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.

SED-MVS		98.18 2	98.10 4	98.41 14	99.63 20	95.24 21	99.77 9	97.72 73	94.17 24	99.30 4	99.54 3	93.32 15	99.98 10	99.70 2	99.81 19	99.99 1

IU-MVS					99.63 20	95.38 19		97.73 71	95.54 15	99.54 1				99.69 4	99.81 19	99.99 1

OPU-MVS				99.49 2	99.64 19	98.51 2	99.77 9				99.19 32	95.12 6	99.97 20	99.90 1	99.92 3	99.99 1

test_241102_TWO								97.72 73	94.17 24	99.23 6	99.54 3	93.14 20	99.98 10	99.70 2	99.82 15	99.99 1

DeepPCF-MVS	93.56 1	96.55 41	97.84 8	92.68 203	98.71 88	78.11 310	99.70 17	97.71 77	98.18 1	97.36 51	99.76 1	90.37 45	99.94 33	99.27 9	99.54 56	99.99 1

MCST-MVS		98.18 2	97.95 7	98.86 3	99.85 3	96.60 7	99.70 17	97.98 43	97.18 2	95.96 78	99.33 21	92.62 22	100.00 1	98.99 13	99.93 1	99.98 6

MG-MVS		97.24 18	96.83 29	98.47 12	99.79 5	95.71 15	99.07 91	99.06 9	94.45 22	96.42 73	98.70 88	88.81 62	99.74 70	95.35 82	99.86 10	99.97 7

test_0728_SECOND				98.77 5	99.66 14	96.37 11	99.72 14	97.68 81					99.98 10	99.64 5	99.82 15	99.96 8

CNVR-MVS		98.46 1	98.38 1	98.72 6	99.80 4	96.19 12	99.80 8	97.99 42	97.05 3	99.41 2	99.59 2	92.89 21	100.00 1	98.99 13	99.90 5	99.96 8

DeepC-MVS_fast	93.52 2	97.16 23	96.84 28	98.13 20	99.61 26	94.45 47	98.85 114	97.64 89	96.51 7	95.88 81	99.39 19	87.35 92	99.99 5	96.61 56	99.69 36	99.96 8
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_0728_THIRD									93.01 47	99.07 8	99.46 11	94.66 10	99.97 20	99.25 11	99.82 15	99.95 11

DPE-MVS		98.11 5	98.00 5	98.44 13	99.50 42	95.39 18	99.29 66	97.72 73	94.50 20	98.64 20	99.54 3	93.32 15	99.97 20	99.58 7	99.90 5	99.95 11

DVP-MVS		97.77 8	98.18 2	96.53 97	99.54 35	90.14 135	99.41 54	97.70 78	95.46 17	98.60 21	99.19 32	95.71 4	99.49 103	98.15 35	99.85 11	99.95 11

testtj		97.23 20	97.05 20	97.75 35	99.75 7	93.34 69	99.16 75	97.74 67	91.28 87	98.40 26	99.29 22	89.95 48	99.98 10	98.20 34	99.70 35	99.94 14

DPM-MVS		97.86 7	97.25 17	99.68 1	98.25 98	99.10 1	99.76 12	97.78 62	96.61 4	98.15 31	99.53 7	93.62 14	100.00 1	91.79 133	99.80 23	99.94 14

NCCC		98.12 4	98.11 3	98.13 20	99.76 6	94.46 46	99.81 6	97.88 48	96.54 5	98.84 14	99.46 11	92.55 23	99.98 10	98.25 33	99.93 1	99.94 14

APDe-MVS		97.53 11	97.47 10	97.70 36	99.58 28	93.63 62	99.56 32	97.52 114	93.59 41	98.01 39	99.12 45	90.80 35	99.55 93	99.26 10	99.79 25	99.93 17

ETH3 D test6400		97.67 10	97.33 16	98.69 7	99.69 9	96.43 9	99.63 25	97.73 71	91.05 90	98.66 19	99.53 7	90.59 38	99.71 73	99.32 8	99.80 23	99.91 18

agg_prior2														97.84 41	99.87 7	99.91 18

test9_res														98.60 19	99.87 7	99.90 20

SteuartSystems-ACMMP		97.25 17	97.34 15	97.01 62	97.38 122	91.46 102	99.75 13	97.66 83	94.14 28	98.13 32	99.26 24	92.16 24	99.66 80	97.91 39	99.64 42	99.90 20
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		96.65 38	96.46 38	97.21 56	99.34 51	91.77 93	99.70 17	98.05 38	86.48 209	98.05 36	99.20 31	89.33 56	99.96 27	98.38 28	99.62 46	99.90 20

ACMMP_NAP		96.59 39	96.18 45	97.81 32	98.82 85	93.55 64	98.88 113	97.59 101	90.66 96	97.98 40	99.14 42	86.59 106	100.00 1	96.47 60	99.46 59	99.89 23

train_agg		97.20 22	97.08 19	97.57 42	99.57 32	93.17 71	99.38 56	97.66 83	90.18 110	98.39 27	99.18 35	90.94 30	99.66 80	98.58 22	99.85 11	99.88 24

MSLP-MVS++		97.50 14	97.45 12	97.63 38	99.65 18	93.21 70	99.70 17	98.13 36	94.61 19	97.78 45	99.46 11	89.85 49	99.81 62	97.97 37	99.91 4	99.88 24

APD-MVS		96.95 29	96.72 32	97.63 38	99.51 41	93.58 63	99.16 75	97.44 131	90.08 115	98.59 22	99.07 49	89.06 58	99.42 113	97.92 38	99.66 38	99.88 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
agg_prior1		97.12 24	97.03 21	97.38 50	99.54 35	92.66 83	99.35 61	97.64 89	90.38 105	97.98 40	99.17 37	90.84 34	99.61 89	98.57 23	99.78 27	99.87 27

MVS		93.92 106	92.28 129	98.83 4	95.69 176	96.82 5	96.22 267	98.17 31	84.89 233	84.34 212	98.61 95	79.32 191	99.83 57	93.88 108	99.43 63	99.86 28

无先验							98.52 153	97.82 54	87.20 194				99.90 40	87.64 177		99.85 29

SMA-MVS		97.24 18	96.99 23	98.00 27	99.30 58	94.20 53	99.16 75	97.65 88	89.55 129	99.22 7	99.52 9	90.34 46	99.99 5	98.32 31	99.83 13	99.82 30

region2R		96.30 50	96.17 47	96.70 88	99.70 8	90.31 131	99.46 45	97.66 83	90.55 100	97.07 55	99.07 49	86.85 100	99.97 20	95.43 80	99.74 28	99.81 31

test222					98.32 97	91.21 106	98.08 195	97.58 103	83.74 248	95.87 82	99.02 55	86.74 103			99.64 42	99.81 31

TSAR-MVS + GP.		96.95 29	96.91 25	97.07 59	98.88 82	91.62 98	99.58 29	96.54 191	95.09 18	96.84 64	98.63 94	91.16 26	99.77 67	99.04 12	96.42 127	99.81 31

test_prior3		97.07 26	97.09 18	97.01 62	99.58 28	91.77 93	99.57 30	97.57 106	91.43 83	98.12 34	98.97 61	90.43 40	99.49 103	98.33 29	99.81 19	99.79 34

test_prior				97.01 62	99.58 28	91.77 93		97.57 106					99.49 103			99.79 34

新几何1				97.40 48	98.92 80	92.51 90		97.77 64	85.52 219	96.69 71	99.06 51	88.08 75	99.89 43	84.88 205	99.62 46	99.79 34

1121		95.19 78	94.45 82	97.42 46	98.88 82	92.58 88	96.22 267	97.75 65	85.50 221	96.86 61	99.01 59	88.59 66	99.90 40	87.64 177	99.60 51	99.79 34

HFP-MVS		96.42 46	96.26 44	96.90 74	99.69 9	90.96 119	99.47 40	97.81 57	90.54 101	96.88 58	99.05 52	87.57 82	99.96 27	95.65 73	99.72 30	99.78 38

#test#		96.48 43	96.34 42	96.90 74	99.69 9	90.96 119	99.53 36	97.81 57	90.94 94	96.88 58	99.05 52	87.57 82	99.96 27	95.87 72	99.72 30	99.78 38

XVS		96.47 44	96.37 40	96.77 81	99.62 24	90.66 127	99.43 51	97.58 103	92.41 65	96.86 61	98.96 64	87.37 88	99.87 47	95.65 73	99.43 63	99.78 38

X-MVStestdata		90.69 173	88.66 190	96.77 81	99.62 24	90.66 127	99.43 51	97.58 103	92.41 65	96.86 61	29.59 351	87.37 88	99.87 47	95.65 73	99.43 63	99.78 38

testdata				95.26 139	98.20 100	87.28 198		97.60 97	85.21 224	98.48 25	99.15 40	88.15 73	98.72 146	90.29 147	99.45 61	99.78 38

xxxxxxxxxxxxxcwj		97.51 12	97.42 13	97.78 34	99.34 51	93.85 59	99.65 23	95.45 262	95.69 11	98.70 17	99.42 17	90.42 42	99.72 71	98.47 25	99.65 39	99.77 43

SF-MVS		97.22 21	96.92 24	98.12 22	99.11 69	94.88 32	99.44 48	97.45 127	89.60 125	98.70 17	99.42 17	90.42 42	99.72 71	98.47 25	99.65 39	99.77 43

SD-MVS		97.51 12	97.40 14	97.81 32	99.01 75	93.79 61	99.33 64	97.38 138	93.73 38	98.83 15	99.02 55	90.87 33	99.88 44	98.69 17	99.74 28	99.77 43
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
SR-MVS		96.13 54	96.16 49	96.07 114	99.42 48	89.04 161	98.59 148	97.33 143	90.44 103	96.84 64	99.12 45	86.75 102	99.41 115	97.47 43	99.44 62	99.76 46

Regformer-1		96.97 28	96.80 30	97.47 44	99.46 46	93.11 73	98.89 111	97.94 44	92.89 52	96.90 57	99.02 55	89.78 50	99.53 96	97.06 47	99.26 72	99.75 47

Regformer-2		96.94 31	96.78 31	97.42 46	99.46 46	92.97 80	98.89 111	97.93 45	92.86 54	96.88 58	99.02 55	89.74 52	99.53 96	97.03 48	99.26 72	99.75 47

ACMMPR		96.28 51	96.14 51	96.73 85	99.68 12	90.47 129	99.47 40	97.80 59	90.54 101	96.83 66	99.03 54	86.51 110	99.95 30	95.65 73	99.72 30	99.75 47

mPP-MVS		95.90 62	95.75 62	96.38 104	99.58 28	89.41 158	99.26 67	97.41 135	90.66 96	94.82 100	98.95 66	86.15 117	99.98 10	95.24 85	99.64 42	99.74 50

