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.

region2R		97.07 21	96.84 23	97.77 34	99.46 1	93.79 53	98.52 10	98.24 34	93.19 74	97.14 37	98.34 37	91.59 52	99.87 7	95.46 61	99.59 14	99.64 10

MSP-MVS		97.91 1	97.81 1	98.22 8	99.45 2	95.36 10	98.21 33	97.85 108	94.92 21	98.73 7	98.87 5	95.08 4	99.84 18	97.52 2	99.67 6	99.48 39

test_0728_SECOND				98.51 2	99.45 2	95.93 3	98.21 33	98.28 26					99.86 8	97.52 2	99.67 6	99.75 3

test0726					99.45 2	95.36 10	98.31 22	98.29 24	94.92 21	98.99 3	98.92 2	95.08 4

ACMMPR		97.07 21	96.84 23	97.79 31	99.44 5	93.88 50	98.52 10	98.31 22	93.21 71	97.15 36	98.33 40	91.35 55	99.86 8	95.63 53	99.59 14	99.62 13

IU-MVS					99.42 6	95.39 9		97.94 98	90.40 165	98.94 4				97.41 6	99.66 8	99.74 5

test_241102_ONE					99.42 6	95.30 15		98.27 28	95.09 17	99.19 1	98.81 7	95.54 3	99.65 52

HFP-MVS		97.14 18	96.92 19	97.83 25	99.42 6	94.12 43	98.52 10	98.32 20	93.21 71	97.18 34	98.29 46	92.08 37	99.83 21	95.63 53	99.59 14	99.54 27

#test#		97.02 25	96.75 30	97.83 25	99.42 6	94.12 43	98.15 36	98.32 20	92.57 98	97.18 34	98.29 46	92.08 37	99.83 21	95.12 67	99.59 14	99.54 27

DVP-MVS		97.59 6	97.54 4	97.73 37	99.40 10	93.77 56	98.53 9	98.29 24	95.55 5	98.56 11	97.81 77	93.90 11	99.65 52	96.62 19	99.21 64	99.77 1

mPP-MVS		96.86 34	96.60 36	97.64 45	99.40 10	93.44 63	98.50 13	98.09 63	93.27 70	95.95 79	98.33 40	91.04 62	99.88 4	95.20 64	99.57 19	99.60 16

MP-MVS		96.77 39	96.45 45	97.72 38	99.39 12	93.80 52	98.41 18	98.06 72	93.37 66	95.54 96	98.34 37	90.59 70	99.88 4	94.83 77	99.54 22	99.49 37
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
XVS		97.18 15	96.96 17	97.81 29	99.38 13	94.03 48	98.59 7	98.20 42	94.85 23	96.59 53	98.29 46	91.70 48	99.80 26	95.66 48	99.40 44	99.62 13

X-MVStestdata		91.71 189	89.67 246	97.81 29	99.38 13	94.03 48	98.59 7	98.20 42	94.85 23	96.59 53	32.69 346	91.70 48	99.80 26	95.66 48	99.40 44	99.62 13

ZNCC-MVS		96.96 29	96.67 34	97.85 24	99.37 15	94.12 43	98.49 14	98.18 46	92.64 97	96.39 63	98.18 54	91.61 50	99.88 4	95.59 58	99.55 20	99.57 19

zzz-MVS		97.07 21	96.77 29	97.97 21	99.37 15	94.42 30	97.15 131	98.08 64	95.07 18	96.11 70	98.59 14	90.88 66	99.90 1	96.18 37	99.50 31	99.58 17

MTAPA		97.08 20	96.78 28	97.97 21	99.37 15	94.42 30	97.24 118	98.08 64	95.07 18	96.11 70	98.59 14	90.88 66	99.90 1	96.18 37	99.50 31	99.58 17

GST-MVS		96.85 35	96.52 41	97.82 28	99.36 18	94.14 42	98.29 24	98.13 54	92.72 94	96.70 45	98.06 59	91.35 55	99.86 8	94.83 77	99.28 56	99.47 42

HPM-MVS		96.69 42	96.45 45	97.40 52	99.36 18	93.11 72	98.87 1	98.06 72	91.17 141	96.40 62	97.99 64	90.99 63	99.58 68	95.61 55	99.61 13	99.49 37
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
PGM-MVS		96.81 37	96.53 40	97.65 43	99.35 20	93.53 61	97.65 79	98.98 1	92.22 104	97.14 37	98.44 24	91.17 60	99.85 14	94.35 86	99.46 37	99.57 19

CP-MVS		97.02 25	96.81 26	97.64 45	99.33 21	93.54 60	98.80 3	98.28 26	92.99 80	96.45 61	98.30 45	91.90 43	99.85 14	95.61 55	99.68 4	99.54 27

HPM-MVS_fast		96.51 48	96.27 49	97.22 63	99.32 22	92.74 80	98.74 4	98.06 72	90.57 161	96.77 44	98.35 34	90.21 74	99.53 84	94.80 80	99.63 11	99.38 52

MCST-MVS		97.18 15	96.84 23	98.20 9	99.30 23	95.35 12	97.12 133	98.07 69	93.54 63	96.08 72	97.69 85	93.86 12	99.71 37	96.50 23	99.39 46	99.55 25

test_part2					99.28 24	95.74 6				98.10 16

CPTT-MVS		95.57 73	95.19 75	96.70 74	99.27 25	91.48 116	98.33 21	98.11 59	87.79 235	95.17 101	98.03 61	87.09 113	99.61 60	93.51 103	99.42 42	99.02 79

TSAR-MVS + MP.		97.42 7	97.33 8	97.69 41	99.25 26	94.24 37	98.07 42	97.85 108	93.72 55	98.57 10	98.35 34	93.69 14	99.40 103	97.06 7	99.46 37	99.44 45
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CSCG		96.05 60	95.91 57	96.46 90	99.24 27	90.47 153	98.30 23	98.57 11	89.01 193	93.97 121	97.57 98	92.62 27	99.76 29	94.66 83	99.27 58	99.15 68

ACMMP		96.27 55	95.93 56	97.28 58	99.24 27	92.62 84	98.25 28	98.81 3	92.99 80	94.56 109	98.39 31	88.96 84	99.85 14	94.57 85	97.63 114	99.36 54
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
MP-MVS-pluss		96.70 41	96.27 49	97.98 20	99.23 29	94.71 25	96.96 146	98.06 72	90.67 152	95.55 94	98.78 9	91.07 61	99.86 8	96.58 21	99.55 20	99.38 52
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DP-MVS Recon		95.68 69	95.12 78	97.37 53	99.19 30	94.19 38	97.03 135	98.08 64	88.35 217	95.09 103	97.65 89	89.97 78	99.48 93	92.08 129	98.59 92	98.44 131

DPE-MVS		97.86 2	97.65 3	98.47 3	99.17 31	95.78 5	97.21 125	98.35 19	95.16 14	98.71 9	98.80 8	95.05 6	99.89 3	96.70 18	99.73 1	99.73 7

APDe-MVS		97.82 3	97.73 2	98.08 14	99.15 32	94.82 24	98.81 2	98.30 23	94.76 31	98.30 12	98.90 3	93.77 13	99.68 46	97.93 1	99.69 3	99.75 3

testtj		96.93 32	96.56 39	98.05 16	99.10 33	94.66 26	97.78 63	98.22 39	92.74 93	97.59 23	98.20 53	91.96 42	99.86 8	94.21 88	99.25 60	99.63 11

SR-MVS		97.01 27	96.86 21	97.47 50	99.09 34	93.27 69	97.98 46	98.07 69	93.75 54	97.45 27	98.48 21	91.43 54	99.59 65	96.22 31	99.27 58	99.54 27

ACMMP_NAP		97.20 14	96.86 21	98.23 7	99.09 34	95.16 19	97.60 85	98.19 44	92.82 90	97.93 19	98.74 10	91.60 51	99.86 8	96.26 28	99.52 24	99.67 8

HPM-MVS++		97.34 12	96.97 16	98.47 3	99.08 36	96.16 2	97.55 89	97.97 95	95.59 4	96.61 51	97.89 67	92.57 29	99.84 18	95.95 42	99.51 28	99.40 49

114514_t		93.95 116	93.06 126	96.63 77	99.07 37	91.61 111	97.46 99	97.96 96	77.99 328	93.00 142	97.57 98	86.14 127	99.33 108	89.22 183	99.15 68	98.94 90

SMA-MVS		97.35 11	97.03 13	98.30 6	99.06 38	95.42 8	97.94 49	98.18 46	90.57 161	98.85 6	98.94 1	93.33 16	99.83 21	96.72 17	99.68 4	99.63 11

APD-MVS		96.95 30	96.60 36	98.01 18	99.03 39	94.93 23	97.72 71	98.10 61	91.50 125	98.01 17	98.32 42	92.33 33	99.58 68	94.85 75	99.51 28	99.53 31
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SF-MVS		97.39 10	97.13 10	98.17 10	99.02 40	95.28 16	98.23 30	98.27 28	92.37 102	98.27 13	98.65 12	93.33 16	99.72 34	96.49 24	99.52 24	99.51 32

APD-MVS_3200maxsize		96.81 37	96.71 32	97.12 67	99.01 41	92.31 91	97.98 46	98.06 72	93.11 77	97.44 28	98.55 18	90.93 64	99.55 79	96.06 39	99.25 60	99.51 32

9.14			96.75 30		98.93 42		97.73 68	98.23 38	91.28 137	97.88 21	98.44 24	93.00 20	99.65 52	95.76 47	99.47 35

CDPH-MVS		95.97 63	95.38 70	97.77 34	98.93 42	94.44 29	96.35 199	97.88 102	86.98 254	96.65 49	97.89 67	91.99 41	99.47 94	92.26 120	99.46 37	99.39 50

xxxxxxxxxxxxxcwj		97.42 7	97.28 9	97.83 25	98.91 44	94.28 33	97.02 138	98.02 83	95.35 8	98.27 13	98.65 12	93.33 16	99.72 34	96.49 24	99.52 24	99.51 32

save fliter					98.91 44	94.28 33	97.02 138	98.02 83	95.35 8

ETH3 D test6400		96.16 58	95.52 64	98.07 15	98.90 46	95.06 21	97.03 135	98.21 40	88.16 224	96.64 50	97.70 84	91.18 59	99.67 48	92.44 119	99.47 35	99.48 39

ETH3D-3000-0.1		97.07 21	96.71 32	98.14 12	98.90 46	95.33 14	97.68 75	98.24 34	91.57 123	97.90 20	98.37 32	92.61 28	99.66 51	95.59 58	99.51 28	99.43 47

