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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

TSAR-MVS + MP.		98.49 8	98.78 7	98.15 20	98.14 51	99.17 28	99.34 5	97.18 30	98.44 4	95.72 20	97.84 16	99.28 11	98.87 7	99.05 1	98.05 25	99.66 1	99.60 6
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SED-MVS		98.90 1	99.07 1	98.69 3	99.38 19	99.61 2	99.33 7	97.80 3	98.25 7	97.60 2	98.87 3	99.89 2	98.67 18	99.02 2	98.26 17	99.36 54	99.61 5

DVP-MVS		98.86 3	98.97 2	98.75 2	99.43 13	99.63 1	99.25 12	97.81 1	98.62 1	97.69 1	97.59 20	99.90 1	98.93 5	98.99 3	98.42 11	99.37 52	99.62 3

SteuartSystems-ACMMP		98.38 14	98.71 9	97.99 24	99.34 21	99.46 7	99.34 5	97.33 25	97.31 35	94.25 30	98.06 13	99.17 18	98.13 28	98.98 4	98.46 9	99.55 11	99.54 9
Skip Steuart: Steuart Systems R&D Blog.
APDe-MVS		98.87 2	98.96 3	98.77 1	99.58 2	99.53 5	99.44 1	97.81 1	98.22 9	97.33 4	98.70 4	99.33 9	98.86 8	98.96 5	98.40 13	99.63 3	99.57 8

DeepPCF-MVS	95.28 2	97.00 40	98.35 20	95.42 58	97.30 62	98.94 48	94.82 112	96.03 39	98.24 8	92.11 48	95.80 39	98.64 32	95.51 83	98.95 6	98.66 5	96.78 184	99.20 38

SMA-MVS		98.66 6	98.89 6	98.39 9	99.60 1	99.41 8	99.00 20	97.63 12	97.78 17	95.83 19	98.33 10	99.83 3	98.85 10	98.93 7	98.56 6	99.41 43	99.40 14
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
CNVR-MVS		98.47 10	98.46 15	98.48 7	99.40 15	99.05 32	99.02 19	97.54 17	97.73 18	96.65 12	97.20 29	99.13 19	98.85 10	98.91 8	98.10 22	99.41 43	99.08 54

DPE-MVS		98.75 4	98.91 5	98.57 4	99.21 24	99.54 4	99.42 2	97.78 5	97.49 31	96.84 9	98.94 1	99.82 4	98.59 21	98.90 9	98.22 18	99.56 10	99.48 11

DeepC-MVS_fast	96.13 1	98.13 20	98.27 25	97.97 25	99.16 27	99.03 39	99.05 18	97.24 27	98.22 9	94.17 32	95.82 38	98.07 39	98.69 17	98.83 10	98.80 2	99.52 14	99.10 51
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP_NAP		98.20 18	98.49 12	97.85 26	99.50 4	99.40 9	99.26 11	97.64 11	97.47 33	92.62 46	97.59 20	99.09 21	98.71 16	98.82 11	97.86 33	99.40 46	99.19 39

zzz-MVS		98.43 11	98.31 23	98.57 4	99.48 5	99.40 9	99.32 8	97.62 13	97.70 22	96.67 11	96.59 32	99.09 21	98.86 8	98.65 12	97.56 43	99.45 30	99.17 45

MSP-MVS		98.73 5	98.93 4	98.50 6	99.44 12	99.57 3	99.36 3	97.65 8	98.14 11	96.51 15	98.49 6	99.65 7	98.67 18	98.60 13	98.42 11	99.40 46	99.63 1

TSAR-MVS + ACMM		97.71 28	98.60 11	96.66 41	98.64 41	99.05 32	98.85 25	97.23 28	98.45 3	89.40 84	97.51 24	99.27 13	96.88 59	98.53 14	97.81 35	98.96 114	99.59 7

DELS-MVS		96.06 53	96.04 59	96.07 50	97.77 56	99.25 23	98.10 42	93.26 56	94.42 103	92.79 43	88.52 107	93.48 70	95.06 88	98.51 15	98.83 1	99.45 30	99.28 24
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
NCCC		98.10 21	98.05 30	98.17 19	99.38 19	99.05 32	99.00 20	97.53 18	98.04 13	95.12 25	94.80 50	99.18 17	98.58 22	98.49 16	97.78 36	99.39 48	98.98 71

APD-MVS		98.36 15	98.32 22	98.41 8	99.47 6	99.26 21	99.12 15	97.77 6	96.73 49	96.12 17	97.27 28	98.88 24	98.46 25	98.47 17	98.39 14	99.52 14	99.22 35
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HPM-MVS++		98.34 16	98.47 14	98.18 17	99.46 8	99.15 29	99.10 16	97.69 7	97.67 25	94.93 27	97.62 19	99.70 6	98.60 20	98.45 18	97.46 46	99.31 61	99.26 29

IS_MVSNet		95.28 62	96.43 54	93.94 85	95.30 89	99.01 43	95.90 92	91.12 87	94.13 108	87.50 100	91.23 81	94.45 66	94.17 102	98.45 18	98.50 7	99.65 2	99.23 33

MP-MVS		98.09 22	98.30 24	97.84 27	99.34 21	99.19 27	99.23 13	97.40 20	97.09 42	93.03 40	97.58 22	98.85 25	98.57 23	98.44 20	97.69 37	99.48 22	99.23 33
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ETV-MVS		96.31 49	97.47 36	94.96 67	94.79 103	98.78 61	96.08 87	91.41 84	96.16 61	90.50 64	95.76 40	96.20 57	97.39 43	98.42 21	97.82 34	99.57 8	99.18 43

X-MVS		97.84 24	98.19 27	97.42 31	99.40 15	99.35 12	99.06 17	97.25 26	97.38 34	90.85 57	96.06 36	98.72 29	98.53 24	98.41 22	98.15 21	99.46 26	99.28 24

MCST-MVS		98.20 18	98.36 18	98.01 23	99.40 15	99.05 32	99.00 20	97.62 13	97.59 29	93.70 34	97.42 27	99.30 10	98.77 14	98.39 23	97.48 45	99.59 4	99.31 23

DeepC-MVS	94.87 4	96.76 47	96.50 52	97.05 36	98.21 49	99.28 19	98.67 27	97.38 21	97.31 35	90.36 69	89.19 100	93.58 69	98.19 27	98.31 24	98.50 7	99.51 19	99.36 17
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CP-MVS		98.32 17	98.34 21	98.29 13	99.34 21	99.30 17	99.15 14	97.35 22	97.49 31	95.58 22	97.72 18	98.62 33	98.82 12	98.29 25	97.67 38	99.51 19	99.28 24

SD-MVS		98.52 7	98.77 8	98.23 16	98.15 50	99.26 21	98.79 26	97.59 16	98.52 2	96.25 16	97.99 15	99.75 5	99.01 3	98.27 26	97.97 27	99.59 4	99.63 1
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
HFP-MVS		98.48 9	98.62 10	98.32 12	99.39 18	99.33 16	99.27 10	97.42 19	98.27 6	95.25 24	98.34 9	98.83 26	99.08 1	98.26 27	98.08 24	99.48 22	99.26 29