PAPR		96.35 47	95.82 58	97.94 29	99.63 20	94.19 54	99.42 53	97.55 109	92.43 61	93.82 119	99.12 45	87.30 93	99.91 38	94.02 105	99.06 77	99.74 50

API-MVS		94.78 85	94.18 89	96.59 93	99.21 64	90.06 142	98.80 119	97.78 62	83.59 252	93.85 117	99.21 30	83.79 143	99.97 20	92.37 129	99.00 80	99.74 50

CSCG		94.87 83	94.71 77	95.36 135	99.54 35	86.49 210	99.34 63	98.15 34	82.71 267	90.15 165	99.25 25	89.48 55	99.86 52	94.97 91	98.82 89	99.72 53

zzz-MVS		96.21 53	95.96 53	96.96 70	99.29 59	91.19 107	98.69 131	97.45 127	92.58 56	94.39 107	99.24 27	86.43 112	99.99 5	96.22 64	99.40 66	99.71 54

MTAPA		96.09 55	95.80 61	96.96 70	99.29 59	91.19 107	97.23 231	97.45 127	92.58 56	94.39 107	99.24 27	86.43 112	99.99 5	96.22 64	99.40 66	99.71 54

ETH3D-3000-0.1		97.29 16	97.01 22	98.12 22	99.18 65	94.97 30	99.47 40	97.52 114	89.85 119	98.79 16	99.46 11	90.41 44	99.69 75	98.78 15	99.67 37	99.70 56

APD-MVS_3200maxsize		95.64 70	95.65 65	95.62 128	99.24 63	87.80 184	98.42 165	97.22 149	88.93 146	96.64 72	98.98 60	85.49 125	99.36 119	96.68 55	99.27 71	99.70 56

CP-MVS		96.22 52	96.15 50	96.42 102	99.67 13	89.62 154	99.70 17	97.61 96	90.07 116	96.00 75	99.16 39	87.43 86	99.92 36	96.03 70	99.72 30	99.70 56

MSP-MVS		98.07 6	98.00 5	98.29 15	99.66 14	95.20 26	99.72 14	97.47 125	93.95 29	99.07 8	99.46 11	93.18 18	99.97 20	99.64 5	99.82 15	99.69 59

ZNCC-MVS		96.09 55	95.81 60	96.95 72	99.42 48	91.19 107	99.55 33	97.53 113	89.72 122	95.86 83	98.94 71	86.59 106	99.97 20	95.13 86	99.56 54	99.68 60

HPM-MVS++		97.72 9	97.59 9	98.14 19	99.53 40	94.76 39	99.19 70	97.75 65	95.66 13	98.21 30	99.29 22	91.10 28	99.99 5	97.68 42	99.87 7	99.68 60

CDPH-MVS		96.56 40	96.18 45	97.70 36	99.59 27	93.92 57	99.13 87	97.44 131	89.02 141	97.90 43	99.22 29	88.90 61	99.49 103	94.63 98	99.79 25	99.68 60

PAPM_NR		95.43 71	95.05 74	96.57 95	99.42 48	90.14 135	98.58 150	97.51 117	90.65 98	92.44 133	98.90 72	87.77 80	99.90 40	90.88 141	99.32 69	99.68 60

canonicalmvs		95.02 81	93.96 97	98.20 17	97.53 120	95.92 14	98.71 126	96.19 215	91.78 76	95.86 83	98.49 103	79.53 189	99.03 135	96.12 67	91.42 189	99.66 64

PGM-MVS		95.85 63	95.65 65	96.45 100	99.50 42	89.77 150	98.22 182	98.90 11	89.19 135	96.74 69	98.95 66	85.91 120	99.92 36	93.94 106	99.46 59	99.66 64

DELS-MVS		97.12 24	96.60 35	98.68 8	98.03 106	96.57 8	99.84 3	97.84 52	96.36 8	95.20 95	98.24 112	88.17 72	99.83 57	96.11 68	99.60 51	99.64 66
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
3Dnovator+	87.72 8	93.43 121	91.84 140	98.17 18	95.73 175	95.08 29	98.92 108	97.04 166	91.42 85	81.48 254	97.60 133	74.60 215	99.79 65	90.84 142	98.97 81	99.64 66

CANet		97.00 27	96.49 37	98.55 9	98.86 84	96.10 13	99.83 5	97.52 114	95.90 9	97.21 52	98.90 72	82.66 160	99.93 35	98.71 16	98.80 90	99.63 68

114514_t		94.06 102	93.05 115	97.06 60	99.08 72	92.26 91	98.97 103	97.01 170	82.58 269	92.57 131	98.22 113	80.68 182	99.30 124	89.34 159	99.02 79	99.63 68

PAPM		96.35 47	95.94 54	97.58 40	94.10 223	95.25 20	98.93 106	98.17 31	94.26 23	93.94 115	98.72 85	89.68 53	97.88 177	96.36 63	99.29 70	99.62 70

ETH3D cwj APD-0.16		96.94 31	96.58 36	98.01 26	98.62 91	94.73 41	99.13 87	97.38 138	88.44 162	98.53 24	99.39 19	89.66 54	99.69 75	98.43 27	99.61 50	99.61 71

TSAR-MVS + MP.		97.44 15	97.46 11	97.39 49	99.12 68	93.49 67	98.52 153	97.50 120	94.46 21	98.99 10	98.64 92	91.58 25	99.08 134	98.49 24	99.83 13	99.60 72
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
旧先验1					98.97 76	92.90 82		97.74 67			99.15 40	91.05 29			99.33 68	99.60 72

test12				97.83 31	99.33 57	94.45 47		97.55 109		97.56 46		88.60 64	99.50 102		99.71 34	99.55 74

HY-MVS	88.56 7	95.29 75	94.23 86	98.48 11	97.72 111	96.41 10	94.03 296	98.74 13	92.42 64	95.65 88	94.76 199	86.52 109	99.49 103	95.29 84	92.97 162	99.53 75

GST-MVS		95.97 59	95.66 63	96.90 74	99.49 44	91.22 105	99.45 47	97.48 123	89.69 123	95.89 80	98.72 85	86.37 114	99.95 30	94.62 99	99.22 75	99.52 76

MP-MVS		96.00 57	95.82 58	96.54 96	99.47 45	90.13 137	99.36 60	97.41 135	90.64 99	95.49 90	98.95 66	85.51 124	99.98 10	96.00 71	99.59 53	99.52 76
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
alignmvs		95.77 68	95.00 75	98.06 25	97.35 123	95.68 16	99.71 16	97.50 120	91.50 81	96.16 74	98.61 95	86.28 115	99.00 136	96.19 66	91.74 183	99.51 78

WTY-MVS		95.97 59	95.11 73	98.54 10	97.62 115	96.65 6	99.44 48	98.74 13	92.25 68	95.21 94	98.46 107	86.56 108	99.46 110	95.00 90	92.69 166	99.50 79

Regformer-3		96.50 42	96.36 41	96.91 73	99.34 51	91.72 96	98.71 126	97.90 47	92.48 60	96.00 75	98.95 66	88.60 64	99.52 99	96.44 61	98.83 87	99.49 80

Regformer-4		96.45 45	96.33 43	96.81 80	99.34 51	91.44 103	98.71 126	97.88 48	92.43 61	95.97 77	98.95 66	88.42 68	99.51 100	96.40 62	98.83 87	99.49 80

DP-MVS Recon		95.85 63	95.15 72	97.95 28	99.87 2	94.38 50	99.60 27	97.48 123	86.58 206	94.42 106	99.13 44	87.36 91	99.98 10	93.64 113	98.33 102	99.48 82

HPM-MVS		95.41 73	95.22 71	95.99 117	99.29 59	89.14 159	99.17 74	97.09 163	87.28 193	95.40 91	98.48 104	84.93 132	99.38 117	95.64 77	99.65 39	99.47 83
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test_yl		95.27 76	94.60 79	97.28 53	98.53 94	92.98 78	99.05 94	98.70 16	86.76 203	94.65 104	97.74 126	87.78 78	99.44 111	95.57 78	92.61 167	99.44 84

DCV-MVSNet		95.27 76	94.60 79	97.28 53	98.53 94	92.98 78	99.05 94	98.70 16	86.76 203	94.65 104	97.74 126	87.78 78	99.44 111	95.57 78	92.61 167	99.44 84

CS-MVS		95.85 63	95.86 57	95.82 122	96.80 142	89.78 149	99.84 3	96.60 183	92.60 55	96.81 68	98.70 88	85.04 130	98.25 157	97.90 40	98.43 100	99.42 86

MVS_111021_HR		96.69 36	96.69 33	96.72 87	98.58 93	91.00 118	99.14 84	99.45 1	93.86 35	95.15 96	98.73 83	88.48 67	99.76 68	97.23 46	99.56 54	99.40 87

lupinMVS		96.32 49	95.94 54	97.44 45	95.05 203	94.87 33	99.86 2	96.50 193	93.82 36	98.04 37	98.77 79	85.52 122	98.09 164	96.98 52	98.97 81	99.37 88

mvs_anonymous		92.50 143	91.65 144	95.06 143	96.60 148	89.64 153	97.06 237	96.44 197	86.64 205	84.14 213	93.93 211	82.49 162	96.17 263	91.47 134	96.08 136	99.35 89

HPM-MVS_fast		94.89 82	94.62 78	95.70 127	99.11 69	88.44 175	99.14 84	97.11 159	85.82 216	95.69 87	98.47 105	83.46 148	99.32 123	93.16 121	99.63 45	99.35 89

1314		93.44 120	91.98 138	97.84 30	95.24 188	94.38 50	96.22 267	97.92 46	90.18 110	82.28 236	97.71 128	77.63 203	99.80 64	91.94 132	98.67 94	99.34 91

LFMVS		92.23 147	90.84 157	96.42 102	98.24 99	91.08 115	98.24 181	96.22 212	83.39 255	94.74 102	98.31 109	61.12 298	98.85 138	94.45 102	92.82 163	99.32 92

Effi-MVS+		93.87 109	93.15 113	96.02 115	95.79 172	90.76 123	96.70 252	95.78 242	86.98 197	95.71 86	97.17 151	79.58 187	98.01 172	94.57 100	96.09 135	99.31 93

CHOSEN 1792x2688		94.35 99	93.82 102	95.95 119	97.40 121	88.74 169	98.41 167	98.27 25	92.18 70	91.43 144	96.40 175	78.88 193	99.81 62	93.59 114	97.81 106	99.30 94

DWT-MVSNet_test		94.36 98	93.95 98	95.62 128	96.99 137	89.47 156	96.62 254	97.38 138	90.96 93	93.07 127	97.27 143	93.73 13	98.09 164	85.86 198	93.65 157	99.29 95