CNVR-MVS		97.68 4	97.44 7	98.37 5	98.90 46	95.86 4	97.27 116	98.08 64	95.81 3	97.87 22	98.31 43	94.26 9	99.68 46	97.02 8	99.49 33	99.57 19

abl_6		96.40 51	96.21 51	96.98 71	98.89 49	92.20 96	97.89 52	98.03 82	93.34 69	97.22 33	98.42 27	87.93 98	99.72 34	95.10 68	99.07 75	99.02 79

PAPM_NR		95.01 86	94.59 88	96.26 105	98.89 49	90.68 148	97.24 118	97.73 115	91.80 118	92.93 147	96.62 148	89.13 83	99.14 125	89.21 184	97.78 111	98.97 86

OPU-MVS				98.55 1	98.82 51	96.86 1	98.25 28				98.26 49	96.04 1	99.24 115	95.36 62	99.59 14	99.56 22

NCCC		97.30 13	97.03 13	98.11 13	98.77 52	95.06 21	97.34 108	98.04 80	95.96 2	97.09 41	97.88 69	93.18 19	99.71 37	95.84 45	99.17 67	99.56 22

DP-MVS		92.76 159	91.51 177	96.52 82	98.77 52	90.99 136	97.38 106	96.08 249	82.38 305	89.29 230	97.87 70	83.77 154	99.69 43	81.37 292	96.69 140	98.89 96

MSLP-MVS++		96.94 31	97.06 12	96.59 80	98.72 54	91.86 106	97.67 76	98.49 12	94.66 34	97.24 32	98.41 30	92.31 35	98.94 145	96.61 20	99.46 37	98.96 87

TEST9					98.70 55	94.19 38	96.41 191	98.02 83	88.17 222	96.03 73	97.56 100	92.74 23	99.59 65

train_agg		96.30 54	95.83 59	97.72 38	98.70 55	94.19 38	96.41 191	98.02 83	88.58 210	96.03 73	97.56 100	92.73 24	99.59 65	95.04 69	99.37 51	99.39 50

test_8					98.67 57	94.06 47	96.37 198	98.01 87	88.58 210	95.98 78	97.55 102	92.73 24	99.58 68

agg_prior1		96.22 57	95.77 60	97.56 47	98.67 57	93.79 53	96.28 207	98.00 89	88.76 207	95.68 88	97.55 102	92.70 26	99.57 76	95.01 70	99.32 52	99.32 56

agg_prior					98.67 57	93.79 53		98.00 89		95.68 88			99.57 76

test_prior3		96.46 50	96.20 52	97.23 61	98.67 57	92.99 74	96.35 199	98.00 89	92.80 91	96.03 73	97.59 96	92.01 39	99.41 101	95.01 70	99.38 47	99.29 58

test_prior				97.23 61	98.67 57	92.99 74		98.00 89					99.41 101			99.29 58

DeepC-MVS_fast	93.89 2	96.93 32	96.64 35	97.78 32	98.64 62	94.30 32	97.41 100	98.04 80	94.81 28	96.59 53	98.37 32	91.24 57	99.64 59	95.16 65	99.52 24	99.42 48
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
新几何1				97.32 55	98.60 63	93.59 59		97.75 113	81.58 311	95.75 85	97.85 73	90.04 77	99.67 48	86.50 234	99.13 70	98.69 111

原ACMM1				96.38 96	98.59 64	91.09 135		97.89 100	87.41 246	95.22 100	97.68 86	90.25 72	99.54 81	87.95 203	99.12 73	98.49 123

AdaColmap		94.34 103	93.68 108	96.31 100	98.59 64	91.68 110	96.59 182	97.81 110	89.87 172	92.15 160	97.06 121	83.62 156	99.54 81	89.34 178	98.07 104	97.70 166

PLC	91.00 6	94.11 110	93.43 118	96.13 110	98.58 66	91.15 134	96.69 171	97.39 162	87.29 249	91.37 172	96.71 134	88.39 93	99.52 88	87.33 222	97.13 132	97.73 164
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
1121		94.71 99	93.83 103	97.34 54	98.57 67	93.64 58	96.04 220	97.73 115	81.56 312	95.68 88	97.85 73	90.23 73	99.65 52	87.68 212	99.12 73	98.73 107

SD-MVS		97.41 9	97.53 5	97.06 68	98.57 67	94.46 28	97.92 51	98.14 53	94.82 27	99.01 2	98.55 18	94.18 10	97.41 290	96.94 9	99.64 10	99.32 56
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
test12				97.65 43	98.46 69	94.26 35		97.66 124		95.52 97		90.89 65	99.46 95		99.25 60	99.22 63

MVS_111021_HR		96.68 44	96.58 38	96.99 70	98.46 69	92.31 91	96.20 214	98.90 2	94.30 43	95.86 81	97.74 82	92.33 33	99.38 106	96.04 40	99.42 42	99.28 61

OMC-MVS		95.09 85	94.70 86	96.25 106	98.46 69	91.28 123	96.43 189	97.57 133	92.04 113	94.77 107	97.96 66	87.01 114	99.09 131	91.31 147	96.77 136	98.36 138

MG-MVS		95.61 71	95.38 70	96.31 100	98.42 72	90.53 151	96.04 220	97.48 141	93.47 64	95.67 91	98.10 56	89.17 82	99.25 114	91.27 148	98.77 85	99.13 70

PHI-MVS		96.77 39	96.46 44	97.71 40	98.40 73	94.07 46	98.21 33	98.45 15	89.86 173	97.11 40	98.01 63	92.52 31	99.69 43	96.03 41	99.53 23	99.36 54

F-COLMAP		93.58 128	92.98 127	95.37 149	98.40 73	88.98 200	97.18 127	97.29 173	87.75 238	90.49 189	97.10 119	85.21 136	99.50 91	86.70 231	96.72 139	97.63 168

SteuartSystems-ACMMP		97.62 5	97.53 5	97.87 23	98.39 75	94.25 36	98.43 17	98.27 28	95.34 10	98.11 15	98.56 16	94.53 8	99.71 37	96.57 22	99.62 12	99.65 9
Skip Steuart: Steuart Systems R&D Blog.
旧先验1					98.38 76	93.38 65		97.75 113			98.09 57	92.30 36			99.01 78	99.16 66

CNLPA		94.28 104	93.53 113	96.52 82	98.38 76	92.55 86	96.59 182	96.88 210	90.13 169	91.91 164	97.24 112	85.21 136	99.09 131	87.64 215	97.83 109	97.92 154

Regformer-3		96.85 35	96.80 27	97.01 69	98.34 78	92.02 102	96.96 146	97.76 112	95.01 20	97.08 42	98.42 27	91.71 47	99.54 81	96.80 13	99.13 70	99.48 39

Regformer-4		96.97 28	96.87 20	97.25 60	98.34 78	92.66 83	96.96 146	98.01 87	95.12 16	97.14 37	98.42 27	91.82 44	99.61 60	96.90 10	99.13 70	99.50 35

TAPA-MVS	90.10 7	92.30 172	91.22 188	95.56 137	98.33 80	89.60 172	96.79 162	97.65 126	81.83 309	91.52 169	97.23 113	87.94 97	98.91 148	71.31 330	98.37 96	98.17 144
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Regformer-1		97.10 19	96.96 17	97.54 48	98.32 81	93.48 62	96.83 158	97.99 93	95.20 13	97.46 26	98.25 50	92.48 32	99.58 68	96.79 15	99.29 54	99.55 25

Regformer-2		97.16 17	96.99 15	97.67 42	98.32 81	93.84 51	96.83 158	98.10 61	95.24 11	97.49 25	98.25 50	92.57 29	99.61 60	96.80 13	99.29 54	99.56 22

TSAR-MVS + GP.		96.69 42	96.49 42	97.27 59	98.31 83	93.39 64	96.79 162	96.72 219	94.17 44	97.44 28	97.66 88	92.76 22	99.33 108	96.86 12	97.76 113	99.08 76

CHOSEN 1792x2688		94.15 107	93.51 114	96.06 113	98.27 84	89.38 184	95.18 260	98.48 14	85.60 272	93.76 125	97.11 118	83.15 163	99.61 60	91.33 146	98.72 87	99.19 64

PVSNet_BlendedMVS		94.06 112	93.92 101	94.47 183	98.27 84	89.46 181	96.73 166	98.36 16	90.17 167	94.36 112	95.24 213	88.02 95	99.58 68	93.44 105	90.72 230	94.36 297

PVSNet_Blended		94.87 94	94.56 89	95.81 123	98.27 84	89.46 181	95.47 246	98.36 16	88.84 201	94.36 112	96.09 173	88.02 95	99.58 68	93.44 105	98.18 101	98.40 134

ETH3D cwj APD-0.16		96.56 47	96.06 54	98.05 16	98.26 87	95.19 17	96.99 143	98.05 79	89.85 175	97.26 31	98.22 52	91.80 45	99.69 43	94.84 76	99.28 56	99.27 62

Anonymous20231211		90.63 240	89.42 250	94.27 191	98.24 88	89.19 196	98.05 43	97.89 100	79.95 320	88.25 255	94.96 220	72.56 291	98.13 206	89.70 169	85.14 285	95.49 233

EI-MVSNet-Vis-set		96.51 48	96.47 43	96.63 77	98.24 88	91.20 129	96.89 153	97.73 115	94.74 32	96.49 57	98.49 20	90.88 66	99.58 68	96.44 26	98.32 97	99.13 70

test222					98.24 88	92.21 94	95.33 251	97.60 130	79.22 324	95.25 99	97.84 76	88.80 87			99.15 68	98.72 108

HyFIR lowres test		93.66 125	92.92 129	95.87 121	98.24 88	89.88 167	94.58 268	98.49 12	85.06 279	93.78 124	95.78 188	82.86 172	98.67 168	91.77 135	95.71 157	99.07 78

MVS_111021_LR		96.24 56	96.19 53	96.39 95	98.23 92	91.35 122	96.24 212	98.79 4	93.99 48	95.80 83	97.65 89	89.92 79	99.24 115	95.87 43	99.20 65	98.58 114

EI-MVSNet-UG-set		96.34 53	96.30 48	96.47 88	98.20 93	90.93 140	96.86 154	97.72 118	94.67 33	96.16 69	98.46 22	90.43 71	99.58 68	96.23 30	97.96 107	98.90 94

PVSNet_Blended_VisFu		95.27 79	94.91 81	96.38 96	98.20 93	90.86 142	97.27 116	98.25 33	90.21 166	94.18 116	97.27 110	87.48 107	99.73 31	93.53 102	97.77 112	98.55 115

Anonymous202405211		92.07 182	90.83 200	95.76 124	98.19 95	88.75 204	97.58 86	95.00 289	86.00 268	93.64 126	97.45 104	66.24 323	99.53 84	90.68 155	92.71 196	99.01 83

