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.

DPE-MVS		97.86 3	97.65 4	98.47 3	99.17 32	95.78 5	97.21 130	98.35 19	95.16 14	98.71 10	98.80 9	95.05 7	99.89 3	96.70 19	99.73 1	99.73 7

test_0728_THIRD									94.78 31	98.73 8	98.87 6	95.87 2	99.84 19	97.45 6	99.72 2	99.77 1

APDe-MVS		97.82 4	97.73 3	98.08 15	99.15 33	94.82 25	98.81 2	98.30 23	94.76 32	98.30 13	98.90 3	93.77 14	99.68 47	97.93 1	99.69 3	99.75 3

SMA-MVS		97.35 12	97.03 14	98.30 6	99.06 40	95.42 8	97.94 54	98.18 46	90.57 165	98.85 7	98.94 1	93.33 17	99.83 22	96.72 18	99.68 4	99.63 11
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
CP-MVS		97.02 26	96.81 28	97.64 46	99.33 22	93.54 62	98.80 3	98.28 26	92.99 84	96.45 66	98.30 49	91.90 45	99.85 14	95.61 59	99.68 4	99.54 29

DVP-MVS		97.91 2	97.81 2	98.22 9	99.45 2	95.36 10	98.21 34	97.85 112	94.92 22	98.73 8	98.87 6	95.08 5	99.84 19	97.52 2	99.67 6	99.48 41

test_0728_SECOND				98.51 2	99.45 2	95.93 3	98.21 34	98.28 26					99.86 8	97.52 2	99.67 6	99.75 3

SED-MVS		98.05 1	97.99 1	98.24 7	99.42 6	95.30 15	98.25 28	98.27 28	95.13 15	99.19 1	98.89 4	95.54 3	99.85 14	97.52 2	99.66 8	99.56 22

IU-MVS					99.42 6	95.39 9		97.94 102	90.40 169	98.94 5				97.41 7	99.66 8	99.74 5

test_241102_TWO								98.27 28	95.13 15	98.93 6	98.89 4	94.99 8	99.85 14	97.52 2	99.65 10	99.74 5

SD-MVS		97.41 9	97.53 6	97.06 71	98.57 72	94.46 30	97.92 56	98.14 53	94.82 28	99.01 3	98.55 19	94.18 11	97.41 296	96.94 10	99.64 11	99.32 60
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
HPM-MVS_fast		96.51 51	96.27 53	97.22 64	99.32 23	92.74 82	98.74 4	98.06 73	90.57 165	96.77 49	98.35 38	90.21 79	99.53 88	94.80 84	99.63 12	99.38 56

SteuartSystems-ACMMP		97.62 6	97.53 6	97.87 24	98.39 80	94.25 38	98.43 17	98.27 28	95.34 10	98.11 16	98.56 17	94.53 9	99.71 38	96.57 23	99.62 13	99.65 9
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS		96.69 45	96.45 49	97.40 53	99.36 19	93.11 74	98.87 1	98.06 73	91.17 145	96.40 67	97.99 69	90.99 67	99.58 70	95.61 59	99.61 14	99.49 39
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
OPU-MVS				98.55 1	98.82 56	96.86 1	98.25 28				98.26 53	96.04 1	99.24 120	95.36 66	99.59 15	99.56 22

HFP-MVS		97.14 19	96.92 20	97.83 26	99.42 6	94.12 45	98.52 10	98.32 20	93.21 75	97.18 38	98.29 50	92.08 39	99.83 22	95.63 57	99.59 15	99.54 29

region2R		97.07 22	96.84 25	97.77 35	99.46 1	93.79 55	98.52 10	98.24 34	93.19 78	97.14 41	98.34 41	91.59 54	99.87 7	95.46 65	99.59 15	99.64 10

#test#		97.02 26	96.75 33	97.83 26	99.42 6	94.12 45	98.15 37	98.32 20	92.57 102	97.18 38	98.29 50	92.08 39	99.83 22	95.12 71	99.59 15	99.54 29

ACMMPR		97.07 22	96.84 25	97.79 32	99.44 5	93.88 52	98.52 10	98.31 22	93.21 75	97.15 40	98.33 44	91.35 59	99.86 8	95.63 57	99.59 15	99.62 13

mPP-MVS		96.86 37	96.60 40	97.64 46	99.40 11	93.44 65	98.50 13	98.09 63	93.27 74	95.95 84	98.33 44	91.04 66	99.88 4	95.20 68	99.57 20	99.60 16

ZNCC-MVS		96.96 30	96.67 38	97.85 25	99.37 16	94.12 45	98.49 14	98.18 46	92.64 101	96.39 68	98.18 58	91.61 52	99.88 4	95.59 62	99.55 21	99.57 19

MP-MVS-pluss		96.70 44	96.27 53	97.98 21	99.23 30	94.71 26	96.96 151	98.06 73	90.67 156	95.55 99	98.78 10	91.07 65	99.86 8	96.58 22	99.55 21	99.38 56
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		96.77 42	96.45 49	97.72 39	99.39 13	93.80 54	98.41 18	98.06 73	93.37 70	95.54 101	98.34 41	90.59 75	99.88 4	94.83 81	99.54 23	99.49 39
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PHI-MVS		96.77 42	96.46 48	97.71 41	98.40 78	94.07 48	98.21 34	98.45 15	89.86 177	97.11 44	98.01 68	92.52 32	99.69 44	96.03 45	99.53 24	99.36 58

xxxxxxxxxxxxxcwj		97.36 11	97.20 10	97.83 26	98.91 49	94.28 35	97.02 143	97.22 182	95.35 8	98.27 14	98.65 13	93.33 17	99.72 35	96.49 25	99.52 25	99.51 34

SF-MVS		97.39 10	97.13 11	98.17 11	99.02 43	95.28 17	98.23 31	98.27 28	92.37 106	98.27 14	98.65 13	93.33 17	99.72 35	96.49 25	99.52 25	99.51 34

ACMMP_NAP		97.20 15	96.86 22	98.23 8	99.09 36	95.16 20	97.60 90	98.19 44	92.82 94	97.93 20	98.74 11	91.60 53	99.86 8	96.26 30	99.52 25	99.67 8

DeepC-MVS_fast	93.89 2	96.93 33	96.64 39	97.78 33	98.64 67	94.30 34	97.41 105	98.04 81	94.81 29	96.59 58	98.37 36	91.24 61	99.64 61	95.16 69	99.52 25	99.42 52
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ETH3D-3000-0.1		97.07 22	96.71 36	98.14 13	98.90 51	95.33 14	97.68 80	98.24 34	91.57 127	97.90 21	98.37 36	92.61 29	99.66 52	95.59 62	99.51 29	99.43 49

HPM-MVS++		97.34 13	96.97 17	98.47 3	99.08 38	96.16 2	97.55 94	97.97 99	95.59 4	96.61 56	97.89 72	92.57 30	99.84 19	95.95 46	99.51 29	99.40 53

APD-MVS		96.95 31	96.60 40	98.01 19	99.03 42	94.93 24	97.72 76	98.10 61	91.50 129	98.01 18	98.32 46	92.33 35	99.58 70	94.85 79	99.51 29	99.53 33
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
zzz-MVS		97.07 22	96.77 32	97.97 22	99.37 16	94.42 32	97.15 136	98.08 64	95.07 19	96.11 75	98.59 15	90.88 70	99.90 1	96.18 39	99.50 32	99.58 17

MTAPA		97.08 21	96.78 31	97.97 22	99.37 16	94.42 32	97.24 123	98.08 64	95.07 19	96.11 75	98.59 15	90.88 70	99.90 1	96.18 39	99.50 32	99.58 17

CNVR-MVS		97.68 5	97.44 8	98.37 5	98.90 51	95.86 4	97.27 121	98.08 64	95.81 3	97.87 23	98.31 47	94.26 10	99.68 47	97.02 9	99.49 34	99.57 19

DeepPCF-MVS	93.97 1	96.61 48	97.09 12	95.15 157	98.09 105	86.63 260	96.00 229	98.15 51	95.43 6	97.95 19	98.56 17	93.40 16	99.36 112	96.77 17	99.48 35	99.45 45

ETH3 D test6400		96.16 61	95.52 68	98.07 16	98.90 51	95.06 22	97.03 140	98.21 40	88.16 229	96.64 55	97.70 89	91.18 63	99.67 49	92.44 124	99.47 36	99.48 41

9.14			96.75 33		98.93 47		97.73 73	98.23 38	91.28 141	97.88 22	98.44 28	93.00 21	99.65 53	95.76 51	99.47 36

TSAR-MVS + MP.		97.42 8	97.33 9	97.69 42	99.25 27	94.24 39	98.07 43	97.85 112	93.72 57	98.57 11	98.35 38	93.69 15	99.40 108	97.06 8	99.46 38	99.44 47
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MSLP-MVS++		96.94 32	97.06 13	96.59 83	98.72 59	91.86 111	97.67 81	98.49 12	94.66 35	97.24 36	98.41 34	92.31 37	98.94 150	96.61 21	99.46 38	98.96 91

PGM-MVS		96.81 40	96.53 44	97.65 44	99.35 21	93.53 63	97.65 84	98.98 1	92.22 108	97.14 41	98.44 28	91.17 64	99.85 14	94.35 91	99.46 38	99.57 19

CDPH-MVS		95.97 66	95.38 74	97.77 35	98.93 47	94.44 31	96.35 204	97.88 106	86.98 259	96.65 54	97.89 72	91.99 43	99.47 99	92.26 125	99.46 38	99.39 54

DELS-MVS		96.61 48	96.38 51	97.30 57	97.79 119	93.19 72	95.96 231	98.18 46	95.23 12	95.87 85	97.65 94	91.45 55	99.70 43	95.87 47	99.44 42	99.00 89
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
CPTT-MVS		95.57 76	95.19 79	96.70 77	99.27 26	91.48 121	98.33 21	98.11 59	87.79 240	95.17 106	98.03 66	87.09 118	99.61 62	93.51 108	99.42 43	99.02 83

MVS_111021_HR		96.68 47	96.58 42	96.99 73	98.46 74	92.31 96	96.20 219	98.90 2	94.30 44	95.86 86	97.74 87	92.33 35	99.38 111	96.04 44	99.42 43	99.28 65

XVS		97.18 16	96.96 18	97.81 30	99.38 14	94.03 50	98.59 7	98.20 42	94.85 24	96.59 58	98.29 50	91.70 50	99.80 27	95.66 52	99.40 45	99.62 13

X-MVStestdata		91.71 192	89.67 250	97.81 30	99.38 14	94.03 50	98.59 7	98.20 42	94.85 24	96.59 58	32.69 352	91.70 50	99.80 27	95.66 52	99.40 45	99.62 13

