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.

test_0728_SECOND				95.01 15	98.79 1	86.43 40	97.09 10	97.49 5					99.61 3	95.62 4	99.08 6	98.99 5

MSP-MVS		95.67 1	96.02 1	94.64 39	98.78 2	85.93 56	97.09 10	96.73 73	90.27 27	97.04 7	98.05 5	91.47 5	99.55 11	95.62 4	99.08 6	98.45 30

test0726					98.78 2	85.93 56	97.19 6	97.47 8	90.27 27	97.64 3	98.13 1	91.47 5

IU-MVS					98.77 4	86.00 53		96.84 62	81.26 232	97.26 5				95.50 6	99.13 3	99.03 4

test_241102_ONE					98.77 4	85.99 54		97.44 13	90.26 29	97.71 1	97.96 7	92.31 2	99.38 28

region2R		94.43 21	94.27 23	94.92 20	98.65 6	86.67 32	96.92 18	97.23 32	88.60 69	93.58 42	97.27 23	85.22 54	99.54 15	92.21 42	98.74 28	98.56 18

ACMMPR		94.43 21	94.28 21	94.91 22	98.63 7	86.69 30	96.94 14	97.32 24	88.63 67	93.53 45	97.26 25	85.04 57	99.54 15	92.35 39	98.78 20	98.50 20

HFP-MVS		94.52 16	94.40 18	94.86 25	98.61 8	86.81 24	96.94 14	97.34 19	88.63 67	93.65 38	97.21 28	86.10 43	99.49 22	92.35 39	98.77 23	98.30 39

#test#		94.32 27	94.14 30	94.86 25	98.61 8	86.81 24	96.43 30	97.34 19	87.51 98	93.65 38	97.21 28	86.10 43	99.49 22	91.68 60	98.77 23	98.30 39

test_part2					98.55 10	87.22 16				96.40 10

XVS		94.45 19	94.32 19	94.85 27	98.54 11	86.60 35	96.93 16	97.19 35	90.66 23	92.85 54	97.16 33	85.02 58	99.49 22	91.99 49	98.56 46	98.47 26

X-MVStestdata		88.31 156	86.13 196	94.85 27	98.54 11	86.60 35	96.93 16	97.19 35	90.66 23	92.85 54	23.41 346	85.02 58	99.49 22	91.99 49	98.56 46	98.47 26

ZNCC-MVS		94.47 17	94.28 21	95.03 14	98.52 13	86.96 17	96.85 22	97.32 24	88.24 78	93.15 50	97.04 38	86.17 42	99.62 1	92.40 37	98.81 17	98.52 19

mPP-MVS		93.99 37	93.78 40	94.63 40	98.50 14	85.90 61	96.87 20	96.91 55	88.70 65	91.83 83	97.17 32	83.96 70	99.55 11	91.44 65	98.64 42	98.43 32

DVP-MVS		95.42 4	95.56 4	94.98 19	98.49 15	86.52 37	96.91 19	97.47 8	91.73 8	96.10 12	96.69 53	89.90 8	99.30 38	94.70 8	98.04 62	99.13 1

MP-MVS		94.25 28	94.07 33	94.77 34	98.47 16	86.31 46	96.71 25	96.98 47	89.04 56	91.98 77	97.19 30	85.43 52	99.56 6	92.06 48	98.79 18	98.44 31
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MCST-MVS		94.45 19	94.20 27	95.19 9	98.46 17	87.50 13	95.00 101	97.12 40	87.13 103	92.51 68	96.30 69	89.24 13	99.34 32	93.46 20	98.62 43	98.73 10

PGM-MVS		93.96 38	93.72 42	94.68 37	98.43 18	86.22 49	95.30 79	97.78 1	87.45 99	93.26 46	97.33 20	84.62 63	99.51 20	90.75 78	98.57 45	98.32 38

zzz-MVS		94.47 17	94.30 20	95.00 16	98.42 19	86.95 18	95.06 99	96.97 48	91.07 13	93.14 51	97.56 13	84.30 65	99.56 6	93.43 21	98.75 26	98.47 26

MTAPA		94.42 23	94.22 24	95.00 16	98.42 19	86.95 18	94.36 149	96.97 48	91.07 13	93.14 51	97.56 13	84.30 65	99.56 6	93.43 21	98.75 26	98.47 26

GST-MVS		94.21 32	93.97 36	94.90 24	98.41 21	86.82 23	96.54 29	97.19 35	88.24 78	93.26 46	96.83 46	85.48 51	99.59 4	91.43 66	98.40 51	98.30 39

HPM-MVS		94.02 36	93.88 37	94.43 49	98.39 22	85.78 63	97.25 5	97.07 44	86.90 111	92.62 65	96.80 50	84.85 61	99.17 48	92.43 35	98.65 41	98.33 37
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		94.34 25	94.21 26	94.74 36	98.39 22	86.64 34	97.60 1	97.24 30	88.53 71	92.73 61	97.23 26	85.20 55	99.32 36	92.15 45	98.83 16	98.25 48

DPE-MVS		95.57 2	95.67 2	95.25 7	98.36 24	87.28 15	95.56 70	97.51 4	89.13 54	97.14 6	97.91 8	91.64 4	99.62 1	94.61 10	99.17 2	98.86 7

HPM-MVS_fast		93.40 52	93.22 50	93.94 60	98.36 24	84.83 74	97.15 8	96.80 68	85.77 132	92.47 69	97.13 34	82.38 81	99.07 56	90.51 80	98.40 51	97.92 73

DP-MVS Recon		91.95 73	91.28 78	93.96 59	98.33 26	85.92 58	94.66 124	96.66 81	82.69 198	90.03 108	95.82 90	82.30 84	99.03 62	84.57 143	96.48 95	96.91 113

APDe-MVS		95.46 3	95.64 3	94.91 22	98.26 27	86.29 48	97.46 2	97.40 17	89.03 57	96.20 11	98.10 2	89.39 12	99.34 32	95.88 1	99.03 8	99.10 3

TSAR-MVS + MP.		94.85 11	94.94 9	94.58 42	98.25 28	86.33 44	96.11 44	96.62 84	88.14 83	96.10 12	96.96 42	89.09 14	98.94 79	94.48 11	98.68 34	98.48 22
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HPM-MVS++		95.14 8	94.91 10	95.83 2	98.25 28	89.65 2	95.92 54	96.96 51	91.75 7	94.02 32	96.83 46	88.12 20	99.55 11	93.41 23	98.94 11	98.28 43

testtj		94.39 24	94.18 28	95.00 16	98.24 30	86.77 28	96.16 39	97.23 32	87.28 101	94.85 23	97.04 38	86.99 35	99.52 19	91.54 62	98.33 54	98.71 11

CPTT-MVS		91.99 72	91.80 72	92.55 104	98.24 30	81.98 148	96.76 24	96.49 91	81.89 217	90.24 104	96.44 66	78.59 126	98.61 101	89.68 85	97.85 68	97.06 105

SR-MVS		94.23 30	94.17 29	94.43 49	98.21 32	85.78 63	96.40 32	96.90 56	88.20 81	94.33 26	97.40 17	84.75 62	99.03 62	93.35 24	97.99 63	98.48 22

MP-MVS-pluss		94.21 32	94.00 35	94.85 27	98.17 33	86.65 33	94.82 113	97.17 38	86.26 123	92.83 56	97.87 9	85.57 50	99.56 6	94.37 13	98.92 12	98.34 36
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SMA-MVS		95.20 6	95.07 8	95.59 3	98.14 34	88.48 6	96.26 36	97.28 28	85.90 129	97.67 2	98.10 2	88.41 16	99.56 6	94.66 9	99.19 1	98.71 11

CNVR-MVS		95.40 5	95.37 5	95.50 5	98.11 35	88.51 5	95.29 81	96.96 51	92.09 3	95.32 18	97.08 36	89.49 11	99.33 35	95.10 7	98.85 14	98.66 13

114514_t		89.51 122	88.50 131	92.54 105	98.11 35	81.99 147	95.16 92	96.36 99	70.19 325	85.81 172	95.25 104	76.70 143	98.63 99	82.07 178	96.86 85	97.00 109

ACMMP		93.24 56	92.88 59	94.30 53	98.09 37	85.33 70	96.86 21	97.45 11	88.33 75	90.15 106	97.03 40	81.44 95	99.51 20	90.85 77	95.74 101	98.04 64
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
APD-MVS		94.24 29	94.07 33	94.75 35	98.06 38	86.90 21	95.88 55	96.94 53	85.68 135	95.05 22	97.18 31	87.31 29	99.07 56	91.90 56	98.61 44	98.28 43
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CSCG		93.23 57	93.05 54	93.76 67	98.04 39	84.07 94	96.22 37	97.37 18	84.15 166	90.05 107	95.66 95	87.77 22	99.15 51	89.91 83	98.27 56	98.07 61

ACMMP_NAP		94.74 14	94.56 15	95.28 6	98.02 40	87.70 10	95.68 63	97.34 19	88.28 77	95.30 19	97.67 12	85.90 47	99.54 15	93.91 16	98.95 10	98.60 16

OPU-MVS				96.21 1	98.00 41	90.85 1	97.13 9				97.08 36	92.59 1	98.94 79	92.25 41	98.99 9	98.84 8

APD-MVS_3200maxsize		93.78 42	93.77 41	93.80 66	97.92 42	84.19 92	96.30 34	96.87 60	86.96 107	93.92 34	97.47 15	83.88 71	98.96 78	92.71 33	97.87 67	98.26 47

xxxxxxxxxxxxxcwj		94.76 13	94.85 12	94.48 46	97.85 43	85.63 66	95.21 87	96.82 65	89.44 43	95.71 14	97.70 10	88.28 18	99.35 30	93.89 17	98.78 20	98.48 22

save fliter					97.85 43	85.63 66	95.21 87	96.82 65	89.44 43

SF-MVS		94.97 9	94.90 11	95.20 8	97.84 45	87.76 8	96.65 27	97.48 7	87.76 92	95.71 14	97.70 10	88.28 18	99.35 30	93.89 17	98.78 20	98.48 22

NCCC		94.81 12	94.69 14	95.17 10	97.83 46	87.46 14	95.66 65	96.93 54	92.34 2	93.94 33	96.58 60	87.74 23	99.44 26	92.83 31	98.40 51	98.62 15

ETH3 D test6400		93.64 46	93.22 50	94.92 20	97.79 47	86.84 22	95.31 76	97.26 29	82.67 199	93.81 36	96.29 70	87.29 30	99.27 41	89.87 84	98.67 36	98.65 14

9.14			94.47 16		97.79 47		96.08 45	97.44 13	86.13 127	95.10 21	97.40 17	88.34 17	99.22 44	93.25 26	98.70 31