ACMMP		94.67 91	94.30 84	95.79 124	99.25 62	88.13 178	98.41 167	98.67 19	90.38 105	91.43 144	98.72 85	82.22 168	99.95 30	93.83 110	95.76 141	99.29 95
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		95.80 66	95.30 68	97.29 52	98.95 79	92.66 83	98.59 148	97.14 155	88.95 144	93.12 125	99.25 25	85.62 121	99.94 33	96.56 58	99.48 58	99.28 97
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
EPMVS		92.59 141	91.59 145	95.59 131	97.22 127	90.03 143	91.78 312	98.04 39	90.42 104	91.66 139	90.65 275	86.49 111	97.46 204	81.78 241	96.31 130	99.28 97

AdaColmap		93.82 110	93.06 114	96.10 113	99.88 1	89.07 160	98.33 174	97.55 109	86.81 202	90.39 162	98.65 91	75.09 212	99.98 10	93.32 119	97.53 114	99.26 99

ET-MVSNet_ETH3D		92.56 142	91.45 147	95.88 120	96.39 154	94.13 55	99.46 45	96.97 172	92.18 70	66.94 327	98.29 111	94.65 11	94.28 312	94.34 103	83.82 233	99.24 100

VNet		95.08 80	94.26 85	97.55 43	98.07 105	93.88 58	98.68 133	98.73 15	90.33 107	97.16 54	97.43 140	79.19 192	99.53 96	96.91 54	91.85 181	99.24 100

CNLPA		93.64 117	92.74 121	96.36 105	98.96 78	90.01 145	99.19 70	95.89 238	86.22 212	89.40 173	98.85 75	80.66 183	99.84 55	88.57 167	96.92 121	99.24 100

3Dnovator	87.35 11	93.17 131	91.77 142	97.37 51	95.41 185	93.07 75	98.82 117	97.85 51	91.53 80	82.56 230	97.58 135	71.97 240	99.82 60	91.01 139	99.23 74	99.22 103

GG-mvs-BLEND				96.98 68	96.53 150	94.81 38	87.20 324	97.74 67		93.91 116	96.40 175	96.56 2	96.94 220	95.08 87	98.95 84	99.20 104

EIA-MVS		95.11 79	95.27 70	94.64 156	96.34 156	86.51 209	99.59 28	96.62 181	92.51 58	94.08 113	98.64 92	86.05 118	98.24 158	95.07 88	98.50 98	99.18 105

Patchmatch-test		86.25 243	84.06 257	92.82 198	94.42 219	82.88 273	82.88 339	94.23 299	71.58 320	79.39 275	90.62 277	89.00 60	96.42 244	63.03 324	91.37 190	99.16 106

gg-mvs-nofinetune		90.00 184	87.71 203	96.89 79	96.15 164	94.69 43	85.15 330	97.74 67	68.32 331	92.97 129	60.16 340	96.10 3	96.84 222	93.89 107	98.87 85	99.14 107

MVS_Test		93.67 116	92.67 123	96.69 89	96.72 145	92.66 83	97.22 232	96.03 221	87.69 186	95.12 97	94.03 207	81.55 175	98.28 156	89.17 163	96.46 125	99.14 107

HyFIR lowres test		93.68 115	93.29 110	94.87 147	97.57 119	88.04 180	98.18 186	98.47 21	87.57 188	91.24 148	95.05 195	85.49 125	97.46 204	93.22 120	92.82 163	99.10 109

Anonymous202405211		88.84 200	87.03 214	94.27 166	98.14 104	84.18 256	98.44 163	95.58 255	76.79 308	89.34 174	96.88 164	53.42 321	99.54 95	87.53 179	87.12 210	99.09 110

baseline		93.91 107	93.30 109	95.72 126	95.10 201	90.07 139	97.48 221	95.91 235	91.03 91	93.54 121	97.68 129	79.58 187	98.02 171	94.27 104	95.14 146	99.08 111

Vis-MVSNet (Re-imp)		93.26 129	93.00 118	94.06 174	96.14 165	86.71 208	98.68 133	96.70 179	88.30 167	89.71 172	97.64 132	85.43 128	96.39 246	88.06 173	96.32 129	99.08 111

PatchmatchNet		92.05 150	91.04 152	95.06 143	96.17 163	89.04 161	91.26 316	97.26 144	89.56 128	90.64 156	90.56 281	88.35 70	97.11 213	79.53 252	96.07 137	99.03 113
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPNet		96.82 34	96.68 34	97.25 55	98.65 89	93.10 74	99.48 39	98.76 12	96.54 5	97.84 44	98.22 113	87.49 85	99.66 80	95.35 82	97.78 109	99.00 114
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		94.85 84	93.94 99	97.58 40	96.43 153	94.09 56	98.93 106	99.16 8	89.50 130	95.27 93	97.85 119	81.50 176	99.65 84	92.79 127	94.02 155	98.99 115

Patchmatch-RL test		81.90 284	80.13 286	87.23 295	80.71 335	70.12 333	84.07 336	88.19 342	83.16 259	70.57 315	82.18 326	87.18 94	92.59 324	82.28 236	62.78 328	98.98 116

PVSNet	87.13 12	93.69 113	92.83 120	96.28 107	97.99 107	90.22 134	99.38 56	98.93 10	91.42 85	93.66 120	97.68 129	71.29 248	99.64 86	87.94 174	97.20 119	98.98 116

MVSFormer		94.71 90	94.08 92	96.61 92	95.05 203	94.87 33	97.77 211	96.17 216	86.84 200	98.04 37	98.52 99	85.52 122	95.99 269	89.83 150	98.97 81	98.96 118

jason		95.40 74	94.86 76	97.03 61	92.91 254	94.23 52	99.70 17	96.30 204	93.56 42	96.73 70	98.52 99	81.46 178	97.91 174	96.08 69	98.47 99	98.96 118
jason: jason.
CostFormer		92.89 134	92.48 127	94.12 172	94.99 205	85.89 230	92.89 306	97.00 171	86.98 197	95.00 99	90.78 268	90.05 47	97.51 203	92.92 125	91.73 184	98.96 118

MAR-MVS		94.43 97	94.09 91	95.45 133	99.10 71	87.47 192	98.39 171	97.79 61	88.37 165	94.02 114	99.17 37	78.64 198	99.91 38	92.48 128	98.85 86	98.96 118
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
MDTV_nov1_ep13_2view						91.17 110	91.38 314		87.45 191	93.08 126		86.67 104		87.02 182		98.95 122

CVMVSNet		90.30 177	90.91 155	88.46 286	94.32 221	73.58 323	97.61 218	97.59 101	90.16 113	88.43 181	97.10 153	76.83 207	92.86 319	82.64 232	93.54 158	98.93 123

ab-mvs		91.05 165	89.17 179	96.69 89	95.96 169	91.72 96	92.62 307	97.23 148	85.61 218	89.74 170	93.89 213	68.55 260	99.42 113	91.09 137	87.84 206	98.92 124

IS-MVSNet		93.00 133	92.51 126	94.49 159	96.14 165	87.36 196	98.31 177	95.70 248	88.58 154	90.17 164	97.50 137	83.02 155	97.22 210	87.06 181	96.07 137	98.90 125

CPTT-MVS		94.60 94	94.43 83	95.09 141	99.66 14	86.85 205	99.44 48	97.47 125	83.22 257	94.34 109	98.96 64	82.50 161	99.55 93	94.81 93	99.50 57	98.88 126

Vis-MVSNet		92.64 138	91.85 139	95.03 145	95.12 197	88.23 176	98.48 160	96.81 176	91.61 78	92.16 136	97.22 147	71.58 246	98.00 173	85.85 199	97.81 106	98.88 126
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
casdiffmvs		93.98 105	93.43 107	95.61 130	95.07 202	89.86 147	98.80 119	95.84 241	90.98 92	92.74 130	97.66 131	79.71 186	98.10 163	94.72 96	95.37 145	98.87 128

GSMVS																98.84 129

sam_mvs1												88.39 69				98.84 129

SCA		90.64 174	89.25 178	94.83 149	94.95 207	88.83 165	96.26 264	97.21 150	90.06 117	90.03 166	90.62 277	66.61 275	96.81 224	83.16 226	94.36 152	98.84 129

PMMVS		93.62 118	93.90 101	92.79 199	96.79 143	81.40 284	98.85 114	96.81 176	91.25 88	96.82 67	98.15 117	77.02 206	98.13 161	93.15 122	96.30 131	98.83 132

ETV-MVS		96.00 57	96.00 52	96.00 116	96.56 149	91.05 116	99.63 25	96.61 182	93.26 46	97.39 50	98.30 110	86.62 105	98.13 161	98.07 36	97.57 111	98.82 133

1112_ss		92.71 136	91.55 146	96.20 108	95.56 180	91.12 111	98.48 160	94.69 288	88.29 168	86.89 194	98.50 101	87.02 97	98.66 148	84.75 206	89.77 201	98.81 134

Test_1112_low_res		92.27 146	90.97 153	96.18 109	95.53 182	91.10 113	98.47 162	94.66 289	88.28 169	86.83 196	93.50 225	87.00 98	98.65 149	84.69 207	89.74 202	98.80 135

PatchT		85.44 256	83.19 262	92.22 208	93.13 251	83.00 268	83.80 338	96.37 200	70.62 322	90.55 157	79.63 333	84.81 135	94.87 301	58.18 334	91.59 186	98.79 136

PVSNet_Blended		95.94 61	95.66 63	96.75 83	98.77 86	91.61 99	99.88 1	98.04 39	93.64 40	94.21 110	97.76 124	83.50 146	99.87 47	97.41 44	97.75 110	98.79 136

DeepC-MVS	91.02 4	94.56 96	93.92 100	96.46 99	97.16 129	90.76 123	98.39 171	97.11 159	93.92 31	88.66 178	98.33 108	78.14 200	99.85 54	95.02 89	98.57 96	98.78 138
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tpmrst		92.78 135	92.16 133	94.65 155	96.27 158	87.45 193	91.83 311	97.10 162	89.10 139	94.68 103	90.69 272	88.22 71	97.73 192	89.78 152	91.80 182	98.77 139

原ACMM1				96.18 109	99.03 74	90.08 138		97.63 93	88.98 142	97.00 56	98.97 61	88.14 74	99.71 73	88.23 171	99.62 46	98.76 140

tpm2		91.77 153	91.09 150	93.82 182	94.83 212	85.56 238	92.51 308	97.16 154	84.00 245	93.83 118	90.66 274	87.54 84	97.17 211	87.73 176	91.55 187	98.72 141