ACMMPR		98.40 12	98.49 12	98.28 14	99.41 14	99.40 9	99.36 3	97.35 22	98.30 5	95.02 26	97.79 17	98.39 37	99.04 2	98.26 27	98.10 22	99.50 21	99.22 35

CANet		96.84 44	97.20 38	96.42 42	97.92 54	99.24 25	98.60 29	93.51 53	97.11 41	93.07 37	91.16 82	97.24 45	96.21 71	98.24 29	98.05 25	99.22 78	99.35 18

PGM-MVS		97.81 25	98.11 28	97.46 30	99.55 3	99.34 15	99.32 8	94.51 46	96.21 60	93.07 37	98.05 14	97.95 42	98.82 12	98.22 30	97.89 32	99.48 22	99.09 53

Vis-MVSNet (Re-imp)		94.46 79	96.24 56	92.40 104	95.23 92	98.64 72	95.56 100	90.99 88	94.42 103	85.02 110	90.88 88	94.65 65	88.01 171	98.17 31	98.37 16	99.57 8	98.53 101

xxxxxxxxxxxxxcwj		97.07 38	95.99 60	98.33 10	99.45 9	99.05 32	98.27 37	97.65 8	97.73 18	97.02 7	98.18 11	81.99 142	98.11 29	98.15 32	97.62 39	99.45 30	99.19 39

SF-MVS		98.39 13	98.45 16	98.33 10	99.45 9	99.05 32	98.27 37	97.65 8	97.73 18	97.02 7	98.18 11	99.25 14	98.11 29	98.15 32	97.62 39	99.45 30	99.19 39

MVS_0304		96.31 49	96.91 47	95.62 54	97.21 64	99.20 26	98.55 31	93.10 61	97.04 44	89.73 78	90.30 92	96.35 53	95.71 77	98.14 34	97.93 31	99.38 49	99.40 14

UA-Net		93.96 88	95.95 61	91.64 111	96.06 76	98.59 76	95.29 102	90.00 99	91.06 150	82.87 117	90.64 89	98.06 40	86.06 181	98.14 34	98.20 19	99.58 6	96.96 156

PVSNet_Blended_VisFu		94.77 72	95.54 66	93.87 87	96.48 71	98.97 46	94.33 121	91.84 76	94.93 95	90.37 68	85.04 130	94.99 63	90.87 149	98.12 36	97.30 54	99.30 63	99.45 13

MVS_111021_HR		97.04 39	98.20 26	95.69 53	98.44 46	99.29 18	96.59 74	93.20 59	97.70 22	89.94 76	98.46 7	96.89 47	96.71 63	98.11 37	97.95 28	99.27 67	99.01 67

PVSNet_BlendedMVS		95.41 60	95.28 70	95.57 55	97.42 60	99.02 41	95.89 94	93.10 61	96.16 61	93.12 35	91.99 71	85.27 120	94.66 93	98.09 38	97.34 52	99.24 72	99.08 54

PVSNet_Blended		95.41 60	95.28 70	95.57 55	97.42 60	99.02 41	95.89 94	93.10 61	96.16 61	93.12 35	91.99 71	85.27 120	94.66 93	98.09 38	97.34 52	99.24 72	99.08 54

MVS_111021_LR		97.16 36	98.01 31	96.16 47	98.47 44	98.98 44	96.94 61	93.89 49	97.64 27	91.44 51	98.89 2	96.41 52	97.20 49	98.02 40	97.29 56	99.04 109	98.85 86

CS-MVS		96.23 52	97.15 41	95.16 61	95.01 99	98.98 44	97.13 57	90.68 92	96.00 68	91.21 54	94.03 54	96.48 51	97.35 45	98.00 41	97.43 47	99.55 11	99.15 47

train_agg		97.65 29	98.06 29	97.18 34	98.94 33	98.91 53	98.98 24	97.07 32	96.71 50	90.66 62	97.43 26	99.08 23	98.20 26	97.96 42	97.14 57	99.22 78	99.19 39

3Dnovator+	93.91 7	97.23 35	97.22 37	97.24 33	98.89 36	98.85 58	98.26 39	93.25 58	97.99 14	95.56 23	90.01 96	98.03 41	98.05 32	97.91 43	98.43 10	99.44 38	99.35 18

3Dnovator	93.79 8	97.08 37	97.20 38	96.95 38	99.09 29	99.03 39	98.20 40	93.33 54	97.99 14	93.82 33	90.61 90	96.80 49	97.82 35	97.90 44	98.78 3	99.47 25	99.26 29

PHI-MVS		97.78 26	98.44 17	97.02 37	98.73 38	99.25 23	98.11 41	95.54 40	96.66 52	92.79 43	98.52 5	99.38 8	97.50 42	97.84 45	98.39 14	99.45 30	99.03 64

gg-mvs-nofinetune		86.17 185	88.57 159	83.36 192	93.44 129	98.15 88	96.58 75	72.05 206	74.12 209	49.23 213	64.81 204	90.85 83	89.90 164	97.83 46	96.84 65	98.97 113	97.41 143

CDPH-MVS		96.84 44	97.49 34	96.09 48	98.92 34	98.85 58	98.61 28	95.09 42	96.00 68	87.29 101	95.45 44	97.42 43	97.16 50	97.83 46	97.94 29	99.44 38	98.92 77

EIA-MVS		95.50 55	96.19 57	94.69 75	94.83 102	98.88 57	95.93 91	91.50 83	94.47 102	89.43 82	93.14 60	92.72 74	97.05 55	97.82 48	97.13 58	99.43 41	99.15 47

QAPM		96.78 46	97.14 42	96.36 44	99.05 30	99.14 30	98.02 43	93.26 56	97.27 37	90.84 60	91.16 82	97.31 44	97.64 40	97.70 49	98.20 19	99.33 56	99.18 43

CHOSEN 280x420		95.46 58	97.01 43	93.66 91	97.28 63	97.98 92	96.40 80	85.39 153	96.10 65	91.07 55	96.53 33	96.34 55	95.61 80	97.65 50	96.95 62	96.21 185	97.49 140

TSAR-MVS + GP.		97.45 31	98.36 18	96.39 43	95.56 83	98.93 50	97.74 49	93.31 55	97.61 28	94.24 31	98.44 8	99.19 16	98.03 33	97.60 51	97.41 49	99.44 38	99.33 20

MVSTER		94.89 66	95.07 76	94.68 76	94.71 107	96.68 121	97.00 59	90.57 94	95.18 92	93.05 39	95.21 45	86.41 112	93.72 111	97.59 52	95.88 91	99.00 110	98.50 103

Vis-MVSNet		92.77 106	95.00 78	90.16 130	94.10 120	98.79 60	94.76 114	88.26 122	92.37 136	79.95 131	88.19 109	91.58 78	84.38 191	97.59 52	97.58 42	99.52 14	98.91 80
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
LS3D		95.46 58	95.14 73	95.84 51	97.91 55	98.90 55	98.58 30	97.79 4	97.07 43	83.65 115	88.71 103	88.64 101	97.82 35	97.49 54	97.42 48	99.26 71	97.72 135

