.85 188	96.42 99	91.26 20	97.49 7	96.80 107	86.50 23	98.49 123	95.54 28	99.03 11	98.33 53
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ETH3D cwj APD-0.16		93.91 35	93.76 33	94.36 31	96.70 97	85.74 39	97.22 95	96.41 100	83.94 150	94.13 44	98.69 18	83.13 46	99.37 68	95.25 33	98.39 46	97.97 88

APDe-MVS		94.56 17	94.75 14	93.96 45	98.84 19	83.40 87	98.04 42	96.41 100	85.79 99	95.00 31	98.28 29	84.32 35	99.18 87	97.35 11	98.77 25	99.28 15

UniMVSNet_NR-MVSNet		85.49 180	84.59 175	88.21 212	89.44 260	79.36 177	96.71 142	96.41 100	85.22 113	78.11 213	90.98 203	76.97 111	95.14 266	79.14 186	68.30 293	90.12 230

test_prior3		94.03 31	94.34 24	93.09 81	98.68 24	81.91 114	98.37 27	96.40 103	86.08 93	94.57 38	98.02 48	83.14 44	99.06 96	95.05 34	98.79 23	98.29 59

test_prior				93.09 81	98.68 24	81.91 114		96.40 103					99.06 96			98.29 59

CP-MVS		92.54 61	92.60 55	92.34 111	98.50 39	79.90 166	98.40 26	96.40 103	84.75 123	90.48 87	98.09 43	77.40 104	99.21 80	91.15 74	98.23 54	97.92 91

CANet		94.89 12	94.64 16	95.63 10	97.55 77	88.12 13	99.06 9	96.39 106	94.07 7	95.34 24	97.80 61	76.83 113	99.87 8	97.08 14	97.64 67	98.89 26

GST-MVS		92.43 63	92.22 63	93.04 84	98.17 55	81.64 127	97.40 90	96.38 107	84.71 126	90.90 83	97.40 83	77.55 101	99.76 20	89.75 96	97.74 65	97.72 102

alignmvs		92.97 46	92.26 61	95.12 16	95.54 118	87.77 17	98.67 19	96.38 107	88.04 62	93.01 55	97.45 78	79.20 79	98.60 117	93.25 56	88.76 157	98.99 25

PAPM		92.87 49	92.40 57	94.30 33	92.25 204	87.85 16	96.40 160	96.38 107	91.07 21	88.72 110	96.90 100	82.11 55	97.37 167	90.05 92	97.70 66	97.67 106

test12				94.25 35	98.34 46	85.55 44		96.35 110		92.36 61		80.84 59	99.22 78		98.31 51	97.98 87

zzz-MVS		92.74 51	92.71 50	92.86 92	97.90 64	80.85 142	96.47 152	96.33 111	87.92 64	90.20 90	98.18 32	76.71 116	99.76 20	92.57 63	98.09 55	97.96 89

MTGPA								96.33 111

MTAPA		92.45 62	92.31 59	92.86 92	97.90 64	80.85 142	92.88 265	96.33 111	87.92 64	90.20 90	98.18 32	76.71 116	99.76 20	92.57 63	98.09 55	97.96 89

ET-MVSNet_ETH3D		90.01 106	89.03 110	92.95 88	94.38 154	86.77 26	98.14 34	96.31 114	89.30 40	63.33 312	96.72 110	90.09 8	93.63 299	90.70 82	82.29 213	98.46 46

EPMVS		87.47 153	85.90 160	92.18 118	95.41 121	82.26 108	87.00 309	96.28 115	85.88 98	84.23 148	85.57 279	75.07 149	96.26 211	71.14 255	92.50 131	98.03 79

CDPH-MVS		93.12 43	92.91 47	93.74 51	98.65 29	83.88 75	97.67 66	96.26 116	83.00 171	93.22 52	98.24 30	81.31 57	99.21 80	89.12 103	98.74 27	98.14 71

WR-MVS		84.32 197	82.96 198	88.41 205	89.38 261	80.32 155	96.59 147	96.25 117	83.97 149	76.63 227	90.36 213	67.53 202	94.86 276	75.82 220	70.09 278	90.06 234

UGNet		87.73 150	86.55 154	91.27 143	95.16 130	79.11 185	96.35 162	96.23 118	88.14 61	87.83 121	90.48 209	50.65 293	99.09 95	80.13 176	94.03 113	95.60 175
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
tfpnnormal		78.14 267	75.42 272	86.31 249	88.33 271	79.24 180	94.41 228	96.22 119	73.51 284	69.81 287	85.52 281	55.43 280	95.75 234	47.65 333	67.86 298	83.95 321

MP-MVS		92.61 59	92.67 53	92.42 109	98.13 57	79.73 172	97.33 93	96.20 120	85.63 102	90.53 85	97.66 65	78.14 93	99.70 35	92.12 68	98.30 52	97.85 95
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PAPR		92.74 51	92.17 64	94.45 28	98.89 17	84.87 63	97.20 99	96.20 120	87.73 70	88.40 114	98.12 41	78.71 85	99.76 20	87.99 114	96.28 92	98.74 31

SD-MVS		94.84 13	95.02 13	94.29 34	97.87 68	84.61 66	97.76 60	96.19 122	89.59 36	96.66 12	98.17 36	84.33 32	99.60 46	96.09 18	98.50 37	98.66 37
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
CHOSEN 280x420		91.71 73	91.85 66	91.29 142	94.94 137	82.69 99	87.89 304	96.17 123	85.94 96	87.27 125	94.31 159	90.27 6	95.65 241	94.04 45	95.86 99	95.53 177

CHOSEN 1792x2688		91.07 86	90.21 90	93.64 57	95.18 129	83.53 84	96.26 168	96.13 124	88.92 44	84.90 140	93.10 178	72.86 168	99.62 45	88.86 104	95.67 102	97.79 100

PAPM_NR		91.46 78	90.82 80	93.37 71	98.50 39	81.81 121	95.03 217	96.13 124	84.65 129	86.10 135	97.65 69	79.24 78	99.75 26	83.20 155	96.88 85	98.56 42

CostFormer		89.08 119	88.39 120	91.15 146	93.13 180	79.15 184	88.61 300	96.11 126	83.14 167	89.58 99	86.93 257	83.83 39	96.87 192	88.22 113	85.92 183	97.42 122

mPP-MVS		91.88 69	91.82 67	92.07 121	98.38 44	78.63 196	97.29 94	96.09 127	85.12 117	88.45 113	97.66 65	75.53 135	99.68 38	89.83 94	98.02 60	97.88 92

APD-MVS		93.61 37	93.59 36	93.69 54	98.76 21	83.26 91	97.21 97	96.09 127	82.41 181	94.65 37	98.21 31	81.96 56	98.81 112	94.65 39	98.36 49	99.01 22
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MDTV_nov1_ep13			83.69 187		94.09 160	81.01 137	86.78 311	96.09 127	83.81 155	84.75 142	84.32 296	74.44 154	96.54 201	63.88 288	85.07 192

QAPM		86.88 160	84.51 176	93.98 43	94.04 162	85.89 36	97.19 100	96.05 130	73.62 283	75.12 251	95.62 130	62.02 236	99.74 28	70.88 256	96.06 96	96.30 163

MP-MVS-pluss		92.58 60	92.35 58	93.29 72	97.30 89	82.53 102	96.44 156	96.04 131	84.68 127	89.12 105	98.37 26	77.48 103	99.74 28	93.31 55	98.38 47	97.59 113
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
tpm2		87.35 154	86.26 156	90.62 159	92.93 186	78.67 195	88.06 303	95.99 132	79.33 232	87.40 122	86.43 268	80.28 66	96.40 205	80.23 174	85.73 187	96.79 145