TAPA-MVS	87.50 9	90.35 175	89.05 181	94.25 168	98.48 96	85.17 244	98.42 165	96.58 188	82.44 273	87.24 190	98.53 98	82.77 159	98.84 139	59.09 332	97.88 105	98.72 141
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EI-MVSNet-Vis-set		95.76 69	95.63 67	96.17 111	99.14 67	90.33 130	98.49 159	97.82 54	91.92 73	94.75 101	98.88 74	87.06 96	99.48 108	95.40 81	97.17 120	98.70 143

diffmvs		94.59 95	94.19 87	95.81 123	95.54 181	90.69 125	98.70 130	95.68 250	91.61 78	95.96 78	97.81 121	80.11 184	98.06 168	96.52 59	95.76 141	98.67 144

DP-MVS		88.75 206	86.56 220	95.34 136	98.92 80	87.45 193	97.64 217	93.52 309	70.55 323	81.49 253	97.25 144	74.43 218	99.88 44	71.14 306	94.09 154	98.67 144

abl_6		94.63 93	94.48 81	95.09 141	98.61 92	86.96 203	98.06 197	96.97 172	89.31 133	95.86 83	98.56 97	79.82 185	99.64 86	94.53 101	98.65 95	98.66 146

TESTMET0.1,1		93.82 110	93.26 111	95.49 132	95.21 190	90.25 132	99.15 81	97.54 112	89.18 136	91.79 137	94.87 197	89.13 57	97.63 196	86.21 191	96.29 132	98.60 147

dp		90.16 181	88.83 186	94.14 171	96.38 155	86.42 212	91.57 313	97.06 165	84.76 235	88.81 177	90.19 293	84.29 139	97.43 206	75.05 284	91.35 191	98.56 148

EPP-MVSNet		93.75 112	93.67 104	94.01 176	95.86 171	85.70 235	98.67 135	97.66 83	84.46 238	91.36 146	97.18 150	91.16 26	97.79 183	92.93 124	93.75 156	98.53 149

Fast-Effi-MVS+		91.72 154	90.79 160	94.49 159	95.89 170	87.40 195	99.54 35	95.70 248	85.01 231	89.28 175	95.68 186	77.75 202	97.57 202	83.22 225	95.06 147	98.51 150

CDS-MVSNet		93.47 119	93.04 116	94.76 150	94.75 214	89.45 157	98.82 117	97.03 168	87.91 178	90.97 151	96.48 173	89.06 58	96.36 248	89.50 154	92.81 165	98.49 151
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
LCM-MVSNet-Re		88.59 208	88.61 191	88.51 285	95.53 182	72.68 326	96.85 244	88.43 341	88.45 159	73.14 309	90.63 276	75.82 208	94.38 311	92.95 123	95.71 143	98.48 152

TAMVS		92.62 139	92.09 136	94.20 169	94.10 223	87.68 186	98.41 167	96.97 172	87.53 190	89.74 170	96.04 182	84.77 136	96.49 239	88.97 166	92.31 173	98.42 153

CR-MVSNet		88.83 202	87.38 208	93.16 191	93.47 242	86.24 218	84.97 332	94.20 300	88.92 147	90.76 154	86.88 317	84.43 137	94.82 303	70.64 307	92.17 177	98.41 154

RPMNet		84.62 263	81.78 276	93.16 191	93.47 242	86.24 218	84.97 332	96.28 208	64.85 337	90.76 154	78.80 334	80.95 181	94.82 303	53.76 337	92.17 177	98.41 154

BH-RMVSNet		91.25 162	89.99 169	95.03 145	96.75 144	88.55 172	98.65 137	94.95 282	87.74 183	87.74 184	97.80 122	68.27 262	98.14 160	80.53 250	97.49 115	98.41 154

UA-Net		93.30 126	92.62 124	95.34 136	96.27 158	88.53 174	95.88 277	96.97 172	90.90 95	95.37 92	97.07 155	82.38 166	99.10 133	83.91 220	94.86 149	98.38 157

tpm		89.67 188	88.95 183	91.82 217	92.54 257	81.43 283	92.95 305	95.92 231	87.81 180	90.50 159	89.44 300	84.99 131	95.65 284	83.67 223	82.71 242	98.38 157

MVS_111021_LR		95.78 67	95.94 54	95.28 138	98.19 102	87.69 185	98.80 119	99.26 7	93.39 43	95.04 98	98.69 90	84.09 141	99.76 68	96.96 53	99.06 77	98.38 157

test-LLR		93.11 132	92.68 122	94.40 162	94.94 208	87.27 199	99.15 81	97.25 145	90.21 108	91.57 140	94.04 205	84.89 133	97.58 199	85.94 195	96.13 133	98.36 160

test-mter		93.27 128	92.89 119	94.40 162	94.94 208	87.27 199	99.15 81	97.25 145	88.95 144	91.57 140	94.04 205	88.03 76	97.58 199	85.94 195	96.13 133	98.36 160

IB-MVS	89.43 6	92.12 148	90.83 159	95.98 118	95.40 186	90.78 122	99.81 6	98.06 37	91.23 89	85.63 202	93.66 219	90.63 37	98.78 140	91.22 136	71.85 311	98.36 160
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
VDD-MVS		91.24 163	90.18 168	94.45 161	97.08 133	85.84 233	98.40 170	96.10 219	86.99 195	93.36 122	98.16 116	54.27 318	99.20 125	96.59 57	90.63 197	98.31 163

PVSNet_Blended_VisFu		94.67 91	94.11 90	96.34 106	97.14 130	91.10 113	99.32 65	97.43 133	92.10 72	91.53 143	96.38 178	83.29 152	99.68 78	93.42 118	96.37 128	98.25 164

thisisatest0515		94.75 86	94.19 87	96.43 101	96.13 168	92.64 87	99.47 40	97.60 97	87.55 189	93.17 124	97.59 134	94.71 9	98.42 151	88.28 170	93.20 159	98.24 165

EI-MVSNet-UG-set		95.43 71	95.29 69	95.86 121	99.07 73	89.87 146	98.43 164	97.80 59	91.78 76	94.11 112	98.77 79	86.25 116	99.48 108	94.95 92	96.45 126	98.22 166

QAPM		91.41 159	89.49 173	97.17 58	95.66 178	93.42 68	98.60 146	97.51 117	80.92 289	81.39 255	97.41 141	72.89 233	99.87 47	82.33 235	98.68 93	98.21 167

CHOSEN 280x420		96.80 35	96.85 27	96.66 91	97.85 109	94.42 49	94.76 289	98.36 23	92.50 59	95.62 89	97.52 136	97.92 1	97.38 207	98.31 32	98.80 90	98.20 168

TR-MVS		90.77 170	89.44 174	94.76 150	96.31 157	88.02 181	97.92 202	95.96 225	85.52 219	88.22 182	97.23 146	66.80 274	98.09 164	84.58 209	92.38 171	98.17 169

GA-MVS		90.10 182	88.69 189	94.33 164	92.44 258	87.97 182	99.08 90	96.26 209	89.65 124	86.92 193	93.11 232	68.09 263	96.96 218	82.54 234	90.15 199	98.05 170

OMC-MVS		93.90 108	93.62 105	94.73 153	98.63 90	87.00 202	98.04 198	96.56 189	92.19 69	92.46 132	98.73 83	79.49 190	99.14 131	92.16 131	94.34 153	98.03 171

xiu_mvs_v2_base		96.66 37	96.17 47	98.11 24	97.11 132	96.96 4	99.01 99	97.04 166	95.51 16	98.86 13	99.11 48	82.19 169	99.36 119	98.59 21	98.14 103	98.00 172

PS-MVSNAJ		96.87 33	96.40 39	98.29 15	97.35 123	97.29 3	99.03 96	97.11 159	95.83 10	98.97 11	99.14 42	82.48 163	99.60 91	98.60 19	99.08 76	98.00 172

thisisatest0530		94.00 104	93.52 106	95.43 134	95.76 174	90.02 144	98.99 101	97.60 97	86.58 206	91.74 138	97.36 142	94.78 8	98.34 152	86.37 190	92.48 170	97.94 174

tpm cat1		88.89 198	87.27 210	93.76 183	95.79 172	85.32 241	90.76 320	97.09 163	76.14 310	85.72 201	88.59 306	82.92 156	98.04 170	76.96 270	91.43 188	97.90 175

tttt0517		93.30 126	93.01 117	94.17 170	95.57 179	86.47 211	98.51 156	97.60 97	85.99 214	90.55 157	97.19 149	94.80 7	98.31 153	85.06 203	91.86 180	97.74 176

ADS-MVSNet2		87.62 223	86.88 216	89.86 258	96.21 160	79.14 301	87.15 325	92.99 312	83.01 260	89.91 168	87.27 313	78.87 194	92.80 322	74.20 291	92.27 174	97.64 177

ADS-MVSNet		88.99 195	87.30 209	94.07 173	96.21 160	87.56 190	87.15 325	96.78 178	83.01 260	89.91 168	87.27 313	78.87 194	97.01 217	74.20 291	92.27 174	97.64 177

BH-w/o		92.32 144	91.79 141	93.91 179	96.85 139	86.18 221	99.11 89	95.74 245	88.13 172	84.81 207	97.00 158	77.26 205	97.91 174	89.16 164	98.03 104	97.64 177

LS3D		90.19 180	88.72 188	94.59 157	98.97 76	86.33 217	96.90 243	96.60 183	74.96 313	84.06 215	98.74 82	75.78 209	99.83 57	74.93 285	97.57 111	97.62 180

VDDNet		90.08 183	88.54 195	94.69 154	94.41 220	87.68 186	98.21 184	96.40 198	76.21 309	93.33 123	97.75 125	54.93 316	98.77 141	94.71 97	90.96 192	97.61 181

EPNet_dtu		92.28 145	92.15 134	92.70 202	97.29 125	84.84 248	98.64 139	97.82 54	92.91 51	93.02 128	97.02 157	85.48 127	95.70 283	72.25 303	94.89 148	97.55 182
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
BH-untuned		91.46 158	90.84 157	93.33 188	96.51 152	84.83 249	98.84 116	95.50 259	86.44 211	83.50 217	96.70 168	75.49 211	97.77 185	86.78 188	97.81 106	97.40 183

thres200		93.69 113	92.59 125	96.97 69	97.76 110	94.74 40	99.35 61	99.36 2	89.23 134	91.21 149	96.97 159	83.42 149	98.77 141	85.08 202	90.96 192	97.39 184

JIA-IIPM		85.97 246	84.85 245	89.33 273	93.23 249	73.68 322	85.05 331	97.13 157	69.62 327	91.56 142	68.03 338	88.03 76	96.96 218	77.89 265	93.12 160	97.34 185

baseline1		92.61 140	91.28 148	96.58 94	97.05 135	94.63 44	97.72 213	96.20 213	89.82 120	88.56 179	96.85 165	86.85 100	97.82 181	88.42 168	80.10 254	97.30 186