MCST-MVS		97.18 16	96.84 25	98.20 10	99.30 24	95.35 12	97.12 138	98.07 70	93.54 65	96.08 77	97.69 90	93.86 13	99.71 38	96.50 24	99.39 47	99.55 26

test9_res														94.81 83	99.38 48	99.45 45

agg_prior2														93.94 99	99.38 48	99.50 37

test_prior3		96.46 53	96.20 56	97.23 62	98.67 62	92.99 76	96.35 204	98.00 93	92.80 95	96.03 78	97.59 101	92.01 41	99.41 106	95.01 74	99.38 48	99.29 62

test_prior2							96.35 204		92.80 95	96.03 78	97.59 101	92.01 41		95.01 74	99.38 48

train_agg		96.30 57	95.83 63	97.72 39	98.70 60	94.19 40	96.41 196	98.02 88	88.58 215	96.03 78	97.56 105	92.73 25	99.59 67	95.04 73	99.37 52	99.39 54

agg_prior1		96.22 60	95.77 64	97.56 48	98.67 62	93.79 55	96.28 212	98.00 93	88.76 212	95.68 93	97.55 107	92.70 27	99.57 78	95.01 74	99.32 53	99.32 60

3Dnovator	91.36 5	95.19 87	94.44 100	97.44 52	96.56 171	93.36 69	98.65 6	98.36 16	94.12 46	89.25 238	98.06 64	82.20 193	99.77 29	93.41 112	99.32 53	99.18 69

ZD-MVS					99.05 41	94.59 28		98.08 64	89.22 192	97.03 47	98.10 60	92.52 32	99.65 53	94.58 89	99.31 55

test1172		96.93 33	96.86 22	97.15 67	99.10 34	92.34 93	97.96 53	98.04 81	93.79 55	97.35 33	98.53 21	91.40 57	99.56 80	96.30 29	99.30 56	99.55 26

Regformer-1		97.10 20	96.96 18	97.54 49	98.32 86	93.48 64	96.83 163	97.99 97	95.20 13	97.46 27	98.25 54	92.48 34	99.58 70	96.79 16	99.29 57	99.55 26

Regformer-2		97.16 18	96.99 16	97.67 43	98.32 86	93.84 53	96.83 163	98.10 61	95.24 11	97.49 26	98.25 54	92.57 30	99.61 62	96.80 14	99.29 57	99.56 22

ETH3D cwj APD-0.16		96.56 50	96.06 58	98.05 17	98.26 92	95.19 18	96.99 148	98.05 80	89.85 179	97.26 35	98.22 56	91.80 47	99.69 44	94.84 80	99.28 59	99.27 66

GST-MVS		96.85 38	96.52 45	97.82 29	99.36 19	94.14 44	98.29 24	98.13 54	92.72 98	96.70 50	98.06 64	91.35 59	99.86 8	94.83 81	99.28 59	99.47 44

SR-MVS		97.01 28	96.86 22	97.47 51	99.09 36	93.27 71	97.98 48	98.07 70	93.75 56	97.45 28	98.48 25	91.43 56	99.59 67	96.22 33	99.27 61	99.54 29

CSCG		96.05 63	95.91 61	96.46 93	99.24 28	90.47 158	98.30 23	98.57 11	89.01 198	93.97 126	97.57 103	92.62 28	99.76 30	94.66 87	99.27 61	99.15 72

SR-MVS-dyc-post		96.88 36	96.80 29	97.11 70	99.02 43	92.34 93	97.98 48	98.03 84	93.52 66	97.43 31	98.51 22	91.40 57	99.56 80	96.05 42	99.26 63	99.43 49

RE-MVS-def			96.72 35		99.02 43	92.34 93	97.98 48	98.03 84	93.52 66	97.43 31	98.51 22	90.71 73		96.05 42	99.26 63	99.43 49

testtj		96.93 33	96.56 43	98.05 17	99.10 34	94.66 27	97.78 68	98.22 39	92.74 97	97.59 24	98.20 57	91.96 44	99.86 8	94.21 93	99.25 65	99.63 11

APD-MVS_3200maxsize		96.81 40	96.71 36	97.12 69	99.01 46	92.31 96	97.98 48	98.06 73	93.11 81	97.44 29	98.55 19	90.93 68	99.55 83	96.06 41	99.25 65	99.51 34

test12				97.65 44	98.46 74	94.26 37		97.66 128		95.52 102		90.89 69	99.46 100		99.25 65	99.22 67

DeepC-MVS	93.07 3	96.06 62	95.66 65	97.29 58	97.96 109	93.17 73	97.30 119	98.06 73	93.92 50	93.38 139	98.66 12	86.83 120	99.73 32	95.60 61	99.22 68	98.96 91
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSP-MVS		97.59 7	97.54 5	97.73 38	99.40 11	93.77 58	98.53 9	98.29 24	95.55 5	98.56 12	97.81 82	93.90 12	99.65 53	96.62 20	99.21 69	99.77 1

CANet		96.39 55	96.02 59	97.50 50	97.62 127	93.38 67	97.02 143	97.96 100	95.42 7	94.86 110	97.81 82	87.38 114	99.82 25	96.88 12	99.20 70	99.29 62

MVS_111021_LR		96.24 59	96.19 57	96.39 98	98.23 97	91.35 127	96.24 217	98.79 4	93.99 49	95.80 88	97.65 94	89.92 84	99.24 120	95.87 47	99.20 70	98.58 118

NCCC		97.30 14	97.03 14	98.11 14	98.77 57	95.06 22	97.34 113	98.04 81	95.96 2	97.09 45	97.88 74	93.18 20	99.71 38	95.84 49	99.17 72	99.56 22

test222					98.24 93	92.21 99	95.33 256	97.60 134	79.22 329	95.25 104	97.84 81	88.80 92			99.15 73	98.72 112

114514_t		93.95 119	93.06 130	96.63 80	99.07 39	91.61 116	97.46 104	97.96 100	77.99 334	93.00 147	97.57 103	86.14 132	99.33 113	89.22 189	99.15 73	98.94 94

Regformer-3		96.85 38	96.80 29	97.01 72	98.34 83	92.02 107	96.96 151	97.76 116	95.01 21	97.08 46	98.42 31	91.71 49	99.54 85	96.80 14	99.13 75	99.48 41

Regformer-4		96.97 29	96.87 21	97.25 61	98.34 83	92.66 85	96.96 151	98.01 91	95.12 17	97.14 41	98.42 31	91.82 46	99.61 62	96.90 11	99.13 75	99.50 37

新几何1				97.32 56	98.60 68	93.59 61		97.75 117	81.58 316	95.75 90	97.85 78	90.04 82	99.67 49	86.50 240	99.13 75	98.69 115

原ACMM1				96.38 99	98.59 69	91.09 140		97.89 104	87.41 251	95.22 105	97.68 91	90.25 77	99.54 85	87.95 209	99.12 78	98.49 127

1121		94.71 102	93.83 107	97.34 55	98.57 72	93.64 60	96.04 225	97.73 119	81.56 317	95.68 93	97.85 78	90.23 78	99.65 53	87.68 218	99.12 78	98.73 111

abl_6		96.40 54	96.21 55	96.98 74	98.89 54	92.20 101	97.89 57	98.03 84	93.34 73	97.22 37	98.42 31	87.93 103	99.72 35	95.10 72	99.07 80	99.02 83

MVSFormer		95.37 79	95.16 80	95.99 120	96.34 184	91.21 132	98.22 32	97.57 138	91.42 133	96.22 72	97.32 113	86.20 130	97.92 250	94.07 95	99.05 81	98.85 103

lupinMVS		94.99 93	94.56 93	96.29 106	96.34 184	91.21 132	95.83 237	96.27 248	88.93 203	96.22 72	96.88 134	86.20 130	98.85 157	95.27 67	99.05 81	98.82 106

旧先验1					98.38 81	93.38 67		97.75 117			98.09 62	92.30 38			99.01 83	99.16 70

testdata				95.46 150	98.18 102	88.90 208		97.66 128	82.73 309	97.03 47	98.07 63	90.06 81	98.85 157	89.67 176	98.98 84	98.64 117

3Dnovator+	91.43 4	95.40 78	94.48 98	98.16 12	96.90 154	95.34 13	98.48 15	97.87 108	94.65 36	88.53 253	98.02 67	83.69 160	99.71 38	93.18 116	98.96 85	99.44 47

DPM-MVS		95.69 71	94.92 84	98.01 19	98.08 106	95.71 7	95.27 261	97.62 133	90.43 168	95.55 99	97.07 125	91.72 48	99.50 96	89.62 178	98.94 86	98.82 106

CHOSEN 280x420		93.12 144	92.72 140	94.34 193	96.71 164	87.27 242	90.29 332	97.72 122	86.61 265	91.34 178	95.29 215	84.29 154	98.41 190	93.25 115	98.94 86	97.35 183

jason		94.84 98	94.39 101	96.18 112	95.52 217	90.93 145	96.09 223	96.52 241	89.28 190	96.01 82	97.32 113	84.70 147	98.77 164	95.15 70	98.91 88	98.85 103
jason: jason.
QAPM		93.45 135	92.27 156	96.98 74	96.77 161	92.62 86	98.39 19	98.12 56	84.50 292	88.27 259	97.77 85	82.39 190	99.81 26	85.40 259	98.81 89	98.51 124

MG-MVS		95.61 74	95.38 74	96.31 103	98.42 77	90.53 156	96.04 225	97.48 146	93.47 68	95.67 96	98.10 60	89.17 87	99.25 119	91.27 153	98.77 90	99.13 74

API-MVS		94.84 98	94.49 97	95.90 123	97.90 115	92.00 108	97.80 66	97.48 146	89.19 194	94.81 111	96.71 139	88.84 91	99.17 126	88.91 196	98.76 91	96.53 200

CHOSEN 1792x2688		94.15 110	93.51 118	96.06 116	98.27 89	89.38 190	95.18 265	98.48 14	85.60 277	93.76 130	97.11 123	83.15 168	99.61 62	91.33 151	98.72 92	99.19 68

EIA-MVS		95.53 77	95.47 70	95.71 133	97.06 149	89.63 175	97.82 64	97.87 108	93.57 61	93.92 127	95.04 224	90.61 74	98.95 149	94.62 88	98.68 93	98.54 120

CS-MVS		95.80 70	95.65 66	96.24 110	97.32 135	91.43 125	98.10 39	97.91 103	93.38 69	95.16 107	94.57 246	90.21 79	98.98 147	95.53 64	98.67 94	98.30 145

OpenMVS	89.19 12	92.86 157	91.68 173	96.40 96	95.34 228	92.73 83	98.27 26	98.12 56	84.86 287	85.78 297	97.75 86	78.89 251	99.74 31	87.50 225	98.65 95	96.73 197