DeepC-MVS	86.58 3	91.53 77	91.06 78	92.94 89	94.52 148	81.89 116	95.95 180	95.98 133	90.76 24	83.76 157	96.76 108	73.24 166	99.71 32	91.67 70	96.96 82	97.22 134
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test-LLR		88.48 136	87.98 124	89.98 175	92.26 202	77.23 234	97.11 110	95.96 134	83.76 156	86.30 133	91.38 195	72.30 174	96.78 197	80.82 168	91.92 138	95.94 167

test-mter		88.95 121	88.60 117	89.98 175	92.26 202	77.23 234	97.11 110	95.96 134	85.32 109	86.30 133	91.38 195	76.37 122	96.78 197	80.82 168	91.92 138	95.94 167

DP-MVS Recon		91.72 72	90.85 79	94.34 32	99.50 1	85.00 60	98.51 25	95.96 134	80.57 206	88.08 119	97.63 70	76.84 112	99.89 7	85.67 128	94.88 109	98.13 72

cdsmvs_eth3d_5k		21.43 320	28.57 322	0.00 337	0.00 356	0.00 357	0.00 348	95.93 137	0.00 352	0.00 353	97.66 65	63.57 226	0.00 354	0.00 351	0.00 351	0.00 350

TAMVS		88.48 136	87.79 128	90.56 160	91.09 232	79.18 182	96.45 155	95.88 138	83.64 159	83.12 163	93.33 174	75.94 129	95.74 237	82.40 160	88.27 164	96.75 149

PVSNet_Blended_VisFu		91.24 83	90.77 81	92.66 101	95.09 131	82.40 105	97.77 56	95.87 139	88.26 59	86.39 131	93.94 167	76.77 114	99.27 73	88.80 106	94.00 116	96.31 162

OpenMVS	79.58 14	86.09 171	83.62 191	93.50 65	90.95 234	86.71 28	97.44 82	95.83 140	75.35 269	72.64 270	95.72 125	57.42 269	99.64 42	71.41 250	95.85 100	94.13 198

CDS-MVSNet		89.50 113	88.96 113	91.14 147	91.94 220	80.93 140	97.09 114	95.81 141	84.26 142	84.72 143	94.20 162	80.31 65	95.64 242	83.37 153	88.96 155	96.85 144
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PS-MVSNAJ		94.17 25	93.52 38	96.10 6	95.65 116	92.35 2	98.21 32	95.79 142	92.42 12	96.24 15	98.18 32	71.04 185	99.17 88	96.77 15	97.39 74	96.79 145

SR-MVS		92.16 65	92.27 60	91.83 131	98.37 45	78.41 202	96.67 145	95.76 143	82.19 185	91.97 64	98.07 47	76.44 119	98.64 116	93.71 46	97.27 76	98.45 48

3Dnovator+	82.88 8	89.63 112	87.85 126	94.99 18	94.49 152	86.76 27	97.84 51	95.74 144	86.10 92	75.47 248	96.02 120	65.00 220	99.51 56	82.91 159	97.07 79	98.72 36

HPM-MVS		91.62 75	91.53 73	91.89 127	97.88 67	79.22 181	96.99 121	95.73 145	82.07 186	89.50 102	97.19 92	75.59 134	98.93 108	90.91 77	97.94 61	97.54 114
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ab-mvs		87.08 156	84.94 172	93.48 67	93.34 176	83.67 82	88.82 297	95.70 146	81.18 195	84.55 146	90.14 218	62.72 230	98.94 107	85.49 130	82.54 212	97.85 95

xiu_mvs_v2_base		93.92 33	93.26 40	95.91 8	95.07 133	92.02 4	98.19 33	95.68 147	92.06 14	96.01 18	98.14 37	70.83 188	98.96 102	96.74 16	96.57 90	96.76 148

CP-MVSNet		81.01 245	80.08 238	83.79 281	87.91 275	70.51 295	94.29 236	95.65 148	80.83 200	72.54 272	88.84 229	63.71 225	92.32 310	68.58 268	68.36 292	88.55 267

PatchmatchNet		86.83 162	85.12 169	91.95 125	94.12 159	82.27 107	86.55 313	95.64 149	84.59 131	82.98 166	84.99 291	77.26 105	95.96 222	68.61 267	91.34 142	97.64 109
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
API-MVS		90.18 104	88.97 112	93.80 49	98.66 27	82.95 97	97.50 79	95.63 150	75.16 272	86.31 132	97.69 64	72.49 171	99.90 5	81.26 167	96.07 95	98.56 42

AdaColmap		88.81 127	87.61 134	92.39 110	99.33 4	79.95 164	96.70 144	95.58 151	77.51 257	83.05 165	96.69 111	61.90 240	99.72 31	84.29 138	93.47 122	97.50 119

SCA		85.63 178	83.64 190	91.60 136	92.30 200	81.86 118	92.88 265	95.56 152	84.85 121	82.52 167	85.12 289	58.04 261	95.39 252	73.89 236	87.58 170	97.54 114

dp		84.30 198	82.31 208	90.28 165	94.24 157	77.97 216	86.57 312	95.53 153	79.94 223	80.75 188	85.16 287	71.49 181	96.39 206	63.73 289	83.36 201	96.48 155

HyFIR lowres test		89.36 115	88.60 117	91.63 135	94.91 139	80.76 145	95.60 196	95.53 153	82.56 180	84.03 150	91.24 198	78.03 94	96.81 195	87.07 122	88.41 163	97.32 127

APD-MVS_3200maxsize		91.23 84	91.35 74	90.89 153	97.89 66	76.35 247	96.30 166	95.52 155	79.82 224	91.03 82	97.88 59	74.70 152	98.54 120	92.11 69	96.89 84	97.77 101

lupinMVS		93.87 36	93.58 37	94.75 23	93.00 183	88.08 14	99.15 4	95.50 156	91.03 22	94.90 32	97.66 65	78.84 82	97.56 154	94.64 40	97.46 69	98.62 40

HPM-MVS_fast		90.38 102	90.17 92	91.03 149	97.61 72	77.35 232	97.15 106	95.48 157	79.51 229	88.79 109	96.90 100	71.64 179	98.81 112	87.01 123	97.44 71	96.94 138

VPNet		84.69 191	82.92 199	90.01 173	89.01 263	83.45 86	96.71 142	95.46 158	85.71 101	79.65 201	92.18 185	56.66 273	96.01 218	83.05 158	67.84 299	90.56 221

114514_t		88.79 129	87.57 135	92.45 107	98.21 54	81.74 123	96.99 121	95.45 159	75.16 272	82.48 168	95.69 127	68.59 199	98.50 122	80.33 172	95.18 107	97.10 135

JIA-IIPM		79.00 262	77.20 259	84.40 276	89.74 254	64.06 321	75.30 336	95.44 160	62.15 322	81.90 179	59.08 337	78.92 81	95.59 246	66.51 277	85.78 186	93.54 205

DU-MVS		84.57 193	83.33 196	88.28 210	88.76 264	79.36 177	96.43 158	95.41 161	85.42 107	78.11 213	90.82 204	67.61 200	95.14 266	79.14 186	68.30 293	90.33 226

EI-MVSNet		85.80 175	85.20 166	87.59 223	91.55 225	77.41 230	95.13 211	95.36 162	80.43 210	80.33 194	94.71 152	73.72 162	95.97 219	76.96 206	78.64 232	89.39 242

MVSTER		89.25 118	88.92 114	90.24 166	95.98 109	84.66 65	96.79 136	95.36 162	87.19 82	80.33 194	90.61 208	90.02 9	95.97 219	85.38 131	78.64 232	90.09 232

CPTT-MVS		89.72 110	89.87 100	89.29 190	98.33 47	73.30 271	97.70 64	95.35 164	75.68 268	87.40 122	97.44 81	70.43 190	98.25 130	89.56 99	96.90 83	96.33 161

EIA-MVS		91.73 71	92.05 65	90.78 156	94.52 148	76.40 246	98.06 40	95.34 165	89.19 41	88.90 108	97.28 89	77.56 100	97.73 147	90.77 80	96.86 88	98.20 64

RRT_test8_iter05		87.14 155	86.41 155	89.32 189	94.41 153	81.10 136	97.06 118	95.33 166	84.67 128	76.27 235	90.48 209	83.60 41	96.33 208	85.10 132	70.78 269	90.53 222

tpmvs		83.04 217	80.77 227	89.84 181	95.43 120	77.96 217	85.59 318	95.32 167	75.31 271	76.27 235	83.70 301	73.89 159	97.41 165	59.53 300	81.93 214	94.14 197