PVSNet_0	83.28 16	87.31 226	85.16 239	93.74 184	94.78 213	84.59 251	98.91 109	98.69 18	89.81 121	78.59 284	93.23 229	61.95 294	99.34 122	94.75 94	55.72 337	97.30 186

PLC	91.07 3	94.23 101	94.01 93	94.87 147	99.17 66	87.49 191	99.25 68	96.55 190	88.43 163	91.26 147	98.21 115	85.92 119	99.86 52	89.77 153	97.57 111	97.24 188
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Anonymous20240529		87.66 222	85.58 234	93.92 178	97.59 118	85.01 247	98.13 189	97.13 157	66.69 335	88.47 180	96.01 183	55.09 315	99.51 100	87.00 183	84.12 229	97.23 189

thres100view900		93.34 125	92.15 134	96.90 74	97.62 115	94.84 35	99.06 93	99.36 2	87.96 176	90.47 160	96.78 166	83.29 152	98.75 143	84.11 216	90.69 194	97.12 190

tfpn200view9		93.43 121	92.27 130	96.90 74	97.68 113	94.84 35	99.18 72	99.36 2	88.45 159	90.79 152	96.90 162	83.31 150	98.75 143	84.11 216	90.69 194	97.12 190

tpmvs		89.16 193	87.76 201	93.35 187	97.19 128	84.75 250	90.58 322	97.36 141	81.99 277	84.56 209	89.31 303	83.98 142	98.17 159	74.85 287	90.00 200	97.12 190

PCF-MVS	89.78 5	91.26 160	89.63 172	96.16 112	95.44 184	91.58 101	95.29 285	96.10 219	85.07 228	82.75 226	97.45 139	78.28 199	99.78 66	80.60 249	95.65 144	97.12 190
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MIMVSNet		84.48 267	81.83 275	92.42 206	91.73 270	87.36 196	85.52 328	94.42 295	81.40 283	81.91 246	87.58 310	51.92 324	92.81 321	73.84 294	88.15 205	97.08 194

CANet_DTU		94.31 100	93.35 108	97.20 57	97.03 136	94.71 42	98.62 142	95.54 257	95.61 14	97.21 52	98.47 105	71.88 241	99.84 55	88.38 169	97.46 116	97.04 195

PatchMatch-RL		91.47 157	90.54 164	94.26 167	98.20 100	86.36 216	96.94 241	97.14 155	87.75 182	88.98 176	95.75 185	71.80 243	99.40 116	80.92 246	97.39 117	97.02 196

UGNet		91.91 152	90.85 156	95.10 140	97.06 134	88.69 170	98.01 199	98.24 27	92.41 65	92.39 134	93.61 220	60.52 299	99.68 78	88.14 172	97.25 118	96.92 197
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
mvs-test1		91.57 155	92.20 132	89.70 263	95.15 195	74.34 319	99.51 37	95.40 266	91.92 73	91.02 150	97.25 144	74.27 220	98.08 167	89.45 155	95.83 140	96.67 198

thres600view7		93.18 130	92.00 137	96.75 83	97.62 115	94.92 31	99.07 91	99.36 2	87.96 176	90.47 160	96.78 166	83.29 152	98.71 147	82.93 230	90.47 198	96.61 199

thres400		93.39 123	92.27 130	96.73 85	97.68 113	94.84 35	99.18 72	99.36 2	88.45 159	90.79 152	96.90 162	83.31 150	98.75 143	84.11 216	90.69 194	96.61 199

xiu_mvs_v1_base_debu		94.73 87	93.98 94	96.99 65	95.19 191	95.24 21	98.62 142	96.50 193	92.99 48	97.52 47	98.83 76	72.37 236	99.15 128	97.03 48	96.74 122	96.58 201

xiu_mvs_v1_base		94.73 87	93.98 94	96.99 65	95.19 191	95.24 21	98.62 142	96.50 193	92.99 48	97.52 47	98.83 76	72.37 236	99.15 128	97.03 48	96.74 122	96.58 201

xiu_mvs_v1_base_debi		94.73 87	93.98 94	96.99 65	95.19 191	95.24 21	98.62 142	96.50 193	92.99 48	97.52 47	98.83 76	72.37 236	99.15 128	97.03 48	96.74 122	96.58 201

F-COLMAP		92.07 149	91.75 143	93.02 194	98.16 103	82.89 272	98.79 123	95.97 223	86.54 208	87.92 183	97.80 122	78.69 197	99.65 84	85.97 193	95.93 139	96.53 204

MSDG		88.29 212	86.37 222	94.04 175	96.90 138	86.15 223	96.52 256	94.36 297	77.89 305	79.22 277	96.95 160	69.72 254	99.59 92	73.20 299	92.58 169	96.37 205

UniMVSNet_ETH3D		85.65 255	83.79 260	91.21 227	90.41 285	80.75 295	95.36 284	95.78 242	78.76 299	81.83 251	94.33 203	49.86 329	96.66 229	84.30 211	83.52 236	96.22 206

OpenMVS	85.28 14	90.75 171	88.84 185	96.48 98	93.58 240	93.51 66	98.80 119	97.41 135	82.59 268	78.62 282	97.49 138	68.00 265	99.82 60	84.52 210	98.55 97	96.11 207

baseline2		94.04 103	93.80 103	94.74 152	93.07 252	90.25 132	98.12 191	98.16 33	89.86 118	86.53 198	96.95 160	95.56 5	98.05 169	91.44 135	94.53 150	95.93 208

DSMNet-mixed		81.60 285	81.43 280	82.10 316	84.36 328	60.79 339	93.63 301	86.74 343	79.00 295	79.32 276	87.15 315	63.87 288	89.78 335	66.89 316	91.92 179	95.73 209

cascas		90.93 168	89.33 177	95.76 125	95.69 176	93.03 77	98.99 101	96.59 185	80.49 291	86.79 197	94.45 202	65.23 283	98.60 150	93.52 115	92.18 176	95.66 210

XVG-OURS-SEG-HR		90.95 167	90.66 163	91.83 216	95.18 194	81.14 291	95.92 274	95.92 231	88.40 164	90.33 163	97.85 119	70.66 251	99.38 117	92.83 126	88.83 203	94.98 211

XVG-OURS		90.83 169	90.49 165	91.86 215	95.23 189	81.25 288	95.79 282	95.92 231	88.96 143	90.02 167	98.03 118	71.60 245	99.35 121	91.06 138	87.78 207	94.98 211

Effi-MVS+-dtu		89.97 185	90.68 162	87.81 290	95.15 195	71.98 328	97.87 206	95.40 266	91.92 73	87.57 185	91.44 256	74.27 220	96.84 222	89.45 155	93.10 161	94.60 213

Fast-Effi-MVS+-dtu		88.84 200	88.59 193	89.58 267	93.44 245	78.18 308	98.65 137	94.62 290	88.46 158	84.12 214	95.37 192	68.91 257	96.52 236	82.06 238	91.70 185	94.06 214

test0.0.03 1		88.96 196	88.61 191	90.03 256	91.09 277	84.43 253	98.97 103	97.02 169	90.21 108	80.29 263	96.31 179	84.89 133	91.93 332	72.98 300	85.70 220	93.73 215

MVS-HIRNet		79.01 295	75.13 302	90.66 241	93.82 236	81.69 282	85.16 329	93.75 305	54.54 339	74.17 305	59.15 342	57.46 306	96.58 232	63.74 322	94.38 151	93.72 216

AllTest		84.97 259	83.12 263	90.52 244	96.82 140	78.84 303	95.89 275	92.17 321	77.96 303	75.94 296	95.50 188	55.48 312	99.18 126	71.15 304	87.14 208	93.55 217

TestCases				90.52 244	96.82 140	78.84 303		92.17 321	77.96 303	75.94 296	95.50 188	55.48 312	99.18 126	71.15 304	87.14 208	93.55 217

RPSCF		85.33 257	85.55 235	84.67 310	94.63 217	62.28 338	93.73 299	93.76 304	74.38 316	85.23 206	97.06 156	64.09 286	98.31 153	80.98 244	86.08 217	93.41 219

HQP4-MVS										87.57 185			97.77 185			92.72 220

HQP-MVS		91.50 156	91.23 149	92.29 207	93.95 227	86.39 214	99.16 75	96.37 200	93.92 31	87.57 185	96.67 169	73.34 227	97.77 185	93.82 111	86.29 212	92.72 220

HQP_MVS		91.26 160	90.95 154	92.16 210	93.84 234	86.07 226	99.02 97	96.30 204	93.38 44	86.99 191	96.52 171	72.92 231	97.75 190	93.46 116	86.17 215	92.67 222

plane_prior5								96.30 204					97.75 190	93.46 116	86.17 215	92.67 222

nrg030		90.23 178	88.87 184	94.32 165	91.53 272	93.54 65	98.79 123	95.89 238	88.12 173	84.55 210	94.61 201	78.80 196	96.88 221	92.35 130	75.21 278	92.53 224

CLD-MVS		91.06 164	90.71 161	92.10 211	94.05 226	86.10 224	99.55 33	96.29 207	94.16 26	84.70 208	97.17 151	69.62 255	97.82 181	94.74 95	86.08 217	92.39 225
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
RRT_test8_iter05		91.04 166	90.40 167	92.95 196	96.20 162	89.75 151	98.97 103	96.38 199	88.52 155	82.00 244	93.51 224	90.69 36	96.73 228	90.43 146	76.91 272	92.38 226

VPNet		88.30 211	86.57 219	93.49 185	91.95 265	91.35 104	98.18 186	97.20 151	88.61 152	84.52 211	94.89 196	62.21 293	96.76 227	89.34 159	72.26 308	92.36 227

DU-MVS		88.83 202	87.51 205	92.79 199	91.46 273	90.07 139	98.71 126	97.62 95	88.87 148	83.21 220	93.68 217	74.63 213	95.93 273	86.95 184	72.47 305	92.36 227

NR-MVSNet		87.74 221	86.00 228	92.96 195	91.46 273	90.68 126	96.65 253	97.42 134	88.02 175	73.42 307	93.68 217	77.31 204	95.83 279	84.26 212	71.82 312	92.36 227

FIs		90.70 172	89.87 170	93.18 190	92.29 259	91.12 111	98.17 188	98.25 26	89.11 138	83.44 218	94.82 198	82.26 167	96.17 263	87.76 175	82.76 241	92.25 230