EPNet		95.20 86	94.56 93	97.14 68	92.80 318	92.68 84	97.85 62	94.87 304	96.64 1	92.46 155	97.80 84	86.23 127	99.65 53	93.72 105	98.62 96	99.10 79
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DP-MVS Recon		95.68 72	95.12 82	97.37 54	99.19 31	94.19 40	97.03 140	98.08 64	88.35 222	95.09 108	97.65 94	89.97 83	99.48 98	92.08 134	98.59 97	98.44 135

Vis-MVSNet		95.23 84	94.81 86	96.51 88	97.18 140	91.58 119	98.26 27	98.12 56	94.38 42	94.90 109	98.15 59	82.28 191	98.92 151	91.45 150	98.58 98	99.01 87
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UGNet		94.04 117	93.28 127	96.31 103	96.85 155	91.19 135	97.88 58	97.68 127	94.40 40	93.00 147	96.18 172	73.39 295	99.61 62	91.72 141	98.46 99	98.13 149
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
CANet_DTU		94.37 105	93.65 113	96.55 84	96.46 178	92.13 103	96.21 218	96.67 233	94.38 42	93.53 135	97.03 127	79.34 240	99.71 38	90.76 157	98.45 100	97.82 166

TAPA-MVS	90.10 7	92.30 175	91.22 192	95.56 140	98.33 85	89.60 177	96.79 167	97.65 130	81.83 314	91.52 174	97.23 118	87.94 102	98.91 153	71.31 336	98.37 101	98.17 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EI-MVSNet-Vis-set		96.51 51	96.47 47	96.63 80	98.24 93	91.20 134	96.89 158	97.73 119	94.74 33	96.49 62	98.49 24	90.88 70	99.58 70	96.44 27	98.32 102	99.13 74

PS-MVSNAJ		95.37 79	95.33 76	95.49 146	97.35 134	90.66 154	95.31 258	97.48 146	93.85 52	96.51 61	95.70 199	88.65 94	99.65 53	94.80 84	98.27 103	96.17 209

LS3D		93.57 132	92.61 144	96.47 91	97.59 130	91.61 116	97.67 81	97.72 122	85.17 282	90.29 199	98.34 41	84.60 148	99.73 32	83.85 278	98.27 103	98.06 154

ETV-MVS		96.02 64	95.89 62	96.40 96	97.16 141	92.44 91	97.47 102	97.77 115	94.55 37	96.48 63	94.51 248	91.23 62	98.92 151	95.65 55	98.19 105	97.82 166

PVSNet_Blended		94.87 97	94.56 93	95.81 126	98.27 89	89.46 186	95.47 251	98.36 16	88.84 206	94.36 117	96.09 178	88.02 100	99.58 70	93.44 110	98.18 106	98.40 138

MAR-MVS		94.22 108	93.46 120	96.51 88	98.00 108	92.19 102	97.67 81	97.47 149	88.13 231	93.00 147	95.84 186	84.86 146	99.51 93	87.99 208	98.17 107	97.83 165
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
MS-PatchMatch		90.27 250	89.77 246	91.78 285	94.33 281	84.72 287	95.55 247	96.73 224	86.17 271	86.36 292	95.28 217	71.28 302	97.80 261	84.09 273	98.14 108	92.81 323

AdaColmap		94.34 106	93.68 112	96.31 103	98.59 69	91.68 115	96.59 187	97.81 114	89.87 176	92.15 165	97.06 126	83.62 161	99.54 85	89.34 184	98.07 109	97.70 170

MVP-Stereo		90.74 239	90.08 233	92.71 262	93.19 312	88.20 224	95.86 235	96.27 248	86.07 272	84.86 305	94.76 236	77.84 265	97.75 266	83.88 277	98.01 110	92.17 331
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Vis-MVSNet (Re-imp)		94.15 110	93.88 106	94.95 168	97.61 128	87.92 231	98.10 39	95.80 263	92.22 108	93.02 146	97.45 109	84.53 150	97.91 253	88.24 204	97.97 111	99.02 83

EI-MVSNet-UG-set		96.34 56	96.30 52	96.47 91	98.20 98	90.93 145	96.86 159	97.72 122	94.67 34	96.16 74	98.46 26	90.43 76	99.58 70	96.23 32	97.96 112	98.90 98

IS-MVSNet		94.90 95	94.52 96	96.05 117	97.67 124	90.56 155	98.44 16	96.22 251	93.21 75	93.99 124	97.74 87	85.55 138	98.45 189	89.98 166	97.86 113	99.14 73

CNLPA		94.28 107	93.53 117	96.52 85	98.38 81	92.55 88	96.59 187	96.88 216	90.13 173	91.91 169	97.24 117	85.21 141	99.09 136	87.64 221	97.83 114	97.92 158

xiu_mvs_v2_base		95.32 81	95.29 77	95.40 151	97.22 137	90.50 157	95.44 252	97.44 161	93.70 59	96.46 65	96.18 172	88.59 97	99.53 88	94.79 86	97.81 115	96.17 209

PAPM_NR		95.01 89	94.59 92	96.26 108	98.89 54	90.68 153	97.24 123	97.73 119	91.80 122	92.93 152	96.62 153	89.13 88	99.14 130	89.21 190	97.78 116	98.97 90

PVSNet_Blended_VisFu		95.27 82	94.91 85	96.38 99	98.20 98	90.86 147	97.27 121	98.25 33	90.21 170	94.18 121	97.27 115	87.48 112	99.73 32	93.53 107	97.77 117	98.55 119

TSAR-MVS + GP.		96.69 45	96.49 46	97.27 60	98.31 88	93.39 66	96.79 167	96.72 225	94.17 45	97.44 29	97.66 93	92.76 23	99.33 113	96.86 13	97.76 118	99.08 80

ACMMP		96.27 58	95.93 60	97.28 59	99.24 28	92.62 86	98.25 28	98.81 3	92.99 84	94.56 114	98.39 35	88.96 89	99.85 14	94.57 90	97.63 119	99.36 58
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
BH-RMVSNet		92.72 163	91.97 164	94.97 166	97.16 141	87.99 230	96.15 221	95.60 269	90.62 161	91.87 170	97.15 122	78.41 256	98.57 182	83.16 280	97.60 120	98.36 142

PatchMatch-RL		92.90 155	92.02 162	95.56 140	98.19 100	90.80 149	95.27 261	97.18 183	87.96 233	91.86 171	95.68 200	80.44 220	98.99 146	84.01 274	97.54 121	96.89 192

xiu_mvs_v1_base_debu		95.01 89	94.76 87	95.75 129	96.58 168	91.71 112	96.25 214	97.35 173	92.99 84	96.70 50	96.63 150	82.67 181	99.44 103	96.22 33	97.46 122	96.11 214

xiu_mvs_v1_base		95.01 89	94.76 87	95.75 129	96.58 168	91.71 112	96.25 214	97.35 173	92.99 84	96.70 50	96.63 150	82.67 181	99.44 103	96.22 33	97.46 122	96.11 214

xiu_mvs_v1_base_debi		95.01 89	94.76 87	95.75 129	96.58 168	91.71 112	96.25 214	97.35 173	92.99 84	96.70 50	96.63 150	82.67 181	99.44 103	96.22 33	97.46 122	96.11 214

MVS		91.71 192	90.44 216	95.51 144	95.20 242	91.59 118	96.04 225	97.45 157	73.44 341	87.36 278	95.60 203	85.42 139	99.10 133	85.97 251	97.46 122	95.83 224

PVSNet	86.66 18	92.24 179	91.74 172	93.73 221	97.77 120	83.69 299	92.88 316	96.72 225	87.91 235	93.00 147	94.86 231	78.51 254	99.05 142	86.53 238	97.45 126	98.47 130

PAPR		94.18 109	93.42 124	96.48 90	97.64 126	91.42 126	95.55 247	97.71 126	88.99 199	92.34 161	95.82 188	89.19 86	99.11 132	86.14 246	97.38 127	98.90 98

LCM-MVSNet-Re		92.50 165	92.52 149	92.44 267	96.82 159	81.89 311	96.92 155	93.71 323	92.41 105	84.30 309	94.60 244	85.08 143	97.03 307	91.51 147	97.36 128	98.40 138

UA-Net		95.95 67	95.53 67	97.20 66	97.67 124	92.98 78	97.65 84	98.13 54	94.81 29	96.61 56	98.35 38	88.87 90	99.51 93	90.36 163	97.35 129	99.11 78

casdiffmvs		95.64 73	95.49 69	96.08 114	96.76 163	90.45 159	97.29 120	97.44 161	94.00 48	95.46 103	97.98 70	87.52 111	98.73 167	95.64 56	97.33 130	99.08 80

PCF-MVS	89.48 11	91.56 199	89.95 238	96.36 101	96.60 166	92.52 89	92.51 321	97.26 179	79.41 328	88.90 242	96.56 155	84.04 157	99.55 83	77.01 320	97.30 131	97.01 186
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-untuned		92.94 153	92.62 143	93.92 215	97.22 137	86.16 269	96.40 199	96.25 250	90.06 174	89.79 218	96.17 174	83.19 166	98.35 195	87.19 231	97.27 132	97.24 184

baseline		95.58 75	95.42 73	96.08 114	96.78 160	90.41 161	97.16 134	97.45 157	93.69 60	95.65 97	97.85 78	87.29 115	98.68 172	95.66 52	97.25 133	99.13 74

gg-mvs-nofinetune		87.82 284	85.61 293	94.44 188	94.46 276	89.27 199	91.21 327	84.61 350	80.88 320	89.89 216	74.98 344	71.50 300	97.53 285	85.75 255	97.21 134	96.51 201

diffmvs		95.25 83	95.13 81	95.63 136	96.43 180	89.34 192	95.99 230	97.35 173	92.83 93	96.31 69	97.37 112	86.44 125	98.67 173	96.26 30	97.19 135	98.87 102

MVS_Test		94.89 96	94.62 91	95.68 134	96.83 158	89.55 180	96.70 174	97.17 185	91.17 145	95.60 98	96.11 177	87.87 104	98.76 165	93.01 121	97.17 136	98.72 112

PLC	91.00 6	94.11 113	93.43 122	96.13 113	98.58 71	91.15 139	96.69 176	97.39 167	87.29 254	91.37 177	96.71 139	88.39 98	99.52 92	87.33 228	97.13 137	97.73 168
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
1314		92.81 161	92.03 161	95.14 158	95.33 231	89.52 183	96.04 225	97.44 161	87.72 244	86.25 293	95.33 214	83.84 158	98.79 161	89.26 187	97.05 138	97.11 185

EPNet_dtu		91.71 192	91.28 188	92.99 253	93.76 297	83.71 297	96.69 176	95.28 282	93.15 79	87.02 285	95.95 181	83.37 165	97.38 298	79.46 309	96.84 139	97.88 161
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+		94.93 94	94.45 99	96.36 101	96.61 165	91.47 122	96.41 196	97.41 166	91.02 150	94.50 115	95.92 182	87.53 110	98.78 162	93.89 101	96.81 140	98.84 105