PS-CasMVS		80.27 251	79.18 246	83.52 288	87.56 279	69.88 300	94.08 238	95.29 168	80.27 215	72.08 274	88.51 236	59.22 254	92.23 312	67.49 270	68.15 295	88.45 271

TSAR-MVS + GP.		94.35 21	94.50 18	93.89 46	97.38 87	83.04 95	98.10 37	95.29 168	91.57 16	93.81 46	97.45 78	86.64 21	99.43 61	96.28 17	94.01 115	99.20 18

CS-MVS		92.88 48	93.09 44	92.26 115	95.21 127	80.70 146	98.84 16	95.26 170	88.83 46	92.50 60	97.48 77	77.49 102	97.63 150	95.34 31	96.88 85	98.46 46

tpmrst		88.36 140	87.38 141	91.31 140	94.36 155	79.92 165	87.32 307	95.26 170	85.32 109	88.34 115	86.13 273	80.60 62	96.70 199	83.78 142	85.34 191	97.30 130

ETV-MVS		92.72 54	92.87 48	92.28 114	94.54 147	81.89 116	97.98 44	95.21 172	89.77 35	93.11 53	96.83 104	77.23 109	97.50 161	95.74 25	95.38 103	97.44 121

NR-MVSNet		83.35 209	81.52 220	88.84 197	88.76 264	81.31 132	94.45 227	95.16 173	84.65 129	67.81 293	90.82 204	70.36 191	94.87 275	74.75 227	66.89 308	90.33 226

MVS_0304		78.43 264	76.70 264	83.60 286	88.22 272	69.81 301	92.91 264	95.10 174	72.32 296	78.71 208	80.29 317	33.78 335	93.37 303	68.77 266	80.23 219	87.63 286

jason		92.73 53	92.23 62	94.21 38	90.50 241	87.30 23	98.65 20	95.09 175	90.61 26	92.76 57	97.13 94	75.28 145	97.30 170	93.32 54	96.75 89	98.02 80
jason: jason.
tpm cat1		83.63 206	81.38 221	90.39 163	93.53 173	78.19 213	85.56 319	95.09 175	70.78 302	78.51 209	83.28 304	74.80 151	97.03 183	66.77 273	84.05 196	95.95 166

cascas		86.50 167	84.48 178	92.55 105	92.64 193	85.95 33	97.04 120	95.07 177	75.32 270	80.50 190	91.02 201	54.33 288	97.98 136	86.79 124	87.62 168	93.71 204

abl_6		89.80 108	89.71 104	90.07 170	96.53 98	75.52 256	94.48 225	95.04 178	81.12 196	89.22 103	97.00 98	68.83 197	98.96 102	89.86 93	95.27 104	95.73 172

CVMVSNet		84.83 189	85.57 161	82.63 295	91.55 225	60.38 330	95.13 211	95.03 179	80.60 205	82.10 177	94.71 152	66.40 211	90.19 328	74.30 233	90.32 147	97.31 129

test0.0.03 1		82.79 221	82.48 206	83.74 283	86.81 283	72.22 278	96.52 149	95.03 179	83.76 156	73.00 266	93.20 175	72.30 174	88.88 330	64.15 287	77.52 240	90.12 230

RRT_MVS		86.89 159	85.96 158	89.68 186	95.01 136	84.13 72	96.33 164	94.98 181	84.20 144	80.10 198	92.07 186	70.52 189	95.01 273	83.30 154	77.14 241	89.91 236

PMMVS		89.46 114	89.92 98	88.06 214	94.64 143	69.57 305	96.22 170	94.95 182	87.27 78	91.37 75	96.54 113	65.88 212	97.39 166	88.54 107	93.89 117	97.23 133

Anonymous20240529		83.15 214	80.60 231	90.80 155	95.74 113	78.27 206	96.81 135	94.92 183	60.10 332	81.89 180	92.54 182	45.82 310	98.82 111	79.25 185	78.32 237	95.31 182

mvs_anonymous		88.68 130	87.62 133	91.86 128	94.80 141	81.69 126	93.53 248	94.92 183	82.03 187	78.87 207	90.43 212	75.77 131	95.34 255	85.04 134	93.16 126	98.55 44

CLD-MVS		87.97 147	87.48 138	89.44 187	92.16 209	80.54 152	98.14 34	94.92 183	91.41 17	79.43 202	95.40 135	62.34 232	97.27 173	90.60 83	82.90 207	90.50 223
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
xiu_mvs_v1_base_debu		90.54 96	89.54 105	93.55 62	92.31 197	87.58 20	96.99 121	94.87 186	87.23 79	93.27 49	97.56 72	57.43 266	98.32 127	92.72 60	93.46 123	94.74 190

xiu_mvs_v1_base		90.54 96	89.54 105	93.55 62	92.31 197	87.58 20	96.99 121	94.87 186	87.23 79	93.27 49	97.56 72	57.43 266	98.32 127	92.72 60	93.46 123	94.74 190

xiu_mvs_v1_base_debi		90.54 96	89.54 105	93.55 62	92.31 197	87.58 20	96.99 121	94.87 186	87.23 79	93.27 49	97.56 72	57.43 266	98.32 127	92.72 60	93.46 123	94.74 190

GA-MVS		85.79 176	84.04 185	91.02 150	89.47 259	80.27 158	96.90 130	94.84 189	85.57 103	80.88 186	89.08 225	56.56 274	96.47 204	77.72 196	85.35 190	96.34 159

TranMVSNet+NR-MVSNet		83.24 213	81.71 216	87.83 217	87.71 277	78.81 193	96.13 175	94.82 190	84.52 132	76.18 238	90.78 206	64.07 224	94.60 281	74.60 231	66.59 310	90.09 232

HQP3-MVS								94.80 191							83.01 204

HQP-MVS		87.91 149	87.55 136	88.98 195	92.08 211	78.48 198	97.63 67	94.80 191	90.52 27	82.30 171	94.56 155	65.40 216	97.32 168	87.67 116	83.01 204	91.13 215

TAPA-MVS	81.61 12	85.02 186	83.67 188	89.06 192	96.79 95	73.27 273	95.92 182	94.79 193	74.81 275	80.47 191	96.83 104	71.07 184	98.19 133	49.82 329	92.57 129	95.71 173
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PEN-MVS		79.47 257	78.26 253	83.08 291	86.36 286	68.58 308	93.85 241	94.77 194	79.76 225	71.37 276	88.55 233	59.79 246	92.46 308	64.50 285	65.40 311	88.19 276

HQP_MVS		87.50 152	87.09 148	88.74 200	91.86 221	77.96 217	97.18 101	94.69 195	89.89 33	81.33 183	94.15 163	64.77 221	97.30 170	87.08 120	82.82 208	90.96 217

plane_prior5								94.69 195					97.30 170	87.08 120	82.82 208	90.96 217

tpm		85.55 179	84.47 179	88.80 199	90.19 246	75.39 258	88.79 298	94.69 195	84.83 122	83.96 153	85.21 285	78.22 92	94.68 280	76.32 214	78.02 239	96.34 159

FMVSNet3		84.71 190	82.71 203	90.70 158	94.55 146	87.71 18	95.92 182	94.67 198	81.73 190	75.82 243	88.08 241	66.99 207	94.47 283	71.23 252	75.38 247	89.91 236

UA-Net		88.92 123	88.48 119	90.24 166	94.06 161	77.18 236	93.04 261	94.66 199	87.39 75	91.09 80	93.89 168	74.92 150	98.18 134	75.83 219	91.43 141	95.35 181

LFMVS		89.27 117	87.64 131	94.16 41	97.16 91	85.52 45	97.18 101	94.66 199	79.17 237	89.63 98	96.57 112	55.35 281	98.22 131	89.52 100	89.54 150	98.74 31