MSLP-MVS++		98.04 23	97.93 32	98.18 17	99.10 28	99.09 31	98.34 36	96.99 33	97.54 30	96.60 13	94.82 49	98.45 35	98.89 6	97.46 55	98.77 4	99.17 86	99.37 16

casdiffmvs		94.38 83	94.15 93	94.64 77	94.70 109	98.51 77	96.03 90	91.66 79	95.70 78	89.36 85	86.48 119	85.03 125	96.60 66	97.40 56	97.30 54	99.52 14	98.67 93

CANet_DTU		93.92 89	96.57 51	90.83 121	95.63 81	98.39 79	96.99 60	87.38 131	96.26 57	71.97 174	96.31 34	93.02 71	94.53 96	97.38 57	96.83 66	98.49 154	97.79 128

EPP-MVSNet		95.27 63	96.18 58	94.20 83	94.88 101	98.64 72	94.97 108	90.70 91	95.34 85	89.67 80	91.66 78	93.84 67	95.42 85	97.32 58	97.00 60	99.58 6	99.47 12

ACMMP		97.37 33	97.48 35	97.25 32	98.88 37	99.28 19	98.47 34	96.86 35	97.04 44	92.15 47	97.57 23	96.05 60	97.67 38	97.27 59	95.99 87	99.46 26	99.14 50
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
OpenMVS	92.33 11	95.50 55	95.22 72	95.82 52	98.98 31	98.97 46	97.67 51	93.04 64	94.64 99	89.18 88	84.44 135	94.79 64	96.79 60	97.23 60	97.61 41	99.24 72	98.88 82

gm-plane-assit		83.26 194	85.29 191	80.89 195	89.52 172	89.89 205	70.26 210	78.24 187	77.11 207	58.01 210	74.16 176	66.90 200	90.63 155	97.20 61	96.05 84	98.66 144	95.68 172

CPTT-MVS		97.78 26	97.54 33	98.05 22	98.91 35	99.05 32	99.00 20	96.96 34	97.14 40	95.92 18	95.50 42	98.78 28	98.99 4	97.20 61	96.07 82	98.54 151	99.04 63

AdaColmap		97.53 30	96.93 45	98.24 15	99.21 24	98.77 62	98.47 34	97.34 24	96.68 51	96.52 14	95.11 47	96.12 58	98.72 15	97.19 63	96.24 78	99.17 86	98.39 111

OMC-MVS		97.00 40	96.92 46	97.09 35	98.69 39	98.66 69	97.85 47	95.02 43	98.09 12	94.47 28	93.15 59	96.90 46	97.38 44	97.16 64	96.82 67	99.13 93	97.65 136

PLC	94.95 3	97.37 33	96.77 49	98.07 21	98.97 32	98.21 84	97.94 46	96.85 36	97.66 26	97.58 3	93.33 58	96.84 48	98.01 34	97.13 65	96.20 80	99.09 98	98.01 123
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DPM-MVS		96.86 43	96.82 48	96.91 39	98.08 52	98.20 85	98.52 33	97.20 29	97.24 38	91.42 52	91.84 75	98.45 35	97.25 47	97.07 66	97.40 50	98.95 115	97.55 139

baseline1		94.59 76	94.47 83	94.72 74	95.16 94	97.97 93	96.07 88	91.94 74	94.86 96	89.98 74	91.60 79	85.87 117	95.64 79	97.07 66	96.90 63	99.52 14	97.06 155

EPNet		96.27 51	96.97 44	95.46 57	98.47 44	98.28 81	97.41 54	93.67 51	95.86 74	92.86 42	97.51 24	93.79 68	91.76 134	97.03 68	97.03 59	98.61 147	99.28 24
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tttt0517		94.52 78	95.44 69	93.44 95	94.51 112	98.68 68	94.61 117	90.72 89	95.61 82	86.84 105	93.78 56	89.26 95	94.74 90	97.02 69	94.86 117	99.20 84	98.87 84

thisisatest0530		94.54 77	95.47 67	93.46 94	94.51 112	98.65 71	94.66 115	90.72 89	95.69 80	86.90 104	93.80 55	89.44 92	94.74 90	96.98 70	94.86 117	99.19 85	98.85 86

tfpn200view9		93.64 94	92.57 113	94.89 68	95.33 87	98.94 48	96.82 65	92.31 67	92.63 127	88.29 92	87.21 111	78.01 156	97.12 53	96.82 71	95.85 92	99.45 30	98.56 98

thres600view7		93.49 99	92.37 124	94.79 73	95.42 84	98.93 50	96.58 75	92.31 67	93.04 121	87.88 97	86.62 117	76.94 161	97.09 54	96.82 71	95.63 96	99.45 30	98.63 95

thres200		93.62 95	92.54 114	94.88 69	95.36 86	98.93 50	96.75 69	92.31 67	92.84 124	88.28 94	86.99 113	77.81 158	97.13 51	96.82 71	95.92 88	99.45 30	98.49 104

MAR-MVS		95.50 55	95.60 64	95.39 59	98.67 40	98.18 87	95.89 94	89.81 104	94.55 101	91.97 49	92.99 61	90.21 88	97.30 46	96.79 74	97.49 44	98.72 137	98.99 69
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
FMVSNet2		93.30 102	93.36 108	93.22 100	91.34 150	95.86 145	96.22 82	88.24 123	95.15 93	89.92 77	81.64 147	89.36 93	94.40 99	96.77 75	96.98 61	99.21 81	97.79 128

thres400		93.56 97	92.43 121	94.87 70	95.40 85	98.91 53	96.70 71	92.38 66	92.93 123	88.19 96	86.69 116	77.35 159	97.13 51	96.75 76	95.85 92	99.42 42	98.56 98

CNLPA		96.90 42	96.28 55	97.64 29	98.56 43	98.63 74	96.85 64	96.60 37	97.73 18	97.08 6	89.78 98	96.28 56	97.80 37	96.73 77	96.63 69	98.94 116	98.14 122

CHOSEN 1792x2688		92.66 108	92.49 117	92.85 102	97.13 65	98.89 56	95.90 92	88.50 120	95.32 86	83.31 116	71.99 189	88.96 99	94.10 104	96.69 78	96.49 71	98.15 164	99.10 51

UGNet		94.92 65	96.63 50	92.93 101	96.03 77	98.63 74	94.53 118	91.52 82	96.23 59	90.03 73	92.87 64	96.10 59	86.28 180	96.68 79	96.60 70	99.16 89	99.32 22
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
Effi-MVS+		92.93 105	93.86 97	91.86 107	94.07 121	98.09 90	95.59 99	85.98 146	94.27 106	79.54 135	91.12 85	81.81 143	96.71 63	96.67 80	96.06 83	99.27 67	98.98 71

Effi-MVS+-dtu		91.78 115	93.59 104	89.68 138	92.44 142	97.11 108	94.40 120	84.94 160	92.43 132	75.48 156	91.09 86	83.75 132	93.55 115	96.61 81	95.47 101	97.24 180	98.67 93