OMC-MVS		95.09 88	94.70 90	96.25 109	98.46 74	91.28 128	96.43 194	97.57 138	92.04 117	94.77 112	97.96 71	87.01 119	99.09 136	91.31 152	96.77 141	98.36 142

test-LLR		91.42 206	91.19 193	92.12 274	94.59 272	80.66 317	94.29 286	92.98 328	91.11 147	90.76 191	92.37 309	79.02 246	98.07 224	88.81 197	96.74 142	97.63 172

test-mter		90.19 254	89.54 253	92.12 274	94.59 272	80.66 317	94.29 286	92.98 328	87.68 245	90.76 191	92.37 309	67.67 321	98.07 224	88.81 197	96.74 142	97.63 172

F-COLMAP		93.58 131	92.98 131	95.37 152	98.40 78	88.98 206	97.18 132	97.29 178	87.75 243	90.49 194	97.10 124	85.21 141	99.50 96	86.70 237	96.72 144	97.63 172

mvs_anonymous		93.82 123	93.74 109	94.06 203	96.44 179	85.41 278	95.81 238	97.05 199	89.85 179	90.09 210	96.36 166	87.44 113	97.75 266	93.97 97	96.69 145	99.02 83

DP-MVS		92.76 162	91.51 181	96.52 85	98.77 57	90.99 141	97.38 111	96.08 255	82.38 310	89.29 235	97.87 75	83.77 159	99.69 44	81.37 298	96.69 145	98.89 100

TESTMET0.1,1		90.06 256	89.42 254	91.97 277	94.41 279	80.62 319	94.29 286	91.97 336	87.28 255	90.44 196	92.47 308	68.79 316	97.67 271	88.50 203	96.60 147	97.61 176

mvs-test1		93.63 129	93.69 111	93.46 236	96.02 200	84.61 288	97.24 123	96.72 225	93.85 52	92.30 162	95.76 194	83.08 170	98.89 155	91.69 144	96.54 148	96.87 193

EPP-MVSNet		95.22 85	95.04 83	95.76 127	97.49 133	89.56 179	98.67 5	97.00 205	90.69 155	94.24 120	97.62 99	89.79 85	98.81 160	93.39 113	96.49 149	98.92 96

PMMVS		92.86 157	92.34 153	94.42 190	94.92 255	86.73 256	94.53 275	96.38 244	84.78 289	94.27 119	95.12 223	83.13 169	98.40 191	91.47 149	96.49 149	98.12 150

Fast-Effi-MVS+		93.46 134	92.75 138	95.59 139	96.77 161	90.03 164	96.81 166	97.13 188	88.19 225	91.30 181	94.27 264	86.21 129	98.63 176	87.66 220	96.46 151	98.12 150

BH-w/o		92.14 184	91.75 170	93.31 242	96.99 153	85.73 273	95.67 242	95.69 265	88.73 213	89.26 237	94.82 234	82.97 175	98.07 224	85.26 261	96.32 152	96.13 213

sss		94.51 104	93.80 108	96.64 78	97.07 146	91.97 109	96.32 208	98.06 73	88.94 202	94.50 115	96.78 136	84.60 148	99.27 118	91.90 136	96.02 153	98.68 116

SCA		91.84 190	91.18 194	93.83 217	95.59 213	84.95 284	94.72 270	95.58 271	90.82 151	92.25 163	93.69 286	75.80 278	98.10 216	86.20 244	95.98 154	98.45 132

CDS-MVSNet		94.14 112	93.54 116	95.93 121	96.18 191	91.46 123	96.33 207	97.04 201	88.97 201	93.56 132	96.51 157	87.55 109	97.89 254	89.80 172	95.95 155	98.44 135
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPM		91.52 202	90.30 222	95.20 155	95.30 235	89.83 173	93.38 308	96.85 220	86.26 269	88.59 251	95.80 189	84.88 145	98.15 210	75.67 324	95.93 156	97.63 172

LFMVS		93.60 130	92.63 142	96.52 85	98.13 104	91.27 129	97.94 54	93.39 326	90.57 165	96.29 70	98.31 47	69.00 315	99.16 127	94.18 94	95.87 157	99.12 77

thisisatest0515		92.29 176	91.30 187	95.25 154	96.60 166	88.90 208	94.36 282	92.32 332	87.92 234	93.43 138	94.57 246	77.28 269	99.00 145	89.42 182	95.86 158	97.86 162

CVMVSNet		91.23 217	91.75 170	89.67 314	95.77 208	74.69 340	96.44 192	94.88 301	85.81 274	92.18 164	97.64 97	79.07 243	95.58 330	88.06 207	95.86 158	98.74 110

TAMVS		94.01 118	93.46 120	95.64 135	96.16 193	90.45 159	96.71 173	96.89 215	89.27 191	93.46 137	96.92 132	87.29 115	97.94 246	88.70 200	95.74 160	98.53 121

Effi-MVS+-dtu		93.08 145	93.21 128	92.68 264	96.02 200	83.25 302	97.14 137	96.72 225	93.85 52	91.20 188	93.44 295	83.08 170	98.30 198	91.69 144	95.73 161	96.50 202

HyFIR lowres test		93.66 128	92.92 133	95.87 124	98.24 93	89.88 172	94.58 273	98.49 12	85.06 284	93.78 129	95.78 193	82.86 177	98.67 173	91.77 140	95.71 162	99.07 82

thisisatest0530		93.03 148	92.21 157	95.49 146	97.07 146	89.11 204	97.49 101	92.19 333	90.16 172	94.09 122	96.41 163	76.43 275	99.05 142	90.38 162	95.68 163	98.31 144

MVS-HIRNet		82.47 309	81.21 311	86.26 324	95.38 223	69.21 347	88.96 340	89.49 344	66.28 343	80.79 324	74.08 346	68.48 318	97.39 297	71.93 334	95.47 164	92.18 330

tttt0517		92.96 151	92.33 154	94.87 171	97.11 144	87.16 248	97.97 52	92.09 334	90.63 160	93.88 128	97.01 128	76.50 272	99.06 141	90.29 165	95.45 165	98.38 140

GG-mvs-BLEND				93.62 227	93.69 299	89.20 200	92.39 323	83.33 351		87.98 268	89.84 327	71.00 304	96.87 314	82.08 291	95.40 166	94.80 286

PatchmatchNet		91.91 188	91.35 183	93.59 229	95.38 223	84.11 293	93.15 312	95.39 275	89.54 183	92.10 166	93.68 288	82.82 179	98.13 211	84.81 265	95.32 167	98.52 122
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VNet		95.89 68	95.45 71	97.21 65	98.07 107	92.94 79	97.50 97	98.15 51	93.87 51	97.52 25	97.61 100	85.29 140	99.53 88	95.81 50	95.27 168	99.16 70

DSMNet-mixed		86.34 295	86.12 291	87.00 322	89.88 336	70.43 344	94.93 268	90.08 343	77.97 335	85.42 302	92.78 303	74.44 286	93.96 338	74.43 326	95.14 169	96.62 199

test_yl		94.78 100	94.23 102	96.43 94	97.74 121	91.22 130	96.85 160	97.10 192	91.23 143	95.71 91	96.93 129	84.30 152	99.31 115	93.10 117	95.12 170	98.75 108

DCV-MVSNet		94.78 100	94.23 102	96.43 94	97.74 121	91.22 130	96.85 160	97.10 192	91.23 143	95.71 91	96.93 129	84.30 152	99.31 115	93.10 117	95.12 170	98.75 108

alignmvs		95.87 69	95.23 78	97.78 33	97.56 132	95.19 18	97.86 59	97.17 185	94.39 41	96.47 64	96.40 164	85.89 133	99.20 122	96.21 37	95.11 172	98.95 93

MSDG		91.42 206	90.24 226	94.96 167	97.15 143	88.91 207	93.69 301	96.32 246	85.72 276	86.93 287	96.47 159	80.24 224	98.98 147	80.57 301	95.05 173	96.98 187

VDD-MVS		93.82 123	93.08 129	96.02 118	97.88 116	89.96 171	97.72 76	95.85 261	92.43 104	95.86 86	98.44 28	68.42 319	99.39 109	96.31 28	94.85 174	98.71 114

VDDNet		93.05 147	92.07 159	96.02 118	96.84 156	90.39 162	98.08 42	95.85 261	86.22 270	95.79 89	98.46 26	67.59 322	99.19 123	94.92 78	94.85 174	98.47 130

canonicalmvs		96.02 64	95.45 71	97.75 37	97.59 130	95.15 21	98.28 25	97.60 134	94.52 38	96.27 71	96.12 175	87.65 107	99.18 125	96.20 38	94.82 176	98.91 97

Patchmatch-test		89.42 266	87.99 273	93.70 224	95.27 236	85.11 280	88.98 339	94.37 314	81.11 318	87.10 283	93.69 286	82.28 191	97.50 288	74.37 327	94.76 177	98.48 129

cascas		91.20 219	90.08 233	94.58 185	94.97 251	89.16 203	93.65 303	97.59 136	79.90 326	89.40 230	92.92 301	75.36 282	98.36 194	92.14 130	94.75 178	96.23 206

Fast-Effi-MVS+-dtu		92.29 176	91.99 163	93.21 247	95.27 236	85.52 276	97.03 140	96.63 237	92.09 115	89.11 240	95.14 221	80.33 223	98.08 221	87.54 224	94.74 179	96.03 217

WTY-MVS		94.71 102	94.02 104	96.79 76	97.71 123	92.05 105	96.59 187	97.35 173	90.61 162	94.64 113	96.93 129	86.41 126	99.39 109	91.20 155	94.71 180	98.94 94

baseline2		91.63 195	90.86 200	93.94 213	94.33 281	86.32 263	95.92 233	91.64 338	89.37 188	86.94 286	94.69 239	81.62 204	98.69 171	88.64 201	94.57 181	96.81 195

HY-MVS	89.66 9	93.87 121	92.95 132	96.63 80	97.10 145	92.49 90	95.64 245	96.64 234	89.05 197	93.00 147	95.79 192	85.77 136	99.45 102	89.16 193	94.35 182	97.96 155