CDPH-MVS		92.83 61	92.30 67	94.44 47	97.79 47	86.11 51	94.06 168	96.66 81	80.09 244	92.77 58	96.63 57	86.62 37	99.04 61	87.40 111	98.66 39	98.17 52

ETH3D-3000-0.1		94.61 15	94.44 17	95.12 11	97.70 50	87.71 9	95.98 51	97.44 13	86.67 116	95.25 20	97.31 21	87.73 24	99.24 42	93.11 29	98.76 25	98.40 33

DP-MVS		87.25 193	85.36 221	92.90 88	97.65 51	83.24 114	94.81 114	92.00 268	74.99 294	81.92 261	95.00 112	72.66 197	99.05 58	66.92 308	92.33 159	96.40 125

PAPM_NR		91.22 87	90.78 89	92.52 106	97.60 52	81.46 160	94.37 148	96.24 106	86.39 121	87.41 143	94.80 121	82.06 90	98.48 107	82.80 167	95.37 109	97.61 83

TEST9					97.53 53	86.49 38	94.07 166	96.78 69	81.61 225	92.77 58	96.20 75	87.71 25	99.12 53

train_agg		93.44 50	93.08 53	94.52 44	97.53 53	86.49 38	94.07 166	96.78 69	81.86 218	92.77 58	96.20 75	87.63 26	99.12 53	92.14 46	98.69 32	97.94 70

abl_6		93.18 58	93.05 54	93.57 70	97.52 55	84.27 91	95.53 71	96.67 80	87.85 89	93.20 49	97.22 27	80.35 102	99.18 47	91.91 53	97.21 78	97.26 96

test_8					97.49 56	86.30 47	94.02 171	96.76 72	81.86 218	92.70 62	96.20 75	87.63 26	99.02 66

DeepC-MVS_fast	89.43 2	94.04 35	93.79 39	94.80 33	97.48 57	86.78 26	95.65 67	96.89 57	89.40 46	92.81 57	96.97 41	85.37 53	99.24 42	90.87 76	98.69 32	98.38 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		89.89 115	89.07 119	92.37 114	97.41 58	83.03 120	94.42 140	95.92 128	82.81 196	86.34 165	94.65 126	73.89 180	99.02 66	80.69 203	95.51 104	95.05 170

agg_prior1		93.29 54	92.97 57	94.26 54	97.38 59	85.92 58	93.92 176	96.72 75	81.96 212	92.16 73	96.23 73	87.85 21	98.97 75	91.95 52	98.55 48	97.90 74

agg_prior					97.38 59	85.92 58		96.72 75		92.16 73			98.97 75

原ACMM1				92.01 124	97.34 61	81.05 170		96.81 67	78.89 256	90.45 102	95.92 86	82.65 78	98.84 90	80.68 204	98.26 57	96.14 133

MSLP-MVS++		93.72 43	94.08 32	92.65 100	97.31 62	83.43 110	95.79 58	97.33 22	90.03 32	93.58 42	96.96 42	84.87 60	97.76 158	92.19 44	98.66 39	96.76 116

新几何1				93.10 78	97.30 63	84.35 90		95.56 157	71.09 322	91.26 95	96.24 72	82.87 77	98.86 85	79.19 225	98.10 60	96.07 140

test_prior3		93.60 47	93.53 45	93.82 63	97.29 64	84.49 81	94.12 159	96.88 58	87.67 95	92.63 63	96.39 67	86.62 37	98.87 82	91.50 63	98.67 36	98.11 59

test_prior				93.82 63	97.29 64	84.49 81		96.88 58					98.87 82			98.11 59

1121		90.42 103	89.49 108	93.20 74	97.27 66	84.46 84	92.63 222	95.51 162	71.01 323	91.20 96	96.21 74	82.92 76	99.05 58	80.56 206	98.07 61	96.10 138

PLC	84.53 7	89.06 137	88.03 144	92.15 122	97.27 66	82.69 134	94.29 151	95.44 169	79.71 249	84.01 227	94.18 142	76.68 144	98.75 94	77.28 242	93.41 140	95.02 171
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SD-MVS		94.96 10	95.33 6	93.88 61	97.25 68	86.69 30	96.19 38	97.11 42	90.42 25	96.95 9	97.27 23	89.53 10	96.91 227	94.38 12	98.85 14	98.03 65
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				94.34 52	97.13 69	86.15 50		96.29 101		91.04 98		85.08 56	99.01 68		98.13 59	97.86 75

MG-MVS		91.77 76	91.70 74	92.00 126	97.08 70	80.03 198	93.60 188	95.18 183	87.85 89	90.89 99	96.47 65	82.06 90	98.36 117	85.07 135	97.04 82	97.62 82

SteuartSystems-ACMMP		95.20 6	95.32 7	94.85 27	96.99 71	86.33 44	97.33 3	97.30 26	91.38 11	95.39 17	97.46 16	88.98 15	99.40 27	94.12 14	98.89 13	98.82 9
Skip Steuart: Steuart Systems R&D Blog.
MVS_111021_HR		93.45 49	93.31 48	93.84 62	96.99 71	84.84 73	93.24 205	97.24 30	88.76 63	91.60 88	95.85 89	86.07 45	98.66 96	91.91 53	98.16 58	98.03 65

CNLPA		89.07 136	87.98 145	92.34 115	96.87 73	84.78 75	94.08 165	93.24 243	81.41 228	84.46 211	95.13 109	75.57 157	96.62 237	77.21 243	93.84 132	95.61 156

PHI-MVS		93.89 41	93.65 43	94.62 41	96.84 74	86.43 40	96.69 26	97.49 5	85.15 150	93.56 44	96.28 71	85.60 49	99.31 37	92.45 34	98.79 18	98.12 57

旧先验1					96.79 75	81.81 151		95.67 148			96.81 48	86.69 36			97.66 71	96.97 110

ETH3D cwj APD-0.16		93.91 39	93.53 45	95.06 13	96.76 76	87.78 7	94.92 106	97.21 34	84.33 164	93.89 35	97.09 35	87.20 31	99.29 40	91.90 56	98.44 50	98.12 57

LFMVS		90.08 108	89.13 118	92.95 86	96.71 77	82.32 143	96.08 45	89.91 316	86.79 112	92.15 75	96.81 48	62.60 285	98.34 120	87.18 115	93.90 130	98.19 51

Anonymous202405211		87.68 171	86.13 196	92.31 117	96.66 78	80.74 179	94.87 110	91.49 283	80.47 240	89.46 113	95.44 98	54.72 322	98.23 126	82.19 176	89.89 183	97.97 68

TAPA-MVS	84.62 6	88.16 160	87.01 167	91.62 144	96.64 79	80.65 180	94.39 143	96.21 111	76.38 280	86.19 168	95.44 98	79.75 110	98.08 140	62.75 323	95.29 111	96.13 134
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MAR-MVS		90.30 104	89.37 112	93.07 81	96.61 80	84.48 83	95.68 63	95.67 148	82.36 203	87.85 135	92.85 186	76.63 145	98.80 92	80.01 214	96.68 88	95.91 145
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
VNet		92.24 71	91.91 71	93.24 73	96.59 81	83.43 110	94.84 112	96.44 92	89.19 52	94.08 31	95.90 87	77.85 136	98.17 130	88.90 93	93.38 141	98.13 56

TSAR-MVS + GP.		93.66 45	93.41 47	94.41 51	96.59 81	86.78 26	94.40 141	93.93 232	89.77 37	94.21 28	95.59 97	87.35 28	98.61 101	92.72 32	96.15 98	97.83 77

test222					96.55 83	81.70 153	92.22 236	95.01 190	68.36 328	90.20 105	96.14 80	80.26 105			97.80 69	96.05 142

Anonymous20240529		88.09 162	86.59 182	92.58 103	96.53 84	81.92 150	95.99 49	95.84 136	74.11 302	89.06 119	95.21 106	61.44 293	98.81 91	83.67 155	87.47 220	97.01 108

Anonymous20231211		86.59 215	85.13 224	90.98 170	96.52 85	81.50 156	96.14 41	96.16 112	73.78 304	83.65 236	92.15 209	63.26 283	97.37 193	82.82 166	81.74 276	94.06 217

DeepPCF-MVS	89.96 1	94.20 34	94.77 13	92.49 107	96.52 85	80.00 200	94.00 173	97.08 43	90.05 31	95.65 16	97.29 22	89.66 9	98.97 75	93.95 15	98.71 29	98.50 20

testdata				90.49 183	96.40 87	77.89 246		95.37 175	72.51 315	93.63 40	96.69 53	82.08 89	97.65 164	83.08 159	97.39 76	95.94 144

PVSNet_Blended_VisFu		91.38 83	90.91 86	92.80 91	96.39 88	83.17 116	94.87 110	96.66 81	83.29 185	89.27 115	94.46 132	80.29 104	99.17 48	87.57 109	95.37 109	96.05 142

API-MVS		90.66 97	90.07 98	92.45 109	96.36 89	84.57 79	96.06 47	95.22 182	82.39 201	89.13 116	94.27 140	80.32 103	98.46 110	80.16 213	96.71 87	94.33 205

F-COLMAP		87.95 165	86.80 172	91.40 150	96.35 90	80.88 175	94.73 119	95.45 167	79.65 250	82.04 259	94.61 127	71.13 210	98.50 106	76.24 253	91.05 170	94.80 184

VDD-MVS		90.74 93	89.92 104	93.20 74	96.27 91	83.02 121	95.73 60	93.86 233	88.42 74	92.53 66	96.84 45	62.09 288	98.64 98	90.95 74	92.62 155	97.93 72

OMC-MVS		91.23 86	90.62 90	93.08 79	96.27 91	84.07 94	93.52 190	95.93 127	86.95 108	89.51 111	96.13 81	78.50 128	98.35 119	85.84 129	92.90 151	96.83 115

DPM-MVS		92.58 66	91.74 73	95.08 12	96.19 93	89.31 3	92.66 221	96.56 89	83.44 181	91.68 87	95.04 111	86.60 40	98.99 72	85.60 131	97.92 66	96.93 112

CHOSEN 1792x2688		88.84 143	87.69 150	92.30 118	96.14 94	81.42 162	90.01 278	95.86 135	74.52 299	87.41 143	93.94 151	75.46 158	98.36 117	80.36 209	95.53 103	97.12 104

thres100view900		87.63 176	86.71 175	90.38 189	96.12 95	78.55 228	95.03 100	91.58 279	87.15 102	88.06 131	92.29 205	68.91 243	98.10 133	70.13 290	91.10 166	94.48 201