MVS_Test		90.29 103	89.18 109	93.62 59	95.23 125	84.93 61	94.41 228	94.66 199	84.31 139	90.37 89	91.02 201	75.13 147	97.82 144	83.11 157	94.42 111	98.12 73

canonicalmvs		92.27 64	91.22 75	95.41 13	95.80 112	88.31 11	97.09 114	94.64 202	88.49 54	92.99 56	97.31 85	72.68 170	98.57 119	93.38 53	88.58 160	99.36 12

VDDNet		86.44 168	84.51 176	92.22 117	91.56 224	81.83 119	97.10 113	94.64 202	69.50 307	87.84 120	95.19 139	48.01 302	97.92 142	89.82 95	86.92 172	96.89 142

baseline1		88.85 126	87.49 137	92.93 90	95.21 127	86.85 25	95.47 200	94.61 204	87.29 76	83.11 164	94.99 149	80.70 61	96.89 190	82.28 161	73.72 254	95.05 184

PatchT		79.75 253	76.85 263	88.42 204	89.55 257	75.49 257	77.37 334	94.61 204	63.07 319	82.46 169	73.32 331	75.52 136	93.41 302	51.36 324	84.43 194	96.36 157

MS-PatchMatch		83.05 216	81.82 215	86.72 244	89.64 255	79.10 186	94.88 220	94.59 206	79.70 227	70.67 282	89.65 221	50.43 295	96.82 194	70.82 259	95.99 98	84.25 319

baseline		90.76 91	90.10 93	92.74 97	92.90 187	82.56 101	94.60 224	94.56 207	87.69 71	89.06 107	95.67 128	73.76 161	97.51 160	90.43 87	92.23 136	98.16 68

OMC-MVS		88.80 128	88.16 122	90.72 157	95.30 124	77.92 220	94.81 221	94.51 208	86.80 86	84.97 139	96.85 103	67.53 202	98.60 117	85.08 133	87.62 168	95.63 174

MVSFormer		91.36 81	90.57 83	93.73 53	93.00 183	88.08 14	94.80 222	94.48 209	80.74 202	94.90 32	97.13 94	78.84 82	95.10 269	83.77 143	97.46 69	98.02 80

test_djsdf		83.00 219	82.45 207	84.64 270	84.07 315	69.78 302	94.80 222	94.48 209	80.74 202	75.41 249	87.70 245	61.32 242	95.10 269	83.77 143	79.76 220	89.04 256

casdiffmvs		90.95 88	90.39 86	92.63 102	92.82 188	82.53 102	96.83 133	94.47 211	87.69 71	88.47 112	95.56 132	74.04 158	97.54 158	90.90 78	92.74 128	97.83 97

PCF-MVS	84.09 5	86.77 165	85.00 171	92.08 120	92.06 214	83.07 94	92.14 273	94.47 211	79.63 228	76.90 224	94.78 151	71.15 183	99.20 84	72.87 241	91.05 143	93.98 200
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VDD-MVS		88.28 142	87.02 149	92.06 122	95.09 131	80.18 162	97.55 75	94.45 213	83.09 168	89.10 106	95.92 123	47.97 303	98.49 123	93.08 58	86.91 173	97.52 118

PLC	83.97 7	88.00 146	87.38 141	89.83 182	98.02 62	76.46 244	97.16 105	94.43 214	79.26 236	81.98 178	96.28 115	69.36 195	99.27 73	77.71 197	92.25 135	93.77 203
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
FMVSNet2		82.79 221	80.44 233	89.83 182	92.66 190	85.43 46	95.42 201	94.35 215	79.06 240	74.46 255	87.28 249	56.38 276	94.31 286	69.72 262	74.68 251	89.76 238

nrg030		86.79 164	85.43 162	90.87 154	88.76 264	85.34 47	97.06 118	94.33 216	84.31 139	80.45 192	91.98 187	72.36 172	96.36 207	88.48 110	71.13 266	90.93 219

ACMM	80.70 13	83.72 205	82.85 201	86.31 249	91.19 230	72.12 281	95.88 185	94.29 217	80.44 208	77.02 222	91.96 188	55.24 282	97.14 181	79.30 184	80.38 218	89.67 239
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XXY-MVS		83.84 202	82.00 212	89.35 188	87.13 281	81.38 130	95.72 191	94.26 218	80.15 218	75.92 242	90.63 207	61.96 239	96.52 202	78.98 188	73.28 259	90.14 229

cl-mvsnet2		85.11 185	84.17 183	87.92 216	95.06 134	78.82 191	95.51 198	94.22 219	79.74 226	76.77 225	87.92 243	75.96 128	95.68 238	79.93 179	72.42 261	89.27 248

testing_2		76.96 276	73.18 286	88.30 209	75.87 337	79.64 174	89.92 290	94.21 220	80.16 217	51.23 335	75.94 326	33.94 334	95.81 229	82.28 161	75.12 250	89.46 241

OPM-MVS		85.84 174	85.10 170	88.06 214	88.34 270	77.83 223	95.72 191	94.20 221	87.89 67	80.45 192	94.05 165	58.57 257	97.26 174	83.88 141	82.76 210	89.09 253

Vis-MVSNet (Re-imp)		88.88 125	88.87 115	88.91 196	93.89 165	74.43 265	96.93 129	94.19 222	84.39 136	83.22 162	95.67 128	78.24 91	94.70 279	78.88 189	94.40 112	97.61 112

Anonymous20231211		79.72 254	77.19 260	87.33 230	95.59 117	77.16 237	95.18 210	94.18 223	59.31 334	72.57 271	86.20 272	47.89 304	95.66 239	74.53 232	69.24 286	89.18 250

PS-MVSNAJss		84.91 188	84.30 181	86.74 240	85.89 296	74.40 266	94.95 218	94.16 224	83.93 151	76.45 230	90.11 219	71.04 185	95.77 232	83.16 156	79.02 229	90.06 234

LPG-MVS_test		84.20 199	83.49 194	86.33 246	90.88 235	73.06 274	95.28 203	94.13 225	82.20 183	76.31 232	93.20 175	54.83 286	96.95 186	83.72 145	80.83 216	88.98 259

LGP-MVS_train				86.33 246	90.88 235	73.06 274		94.13 225	82.20 183	76.31 232	93.20 175	54.83 286	96.95 186	83.72 145	80.83 216	88.98 259

V42		83.04 217	81.53 219	87.57 225	86.27 289	79.09 187	95.87 186	94.11 227	80.35 212	77.22 220	86.79 260	65.32 218	96.02 217	77.74 195	70.14 274	87.61 288

XVG-OURS-SEG-HR		85.74 177	85.16 168	87.49 228	90.22 245	71.45 291	91.29 282	94.09 228	81.37 193	83.90 155	95.22 137	60.30 245	97.53 159	85.58 129	84.42 195	93.50 206

XVG-OURS		85.18 184	84.38 180	87.59 223	90.42 243	71.73 288	91.06 285	94.07 229	82.00 188	83.29 161	95.08 146	56.42 275	97.55 155	83.70 147	83.42 200	93.49 207

miper_enhance_ethall		85.95 173	85.20 166	88.19 213	94.85 140	79.76 168	96.00 177	94.06 230	82.98 172	77.74 215	88.76 230	79.42 73	95.46 251	80.58 170	72.42 261	89.36 247

v2v482		83.46 208	81.86 214	88.25 211	86.19 290	79.65 173	96.34 163	94.02 231	81.56 192	77.32 218	88.23 238	65.62 213	96.03 216	77.77 194	69.72 282	89.09 253

jajsoiax		82.12 232	81.15 224	85.03 264	84.19 313	70.70 294	94.22 237	93.95 232	83.07 169	73.48 260	89.75 220	49.66 298	95.37 254	82.24 163	79.76 220	89.02 257

v1144		82.90 220	81.27 223	87.78 219	86.29 288	79.07 188	96.14 173	93.93 233	80.05 220	77.38 216	86.80 259	65.50 214	95.93 224	75.21 224	70.13 275	88.33 274