MDTV_nov1_ep13			90.76 206		95.22 240	80.33 322	93.03 315	95.28 282	88.14 230	92.84 153	93.83 280	81.34 206	98.08 221	82.86 283	94.34 183

thres200		92.23 180	91.39 182	94.75 179	97.61 128	89.03 205	96.60 186	95.09 292	92.08 116	93.28 142	94.00 276	78.39 257	99.04 144	81.26 299	94.18 184	96.19 208

thres100view900		92.43 168	91.58 176	94.98 165	97.92 113	89.37 191	97.71 78	94.66 306	92.20 110	93.31 141	94.90 229	78.06 262	99.08 138	81.40 295	94.08 185	96.48 203

tfpn200view9		92.38 171	91.52 179	94.95 168	97.85 117	89.29 196	97.41 105	94.88 301	92.19 112	93.27 143	94.46 253	78.17 259	99.08 138	81.40 295	94.08 185	96.48 203

thres400		92.42 169	91.52 179	95.12 160	97.85 117	89.29 196	97.41 105	94.88 301	92.19 112	93.27 143	94.46 253	78.17 259	99.08 138	81.40 295	94.08 185	96.98 187

thres600view7		92.49 167	91.60 175	95.18 156	97.91 114	89.47 184	97.65 84	94.66 306	92.18 114	93.33 140	94.91 228	78.06 262	99.10 133	81.61 292	94.06 188	96.98 187

CR-MVSNet		90.82 235	89.77 246	93.95 211	94.45 277	87.19 246	90.23 333	95.68 267	86.89 261	92.40 156	92.36 312	80.91 212	97.05 306	81.09 300	93.95 189	97.60 177

RPMNet		88.98 269	87.05 284	94.77 177	94.45 277	87.19 246	90.23 333	98.03 84	77.87 336	92.40 156	87.55 337	80.17 226	99.51 93	68.84 340	93.95 189	97.60 177

1112_ss		93.37 136	92.42 152	96.21 111	97.05 151	90.99 141	96.31 209	96.72 225	86.87 262	89.83 217	96.69 143	86.51 124	99.14 130	88.12 206	93.67 191	98.50 125

PatchT		88.87 273	87.42 278	93.22 246	94.08 288	85.10 281	89.51 337	94.64 308	81.92 313	92.36 159	88.15 335	80.05 228	97.01 310	72.43 332	93.65 192	97.54 180

COLMAP_ROB	87.81 15	90.40 248	89.28 257	93.79 220	97.95 110	87.13 249	96.92 155	95.89 260	82.83 308	86.88 289	97.18 119	73.77 292	99.29 117	78.44 314	93.62 193	94.95 270
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
GA-MVS		91.38 208	90.31 221	94.59 181	94.65 269	87.62 238	94.34 283	96.19 252	90.73 154	90.35 198	93.83 280	71.84 298	97.96 243	87.22 230	93.61 194	98.21 147

TR-MVS		91.48 204	90.59 212	94.16 200	96.40 181	87.33 240	95.67 242	95.34 281	87.68 245	91.46 175	95.52 209	76.77 271	98.35 195	82.85 284	93.61 194	96.79 196

Test_1112_low_res		92.84 159	91.84 168	95.85 125	97.04 152	89.97 170	95.53 249	96.64 234	85.38 278	89.65 223	95.18 219	85.86 134	99.10 133	87.70 215	93.58 196	98.49 127

ab-mvs		93.57 132	92.55 146	96.64 78	97.28 136	91.96 110	95.40 253	97.45 157	89.81 181	93.22 145	96.28 169	79.62 237	99.46 100	90.74 158	93.11 197	98.50 125

AllTest		90.23 252	88.98 261	93.98 207	97.94 111	86.64 257	96.51 191	95.54 272	85.38 278	85.49 300	96.77 137	70.28 309	99.15 128	80.02 304	92.87 198	96.15 211

TestCases				93.98 207	97.94 111	86.64 257		95.54 272	85.38 278	85.49 300	96.77 137	70.28 309	99.15 128	80.02 304	92.87 198	96.15 211

MIMVSNet		88.50 278	86.76 286	93.72 223	94.84 261	87.77 236	91.39 325	94.05 319	86.41 267	87.99 267	92.59 306	63.27 335	95.82 326	77.44 316	92.84 200	97.57 179

Anonymous202405211		92.07 185	90.83 204	95.76 127	98.19 100	88.75 210	97.58 91	95.00 295	86.00 273	93.64 131	97.45 109	66.24 329	99.53 88	90.68 160	92.71 201	99.01 87

EPMVS		90.70 241	89.81 244	93.37 240	94.73 266	84.21 291	93.67 302	88.02 345	89.50 185	92.38 158	93.49 293	77.82 266	97.78 263	86.03 250	92.68 202	98.11 153

XVG-OURS		93.72 127	93.35 125	94.80 175	97.07 146	88.61 213	94.79 269	97.46 151	91.97 120	93.99 124	97.86 77	81.74 202	98.88 156	92.64 123	92.67 203	96.92 191

MVS_0304		88.79 274	87.57 276	92.46 266	94.65 269	86.15 270	96.40 199	97.17 185	86.44 266	88.02 266	91.71 321	56.68 343	97.03 307	84.47 270	92.58 204	94.19 308

XVG-OURS-SEG-HR		93.86 122	93.55 115	94.81 174	97.06 149	88.53 215	95.28 259	97.45 157	91.68 125	94.08 123	97.68 91	82.41 189	98.90 154	93.84 103	92.47 205	96.98 187

CLD-MVS		92.98 150	92.53 148	94.32 194	96.12 197	89.20 200	95.28 259	97.47 149	92.66 99	89.90 214	95.62 202	80.58 217	98.40 191	92.73 122	92.40 206	95.38 251
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OPM-MVS		93.28 139	92.76 136	94.82 172	94.63 271	90.77 151	96.65 179	97.18 183	93.72 57	91.68 172	97.26 116	79.33 241	98.63 176	92.13 131	92.28 207	95.07 266

HQP_MVS		93.78 125	93.43 122	94.82 172	96.21 188	89.99 167	97.74 71	97.51 144	94.85 24	91.34 178	96.64 146	81.32 207	98.60 179	93.02 119	92.23 208	95.86 220

plane_prior5								97.51 144					98.60 179	93.02 119	92.23 208	95.86 220

RPSCF		90.75 238	90.86 200	90.42 308	96.84 156	76.29 338	95.61 246	96.34 245	83.89 298	91.38 176	97.87 75	76.45 273	98.78 162	87.16 233	92.23 208	96.20 207

CostFormer		91.18 223	90.70 208	92.62 265	94.84 261	81.76 312	94.09 292	94.43 311	84.15 295	92.72 154	93.77 284	79.43 239	98.20 205	90.70 159	92.18 211	97.90 159

plane_prior						89.99 167	97.24 123		94.06 47						92.16 212

HQP3-MVS								97.39 167							92.10 213

HQP-MVS		93.19 143	92.74 139	94.54 186	95.86 203	89.33 193	96.65 179	97.39 167	93.55 62	90.14 201	95.87 184	80.95 210	98.50 186	92.13 131	92.10 213	95.78 227

tpm2		89.96 257	89.21 258	92.23 273	94.91 258	81.25 314	93.78 298	94.42 312	80.62 323	91.56 173	93.44 295	76.44 274	97.94 246	85.60 256	92.08 215	97.49 181

LPG-MVS_test		92.94 153	92.56 145	94.10 201	96.16 193	88.26 221	97.65 84	97.46 151	91.29 138	90.12 207	97.16 120	79.05 244	98.73 167	92.25 127	91.89 216	95.31 255

LGP-MVS_train				94.10 201	96.16 193	88.26 221		97.46 151	91.29 138	90.12 207	97.16 120	79.05 244	98.73 167	92.25 127	91.89 216	95.31 255

ACMM	89.79 8	92.96 151	92.50 150	94.35 192	96.30 186	88.71 211	97.58 91	97.36 172	91.40 136	90.53 193	96.65 145	79.77 233	98.75 166	91.24 154	91.64 218	95.59 236
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
JIA-IIPM		88.26 281	87.04 285	91.91 278	93.52 303	81.42 313	89.38 338	94.38 313	80.84 321	90.93 190	80.74 342	79.22 242	97.92 250	82.76 285	91.62 219	96.38 205

test_djsdf		93.07 146	92.76 136	94.00 206	93.49 305	88.70 212	98.22 32	97.57 138	91.42 133	90.08 211	95.55 207	82.85 178	97.92 250	94.07 95	91.58 220	95.40 248

jajsoiax		92.42 169	91.89 167	94.03 205	93.33 310	88.50 216	97.73 73	97.53 142	92.00 119	88.85 245	96.50 158	75.62 281	98.11 215	93.88 102	91.56 221	95.48 238

mvs_tets		92.31 174	91.76 169	93.94 213	93.41 307	88.29 219	97.63 89	97.53 142	92.04 117	88.76 248	96.45 160	74.62 285	98.09 220	93.91 100	91.48 222	95.45 243

ACMP	89.59 10	92.62 164	92.14 158	94.05 204	96.40 181	88.20 224	97.36 112	97.25 181	91.52 128	88.30 257	96.64 146	78.46 255	98.72 170	91.86 139	91.48 222	95.23 262
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ADS-MVSNet2		89.45 265	88.59 266	92.03 276	95.86 203	82.26 310	90.93 328	94.32 316	83.23 306	91.28 184	91.81 319	79.01 248	95.99 323	79.52 306	91.39 224	97.84 163

ADS-MVSNet		89.89 259	88.68 265	93.53 232	95.86 203	84.89 285	90.93 328	95.07 293	83.23 306	91.28 184	91.81 319	79.01 248	97.85 256	79.52 306	91.39 224	97.84 163

anonymousdsp		92.16 182	91.55 177	93.97 209	92.58 322	89.55 180	97.51 96	97.42 165	89.42 187	88.40 254	94.84 232	80.66 216	97.88 255	91.87 138	91.28 226	94.48 298

CMPMVS	62.92 21	85.62 301	84.92 298	87.74 319	89.14 339	73.12 343	94.17 289	96.80 223	73.98 339	73.65 339	94.93 227	66.36 326	97.61 278	83.95 276	91.28 226	92.48 327
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20240529		91.98 187	90.73 207	95.73 132	98.14 103	89.40 189	97.99 47	97.72 122	79.63 327	93.54 134	97.41 111	69.94 312	99.56 80	91.04 156	91.11 228	98.22 146

XVG-ACMP-BASELINE		90.93 232	90.21 230	93.09 250	94.31 283	85.89 271	95.33 256	97.26 179	91.06 149	89.38 231	95.44 212	68.61 317	98.60 179	89.46 181	91.05 229	94.79 288

ACMMP++															91.02 230

UniMVSNet_ETH3D		91.34 213	90.22 229	94.68 180	94.86 260	87.86 234	97.23 128	97.46 151	87.99 232	89.90 214	96.92 132	66.35 327	98.23 201	90.30 164	90.99 231	97.96 155

D2MVS		91.30 215	90.95 197	92.35 270	94.71 267	85.52 276	96.18 220	98.21 40	88.89 204	86.60 290	93.82 282	79.92 231	97.95 245	89.29 186	90.95 232	93.56 316

PS-MVSNAJss		93.74 126	93.51 118	94.44 188	93.91 292	89.28 198	97.75 70	97.56 141	92.50 103	89.94 213	96.54 156	88.65 94	98.18 208	93.83 104	90.90 233	95.86 220