PVSNet_BlendedMVS		89.98 110	89.70 105	90.82 172	96.12 95	81.25 165	93.92 176	96.83 63	83.49 180	89.10 117	92.26 206	81.04 99	98.85 88	86.72 124	87.86 218	92.35 287

PVSNet_Blended		90.73 94	90.32 93	91.98 127	96.12 95	81.25 165	92.55 226	96.83 63	82.04 210	89.10 117	92.56 196	81.04 99	98.85 88	86.72 124	95.91 99	95.84 149

UA-Net		92.83 61	92.54 64	93.68 68	96.10 98	84.71 76	95.66 65	96.39 97	91.92 4	93.22 48	96.49 64	83.16 74	98.87 82	84.47 144	95.47 106	97.45 91

thres600view7		87.65 173	86.67 177	90.59 175	96.08 99	78.72 224	94.88 109	91.58 279	87.06 105	88.08 130	92.30 204	68.91 243	98.10 133	70.05 293	91.10 166	94.96 175

DeepC-MVS	88.79 3	93.31 53	92.99 56	94.26 54	96.07 100	85.83 62	94.89 108	96.99 46	89.02 58	89.56 110	97.37 19	82.51 80	99.38 28	92.20 43	98.30 55	97.57 86
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LS3D		87.89 166	86.32 191	92.59 102	96.07 100	82.92 125	95.23 85	94.92 198	75.66 287	82.89 249	95.98 84	72.48 200	99.21 45	68.43 300	95.23 114	95.64 155

HyFIR lowres test		88.09 162	86.81 171	91.93 131	96.00 102	80.63 181	90.01 278	95.79 140	73.42 307	87.68 140	92.10 214	73.86 181	97.96 149	80.75 202	91.70 162	97.19 100

tfpn200view9		87.58 180	86.64 178	90.41 186	95.99 103	78.64 226	94.58 127	91.98 270	86.94 109	88.09 128	91.77 224	69.18 240	98.10 133	70.13 290	91.10 166	94.48 201

thres400		87.62 178	86.64 178	90.57 176	95.99 103	78.64 226	94.58 127	91.98 270	86.94 109	88.09 128	91.77 224	69.18 240	98.10 133	70.13 290	91.10 166	94.96 175

MVS_111021_LR		92.47 68	92.29 68	92.98 84	95.99 103	84.43 88	93.08 210	96.09 116	88.20 81	91.12 97	95.72 94	81.33 97	97.76 158	91.74 58	97.37 77	96.75 117

PatchMatch-RL		86.77 211	85.54 215	90.47 185	95.88 106	82.71 133	90.54 268	92.31 259	79.82 248	84.32 219	91.57 234	68.77 245	96.39 256	73.16 276	93.48 139	92.32 288

EPP-MVSNet		91.70 79	91.56 75	92.13 123	95.88 106	80.50 186	97.33 3	95.25 179	86.15 125	89.76 109	95.60 96	83.42 73	98.32 123	87.37 113	93.25 144	97.56 87

IS-MVSNet		91.43 82	91.09 83	92.46 108	95.87 108	81.38 163	96.95 13	93.69 238	89.72 39	89.50 112	95.98 84	78.57 127	97.77 157	83.02 161	96.50 94	98.22 50

PAPR		90.02 109	89.27 116	92.29 119	95.78 109	80.95 173	92.68 220	96.22 108	81.91 215	86.66 158	93.75 164	82.23 85	98.44 114	79.40 224	94.79 116	97.48 89

Vis-MVSNet (Re-imp)		89.59 120	89.44 110	90.03 204	95.74 110	75.85 277	95.61 68	90.80 301	87.66 97	87.83 136	95.40 101	76.79 141	96.46 253	78.37 230	96.73 86	97.80 78

test_yl		90.69 95	90.02 102	92.71 96	95.72 111	82.41 140	94.11 161	95.12 185	85.63 136	91.49 89	94.70 122	74.75 165	98.42 115	86.13 127	92.53 156	97.31 94

DCV-MVSNet		90.69 95	90.02 102	92.71 96	95.72 111	82.41 140	94.11 161	95.12 185	85.63 136	91.49 89	94.70 122	74.75 165	98.42 115	86.13 127	92.53 156	97.31 94

canonicalmvs		93.27 55	92.75 60	94.85 27	95.70 113	87.66 11	96.33 33	96.41 95	90.00 33	94.09 30	94.60 128	82.33 83	98.62 100	92.40 37	92.86 152	98.27 45

CANet		93.54 48	93.20 52	94.55 43	95.65 114	85.73 65	94.94 104	96.69 79	91.89 5	90.69 100	95.88 88	81.99 92	99.54 15	93.14 28	97.95 65	98.39 34

3Dnovator+	87.14 4	92.42 69	91.37 76	95.55 4	95.63 115	88.73 4	97.07 12	96.77 71	90.84 16	84.02 226	96.62 58	75.95 151	99.34 32	87.77 106	97.68 70	98.59 17

alignmvs		93.08 59	92.50 65	94.81 32	95.62 116	87.61 12	95.99 49	96.07 118	89.77 37	94.12 29	94.87 116	80.56 101	98.66 96	92.42 36	93.10 147	98.15 54

CS-MVS		92.60 65	92.56 63	92.73 95	95.55 117	82.35 142	96.14 41	96.85 61	88.71 64	91.44 91	91.51 235	84.13 67	98.48 107	91.27 67	97.47 75	97.34 93

Regformer-1		94.22 31	94.13 31	94.51 45	95.54 118	86.36 43	94.57 129	96.44 92	91.69 9	94.32 27	96.56 62	87.05 34	99.03 62	93.35 24	97.65 72	98.15 54

Regformer-2		94.33 26	94.22 24	94.68 37	95.54 118	86.75 29	94.57 129	96.70 77	91.84 6	94.41 24	96.56 62	87.19 32	99.13 52	93.50 19	97.65 72	98.16 53

WTY-MVS		89.60 119	88.92 123	91.67 143	95.47 120	81.15 169	92.38 230	94.78 208	83.11 188	89.06 119	94.32 135	78.67 125	96.61 240	81.57 190	90.89 172	97.24 97

DELS-MVS		93.43 51	93.25 49	93.97 58	95.42 121	85.04 72	93.06 212	97.13 39	90.74 20	91.84 81	95.09 110	86.32 41	99.21 45	91.22 68	98.45 49	97.65 81
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
Regformer-3		93.68 44	93.64 44	93.81 65	95.36 122	84.61 77	94.68 121	95.83 137	91.27 12	93.60 41	96.71 51	85.75 48	98.86 85	92.87 30	96.65 89	97.96 69

Regformer-4		93.91 39	93.81 38	94.19 56	95.36 122	85.47 68	94.68 121	96.41 95	91.60 10	93.75 37	96.71 51	85.95 46	99.10 55	93.21 27	96.65 89	98.01 67

thres200		87.21 197	86.24 194	90.12 199	95.36 122	78.53 229	93.26 202	92.10 264	86.42 120	88.00 133	91.11 248	69.24 239	98.00 146	69.58 294	91.04 171	93.83 231

Vis-MVSNet		91.75 77	91.23 79	93.29 71	95.32 125	83.78 101	96.14 41	95.98 123	89.89 34	90.45 102	96.58 60	75.09 161	98.31 124	84.75 141	96.90 83	97.78 80
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
BH-RMVSNet		88.37 154	87.48 155	91.02 165	95.28 126	79.45 210	92.89 217	93.07 246	85.45 142	86.91 153	94.84 120	70.35 223	97.76 158	73.97 271	94.59 121	95.85 148

COLMAP_ROB	80.39 16	83.96 256	82.04 262	89.74 216	95.28 126	79.75 205	94.25 153	92.28 260	75.17 292	78.02 298	93.77 162	58.60 311	97.84 155	65.06 316	85.92 232	91.63 298
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PS-MVSNAJ		91.18 88	90.92 85	91.96 129	95.26 128	82.60 137	92.09 241	95.70 146	86.27 122	91.84 81	92.46 198	79.70 112	98.99 72	89.08 91	95.86 100	94.29 206

BH-untuned		88.60 150	88.13 143	90.01 206	95.24 129	78.50 231	93.29 200	94.15 226	84.75 158	84.46 211	93.40 166	75.76 152	97.40 189	77.59 239	94.52 123	94.12 212

ETV-MVS		92.74 63	92.66 61	92.97 85	95.20 130	84.04 96	95.07 96	96.51 90	90.73 21	92.96 53	91.19 242	84.06 68	98.34 120	91.72 59	96.54 92	96.54 124

EIA-MVS		91.95 73	91.94 70	91.98 127	95.16 131	80.01 199	95.36 73	96.73 73	88.44 72	89.34 114	92.16 208	83.82 72	98.45 113	89.35 88	97.06 81	97.48 89

ab-mvs		89.41 128	88.35 135	92.60 101	95.15 132	82.65 135	92.20 237	95.60 155	83.97 170	88.55 123	93.70 165	74.16 176	98.21 129	82.46 172	89.37 191	96.94 111

VDDNet		89.56 121	88.49 133	92.76 93	95.07 133	82.09 145	96.30 34	93.19 244	81.05 236	91.88 79	96.86 44	61.16 298	98.33 122	88.43 99	92.49 158	97.84 76

AllTest		83.42 261	81.39 266	89.52 224	95.01 134	77.79 250	93.12 207	90.89 299	77.41 272	76.12 307	93.34 167	54.08 325	97.51 173	68.31 301	84.27 245	93.26 254

TestCases				89.52 224	95.01 134	77.79 250		90.89 299	77.41 272	76.12 307	93.34 167	54.08 325	97.51 173	68.31 301	84.27 245	93.26 254

EI-MVSNet-Vis-set		93.01 60	92.92 58	93.29 71	95.01 134	83.51 109	94.48 133	95.77 141	90.87 15	92.52 67	96.67 55	84.50 64	99.00 71	91.99 49	94.44 126	97.36 92

xiu_mvs_v2_base		91.13 89	90.89 87	91.86 134	94.97 137	82.42 138	92.24 235	95.64 153	86.11 128	91.74 86	93.14 178	79.67 115	98.89 81	89.06 92	95.46 107	94.28 207

tttt0517		88.61 149	87.78 149	91.11 160	94.96 138	77.81 249	95.35 74	89.69 320	85.09 152	88.05 132	94.59 129	66.93 257	98.48 107	83.27 158	92.13 161	97.03 107

baseline1		88.10 161	87.28 161	90.57 176	94.96 138	80.07 194	94.27 152	91.29 288	86.74 113	87.41 143	94.00 148	76.77 142	96.20 264	80.77 201	79.31 308	95.44 160