PatchMatch-RL		92.90 152	92.02 158	95.56 137	98.19 95	90.80 144	95.27 256	97.18 177	87.96 228	91.86 166	95.68 195	80.44 215	98.99 141	84.01 268	97.54 116	96.89 188

testdata				95.46 147	98.18 97	88.90 202		97.66 124	82.73 304	97.03 43	98.07 58	90.06 76	98.85 152	89.67 170	98.98 79	98.64 113

Anonymous20240529		91.98 184	90.73 203	95.73 129	98.14 98	89.40 183	97.99 45	97.72 118	79.63 322	93.54 129	97.41 106	69.94 307	99.56 78	91.04 151	91.11 223	98.22 142

LFMVS		93.60 127	92.63 138	96.52 82	98.13 99	91.27 124	97.94 49	93.39 321	90.57 161	96.29 65	98.31 43	69.00 309	99.16 122	94.18 89	95.87 152	99.12 73

DeepPCF-MVS	93.97 1	96.61 45	97.09 11	95.15 154	98.09 100	86.63 254	96.00 224	98.15 51	95.43 6	97.95 18	98.56 16	93.40 15	99.36 107	96.77 16	99.48 34	99.45 43

DPM-MVS		95.69 68	94.92 80	98.01 18	98.08 101	95.71 7	95.27 256	97.62 129	90.43 164	95.55 94	97.07 120	91.72 46	99.50 91	89.62 172	98.94 81	98.82 102

VNet		95.89 65	95.45 67	97.21 64	98.07 102	92.94 77	97.50 92	98.15 51	93.87 50	97.52 24	97.61 95	85.29 135	99.53 84	95.81 46	95.27 163	99.16 66

MAR-MVS		94.22 105	93.46 116	96.51 85	98.00 103	92.19 97	97.67 76	97.47 144	88.13 226	93.00 142	95.84 181	84.86 141	99.51 89	87.99 202	98.17 102	97.83 161
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
DeepC-MVS	93.07 3	96.06 59	95.66 61	97.29 57	97.96 104	93.17 71	97.30 114	98.06 72	93.92 49	93.38 134	98.66 11	86.83 115	99.73 31	95.60 57	99.22 63	98.96 87
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
COLMAP_ROB	87.81 15	90.40 245	89.28 253	93.79 216	97.95 105	87.13 243	96.92 150	95.89 254	82.83 303	86.88 284	97.18 114	73.77 287	99.29 112	78.44 308	93.62 188	94.95 265
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		90.23 249	88.98 257	93.98 202	97.94 106	86.64 251	96.51 186	95.54 266	85.38 273	85.49 294	96.77 132	70.28 304	99.15 123	80.02 298	92.87 193	96.15 207

TestCases				93.98 202	97.94 106	86.64 251		95.54 266	85.38 273	85.49 294	96.77 132	70.28 304	99.15 123	80.02 298	92.87 193	96.15 207

thres100view900		92.43 165	91.58 172	94.98 162	97.92 108	89.37 185	97.71 73	94.66 301	92.20 106	93.31 136	94.90 224	78.06 257	99.08 133	81.40 289	94.08 180	96.48 199

thres600view7		92.49 164	91.60 171	95.18 153	97.91 109	89.47 179	97.65 79	94.66 301	92.18 110	93.33 135	94.91 223	78.06 257	99.10 128	81.61 286	94.06 183	96.98 183

API-MVS		94.84 95	94.49 93	95.90 120	97.90 110	92.00 103	97.80 61	97.48 141	89.19 189	94.81 106	96.71 134	88.84 86	99.17 121	88.91 190	98.76 86	96.53 196

VDD-MVS		93.82 120	93.08 125	96.02 115	97.88 111	89.96 166	97.72 71	95.85 255	92.43 100	95.86 81	98.44 24	68.42 313	99.39 104	96.31 27	94.85 169	98.71 110

tfpn200view9		92.38 168	91.52 175	94.95 165	97.85 112	89.29 190	97.41 100	94.88 296	92.19 108	93.27 138	94.46 247	78.17 254	99.08 133	81.40 289	94.08 180	96.48 199

thres400		92.42 166	91.52 175	95.12 157	97.85 112	89.29 190	97.41 100	94.88 296	92.19 108	93.27 138	94.46 247	78.17 254	99.08 133	81.40 289	94.08 180	96.98 183

DELS-MVS		96.61 45	96.38 47	97.30 56	97.79 114	93.19 70	95.96 226	98.18 46	95.23 12	95.87 80	97.65 89	91.45 53	99.70 42	95.87 43	99.44 41	99.00 85
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
PVSNet	86.66 18	92.24 176	91.74 168	93.73 217	97.77 115	83.69 293	92.88 311	96.72 219	87.91 230	93.00 142	94.86 226	78.51 249	99.05 137	86.53 232	97.45 121	98.47 126

test_yl		94.78 97	94.23 98	96.43 91	97.74 116	91.22 125	96.85 155	97.10 186	91.23 139	95.71 86	96.93 124	84.30 147	99.31 110	93.10 112	95.12 165	98.75 104

DCV-MVSNet		94.78 97	94.23 98	96.43 91	97.74 116	91.22 125	96.85 155	97.10 186	91.23 139	95.71 86	96.93 124	84.30 147	99.31 110	93.10 112	95.12 165	98.75 104

WTY-MVS		94.71 99	94.02 100	96.79 73	97.71 118	92.05 100	96.59 182	97.35 168	90.61 158	94.64 108	96.93 124	86.41 121	99.39 104	91.20 150	94.71 175	98.94 90

UA-Net		95.95 64	95.53 63	97.20 65	97.67 119	92.98 76	97.65 79	98.13 54	94.81 28	96.61 51	98.35 34	88.87 85	99.51 89	90.36 158	97.35 124	99.11 74

IS-MVSNet		94.90 92	94.52 92	96.05 114	97.67 119	90.56 150	98.44 16	96.22 245	93.21 71	93.99 119	97.74 82	85.55 133	98.45 184	89.98 161	97.86 108	99.14 69

PAPR		94.18 106	93.42 120	96.48 87	97.64 121	91.42 121	95.55 242	97.71 122	88.99 194	92.34 156	95.82 183	89.19 81	99.11 127	86.14 240	97.38 122	98.90 94

CANet		96.39 52	96.02 55	97.50 49	97.62 122	93.38 65	97.02 138	97.96 96	95.42 7	94.86 105	97.81 77	87.38 109	99.82 24	96.88 11	99.20 65	99.29 58

thres200		92.23 177	91.39 178	94.75 175	97.61 123	89.03 199	96.60 181	95.09 286	92.08 112	93.28 137	94.00 270	78.39 252	99.04 139	81.26 293	94.18 179	96.19 204

Vis-MVSNet (Re-imp)		94.15 107	93.88 102	94.95 165	97.61 123	87.92 225	98.10 38	95.80 257	92.22 104	93.02 141	97.45 104	84.53 145	97.91 247	88.24 198	97.97 106	99.02 79

canonicalmvs		96.02 61	95.45 67	97.75 36	97.59 125	95.15 20	98.28 25	97.60 130	94.52 37	96.27 66	96.12 170	87.65 102	99.18 120	96.20 36	94.82 171	98.91 93

LS3D		93.57 129	92.61 140	96.47 88	97.59 125	91.61 111	97.67 76	97.72 118	85.17 277	90.29 194	98.34 37	84.60 143	99.73 31	83.85 272	98.27 98	98.06 150

alignmvs		95.87 66	95.23 74	97.78 32	97.56 127	95.19 17	97.86 54	97.17 179	94.39 40	96.47 59	96.40 159	85.89 128	99.20 117	96.21 35	95.11 167	98.95 89

EPP-MVSNet		95.22 82	95.04 79	95.76 124	97.49 128	89.56 174	98.67 5	97.00 199	90.69 151	94.24 115	97.62 94	89.79 80	98.81 155	93.39 108	96.49 144	98.92 92

PS-MVSNAJ		95.37 76	95.33 72	95.49 143	97.35 129	90.66 149	95.31 253	97.48 141	93.85 51	96.51 56	95.70 194	88.65 89	99.65 52	94.80 80	98.27 98	96.17 205

CS-MVS		95.80 67	95.65 62	96.24 107	97.32 130	91.43 120	98.10 38	97.91 99	93.38 65	95.16 102	94.57 240	90.21 74	98.98 142	95.53 60	98.67 89	98.30 141

ab-mvs		93.57 129	92.55 142	96.64 75	97.28 131	91.96 105	95.40 248	97.45 152	89.81 177	93.22 140	96.28 164	79.62 232	99.46 95	90.74 153	93.11 192	98.50 121

xiu_mvs_v2_base		95.32 78	95.29 73	95.40 148	97.22 132	90.50 152	95.44 247	97.44 156	93.70 57	96.46 60	96.18 167	88.59 92	99.53 84	94.79 82	97.81 110	96.17 205

BH-untuned		92.94 150	92.62 139	93.92 211	97.22 132	86.16 263	96.40 194	96.25 244	90.06 170	89.79 213	96.17 169	83.19 161	98.35 190	87.19 225	97.27 127	97.24 180

baseline1		92.82 157	91.90 162	95.55 139	97.20 134	90.77 146	97.19 126	94.58 304	92.20 106	92.36 154	96.34 162	84.16 150	98.21 198	89.20 185	83.90 305	97.68 167

Vis-MVSNet		95.23 81	94.81 82	96.51 85	97.18 135	91.58 114	98.26 27	98.12 56	94.38 41	94.90 104	98.15 55	82.28 186	98.92 146	91.45 145	98.58 93	99.01 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ETV-MVS		96.02 61	95.89 58	96.40 93	97.16 136	92.44 89	97.47 97	97.77 111	94.55 36	96.48 58	94.51 242	91.23 58	98.92 146	95.65 51	98.19 100	97.82 162

BH-RMVSNet		92.72 160	91.97 160	94.97 163	97.16 136	87.99 224	96.15 216	95.60 263	90.62 157	91.87 165	97.15 117	78.41 251	98.57 177	83.16 274	97.60 115	98.36 138

MSDG		91.42 203	90.24 222	94.96 164	97.15 138	88.91 201	93.69 296	96.32 240	85.72 271	86.93 282	96.47 154	80.24 219	98.98 142	80.57 295	95.05 168	96.98 183

tttt0517		92.96 148	92.33 150	94.87 168	97.11 139	87.16 242	97.97 48	92.09 329	90.63 156	93.88 123	97.01 123	76.50 267	99.06 136	90.29 160	95.45 160	98.38 136