EG-PatchMatch MVS		87.02 291	85.44 294	91.76 287	92.67 320	85.00 282	96.08 224	96.45 242	83.41 305	79.52 331	93.49 293	57.10 342	97.72 268	79.34 311	90.87 234	92.56 325

PVSNet_BlendedMVS		94.06 115	93.92 105	94.47 187	98.27 89	89.46 186	96.73 171	98.36 16	90.17 171	94.36 117	95.24 218	88.02 100	99.58 70	93.44 110	90.72 235	94.36 302

DWT-MVSNet_test		90.76 236	89.89 240	93.38 239	95.04 249	83.70 298	95.85 236	94.30 317	88.19 225	90.46 195	92.80 302	73.61 293	98.50 186	88.16 205	90.58 236	97.95 157

EI-MVSNet		93.03 148	92.88 134	93.48 234	95.77 208	86.98 251	96.44 192	97.12 189	90.66 158	91.30 181	97.64 97	86.56 122	98.05 227	89.91 168	90.55 237	95.41 244

MVSTER		93.20 142	92.81 135	94.37 191	96.56 171	89.59 178	97.06 139	97.12 189	91.24 142	91.30 181	95.96 180	82.02 196	98.05 227	93.48 109	90.55 237	95.47 240

FIs		94.09 114	93.70 110	95.27 153	95.70 211	92.03 106	98.10 39	98.68 7	93.36 72	90.39 197	96.70 141	87.63 108	97.94 246	92.25 127	90.50 239	95.84 223

FC-MVSNet-test		93.94 120	93.57 114	95.04 161	95.48 219	91.45 124	98.12 38	98.71 5	93.37 70	90.23 200	96.70 141	87.66 106	97.85 256	91.49 148	90.39 240	95.83 224

ACMMP++_ref															90.30 241

RRT_MVS		93.21 141	92.32 155	95.91 122	94.92 255	94.15 43	96.92 155	96.86 219	91.42 133	91.28 184	96.43 161	79.66 236	98.10 216	93.29 114	90.06 242	95.46 241

LTVRE_ROB	88.41 13	90.99 228	89.92 239	94.19 198	96.18 191	89.55 180	96.31 209	97.09 194	87.88 236	85.67 298	95.91 183	78.79 252	98.57 182	81.50 293	89.98 243	94.44 300
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
tpmvs		89.83 262	89.15 260	91.89 279	94.92 255	80.30 323	93.11 313	95.46 274	86.28 268	88.08 264	92.65 304	80.44 220	98.52 185	81.47 294	89.92 244	96.84 194

ITE_SJBPF				92.43 268	95.34 228	85.37 279		95.92 258	91.47 130	87.75 271	96.39 165	71.00 304	97.96 243	82.36 289	89.86 245	93.97 312

ET-MVSNet_ETH3D		91.49 203	90.11 232	95.63 136	96.40 181	91.57 120	95.34 255	93.48 325	90.60 164	75.58 337	95.49 210	80.08 227	96.79 316	94.25 92	89.76 246	98.52 122

USDC		88.94 270	87.83 275	92.27 272	94.66 268	84.96 283	93.86 296	95.90 259	87.34 253	83.40 317	95.56 206	67.43 323	98.19 207	82.64 288	89.67 247	93.66 315

ACMH	87.59 16	90.53 245	89.42 254	93.87 216	96.21 188	87.92 231	97.24 123	96.94 208	88.45 219	83.91 315	96.27 170	71.92 297	98.62 178	84.43 271	89.43 248	95.05 268
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		91.44 205	91.32 185	91.79 284	95.15 243	79.20 332	93.42 307	95.37 277	88.55 218	93.49 136	93.67 289	82.49 187	98.27 199	90.41 161	89.34 249	97.90 159

test0.0.03 1		89.37 267	88.70 264	91.41 294	92.47 323	85.63 274	95.22 264	92.70 330	91.11 147	86.91 288	93.65 290	79.02 246	93.19 342	78.00 315	89.18 250	95.41 244

OpenMVS_ROB	81.14 20	84.42 305	82.28 308	90.83 300	90.06 334	84.05 294	95.73 241	94.04 320	73.89 340	80.17 330	91.53 323	59.15 340	97.64 274	66.92 342	89.05 251	90.80 337

GBi-Net		91.35 211	90.27 224	94.59 181	96.51 174	91.18 136	97.50 97	96.93 209	88.82 208	89.35 232	94.51 248	73.87 289	97.29 302	86.12 247	88.82 252	95.31 255

test1		91.35 211	90.27 224	94.59 181	96.51 174	91.18 136	97.50 97	96.93 209	88.82 208	89.35 232	94.51 248	73.87 289	97.29 302	86.12 247	88.82 252	95.31 255

FMVSNet3		91.78 191	90.69 209	95.03 162	96.53 173	92.27 98	97.02 143	96.93 209	89.79 182	89.35 232	94.65 242	77.01 270	97.47 290	86.12 247	88.82 252	95.35 253

tpm cat1		88.36 279	87.21 282	91.81 283	95.13 245	80.55 320	92.58 320	95.70 264	74.97 338	87.45 274	91.96 317	78.01 264	98.17 209	80.39 303	88.74 255	96.72 198

testing_2		87.33 288	85.03 297	94.22 197	87.77 344	89.32 195	94.97 267	97.11 191	89.22 192	71.64 340	88.73 330	55.16 345	97.94 246	91.95 135	88.73 256	95.41 244

test_0402		86.46 294	84.79 299	91.45 292	95.02 250	85.55 275	96.29 211	94.89 300	80.90 319	82.21 319	93.97 278	68.21 320	97.29 302	62.98 344	88.68 257	91.51 334

FMVSNet2		91.31 214	90.08 233	94.99 163	96.51 174	92.21 99	97.41 105	96.95 207	88.82 208	88.62 250	94.75 237	73.87 289	97.42 295	85.20 262	88.55 258	95.35 253

testgi		87.97 282	87.21 282	90.24 310	92.86 316	80.76 316	96.67 178	94.97 297	91.74 123	85.52 299	95.83 187	62.66 337	94.47 336	76.25 321	88.36 259	95.48 238

ACMH+	87.92 14	90.20 253	89.18 259	93.25 244	96.48 177	86.45 262	96.99 148	96.68 231	88.83 207	84.79 306	96.22 171	70.16 311	98.53 184	84.42 272	88.04 260	94.77 291

tpm		90.25 251	89.74 249	91.76 287	93.92 291	79.73 328	93.98 293	93.54 324	88.28 223	91.99 168	93.25 298	77.51 268	97.44 293	87.30 229	87.94 261	98.12 150

pmmvs4		90.93 232	89.85 242	94.17 199	93.34 309	90.79 150	94.60 272	96.02 256	84.62 290	87.45 274	95.15 220	81.88 200	97.45 292	87.70 215	87.87 262	94.27 307

XXY-MVS		92.16 182	91.23 191	94.95 168	94.75 265	90.94 144	97.47 102	97.43 164	89.14 195	88.90 242	96.43 161	79.71 234	98.24 200	89.56 179	87.68 263	95.67 235

pmmvs5		89.86 261	88.87 263	92.82 259	92.86 316	86.23 266	96.26 213	95.39 275	84.24 294	87.12 281	94.51 248	74.27 287	97.36 299	87.61 223	87.57 264	94.86 279

LF4IMVS		87.94 283	87.25 280	89.98 312	92.38 326	80.05 327	94.38 281	95.25 285	87.59 247	84.34 308	94.74 238	64.31 334	97.66 273	84.83 264	87.45 265	92.23 329

FMVSNet1		89.88 260	88.31 269	94.59 181	95.41 221	91.18 136	97.50 97	96.93 209	86.62 264	87.41 276	94.51 248	65.94 331	97.29 302	83.04 282	87.43 266	95.31 255

dp		88.90 272	88.26 271	90.81 301	94.58 274	76.62 337	92.85 317	94.93 299	85.12 283	90.07 212	93.07 299	75.81 277	98.12 214	80.53 302	87.42 267	97.71 169

OurMVSNet-221017-0		90.51 246	90.19 231	91.44 293	93.41 307	81.25 314	96.98 150	96.28 247	91.68 125	86.55 291	96.30 168	74.20 288	97.98 236	88.96 195	87.40 268	95.09 265

TinyColmap		86.82 292	85.35 296	91.21 295	94.91 258	82.99 304	93.94 295	94.02 321	83.58 302	81.56 321	94.68 240	62.34 338	98.13 211	75.78 322	87.35 269	92.52 326

cl-mvsnet2		91.21 218	90.56 214	93.14 249	96.09 199	86.80 254	94.41 280	96.58 240	87.80 239	88.58 252	93.99 277	80.85 215	97.62 277	89.87 171	86.93 270	94.99 269

miper_ehance_all_eth		91.59 196	91.13 195	92.97 254	95.55 216	86.57 261	94.47 276	96.88 216	87.77 241	88.88 244	94.01 275	86.22 128	97.54 283	89.49 180	86.93 270	94.79 288

miper_enhance_ethall		91.54 201	91.01 196	93.15 248	95.35 227	87.07 250	93.97 294	96.90 213	86.79 263	89.17 239	93.43 297	86.55 123	97.64 274	89.97 167	86.93 270	94.74 292

IterMVS		90.15 255	89.67 250	91.61 289	95.48 219	83.72 296	94.33 284	96.12 254	89.99 175	87.31 280	94.15 272	75.78 280	96.27 322	86.97 235	86.89 273	94.83 281
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		90.31 249	89.81 244	91.82 282	95.52 217	84.20 292	94.30 285	96.15 253	90.61 162	87.39 277	94.27 264	75.80 278	96.44 319	87.34 227	86.88 274	94.82 283

RRT_test8_iter05		91.19 222	90.78 205	92.41 269	95.76 210	83.14 303	97.32 116	97.46 151	91.37 137	89.07 241	95.57 204	70.33 308	98.21 203	93.56 106	86.62 275	95.89 219

our_test_3		88.78 275	87.98 274	91.20 296	92.45 324	82.53 306	93.61 305	95.69 265	85.77 275	84.88 304	93.71 285	79.99 229	96.78 317	79.47 308	86.24 276	94.28 306

EU-MVSNet		88.72 276	88.90 262	88.20 317	93.15 313	74.21 341	96.63 183	94.22 318	85.18 281	87.32 279	95.97 179	76.16 276	94.98 334	85.27 260	86.17 277	95.41 244

Anonymous20231206		87.09 290	86.14 290	89.93 313	91.22 331	80.35 321	96.11 222	95.35 278	83.57 303	84.16 311	93.02 300	73.54 294	95.61 328	72.16 333	86.14 278	93.84 314

