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.

HFP-MVS		97.11 10	97.19 12	97.00 10	98.97 13	98.73 10	98.37 10	95.69 18	96.60 5	93.28 18	96.87 6	96.64 24	97.27 1	96.64 26	96.33 29	98.44 25	98.56 17

APDe-MVS		97.79 2	97.96 2	97.60 1	99.20 2	99.10 4	98.88 2	96.68 3	96.81 3	94.64 4	97.84 2	98.02 9	97.24 2	97.74 6	97.02 10	98.97 2	99.16 2

TSAR-MVS + MP.		97.31 6	97.64 6	96.92 11	97.28 42	98.56 21	98.61 4	95.48 24	96.72 4	94.03 11	96.73 10	98.29 7	97.15 3	97.61 10	96.42 23	98.96 3	99.13 3

ACMMPR		96.92 14	96.96 15	96.87 13	98.99 12	98.78 9	98.38 9	95.52 21	96.57 6	92.81 22	96.06 17	95.90 31	97.07 4	96.60 28	96.34 28	98.46 21	98.42 28

ESAPD		97.83 1	98.13 1	97.48 2	98.83 19	99.19 1	98.99 1	96.70 2	96.05 14	94.39 6	98.30 1	99.47 2	97.02 5	97.75 5	97.02 10	98.98 1	99.10 5

SD-MVS		97.35 5	97.73 4	96.90 12	97.35 40	98.66 12	97.85 24	96.25 8	96.86 2	94.54 5	96.75 9	99.13 4	96.99 6	96.94 21	96.58 20	98.39 34	99.20 1

CNVR-MVS		97.30 7	97.41 8	97.18 7	99.02 10	98.60 19	98.15 15	96.24 10	96.12 12	94.10 9	95.54 22	97.99 10	96.99 6	97.97 1	97.17 6	98.57 12	98.50 23

CPTT-MVS		95.54 28	95.07 33	96.10 23	97.88 32	97.98 44	97.92 23	94.86 28	94.56 36	92.16 24	91.01 39	95.71 32	96.97 8	94.56 73	93.50 82	96.81 164	98.14 39

HSP-MVS		97.51 4	97.70 5	97.29 4	99.00 11	99.17 2	98.61 4	96.41 6	95.88 16	94.34 8	97.72 3	99.04 5	96.93 9	97.29 14	95.90 37	98.45 24	98.94 8

zzz-MVS		96.98 12	96.68 20	97.33 3	99.09 4	98.71 11	98.43 7	96.01 13	96.11 13	95.19 3	92.89 30	97.32 19	96.84 10	97.20 15	96.09 34	98.44 25	98.46 27

MSLP-MVS++		96.05 24	95.63 28	96.55 19	98.33 26	98.17 37	96.94 33	94.61 30	94.70 35	94.37 7	89.20 47	95.96 30	96.81 11	95.57 47	97.33 5	98.24 60	98.47 25

APD-MVS		97.12 9	97.05 14	97.19 6	99.04 7	98.63 17	98.45 6	96.54 4	94.81 33	93.50 13	96.10 16	97.40 18	96.81 11	97.05 18	96.82 16	98.80 6	98.56 17
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		96.68 17	96.59 23	96.77 15	98.85 18	98.58 20	98.18 14	95.51 22	95.34 23	92.94 21	95.21 25	96.25 26	96.79 13	96.44 33	95.77 39	98.35 37	98.56 17

SMA-MVS		97.53 3	97.93 3	97.07 8	99.21 1	99.02 6	98.08 18	96.25 8	96.36 8	93.57 12	96.56 11	99.27 3	96.78 14	97.91 2	97.43 3	98.51 14	98.94 8

v1.0		90.03 83	83.83 153	97.27 5	99.12 3	99.14 3	98.66 3	96.80 1	95.74 17	93.46 15	97.72 3	99.48 1	96.76 15	97.77 3	96.92 14	98.83 5	0.00 242

HPM-MVS++		97.22 8	97.40 9	97.01 9	99.08 5	98.55 22	98.19 13	96.48 5	96.02 15	93.28 18	96.26 14	98.71 6	96.76 15	97.30 13	96.25 31	98.30 48	98.68 11

PGM-MVS		96.16 21	96.33 25	95.95 24	99.04 7	98.63 17	98.32 11	92.76 38	93.42 45	90.49 36	96.30 13	95.31 36	96.71 17	96.46 31	96.02 35	98.38 35	98.19 36