TAPA-MVS	94.18 5	96.38 48	96.49 53	96.25 45	98.26 48	98.66 69	98.00 44	94.96 44	97.17 39	89.48 81	92.91 63	96.35 53	97.53 41	96.59 82	95.90 90	99.28 65	97.82 127
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchMatch-RL		94.69 74	94.41 84	95.02 64	97.63 59	98.15 88	94.50 119	91.99 73	95.32 86	91.31 53	95.47 43	83.44 134	96.02 74	96.56 83	95.23 108	98.69 140	96.67 163

Fast-Effi-MVS+		91.87 113	92.08 128	91.62 113	92.91 136	97.21 107	94.93 109	84.60 164	93.61 116	81.49 126	83.50 140	78.95 151	96.62 65	96.55 84	96.22 79	99.16 89	98.51 102

FC-MVSNet-train		93.85 90	93.91 95	93.78 89	94.94 100	96.79 118	94.29 122	91.13 86	93.84 113	88.26 95	90.40 91	85.23 122	94.65 95	96.54 85	95.31 105	99.38 49	99.28 24

GBi-Net		93.81 91	94.18 89	93.38 96	91.34 150	95.86 145	96.22 82	88.68 116	95.23 89	90.40 65	86.39 120	91.16 79	94.40 99	96.52 86	96.30 74	99.21 81	97.79 128

test1		93.81 91	94.18 89	93.38 96	91.34 150	95.86 145	96.22 82	88.68 116	95.23 89	90.40 65	86.39 120	91.16 79	94.40 99	96.52 86	96.30 74	99.21 81	97.79 128

FMVSNet1		91.54 120	90.93 141	92.26 105	90.35 160	95.27 168	95.22 105	87.16 134	91.37 147	87.62 99	75.45 165	83.84 131	94.43 97	96.52 86	96.30 74	98.82 126	97.74 134

MVS_Test		94.82 68	95.66 63	93.84 88	94.79 103	98.35 80	96.49 78	89.10 114	96.12 64	87.09 103	92.58 66	90.61 85	96.48 67	96.51 89	96.89 64	99.11 96	98.54 100

GG-mvs-BLEND		66.17 204	94.91 79	32.63 210	1.32 218	96.64 122	91.40 169	0.85 216	94.39 105	2.20 219	90.15 95	95.70 61	2.27 215	96.39 90	95.44 102	97.78 173	95.68 172

thres100view900		93.55 98	92.47 120	94.81 72	95.33 87	98.74 63	96.78 68	92.30 70	92.63 127	88.29 92	87.21 111	78.01 156	96.78 61	96.38 91	95.92 88	99.38 49	98.40 110

baseline2		93.01 104	94.17 91	91.64 111	92.83 138	97.49 99	93.40 133	87.53 129	93.67 115	86.07 106	91.83 76	86.58 109	91.36 138	96.38 91	95.06 111	98.67 141	98.20 120

test-mter		90.95 126	93.54 107	87.93 163	90.28 161	96.80 115	91.44 168	82.68 176	92.15 141	74.37 167	89.57 99	88.23 106	90.88 148	96.37 93	94.31 136	97.93 171	97.37 144

LGP-MVS_train		94.12 85	94.62 80	93.53 92	96.44 72	97.54 97	97.40 55	91.84 76	94.66 98	81.09 128	95.70 41	83.36 135	95.10 87	96.36 94	95.71 95	99.32 58	99.03 64

DI_MVS_plusplus_trai		94.01 87	93.63 102	94.44 79	94.54 111	98.26 83	97.51 53	90.63 93	95.88 73	89.34 86	80.54 152	89.36 93	95.48 84	96.33 95	96.27 77	99.17 86	98.78 91

TSAR-MVS + COLMAP		94.79 70	94.51 82	95.11 62	96.50 70	97.54 97	97.99 45	94.54 45	97.81 16	85.88 107	96.73 31	81.28 146	96.99 56	96.29 96	95.21 109	98.76 136	96.73 162

OPM-MVS		93.61 96	92.43 121	95.00 65	96.94 67	97.34 103	97.78 48	94.23 47	89.64 162	85.53 108	88.70 104	82.81 138	96.28 70	96.28 97	95.00 115	99.24 72	97.22 148

anonymousdsp		88.90 156	91.00 140	86.44 182	88.74 191	95.97 140	90.40 181	82.86 174	88.77 169	67.33 194	81.18 149	81.44 145	90.22 160	96.23 98	94.27 137	99.12 95	99.16 46

ACMM	92.75 10	94.41 82	93.84 98	95.09 63	96.41 73	96.80 115	94.88 111	93.54 52	96.41 55	90.16 70	92.31 69	83.11 136	96.32 69	96.22 99	94.65 122	99.22 78	97.35 145
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CSCG		97.44 32	97.18 40	97.75 28	99.47 6	99.52 6	98.55 31	95.41 41	97.69 24	95.72 20	94.29 53	95.53 62	98.10 31	96.20 100	97.38 51	99.24 72	99.62 3

test-LLR		91.62 118	93.56 105	89.35 142	93.31 132	96.57 124	92.02 162	87.06 135	92.34 137	75.05 163	90.20 93	88.64 101	90.93 145	96.19 101	94.07 140	97.75 175	96.90 159

TESTMET0.1,1		91.07 125	93.56 105	88.17 153	90.43 157	96.57 124	92.02 162	82.83 175	92.34 137	75.05 163	90.20 93	88.64 101	90.93 145	96.19 101	94.07 140	97.75 175	96.90 159

MSDG		94.82 68	93.73 100	96.09 48	98.34 47	97.43 102	97.06 58	96.05 38	95.84 75	90.56 63	86.30 124	89.10 98	95.55 82	96.13 103	95.61 97	99.00 110	95.73 171

diffmvs		94.31 84	94.21 88	94.42 80	94.64 110	98.28 81	96.36 81	91.56 80	96.77 48	88.89 91	88.97 101	84.23 128	96.01 75	96.05 104	96.41 73	99.05 108	98.79 90

FMVSNet3		93.79 93	94.17 91	93.35 98	91.21 153	95.99 138	96.62 72	88.68 116	95.23 89	90.40 65	86.39 120	91.16 79	94.11 103	95.96 105	96.67 68	99.07 101	97.79 128

EPNet_dtu		92.45 110	95.02 77	89.46 139	98.02 53	95.47 160	94.79 113	92.62 65	94.97 94	70.11 185	94.76 51	92.61 75	84.07 194	95.94 106	95.56 98	97.15 181	95.82 170
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CDS-MVSNet		92.77 106	93.60 103	91.80 109	92.63 140	96.80 115	95.24 104	89.14 113	90.30 159	84.58 111	86.76 114	90.65 84	90.42 157	95.89 107	96.49 71	98.79 133	98.32 116
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HyFIR lowres test		92.03 111	91.55 135	92.58 103	97.13 65	98.72 66	94.65 116	86.54 139	93.58 117	82.56 119	67.75 200	90.47 86	95.67 78	95.87 108	95.54 99	98.91 119	98.93 76

GA-MVS		89.28 149	90.75 144	87.57 170	91.77 146	96.48 126	92.29 154	87.58 128	90.61 156	65.77 196	84.48 134	76.84 162	89.46 165	95.84 109	93.68 150	98.52 152	97.34 146