UniMVSNet_NR-MVSNet		89.60 189	88.55 194	92.75 201	92.17 262	90.07 139	98.74 125	98.15 34	88.37 165	83.21 220	93.98 210	82.86 157	95.93 273	86.95 184	72.47 305	92.25 230

VPA-MVSNet		89.10 194	87.66 204	93.45 186	92.56 256	91.02 117	97.97 201	98.32 24	86.92 199	86.03 200	92.01 245	68.84 259	97.10 215	90.92 140	75.34 277	92.23 232

TranMVSNet+NR-MVSNet		87.75 219	86.31 223	92.07 212	90.81 280	88.56 171	98.33 174	97.18 152	87.76 181	81.87 248	93.90 212	72.45 235	95.43 289	83.13 228	71.30 315	92.23 232

FC-MVSNet-test		90.22 179	89.40 175	92.67 204	91.78 269	89.86 147	97.89 203	98.22 28	88.81 149	82.96 225	94.66 200	81.90 173	95.96 271	85.89 197	82.52 244	92.20 234

PS-MVSNAJss		89.54 191	89.05 181	91.00 232	88.77 304	84.36 254	97.39 222	95.97 223	88.47 156	81.88 247	93.80 215	82.48 163	96.50 237	89.34 159	83.34 238	92.15 235

testgi		82.29 280	81.00 284	86.17 301	87.24 320	74.84 318	97.39 222	91.62 329	88.63 151	75.85 298	95.42 191	46.07 334	91.55 333	66.87 317	79.94 255	92.12 236

WR-MVS		88.54 209	87.22 212	92.52 205	91.93 267	89.50 155	98.56 151	97.84 52	86.99 195	81.87 248	93.81 214	74.25 222	95.92 275	85.29 200	74.43 286	92.12 236

MVSTER		92.71 136	92.32 128	93.86 180	97.29 125	92.95 81	99.01 99	96.59 185	90.09 114	85.51 203	94.00 209	94.61 12	96.56 233	90.77 144	83.03 239	92.08 238

ACMM	86.95 13	88.77 205	88.22 199	90.43 246	93.61 239	81.34 286	98.50 157	95.92 231	87.88 179	83.85 216	95.20 193	67.20 271	97.89 176	86.90 186	84.90 223	92.06 239
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
RRT_MVS		91.95 151	91.09 150	94.53 158	96.71 147	95.12 28	98.64 139	96.23 211	89.04 140	85.24 205	95.06 194	87.71 81	96.43 243	89.10 165	82.06 246	92.05 240

XXY-MVS		87.75 219	86.02 227	92.95 196	90.46 284	89.70 152	97.71 215	95.90 236	84.02 244	80.95 256	94.05 204	67.51 269	97.10 215	85.16 201	78.41 261	92.04 241

FMVSNet3		88.81 204	87.08 213	93.99 177	96.52 151	94.59 45	98.08 195	96.20 213	85.85 215	82.12 239	91.60 253	74.05 223	95.40 291	79.04 256	80.24 251	91.99 242

FMVSNet2		86.90 230	84.79 247	93.24 189	95.11 198	92.54 89	97.67 216	95.86 240	82.94 262	80.55 260	91.17 262	62.89 290	95.29 293	77.23 267	79.71 258	91.90 243

UniMVSNet (Re)		89.50 192	88.32 197	93.03 193	92.21 261	90.96 119	98.90 110	98.39 22	89.13 137	83.22 219	92.03 243	81.69 174	96.34 254	86.79 187	72.53 304	91.81 244

testing_2		80.92 287	77.24 295	91.98 213	78.88 339	87.83 183	93.96 297	95.72 246	84.27 241	56.20 336	80.42 330	38.64 342	96.40 245	87.20 180	79.85 256	91.72 245

EU-MVSNet		84.19 271	84.42 254	83.52 313	88.64 307	67.37 336	96.04 273	95.76 244	85.29 223	78.44 285	93.18 230	70.67 250	91.48 334	75.79 281	75.98 274	91.70 246

EI-MVSNet		89.87 186	89.38 176	91.36 226	94.32 221	85.87 231	97.61 218	96.59 185	85.10 226	85.51 203	97.10 153	81.30 180	96.56 233	83.85 222	83.03 239	91.64 247

IterMVS-LS		88.34 210	87.44 206	91.04 231	94.10 223	85.85 232	98.10 193	95.48 260	85.12 225	82.03 243	91.21 261	81.35 179	95.63 285	83.86 221	75.73 276	91.63 248
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GBi-Net		86.67 235	84.96 241	91.80 218	95.11 198	88.81 166	96.77 246	95.25 273	82.94 262	82.12 239	90.25 288	62.89 290	94.97 298	79.04 256	80.24 251	91.62 249

test1		86.67 235	84.96 241	91.80 218	95.11 198	88.81 166	96.77 246	95.25 273	82.94 262	82.12 239	90.25 288	62.89 290	94.97 298	79.04 256	80.24 251	91.62 249

FMVSNet1		83.94 275	81.32 282	91.80 218	91.94 266	88.81 166	96.77 246	95.25 273	77.98 301	78.25 287	90.25 288	50.37 328	94.97 298	73.27 298	77.81 268	91.62 249

cl-mvsnet2		89.57 190	88.79 187	91.91 214	97.94 108	87.62 188	97.98 200	96.51 192	85.03 229	82.37 235	91.79 249	83.65 144	96.50 237	85.96 194	77.89 264	91.61 252

eth_miper_zixun_eth		87.76 218	87.00 215	90.06 253	94.67 216	82.65 276	97.02 240	95.37 269	84.19 242	81.86 250	91.58 254	81.47 177	95.90 277	83.24 224	73.61 295	91.61 252

Anonymous20231211		84.72 261	82.65 272	90.91 234	97.71 112	84.55 252	97.28 227	96.67 180	66.88 334	79.18 278	90.87 267	58.47 303	96.60 231	82.61 233	74.20 290	91.59 254

miper_enhance_ethall		90.33 176	89.70 171	92.22 208	97.12 131	88.93 163	98.35 173	95.96 225	88.60 153	83.14 224	92.33 241	87.38 87	96.18 262	86.49 189	77.89 264	91.55 255

jajsoiax		87.35 225	86.51 221	89.87 257	87.75 318	81.74 281	97.03 238	95.98 222	88.47 156	80.15 265	93.80 215	61.47 295	96.36 248	89.44 157	84.47 227	91.50 256

ACMP	87.39 10	88.71 207	88.24 198	90.12 252	93.91 232	81.06 292	98.50 157	95.67 251	89.43 131	80.37 262	95.55 187	65.67 280	97.83 180	90.55 145	84.51 225	91.47 257
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		88.86 199	88.47 196	90.06 253	93.35 247	80.95 293	98.22 182	95.94 228	87.73 184	83.17 222	96.11 180	66.28 278	97.77 185	90.19 148	85.19 221	91.46 258

LGP-MVS_train				90.06 253	93.35 247	80.95 293		95.94 228	87.73 184	83.17 222	96.11 180	66.28 278	97.77 185	90.19 148	85.19 221	91.46 258

mvs_tets		87.09 228	86.22 224	89.71 262	87.87 314	81.39 285	96.73 251	95.90 236	88.19 171	79.99 267	93.61 220	59.96 301	96.31 256	89.40 158	84.34 228	91.43 260

cl-mvsnet1		87.82 216	86.81 217	90.87 237	94.87 211	85.39 240	97.81 208	95.22 280	82.92 265	80.76 258	91.31 259	81.99 170	95.81 280	81.36 242	75.04 280	91.42 261

cl-mvsnet_		87.82 216	86.79 218	90.89 236	94.88 210	85.43 239	97.81 208	95.24 276	82.91 266	80.71 259	91.22 260	81.97 172	95.84 278	81.34 243	75.06 279	91.40 262

miper_ehance_all_eth		88.94 197	88.12 200	91.40 224	95.32 187	86.93 204	97.85 207	95.55 256	84.19 242	81.97 245	91.50 255	84.16 140	95.91 276	84.69 207	77.89 264	91.36 263

CP-MVSNet		86.54 238	85.45 237	89.79 261	91.02 279	82.78 275	97.38 224	97.56 108	85.37 222	79.53 274	93.03 233	71.86 242	95.25 294	79.92 251	73.43 299	91.34 264

test_djsdf		88.26 213	87.73 202	89.84 259	88.05 313	82.21 278	97.77 211	96.17 216	86.84 200	82.41 234	91.95 248	72.07 239	95.99 269	89.83 150	84.50 226	91.32 265

v2v482		87.27 227	85.76 231	91.78 222	89.59 293	87.58 189	98.56 151	95.54 257	84.53 237	82.51 231	91.78 250	73.11 230	96.47 240	82.07 237	74.14 292	91.30 266

cl_fuxian		88.19 214	87.23 211	91.06 230	94.97 206	86.17 222	97.72 213	95.38 268	83.43 254	81.68 252	91.37 257	82.81 158	95.72 282	84.04 219	73.70 294	91.29 267

OPM-MVS		89.76 187	89.15 180	91.57 223	90.53 283	85.58 237	98.11 192	95.93 230	92.88 53	86.05 199	96.47 174	67.06 273	97.87 178	89.29 162	86.08 217	91.26 268

PS-CasMVS		85.81 250	84.58 251	89.49 271	90.77 281	82.11 279	97.20 233	97.36 141	84.83 234	79.12 279	92.84 236	67.42 270	95.16 296	78.39 263	73.25 300	91.21 269

pmmvs5		85.87 247	84.40 255	90.30 249	88.53 308	84.23 255	98.60 146	93.71 306	81.53 282	80.29 263	92.02 244	64.51 285	95.52 287	82.04 239	78.34 262	91.15 270

miper_lstm_enhance		86.90 230	86.20 225	89.00 279	94.53 218	81.19 289	96.74 250	95.24 276	82.33 274	80.15 265	90.51 284	81.99 170	94.68 308	80.71 248	73.58 296	91.12 271

COLMAP_ROB	82.69 18	84.54 266	82.82 265	89.70 263	96.72 145	78.85 302	95.89 275	92.83 315	71.55 321	77.54 291	95.89 184	59.40 302	99.14 131	67.26 314	88.26 204	91.11 272
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		85.21 258	83.93 259	89.07 278	89.89 290	81.31 287	97.09 236	97.24 147	84.45 239	78.66 281	92.68 238	68.44 261	94.87 301	75.98 279	70.92 316	91.04 273

ACMH	83.09 17	84.60 264	82.61 273	90.57 242	93.18 250	82.94 269	96.27 262	94.92 283	81.01 287	72.61 314	93.61 220	56.54 308	97.79 183	74.31 290	81.07 250	90.99 274
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		84.13 273	83.59 261	85.77 304	87.81 315	70.24 331	94.89 288	93.65 308	86.08 213	76.53 292	93.28 228	61.41 296	96.14 265	80.95 245	77.69 269	90.93 275