RPMNet		79.32 259	75.75 270	90.06 171	90.16 247	79.75 169	79.02 330	93.92 234	58.43 336	82.27 175	72.55 332	73.03 167	93.67 297	46.10 335	86.25 178	96.75 149

UnsupCasMVSNet_eth		73.25 293	70.57 296	81.30 301	77.53 331	66.33 315	87.24 308	93.89 235	80.38 211	57.90 328	81.59 309	42.91 319	90.56 325	65.18 283	48.51 335	87.01 298

v7n		79.32 259	77.34 258	85.28 261	84.05 316	72.89 277	93.38 250	93.87 236	75.02 274	70.68 281	84.37 295	59.58 249	95.62 244	67.60 269	67.50 302	87.32 295

Vis-MVSNet		88.67 131	87.82 127	91.24 144	92.68 189	78.82 191	96.95 127	93.85 237	87.55 73	87.07 127	95.13 144	63.43 227	97.21 175	77.58 199	96.15 93	97.70 105
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v148		82.41 229	80.89 225	86.99 238	86.18 291	76.81 240	96.27 167	93.82 238	80.49 207	75.28 250	86.11 274	67.32 205	95.75 234	75.48 222	67.03 307	88.42 272

BH-w/o		88.24 143	87.47 139	90.54 161	95.03 135	78.54 197	97.41 89	93.82 238	84.08 145	78.23 212	94.51 157	69.34 196	97.21 175	80.21 175	94.58 110	95.87 169

TR-MVS		86.30 169	84.93 173	90.42 162	94.63 144	77.58 227	96.57 148	93.82 238	80.30 213	82.42 170	95.16 141	58.74 256	97.55 155	74.88 226	87.82 167	96.13 165

v1192		82.31 230	80.55 232	87.60 222	85.94 294	78.47 201	95.85 188	93.80 241	79.33 232	76.97 223	86.51 263	63.33 228	95.87 226	73.11 240	70.13 275	88.46 270

ACMP	81.66 11	84.00 200	83.22 197	86.33 246	91.53 227	72.95 276	95.91 184	93.79 242	83.70 158	73.79 258	92.22 184	54.31 289	96.89 190	83.98 140	79.74 222	89.16 251
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v144192		82.43 226	80.73 228	87.54 226	85.81 297	78.22 208	95.98 178	93.78 243	79.09 239	77.11 221	86.49 264	64.66 223	95.91 225	74.20 234	69.42 283	88.49 268

mvs_tets		81.74 235	80.71 229	84.84 265	84.22 312	70.29 297	93.91 240	93.78 243	82.77 175	73.37 261	89.46 223	47.36 307	95.31 258	81.99 164	79.55 225	88.92 263

F-COLMAP		84.50 195	83.44 195	87.67 220	95.22 126	72.22 278	95.95 180	93.78 243	75.74 267	76.30 234	95.18 140	59.50 250	98.45 125	72.67 243	86.59 176	92.35 212

UniMVSNet_ETH3D		80.86 247	78.75 250	87.22 235	86.31 287	72.02 282	91.95 274	93.76 246	73.51 284	75.06 252	90.16 217	43.04 318	95.66 239	76.37 213	78.55 235	93.98 200

Fast-Effi-MVS+		87.93 148	86.94 151	90.92 152	94.04 162	79.16 183	98.26 30	93.72 247	81.29 194	83.94 154	92.90 179	69.83 194	96.68 200	76.70 208	91.74 140	96.93 139

v1921920		82.02 233	80.23 236	87.41 229	85.62 298	77.92 220	95.79 190	93.69 248	78.86 243	76.67 226	86.44 266	62.50 231	95.83 228	72.69 242	69.77 281	88.47 269

DTE-MVSNet		78.37 265	77.06 261	82.32 298	85.22 305	67.17 313	93.40 249	93.66 249	78.71 245	70.53 283	88.29 237	59.06 255	92.23 312	61.38 297	63.28 318	87.56 290

v8		81.88 234	80.06 240	87.32 231	86.63 284	79.04 189	94.41 228	93.65 250	78.77 244	73.19 265	85.57 279	66.87 208	95.81 229	73.84 238	67.61 301	87.11 296

diffmvs		91.17 85	90.74 82	92.44 108	93.11 182	82.50 104	96.25 169	93.62 251	87.79 68	90.40 88	95.93 121	73.44 165	97.42 164	93.62 48	92.55 130	97.41 123

ADS-MVSNet		81.26 242	78.36 251	89.96 177	93.78 166	79.78 167	79.48 326	93.60 252	73.09 289	80.14 196	79.99 318	62.15 234	95.24 261	59.49 301	83.52 198	94.85 187

PatchMatch-RL		85.00 187	83.66 189	89.02 194	95.86 111	74.55 264	92.49 269	93.60 252	79.30 234	79.29 204	91.47 193	58.53 258	98.45 125	70.22 260	92.17 137	94.07 199

anonymousdsp		80.98 246	79.97 241	84.01 278	81.73 320	70.44 296	92.49 269	93.58 254	77.10 263	72.98 267	86.31 270	57.58 265	94.90 274	79.32 183	78.63 234	86.69 301

miper_ehance_all_eth		84.57 193	83.60 192	87.50 227	92.64 193	78.25 207	95.40 202	93.47 255	79.28 235	76.41 231	87.64 246	76.53 118	95.24 261	78.58 190	72.42 261	89.01 258

v1240		81.70 236	79.83 243	87.30 233	85.50 299	77.70 226	95.48 199	93.44 256	78.46 248	76.53 229	86.44 266	60.85 243	95.84 227	71.59 249	70.17 273	88.35 273

v10		81.43 240	79.53 245	87.11 236	86.38 285	78.87 190	94.31 232	93.43 257	77.88 252	73.24 264	85.26 283	65.44 215	95.75 234	72.14 246	67.71 300	86.72 300

IterMVS-LS		83.93 201	82.80 202	87.31 232	91.46 228	77.39 231	95.66 194	93.43 257	80.44 208	75.51 247	87.26 251	73.72 162	95.16 265	76.99 204	70.72 271	89.39 242
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GBi-Net		82.42 227	80.43 234	88.39 206	92.66 190	81.95 111	94.30 233	93.38 259	79.06 240	75.82 243	85.66 275	56.38 276	93.84 293	71.23 252	75.38 247	89.38 244

test1		82.42 227	80.43 234	88.39 206	92.66 190	81.95 111	94.30 233	93.38 259	79.06 240	75.82 243	85.66 275	56.38 276	93.84 293	71.23 252	75.38 247	89.38 244

FMVSNet1		79.50 256	76.54 266	88.39 206	88.47 269	81.95 111	94.30 233	93.38 259	73.14 288	72.04 275	85.66 275	43.86 312	93.84 293	65.48 281	72.53 260	89.38 244

BH-untuned		86.95 158	85.94 159	89.99 174	94.52 148	77.46 229	96.78 137	93.37 262	81.80 189	76.62 228	93.81 171	66.64 210	97.02 184	76.06 216	93.88 118	95.48 178

Effi-MVS+-dtu		84.61 192	84.90 174	83.72 284	91.96 217	63.14 324	94.95 218	93.34 263	85.57 103	79.79 200	87.12 254	61.99 237	95.61 245	83.55 149	85.83 185	92.41 211

mvs-test1		86.83 162	87.17 144	85.81 255	91.96 217	65.24 317	97.90 49	93.34 263	85.57 103	84.51 147	95.14 143	61.99 237	97.19 177	83.55 149	90.55 146	95.00 185