IterMVS-LS		92.29 176	91.94 165	93.34 241	96.25 187	86.97 252	96.57 190	97.05 199	90.67 156	89.50 229	94.80 235	86.59 121	97.64 274	89.91 168	86.11 279	95.40 248
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VPA-MVSNet		93.24 140	92.48 151	95.51 144	95.70 211	92.39 92	97.86 59	98.66 9	92.30 107	92.09 167	95.37 213	80.49 219	98.40 191	93.95 98	85.86 280	95.75 231

nrg030		94.05 116	93.31 126	96.27 107	95.22 240	94.59 28	98.34 20	97.46 151	92.93 91	91.21 187	96.64 146	87.23 117	98.22 202	94.99 77	85.80 281	95.98 218

cl-mvsnet_		90.96 231	90.32 220	92.89 256	95.37 225	86.21 267	94.46 278	96.64 234	87.82 237	88.15 263	94.18 270	82.98 174	97.54 283	87.70 215	85.59 282	94.92 276

cl-mvsnet1		90.97 230	90.33 219	92.88 257	95.36 226	86.19 268	94.46 278	96.63 237	87.82 237	88.18 262	94.23 267	82.99 173	97.53 285	87.72 213	85.57 283	94.93 274

v1192		91.07 224	90.23 227	93.58 230	93.70 298	87.82 235	96.73 171	97.07 196	87.77 241	89.58 224	94.32 261	80.90 214	97.97 239	86.52 239	85.48 284	94.95 270

v1240		90.70 241	89.85 242	93.23 245	93.51 304	86.80 254	96.61 184	97.02 204	87.16 257	89.58 224	94.31 262	79.55 238	97.98 236	85.52 257	85.44 285	94.90 277

v1144		91.37 210	90.60 211	93.68 226	93.89 293	88.23 223	96.84 162	97.03 203	88.37 221	89.69 221	94.39 255	82.04 195	97.98 236	87.80 212	85.37 286	94.84 280

FMVSNet5		87.29 289	85.79 292	91.78 285	94.80 263	87.28 241	95.49 250	95.28 282	84.09 296	83.85 316	91.82 318	62.95 336	94.17 337	78.48 313	85.34 287	93.91 313

WR-MVS		92.34 172	91.53 178	94.77 177	95.13 245	90.83 148	96.40 199	97.98 98	91.88 121	89.29 235	95.54 208	82.50 186	97.80 261	89.79 173	85.27 288	95.69 234

v1921920		90.85 234	90.03 237	93.29 243	93.55 301	86.96 253	96.74 170	97.04 201	87.36 252	89.52 228	94.34 258	80.23 225	97.97 239	86.27 242	85.21 289	94.94 272

Anonymous20231211		90.63 243	89.42 254	94.27 195	98.24 93	89.19 202	98.05 44	97.89 104	79.95 325	88.25 260	94.96 225	72.56 296	98.13 211	89.70 175	85.14 290	95.49 237

Patchmtry		88.64 277	87.25 280	92.78 261	94.09 287	86.64 257	89.82 336	95.68 267	80.81 322	87.63 273	92.36 312	80.91 212	97.03 307	78.86 312	85.12 291	94.67 294

V42		91.58 198	90.87 199	93.73 221	94.05 289	88.50 216	97.32 116	96.97 206	88.80 211	89.71 219	94.33 259	82.54 185	98.05 227	89.01 194	85.07 292	94.64 296

SixPastTwentyTwo		89.15 268	88.54 267	90.98 298	93.49 305	80.28 324	96.70 174	94.70 305	90.78 152	84.15 312	95.57 204	71.78 299	97.71 269	84.63 268	85.07 292	94.94 272

v2v482		91.59 196	90.85 202	93.80 219	93.87 294	88.17 226	96.94 154	96.88 216	89.54 183	89.53 227	94.90 229	81.70 203	98.02 232	89.25 188	85.04 294	95.20 263

ppachtmachnet_test		88.35 280	87.29 279	91.53 290	92.45 324	83.57 300	93.75 299	95.97 257	84.28 293	85.32 303	94.18 270	79.00 250	96.93 312	75.71 323	84.99 295	94.10 309

v144192		91.06 225	90.28 223	93.39 238	93.66 300	87.23 245	96.83 163	97.07 196	87.43 250	89.69 221	94.28 263	81.48 205	98.00 235	87.18 232	84.92 296	94.93 274

CP-MVSNet		91.89 189	91.24 190	93.82 218	95.05 248	88.57 214	97.82 64	98.19 44	91.70 124	88.21 261	95.76 194	81.96 197	97.52 287	87.86 210	84.65 297	95.37 252

cl_fuxian		91.38 208	90.89 198	92.88 257	95.58 214	86.30 264	94.68 271	96.84 221	88.17 227	88.83 247	94.23 267	85.65 137	97.47 290	89.36 183	84.63 298	94.89 278

miper_lstm_enhance		90.50 247	90.06 236	91.83 281	95.33 231	83.74 295	93.86 296	96.70 230	87.56 248	87.79 269	93.81 283	83.45 164	96.92 313	87.39 226	84.62 299	94.82 283

tfpnnormal		89.70 263	88.40 268	93.60 228	95.15 243	90.10 163	97.56 93	98.16 50	87.28 255	86.16 294	94.63 243	77.57 267	98.05 227	74.48 325	84.59 300	92.65 324

PS-CasMVS		91.55 200	90.84 203	93.69 225	94.96 252	88.28 220	97.84 63	98.24 34	91.46 131	88.04 265	95.80 189	79.67 235	97.48 289	87.02 234	84.54 301	95.31 255

N_pmnet		78.73 312	78.71 314	78.79 327	92.80 318	46.50 356	94.14 290	43.71 358	78.61 331	80.83 323	91.66 322	74.94 284	96.36 320	67.24 341	84.45 302	93.50 317

eth_miper_zixun_eth		91.02 227	90.59 212	92.34 271	95.33 231	84.35 289	94.10 291	96.90 213	88.56 217	88.84 246	94.33 259	84.08 156	97.60 279	88.77 199	84.37 303	95.06 267

WR-MVS_H		92.00 186	91.35 183	93.95 211	95.09 247	89.47 184	98.04 45	98.68 7	91.46 131	88.34 255	94.68 240	85.86 134	97.56 281	85.77 254	84.24 304	94.82 283

v10		91.04 226	90.23 227	93.49 233	94.12 286	88.16 227	97.32 116	97.08 195	88.26 224	88.29 258	94.22 269	82.17 194	97.97 239	86.45 241	84.12 305	94.33 303

UniMVSNet (Re)		93.31 138	92.55 146	95.61 138	95.39 222	93.34 70	97.39 109	98.71 5	93.14 80	90.10 209	94.83 233	87.71 105	98.03 231	91.67 146	83.99 306	95.46 241

UniMVSNet_NR-MVSNet		93.37 136	92.67 141	95.47 149	95.34 228	92.83 80	97.17 133	98.58 10	92.98 89	90.13 205	95.80 189	88.37 99	97.85 256	91.71 142	83.93 307	95.73 233

DU-MVS		92.90 155	92.04 160	95.49 146	94.95 253	92.83 80	97.16 134	98.24 34	93.02 83	90.13 205	95.71 197	83.47 162	97.85 256	91.71 142	83.93 307	95.78 227

v8		91.29 216	90.53 215	93.57 231	94.15 285	88.12 228	97.34 113	97.06 198	88.99 199	88.32 256	94.26 266	83.08 170	98.01 233	87.62 222	83.92 309	94.57 297

baseline1		92.82 160	91.90 166	95.55 142	97.20 139	90.77 151	97.19 131	94.58 309	92.20 110	92.36 159	96.34 167	84.16 155	98.21 203	89.20 191	83.90 310	97.68 171

v7n		90.76 236	89.86 241	93.45 237	93.54 302	87.60 239	97.70 79	97.37 170	88.85 205	87.65 272	94.08 274	81.08 209	98.10 216	84.68 267	83.79 311	94.66 295

VPNet		92.23 180	91.31 186	94.99 163	95.56 215	90.96 143	97.22 129	97.86 111	92.96 90	90.96 189	96.62 153	75.06 283	98.20 205	91.90 136	83.65 312	95.80 226

NR-MVSNet		92.34 172	91.27 189	95.53 143	94.95 253	93.05 75	97.39 109	98.07 70	92.65 100	84.46 307	95.71 197	85.00 144	97.77 265	89.71 174	83.52 313	95.78 227

v148		90.99 228	90.38 218	92.81 260	93.83 295	85.80 272	96.78 169	96.68 231	89.45 186	88.75 249	93.93 279	82.96 176	97.82 260	87.83 211	83.25 314	94.80 286

Baseline_NR-MVSNet		91.20 219	90.62 210	92.95 255	93.83 295	88.03 229	97.01 147	95.12 291	88.42 220	89.70 220	95.13 222	83.47 162	97.44 293	89.66 177	83.24 315	93.37 320

TranMVSNet+NR-MVSNet		92.50 165	91.63 174	95.14 158	94.76 264	92.07 104	97.53 95	98.11 59	92.90 92	89.56 226	96.12 175	83.16 167	97.60 279	89.30 185	83.20 316	95.75 231

PEN-MVS		91.20 219	90.44 216	93.48 234	94.49 275	87.91 233	97.76 69	98.18 46	91.29 138	87.78 270	95.74 196	80.35 222	97.33 300	85.46 258	82.96 317	95.19 264

new_pmnet		82.89 308	81.12 312	88.18 318	89.63 337	80.18 325	91.77 324	92.57 331	76.79 337	75.56 338	88.23 334	61.22 339	94.48 335	71.43 335	82.92 318	89.87 339

test_part1		89.59 264	88.03 272	94.27 195	95.32 234	89.42 188	98.03 46	97.58 137	78.01 333	86.10 296	94.59 245	69.87 313	98.01 233	89.88 170	82.85 319	95.40 248

FPMVS		71.27 314	69.85 316	75.50 329	74.64 350	59.03 352	91.30 326	91.50 339	58.80 346	57.92 347	88.28 333	29.98 353	85.53 348	53.43 346	82.84 320	81.95 343

MIMVSNet1		84.93 304	83.05 306	90.56 306	89.56 338	84.84 286	95.40 253	95.35 278	83.91 297	80.38 327	92.21 316	57.23 341	93.34 341	70.69 339	82.75 321	93.50 317

pm-mvs1		90.72 240	89.65 252	93.96 210	94.29 284	89.63 175	97.79 67	96.82 222	89.07 196	86.12 295	95.48 211	78.61 253	97.78 263	86.97 235	81.67 322	94.46 299

DTE-MVSNet		90.56 244	89.75 248	93.01 252	93.95 290	87.25 243	97.64 88	97.65 130	90.74 153	87.12 281	95.68 200	79.97 230	97.00 311	83.33 279	81.66 323	94.78 290