Test_1112_low_res		87.65 173	86.51 184	91.08 161	94.94 140	79.28 218	91.77 246	94.30 220	76.04 285	83.51 240	92.37 201	77.86 135	97.73 162	78.69 229	89.13 197	96.22 131

1112_ss		88.42 152	87.33 159	91.72 141	94.92 141	80.98 171	92.97 215	94.54 212	78.16 269	83.82 231	93.88 156	78.78 123	97.91 153	79.45 220	89.41 190	96.26 130

QAPM		89.51 122	88.15 142	93.59 69	94.92 141	84.58 78	96.82 23	96.70 77	78.43 264	83.41 242	96.19 78	73.18 192	99.30 38	77.11 245	96.54 92	96.89 114

BH-w/o		87.57 181	87.05 166	89.12 233	94.90 143	77.90 245	92.41 228	93.51 240	82.89 195	83.70 234	91.34 236	75.75 153	97.07 217	75.49 258	93.49 137	92.39 285

thisisatest0530		88.67 147	87.61 153	91.86 134	94.87 144	80.07 194	94.63 125	89.90 317	84.00 169	88.46 125	93.78 161	66.88 259	98.46 110	83.30 157	92.65 154	97.06 105

EI-MVSNet-UG-set		92.74 63	92.62 62	93.12 77	94.86 145	83.20 115	94.40 141	95.74 144	90.71 22	92.05 76	96.60 59	84.00 69	98.99 72	91.55 61	93.63 134	97.17 101

HY-MVS	83.01 12	89.03 138	87.94 147	92.29 119	94.86 145	82.77 127	92.08 242	94.49 213	81.52 227	86.93 152	92.79 192	78.32 131	98.23 126	79.93 215	90.55 173	95.88 147

Fast-Effi-MVS+		89.41 128	88.64 128	91.71 142	94.74 147	80.81 177	93.54 189	95.10 187	83.11 188	86.82 156	90.67 259	79.74 111	97.75 161	80.51 208	93.55 135	96.57 122

ACMP	84.23 8	89.01 140	88.35 135	90.99 168	94.73 148	81.27 164	95.07 96	95.89 133	86.48 118	83.67 235	94.30 136	69.33 235	97.99 147	87.10 120	88.55 202	93.72 240
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet	78.82 18	85.55 233	84.65 235	88.23 258	94.72 149	71.93 306	87.12 312	92.75 252	78.80 259	84.95 202	90.53 261	64.43 279	96.71 234	74.74 266	93.86 131	96.06 141

LCM-MVSNet-Re		88.30 157	88.32 138	88.27 255	94.71 150	72.41 305	93.15 206	90.98 295	87.77 91	79.25 292	91.96 220	78.35 130	95.75 284	83.04 160	95.62 102	96.65 120

HQP_MVS		90.60 101	90.19 95	91.82 137	94.70 151	82.73 131	95.85 56	96.22 108	90.81 17	86.91 153	94.86 117	74.23 172	98.12 131	88.15 101	89.99 179	94.63 187

plane_prior7					94.70 151	82.74 130

ACMH+	81.04 14	85.05 244	83.46 251	89.82 212	94.66 153	79.37 212	94.44 138	94.12 229	82.19 206	78.04 297	92.82 189	58.23 312	97.54 171	73.77 273	82.90 262	92.54 279

ACMM	84.12 9	89.14 134	88.48 134	91.12 157	94.65 154	81.22 167	95.31 76	96.12 115	85.31 146	85.92 171	94.34 133	70.19 226	98.06 142	85.65 130	88.86 200	94.08 216
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
plane_prior1					94.59 155

3Dnovator	86.66 5	91.73 78	90.82 88	94.44 47	94.59 155	86.37 42	97.18 7	97.02 45	89.20 51	84.31 221	96.66 56	73.74 184	99.17 48	86.74 121	97.96 64	97.79 79

plane_prior6					94.52 157	82.75 128						74.23 172

UGNet		89.95 112	88.95 122	92.95 86	94.51 158	83.31 113	95.70 62	95.23 180	89.37 47	87.58 141	93.94 151	64.00 280	98.78 93	83.92 150	96.31 97	96.74 118
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
LPG-MVS_test		89.45 125	88.90 124	91.12 157	94.47 159	81.49 158	95.30 79	96.14 113	86.73 114	85.45 186	95.16 107	69.89 228	98.10 133	87.70 107	89.23 195	93.77 236

LGP-MVS_train				91.12 157	94.47 159	81.49 158		96.14 113	86.73 114	85.45 186	95.16 107	69.89 228	98.10 133	87.70 107	89.23 195	93.77 236

baseline		92.39 70	92.29 68	92.69 99	94.46 161	81.77 152	94.14 158	96.27 102	89.22 50	91.88 79	96.00 83	82.35 82	97.99 147	91.05 70	95.27 113	98.30 39

ACMH	80.38 17	85.36 236	83.68 247	90.39 187	94.45 162	80.63 181	94.73 119	94.85 202	82.09 207	77.24 302	92.65 194	60.01 305	97.58 168	72.25 279	84.87 241	92.96 268
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	82.13 13	86.26 223	84.90 230	90.34 192	94.44 163	81.50 156	92.31 234	94.89 199	83.03 190	79.63 289	92.67 193	69.69 231	97.79 156	71.20 282	86.26 231	91.72 296
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
casdiffmvs		92.51 67	92.43 66	92.74 94	94.41 164	81.98 148	94.54 131	96.23 107	89.57 41	91.96 78	96.17 79	82.58 79	98.01 145	90.95 74	95.45 108	98.23 49

MVS_Test		91.31 85	91.11 81	91.93 131	94.37 165	80.14 191	93.46 193	95.80 139	86.46 119	91.35 94	93.77 162	82.21 86	98.09 139	87.57 109	94.95 115	97.55 88

NP-MVS					94.37 165	82.42 138					93.98 149

TR-MVS		86.78 208	85.76 213	89.82 212	94.37 165	78.41 233	92.47 227	92.83 249	81.11 235	86.36 164	92.40 200	68.73 246	97.48 175	73.75 274	89.85 185	93.57 244

Effi-MVS+		91.59 81	91.11 81	93.01 83	94.35 168	83.39 112	94.60 126	95.10 187	87.10 104	90.57 101	93.10 180	81.43 96	98.07 141	89.29 89	94.48 124	97.59 85

CLD-MVS		89.47 124	88.90 124	91.18 156	94.22 169	82.07 146	92.13 239	96.09 116	87.90 87	85.37 195	92.45 199	74.38 170	97.56 170	87.15 116	90.43 174	93.93 222
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP-NCC					94.17 170		94.39 143		88.81 60	85.43 189

ACMP_Plane					94.17 170		94.39 143		88.81 60	85.43 189

HQP-MVS		89.80 116	89.28 115	91.34 152	94.17 170	81.56 154	94.39 143	96.04 121	88.81 60	85.43 189	93.97 150	73.83 182	97.96 149	87.11 118	89.77 186	94.50 198

XVG-OURS		89.40 130	88.70 127	91.52 146	94.06 173	81.46 160	91.27 258	96.07 118	86.14 126	88.89 121	95.77 92	68.73 246	97.26 201	87.39 112	89.96 181	95.83 150

sss		88.93 141	88.26 141	90.94 171	94.05 174	80.78 178	91.71 249	95.38 173	81.55 226	88.63 122	93.91 155	75.04 162	95.47 296	82.47 171	91.61 163	96.57 122

PCF-MVS	84.11 10	87.74 170	86.08 200	92.70 98	94.02 175	84.43 88	89.27 288	95.87 134	73.62 306	84.43 213	94.33 134	78.48 129	98.86 85	70.27 286	94.45 125	94.81 183
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GBi-Net		87.26 191	85.98 203	91.08 161	94.01 176	83.10 117	95.14 93	94.94 193	83.57 176	84.37 214	91.64 227	66.59 264	96.34 260	78.23 233	85.36 237	93.79 232

test1		87.26 191	85.98 203	91.08 161	94.01 176	83.10 117	95.14 93	94.94 193	83.57 176	84.37 214	91.64 227	66.59 264	96.34 260	78.23 233	85.36 237	93.79 232

FMVSNet2		87.19 198	85.82 209	91.30 153	94.01 176	83.67 104	94.79 115	94.94 193	83.57 176	83.88 229	92.05 218	66.59 264	96.51 248	77.56 240	85.01 240	93.73 239

XVG-OURS-SEG-HR		89.95 112	89.45 109	91.47 148	94.00 179	81.21 168	91.87 244	96.06 120	85.78 131	88.55 123	95.73 93	74.67 168	97.27 199	88.71 96	89.64 188	95.91 145

FIs		90.51 102	90.35 92	90.99 168	93.99 180	80.98 171	95.73 60	97.54 3	89.15 53	86.72 157	94.68 124	81.83 94	97.24 203	85.18 134	88.31 210	94.76 185

xiu_mvs_v1_base_debu		90.64 98	90.05 99	92.40 110	93.97 181	84.46 84	93.32 195	95.46 164	85.17 147	92.25 70	94.03 143	70.59 218	98.57 103	90.97 71	94.67 117	94.18 208

xiu_mvs_v1_base		90.64 98	90.05 99	92.40 110	93.97 181	84.46 84	93.32 195	95.46 164	85.17 147	92.25 70	94.03 143	70.59 218	98.57 103	90.97 71	94.67 117	94.18 208

xiu_mvs_v1_base_debi		90.64 98	90.05 99	92.40 110	93.97 181	84.46 84	93.32 195	95.46 164	85.17 147	92.25 70	94.03 143	70.59 218	98.57 103	90.97 71	94.67 117	94.18 208

VPA-MVSNet		89.62 118	88.96 121	91.60 145	93.86 184	82.89 126	95.46 72	97.33 22	87.91 86	88.43 126	93.31 170	74.17 175	97.40 189	87.32 114	82.86 263	94.52 196

MVSFormer		91.68 80	91.30 77	92.80 91	93.86 184	83.88 99	95.96 52	95.90 131	84.66 160	91.76 84	94.91 114	77.92 133	97.30 195	89.64 86	97.11 79	97.24 97

lupinMVS		90.92 91	90.21 94	93.03 82	93.86 184	83.88 99	92.81 218	93.86 233	79.84 247	91.76 84	94.29 137	77.92 133	98.04 143	90.48 81	97.11 79	97.17 101

IterMVS-LS		88.36 155	87.91 148	89.70 219	93.80 187	78.29 237	93.73 182	95.08 189	85.73 133	84.75 204	91.90 222	79.88 108	96.92 226	83.83 151	82.51 264	93.89 224
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		84.86 248	83.09 254	90.14 198	93.80 187	80.05 196	89.18 291	93.09 245	78.89 256	78.19 295	91.91 221	65.86 273	97.27 199	68.47 299	88.45 206	93.11 263