CMPMVS	54.94 21	75.71 284	74.56 278	79.17 311	79.69 326	55.98 336	89.59 291	93.30 265	60.28 330	53.85 333	89.07 226	47.68 306	96.33 208	76.55 209	81.02 215	85.22 313
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
cl-mvsnet_		83.27 211	82.12 209	86.74 240	92.20 205	75.95 252	95.11 213	93.27 266	78.44 249	74.82 253	87.02 256	74.19 156	95.19 263	74.67 229	69.32 284	89.09 253

cl-mvsnet1		83.27 211	82.12 209	86.74 240	92.19 206	75.92 253	95.11 213	93.26 267	78.44 249	74.81 254	87.08 255	74.19 156	95.19 263	74.66 230	69.30 285	89.11 252

miper_lstm_enhance		81.66 238	80.66 230	84.67 269	91.19 230	71.97 284	91.94 275	93.19 268	77.86 253	72.27 273	85.26 283	73.46 164	93.42 301	73.71 239	67.05 306	88.61 266

eth_miper_zixun_eth		83.12 215	82.01 211	86.47 245	91.85 223	74.80 261	94.33 231	93.18 269	79.11 238	75.74 246	87.25 252	72.71 169	95.32 257	76.78 207	67.13 305	89.27 248

pmmvs4		82.54 225	80.79 226	87.79 218	86.11 292	80.49 154	93.55 247	93.18 269	77.29 260	73.35 262	89.40 224	65.26 219	95.05 272	75.32 223	73.61 255	87.83 282

XVG-ACMP-BASELINE		79.38 258	77.90 255	83.81 280	84.98 307	67.14 314	89.03 296	93.18 269	80.26 216	72.87 268	88.15 240	38.55 327	96.26 211	76.05 217	78.05 238	88.02 279

CANet_DTU		90.98 87	90.04 94	93.83 48	94.76 142	86.23 30	96.32 165	93.12 272	93.11 10	93.71 47	96.82 106	63.08 229	99.48 58	84.29 138	95.12 108	95.77 171

IS-MVSNet		88.67 131	88.16 122	90.20 168	93.61 170	76.86 239	96.77 139	93.07 273	84.02 147	83.62 158	95.60 131	74.69 153	96.24 213	78.43 192	93.66 121	97.49 120

cl_fuxian		83.80 203	82.65 204	87.25 234	92.10 210	77.74 225	95.25 206	93.04 274	78.58 246	76.01 239	87.21 253	75.25 146	95.11 268	77.54 200	68.89 288	88.91 264

UnsupCasMVSNet_bld		68.60 306	64.50 308	80.92 304	74.63 338	67.80 310	83.97 321	92.94 275	65.12 317	54.63 332	68.23 335	35.97 330	92.17 314	60.13 299	44.83 338	82.78 324

MVP-Stereo		82.65 224	81.67 217	85.59 258	86.10 293	78.29 205	93.33 252	92.82 276	77.75 254	69.17 291	87.98 242	59.28 253	95.76 233	71.77 247	96.88 85	82.73 325
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Effi-MVS+		90.70 92	89.90 99	93.09 81	93.61 170	83.48 85	95.20 208	92.79 277	83.22 165	91.82 66	95.70 126	71.82 176	97.48 162	91.25 73	93.67 120	98.32 54

EU-MVSNet		76.92 278	76.95 262	76.83 315	84.10 314	54.73 339	91.77 278	92.71 278	72.74 292	69.57 288	88.69 231	58.03 263	87.43 334	64.91 284	70.00 279	88.33 274

xxxxxxxxxxxxxcwj		94.38 20	94.62 17	93.68 55	98.24 51	83.34 88	98.61 23	92.69 279	91.32 18	95.07 29	98.74 14	82.93 48	99.38 64	95.42 29	98.51 34	98.32 54

pm-mvs1		80.05 252	78.02 254	86.15 251	85.42 300	75.81 254	95.11 213	92.69 279	77.13 261	70.36 284	87.43 248	58.44 259	95.27 260	71.36 251	64.25 314	87.36 294

1112_ss		88.60 134	87.47 139	92.00 124	93.21 177	80.97 139	96.47 152	92.46 281	83.64 159	80.86 187	97.30 87	80.24 67	97.62 151	77.60 198	85.49 188	97.40 124

Test_1112_low_res		88.03 145	86.73 152	91.94 126	93.15 179	80.88 141	96.44 156	92.41 282	83.59 162	80.74 189	91.16 199	80.18 68	97.59 152	77.48 201	85.40 189	97.36 126

BH-RMVSNet		86.84 161	85.28 165	91.49 138	95.35 123	80.26 159	96.95 127	92.21 283	82.86 174	81.77 182	95.46 134	59.34 252	97.64 149	69.79 261	93.81 119	96.57 153

LS3D		82.22 231	79.94 242	89.06 192	97.43 81	74.06 269	93.20 259	92.05 284	61.90 323	73.33 263	95.21 138	59.35 251	99.21 80	54.54 317	92.48 132	93.90 202

EG-PatchMatch MVS		74.92 286	72.02 290	83.62 285	83.76 318	73.28 272	93.62 245	92.04 285	68.57 309	58.88 324	83.80 300	31.87 339	95.57 248	56.97 310	78.67 231	82.00 331

IterMVS		80.67 248	79.16 247	85.20 262	89.79 251	76.08 249	92.97 263	91.86 286	80.28 214	71.20 278	85.14 288	57.93 264	91.34 319	72.52 244	70.74 270	88.18 277
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MIMVSNet		79.18 261	75.99 269	88.72 201	87.37 280	80.66 148	79.96 325	91.82 287	77.38 259	74.33 256	81.87 308	41.78 321	90.74 324	66.36 279	83.10 203	94.76 189

IterMVS-SCA-FT		80.51 250	79.10 248	84.73 267	89.63 256	74.66 262	92.98 262	91.81 288	80.05 220	71.06 280	85.18 286	58.04 261	91.40 318	72.48 245	70.70 272	88.12 278

our_test_3		77.90 270	75.37 273	85.48 260	85.39 301	76.74 241	93.63 244	91.67 289	73.39 287	65.72 303	84.65 294	58.20 260	93.13 305	57.82 307	67.87 297	86.57 302

pmmvs5		81.34 241	79.54 244	86.73 243	85.02 306	76.91 238	96.22 170	91.65 290	77.65 255	73.55 259	88.61 232	55.70 279	94.43 284	74.12 235	73.35 258	88.86 265

ACMH	75.40 17	77.99 268	74.96 274	87.10 237	90.67 239	76.41 245	93.19 260	91.64 291	72.47 295	63.44 311	87.61 247	43.34 315	97.16 178	58.34 305	73.94 253	87.72 283
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Fast-Effi-MVS+-dtu		83.33 210	82.60 205	85.50 259	89.55 257	69.38 306	96.09 176	91.38 292	82.30 182	75.96 241	91.41 194	56.71 271	95.58 247	75.13 225	84.90 193	91.54 213

YYNet1		73.53 292	70.43 297	82.85 293	84.52 310	71.73 288	91.69 280	91.37 293	67.63 310	46.79 336	81.21 311	55.04 284	90.43 326	55.93 313	59.70 323	86.38 304

ppachtmachnet_test		77.19 274	74.22 281	86.13 252	85.39 301	78.22 208	93.98 239	91.36 294	71.74 299	67.11 296	84.87 292	56.67 272	93.37 303	52.21 322	64.59 313	86.80 299

Anonymous202405211		84.41 196	81.93 213	91.85 130	96.78 96	78.41 202	97.44 82	91.34 295	70.29 304	84.06 149	94.26 161	41.09 324	98.96 102	79.46 182	82.65 211	98.17 67

MDA-MVSNet_test_wron		73.54 291	70.43 297	82.86 292	84.55 308	71.85 285	91.74 279	91.32 296	67.63 310	46.73 337	81.09 312	55.11 283	90.42 327	55.91 314	59.76 322	86.31 305

CR-MVSNet		83.53 207	81.36 222	90.06 171	90.16 247	79.75 169	79.02 330	91.12 297	84.24 143	82.27 175	80.35 315	75.45 137	93.67 297	63.37 292	86.25 178	96.75 149

Patchmtry		77.36 273	74.59 277	85.67 257	89.75 252	75.75 255	77.85 333	91.12 297	60.28 330	71.23 277	80.35 315	75.45 137	93.56 300	57.94 306	67.34 304	87.68 285