MIMVSNet		88.99 155	91.07 139	86.57 181	86.78 200	95.62 153	91.20 174	75.40 200	90.65 155	76.57 148	84.05 137	82.44 141	91.01 144	95.84 109	95.38 103	98.48 155	93.50 192

pm-mvs1		89.19 152	89.02 155	89.38 141	90.40 158	95.74 152	92.05 160	88.10 125	86.13 188	77.70 140	73.72 179	79.44 150	88.97 168	95.81 111	94.51 132	99.08 99	97.78 133

canonicalmvs		95.25 64	95.45 68	95.00 65	95.27 91	98.72 66	96.89 62	89.82 103	96.51 53	90.84 60	93.72 57	86.01 115	97.66 39	95.78 112	97.94 29	99.54 13	99.50 10

baseline		94.83 67	95.82 62	93.68 90	94.75 106	97.80 94	96.51 77	88.53 119	97.02 46	89.34 86	92.93 62	92.18 76	94.69 92	95.78 112	96.08 81	98.27 162	98.97 75

Fast-Effi-MVS+-dtu		91.19 124	93.64 101	88.33 151	92.19 144	96.46 127	93.99 125	81.52 181	92.59 129	71.82 175	92.17 70	85.54 118	91.68 135	95.73 114	94.64 123	98.80 131	98.34 113

ACMP	92.88 9	94.43 80	94.38 85	94.50 78	96.01 78	97.69 95	95.85 97	92.09 72	95.74 77	89.12 89	95.14 46	82.62 140	94.77 89	95.73 114	94.67 121	99.14 92	99.06 58
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
FC-MVSNet-test		91.63 117	93.82 99	89.08 143	92.02 145	96.40 130	93.26 136	87.26 132	93.72 114	77.26 143	88.61 106	89.86 90	85.50 184	95.72 116	95.02 113	99.16 89	97.44 142

tfpnnormal		88.50 159	87.01 180	90.23 128	91.36 149	95.78 151	92.74 143	90.09 98	83.65 197	76.33 151	71.46 192	69.58 193	91.84 132	95.54 117	94.02 142	99.06 104	99.03 64

DCV-MVSNet		94.76 73	95.12 75	94.35 81	95.10 97	95.81 149	96.46 79	89.49 109	96.33 56	90.16 70	92.55 67	90.26 87	95.83 76	95.52 118	96.03 85	99.06 104	99.33 20

CLD-MVS		94.79 70	94.36 86	95.30 60	95.21 93	97.46 100	97.23 56	92.24 71	96.43 54	91.77 50	92.69 65	84.31 127	96.06 72	95.52 118	95.03 112	99.31 61	99.06 58
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP-MVS		94.43 80	94.57 81	94.27 82	96.41 73	97.23 106	96.89 62	93.98 48	95.94 71	83.68 114	95.01 48	84.46 126	95.58 81	95.47 120	94.85 120	99.07 101	99.00 68

PMMVS		94.61 75	95.56 65	93.50 93	94.30 116	96.74 119	94.91 110	89.56 108	95.58 83	87.72 98	96.15 35	92.86 72	96.06 72	95.47 120	95.02 113	98.43 159	97.09 151

thisisatest0515		90.12 140	92.06 129	87.85 164	90.03 164	96.17 135	87.83 190	87.45 130	91.71 144	77.15 144	85.40 128	84.01 130	85.74 183	95.41 122	93.30 157	98.88 121	98.43 106

IterMVS-SCA-FT		90.24 136	92.48 119	87.63 168	92.85 137	94.30 191	93.79 127	81.47 182	92.66 126	69.95 186	84.66 133	88.38 104	89.99 162	95.39 123	94.34 135	97.74 177	97.63 137

IterMVS-LS		92.56 109	93.18 109	91.84 108	93.90 122	94.97 175	94.99 107	86.20 143	94.18 107	82.68 118	85.81 126	87.36 108	94.43 97	95.31 124	96.02 86	98.87 122	98.60 97
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS		90.20 137	92.43 121	87.61 169	92.82 139	94.31 190	94.11 123	81.54 180	92.97 122	69.90 187	84.71 132	88.16 107	89.96 163	95.25 125	94.17 138	97.31 179	97.46 141
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CR-MVSNet		90.16 139	91.96 131	88.06 157	93.32 131	95.95 142	93.36 134	75.99 198	92.40 134	75.19 160	83.18 141	85.37 119	92.05 129	95.21 126	94.56 128	98.47 156	97.08 153

PatchT		89.13 153	91.71 132	86.11 185	92.92 135	95.59 156	83.64 200	75.09 201	91.87 143	75.19 160	82.63 144	85.06 124	92.05 129	95.21 126	94.56 128	97.76 174	97.08 153

Anonymous202405211			92.18 126		95.04 98	98.20 85	96.14 85	91.79 78	93.93 109		74.60 171	88.38 104	96.48 67	95.17 128	95.82 94	99.00 110	99.15 47

test0.0.03 1		91.97 112	93.91 95	89.72 135	93.31 132	96.40 130	91.34 171	87.06 135	93.86 111	81.67 124	91.15 84	89.16 97	86.02 182	95.08 129	95.09 110	98.91 119	96.64 165

MS-PatchMatch		91.82 114	92.51 115	91.02 117	95.83 80	96.88 111	95.05 106	84.55 166	93.85 112	82.01 121	82.51 145	91.71 77	90.52 156	95.07 130	93.03 161	98.13 165	94.52 179

USDC		90.69 129	90.52 145	90.88 120	94.17 119	96.43 128	95.82 98	86.76 137	93.92 110	76.27 152	86.49 118	74.30 171	93.67 114	95.04 131	93.36 154	98.61 147	94.13 184

PCF-MVS	93.95 6	95.65 54	95.14 73	96.25 45	97.73 58	98.73 65	97.59 52	97.13 31	92.50 131	89.09 90	89.85 97	96.65 50	96.90 58	94.97 132	94.89 116	99.08 99	98.38 112
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Anonymous20231211		93.49 99	92.33 125	94.84 71	94.78 105	98.00 91	96.11 86	91.85 75	94.86 96	90.91 56	74.69 170	89.18 96	96.73 62	94.82 133	95.51 100	98.67 141	99.24 32

FMVSNet5		90.36 134	90.93 141	89.70 136	87.99 194	92.25 199	92.03 161	83.51 170	92.20 140	84.13 112	85.59 127	86.48 110	92.43 126	94.61 134	94.52 131	98.13 165	90.85 199

ACMH	90.77 13	91.51 121	91.63 134	91.38 114	95.62 82	96.87 113	91.76 166	89.66 106	91.58 145	78.67 137	86.73 115	78.12 154	93.77 110	94.59 135	94.54 130	98.78 134	98.98 71
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		90.54 133	90.87 143	90.16 130	91.48 148	96.61 123	93.26 136	86.08 144	87.71 178	81.66 125	83.11 143	84.04 129	90.42 157	94.54 136	94.60 125	98.04 169	95.48 175