FMVSNet3		87.40 188	86.11 198	91.30 153	93.79 189	83.64 105	94.20 156	94.81 206	83.89 171	84.37 214	91.87 223	68.45 249	96.56 245	78.23 233	85.36 237	93.70 241

FC-MVSNet-test		90.27 105	90.18 96	90.53 179	93.71 190	79.85 204	95.77 59	97.59 2	89.31 48	86.27 166	94.67 125	81.93 93	97.01 221	84.26 146	88.09 214	94.71 186

TAMVS		89.21 133	88.29 139	91.96 129	93.71 190	82.62 136	93.30 199	94.19 224	82.22 205	87.78 138	93.94 151	78.83 121	96.95 224	77.70 238	92.98 150	96.32 127

ET-MVSNet_ETH3D		87.51 183	85.91 207	92.32 116	93.70 192	83.93 97	92.33 232	90.94 297	84.16 165	72.09 324	92.52 197	69.90 227	95.85 279	89.20 90	88.36 209	97.17 101

CDS-MVSNet		89.45 125	88.51 130	92.29 119	93.62 193	83.61 107	93.01 213	94.68 211	81.95 213	87.82 137	93.24 174	78.69 124	96.99 222	80.34 210	93.23 145	96.28 129
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UniMVSNet (Re)		89.80 116	89.07 119	92.01 124	93.60 194	84.52 80	94.78 116	97.47 8	89.26 49	86.44 163	92.32 203	82.10 88	97.39 192	84.81 140	80.84 290	94.12 212

VPNet		88.20 159	87.47 156	90.39 187	93.56 195	79.46 209	94.04 169	95.54 160	88.67 66	86.96 151	94.58 130	69.33 235	97.15 209	84.05 149	80.53 295	94.56 194

thisisatest0515		87.33 189	85.99 202	91.37 151	93.49 196	79.55 207	90.63 267	89.56 323	80.17 242	87.56 142	90.86 253	67.07 256	98.28 125	81.50 191	93.02 149	96.29 128

mvs_anonymous		89.37 131	89.32 113	89.51 226	93.47 197	74.22 286	91.65 252	94.83 204	82.91 194	85.45 186	93.79 160	81.23 98	96.36 259	86.47 126	94.09 128	97.94 70

CANet_DTU		90.26 106	89.41 111	92.81 90	93.46 198	83.01 122	93.48 191	94.47 214	89.43 45	87.76 139	94.23 141	70.54 222	99.03 62	84.97 136	96.39 96	96.38 126

UniMVSNet_NR-MVSNet		89.92 114	89.29 114	91.81 139	93.39 199	83.72 102	94.43 139	97.12 40	89.80 36	86.46 160	93.32 169	83.16 74	97.23 205	84.92 137	81.02 286	94.49 200

Effi-MVS+-dtu		88.65 148	88.35 135	89.54 223	93.33 200	76.39 272	94.47 136	94.36 217	87.70 93	85.43 189	89.56 281	73.45 187	97.26 201	85.57 132	91.28 165	94.97 172

mvs-test1		89.45 125	89.14 117	90.38 189	93.33 200	77.63 255	94.95 103	94.36 217	87.70 93	87.10 150	92.81 190	73.45 187	98.03 144	85.57 132	93.04 148	95.48 158

WR-MVS		88.38 153	87.67 152	90.52 181	93.30 202	80.18 189	93.26 202	95.96 125	88.57 70	85.47 185	92.81 190	76.12 147	96.91 227	81.24 193	82.29 266	94.47 203

WR-MVS_H		87.80 169	87.37 158	89.10 234	93.23 203	78.12 240	95.61 68	97.30 26	87.90 87	83.72 233	92.01 219	79.65 116	96.01 272	76.36 250	80.54 294	93.16 261

test_0402		81.30 284	79.17 289	87.67 268	93.19 204	78.17 239	92.98 214	91.71 275	75.25 291	76.02 309	90.31 265	59.23 309	96.37 257	50.22 337	83.63 252	88.47 329

OPM-MVS		90.12 107	89.56 107	91.82 137	93.14 205	83.90 98	94.16 157	95.74 144	88.96 59	87.86 134	95.43 100	72.48 200	97.91 153	88.10 104	90.18 178	93.65 242

CP-MVSNet		87.63 176	87.26 163	88.74 244	93.12 206	76.59 269	95.29 81	96.58 87	88.43 73	83.49 241	92.98 183	75.28 159	95.83 280	78.97 226	81.15 282	93.79 232

diffmvs		91.37 84	91.23 79	91.77 140	93.09 207	80.27 188	92.36 231	95.52 161	87.03 106	91.40 93	94.93 113	80.08 106	97.44 180	92.13 47	94.56 122	97.61 83

nrg030		91.08 90	90.39 91	93.17 76	93.07 208	86.91 20	96.41 31	96.26 103	88.30 76	88.37 127	94.85 119	82.19 87	97.64 166	91.09 69	82.95 258	94.96 175

PAPM		86.68 212	85.39 219	90.53 179	93.05 209	79.33 217	89.79 281	94.77 209	78.82 258	81.95 260	93.24 174	76.81 140	97.30 195	66.94 306	93.16 146	94.95 178

DU-MVS		89.34 132	88.50 131	91.85 136	93.04 210	83.72 102	94.47 136	96.59 86	89.50 42	86.46 160	93.29 172	77.25 137	97.23 205	84.92 137	81.02 286	94.59 191

NR-MVSNet		88.58 151	87.47 156	91.93 131	93.04 210	84.16 93	94.77 117	96.25 105	89.05 55	80.04 285	93.29 172	79.02 120	97.05 219	81.71 189	80.05 300	94.59 191

jason		90.80 92	90.10 97	92.90 88	93.04 210	83.53 108	93.08 210	94.15 226	80.22 241	91.41 92	94.91 114	76.87 139	97.93 152	90.28 82	96.90 83	97.24 97
jason: jason.
RRT_test8_iter05		86.90 204	86.36 188	88.52 249	93.00 213	73.27 294	94.32 150	95.96 125	85.50 141	84.26 222	92.86 185	60.76 300	97.70 163	88.32 100	82.29 266	94.60 190

PS-CasMVS		87.32 190	86.88 168	88.63 247	92.99 214	76.33 274	95.33 75	96.61 85	88.22 80	83.30 246	93.07 181	73.03 194	95.79 283	78.36 231	81.00 288	93.75 238

MVSTER		88.84 143	88.29 139	90.51 182	92.95 215	80.44 187	93.73 182	95.01 190	84.66 160	87.15 147	93.12 179	72.79 196	97.21 207	87.86 105	87.36 223	93.87 227

RPSCF		85.07 243	84.27 239	87.48 274	92.91 216	70.62 318	91.69 251	92.46 257	76.20 284	82.67 252	95.22 105	63.94 281	97.29 198	77.51 241	85.80 234	94.53 195

FMVSNet1		85.85 229	84.11 241	91.08 161	92.81 217	83.10 117	95.14 93	94.94 193	81.64 223	82.68 251	91.64 227	59.01 310	96.34 260	75.37 260	83.78 248	93.79 232

tfpnnormal		84.72 250	83.23 253	89.20 231	92.79 218	80.05 196	94.48 133	95.81 138	82.38 202	81.08 269	91.21 241	69.01 242	96.95 224	61.69 325	80.59 293	90.58 316

OpenMVS	83.78 11	88.74 146	87.29 160	93.08 79	92.70 219	85.39 69	96.57 28	96.43 94	78.74 261	80.85 271	96.07 82	69.64 232	99.01 68	78.01 236	96.65 89	94.83 182

TranMVSNet+NR-MVSNet		88.84 143	87.95 146	91.49 147	92.68 220	83.01 122	94.92 106	96.31 100	89.88 35	85.53 180	93.85 158	76.63 145	96.96 223	81.91 182	79.87 303	94.50 198

MVS		87.44 186	86.10 199	91.44 149	92.61 221	83.62 106	92.63 222	95.66 150	67.26 329	81.47 263	92.15 209	77.95 132	98.22 128	79.71 217	95.48 105	92.47 282

CHOSEN 280x420		85.15 242	83.99 243	88.65 246	92.47 222	78.40 234	79.68 337	92.76 251	74.90 296	81.41 265	89.59 279	69.85 230	95.51 292	79.92 216	95.29 111	92.03 292

UniMVSNet_ETH3D		87.53 182	86.37 187	91.00 167	92.44 223	78.96 223	94.74 118	95.61 154	84.07 168	85.36 196	94.52 131	59.78 307	97.34 194	82.93 162	87.88 217	96.71 119

1314		87.51 183	86.57 183	90.34 192	92.42 224	79.74 206	92.63 222	95.35 177	78.35 265	80.14 282	91.62 231	74.05 177	97.15 209	81.05 194	93.53 136	94.12 212

cl-mvsnet2		86.78 208	85.98 203	89.18 232	92.34 225	77.62 256	90.84 264	94.13 228	81.33 230	83.97 228	90.15 268	73.96 179	96.60 242	84.19 147	82.94 259	93.33 252

PEN-MVS		86.80 207	86.27 193	88.40 251	92.32 226	75.71 279	95.18 90	96.38 98	87.97 84	82.82 250	93.15 177	73.39 190	95.92 275	76.15 254	79.03 310	93.59 243

cl_fuxian		87.14 200	86.50 185	89.04 236	92.20 227	77.26 261	91.22 260	94.70 210	82.01 211	84.34 218	90.43 263	78.81 122	96.61 240	83.70 154	81.09 283	93.25 256

SCA		86.32 222	85.18 223	89.73 218	92.15 228	76.60 268	91.12 261	91.69 277	83.53 179	85.50 183	88.81 288	66.79 260	96.48 250	76.65 248	90.35 176	96.12 135

XXY-MVS		87.65 173	86.85 170	90.03 204	92.14 229	80.60 183	93.76 181	95.23 180	82.94 192	84.60 206	94.02 146	74.27 171	95.49 295	81.04 195	83.68 251	94.01 220

miper_ehance_all_eth		87.22 196	86.62 181	89.02 237	92.13 230	77.40 260	90.91 263	94.81 206	81.28 231	84.32 219	90.08 270	79.26 118	96.62 237	83.81 152	82.94 259	93.04 266

IB-MVS	80.51 15	85.24 241	83.26 252	91.19 155	92.13 230	79.86 203	91.75 247	91.29 288	83.28 186	80.66 274	88.49 294	61.28 294	98.46 110	80.99 198	79.46 306	95.25 166
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
cascas		86.43 221	84.98 227	90.80 173	92.10 232	80.92 174	90.24 272	95.91 130	73.10 310	83.57 239	88.39 295	65.15 275	97.46 177	84.90 139	91.43 164	94.03 219