HY-MVS	89.66 9	93.87 118	92.95 128	96.63 77	97.10 140	92.49 88	95.64 240	96.64 228	89.05 192	93.00 142	95.79 187	85.77 131	99.45 97	89.16 187	94.35 177	97.96 151

thisisatest0530		93.03 145	92.21 153	95.49 143	97.07 141	89.11 198	97.49 96	92.19 328	90.16 168	94.09 117	96.41 158	76.43 270	99.05 137	90.38 157	95.68 158	98.31 140

XVG-OURS		93.72 124	93.35 121	94.80 172	97.07 141	88.61 207	94.79 264	97.46 146	91.97 116	93.99 119	97.86 72	81.74 197	98.88 151	92.64 118	92.67 198	96.92 187

sss		94.51 101	93.80 104	96.64 75	97.07 141	91.97 104	96.32 203	98.06 72	88.94 197	94.50 110	96.78 131	84.60 143	99.27 113	91.90 131	96.02 148	98.68 112

EIA-MVS		95.53 74	95.47 66	95.71 130	97.06 144	89.63 170	97.82 59	97.87 104	93.57 59	93.92 122	95.04 219	90.61 69	98.95 144	94.62 84	98.68 88	98.54 116

XVG-OURS-SEG-HR		93.86 119	93.55 111	94.81 171	97.06 144	88.53 209	95.28 254	97.45 152	91.68 121	94.08 118	97.68 86	82.41 184	98.90 149	93.84 98	92.47 200	96.98 183

1112_ss		93.37 133	92.42 148	96.21 108	97.05 146	90.99 136	96.31 204	96.72 219	86.87 257	89.83 212	96.69 138	86.51 119	99.14 125	88.12 200	93.67 186	98.50 121

Test_1112_low_res		92.84 156	91.84 164	95.85 122	97.04 147	89.97 165	95.53 244	96.64 228	85.38 273	89.65 218	95.18 214	85.86 129	99.10 128	87.70 209	93.58 191	98.49 123

BH-w/o		92.14 181	91.75 166	93.31 238	96.99 148	85.73 267	95.67 237	95.69 259	88.73 208	89.26 232	94.82 229	82.97 170	98.07 219	85.26 255	96.32 147	96.13 209

3Dnovator+	91.43 4	95.40 75	94.48 94	98.16 11	96.90 149	95.34 13	98.48 15	97.87 104	94.65 35	88.53 248	98.02 62	83.69 155	99.71 37	93.18 111	98.96 80	99.44 45

UGNet		94.04 114	93.28 123	96.31 100	96.85 150	91.19 130	97.88 53	97.68 123	94.40 39	93.00 142	96.18 167	73.39 290	99.61 60	91.72 136	98.46 94	98.13 145
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
VDDNet		93.05 144	92.07 155	96.02 115	96.84 151	90.39 157	98.08 41	95.85 255	86.22 265	95.79 84	98.46 22	67.59 316	99.19 118	94.92 74	94.85 169	98.47 126

RPSCF		90.75 235	90.86 196	90.42 304	96.84 151	76.29 332	95.61 241	96.34 239	83.89 293	91.38 171	97.87 70	76.45 268	98.78 157	87.16 227	92.23 203	96.20 203

MVS_Test		94.89 93	94.62 87	95.68 131	96.83 153	89.55 175	96.70 169	97.17 179	91.17 141	95.60 93	96.11 172	87.87 99	98.76 160	93.01 116	97.17 131	98.72 108

LCM-MVSNet-Re		92.50 162	92.52 145	92.44 263	96.82 154	81.89 305	96.92 150	93.71 318	92.41 101	84.30 303	94.60 239	85.08 138	97.03 302	91.51 142	97.36 123	98.40 134

baseline		95.58 72	95.42 69	96.08 111	96.78 155	90.41 156	97.16 129	97.45 152	93.69 58	95.65 92	97.85 73	87.29 110	98.68 167	95.66 48	97.25 128	99.13 70

Fast-Effi-MVS+		93.46 131	92.75 134	95.59 136	96.77 156	90.03 159	96.81 161	97.13 182	88.19 220	91.30 176	94.27 258	86.21 124	98.63 171	87.66 214	96.46 146	98.12 146

QAPM		93.45 132	92.27 152	96.98 71	96.77 156	92.62 84	98.39 19	98.12 56	84.50 287	88.27 254	97.77 80	82.39 185	99.81 25	85.40 253	98.81 84	98.51 120

casdiffmvs		95.64 70	95.49 65	96.08 111	96.76 158	90.45 154	97.29 115	97.44 156	94.00 47	95.46 98	97.98 65	87.52 106	98.73 162	95.64 52	97.33 125	99.08 76

CHOSEN 280x420		93.12 141	92.72 136	94.34 189	96.71 159	87.27 236	90.29 327	97.72 118	86.61 260	91.34 173	95.29 210	84.29 149	98.41 185	93.25 110	98.94 81	97.35 179

Effi-MVS+		94.93 91	94.45 95	96.36 98	96.61 160	91.47 117	96.41 191	97.41 161	91.02 146	94.50 110	95.92 177	87.53 105	98.78 157	93.89 96	96.81 135	98.84 101

thisisatest0515		92.29 173	91.30 183	95.25 151	96.60 161	88.90 202	94.36 277	92.32 327	87.92 229	93.43 133	94.57 240	77.28 264	99.00 140	89.42 176	95.86 153	97.86 158

PCF-MVS	89.48 11	91.56 196	89.95 234	96.36 98	96.60 161	92.52 87	92.51 316	97.26 174	79.41 323	88.90 237	96.56 150	84.04 152	99.55 79	77.01 314	97.30 126	97.01 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
xiu_mvs_v1_base_debu		95.01 86	94.76 83	95.75 126	96.58 163	91.71 107	96.25 209	97.35 168	92.99 80	96.70 45	96.63 145	82.67 176	99.44 98	96.22 31	97.46 117	96.11 210

xiu_mvs_v1_base		95.01 86	94.76 83	95.75 126	96.58 163	91.71 107	96.25 209	97.35 168	92.99 80	96.70 45	96.63 145	82.67 176	99.44 98	96.22 31	97.46 117	96.11 210

xiu_mvs_v1_base_debi		95.01 86	94.76 83	95.75 126	96.58 163	91.71 107	96.25 209	97.35 168	92.99 80	96.70 45	96.63 145	82.67 176	99.44 98	96.22 31	97.46 117	96.11 210

MVSTER		93.20 139	92.81 131	94.37 187	96.56 166	89.59 173	97.06 134	97.12 183	91.24 138	91.30 176	95.96 175	82.02 191	98.05 222	93.48 104	90.55 232	95.47 236

3Dnovator	91.36 5	95.19 84	94.44 96	97.44 51	96.56 166	93.36 67	98.65 6	98.36 16	94.12 45	89.25 233	98.06 59	82.20 188	99.77 28	93.41 107	99.32 52	99.18 65

FMVSNet3		91.78 188	90.69 205	95.03 159	96.53 168	92.27 93	97.02 138	96.93 203	89.79 178	89.35 227	94.65 237	77.01 265	97.47 284	86.12 241	88.82 247	95.35 248

GBi-Net		91.35 208	90.27 220	94.59 177	96.51 169	91.18 131	97.50 92	96.93 203	88.82 203	89.35 227	94.51 242	73.87 284	97.29 296	86.12 241	88.82 247	95.31 250

test1		91.35 208	90.27 220	94.59 177	96.51 169	91.18 131	97.50 92	96.93 203	88.82 203	89.35 227	94.51 242	73.87 284	97.29 296	86.12 241	88.82 247	95.31 250

FMVSNet2		91.31 211	90.08 229	94.99 160	96.51 169	92.21 94	97.41 100	96.95 201	88.82 203	88.62 245	94.75 232	73.87 284	97.42 289	85.20 256	88.55 253	95.35 248

ACMH+	87.92 14	90.20 250	89.18 255	93.25 240	96.48 172	86.45 256	96.99 143	96.68 225	88.83 202	84.79 300	96.22 166	70.16 306	98.53 179	84.42 266	88.04 255	94.77 286

CANet_DTU		94.37 102	93.65 109	96.55 81	96.46 173	92.13 98	96.21 213	96.67 227	94.38 41	93.53 130	97.03 122	79.34 235	99.71 37	90.76 152	98.45 95	97.82 162

mvs_anonymous		93.82 120	93.74 105	94.06 198	96.44 174	85.41 272	95.81 233	97.05 193	89.85 175	90.09 205	96.36 161	87.44 108	97.75 260	93.97 92	96.69 140	99.02 79

diffmvs		95.25 80	95.13 77	95.63 133	96.43 175	89.34 186	95.99 225	97.35 168	92.83 89	96.31 64	97.37 107	86.44 120	98.67 168	96.26 28	97.19 130	98.87 98

ET-MVSNet_ETH3D		91.49 200	90.11 228	95.63 133	96.40 176	91.57 115	95.34 250	93.48 320	90.60 160	75.58 331	95.49 205	80.08 222	96.79 311	94.25 87	89.76 241	98.52 118

TR-MVS		91.48 201	90.59 208	94.16 195	96.40 176	87.33 234	95.67 237	95.34 275	87.68 240	91.46 170	95.52 204	76.77 266	98.35 190	82.85 278	93.61 189	96.79 192

ACMP	89.59 10	92.62 161	92.14 154	94.05 199	96.40 176	88.20 218	97.36 107	97.25 176	91.52 124	88.30 252	96.64 141	78.46 250	98.72 165	91.86 134	91.48 217	95.23 257
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSFormer		95.37 76	95.16 76	95.99 117	96.34 179	91.21 127	98.22 31	97.57 133	91.42 129	96.22 67	97.32 108	86.20 125	97.92 244	94.07 90	99.05 76	98.85 99

lupinMVS		94.99 90	94.56 89	96.29 103	96.34 179	91.21 127	95.83 232	96.27 242	88.93 198	96.22 67	96.88 129	86.20 125	98.85 152	95.27 63	99.05 76	98.82 102