NCCC		96.75 16	96.67 21	96.85 14	99.03 9	98.44 30	98.15 15	96.28 7	96.32 9	92.39 23	92.16 32	97.55 16	96.68 18	97.32 11	96.65 19	98.55 13	98.26 32

ACMMP_Plus		96.93 13	97.27 11	96.53 21	99.06 6	98.95 7	98.24 12	96.06 12	95.66 19	90.96 31	95.63 21	97.71 13	96.53 19	97.66 8	96.68 17	98.30 48	98.61 16

AdaColmap		95.02 33	93.71 42	96.54 20	98.51 23	97.76 50	96.69 36	95.94 16	93.72 43	93.50 13	89.01 48	90.53 58	96.49 20	94.51 75	93.76 72	98.07 79	96.69 87

MCST-MVS		96.83 15	97.06 13	96.57 17	98.88 17	98.47 28	98.02 20	96.16 11	95.58 21	90.96 31	95.78 20	97.84 12	96.46 21	97.00 20	96.17 33	98.94 4	98.55 22

DeepC-MVS_fast	93.32 1	96.48 19	96.42 24	96.56 18	98.70 22	98.31 34	97.97 21	95.76 17	96.31 10	92.01 25	91.43 37	95.42 35	96.46 21	97.65 9	97.69 1	98.49 18	98.12 41
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SteuartSystems-ACMMP		97.10 11	97.49 7	96.65 16	98.97 13	98.95 7	98.43 7	95.96 14	95.12 26	91.46 26	96.85 7	97.60 15	96.37 23	97.76 4	97.16 7	98.68 7	98.97 7
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		96.56 18	96.72 19	96.37 22	98.93 16	98.48 26	98.04 19	95.55 20	94.32 37	90.95 33	95.88 19	97.02 21	96.29 24	96.77 24	96.01 36	98.47 19	98.56 17
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
X-MVS		96.07 23	96.33 25	95.77 27	98.94 15	98.66 12	97.94 22	95.41 26	95.12 26	88.03 49	93.00 29	96.06 27	95.85 25	96.65 25	96.35 26	98.47 19	98.48 24

DeepC-MVS	92.10 3	95.22 31	94.77 35	95.75 28	97.77 34	98.54 23	97.63 26	95.96 14	95.07 29	88.85 44	85.35 64	91.85 48	95.82 26	96.88 23	97.10 8	98.44 25	98.63 13
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
train_agg		96.15 22	96.64 22	95.58 31	98.44 24	98.03 41	98.14 17	95.40 27	93.90 42	87.72 53	96.26 14	98.10 8	95.75 27	96.25 38	95.45 44	98.01 86	98.47 25

TSAR-MVS + GP.		95.86 25	96.95 17	94.60 39	94.07 81	98.11 39	96.30 40	91.76 46	95.67 18	91.07 29	96.82 8	97.69 14	95.71 28	95.96 42	95.75 40	98.68 7	98.63 13

PLC	90.69 4	94.32 40	92.99 50	95.87 26	97.91 30	96.49 88	95.95 47	94.12 31	94.94 30	94.09 10	85.90 60	90.77 55	95.58 29	94.52 74	93.32 88	97.55 111	95.00 151
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMMP		95.54 28	95.49 31	95.61 30	98.27 27	98.53 24	97.16 31	94.86 28	94.88 32	89.34 40	95.36 24	91.74 49	95.50 30	95.51 48	94.16 61	98.50 17	98.22 34
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
3Dnovator+	90.56 5	95.06 32	94.56 37	95.65 29	98.11 28	98.15 38	97.19 30	91.59 48	95.11 28	93.23 20	81.99 94	94.71 38	95.43 31	96.48 30	96.88 15	98.35 37	98.63 13

CSCG		95.68 27	95.46 32	95.93 25	98.71 21	99.07 5	97.13 32	93.55 33	95.48 22	93.35 17	90.61 41	93.82 41	95.16 32	94.60 72	95.57 42	97.70 103	99.08 6

canonicalmvs		93.08 48	93.09 48	93.07 58	94.24 74	97.86 46	95.45 54	87.86 102	94.00 41	87.47 55	88.32 50	82.37 92	95.13 33	93.96 89	96.41 24	98.27 55	98.73 10

CNLPA		93.69 45	92.50 55	95.06 33	97.11 45	97.36 59	93.88 80	93.30 34	95.64 20	93.44 16	80.32 101	90.73 56	94.99 34	93.58 97	93.33 86	97.67 106	96.57 97