Fast-Effi-MVS+-dtu		87.44 186	86.72 174	89.63 221	92.04 233	77.68 254	94.03 170	93.94 231	85.81 130	82.42 253	91.32 239	70.33 224	97.06 218	80.33 211	90.23 177	94.14 211

cl-mvsnet_		86.52 217	85.78 210	88.75 242	92.03 234	76.46 270	90.74 265	94.30 220	81.83 220	83.34 244	90.78 257	75.74 155	96.57 243	81.74 187	81.54 278	93.22 258

cl-mvsnet1		86.53 216	85.78 210	88.75 242	92.02 235	76.45 271	90.74 265	94.30 220	81.83 220	83.34 244	90.82 255	75.75 153	96.57 243	81.73 188	81.52 279	93.24 257

RRT_MVS		88.86 142	87.68 151	92.39 113	92.02 235	86.09 52	94.38 147	94.94 193	85.45 142	87.14 149	93.84 159	65.88 272	97.11 213	88.73 95	86.77 230	93.98 221

eth_miper_zixun_eth		86.50 218	85.77 212	88.68 245	91.94 237	75.81 278	90.47 269	94.89 199	82.05 208	84.05 225	90.46 262	75.96 150	96.77 231	82.76 168	79.36 307	93.46 250

PS-MVSNAJss		89.97 111	89.62 106	91.02 165	91.90 238	80.85 176	95.26 84	95.98 123	86.26 123	86.21 167	94.29 137	79.70 112	97.65 164	88.87 94	88.10 212	94.57 193

ITE_SJBPF				88.24 257	91.88 239	77.05 264		92.92 247	85.54 139	80.13 283	93.30 171	57.29 314	96.20 264	72.46 278	84.71 242	91.49 300

EI-MVSNet		89.10 135	88.86 126	89.80 215	91.84 240	78.30 236	93.70 185	95.01 190	85.73 133	87.15 147	95.28 102	79.87 109	97.21 207	83.81 152	87.36 223	93.88 226

CVMVSNet		84.69 251	84.79 233	84.37 308	91.84 240	64.92 335	93.70 185	91.47 284	66.19 331	86.16 169	95.28 102	67.18 255	93.33 319	80.89 200	90.42 175	94.88 180

MVS-HIRNet		73.70 305	72.20 307	78.18 320	91.81 242	56.42 342	82.94 332	82.58 338	55.24 337	68.88 329	66.48 338	55.32 320	95.13 300	58.12 332	88.42 207	83.01 334

PatchmatchNet		85.85 229	84.70 234	89.29 229	91.76 243	75.54 280	88.49 299	91.30 287	81.63 224	85.05 200	88.70 292	71.71 204	96.24 263	74.61 268	89.05 198	96.08 139
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TransMVSNet (Re)		84.43 253	83.06 255	88.54 248	91.72 244	78.44 232	95.18 90	92.82 250	82.73 197	79.67 288	92.12 211	73.49 186	95.96 274	71.10 285	68.73 331	91.21 307

IterMVS-SCA-FT		85.45 234	84.53 238	88.18 259	91.71 245	76.87 266	90.19 275	92.65 255	85.40 144	81.44 264	90.54 260	66.79 260	95.00 304	81.04 195	81.05 284	92.66 277

TinyColmap		79.76 294	77.69 295	85.97 297	91.71 245	73.12 295	89.55 282	90.36 307	75.03 293	72.03 325	90.19 266	46.22 338	96.19 266	63.11 321	81.03 285	88.59 328

MDTV_nov1_ep13			83.56 250		91.69 247	69.93 322	87.75 307	91.54 281	78.60 262	84.86 203	88.90 287	69.54 233	96.03 270	70.25 287	88.93 199

miper_enhance_ethall		86.90 204	86.18 195	89.06 235	91.66 248	77.58 257	90.22 274	94.82 205	79.16 253	84.48 210	89.10 284	79.19 119	96.66 235	84.06 148	82.94 259	92.94 269

DTE-MVSNet		86.11 224	85.48 217	87.98 263	91.65 249	74.92 282	94.93 105	95.75 143	87.36 100	82.26 255	93.04 182	72.85 195	95.82 281	74.04 270	77.46 315	93.20 259

MIMVSNet		82.59 269	80.53 273	88.76 241	91.51 250	78.32 235	86.57 315	90.13 310	79.32 251	80.70 273	88.69 293	52.98 327	93.07 323	66.03 311	88.86 200	94.90 179

pm-mvs1		86.61 213	85.54 215	89.82 212	91.44 251	80.18 189	95.28 83	94.85 202	83.84 172	81.66 262	92.62 195	72.45 202	96.48 250	79.67 218	78.06 311	92.82 274

Baseline_NR-MVSNet		87.07 201	86.63 180	88.40 251	91.44 251	77.87 247	94.23 155	92.57 256	84.12 167	85.74 174	92.08 215	77.25 137	96.04 269	82.29 175	79.94 301	91.30 304

IterMVS		84.88 247	83.98 244	87.60 269	91.44 251	76.03 276	90.18 276	92.41 258	83.24 187	81.06 270	90.42 264	66.60 263	94.28 310	79.46 219	80.98 289	92.48 281
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DWT-MVSNet_test		84.95 246	83.68 247	88.77 240	91.43 254	73.75 290	91.74 248	90.98 295	80.66 239	83.84 230	87.36 309	62.44 286	97.11 213	78.84 228	85.81 233	95.46 159

MS-PatchMatch		85.05 244	84.16 240	87.73 267	91.42 255	78.51 230	91.25 259	93.53 239	77.50 271	80.15 281	91.58 232	61.99 289	95.51 292	75.69 257	94.35 127	89.16 323

tpm2		84.08 255	82.94 256	87.48 274	91.39 256	71.27 310	89.23 290	90.37 306	71.95 318	84.64 205	89.33 282	67.30 252	96.55 247	75.17 262	87.09 227	94.63 187

v8		87.50 185	86.71 175	89.89 209	91.37 257	79.40 211	94.50 132	95.38 173	84.81 157	83.60 238	91.33 237	76.05 148	97.42 182	82.84 165	80.51 297	92.84 273

ADS-MVSNet2		81.66 277	79.71 284	87.50 272	91.35 258	74.19 287	83.33 329	88.48 326	72.90 312	82.24 256	85.77 317	64.98 276	93.20 321	64.57 317	83.74 249	95.12 168

ADS-MVSNet		81.56 279	79.78 282	86.90 288	91.35 258	71.82 307	83.33 329	89.16 324	72.90 312	82.24 256	85.77 317	64.98 276	93.76 314	64.57 317	83.74 249	95.12 168

GA-MVS		86.61 213	85.27 222	90.66 174	91.33 260	78.71 225	90.40 270	93.81 236	85.34 145	85.12 199	89.57 280	61.25 295	97.11 213	80.99 198	89.59 189	96.15 132

miper_lstm_enhance		85.27 240	84.59 237	87.31 276	91.28 261	74.63 283	87.69 308	94.09 230	81.20 234	81.36 266	89.85 276	74.97 164	94.30 309	81.03 197	79.84 304	93.01 267

XVG-ACMP-BASELINE		86.00 225	84.84 232	89.45 227	91.20 262	78.00 242	91.70 250	95.55 158	85.05 153	82.97 248	92.25 207	54.49 323	97.48 175	82.93 162	87.45 222	92.89 271

v10		87.25 193	86.38 186	89.85 210	91.19 263	79.50 208	94.48 133	95.45 167	83.79 173	83.62 237	91.19 242	75.13 160	97.42 182	81.94 181	80.60 292	92.63 278

FMVSNet5		81.52 280	79.60 285	87.27 277	91.17 264	77.95 243	91.49 254	92.26 261	76.87 277	76.16 306	87.91 304	51.67 328	92.34 325	67.74 305	81.16 280	91.52 299

USDC		82.76 266	81.26 268	87.26 278	91.17 264	74.55 284	89.27 288	93.39 242	78.26 267	75.30 311	92.08 215	54.43 324	96.63 236	71.64 280	85.79 235	90.61 313

CostFormer		85.77 231	84.94 229	88.26 256	91.16 266	72.58 304	89.47 286	91.04 294	76.26 283	86.45 162	89.97 273	70.74 216	96.86 230	82.35 173	87.07 228	95.34 165

baseline2		86.50 218	85.39 219	89.84 211	91.12 267	76.70 267	91.88 243	88.58 325	82.35 204	79.95 286	90.95 252	73.42 189	97.63 167	80.27 212	89.95 182	95.19 167

tpm cat1		81.96 272	80.27 276	87.01 285	91.09 268	71.02 314	87.38 311	91.53 282	66.25 330	80.17 280	86.35 315	68.22 251	96.15 267	69.16 295	82.29 266	93.86 229

tpmvs		83.35 264	82.07 261	87.20 283	91.07 269	71.00 315	88.31 302	91.70 276	78.91 255	80.49 277	87.18 312	69.30 238	97.08 216	68.12 304	83.56 253	93.51 248

v1144		87.61 179	86.79 173	90.06 203	91.01 270	79.34 214	93.95 175	95.42 172	83.36 184	85.66 176	91.31 240	74.98 163	97.42 182	83.37 156	82.06 269	93.42 251

v2v482		87.84 167	87.06 165	90.17 195	90.99 271	79.23 221	94.00 173	95.13 184	84.87 155	85.53 180	92.07 217	74.45 169	97.45 178	84.71 142	81.75 275	93.85 230

SixPastTwentyTwo		83.91 257	82.90 257	86.92 287	90.99 271	70.67 317	93.48 191	91.99 269	85.54 139	77.62 301	92.11 213	60.59 301	96.87 229	76.05 255	77.75 312	93.20 259

test-LLR		85.87 228	85.41 218	87.25 279	90.95 273	71.67 308	89.55 282	89.88 318	83.41 182	84.54 208	87.95 302	67.25 253	95.11 301	81.82 184	93.37 142	94.97 172

test-mter		84.54 252	83.64 249	87.25 279	90.95 273	71.67 308	89.55 282	89.88 318	79.17 252	84.54 208	87.95 302	55.56 318	95.11 301	81.82 184	93.37 142	94.97 172

v148		87.04 202	86.32 191	89.21 230	90.94 275	77.26 261	93.71 184	94.43 215	84.84 156	84.36 217	90.80 256	76.04 149	97.05 219	82.12 177	79.60 305	93.31 253

mvs_tets		88.06 164	87.28 161	90.38 189	90.94 275	79.88 202	95.22 86	95.66 150	85.10 151	84.21 224	93.94 151	63.53 282	97.40 189	88.50 98	88.40 208	93.87 227