ACMM	89.79 8	92.96 148	92.50 146	94.35 188	96.30 181	88.71 205	97.58 86	97.36 167	91.40 132	90.53 188	96.65 140	79.77 228	98.75 161	91.24 149	91.64 213	95.59 232
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS-LS		92.29 173	91.94 161	93.34 237	96.25 182	86.97 246	96.57 185	97.05 193	90.67 152	89.50 224	94.80 230	86.59 116	97.64 268	89.91 163	86.11 274	95.40 244
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HQP_MVS		93.78 122	93.43 118	94.82 169	96.21 183	89.99 162	97.74 66	97.51 139	94.85 23	91.34 173	96.64 141	81.32 202	98.60 174	93.02 114	92.23 203	95.86 216

plane_prior7					96.21 183	89.98 164

ACMH	87.59 16	90.53 242	89.42 250	93.87 212	96.21 183	87.92 225	97.24 118	96.94 202	88.45 214	83.91 309	96.27 165	71.92 292	98.62 173	84.43 265	89.43 243	95.05 263
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CDS-MVSNet		94.14 109	93.54 112	95.93 118	96.18 186	91.46 118	96.33 202	97.04 195	88.97 196	93.56 127	96.51 152	87.55 104	97.89 248	89.80 166	95.95 150	98.44 131
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
LTVRE_ROB	88.41 13	90.99 225	89.92 235	94.19 193	96.18 186	89.55 175	96.31 204	97.09 188	87.88 231	85.67 292	95.91 178	78.79 247	98.57 177	81.50 287	89.98 238	94.44 295
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
LPG-MVS_test		92.94 150	92.56 141	94.10 196	96.16 188	88.26 215	97.65 79	97.46 146	91.29 134	90.12 202	97.16 115	79.05 239	98.73 162	92.25 122	91.89 211	95.31 250

LGP-MVS_train				94.10 196	96.16 188	88.26 215		97.46 146	91.29 134	90.12 202	97.16 115	79.05 239	98.73 162	92.25 122	91.89 211	95.31 250

TAMVS		94.01 115	93.46 116	95.64 132	96.16 188	90.45 154	96.71 168	96.89 209	89.27 187	93.46 132	96.92 127	87.29 110	97.94 240	88.70 194	95.74 155	98.53 117

plane_prior1					96.14 191

CLD-MVS		92.98 147	92.53 144	94.32 190	96.12 192	89.20 194	95.28 254	97.47 144	92.66 95	89.90 209	95.62 197	80.58 212	98.40 186	92.73 117	92.40 201	95.38 246
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
plane_prior6					96.10 193	90.00 160						81.32 202

cl-mvsnet2		91.21 215	90.56 210	93.14 245	96.09 194	86.80 248	94.41 275	96.58 234	87.80 234	88.58 247	93.99 271	80.85 210	97.62 271	89.87 165	86.93 265	94.99 264

Effi-MVS+-dtu		93.08 142	93.21 124	92.68 260	96.02 195	83.25 296	97.14 132	96.72 219	93.85 51	91.20 183	93.44 289	83.08 165	98.30 193	91.69 139	95.73 156	96.50 198

mvs-test1		93.63 126	93.69 107	93.46 232	96.02 195	84.61 282	97.24 118	96.72 219	93.85 51	92.30 157	95.76 189	83.08 165	98.89 150	91.69 139	96.54 143	96.87 189

NP-MVS					95.99 197	89.81 169					95.87 179

ADS-MVSNet2		89.45 261	88.59 262	92.03 272	95.86 198	82.26 304	90.93 323	94.32 311	83.23 301	91.28 179	91.81 313	79.01 243	95.99 318	79.52 300	91.39 219	97.84 159

ADS-MVSNet		89.89 256	88.68 261	93.53 228	95.86 198	84.89 279	90.93 323	95.07 287	83.23 301	91.28 179	91.81 313	79.01 243	97.85 250	79.52 300	91.39 219	97.84 159

HQP-NCC					95.86 198		96.65 174		93.55 60	90.14 196

ACMP_Plane					95.86 198		96.65 174		93.55 60	90.14 196

HQP-MVS		93.19 140	92.74 135	94.54 182	95.86 198	89.33 187	96.65 174	97.39 162	93.55 60	90.14 196	95.87 179	80.95 205	98.50 181	92.13 126	92.10 208	95.78 223

EI-MVSNet		93.03 145	92.88 130	93.48 230	95.77 203	86.98 245	96.44 187	97.12 183	90.66 154	91.30 176	97.64 92	86.56 117	98.05 222	89.91 163	90.55 232	95.41 240

CVMVSNet		91.23 214	91.75 166	89.67 310	95.77 203	74.69 334	96.44 187	94.88 296	85.81 269	92.18 159	97.64 92	79.07 238	95.58 325	88.06 201	95.86 153	98.74 106

RRT_test8_iter05		91.19 219	90.78 201	92.41 265	95.76 205	83.14 297	97.32 111	97.46 146	91.37 133	89.07 236	95.57 199	70.33 303	98.21 198	93.56 101	86.62 270	95.89 215

FIs		94.09 111	93.70 106	95.27 150	95.70 206	92.03 101	98.10 38	98.68 7	93.36 68	90.39 192	96.70 136	87.63 103	97.94 240	92.25 122	90.50 234	95.84 219

VPA-MVSNet		93.24 137	92.48 147	95.51 141	95.70 206	92.39 90	97.86 54	98.66 9	92.30 103	92.09 162	95.37 208	80.49 214	98.40 186	93.95 93	85.86 275	95.75 227

SCA		91.84 187	91.18 190	93.83 213	95.59 208	84.95 278	94.72 265	95.58 265	90.82 147	92.25 158	93.69 280	75.80 273	98.10 211	86.20 238	95.98 149	98.45 128

cl_fuxian		91.38 205	90.89 194	92.88 253	95.58 209	86.30 258	94.68 266	96.84 215	88.17 222	88.83 242	94.23 261	85.65 132	97.47 284	89.36 177	84.63 293	94.89 273

VPNet		92.23 177	91.31 182	94.99 160	95.56 210	90.96 138	97.22 124	97.86 107	92.96 86	90.96 184	96.62 148	75.06 278	98.20 200	91.90 131	83.65 307	95.80 222

miper_ehance_all_eth		91.59 193	91.13 191	92.97 250	95.55 211	86.57 255	94.47 271	96.88 210	87.77 236	88.88 239	94.01 269	86.22 123	97.54 277	89.49 174	86.93 265	94.79 283

IterMVS-SCA-FT		90.31 246	89.81 240	91.82 278	95.52 212	84.20 286	94.30 280	96.15 247	90.61 158	87.39 272	94.27 258	75.80 273	96.44 314	87.34 221	86.88 269	94.82 278

jason		94.84 95	94.39 97	96.18 109	95.52 212	90.93 140	96.09 218	96.52 235	89.28 186	96.01 77	97.32 108	84.70 142	98.77 159	95.15 66	98.91 83	98.85 99
jason: jason.
FC-MVSNet-test		93.94 117	93.57 110	95.04 158	95.48 214	91.45 119	98.12 37	98.71 5	93.37 66	90.23 195	96.70 136	87.66 101	97.85 250	91.49 143	90.39 235	95.83 220

IterMVS		90.15 252	89.67 246	91.61 285	95.48 214	83.72 290	94.33 279	96.12 248	89.99 171	87.31 275	94.15 266	75.78 275	96.27 317	86.97 229	86.89 268	94.83 276
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet1		89.88 257	88.31 265	94.59 177	95.41 216	91.18 131	97.50 92	96.93 203	86.62 259	87.41 271	94.51 242	65.94 325	97.29 296	83.04 276	87.43 261	95.31 250

UniMVSNet (Re)		93.31 135	92.55 142	95.61 135	95.39 217	93.34 68	97.39 104	98.71 5	93.14 76	90.10 204	94.83 228	87.71 100	98.03 226	91.67 141	83.99 301	95.46 237

MVS-HIRNet		82.47 305	81.21 306	86.26 320	95.38 218	69.21 341	88.96 335	89.49 339	66.28 337	80.79 318	74.08 340	68.48 312	97.39 291	71.93 328	95.47 159	92.18 325

PatchmatchNet		91.91 185	91.35 179	93.59 225	95.38 218	84.11 287	93.15 307	95.39 269	89.54 179	92.10 161	93.68 282	82.82 174	98.13 206	84.81 259	95.32 162	98.52 118
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cl-mvsnet_		90.96 228	90.32 216	92.89 252	95.37 220	86.21 261	94.46 273	96.64 228	87.82 232	88.15 258	94.18 264	82.98 169	97.54 277	87.70 209	85.59 277	94.92 271

cl-mvsnet1		90.97 227	90.33 215	92.88 253	95.36 221	86.19 262	94.46 273	96.63 231	87.82 232	88.18 257	94.23 261	82.99 168	97.53 279	87.72 207	85.57 278	94.93 269

miper_enhance_ethall		91.54 198	91.01 192	93.15 244	95.35 222	87.07 244	93.97 289	96.90 207	86.79 258	89.17 234	93.43 291	86.55 118	97.64 268	89.97 162	86.93 265	94.74 287

UniMVSNet_NR-MVSNet		93.37 133	92.67 137	95.47 146	95.34 223	92.83 78	97.17 128	98.58 10	92.98 85	90.13 200	95.80 184	88.37 94	97.85 250	91.71 137	83.93 302	95.73 229

ITE_SJBPF				92.43 264	95.34 223	85.37 273		95.92 252	91.47 126	87.75 266	96.39 160	71.00 299	97.96 237	82.36 283	89.86 240	93.97 307

OpenMVS	89.19 12	92.86 154	91.68 169	96.40 93	95.34 223	92.73 81	98.27 26	98.12 56	84.86 282	85.78 291	97.75 81	78.89 246	99.74 30	87.50 219	98.65 90	96.73 193

eth_miper_zixun_eth		91.02 224	90.59 208	92.34 267	95.33 226	84.35 283	94.10 286	96.90 207	88.56 212	88.84 241	94.33 253	84.08 151	97.60 273	88.77 193	84.37 298	95.06 262

miper_lstm_enhance		90.50 244	90.06 232	91.83 277	95.33 226	83.74 289	93.86 291	96.70 224	87.56 243	87.79 264	93.81 277	83.45 159	96.92 308	87.39 220	84.62 294	94.82 278

1314		92.81 158	92.03 157	95.14 155	95.33 226	89.52 178	96.04 220	97.44 156	87.72 239	86.25 288	95.33 209	83.84 153	98.79 156	89.26 181	97.05 133	97.11 181

PAPM		91.52 199	90.30 218	95.20 152	95.30 229	89.83 168	93.38 303	96.85 214	86.26 264	88.59 246	95.80 184	84.88 140	98.15 205	75.67 318	95.93 151	97.63 168

Fast-Effi-MVS+-dtu		92.29 173	91.99 159	93.21 243	95.27 230	85.52 270	97.03 135	96.63 231	92.09 111	89.11 235	95.14 216	80.33 218	98.08 216	87.54 218	94.74 174	96.03 213

Patchmatch-test		89.42 262	87.99 268	93.70 220	95.27 230	85.11 274	88.98 334	94.37 309	81.11 313	87.10 278	93.69 280	82.28 186	97.50 282	74.37 321	94.76 172	98.48 125

PVSNet_0	82.17 19	85.46 298	83.64 300	90.92 295	95.27 230	79.49 323	90.55 326	95.60 263	83.76 296	83.00 312	89.95 319	71.09 298	97.97 233	82.75 280	60.79 340	95.31 250