LTVRE_ROB	73.68 18	77.99 268	75.74 271	84.74 266	90.45 242	72.02 282	86.41 314	91.12 297	72.57 294	66.63 298	87.27 250	54.95 285	96.98 185	56.29 312	75.98 243	85.21 314
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
OurMVSNet-221017-0		77.18 275	76.06 268	80.55 305	83.78 317	60.00 331	90.35 287	91.05 300	77.01 265	66.62 299	87.92 243	47.73 305	94.03 290	71.63 248	68.44 291	87.62 287

CNLPA		86.96 157	85.37 164	91.72 133	97.59 74	79.34 179	97.21 97	91.05 300	74.22 279	78.90 205	96.75 109	67.21 206	98.95 105	74.68 228	90.77 145	96.88 143

pmmvs6		74.65 288	71.67 291	83.60 286	79.13 328	69.94 299	93.31 256	90.88 302	61.05 329	65.83 302	84.15 298	43.43 314	94.83 277	66.62 274	60.63 321	86.02 310

Anonymous20231206		75.29 285	73.64 284	80.22 306	80.75 321	63.38 323	93.36 251	90.71 303	73.09 289	67.12 295	83.70 301	50.33 296	90.85 323	53.63 320	70.10 277	86.44 303

USDC		78.65 263	76.25 267	85.85 254	87.58 278	74.60 263	89.58 292	90.58 304	84.05 146	63.13 313	88.23 238	40.69 326	96.86 193	66.57 276	75.81 245	86.09 309

MSDG		80.62 249	77.77 256	89.14 191	93.43 175	77.24 233	91.89 276	90.18 305	69.86 306	68.02 292	91.94 190	52.21 291	98.84 110	59.32 303	83.12 202	91.35 214

ACMH+	76.62 16	77.47 272	74.94 275	85.05 263	91.07 233	71.58 290	93.26 257	90.01 306	71.80 298	64.76 306	88.55 233	41.62 322	96.48 203	62.35 295	71.00 267	87.09 297

FMVSNet5		76.46 280	74.16 282	83.35 290	90.05 249	76.17 248	89.58 292	89.85 307	71.39 301	65.29 305	80.42 314	50.61 294	87.70 333	61.05 298	69.24 286	86.18 307

ambc				76.02 318	68.11 341	51.43 340	64.97 342	89.59 308		60.49 321	74.49 327	17.17 345	92.46 308	61.50 296	52.85 331	84.17 320

ITE_SJBPF				82.38 296	87.00 282	65.59 316		89.55 309	79.99 222	69.37 289	91.30 197	41.60 323	95.33 256	62.86 294	74.63 252	86.24 306

pmmvs-eth3d		73.59 290	70.66 295	82.38 296	76.40 335	73.38 270	89.39 295	89.43 310	72.69 293	60.34 322	77.79 323	46.43 309	91.26 321	66.42 278	57.06 325	82.51 326

test20.03		72.36 298	71.15 293	75.98 319	77.79 330	59.16 333	92.40 271	89.35 311	74.09 280	61.50 318	84.32 296	48.09 301	85.54 339	50.63 327	62.15 320	83.24 322

SixPastTwentyTwo		76.04 281	74.32 280	81.22 302	84.54 309	61.43 329	91.16 283	89.30 312	77.89 251	64.04 308	86.31 270	48.23 300	94.29 287	63.54 291	63.84 316	87.93 281

TransMVSNet (Re)		76.94 277	74.38 279	84.62 271	85.92 295	75.25 259	95.28 203	89.18 313	73.88 282	67.22 294	86.46 265	59.64 247	94.10 289	59.24 304	52.57 332	84.50 318

MIMVSNet1		69.44 302	66.65 305	77.84 312	76.48 334	62.84 325	87.42 306	88.97 314	66.96 315	57.75 329	79.72 320	32.77 338	85.83 338	46.32 334	63.42 317	84.85 316

K. test v3		73.62 289	71.59 292	79.69 308	82.98 319	59.85 332	90.85 286	88.83 315	77.13 261	58.90 323	82.11 306	43.62 313	91.72 316	65.83 280	54.10 329	87.50 292

Baseline_NR-MVSNet		81.22 243	80.07 239	84.68 268	85.32 304	75.12 260	96.48 151	88.80 316	76.24 266	77.28 219	86.40 269	67.61 200	94.39 285	75.73 221	66.73 309	84.54 317

MDA-MVSNet-bldmvs		71.45 300	67.94 302	81.98 300	85.33 303	68.50 309	92.35 272	88.76 317	70.40 303	42.99 338	81.96 307	46.57 308	91.31 320	48.75 332	54.39 328	86.11 308

new-patchmatchnet		68.85 305	65.93 306	77.61 313	73.57 340	63.94 322	90.11 289	88.73 318	71.62 300	55.08 331	73.60 329	40.84 325	87.22 335	51.35 325	48.49 336	81.67 332

Patchmatch-test		78.25 266	74.72 276	88.83 198	91.20 229	74.10 268	73.91 339	88.70 319	59.89 333	66.82 297	85.12 289	78.38 89	94.54 282	48.84 331	79.58 224	97.86 94

OpenMVS_ROB	68.52 20	73.02 295	69.57 299	83.37 289	80.54 324	71.82 286	93.60 246	88.22 320	62.37 321	61.98 316	83.15 305	35.31 333	95.47 250	45.08 336	75.88 244	82.82 323

RPSCF		77.73 271	76.63 265	81.06 303	88.66 268	55.76 338	87.77 305	87.88 321	64.82 318	74.14 257	92.79 180	49.22 299	96.81 195	67.47 271	76.88 242	90.62 220

MVS-HIRNet		71.36 301	67.00 303	84.46 275	90.58 240	69.74 303	79.15 329	87.74 322	46.09 339	61.96 317	50.50 340	45.14 311	95.64 242	53.74 319	88.11 166	88.00 280

DP-MVS		81.47 239	78.28 252	91.04 148	98.14 56	78.48 198	95.09 216	86.97 323	61.14 328	71.12 279	92.78 181	59.59 248	99.38 64	53.11 321	86.61 175	95.27 183

COLMAP_ROB	73.24 19	75.74 283	73.00 288	83.94 279	92.38 196	69.08 307	91.85 277	86.93 324	61.48 326	65.32 304	90.27 214	42.27 320	96.93 189	50.91 326	75.63 246	85.80 312
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test_0402		72.68 296	69.54 300	82.09 299	88.67 267	71.81 287	92.72 267	86.77 325	61.52 325	62.21 315	83.91 299	43.22 316	93.76 296	34.60 340	72.23 264	80.72 333

testgi		74.88 287	73.40 285	79.32 310	80.13 325	61.75 327	93.21 258	86.64 326	79.49 230	66.56 300	91.06 200	35.51 332	88.67 331	56.79 311	71.25 265	87.56 290

TDRefinement		69.20 304	65.78 307	79.48 309	66.04 343	62.21 326	88.21 302	86.12 327	62.92 320	61.03 320	85.61 278	33.23 336	94.16 288	55.82 315	53.02 330	82.08 330

ADS-MVSNet2		79.57 255	77.53 257	85.71 256	93.78 166	72.13 280	79.48 326	86.11 328	73.09 289	80.14 196	79.99 318	62.15 234	90.14 329	59.49 301	83.52 198	94.85 187

LF4IMVS		72.36 298	70.82 294	76.95 314	79.18 327	56.33 335	86.12 315	86.11 328	69.30 308	63.06 314	86.66 261	33.03 337	92.25 311	65.33 282	68.64 290	82.28 329

TinyColmap		72.41 297	68.99 301	82.68 294	88.11 273	69.59 304	88.41 301	85.20 330	65.55 316	57.91 327	84.82 293	30.80 341	95.94 223	51.38 323	68.70 289	82.49 328

pmmvs3		65.75 308	62.18 310	76.45 317	67.12 342	64.54 318	88.68 299	85.05 331	54.77 338	57.54 330	73.79 328	29.40 342	86.21 337	55.49 316	47.77 337	78.62 334

new_pmnet		66.18 307	63.18 309	75.18 321	76.27 336	61.74 328	83.79 322	84.66 332	56.64 337	51.57 334	71.85 334	31.29 340	87.93 332	49.98 328	62.55 319	75.86 336