TransMVSNet (Re)		87.73 172	86.79 182	88.83 145	90.76 154	94.40 188	91.33 172	89.62 107	84.73 194	75.41 158	72.73 184	71.41 184	86.80 177	94.53 137	93.93 144	99.06 104	95.83 169

pmmvs5		87.83 171	88.09 164	87.51 173	89.59 171	95.48 159	89.75 185	84.73 162	86.07 190	71.44 177	80.57 151	70.09 191	90.74 152	94.47 138	92.87 165	98.82 126	97.10 150

TinyColmap		89.42 146	88.58 158	90.40 127	93.80 126	95.45 161	93.96 126	86.54 139	92.24 139	76.49 149	80.83 150	70.44 188	93.37 117	94.45 139	93.30 157	98.26 163	93.37 194

RPMNet		90.19 138	92.03 130	88.05 158	93.46 128	95.95 142	93.41 132	74.59 203	92.40 134	75.91 154	84.22 136	86.41 112	92.49 125	94.42 140	93.85 147	98.44 157	96.96 156

testgi		89.42 146	91.50 136	87.00 178	92.40 143	95.59 156	89.15 187	85.27 157	92.78 125	72.42 172	91.75 77	76.00 164	84.09 193	94.38 141	93.82 149	98.65 145	96.15 166

LTVRE_ROB	87.32 16	87.55 173	88.25 162	86.73 179	90.66 155	95.80 150	93.05 139	84.77 161	83.35 198	60.32 206	83.12 142	67.39 199	93.32 118	94.36 142	94.86 117	98.28 161	98.87 84
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
CVMVSNet		89.77 144	91.66 133	87.56 171	93.21 134	95.45 161	91.94 165	89.22 112	89.62 163	69.34 191	83.99 138	85.90 116	84.81 189	94.30 143	95.28 106	96.85 183	97.09 151

COLMAP_ROB	90.49 14	93.27 103	92.71 112	93.93 86	97.75 57	97.44 101	96.07 88	93.17 60	95.40 84	83.86 113	83.76 139	88.72 100	93.87 107	94.25 144	94.11 139	98.87 122	95.28 177
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
NR-MVSNet		89.34 148	88.66 157	90.13 133	90.40 158	95.61 154	93.04 140	89.91 100	91.22 148	78.96 136	77.72 160	68.90 196	89.16 167	94.24 145	93.95 143	99.32 58	98.99 69

EG-PatchMatch MVS		86.68 181	87.24 177	86.02 186	90.58 156	96.26 133	91.08 175	81.59 179	84.96 193	69.80 189	71.35 193	75.08 168	84.23 192	94.24 145	93.35 155	98.82 126	95.46 176

v7n		86.43 183	86.52 186	86.33 183	87.91 195	94.93 177	90.15 183	83.05 172	86.57 185	70.21 184	71.48 191	66.78 202	87.72 172	94.19 147	92.96 162	98.92 118	98.76 92

ET-MVSNet_ETH3D		93.34 101	94.33 87	92.18 106	83.26 206	97.66 96	96.72 70	89.89 102	95.62 81	87.17 102	96.00 37	83.69 133	96.99 56	93.78 148	95.34 104	99.06 104	98.18 121

UniMVSNet (Re)		90.03 142	89.61 150	90.51 126	89.97 166	96.12 136	92.32 152	89.26 111	90.99 151	80.95 129	78.25 159	75.08 168	91.14 141	93.78 148	93.87 146	99.41 43	99.21 37

v1192		87.51 174	87.31 175	87.74 166	89.04 185	94.87 180	92.07 159	85.03 158	88.49 172	70.32 182	72.65 185	70.35 189	91.21 140	93.59 150	92.80 166	98.78 134	98.42 108

v10		88.00 165	87.96 167	88.05 158	89.44 173	94.68 182	92.36 151	83.35 171	89.37 164	72.96 171	73.98 177	72.79 178	91.35 139	93.59 150	92.88 164	98.81 129	98.42 108

pmmvs6		85.98 186	84.89 194	87.25 175	88.83 189	94.35 189	89.36 186	85.30 156	78.51 206	75.44 157	62.71 205	75.41 165	87.65 173	93.58 152	92.40 174	96.89 182	97.29 147

pmmvs4		90.55 132	89.91 148	91.30 116	90.26 162	94.95 176	92.73 144	87.94 126	93.44 119	85.35 109	82.28 146	76.09 163	93.02 123	93.56 153	92.26 177	98.51 153	96.77 161

v1144		87.92 169	87.79 171	88.07 155	89.27 177	95.15 171	92.17 157	85.62 150	88.52 171	71.52 176	73.80 178	72.40 180	91.06 143	93.54 154	92.80 166	98.81 129	98.33 114

ACMH+	90.88 12	91.41 122	91.13 138	91.74 110	95.11 96	96.95 110	93.13 138	89.48 110	92.42 133	79.93 132	85.13 129	78.02 155	93.82 109	93.49 155	93.88 145	98.94 116	97.99 124

Anonymous20231206		83.84 193	85.19 192	82.26 194	87.38 198	92.87 196	85.49 197	83.65 169	86.07 190	63.44 202	68.42 197	69.01 195	75.45 202	93.34 156	92.44 173	98.12 167	94.20 183

v1921920		87.31 178	87.13 179	87.52 172	88.87 188	94.72 181	91.96 164	84.59 165	88.28 174	69.86 188	72.50 187	70.03 192	91.10 142	93.33 157	92.61 171	98.71 138	98.44 105

v1240		86.89 180	86.75 184	87.06 177	88.75 190	94.65 184	91.30 173	84.05 167	87.49 181	68.94 192	71.96 190	68.86 197	90.65 154	93.33 157	92.72 170	98.67 141	98.24 118

test_part1		91.21 123	89.47 151	93.24 99	94.26 117	95.45 161	95.26 103	88.36 121	88.49 172	90.04 72	72.61 186	82.82 137	93.69 113	93.25 159	94.62 124	97.84 172	99.06 58

WR-MVS_H		87.93 167	87.85 170	88.03 160	89.62 169	95.58 158	90.47 180	85.55 151	87.20 183	76.83 147	74.42 174	72.67 179	86.37 179	93.22 160	93.04 160	99.33 56	98.83 88

TDRefinement		89.07 154	88.15 163	90.14 132	95.16 94	96.88 111	95.55 101	90.20 97	89.68 161	76.42 150	76.67 162	74.30 171	84.85 188	93.11 161	91.91 179	98.64 146	94.47 180

MVS-HIRNet		85.36 188	86.89 181	83.57 191	90.13 163	94.51 186	83.57 201	72.61 205	88.27 175	71.22 179	68.97 196	81.81 143	88.91 169	93.08 162	91.94 178	94.97 200	89.64 202

CP-MVSNet		87.89 170	87.27 176	88.62 147	89.30 176	95.06 172	90.60 179	85.78 148	87.43 182	75.98 153	74.60 171	68.14 198	90.76 150	93.07 163	93.60 151	99.30 63	98.98 71

PS-CasMVS		87.33 177	86.68 185	88.10 154	89.22 183	94.93 177	90.35 182	85.70 149	86.44 187	74.01 168	73.43 181	66.59 204	90.04 161	92.92 164	93.52 152	99.28 65	98.91 80