XVG-ACMP-BASELINE		85.86 248	84.95 243	88.57 283	89.90 289	77.12 313	94.30 292	95.60 254	87.40 192	82.12 239	92.99 235	53.42 321	97.66 194	85.02 204	83.83 231	90.92 276

Patchmtry		83.61 278	81.64 277	89.50 269	93.36 246	82.84 274	84.10 335	94.20 300	69.47 328	79.57 273	86.88 317	84.43 137	94.78 305	68.48 312	74.30 288	90.88 277

IterMVS		85.81 250	84.67 249	89.22 274	93.51 241	83.67 262	96.32 261	94.80 284	85.09 227	78.69 280	90.17 294	66.57 277	93.17 318	79.48 254	77.42 270	90.81 278
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		86.02 245	84.44 253	90.77 239	89.32 299	85.20 242	98.10 193	95.35 271	82.19 275	82.25 237	90.71 270	70.73 249	96.30 259	76.85 272	74.49 285	90.80 279

v144192		86.40 240	84.89 244	90.91 234	89.48 297	85.59 236	98.21 184	95.43 265	82.45 272	82.62 229	90.58 280	72.79 234	96.36 248	78.45 262	74.04 293	90.79 280

v1192		86.32 242	84.71 248	91.17 228	89.53 296	86.40 213	98.13 189	95.44 264	82.52 271	82.42 233	90.62 277	71.58 246	96.33 255	77.23 267	74.88 281	90.79 280

IterMVS-SCA-FT		85.73 253	84.64 250	89.00 279	93.46 244	82.90 271	96.27 262	94.70 287	85.02 230	78.62 282	90.35 286	66.61 275	93.33 317	79.38 255	77.36 271	90.76 282

SixPastTwentyTwo		82.63 279	81.58 278	85.79 303	88.12 312	71.01 330	95.17 286	92.54 317	84.33 240	72.93 312	92.08 242	60.41 300	95.61 286	74.47 289	74.15 291	90.75 283

MVS_0304		84.13 273	82.66 271	88.52 284	93.07 252	80.15 296	95.81 281	98.21 29	79.27 294	86.85 195	86.40 320	41.33 339	94.69 307	76.36 276	86.69 211	90.73 284

v1240		85.77 252	84.11 256	90.73 240	89.26 300	85.15 245	97.88 205	95.23 279	81.89 280	82.16 238	90.55 282	69.60 256	96.31 256	75.59 282	74.87 282	90.72 285

v148		86.38 241	85.06 240	90.37 248	89.47 298	84.10 257	98.52 153	95.48 260	83.80 247	80.93 257	90.22 291	74.60 215	96.31 256	80.92 246	71.55 313	90.69 286

K. test v3		81.04 286	79.77 288	84.83 308	87.41 319	70.23 332	95.60 283	93.93 303	83.70 250	67.51 325	89.35 302	55.76 310	93.58 316	76.67 274	68.03 321	90.67 287

v1144		86.83 232	85.31 238	91.40 224	89.75 291	87.21 201	98.31 177	95.45 262	83.22 257	82.70 228	90.78 268	73.36 226	96.36 248	79.49 253	74.69 284	90.63 288

ACMH+	83.78 15	84.21 270	82.56 274	89.15 276	93.73 238	79.16 300	96.43 257	94.28 298	81.09 286	74.00 306	94.03 207	54.58 317	97.67 193	76.10 278	78.81 260	90.63 288

lessismore_v0				85.08 306	85.59 325	69.28 334		90.56 335		67.68 324	90.21 292	54.21 319	95.46 288	73.88 293	62.64 329	90.50 290

pmmvs4		87.58 224	86.17 226	91.80 218	89.58 294	88.92 164	97.25 229	95.28 272	82.54 270	80.49 261	93.17 231	75.62 210	96.05 268	82.75 231	78.90 259	90.42 291

WR-MVS_H		86.53 239	85.49 236	89.66 266	91.04 278	83.31 266	97.53 220	98.20 30	84.95 232	79.64 271	90.90 266	78.01 201	95.33 292	76.29 277	72.81 301	90.35 292

V42		87.00 229	85.68 233	90.98 233	89.91 288	86.08 225	98.32 176	95.61 253	83.67 251	82.72 227	90.67 273	74.00 224	96.53 235	81.94 240	74.28 289	90.32 293

DTE-MVSNet		84.14 272	82.80 266	88.14 287	88.95 303	79.87 299	96.81 245	96.24 210	83.50 253	77.60 290	92.52 240	67.89 267	94.24 313	72.64 302	69.05 319	90.32 293

YYNet1		79.64 294	77.04 297	87.43 294	87.80 316	79.98 298	96.23 266	94.44 293	73.83 318	51.83 337	87.53 311	67.96 266	92.07 331	66.00 319	67.75 323	90.23 295

MDA-MVSNet_test_wron		79.65 293	77.05 296	87.45 293	87.79 317	80.13 297	96.25 265	94.44 293	73.87 317	51.80 338	87.47 312	68.04 264	92.12 330	66.02 318	67.79 322	90.09 296

MDA-MVSNet-bldmvs		77.82 302	74.75 304	87.03 296	88.33 309	78.52 306	96.34 260	92.85 314	75.57 311	48.87 340	87.89 308	57.32 307	92.49 326	60.79 328	64.80 327	90.08 297

our_test_3		84.47 268	82.80 266	89.50 269	89.01 301	83.90 260	97.03 238	94.56 291	81.33 284	75.36 301	90.52 283	71.69 244	94.54 310	68.81 310	76.84 273	90.07 298

v7n		84.42 269	82.75 269	89.43 272	88.15 311	81.86 280	96.75 249	95.67 251	80.53 290	78.38 286	89.43 301	69.89 252	96.35 253	73.83 295	72.13 309	90.07 298

v8		86.11 244	84.45 252	91.10 229	89.99 287	86.85 205	97.24 230	95.36 270	81.99 277	79.89 269	89.86 296	74.53 217	96.39 246	78.83 260	72.32 307	90.05 300

PVSNet_BlendedMVS		93.36 124	93.20 112	93.84 181	98.77 86	91.61 99	99.47 40	98.04 39	91.44 82	94.21 110	92.63 239	83.50 146	99.87 47	97.41 44	83.37 237	90.05 300

ITE_SJBPF				87.93 288	92.26 260	76.44 314		93.47 310	87.67 187	79.95 268	95.49 190	56.50 309	97.38 207	75.24 283	82.33 245	89.98 302

pm-mvs1		84.68 262	82.78 268	90.40 247	89.58 294	85.18 243	97.31 225	94.73 286	81.93 279	76.05 295	92.01 245	65.48 282	96.11 266	78.75 261	69.14 318	89.91 303

LTVRE_ROB	81.71 19	84.59 265	82.72 270	90.18 250	92.89 255	83.18 267	93.15 304	94.74 285	78.99 296	75.14 302	92.69 237	65.64 281	97.63 196	69.46 308	81.82 248	89.74 304
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
anonymousdsp		86.69 234	85.75 232	89.53 268	86.46 324	82.94 269	96.39 258	95.71 247	83.97 246	79.63 272	90.70 271	68.85 258	95.94 272	86.01 192	84.02 230	89.72 305

ppachtmachnet_test		83.63 277	81.57 279	89.80 260	89.01 301	85.09 246	97.13 235	94.50 292	78.84 297	76.14 294	91.00 264	69.78 253	94.61 309	63.40 323	74.36 287	89.71 306

v10		85.73 253	84.01 258	90.87 237	90.03 286	86.73 207	97.20 233	95.22 280	81.25 285	79.85 270	89.75 297	73.30 229	96.28 260	76.87 271	72.64 303	89.61 307

UnsupCasMVSNet_eth		78.90 296	76.67 298	85.58 305	82.81 333	74.94 317	91.98 310	96.31 203	84.64 236	65.84 330	87.71 309	51.33 325	92.23 328	72.89 301	56.50 336	89.56 308

USDC		84.74 260	82.93 264	90.16 251	91.73 270	83.54 263	95.00 287	93.30 311	88.77 150	73.19 308	93.30 227	53.62 320	97.65 195	75.88 280	81.54 249	89.30 309

FMVSNet5		82.29 280	80.54 285	87.52 292	93.79 237	84.01 258	93.73 299	92.47 318	76.92 307	74.27 304	86.15 322	63.69 289	89.24 336	69.07 309	74.79 283	89.29 310

Anonymous20231206		80.76 288	79.42 290	84.79 309	84.78 327	72.98 324	96.53 255	92.97 313	79.56 293	74.33 303	88.83 304	61.27 297	92.15 329	60.59 329	75.92 275	89.24 311

pmmvs6		79.90 292	77.31 294	87.67 291	84.17 329	78.13 309	95.86 279	93.68 307	67.94 332	72.67 313	89.62 299	50.98 327	95.75 281	74.80 288	66.04 324	89.14 312

N_pmnet		70.19 309	69.87 310	71.12 324	88.24 310	30.63 353	95.85 280	28.70 354	70.18 325	68.73 319	86.55 319	64.04 287	93.81 314	53.12 338	73.46 298	88.94 313

D2MVS		87.96 215	87.39 207	89.70 263	91.84 268	83.40 264	98.31 177	98.49 20	88.04 174	78.23 288	90.26 287	73.57 225	96.79 226	84.21 213	83.53 235	88.90 314

MIMVSNet1		75.92 304	73.30 306	83.81 312	81.29 334	75.57 316	92.26 309	92.05 324	73.09 319	67.48 326	86.18 321	40.87 340	87.64 339	55.78 335	70.68 317	88.21 315

TransMVSNet (Re)		81.97 282	79.61 289	89.08 277	89.70 292	84.01 258	97.26 228	91.85 327	78.84 297	73.07 311	91.62 252	67.17 272	95.21 295	67.50 313	59.46 334	88.02 316

MS-PatchMatch		86.75 233	85.92 229	89.22 274	91.97 264	82.47 277	96.91 242	96.14 218	83.74 248	77.73 289	93.53 223	58.19 304	97.37 209	76.75 273	98.35 101	87.84 317

Baseline_NR-MVSNet		85.83 249	84.82 246	88.87 282	88.73 305	83.34 265	98.63 141	91.66 328	80.41 292	82.44 232	91.35 258	74.63 213	95.42 290	84.13 215	71.39 314	87.84 317

ambc				79.60 321	72.76 342	56.61 342	76.20 341	92.01 325		68.25 321	80.23 331	23.34 345	94.73 306	73.78 296	60.81 332	87.48 319