IB-MVS	87.33 17	89.91 255	88.28 266	94.79 173	95.26 233	87.70 231	95.12 261	93.95 317	89.35 185	87.03 279	92.49 301	70.74 301	99.19 118	89.18 186	81.37 318	97.49 177
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
nrg030		94.05 113	93.31 122	96.27 104	95.22 234	94.59 27	98.34 20	97.46 146	92.93 87	91.21 182	96.64 141	87.23 112	98.22 197	94.99 73	85.80 276	95.98 214

MDTV_nov1_ep13			90.76 202		95.22 234	80.33 316	93.03 310	95.28 276	88.14 225	92.84 148	93.83 274	81.34 201	98.08 216	82.86 277	94.34 178

MVS		91.71 189	90.44 212	95.51 141	95.20 236	91.59 113	96.04 220	97.45 152	73.44 335	87.36 273	95.60 198	85.42 134	99.10 128	85.97 245	97.46 117	95.83 220

tfpnnormal		89.70 260	88.40 264	93.60 224	95.15 237	90.10 158	97.56 88	98.16 50	87.28 250	86.16 289	94.63 238	77.57 262	98.05 222	74.48 319	84.59 295	92.65 319

tpmrst		91.44 202	91.32 181	91.79 280	95.15 237	79.20 326	93.42 302	95.37 271	88.55 213	93.49 131	93.67 283	82.49 182	98.27 194	90.41 156	89.34 244	97.90 155

WR-MVS		92.34 169	91.53 174	94.77 174	95.13 239	90.83 143	96.40 194	97.98 94	91.88 117	89.29 230	95.54 203	82.50 181	97.80 255	89.79 167	85.27 283	95.69 230

tpm cat1		88.36 275	87.21 277	91.81 279	95.13 239	80.55 314	92.58 315	95.70 258	74.97 332	87.45 269	91.96 311	78.01 259	98.17 204	80.39 297	88.74 250	96.72 194

WR-MVS_H		92.00 183	91.35 179	93.95 206	95.09 241	89.47 179	98.04 44	98.68 7	91.46 127	88.34 250	94.68 235	85.86 129	97.56 275	85.77 248	84.24 299	94.82 278

CP-MVSNet		91.89 186	91.24 186	93.82 214	95.05 242	88.57 208	97.82 59	98.19 44	91.70 120	88.21 256	95.76 189	81.96 192	97.52 281	87.86 204	84.65 292	95.37 247

DWT-MVSNet_test		90.76 233	89.89 236	93.38 235	95.04 243	83.70 292	95.85 231	94.30 312	88.19 220	90.46 190	92.80 296	73.61 288	98.50 181	88.16 199	90.58 231	97.95 153

test_0402		86.46 290	84.79 294	91.45 288	95.02 244	85.55 269	96.29 206	94.89 295	80.90 314	82.21 313	93.97 272	68.21 314	97.29 296	62.98 338	88.68 252	91.51 329

cascas		91.20 216	90.08 229	94.58 181	94.97 245	89.16 197	93.65 298	97.59 132	79.90 321	89.40 225	92.92 295	75.36 277	98.36 189	92.14 125	94.75 173	96.23 202

PS-CasMVS		91.55 197	90.84 199	93.69 221	94.96 246	88.28 214	97.84 58	98.24 34	91.46 127	88.04 260	95.80 184	79.67 230	97.48 283	87.02 228	84.54 296	95.31 250

DU-MVS		92.90 152	92.04 156	95.49 143	94.95 247	92.83 78	97.16 129	98.24 34	93.02 79	90.13 200	95.71 192	83.47 157	97.85 250	91.71 137	83.93 302	95.78 223

NR-MVSNet		92.34 169	91.27 185	95.53 140	94.95 247	93.05 73	97.39 104	98.07 69	92.65 96	84.46 301	95.71 192	85.00 139	97.77 259	89.71 168	83.52 308	95.78 223

RRT_MVS		93.21 138	92.32 151	95.91 119	94.92 249	94.15 41	96.92 150	96.86 213	91.42 129	91.28 179	96.43 156	79.66 231	98.10 211	93.29 109	90.06 237	95.46 237

tpmvs		89.83 259	89.15 256	91.89 275	94.92 249	80.30 317	93.11 308	95.46 268	86.28 263	88.08 259	92.65 298	80.44 215	98.52 180	81.47 288	89.92 239	96.84 190

PMMVS		92.86 154	92.34 149	94.42 186	94.92 249	86.73 250	94.53 270	96.38 238	84.78 284	94.27 114	95.12 218	83.13 164	98.40 186	91.47 144	96.49 144	98.12 146

tpm2		89.96 254	89.21 254	92.23 269	94.91 252	81.25 308	93.78 293	94.42 307	80.62 318	91.56 168	93.44 289	76.44 269	97.94 240	85.60 250	92.08 210	97.49 177

TinyColmap		86.82 288	85.35 291	91.21 291	94.91 252	82.99 298	93.94 290	94.02 316	83.58 297	81.56 315	94.68 235	62.34 332	98.13 206	75.78 316	87.35 264	92.52 321

UniMVSNet_ETH3D		91.34 210	90.22 225	94.68 176	94.86 254	87.86 228	97.23 123	97.46 146	87.99 227	89.90 209	96.92 127	66.35 321	98.23 196	90.30 159	90.99 226	97.96 151

CostFormer		91.18 220	90.70 204	92.62 261	94.84 255	81.76 306	94.09 287	94.43 306	84.15 290	92.72 149	93.77 278	79.43 234	98.20 200	90.70 154	92.18 206	97.90 155

MIMVSNet		88.50 274	86.76 280	93.72 219	94.84 255	87.77 230	91.39 320	94.05 314	86.41 262	87.99 262	92.59 300	63.27 329	95.82 321	77.44 310	92.84 195	97.57 175

FMVSNet5		87.29 285	85.79 287	91.78 281	94.80 257	87.28 235	95.49 245	95.28 276	84.09 291	83.85 310	91.82 312	62.95 330	94.17 332	78.48 307	85.34 282	93.91 308

TranMVSNet+NR-MVSNet		92.50 162	91.63 170	95.14 155	94.76 258	92.07 99	97.53 90	98.11 59	92.90 88	89.56 221	96.12 170	83.16 162	97.60 273	89.30 179	83.20 311	95.75 227

XXY-MVS		92.16 179	91.23 187	94.95 165	94.75 259	90.94 139	97.47 97	97.43 159	89.14 190	88.90 237	96.43 156	79.71 229	98.24 195	89.56 173	87.68 258	95.67 231

EPMVS		90.70 238	89.81 240	93.37 236	94.73 260	84.21 285	93.67 297	88.02 340	89.50 181	92.38 153	93.49 287	77.82 261	97.78 257	86.03 244	92.68 197	98.11 149

D2MVS		91.30 212	90.95 193	92.35 266	94.71 261	85.52 270	96.18 215	98.21 40	88.89 199	86.60 285	93.82 276	79.92 226	97.95 239	89.29 180	90.95 227	93.56 311

USDC		88.94 265	87.83 270	92.27 268	94.66 262	84.96 277	93.86 291	95.90 253	87.34 248	83.40 311	95.56 201	67.43 317	98.19 202	82.64 282	89.67 242	93.66 310

MVS_0304		88.79 269	87.57 271	92.46 262	94.65 263	86.15 264	96.40 194	97.17 179	86.44 261	88.02 261	91.71 315	56.68 337	97.03 302	84.47 264	92.58 199	94.19 303

GA-MVS		91.38 205	90.31 217	94.59 177	94.65 263	87.62 232	94.34 278	96.19 246	90.73 150	90.35 193	93.83 274	71.84 293	97.96 237	87.22 224	93.61 189	98.21 143

OPM-MVS		93.28 136	92.76 132	94.82 169	94.63 265	90.77 146	96.65 174	97.18 177	93.72 55	91.68 167	97.26 111	79.33 236	98.63 171	92.13 126	92.28 202	95.07 261

test-LLR		91.42 203	91.19 189	92.12 270	94.59 266	80.66 311	94.29 281	92.98 323	91.11 143	90.76 186	92.37 303	79.02 241	98.07 219	88.81 191	96.74 137	97.63 168

test-mter		90.19 251	89.54 249	92.12 270	94.59 266	80.66 311	94.29 281	92.98 323	87.68 240	90.76 186	92.37 303	67.67 315	98.07 219	88.81 191	96.74 137	97.63 168

dp		88.90 267	88.26 267	90.81 297	94.58 268	76.62 331	92.85 312	94.93 294	85.12 278	90.07 207	93.07 293	75.81 272	98.12 209	80.53 296	87.42 262	97.71 165

PEN-MVS		91.20 216	90.44 212	93.48 230	94.49 269	87.91 227	97.76 64	98.18 46	91.29 134	87.78 265	95.74 191	80.35 217	97.33 294	85.46 252	82.96 312	95.19 259

gg-mvs-nofinetune		87.82 280	85.61 288	94.44 184	94.46 270	89.27 193	91.21 322	84.61 345	80.88 315	89.89 211	74.98 338	71.50 295	97.53 279	85.75 249	97.21 129	96.51 197

CR-MVSNet		90.82 232	89.77 242	93.95 206	94.45 271	87.19 240	90.23 328	95.68 261	86.89 256	92.40 151	92.36 306	80.91 207	97.05 300	81.09 294	93.95 184	97.60 173

RPMNet		88.52 273	86.72 282	93.95 206	94.45 271	87.19 240	90.23 328	94.99 291	77.87 330	92.40 151	87.55 331	80.17 221	97.05 300	68.84 334	93.95 184	97.60 173

TESTMET0.1,1		90.06 253	89.42 250	91.97 273	94.41 273	80.62 313	94.29 281	91.97 331	87.28 250	90.44 191	92.47 302	68.79 310	97.67 265	88.50 197	96.60 142	97.61 172

TransMVSNet (Re)		88.94 265	87.56 272	93.08 247	94.35 274	88.45 212	97.73 68	95.23 280	87.47 244	84.26 304	95.29 210	79.86 227	97.33 294	79.44 304	74.44 332	93.45 314

MS-PatchMatch		90.27 247	89.77 242	91.78 281	94.33 275	84.72 281	95.55 242	96.73 218	86.17 266	86.36 287	95.28 212	71.28 297	97.80 255	84.09 267	98.14 103	92.81 318

baseline2		91.63 192	90.86 196	93.94 209	94.33 275	86.32 257	95.92 228	91.64 333	89.37 184	86.94 281	94.69 234	81.62 199	98.69 166	88.64 195	94.57 176	96.81 191