MVP-Stereo		85.97 226	84.86 231	89.32 228	90.92 277	82.19 144	92.11 240	94.19 224	78.76 260	78.77 294	91.63 230	68.38 250	96.56 245	75.01 265	93.95 129	89.20 322
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Patchmatch-test		81.37 282	79.30 287	87.58 270	90.92 277	74.16 288	80.99 335	87.68 330	70.52 324	76.63 305	88.81 288	71.21 209	92.76 324	60.01 331	86.93 229	95.83 150

jajsoiax		88.24 158	87.50 154	90.48 184	90.89 279	80.14 191	95.31 76	95.65 152	84.97 154	84.24 223	94.02 146	65.31 274	97.42 182	88.56 97	88.52 204	93.89 224

tpmrst		85.35 237	84.99 226	86.43 293	90.88 280	67.88 328	88.71 296	91.43 285	80.13 243	86.08 170	88.80 290	73.05 193	96.02 271	82.48 170	83.40 257	95.40 162

gg-mvs-nofinetune		81.77 275	79.37 286	88.99 238	90.85 281	77.73 253	86.29 316	79.63 343	74.88 297	83.19 247	69.05 337	60.34 302	96.11 268	75.46 259	94.64 120	93.11 263

D2MVS		85.90 227	85.09 225	88.35 253	90.79 282	77.42 259	91.83 245	95.70 146	80.77 238	80.08 284	90.02 271	66.74 262	96.37 257	81.88 183	87.97 216	91.26 305

OurMVSNet-221017-0		85.35 237	84.64 236	87.49 273	90.77 283	72.59 303	94.01 172	94.40 216	84.72 159	79.62 290	93.17 176	61.91 290	96.72 232	81.99 180	81.16 280	93.16 261

v1192		87.25 193	86.33 190	90.00 207	90.76 284	79.04 222	93.80 179	95.48 163	82.57 200	85.48 184	91.18 244	73.38 191	97.42 182	82.30 174	82.06 269	93.53 245

test_djsdf		89.03 138	88.64 128	90.21 194	90.74 285	79.28 218	95.96 52	95.90 131	84.66 160	85.33 197	92.94 184	74.02 178	97.30 195	89.64 86	88.53 203	94.05 218

v7n		86.81 206	85.76 213	89.95 208	90.72 286	79.25 220	95.07 96	95.92 128	84.45 163	82.29 254	90.86 253	72.60 199	97.53 172	79.42 223	80.52 296	93.08 265

PVSNet_0	73.20 20	77.22 301	74.83 305	84.37 308	90.70 287	71.10 313	83.09 331	89.67 321	72.81 314	73.93 318	83.13 326	60.79 299	93.70 315	68.54 298	50.84 340	88.30 330

v144192		87.19 198	86.35 189	89.74 216	90.64 288	78.24 238	93.92 176	95.43 170	81.93 214	85.51 182	91.05 250	74.21 174	97.45 178	82.86 164	81.56 277	93.53 245

MVS_0304		83.46 260	81.92 263	88.10 261	90.63 289	77.49 258	93.26 202	93.75 237	80.04 245	80.44 278	87.24 311	47.94 335	95.55 289	75.79 256	88.16 211	91.26 305

V42		87.68 171	86.86 169	90.15 197	90.58 290	80.14 191	94.24 154	95.28 178	83.66 175	85.67 175	91.33 237	74.73 167	97.41 187	84.43 145	81.83 273	92.89 271

CR-MVSNet		85.35 237	83.76 246	90.12 199	90.58 290	79.34 214	85.24 322	91.96 272	78.27 266	85.55 178	87.87 305	71.03 212	95.61 286	73.96 272	89.36 192	95.40 162

RPMNet		83.18 265	80.87 272	90.12 199	90.58 290	79.34 214	85.24 322	90.78 302	71.44 320	85.55 178	82.97 327	70.87 214	95.61 286	61.01 327	89.36 192	95.40 162

v1921920		86.97 203	86.06 201	89.69 220	90.53 293	78.11 241	93.80 179	95.43 170	81.90 216	85.33 197	91.05 250	72.66 197	97.41 187	82.05 179	81.80 274	93.53 245

v1240		86.78 208	85.85 208	89.56 222	90.45 294	77.79 250	93.61 187	95.37 175	81.65 222	85.43 189	91.15 246	71.50 207	97.43 181	81.47 192	82.05 271	93.47 249

tpm		84.73 249	84.02 242	86.87 290	90.33 295	68.90 325	89.06 292	89.94 315	80.85 237	85.75 173	89.86 275	68.54 248	95.97 273	77.76 237	84.05 247	95.75 153

EG-PatchMatch MVS		82.37 271	80.34 275	88.46 250	90.27 296	79.35 213	92.80 219	94.33 219	77.14 276	73.26 321	90.18 267	47.47 337	96.72 232	70.25 287	87.32 225	89.30 320

EPNet_dtu		86.49 220	85.94 206	88.14 260	90.24 297	72.82 298	94.11 161	92.20 262	86.66 117	79.42 291	92.36 202	73.52 185	95.81 282	71.26 281	93.66 133	95.80 152
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPMVS		83.90 258	82.70 260	87.51 271	90.23 298	72.67 300	88.62 298	81.96 340	81.37 229	85.01 201	88.34 296	66.31 267	94.45 306	75.30 261	87.12 226	95.43 161

EPNet		91.79 75	91.02 84	94.10 57	90.10 299	85.25 71	96.03 48	92.05 266	92.83 1	87.39 146	95.78 91	79.39 117	99.01 68	88.13 103	97.48 74	98.05 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchT		82.68 268	81.27 267	86.89 289	90.09 300	70.94 316	84.06 326	90.15 309	74.91 295	85.63 177	83.57 324	69.37 234	94.87 305	65.19 313	88.50 205	94.84 181

Patchmtry		82.71 267	80.93 271	88.06 262	90.05 301	76.37 273	84.74 324	91.96 272	72.28 317	81.32 267	87.87 305	71.03 212	95.50 294	68.97 296	80.15 299	92.32 288

pmmvs4		85.43 235	83.86 245	90.16 196	90.02 302	82.97 124	90.27 271	92.67 254	75.93 286	80.73 272	91.74 226	71.05 211	95.73 285	78.85 227	83.46 255	91.78 295

TESTMET0.1,1		83.74 259	82.85 258	86.42 294	89.96 303	71.21 312	89.55 282	87.88 327	77.41 272	83.37 243	87.31 310	56.71 315	93.65 316	80.62 205	92.85 153	94.40 204

dp		81.47 281	80.23 277	85.17 303	89.92 304	65.49 334	86.74 313	90.10 311	76.30 282	81.10 268	87.12 313	62.81 284	95.92 275	68.13 303	79.88 302	94.09 215

K. test v3		81.59 278	80.15 279	85.91 298	89.89 305	69.42 324	92.57 225	87.71 329	85.56 138	73.44 320	89.71 278	55.58 317	95.52 291	77.17 244	69.76 327	92.78 275

MDA-MVSNet-bldmvs		78.85 299	76.31 301	86.46 292	89.76 306	73.88 289	88.79 295	90.42 305	79.16 253	59.18 337	88.33 297	60.20 303	94.04 312	62.00 324	68.96 329	91.48 301

GG-mvs-BLEND				87.94 265	89.73 307	77.91 244	87.80 305	78.23 345		80.58 275	83.86 322	59.88 306	95.33 298	71.20 282	92.22 160	90.60 315

gm-plane-assit					89.60 308	68.00 327			77.28 275		88.99 285		97.57 169	79.44 221

anonymousdsp		87.84 167	87.09 164	90.12 199	89.13 309	80.54 184	94.67 123	95.55 158	82.05 208	83.82 231	92.12 211	71.47 208	97.15 209	87.15 116	87.80 219	92.67 276

N_pmnet		68.89 308	68.44 310	70.23 324	89.07 310	28.79 352	88.06 303	19.50 353	69.47 326	71.86 326	84.93 320	61.24 296	91.75 330	54.70 335	77.15 316	90.15 317

pmmvs5		84.21 254	82.84 259	88.34 254	88.95 311	76.94 265	92.41 228	91.91 274	75.63 288	80.28 279	91.18 244	64.59 278	95.57 288	77.09 246	83.47 254	92.53 280

PMMVS		85.71 232	84.96 228	87.95 264	88.90 312	77.09 263	88.68 297	90.06 312	72.32 316	86.47 159	90.76 258	72.15 203	94.40 307	81.78 186	93.49 137	92.36 286

JIA-IIPM		81.04 285	78.98 292	87.25 279	88.64 313	73.48 292	81.75 334	89.61 322	73.19 309	82.05 258	73.71 334	66.07 271	95.87 278	71.18 284	84.60 243	92.41 284

Gipuma		57.99 313	54.91 314	67.24 326	88.51 314	65.59 333	52.21 345	90.33 308	43.58 342	42.84 342	51.18 343	20.29 348	85.07 340	34.77 342	70.45 326	51.05 342
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EU-MVSNet		81.32 283	80.95 270	82.42 315	88.50 315	63.67 336	93.32 195	91.33 286	64.02 333	80.57 276	92.83 188	61.21 297	92.27 326	76.34 251	80.38 298	91.32 303

our_test_3		81.93 273	80.46 274	86.33 295	88.46 316	73.48 292	88.46 300	91.11 290	76.46 278	76.69 304	88.25 298	66.89 258	94.36 308	68.75 297	79.08 309	91.14 309

ppachtmachnet_test		81.84 274	80.07 280	87.15 284	88.46 316	74.43 285	89.04 293	92.16 263	75.33 290	77.75 299	88.99 285	66.20 268	95.37 297	65.12 315	77.60 313	91.65 297

lessismore_v0				86.04 296	88.46 316	68.78 326		80.59 341		73.01 322	90.11 269	55.39 319	96.43 255	75.06 264	65.06 333	92.90 270

test0.0.03 1		82.41 270	81.69 264	84.59 306	88.23 319	72.89 297	90.24 272	87.83 328	83.41 182	79.86 287	89.78 277	67.25 253	88.99 336	65.18 314	83.42 256	91.90 294

MDA-MVSNet_test_wron		79.21 298	77.19 299	85.29 301	88.22 320	72.77 299	85.87 318	90.06 312	74.34 300	62.62 336	87.56 308	66.14 269	91.99 328	66.90 309	73.01 321	91.10 311