OMC-MVS		94.49 39	94.36 39	94.64 37	97.17 44	97.73 51	95.49 53	92.25 40	96.18 11	90.34 37	88.51 49	92.88 45	94.90 35	94.92 57	94.17 60	97.69 104	96.15 112

3Dnovator	90.28 7	94.70 38	94.34 40	95.11 32	98.06 29	98.21 35	96.89 34	91.03 54	94.72 34	91.45 27	82.87 85	93.10 44	94.61 36	96.24 39	97.08 9	98.63 10	98.16 37

TAPA-MVS	90.35 6	93.69 45	93.52 43	93.90 44	96.89 48	97.62 55	96.15 41	91.67 47	94.94 30	85.97 64	87.72 52	91.96 47	94.40 37	93.76 91	93.06 102	98.30 48	95.58 130
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LS3D		91.97 60	90.98 73	93.12 56	97.03 47	97.09 74	95.33 55	95.59 19	92.47 51	79.26 108	81.60 97	82.77 88	94.39 38	94.28 78	94.23 59	97.14 124	94.45 156

MAR-MVS		92.71 52	92.63 53	92.79 60	97.70 36	97.15 71	93.75 83	87.98 96	90.71 65	85.76 69	86.28 57	86.38 67	94.35 39	94.95 55	95.49 43	97.22 119	97.44 66
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
MVS_111021_LR		94.84 35	95.57 29	94.00 41	97.11 45	97.72 53	94.88 59	91.16 52	95.24 25	88.74 45	96.03 18	91.52 52	94.33 40	95.96 42	95.01 49	97.79 95	97.49 64

abl_6				94.78 34	97.46 39	97.99 43	95.76 48	91.80 45	93.72 43	91.25 28	91.33 38	96.47 25	94.28 41			98.14 68	97.39 68

QAPM		94.13 42	94.33 41	93.90 44	97.82 33	98.37 33	96.47 38	90.89 55	92.73 50	85.63 70	85.35 64	93.87 40	94.17 42	95.71 46	95.90 37	98.40 32	98.42 28

TSAR-MVS + COLMAP		92.39 55	92.31 60	92.47 61	95.35 68	96.46 89	96.13 42	92.04 43	95.33 24	80.11 103	94.95 26	77.35 118	94.05 43	94.49 76	93.08 99	97.15 122	94.53 154

TSAR-MVS + ACMM		96.19 20	97.39 10	94.78 34	97.70 36	98.41 31	97.72 25	95.49 23	96.47 7	86.66 61	96.35 12	97.85 11	93.99 44	97.19 16	96.37 25	97.12 125	99.13 3

ACMM	88.76 10	91.70 66	90.43 76	93.19 54	95.56 61	95.14 106	93.35 94	91.48 49	92.26 53	87.12 57	84.02 79	79.34 105	93.99 44	94.07 84	92.68 108	97.62 110	95.50 131
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OPM-MVS		91.08 69	89.34 86	93.11 57	96.18 55	96.13 100	96.39 39	92.39 39	82.97 149	81.74 89	82.55 91	80.20 102	93.97 46	94.62 70	93.23 90	98.00 87	95.73 125

casdiffmvs1		93.20 47	93.17 47	93.25 51	94.35 73	97.64 54	95.59 52	87.34 108	94.26 38	90.22 38	89.46 44	85.25 75	93.90 47	92.68 112	94.94 51	98.11 72	97.92 49

PHI-MVS		95.86 25	96.93 18	94.61 38	97.60 38	98.65 16	96.49 37	93.13 36	94.07 40	87.91 52	97.12 5	97.17 20	93.90 47	96.46 31	96.93 13	98.64 9	98.10 43

DeepPCF-MVS	92.65 2	95.50 30	96.96 15	93.79 47	96.44 52	98.21 35	93.51 89	94.08 32	96.94 1	89.29 41	93.08 28	96.77 23	93.82 49	97.68 7	97.40 4	95.59 188	98.65 12

MVS_111021_HR		94.84 35	95.91 27	93.60 48	97.35 40	98.46 29	95.08 56	91.19 51	94.18 39	85.97 64	95.38 23	92.56 46	93.61 50	96.61 27	96.25 31	98.40 32	97.92 49