XVG-ACMP-BASELINE		90.93 229	90.21 226	93.09 246	94.31 277	85.89 265	95.33 251	97.26 174	91.06 145	89.38 226	95.44 207	68.61 311	98.60 174	89.46 175	91.05 224	94.79 283

pm-mvs1		90.72 237	89.65 248	93.96 205	94.29 278	89.63 170	97.79 62	96.82 216	89.07 191	86.12 290	95.48 206	78.61 248	97.78 257	86.97 229	81.67 316	94.46 294

v8		91.29 213	90.53 211	93.57 227	94.15 279	88.12 222	97.34 108	97.06 192	88.99 194	88.32 251	94.26 260	83.08 165	98.01 228	87.62 216	83.92 304	94.57 292

v10		91.04 223	90.23 223	93.49 229	94.12 280	88.16 221	97.32 111	97.08 189	88.26 219	88.29 253	94.22 263	82.17 189	97.97 233	86.45 235	84.12 300	94.33 298

Patchmtry		88.64 272	87.25 275	92.78 257	94.09 281	86.64 251	89.82 331	95.68 261	80.81 317	87.63 268	92.36 306	80.91 207	97.03 302	78.86 306	85.12 286	94.67 289

PatchT		88.87 268	87.42 273	93.22 242	94.08 282	85.10 275	89.51 332	94.64 303	81.92 308	92.36 154	88.15 329	80.05 223	97.01 305	72.43 326	93.65 187	97.54 176

V42		91.58 195	90.87 195	93.73 217	94.05 283	88.50 210	97.32 111	96.97 200	88.80 206	89.71 214	94.33 253	82.54 180	98.05 222	89.01 188	85.07 287	94.64 291

DTE-MVSNet		90.56 241	89.75 244	93.01 248	93.95 284	87.25 237	97.64 83	97.65 126	90.74 149	87.12 276	95.68 195	79.97 225	97.00 306	83.33 273	81.66 317	94.78 285

tpm		90.25 248	89.74 245	91.76 283	93.92 285	79.73 322	93.98 288	93.54 319	88.28 218	91.99 163	93.25 292	77.51 263	97.44 287	87.30 223	87.94 256	98.12 146

PS-MVSNAJss		93.74 123	93.51 114	94.44 184	93.91 286	89.28 192	97.75 65	97.56 136	92.50 99	89.94 208	96.54 151	88.65 89	98.18 203	93.83 99	90.90 228	95.86 216

v1144		91.37 207	90.60 207	93.68 222	93.89 287	88.23 217	96.84 157	97.03 197	88.37 216	89.69 216	94.39 249	82.04 190	97.98 230	87.80 206	85.37 281	94.84 275

v2v482		91.59 193	90.85 198	93.80 215	93.87 288	88.17 220	96.94 149	96.88 210	89.54 179	89.53 222	94.90 224	81.70 198	98.02 227	89.25 182	85.04 289	95.20 258

v148		90.99 225	90.38 214	92.81 256	93.83 289	85.80 266	96.78 164	96.68 225	89.45 182	88.75 244	93.93 273	82.96 171	97.82 254	87.83 205	83.25 309	94.80 281

Baseline_NR-MVSNet		91.20 216	90.62 206	92.95 251	93.83 289	88.03 223	97.01 142	95.12 285	88.42 215	89.70 215	95.13 217	83.47 157	97.44 287	89.66 171	83.24 310	93.37 315

EPNet_dtu		91.71 189	91.28 184	92.99 249	93.76 291	83.71 291	96.69 171	95.28 276	93.15 75	87.02 280	95.95 176	83.37 160	97.38 292	79.46 303	96.84 134	97.88 157
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1192		91.07 221	90.23 223	93.58 226	93.70 292	87.82 229	96.73 166	97.07 190	87.77 236	89.58 219	94.32 255	80.90 209	97.97 233	86.52 233	85.48 279	94.95 265

GG-mvs-BLEND				93.62 223	93.69 293	89.20 194	92.39 318	83.33 346		87.98 263	89.84 321	71.00 299	96.87 309	82.08 285	95.40 161	94.80 281

v144192		91.06 222	90.28 219	93.39 234	93.66 294	87.23 239	96.83 158	97.07 190	87.43 245	89.69 216	94.28 257	81.48 200	98.00 229	87.18 226	84.92 291	94.93 269

v1921920		90.85 231	90.03 233	93.29 239	93.55 295	86.96 247	96.74 165	97.04 195	87.36 247	89.52 223	94.34 252	80.23 220	97.97 233	86.27 236	85.21 284	94.94 267

v7n		90.76 233	89.86 237	93.45 233	93.54 296	87.60 233	97.70 74	97.37 165	88.85 200	87.65 267	94.08 268	81.08 204	98.10 211	84.68 261	83.79 306	94.66 290

JIA-IIPM		88.26 277	87.04 279	91.91 274	93.52 297	81.42 307	89.38 333	94.38 308	80.84 316	90.93 185	80.74 336	79.22 237	97.92 244	82.76 279	91.62 214	96.38 201

v1240		90.70 238	89.85 238	93.23 241	93.51 298	86.80 248	96.61 179	97.02 198	87.16 252	89.58 219	94.31 256	79.55 233	97.98 230	85.52 251	85.44 280	94.90 272

test_djsdf		93.07 143	92.76 132	94.00 201	93.49 299	88.70 206	98.22 31	97.57 133	91.42 129	90.08 206	95.55 202	82.85 173	97.92 244	94.07 90	91.58 215	95.40 244

SixPastTwentyTwo		89.15 264	88.54 263	90.98 294	93.49 299	80.28 318	96.70 169	94.70 300	90.78 148	84.15 306	95.57 199	71.78 294	97.71 263	84.63 262	85.07 287	94.94 267

mvs_tets		92.31 171	91.76 165	93.94 209	93.41 301	88.29 213	97.63 84	97.53 137	92.04 113	88.76 243	96.45 155	74.62 280	98.09 215	93.91 95	91.48 217	95.45 239

OurMVSNet-221017-0		90.51 243	90.19 227	91.44 289	93.41 301	81.25 308	96.98 145	96.28 241	91.68 121	86.55 286	96.30 163	74.20 283	97.98 230	88.96 189	87.40 263	95.09 260

pmmvs4		90.93 229	89.85 238	94.17 194	93.34 303	90.79 145	94.60 267	96.02 250	84.62 285	87.45 269	95.15 215	81.88 195	97.45 286	87.70 209	87.87 257	94.27 302

jajsoiax		92.42 166	91.89 163	94.03 200	93.33 304	88.50 210	97.73 68	97.53 137	92.00 115	88.85 240	96.50 153	75.62 276	98.11 210	93.88 97	91.56 216	95.48 234

gm-plane-assit					93.22 305	78.89 328			84.82 283		93.52 286		98.64 170	87.72 207

MVP-Stereo		90.74 236	90.08 229	92.71 258	93.19 306	88.20 218	95.86 230	96.27 242	86.07 267	84.86 299	94.76 231	77.84 260	97.75 260	83.88 271	98.01 105	92.17 326
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
EU-MVSNet		88.72 271	88.90 258	88.20 313	93.15 307	74.21 335	96.63 178	94.22 313	85.18 276	87.32 274	95.97 174	76.16 271	94.98 329	85.27 254	86.17 272	95.41 240

MDA-MVSNet-bldmvs		85.00 299	82.95 302	91.17 293	93.13 308	83.33 295	94.56 269	95.00 289	84.57 286	65.13 339	92.65 298	70.45 302	95.85 319	73.57 324	77.49 325	94.33 298

K. test v3		87.64 282	86.75 281	90.32 305	93.02 309	79.48 324	96.61 179	92.08 330	90.66 154	80.25 323	94.09 267	67.21 319	96.65 313	85.96 246	80.83 320	94.83 276

pmmvs5		89.86 258	88.87 259	92.82 255	92.86 310	86.23 260	96.26 208	95.39 269	84.24 289	87.12 276	94.51 242	74.27 282	97.36 293	87.61 217	87.57 259	94.86 274

testgi		87.97 278	87.21 277	90.24 306	92.86 310	80.76 310	96.67 173	94.97 292	91.74 119	85.52 293	95.83 182	62.66 331	94.47 331	76.25 315	88.36 254	95.48 234

EPNet		95.20 83	94.56 89	97.14 66	92.80 312	92.68 82	97.85 57	94.87 299	96.64 1	92.46 150	97.80 79	86.23 122	99.65 52	93.72 100	98.62 91	99.10 75
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
N_pmnet		78.73 308	78.71 309	78.79 323	92.80 312	46.50 350	94.14 285	43.71 353	78.61 326	80.83 317	91.66 316	74.94 279	96.36 315	67.24 335	84.45 297	93.50 312

EG-PatchMatch MVS		87.02 287	85.44 289	91.76 283	92.67 314	85.00 276	96.08 219	96.45 236	83.41 300	79.52 325	93.49 287	57.10 336	97.72 262	79.34 305	90.87 229	92.56 320

Gipuma		67.86 312	65.41 314	75.18 326	92.66 315	73.45 336	66.50 345	94.52 305	53.33 342	57.80 342	66.07 342	30.81 345	89.20 340	48.15 342	78.88 324	62.90 342
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
anonymousdsp		92.16 179	91.55 173	93.97 204	92.58 316	89.55 175	97.51 91	97.42 160	89.42 183	88.40 249	94.84 227	80.66 211	97.88 249	91.87 133	91.28 221	94.48 293

test0.0.03 1		89.37 263	88.70 260	91.41 290	92.47 317	85.63 268	95.22 259	92.70 325	91.11 143	86.91 283	93.65 284	79.02 241	93.19 337	78.00 309	89.18 245	95.41 240

our_test_3		88.78 270	87.98 269	91.20 292	92.45 318	82.53 300	93.61 300	95.69 259	85.77 270	84.88 298	93.71 279	79.99 224	96.78 312	79.47 302	86.24 271	94.28 301

ppachtmachnet_test		88.35 276	87.29 274	91.53 286	92.45 318	83.57 294	93.75 294	95.97 251	84.28 288	85.32 297	94.18 264	79.00 245	96.93 307	75.71 317	84.99 290	94.10 304

YYNet1		85.87 295	84.23 298	90.78 300	92.38 320	82.46 302	93.17 305	95.14 284	82.12 307	67.69 335	92.36 306	78.16 256	95.50 327	77.31 312	79.73 322	94.39 296

MDA-MVSNet_test_wron		85.87 295	84.23 298	90.80 299	92.38 320	82.57 299	93.17 305	95.15 283	82.15 306	67.65 336	92.33 309	78.20 253	95.51 326	77.33 311	79.74 321	94.31 300