TinyColmap		80.42 290	77.94 291	87.85 289	92.09 263	78.58 305	93.74 298	89.94 337	74.99 312	69.77 317	91.78 250	46.09 333	97.58 199	65.17 321	77.89 264	87.38 320

TDRefinement		78.01 300	75.31 301	86.10 302	70.06 343	73.84 321	93.59 302	91.58 330	74.51 315	73.08 310	91.04 263	49.63 330	97.12 212	74.88 286	59.47 333	87.33 321

CMPMVS	58.40 21	80.48 289	80.11 287	81.59 319	85.10 326	59.56 340	94.14 295	95.95 227	68.54 330	60.71 334	93.31 226	55.35 314	97.87 178	83.06 229	84.85 224	87.33 321
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LF4IMVS		81.94 283	81.17 283	84.25 311	87.23 321	68.87 335	93.35 303	91.93 326	83.35 256	75.40 300	93.00 234	49.25 331	96.65 230	78.88 259	78.11 263	87.22 323

tfpnnormal		83.65 276	81.35 281	90.56 243	91.37 275	88.06 179	97.29 226	97.87 50	78.51 300	76.20 293	90.91 265	64.78 284	96.47 240	61.71 327	73.50 297	87.13 324

EG-PatchMatch MVS		79.92 291	77.59 292	86.90 297	87.06 322	77.90 312	96.20 270	94.06 302	74.61 314	66.53 329	88.76 305	40.40 341	96.20 261	67.02 315	83.66 234	86.61 325

test20.03		78.51 299	77.48 293	81.62 318	83.07 332	71.03 329	96.11 271	92.83 315	81.66 281	69.31 318	89.68 298	57.53 305	87.29 340	58.65 333	68.47 320	86.53 326

MVP-Stereo		86.61 237	85.83 230	88.93 281	88.70 306	83.85 261	96.07 272	94.41 296	82.15 276	75.64 299	91.96 247	67.65 268	96.45 242	77.20 269	98.72 92	86.51 327
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OpenMVS_ROB	73.86 20	77.99 301	75.06 303	86.77 298	83.81 331	77.94 311	96.38 259	91.53 331	67.54 333	68.38 320	87.13 316	43.94 335	96.08 267	55.03 336	81.83 247	86.29 328

UnsupCasMVSNet_bld		73.85 307	70.14 309	84.99 307	79.44 337	75.73 315	88.53 323	95.24 276	70.12 326	61.94 333	74.81 335	41.41 338	93.62 315	68.65 311	51.13 341	85.62 329

pmmvs-eth3d		78.71 298	76.16 300	86.38 299	80.25 336	81.19 289	94.17 294	92.13 323	77.97 302	66.90 328	82.31 325	55.76 310	92.56 325	73.63 297	62.31 331	85.38 330

PM-MVS		74.88 305	72.85 307	80.98 320	78.98 338	64.75 337	90.81 319	85.77 344	80.95 288	68.23 322	82.81 324	29.08 344	92.84 320	76.54 275	62.46 330	85.36 331

test_0402		78.81 297	76.33 299	86.26 300	91.18 276	78.44 307	95.88 277	91.34 332	68.55 329	70.51 316	89.91 295	52.65 323	94.99 297	47.14 340	79.78 257	85.34 332

new-patchmatchnet		74.80 306	72.40 308	81.99 317	78.36 340	72.20 327	94.44 290	92.36 319	77.06 306	63.47 331	79.98 332	51.04 326	88.85 337	60.53 330	54.35 338	84.92 333

DeepMVS_CX				76.08 322	90.74 282	51.65 345		90.84 334	86.47 210	57.89 335	87.98 307	35.88 343	92.60 323	65.77 320	65.06 326	83.97 334

pmmvs3		72.86 308	69.76 311	82.17 315	73.86 341	74.19 320	94.20 293	89.01 340	64.23 338	67.72 323	80.91 329	41.48 337	88.65 338	62.40 325	54.02 339	83.68 335

new_pmnet		76.02 303	73.71 305	82.95 314	83.88 330	72.85 325	91.26 316	92.26 320	70.44 324	62.60 332	81.37 327	47.64 332	92.32 327	61.85 326	72.10 310	83.68 335

LCM-MVSNet		60.07 311	56.37 313	71.18 323	54.81 349	48.67 346	82.17 340	89.48 339	37.95 342	49.13 339	69.12 336	13.75 352	81.76 341	59.28 331	51.63 340	83.10 337

PMMVS2		58.97 312	55.07 314	70.69 325	62.72 344	55.37 343	85.97 327	80.52 347	49.48 340	45.94 341	68.31 337	15.73 350	80.78 343	49.79 339	37.12 342	75.91 338

FPMVS		61.57 310	60.32 312	65.34 326	60.14 347	42.44 348	91.02 318	89.72 338	44.15 341	42.63 342	80.93 328	19.02 346	80.59 344	42.50 341	72.76 302	73.00 339

ANet_high		50.71 315	46.17 317	64.33 327	44.27 351	52.30 344	76.13 342	78.73 348	64.95 336	27.37 346	55.23 343	14.61 351	67.74 346	36.01 342	18.23 345	72.95 340

tmp_tt		53.66 314	52.86 315	56.05 329	32.75 353	41.97 349	73.42 343	76.12 350	21.91 348	39.68 344	96.39 177	42.59 336	65.10 347	78.00 264	14.92 347	61.08 341

PMVS	41.42 23	45.67 316	42.50 318	55.17 330	34.28 352	32.37 351	66.24 344	78.71 349	30.72 344	22.04 349	59.59 341	4.59 353	77.85 345	27.49 344	58.84 335	55.29 342
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	44.00 22	41.70 317	37.64 321	53.90 331	49.46 350	43.37 347	65.09 345	66.66 351	26.19 347	25.77 348	48.53 345	3.58 355	63.35 348	26.15 345	27.28 343	54.97 343
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		54.77 313	52.22 316	62.40 328	86.50 323	59.37 341	50.20 346	90.35 336	36.52 343	41.20 343	49.49 344	18.33 348	81.29 342	32.10 343	65.34 325	46.54 344
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		41.02 318	40.93 319	41.29 332	61.97 345	33.83 350	84.00 337	65.17 352	27.17 345	27.56 345	46.72 346	17.63 349	60.41 349	19.32 346	18.82 344	29.61 345

EMVS		39.96 319	39.88 320	40.18 333	59.57 348	32.12 352	84.79 334	64.57 353	26.27 346	26.14 347	44.18 349	18.73 347	59.29 350	17.03 347	17.67 346	29.12 346

test123		16.58 323	19.47 324	7.91 335	3.59 355	5.37 355	94.32 291	1.39 357	2.49 351	13.98 351	44.60 348	2.91 356	2.65 352	11.35 350	0.57 350	15.70 347

testmvs		18.81 321	23.05 323	6.10 336	4.48 354	2.29 356	97.78 210	3.00 356	3.27 350	18.60 350	62.71 339	1.53 357	2.49 353	14.26 349	1.80 349	13.50 348

wuyk23d		16.71 322	16.73 325	16.65 334	60.15 346	25.22 354	41.24 347	5.17 355	6.56 349	5.48 352	3.61 352	3.64 354	22.72 351	15.20 348	9.52 348	1.99 349

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

test_part1				0.00 337		0.00 357	0.00 348	97.69 80				0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

cdsmvs_eth3d_5k		22.52 320	30.03 322	0.00 337	0.00 356	0.00 357	0.00 348	97.17 153	0.00 352	0.00 353	98.77 79	74.35 219	0.00 354	0.00 351	0.00 351	0.00 350

pcd_1.5k_mvsjas		6.87 325	9.16 327	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	82.48 163	0.00 354	0.00 351	0.00 351	0.00 350

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

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

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

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

ab-mvs-re		8.21 324	10.94 326	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	98.50 101	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

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

test_241102_ONE					99.63 20	95.24 21		97.72 73	94.16 26	99.30 4	99.49 10	93.32 15	99.98 10

9.14			96.87 26		99.34 51		99.50 38	97.49 122	89.41 132	98.59 22	99.43 16	89.78 50	99.69 75	98.69 17	99.62 46

save fliter					99.34 51	93.85 59	99.65 23	97.63 93	95.69 11

test0726					99.66 14	95.20 26	99.77 9	97.70 78	93.95 29	99.35 3	99.54 3	93.18 18

test_part2					99.54 35	95.42 17				98.13 32

sam_mvs												87.08 95

MTGPA								97.45 127

test_post1							90.74 321				41.37 350	85.38 129	96.36 248	83.16 226

test_post											46.00 347	87.37 88	97.11 213

patchmatchnet-post											84.86 323	88.73 63	96.81 224

MTMP							99.21 69	91.09 333

gm-plane-assit					94.69 215	88.14 177			88.22 170		97.20 148		98.29 155	90.79 143

TEST9					99.57 32	93.17 71	99.38 56	97.66 83	89.57 127	98.39 27	99.18 35	90.88 32	99.66 80

test_8					99.55 34	93.07 75	99.37 59	97.64 89	90.18 110	98.36 29	99.19 32	90.94 30	99.64 86

agg_prior					99.54 35	92.66 83		97.64 89		97.98 40			99.61 89

test_prior4						92.00 92	99.41 54

test_prior2							99.57 30		91.43 83	98.12 34	98.97 61	90.43 40		98.33 29	99.81 19

旧先验2							98.67 135		85.75 217	98.96 12			98.97 137	93.84 109

新几何2							98.26 180

原ACMM2							98.69 131

testdata2													99.88 44	84.16 214

segment_acmp												90.56 39

testdata1							97.89 203		92.43 61

plane_prior7					93.84 234	85.73 234

plane_prior6					93.92 231	86.02 228						72.92 231

plane_prior4											96.52 171

plane_prior3						85.91 229			93.65 39	86.99 191

plane_prior2							99.02 97		93.38 44

plane_prior1					93.90 233

plane_prior						86.07 226	99.14 84		93.81 37						86.26 214

n2								0.00 358

nn								0.00 358

door-mid								84.90 346

test11								97.68 81

door								85.30 345

HQP5-MVS						86.39 214

HQP-NCC					93.95 227		99.16 75		93.92 31	87.57 185

ACMP_Plane					93.95 227		99.16 75		93.92 31	87.57 185

BP-MVS														93.82 111

HQP3-MVS								96.37 200							86.29 212

HQP2-MVS												73.34 227

NP-MVS					93.94 230	86.22 220					96.67 169

MDTV_nov1_ep13			90.47 166		96.14 165	88.55 172	91.34 315	97.51 117	89.58 126	92.24 135	90.50 285	86.99 99	97.61 198	77.64 266	92.34 172

ACMMP++_ref															82.64 243

ACMMP++															83.83 231

Test By Simon												83.62 145

High-resolution multi-view benchmark