YYNet1		79.22 297	77.20 298	85.28 302	88.20 321	72.66 301	85.87 318	90.05 314	74.33 301	62.70 335	87.61 307	66.09 270	92.03 327	66.94 306	72.97 322	91.15 308

pmmvs6		83.42 261	81.60 265	88.87 239	88.01 322	77.87 247	94.96 102	94.24 223	74.67 298	78.80 293	91.09 249	60.17 304	96.49 249	77.06 247	75.40 319	92.23 290

testgi		80.94 288	80.20 278	83.18 312	87.96 323	66.29 331	91.28 257	90.70 304	83.70 174	78.12 296	92.84 187	51.37 329	90.82 333	63.34 320	82.46 265	92.43 283

Anonymous20231206		81.03 286	79.77 283	84.82 305	87.85 324	70.26 320	91.42 255	92.08 265	73.67 305	77.75 299	89.25 283	62.43 287	93.08 322	61.50 326	82.00 272	91.12 310

OpenMVS_ROB	74.94 19	79.51 295	77.03 300	86.93 286	87.00 325	76.23 275	92.33 232	90.74 303	68.93 327	74.52 315	88.23 299	49.58 331	96.62 237	57.64 333	84.29 244	87.94 331

LF4IMVS		80.37 290	79.07 291	84.27 310	86.64 326	69.87 323	89.39 287	91.05 293	76.38 280	74.97 313	90.00 272	47.85 336	94.25 311	74.55 269	80.82 291	88.69 327

MIMVSNet1		79.38 296	77.28 297	85.69 299	86.35 327	73.67 291	91.61 253	92.75 252	78.11 270	72.64 323	88.12 300	48.16 334	91.97 329	60.32 328	77.49 314	91.43 302

test20.03		79.95 292	79.08 290	82.55 314	85.79 328	67.74 329	91.09 262	91.08 291	81.23 233	74.48 316	89.96 274	61.63 291	90.15 334	60.08 329	76.38 317	89.76 318

Patchmatch-RL test		81.67 276	79.96 281	86.81 291	85.42 329	71.23 311	82.17 333	87.50 331	78.47 263	77.19 303	82.50 328	70.81 215	93.48 317	82.66 169	72.89 323	95.71 154

UnsupCasMVSNet_eth		80.07 291	78.27 294	85.46 300	85.24 330	72.63 302	88.45 301	94.87 201	82.99 191	71.64 327	88.07 301	56.34 316	91.75 330	73.48 275	63.36 336	92.01 293

testing_2		83.40 263	81.02 269	90.56 178	85.06 331	80.51 185	91.37 256	95.57 156	82.92 193	67.06 332	85.54 319	49.47 332	97.24 203	86.74 121	85.44 236	93.93 222

pmmvs-eth3d		80.97 287	78.72 293	87.74 266	84.99 332	79.97 201	90.11 277	91.65 278	75.36 289	73.51 319	86.03 316	59.45 308	93.96 313	75.17 262	72.21 324	89.29 321

CMPMVS	59.16 21	80.52 289	79.20 288	84.48 307	83.98 333	67.63 330	89.95 280	93.84 235	64.79 332	66.81 333	91.14 247	57.93 313	95.17 299	76.25 252	88.10 212	90.65 312
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UnsupCasMVSNet_bld		76.23 304	73.27 306	85.09 304	83.79 334	72.92 296	85.65 321	93.47 241	71.52 319	68.84 330	79.08 332	49.77 330	93.21 320	66.81 310	60.52 338	89.13 325

PM-MVS		78.11 300	76.12 303	84.09 311	83.54 335	70.08 321	88.97 294	85.27 335	79.93 246	74.73 314	86.43 314	34.70 342	93.48 317	79.43 222	72.06 325	88.72 326

DSMNet-mixed		76.94 302	76.29 302	78.89 318	83.10 336	56.11 343	87.78 306	79.77 342	60.65 335	75.64 310	88.71 291	61.56 292	88.34 337	60.07 330	89.29 194	92.21 291

new_pmnet		72.15 306	70.13 308	78.20 319	82.95 337	65.68 332	83.91 327	82.40 339	62.94 334	64.47 334	79.82 331	42.85 340	86.26 339	57.41 334	74.44 320	82.65 335

new-patchmatchnet		76.41 303	75.17 304	80.13 317	82.65 338	59.61 338	87.66 309	91.08 291	78.23 268	69.85 328	83.22 325	54.76 321	91.63 332	64.14 319	64.89 334	89.16 323

ambc				83.06 313	79.99 339	63.51 337	77.47 338	92.86 248		74.34 317	84.45 321	28.74 343	95.06 303	73.06 277	68.89 330	90.61 313

TDRefinement		79.81 293	77.34 296	87.22 282	79.24 340	75.48 281	93.12 207	92.03 267	76.45 279	75.01 312	91.58 232	49.19 333	96.44 254	70.22 289	69.18 328	89.75 319

pmmvs3		71.81 307	68.71 309	81.11 316	75.86 341	70.42 319	86.74 313	83.66 337	58.95 336	68.64 331	80.89 330	36.93 341	89.52 335	63.10 322	63.59 335	83.39 333

DeepMVS_CX				56.31 329	74.23 342	51.81 345		56.67 351	44.85 341	48.54 341	75.16 333	27.87 344	58.74 348	40.92 340	52.22 339	58.39 341

FPMVS		64.63 310	62.55 311	70.88 323	70.80 343	56.71 340	84.42 325	84.42 336	51.78 339	49.57 339	81.61 329	23.49 345	81.48 342	40.61 341	76.25 318	74.46 338

PMMVS2		59.60 311	56.40 313	69.21 325	68.83 344	46.58 347	73.02 342	77.48 346	55.07 338	49.21 340	72.95 336	17.43 350	80.04 343	49.32 338	44.33 341	80.99 337

wuyk23d		21.27 320	20.48 322	23.63 333	68.59 345	36.41 350	49.57 346	6.85 354	9.37 347	7.89 349	4.46 351	4.03 354	31.37 349	17.47 347	16.07 347	3.12 346

E-PMN		43.23 316	42.29 317	46.03 330	65.58 346	37.41 349	73.51 340	64.62 347	33.99 344	28.47 347	47.87 344	19.90 349	67.91 345	22.23 345	24.45 343	32.77 343

LCM-MVSNet		66.00 309	62.16 312	77.51 321	64.51 347	58.29 339	83.87 328	90.90 298	48.17 340	54.69 338	73.31 335	16.83 351	86.75 338	65.47 312	61.67 337	87.48 332

EMVS		42.07 317	41.12 318	44.92 331	63.45 348	35.56 351	73.65 339	63.48 348	33.05 345	26.88 348	45.45 345	21.27 347	67.14 346	19.80 346	23.02 345	32.06 344

MVE	39.65 23	43.39 315	38.59 320	57.77 328	56.52 349	48.77 346	55.38 344	58.64 350	29.33 346	28.96 346	52.65 342	4.68 353	64.62 347	28.11 344	33.07 342	59.93 340
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		58.88 312	54.22 315	72.86 322	56.50 350	56.67 341	80.75 336	86.00 332	73.09 311	37.39 343	64.63 340	22.17 346	79.49 344	43.51 339	23.96 344	82.43 336

PMVS	47.18 22	52.22 314	48.46 316	63.48 327	45.72 351	46.20 348	73.41 341	78.31 344	41.03 343	30.06 345	65.68 339	6.05 352	83.43 341	30.04 343	65.86 332	60.80 339
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		35.64 318	39.24 319	24.84 332	14.87 352	23.90 353	62.71 343	51.51 352	6.58 348	36.66 344	62.08 341	44.37 339	30.34 350	52.40 336	22.00 346	20.27 345

testmvs		8.92 321	11.52 323	1.12 335	1.06 353	0.46 355	86.02 317	0.65 355	0.62 349	2.74 350	9.52 349	0.31 356	0.45 352	2.38 348	0.39 348	2.46 348

test123		8.76 322	11.22 324	1.39 334	0.85 354	0.97 354	85.76 320	0.35 356	0.54 350	2.45 351	8.14 350	0.60 355	0.48 351	2.16 349	0.17 349	2.71 347

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		22.14 319	29.52 321	0.00 336	0.00 355	0.00 356	0.00 347	95.76 142	0.00 351	0.00 352	94.29 137	75.66 156	0.00 353	0.00 350	0.00 350	0.00 349

pcd_1.5k_mvsjas		6.64 324	8.86 326	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	0.00 352	79.70 112	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		7.82 323	10.43 325	0.00 336	0.00 355	0.00 356	0.00 347	0.00 357	0.00 351	0.00 352	93.88 156	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								97.44 13	90.31 26	97.62 4	98.07 4	91.46 7	99.58 5	95.66 2	99.12 5	98.98 6

test_0728_THIRD									90.75 19	97.04 7	98.05 5	92.09 3	99.55 11	95.64 3	99.13 3	99.13 1

GSMVS																96.12 135

test_part1				0.00 336		0.00 356	0.00 347	97.45 11				0.00 357	0.00 353	0.00 350	0.00 350	0.00 349

sam_mvs1												71.70 205				96.12 135

sam_mvs												70.60 217

MTGPA								96.97 48

test_post1							88.00 304				9.81 348	69.31 237	95.53 290	76.65 248

test_post											10.29 347	70.57 221	95.91 277

patchmatchnet-post											83.76 323	71.53 206	96.48 250

MTMP							96.16 39	60.64 349

test9_res														91.91 53	98.71 29	98.07 61

agg_prior2														90.54 79	98.68 34	98.27 45

test_prior4						85.96 55	94.11 161

test_prior2							94.12 159		87.67 95	92.63 63	96.39 67	86.62 37		91.50 63	98.67 36

旧先验2							93.36 194		71.25 321	94.37 25			97.13 212	86.74 121

新几何2							93.11 209

无先验							93.28 201	96.26 103	73.95 303				99.05 58	80.56 206		96.59 121

原ACMM2							92.94 216

testdata2													98.75 94	78.30 232

segment_acmp												87.16 33

testdata1							92.15 238		87.94 85

plane_prior5								96.22 108					98.12 131	88.15 101	89.99 179	94.63 187

plane_prior4											94.86 117

plane_prior3						82.75 128			90.26 29	86.91 153

plane_prior2							95.85 56		90.81 17

plane_prior						82.73 131	95.21 87		89.66 40						89.88 184

n2								0.00 357

nn								0.00 357

door-mid								85.49 333

test11								96.57 88

door								85.33 334

HQP5-MVS						81.56 154

BP-MVS														87.11 118

HQP4-MVS										85.43 189			97.96 149			94.51 197

HQP3-MVS								96.04 121							89.77 186

HQP2-MVS												73.83 182

MDTV_nov1_ep13_2view						55.91 344	87.62 310		73.32 308	84.59 207		70.33 224		74.65 267		95.50 157

ACMMP++_ref															87.47 220

ACMMP++															88.01 215

Test By Simon												80.02 107

High-resolution multi-view benchmark