AllTest		75.92 282	73.06 287	84.47 273	92.18 207	67.29 311	91.07 284	84.43 333	67.63 310	63.48 309	90.18 215	38.20 328	97.16 178	57.04 308	73.37 256	88.97 261

TestCases				84.47 273	92.18 207	67.29 311		84.43 333	67.63 310	63.48 309	90.18 215	38.20 328	97.16 178	57.04 308	73.37 256	88.97 261

LCM-MVSNet-Re		83.75 204	83.54 193	84.39 277	93.54 172	64.14 320	92.51 268	84.03 335	83.90 152	66.14 301	86.59 262	67.36 204	92.68 306	84.89 136	92.87 127	96.35 158

Gipuma		45.11 314	42.05 315	54.30 328	80.69 322	51.30 341	35.80 346	83.81 336	28.13 343	27.94 344	34.53 344	11.41 350	76.70 343	21.45 343	54.65 327	34.90 344
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		52.52 311	48.24 313	65.35 322	47.63 349	41.45 346	72.55 340	83.62 337	31.75 342	37.66 340	57.92 338	9.19 352	76.76 342	49.26 330	44.60 339	77.84 335

FPMVS		55.09 310	52.93 312	61.57 326	55.98 344	40.51 347	83.11 323	83.41 338	37.61 341	34.95 341	71.95 333	14.40 346	76.95 341	29.81 341	65.16 312	67.25 340

Patchmatch-RL test		76.65 279	74.01 283	84.55 272	77.37 333	64.23 319	78.49 332	82.84 339	78.48 247	64.63 307	73.40 330	76.05 127	91.70 317	76.99 204	57.84 324	97.72 102

DSMNet-mixed		73.13 294	72.45 289	75.19 320	77.51 332	46.82 342	85.09 320	82.01 340	67.61 314	69.27 290	81.33 310	50.89 292	86.28 336	54.54 317	83.80 197	92.46 210

lessismore_v0				79.98 307	80.59 323	58.34 334		80.87 341		58.49 325	83.46 303	43.10 317	93.89 292	63.11 293	48.68 334	87.72 283

door								80.13 342

door-mid								79.75 343

PM-MVS		69.32 303	66.93 304	76.49 316	73.60 339	55.84 337	85.91 316	79.32 344	74.72 276	61.09 319	78.18 322	21.76 343	91.10 322	70.86 257	56.90 326	82.51 326

ANet_high		46.22 313	41.28 317	61.04 327	39.91 351	46.25 344	70.59 341	76.18 345	58.87 335	23.09 345	48.00 342	12.58 348	66.54 346	28.65 342	13.62 345	70.35 338

PMMVS2		50.90 312	46.31 314	64.67 323	55.53 345	46.67 343	77.30 335	71.02 346	40.89 340	34.16 342	59.32 336	9.83 351	76.14 344	40.09 339	28.63 342	71.21 337

MTMP							97.53 76	68.16 347

DeepMVS_CX				64.06 325	78.53 329	43.26 345		68.11 348	69.94 305	38.55 339	76.14 325	18.53 344	79.34 340	43.72 337	41.62 341	69.57 339

PMVS	34.80 23	39.19 316	35.53 318	50.18 329	29.72 352	30.30 349	59.60 344	66.20 349	26.06 344	17.91 347	49.53 341	3.12 353	74.09 345	18.19 345	49.40 333	46.14 341
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		41.54 315	41.93 316	40.38 331	20.10 353	26.84 350	61.93 343	59.09 350	14.81 348	28.51 343	80.58 313	35.53 331	48.33 350	63.70 290	13.11 346	45.96 343

MVE	35.65 22	33.85 317	29.49 321	46.92 330	41.86 350	36.28 348	50.45 345	56.52 351	18.75 347	18.28 346	37.84 343	2.41 354	58.41 347	18.71 344	20.62 343	46.06 342
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		32.70 318	32.39 319	33.65 332	53.35 347	25.70 351	74.07 338	53.33 352	21.08 345	17.17 348	33.63 346	11.85 349	54.84 348	12.98 346	14.04 344	20.42 345

EMVS		31.70 319	31.45 320	32.48 333	50.72 348	23.95 352	74.78 337	52.30 353	20.36 346	16.08 349	31.48 347	12.80 347	53.60 349	11.39 347	13.10 347	19.88 346

N_pmnet		61.30 309	60.20 311	64.60 324	84.32 311	17.00 354	91.67 281	10.98 354	61.77 324	58.45 326	78.55 321	49.89 297	91.83 315	42.27 338	63.94 315	84.97 315

wuyk23d		14.10 321	13.89 323	14.72 334	55.23 346	22.91 353	33.83 347	3.56 355	4.94 349	4.11 350	2.28 352	2.06 355	19.66 351	10.23 348	8.74 348	1.59 349

testmvs		9.92 322	12.94 324	0.84 336	0.65 354	0.29 356	93.78 242	0.39 356	0.42 350	2.85 351	15.84 350	0.17 357	0.30 353	2.18 349	0.21 349	1.91 348

test123		9.07 323	11.73 325	1.11 335	0.50 355	0.77 355	89.44 294	0.20 357	0.34 351	2.15 352	10.72 351	0.34 356	0.32 352	1.79 350	0.08 350	2.23 347

uanet_test		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

pcd_1.5k_mvsjas		5.92 325	7.89 327	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	71.04 185	0.00 354	0.00 351	0.00 351	0.00 350

sosnet-low-res		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

sosnet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uncertanet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

Regformer		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

n2								0.00 358

nn								0.00 358

ab-mvs-re		8.11 324	10.81 326	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	97.30 87	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uanet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

OPU-MVS				97.30 2	99.19 8	92.31 3	99.12 6				98.54 20	92.06 2	99.84 12	99.11 1	99.37 1	99.74 1

test_0728_THIRD									88.38 56	96.69 10	98.76 12	89.64 10	99.76 20	97.47 10	98.84 22	99.38 10

GSMVS																97.54 114

test_part2					98.90 16	85.14 57				96.07 17

sam_mvs1												77.59 99				97.54 114

sam_mvs												75.35 144

test_post1							85.88 317				30.24 348	73.77 160	95.07 271	73.89 236

test_post											33.80 345	76.17 125	95.97 219

patchmatchnet-post											77.09 324	77.78 98	95.39 252

gm-plane-assit					92.27 201	79.64 174			84.47 135		95.15 142		97.93 137	85.81 127

test9_res														96.00 21	99.03 11	98.31 57

agg_prior2														94.30 41	99.00 13	98.57 41

test_prior4						82.34 106	97.75 61

test_prior2							98.37 27		86.08 93	94.57 38	98.02 48	83.14 44		95.05 34	98.79 23

旧先验2							96.97 126		74.06 281	96.10 16			97.76 146	88.38 111

新几何2							96.42 159

原ACMM2							96.84 132

testdata2													99.48 58	76.45 211

segment_acmp												82.69 52

testdata1							95.57 197		87.44 74

plane_prior7					91.86 221	77.55 228

plane_prior6					91.98 216	77.92 220						64.77 221

plane_prior4											94.15 163

plane_prior3						77.75 224			90.17 31	81.33 183

plane_prior2							97.18 101		89.89 33

plane_prior1					91.95 219

plane_prior						77.96 217	97.52 78		90.36 30						82.96 206

HQP5-MVS						78.48 198

HQP-NCC					92.08 211		97.63 67		90.52 27	82.30 171

ACMP_Plane					92.08 211		97.63 67		90.52 27	82.30 171

BP-MVS														87.67 116

HQP4-MVS										82.30 171			97.32 168			91.13 215

HQP2-MVS												65.40 216

NP-MVS					92.04 215	78.22 208					94.56 155

MDTV_nov1_ep13_2view						81.74 123	86.80 310		80.65 204	85.65 136		74.26 155		76.52 210		96.98 137

ACMMP++_ref															78.45 236

ACMMP++															79.05 228

Test By Simon												71.65 178

High-resolution multi-view benchmark