UniMVSNet_NR-MVSNet		90.35 135	89.96 147	90.80 122	89.66 168	95.83 148	92.48 148	90.53 95	90.96 152	79.57 133	79.33 156	77.14 160	93.21 121	92.91 165	94.50 133	99.37 52	99.05 61

SixPastTwentyTwo		88.37 161	89.47 151	87.08 176	90.01 165	95.93 144	87.41 191	85.32 154	90.26 160	70.26 183	86.34 123	71.95 181	90.93 145	92.89 166	91.72 180	98.55 150	97.22 148

v144192		87.40 176	87.20 178	87.64 167	88.89 186	94.88 179	91.65 167	84.70 163	87.80 177	71.17 180	73.20 183	70.91 186	90.75 151	92.69 167	92.49 172	98.71 138	98.43 106

PM-MVS		84.72 191	84.47 195	85.03 188	84.67 202	91.57 201	86.27 195	82.31 178	87.65 179	70.62 181	76.54 164	56.41 212	88.75 170	92.59 168	89.85 189	97.54 178	96.66 164

DU-MVS		89.67 145	88.84 156	90.63 125	89.26 178	95.61 154	92.48 148	89.91 100	91.22 148	79.57 133	77.72 160	71.18 185	93.21 121	92.53 169	94.57 127	99.35 55	99.05 61

Baseline_NR-MVSNet		89.27 150	88.01 166	90.73 124	89.26 178	93.71 194	92.71 145	89.78 105	90.73 153	81.28 127	73.53 180	72.85 177	92.30 128	92.53 169	93.84 148	99.07 101	98.88 82

IB-MVS	89.56 15	91.71 116	92.50 116	90.79 123	95.94 79	98.44 78	87.05 193	91.38 85	93.15 120	92.98 41	84.78 131	85.14 123	78.27 199	92.47 171	94.44 134	99.10 97	99.08 54
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
WR-MVS		87.93 167	88.09 164	87.75 165	89.26 178	95.28 166	90.81 177	86.69 138	88.90 166	75.29 159	74.31 175	73.72 174	85.19 187	92.26 172	93.32 156	99.27 67	98.81 89

EU-MVSNet		85.62 187	87.65 174	83.24 193	88.54 192	92.77 198	87.12 192	85.32 154	86.71 184	64.54 198	78.52 158	75.11 167	78.35 198	92.25 173	92.28 176	95.58 192	95.93 168

pmmvs-eth3d		84.33 192	82.94 197	85.96 187	84.16 203	90.94 202	86.55 194	83.79 168	84.25 195	75.85 155	70.64 194	56.43 211	87.44 176	92.20 174	90.41 186	97.97 170	95.68 172

TranMVSNet+NR-MVSNet		89.23 151	88.48 160	90.11 134	89.07 184	95.25 169	92.91 141	90.43 96	90.31 158	77.10 145	76.62 163	71.57 183	91.83 133	92.12 175	94.59 126	99.32 58	98.92 77

v8		88.21 164	87.94 169	88.51 148	89.62 169	95.01 174	92.31 153	84.99 159	88.94 165	74.70 165	75.03 167	73.51 175	90.67 153	92.11 176	92.74 169	98.80 131	98.24 118

v2v482		88.25 163	87.71 173	88.88 144	89.23 182	95.28 166	92.10 158	87.89 127	88.69 170	73.31 170	75.32 166	71.64 182	91.89 131	92.10 177	92.92 163	98.86 124	97.99 124

V42		88.31 162	87.95 168	88.73 146	89.44 173	95.34 165	92.23 156	87.21 133	88.83 167	74.49 166	74.89 169	73.43 176	90.41 159	92.08 178	92.77 168	98.60 149	98.33 114

test20.03		82.92 195	85.52 190	79.90 198	87.75 196	91.84 200	82.80 202	82.99 173	82.65 202	60.32 206	78.90 157	70.50 187	67.10 205	92.05 179	90.89 182	98.44 157	91.80 197

MDTV_nov1_ep13		91.57 119	93.18 109	89.70 136	93.39 130	96.97 109	93.53 130	80.91 183	95.70 78	81.86 122	92.40 68	89.93 89	93.25 120	91.97 180	90.80 183	95.25 197	94.46 181

MIMVSNet1		80.03 198	80.93 199	78.97 199	72.46 212	90.73 203	80.81 205	82.44 177	80.39 203	63.64 200	57.57 206	64.93 206	76.37 200	91.66 181	91.55 181	98.07 168	89.70 201

RPSCF		94.05 86	94.00 94	94.12 84	96.20 75	96.41 129	96.61 73	91.54 81	95.83 76	89.73 78	96.94 30	92.80 73	95.35 86	91.63 182	90.44 185	95.27 196	93.94 187

UniMVSNet_ETH3D		88.47 160	86.00 189	91.35 115	91.55 147	96.29 132	92.53 147	88.81 115	85.58 192	82.33 120	67.63 201	66.87 201	94.04 105	91.49 183	95.24 107	98.84 125	98.92 77

ambc			73.83 204		76.23 210	85.13 209	82.27 203		84.16 196	65.58 197	52.82 208	23.31 219	73.55 203	91.41 184	85.26 203	92.97 206	94.70 178

PEN-MVS		87.22 179	86.50 187	88.07 155	88.88 187	94.44 187	90.99 176	86.21 141	86.53 186	73.66 169	74.97 168	66.56 205	89.42 166	91.20 185	93.48 153	99.24 72	98.31 117

SCA		90.92 127	93.04 111	88.45 149	93.72 127	97.33 104	92.77 142	76.08 197	96.02 67	78.26 139	91.96 73	90.86 82	93.99 106	90.98 186	90.04 188	95.88 188	94.06 186

v148		87.51 174	86.79 182	88.36 150	89.39 175	95.21 170	89.84 184	88.20 124	87.61 180	77.56 141	73.38 182	70.32 190	86.80 177	90.70 187	92.31 175	98.37 160	97.98 126

DTE-MVSNet		86.67 182	86.09 188	87.35 174	88.45 193	94.08 192	90.65 178	86.05 145	86.13 188	72.19 173	74.58 173	66.77 203	87.61 174	90.31 188	93.12 159	99.13 93	97.62 138

EPMVS		90.88 128	92.12 127	89.44 140	94.71 107	97.24 105	93.55 129	76.81 192	95.89 72	81.77 123	91.49 80	86.47 111	93.87 107	90.21 189	90.07 187	95.92 187	93.49 193

pmmvs3		79.16 199	80.12 201	78.05 201	79.36 207	86.59 208	78.13 208	73.87 204	76.42 208	57.51 211	70.59 195	57.02 210	84.66 190	90.10 190	88.32 193	94.75 202	91.77 198

PatchmatchNet		90.56 131	92.49 117	88.31 152	93.83 125	96.86 114	92.42 150	76.50 194	95.96 70	78.31 138	91.96 73	89.66 91	93.48 116	90.04 191	89.20 191	95.32 194	93.73 191
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13_2view		86.30 184	88.27 161	84.01 190	87.71 197	94.67 183	88.08 189	76.78 193	90.59 157	68.66 193	80.46 153	80.12 148	87.58 175	89.95 192	88.20 194	95.25 197	93.90 189

tpm		87.95 166	89.44 153	86.21 184	92.53 141	94.62 185	91.40 169	76.36 195	91.46 146	69.80 189	87.43 110	75.14 166	91.55 136	89.85 193	90.60 184	95.61 191	96.96 156

new_pmnet		81.53 196	82.68 198	80.20 196	83.47 205	89.47 206	82.21 204	78.36 186	87.86 176	60.14 208	67.90 199	69.43 194	82.03 196	89.22 194	87.47 197	94.99 199	87.39 204