IB-MVS	87.33 17	89.91 258	88.28 270	94.79 176	95.26 239	87.70 237	95.12 266	93.95 322	89.35 189	87.03 284	92.49 307	70.74 306	99.19 123	89.18 192	81.37 324	97.49 181
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
test20.03		86.14 297	85.40 295	88.35 315	90.12 333	80.06 326	95.90 234	95.20 287	88.59 214	81.29 322	93.62 291	71.43 301	92.65 343	71.26 337	81.17 325	92.34 328

K. test v3		87.64 286	86.75 287	90.32 309	93.02 315	79.48 330	96.61 184	92.08 335	90.66 158	80.25 329	94.09 273	67.21 325	96.65 318	85.96 252	80.83 326	94.83 281

MDA-MVSNet_test_wron		85.87 299	84.23 303	90.80 303	92.38 326	82.57 305	93.17 310	95.15 289	82.15 311	67.65 342	92.33 315	78.20 258	95.51 331	77.33 317	79.74 327	94.31 305

YYNet1		85.87 299	84.23 303	90.78 304	92.38 326	82.46 308	93.17 310	95.14 290	82.12 312	67.69 341	92.36 312	78.16 261	95.50 332	77.31 318	79.73 328	94.39 301

pmmvs6		87.81 285	86.19 289	92.69 263	91.32 330	86.30 264	97.34 113	96.41 243	80.59 324	84.05 314	94.37 257	67.37 324	97.67 271	84.75 266	79.51 329	94.09 311

Gipuma		67.86 316	65.41 319	75.18 330	92.66 321	73.45 342	66.50 350	94.52 310	53.33 348	57.80 348	66.07 348	30.81 351	89.20 345	48.15 348	78.88 330	62.90 347
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet-bldmvs		85.00 303	82.95 307	91.17 297	93.13 314	83.33 301	94.56 274	95.00 295	84.57 291	65.13 345	92.65 304	70.45 307	95.85 324	73.57 330	77.49 331	94.33 303

Patchmatch-RL test		87.38 287	86.24 288	90.81 301	88.74 340	78.40 335	88.12 341	93.17 327	87.11 258	82.17 320	89.29 329	81.95 198	95.60 329	88.64 201	77.02 332	98.41 137

lessismore_v0				90.45 307	91.96 329	79.09 333		87.19 348		80.32 328	94.39 255	66.31 328	97.55 282	84.00 275	76.84 333	94.70 293

pmmvs-eth3d		86.22 296	84.45 301	91.53 290	88.34 341	87.25 243	94.47 276	95.01 294	83.47 304	79.51 332	89.61 328	69.75 314	95.71 327	83.13 281	76.73 334	91.64 332

PM-MVS		83.48 306	81.86 310	88.31 316	87.83 343	77.59 336	93.43 306	91.75 337	86.91 260	80.63 325	89.91 326	44.42 349	95.84 325	85.17 263	76.73 334	91.50 335

ambc				86.56 323	83.60 346	70.00 346	85.69 343	94.97 297		80.60 326	88.45 331	37.42 350	96.84 315	82.69 287	75.44 336	92.86 322

TDRefinement		86.53 293	84.76 300	91.85 280	82.23 348	84.25 290	96.38 202	95.35 278	84.97 286	84.09 313	94.94 226	65.76 332	98.34 197	84.60 269	74.52 337	92.97 321

TransMVSNet (Re)		88.94 270	87.56 277	93.08 251	94.35 280	88.45 218	97.73 73	95.23 286	87.47 249	84.26 310	95.29 215	79.86 232	97.33 300	79.44 310	74.44 338	93.45 319

PMVS	53.92 22	58.58 318	55.40 321	68.12 332	51.00 357	48.64 354	78.86 347	87.10 349	46.77 349	35.84 354	74.28 345	8.76 358	86.34 347	42.07 349	73.91 339	69.38 345
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepMVS_CX				74.68 331	90.84 332	64.34 351		81.61 353	65.34 344	67.47 343	88.01 336	48.60 348	80.13 350	62.33 345	73.68 340	79.58 344

UnsupCasMVSNet_eth		85.99 298	84.45 301	90.62 305	89.97 335	82.40 309	93.62 304	97.37 170	89.86 177	78.59 334	92.37 309	65.25 333	95.35 333	82.27 290	70.75 341	94.10 309

new-patchmatchnet		83.18 307	81.87 309	87.11 321	86.88 345	75.99 339	93.70 300	95.18 288	85.02 285	77.30 335	88.40 332	65.99 330	93.88 339	74.19 329	70.18 342	91.47 336

pmmvs3		79.97 311	77.50 315	87.39 320	82.80 347	79.38 331	92.70 319	90.75 342	70.69 342	78.66 333	87.47 338	51.34 347	93.40 340	73.39 331	69.65 343	89.38 340

LCM-MVSNet		72.55 313	69.39 317	82.03 325	70.81 354	65.42 350	90.12 335	94.36 315	55.02 347	65.88 344	81.72 341	24.16 357	89.96 344	74.32 328	68.10 344	90.71 338

UnsupCasMVSNet_bld		82.13 310	79.46 313	90.14 311	88.00 342	82.47 307	90.89 330	96.62 239	78.94 330	75.61 336	84.40 340	56.63 344	96.31 321	77.30 319	66.77 345	91.63 333

PVSNet_0	82.17 19	85.46 302	83.64 305	90.92 299	95.27 236	79.49 329	90.55 331	95.60 269	83.76 301	83.00 318	89.95 325	71.09 303	97.97 239	82.75 286	60.79 346	95.31 255

PMMVS2		70.19 315	66.92 318	80.01 326	76.35 349	65.67 349	86.22 342	87.58 347	64.83 345	62.38 346	80.29 343	26.78 355	88.49 346	63.79 343	54.07 347	85.88 341

MVE	50.73 23	53.25 320	48.81 325	66.58 333	65.34 355	57.50 353	72.49 349	70.94 356	40.15 352	39.28 353	63.51 349	6.89 360	73.48 353	38.29 350	42.38 348	68.76 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		53.28 319	52.56 323	55.43 334	74.43 351	47.13 355	83.63 346	76.30 354	42.23 350	42.59 351	62.22 350	28.57 354	74.40 351	31.53 351	31.51 349	44.78 348

ANet_high		63.94 317	59.58 320	77.02 328	61.24 356	66.06 348	85.66 344	87.93 346	78.53 332	42.94 350	71.04 347	25.42 356	80.71 349	52.60 347	30.83 350	84.28 342

EMVS		52.08 321	51.31 324	54.39 335	72.62 353	45.39 357	83.84 345	75.51 355	41.13 351	40.77 352	59.65 351	30.08 352	73.60 352	28.31 352	29.90 351	44.18 349

tmp_tt		51.94 322	53.82 322	46.29 336	33.73 358	45.30 358	78.32 348	67.24 357	18.02 353	50.93 349	87.05 339	52.99 346	53.11 354	70.76 338	25.29 352	40.46 350

wuyk23d		25.11 323	24.57 327	26.74 337	73.98 352	39.89 359	57.88 351	9.80 359	12.27 354	10.39 355	6.97 357	7.03 359	36.44 355	25.43 353	17.39 353	3.89 353

testmvs		13.36 325	16.33 328	4.48 339	5.04 359	2.26 361	93.18 309	3.28 360	2.70 355	8.24 356	21.66 353	2.29 362	2.19 356	7.58 354	2.96 354	9.00 352

test123		13.04 326	15.66 329	5.18 338	4.51 360	3.45 360	92.50 322	1.81 361	2.50 356	7.58 357	20.15 354	3.67 361	2.18 357	7.13 355	1.07 355	9.90 351

uanet_test		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

cdsmvs_eth3d_5k		23.24 324	30.99 326	0.00 340	0.00 361	0.00 362	0.00 352	97.63 132	0.00 357	0.00 358	96.88 134	84.38 151	0.00 358	0.00 356	0.00 356	0.00 354

pcd_1.5k_mvsjas		7.39 328	9.85 331	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	88.65 94	0.00 358	0.00 356	0.00 356	0.00 354

sosnet-low-res		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

sosnet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uncertanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

Regformer		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

ab-mvs-re		8.06 327	10.74 330	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	96.69 143	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

test_241102_ONE					99.42 6	95.30 15		98.27 28	95.09 18	99.19 1	98.81 8	95.54 3	99.65 53

save fliter					98.91 49	94.28 35	97.02 143	98.02 88	95.35 8

test0726					99.45 2	95.36 10	98.31 22	98.29 24	94.92 22	98.99 4	98.92 2	95.08 5

GSMVS																98.45 132

test_part2					99.28 25	95.74 6				98.10 17

sam_mvs1												82.76 180				98.45 132

sam_mvs												81.94 199

MTGPA								98.08 64

test_post1							92.81 318				16.58 356	80.53 218	97.68 270	86.20 244

test_post											17.58 355	81.76 201	98.08 221

patchmatchnet-post											90.45 324	82.65 184	98.10 216

MTMP							97.86 59	82.03 352

gm-plane-assit					93.22 311	78.89 334			84.82 288		93.52 292		98.64 175	87.72 213

TEST9					98.70 60	94.19 40	96.41 196	98.02 88	88.17 227	96.03 78	97.56 105	92.74 24	99.59 67

test_8					98.67 62	94.06 49	96.37 203	98.01 91	88.58 215	95.98 83	97.55 107	92.73 25	99.58 70

agg_prior					98.67 62	93.79 55		98.00 93		95.68 93			99.57 78

test_prior4						93.66 59	96.42 195

test_prior				97.23 62	98.67 62	92.99 76		98.00 93					99.41 106			99.29 62

旧先验2							95.94 232		81.66 315	97.34 34			98.82 159	92.26 125

新几何2							95.79 239

无先验							95.79 239	97.87 108	83.87 300				99.65 53	87.68 218		98.89 100

原ACMM2							95.67 242

testdata2													99.67 49	85.96 252

segment_acmp												92.89 22

testdata1							95.26 263		93.10 82

plane_prior7					96.21 188	89.98 169

plane_prior6					96.10 198	90.00 165						81.32 207

plane_prior4											96.64 146

plane_prior3						90.00 165			94.46 39	91.34 178

plane_prior2							97.74 71		94.85 24

plane_prior1					96.14 196

n2								0.00 362

nn								0.00 362

door-mid								91.06 341

test11								97.88 106

door								91.13 340

HQP5-MVS						89.33 193

HQP-NCC					95.86 203		96.65 179		93.55 62	90.14 201

ACMP_Plane					95.86 203		96.65 179		93.55 62	90.14 201

BP-MVS														92.13 131

HQP4-MVS										90.14 201			98.50 186			95.78 227

HQP2-MVS												80.95 210

NP-MVS					95.99 202	89.81 174					95.87 184

MDTV_nov1_ep13_2view						70.35 345	93.10 314		83.88 299	93.55 133		82.47 188		86.25 243		98.38 140

Test By Simon												88.73 93

High-resolution multi-view benchmark