LF4IMVS		87.94 279	87.25 275	89.98 308	92.38 320	80.05 321	94.38 276	95.25 279	87.59 242	84.34 302	94.74 233	64.31 328	97.66 267	84.83 258	87.45 260	92.23 324

lessismore_v0				90.45 303	91.96 323	79.09 327		87.19 343		80.32 322	94.39 249	66.31 322	97.55 276	84.00 269	76.84 327	94.70 288

pmmvs6		87.81 281	86.19 284	92.69 259	91.32 324	86.30 258	97.34 108	96.41 237	80.59 319	84.05 308	94.37 251	67.37 318	97.67 265	84.75 260	79.51 323	94.09 306

Anonymous20231206		87.09 286	86.14 285	89.93 309	91.22 325	80.35 315	96.11 217	95.35 272	83.57 298	84.16 305	93.02 294	73.54 289	95.61 323	72.16 327	86.14 273	93.84 309

DeepMVS_CX				74.68 327	90.84 326	64.34 345		81.61 348	65.34 338	67.47 337	88.01 330	48.60 342	80.13 345	62.33 339	73.68 334	79.58 339

test20.03		86.14 293	85.40 290	88.35 311	90.12 327	80.06 320	95.90 229	95.20 281	88.59 209	81.29 316	93.62 285	71.43 296	92.65 338	71.26 331	81.17 319	92.34 323

OpenMVS_ROB	81.14 20	84.42 301	82.28 303	90.83 296	90.06 328	84.05 288	95.73 236	94.04 315	73.89 334	80.17 324	91.53 317	59.15 334	97.64 268	66.92 336	89.05 246	90.80 332

UnsupCasMVSNet_eth		85.99 294	84.45 296	90.62 301	89.97 329	82.40 303	93.62 299	97.37 165	89.86 173	78.59 328	92.37 303	65.25 327	95.35 328	82.27 284	70.75 335	94.10 304

DSMNet-mixed		86.34 291	86.12 286	87.00 318	89.88 330	70.43 338	94.93 263	90.08 338	77.97 329	85.42 296	92.78 297	74.44 281	93.96 333	74.43 320	95.14 164	96.62 195

new_pmnet		82.89 304	81.12 307	88.18 314	89.63 331	80.18 319	91.77 319	92.57 326	76.79 331	75.56 332	88.23 328	61.22 333	94.48 330	71.43 329	82.92 313	89.87 334

MIMVSNet1		84.93 300	83.05 301	90.56 302	89.56 332	84.84 280	95.40 248	95.35 272	83.91 292	80.38 321	92.21 310	57.23 335	93.34 336	70.69 333	82.75 315	93.50 312

CMPMVS	62.92 21	85.62 297	84.92 293	87.74 315	89.14 333	73.12 337	94.17 284	96.80 217	73.98 333	73.65 333	94.93 222	66.36 320	97.61 272	83.95 270	91.28 221	92.48 322
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Patchmatch-RL test		87.38 283	86.24 283	90.81 297	88.74 334	78.40 329	88.12 336	93.17 322	87.11 253	82.17 314	89.29 323	81.95 193	95.60 324	88.64 195	77.02 326	98.41 133

pmmvs-eth3d		86.22 292	84.45 296	91.53 286	88.34 335	87.25 237	94.47 271	95.01 288	83.47 299	79.51 326	89.61 322	69.75 308	95.71 322	83.13 275	76.73 328	91.64 327

UnsupCasMVSNet_bld		82.13 306	79.46 308	90.14 307	88.00 336	82.47 301	90.89 325	96.62 233	78.94 325	75.61 330	84.40 334	56.63 338	96.31 316	77.30 313	66.77 339	91.63 328

PM-MVS		83.48 302	81.86 305	88.31 312	87.83 337	77.59 330	93.43 301	91.75 332	86.91 255	80.63 319	89.91 320	44.42 343	95.84 320	85.17 257	76.73 328	91.50 330

testing_2		87.33 284	85.03 292	94.22 192	87.77 338	89.32 189	94.97 262	97.11 185	89.22 188	71.64 334	88.73 324	55.16 339	97.94 240	91.95 130	88.73 251	95.41 240

new-patchmatchnet		83.18 303	81.87 304	87.11 317	86.88 339	75.99 333	93.70 295	95.18 282	85.02 280	77.30 329	88.40 326	65.99 324	93.88 334	74.19 323	70.18 336	91.47 331

ambc				86.56 319	83.60 340	70.00 340	85.69 338	94.97 292		80.60 320	88.45 325	37.42 344	96.84 310	82.69 281	75.44 330	92.86 317

pmmvs3		79.97 307	77.50 310	87.39 316	82.80 341	79.38 325	92.70 314	90.75 337	70.69 336	78.66 327	87.47 332	51.34 341	93.40 335	73.39 325	69.65 337	89.38 335

TDRefinement		86.53 289	84.76 295	91.85 276	82.23 342	84.25 284	96.38 197	95.35 272	84.97 281	84.09 307	94.94 221	65.76 326	98.34 192	84.60 263	74.52 331	92.97 316

PMMVS2		70.19 311	66.92 313	80.01 322	76.35 343	65.67 343	86.22 337	87.58 342	64.83 339	62.38 340	80.29 337	26.78 349	88.49 341	63.79 337	54.07 341	85.88 336

FPMVS		71.27 310	69.85 311	75.50 325	74.64 344	59.03 346	91.30 321	91.50 334	58.80 340	57.92 341	88.28 327	29.98 347	85.53 343	53.43 340	82.84 314	81.95 338

E-PMN		53.28 315	52.56 318	55.43 330	74.43 345	47.13 349	83.63 341	76.30 349	42.23 344	42.59 345	62.22 344	28.57 348	74.40 346	31.53 345	31.51 343	44.78 343

wuyk23d		25.11 319	24.57 322	26.74 333	73.98 346	39.89 353	57.88 346	9.80 354	12.27 348	10.39 349	6.97 351	7.03 353	36.44 350	25.43 347	17.39 347	3.89 348

EMVS		52.08 317	51.31 319	54.39 331	72.62 347	45.39 351	83.84 340	75.51 350	41.13 345	40.77 346	59.65 345	30.08 346	73.60 347	28.31 346	29.90 345	44.18 344

LCM-MVSNet		72.55 309	69.39 312	82.03 321	70.81 348	65.42 344	90.12 330	94.36 310	55.02 341	65.88 338	81.72 335	24.16 351	89.96 339	74.32 322	68.10 338	90.71 333

MVE	50.73 23	53.25 316	48.81 320	66.58 329	65.34 349	57.50 347	72.49 344	70.94 351	40.15 346	39.28 347	63.51 343	6.89 354	73.48 348	38.29 344	42.38 342	68.76 341
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		63.94 313	59.58 315	77.02 324	61.24 350	66.06 342	85.66 339	87.93 341	78.53 327	42.94 344	71.04 341	25.42 350	80.71 344	52.60 341	30.83 344	84.28 337

PMVS	53.92 22	58.58 314	55.40 316	68.12 328	51.00 351	48.64 348	78.86 342	87.10 344	46.77 343	35.84 348	74.28 339	8.76 352	86.34 342	42.07 343	73.91 333	69.38 340
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		51.94 318	53.82 317	46.29 332	33.73 352	45.30 352	78.32 343	67.24 352	18.02 347	50.93 343	87.05 333	52.99 340	53.11 349	70.76 332	25.29 346	40.46 345

testmvs		13.36 321	16.33 323	4.48 335	5.04 353	2.26 355	93.18 304	3.28 355	2.70 349	8.24 350	21.66 347	2.29 356	2.19 351	7.58 348	2.96 348	9.00 347

test123		13.04 322	15.66 324	5.18 334	4.51 354	3.45 354	92.50 317	1.81 356	2.50 350	7.58 351	20.15 348	3.67 355	2.18 352	7.13 349	1.07 349	9.90 346

uanet_test		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

cdsmvs_eth3d_5k		23.24 320	30.99 321	0.00 336	0.00 355	0.00 356	0.00 347	97.63 128	0.00 351	0.00 352	96.88 129	84.38 146	0.00 353	0.00 350	0.00 350	0.00 349

pcd_1.5k_mvsjas		7.39 324	9.85 326	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	88.65 89	0.00 353	0.00 350	0.00 350	0.00 349

sosnet-low-res		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

sosnet		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

uncertanet		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

Regformer		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

ab-mvs-re		8.06 323	10.74 325	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	96.69 138	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

uanet		0.00 325	0.00 327	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

test_241102_TWO								98.27 28	95.13 15	98.93 5	98.89 4	94.99 7	99.85 14	97.52 2	99.65 9	99.74 5

test_0728_THIRD									94.78 30	98.73 7	98.87 5	95.87 2	99.84 18	97.45 5	99.72 2	99.77 1

GSMVS																98.45 128

test_part1				0.00 336		0.00 356	0.00 347	98.26 32				0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

sam_mvs1												82.76 175				98.45 128

sam_mvs												81.94 194

MTGPA								98.08 64

test_post1							92.81 313				16.58 350	80.53 213	97.68 264	86.20 238

test_post											17.58 349	81.76 196	98.08 216

patchmatchnet-post											90.45 318	82.65 179	98.10 211

MTMP							97.86 54	82.03 347

test9_res														94.81 79	99.38 47	99.45 43

agg_prior2														93.94 94	99.38 47	99.50 35

test_prior4						93.66 57	96.42 190

test_prior2							96.35 199		92.80 91	96.03 73	97.59 96	92.01 39		95.01 70	99.38 47

旧先验2							95.94 227		81.66 310	97.34 30			98.82 154	92.26 120

新几何2							95.79 234

无先验							95.79 234	97.87 104	83.87 295				99.65 52	87.68 212		98.89 96

原ACMM2							95.67 237

testdata2													99.67 48	85.96 246

segment_acmp												92.89 21

testdata1							95.26 258		93.10 78

plane_prior5								97.51 139					98.60 174	93.02 114	92.23 203	95.86 216

plane_prior4											96.64 141

plane_prior3						90.00 160			94.46 38	91.34 173

plane_prior2							97.74 66		94.85 23

plane_prior						89.99 162	97.24 118		94.06 46						92.16 207

n2								0.00 357

nn								0.00 357

door-mid								91.06 336

test11								97.88 102

door								91.13 335

HQP5-MVS						89.33 187

BP-MVS														92.13 126

HQP4-MVS										90.14 196			98.50 181			95.78 223

HQP3-MVS								97.39 162							92.10 208

HQP2-MVS												80.95 205

MDTV_nov1_ep13_2view						70.35 339	93.10 309		83.88 294	93.55 128		82.47 183		86.25 237		98.38 136

ACMMP++_ref															90.30 236

ACMMP++															91.02 225

Test By Simon												88.73 88

High-resolution multi-view benchmark