ADS-MVSNet		89.80 143	91.33 137	88.00 161	94.43 114	96.71 120	92.29 154	74.95 202	96.07 66	77.39 142	88.67 105	86.09 114	93.26 119	88.44 195	89.57 190	95.68 190	93.81 190

N_pmnet		84.80 189	85.10 193	84.45 189	89.25 181	92.86 197	84.04 199	86.21 141	88.78 168	66.73 195	72.41 188	74.87 170	85.21 186	88.32 196	86.45 199	95.30 195	92.04 196

CMPMVS	65.18 17	84.76 190	83.10 196	86.69 180	95.29 90	95.05 173	88.37 188	85.51 152	80.27 204	71.31 178	68.37 198	73.85 173	85.25 185	87.72 197	87.75 195	94.38 204	88.70 203
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
dps		90.11 141	89.37 154	90.98 118	93.89 123	96.21 134	93.49 131	77.61 190	91.95 142	92.74 45	88.85 102	78.77 153	92.37 127	87.71 198	87.71 196	95.80 189	94.38 182

CostFormer		90.69 129	90.48 146	90.93 119	94.18 118	96.08 137	94.03 124	78.20 188	93.47 118	89.96 75	90.97 87	80.30 147	93.72 111	87.66 199	88.75 192	95.51 193	96.12 167

tpmrst		88.86 158	89.62 149	87.97 162	94.33 115	95.98 139	92.62 146	76.36 195	94.62 100	76.94 146	85.98 125	82.80 139	92.80 124	86.90 200	87.15 198	94.77 201	93.93 188

tpm cat1		88.90 156	87.78 172	90.22 129	93.88 124	95.39 164	93.79 127	78.11 189	92.55 130	89.43 82	81.31 148	79.84 149	91.40 137	84.95 201	86.34 201	94.68 203	94.09 185

new-patchmatchnet		78.49 200	78.19 202	78.84 200	84.13 204	90.06 204	77.11 209	80.39 184	79.57 205	59.64 209	66.01 202	55.65 213	75.62 201	84.55 202	80.70 204	96.14 186	90.77 200

Gipuma		68.35 202	66.71 205	70.27 203	74.16 211	68.78 213	63.93 213	71.77 207	83.34 199	54.57 212	34.37 210	31.88 216	68.69 204	83.30 203	85.53 202	88.48 209	79.78 208
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt				66.88 205	86.07 201	73.86 212	68.22 211	33.38 213	96.88 47	80.67 130	88.23 108	78.82 152	49.78 210	82.68 204	77.47 206	83.19 212

DeepMVS_CX						86.86 207	79.50 206	70.43 208	90.73 153	63.66 199	80.36 154	60.83 207	79.68 197	76.23 205		89.46 208	86.53 205

MDA-MVSNet-bldmvs		80.11 197	80.24 200	79.94 197	77.01 209	93.21 195	78.86 207	85.94 147	82.71 201	60.86 203	79.71 155	51.77 214	83.71 195	75.60 206	86.37 200	93.28 205	92.35 195

FPMVS		75.84 201	74.59 203	77.29 202	86.92 199	83.89 210	85.01 198	80.05 185	82.91 200	60.61 205	65.25 203	60.41 208	63.86 206	75.60 206	73.60 208	87.29 210	80.47 207

PMMVS2		64.36 205	65.94 207	62.52 206	67.37 213	77.44 211	64.39 212	69.32 211	61.47 211	34.59 214	46.09 209	41.03 215	48.02 212	74.56 208	78.23 205	91.43 207	82.76 206

PMVS	63.12 18	67.27 203	66.39 206	68.30 204	77.98 208	60.24 214	59.53 214	76.82 191	66.65 210	60.74 204	54.39 207	59.82 209	51.24 209	73.92 209	70.52 209	83.48 211	79.17 209
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	50.86 19	49.54 208	51.43 208	47.33 209	44.14 216	59.20 215	36.45 217	60.59 212	41.47 214	31.14 215	29.58 211	17.06 220	48.52 211	62.22 210	74.63 207	63.12 215	75.87 210
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		50.67 206	47.85 209	53.96 207	64.13 215	50.98 217	38.06 215	69.51 209	51.40 213	24.60 216	29.46 213	24.39 218	56.07 208	48.17 211	59.70 210	71.40 213	70.84 211

EMVS		49.98 207	46.76 210	53.74 208	64.96 214	51.29 216	37.81 216	69.35 210	51.83 212	22.69 217	29.57 212	25.06 217	57.28 207	44.81 212	56.11 211	70.32 214	68.64 212

testmvs		12.09 209	16.94 211	6.42 211	3.15 217	6.08 218	9.51 219	3.84 214	21.46 215	5.31 218	27.49 214	6.76 221	10.89 213	17.06 213	15.01 212	5.84 216	24.75 213

test123		9.58 210	13.53 212	4.97 212	1.31 219	5.47 219	8.32 220	2.95 215	18.14 216	2.03 220	20.82 215	2.34 222	10.60 214	10.00 214	14.16 213	4.60 217	23.77 214

uanet_test		0.00 211	0.00 213	0.00 213	0.00 220	0.00 220	0.00 221	0.00 217	0.00 217	0.00 221	0.00 216	0.00 223	0.00 216	0.00 215	0.00 214	0.00 218	0.00 215

sosnet-low-res		0.00 211	0.00 213	0.00 213	0.00 220	0.00 220	0.00 221	0.00 217	0.00 217	0.00 221	0.00 216	0.00 223	0.00 216	0.00 215	0.00 214	0.00 218	0.00 215

sosnet		0.00 211	0.00 213	0.00 213	0.00 220	0.00 220	0.00 221	0.00 217	0.00 217	0.00 221	0.00 216	0.00 223	0.00 216	0.00 215	0.00 214	0.00 218	0.00 215

RE-MVS-def										63.50 201

9.14												99.28 11

SR-MVS					99.45 9			97.61 15				99.20 15

our_test_3					89.78 167	93.84 193	85.59 196

MTAPA										96.83 10		99.12 20

MTMP										97.18 5		98.83 26

Patchmatch-RL test							34.61 218

XVS					96.60 68	99.35 12	96.82 65			90.85 57		98.72 29				99.46 26

X-MVStestdata					96.60 68	99.35 12	96.82 65			90.85 57		98.72 29				99.46 26

abl_6				96.82 40	98.60 42	98.74 63	97.74 49	93.73 50	96.25 58	94.37 29	94.55 52	98.60 34	97.25 47			99.27 67	98.61 96

mPP-MVS					99.21 24							98.29 38

NP-MVS									95.32 86

Patchmtry						95.96 141	93.36 134	75.99 198		75.19 160

High-resolution multi-view benchmark