CDPH-MVS		94.80 37	95.50 30	93.98 43	98.34 25	98.06 40	97.41 27	93.23 35	92.81 48	82.98 87	92.51 31	94.82 37	93.53 51	96.08 41	96.30 30	98.42 28	97.94 47

Effi-MVS+		89.79 86	89.83 84	89.74 95	92.98 109	96.45 90	93.48 91	84.24 137	87.62 103	76.45 119	81.76 95	77.56 116	93.48 52	94.61 71	93.59 75	97.82 94	97.22 72

PCF-MVS	90.19 8	92.98 49	92.07 63	94.04 40	96.39 53	97.87 45	96.03 44	95.47 25	87.16 106	85.09 79	84.81 73	93.21 43	93.46 53	91.98 127	91.98 123	97.78 96	97.51 63
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Fast-Effi-MVS+		88.56 109	87.99 105	89.22 102	91.56 127	95.21 104	92.29 108	82.69 155	86.82 108	77.73 113	76.24 126	73.39 130	93.36 54	94.22 81	93.64 73	97.65 107	96.43 99

CLD-MVS		92.50 54	91.96 65	93.13 55	93.93 86	96.24 97	95.69 49	88.77 86	92.92 47	89.01 43	88.19 51	81.74 97	93.13 55	93.63 95	93.08 99	98.23 61	97.91 52
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CANet		94.85 34	94.92 34	94.78 34	97.25 43	98.52 25	97.20 29	91.81 44	93.25 46	91.06 30	86.29 56	94.46 39	92.99 56	97.02 19	96.68 17	98.34 39	98.20 35

MVS_Test		91.81 64	92.19 61	91.37 80	93.24 106	96.95 76	94.43 61	86.25 113	91.45 61	83.45 84	86.31 55	85.15 76	92.93 57	93.99 85	94.71 54	97.92 91	96.77 85

HQP-MVS		92.39 55	92.49 56	92.29 64	95.65 60	95.94 102	95.64 51	92.12 42	92.46 52	79.65 106	91.97 34	82.68 89	92.92 58	93.47 102	92.77 106	97.74 99	98.12 41

casdiffmvs		92.13 57	91.95 66	92.34 63	93.87 92	97.44 57	94.36 63	86.99 111	92.00 54	88.04 48	87.23 53	81.81 96	92.73 59	93.78 90	94.06 66	98.03 83	97.30 71

OpenMVS	88.18 11	92.51 53	91.61 68	93.55 49	97.74 35	98.02 42	95.66 50	90.46 58	89.14 88	86.50 62	75.80 128	90.38 61	92.69 60	94.99 53	95.30 45	98.27 55	97.63 59

MVS_0304		94.30 41	94.68 36	93.86 46	96.33 54	98.48 26	97.41 27	91.20 50	92.75 49	86.96 59	86.03 59	93.81 42	92.64 61	96.89 22	96.54 22	98.61 11	98.24 33

PatchMatch-RL		90.30 78	88.93 92	91.89 66	95.41 67	95.68 103	90.94 125	88.67 88	89.80 83	86.95 60	85.90 60	72.51 131	92.46 62	93.56 100	92.18 116	96.93 148	92.89 176

ACMP	89.13 9	92.03 59	91.70 67	92.41 62	94.92 69	96.44 91	93.95 78	89.96 61	91.81 58	85.48 75	90.97 40	79.12 106	92.42 63	93.28 107	92.55 109	97.76 97	97.74 58
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		89.82 85	88.18 102	91.74 68	92.52 117	96.09 101	93.38 92	89.30 78	88.95 90	85.90 67	64.55 204	84.39 79	92.41 64	92.24 122	93.06 102	96.93 148	97.95 46

LGP-MVS_train		91.83 63	92.04 64	91.58 71	95.46 64	96.18 99	95.97 46	89.85 62	90.45 69	77.76 112	91.92 35	80.07 103	92.34 65	94.27 79	93.47 83	98.11 72	97.90 53

Anonymous202405211			88.00 104		93.16 108	96.38 94	93.58 88	89.34 77	87.92 101		65.04 200	83.03 85	92.07 66	92.67 113	93.33 86	96.96 140	97.63 59

DI_MVS_plusplus_trai		91.05 70	90.15 79	92.11 65	92.67 116	96.61 83	96.03 44	88.44 90	90.25 71	85.92 66	73.73 138	84.89 78	91.92 67	94.17 82	94.07 65	97.68 105	97.31 70

thres400		89.40 93	87.58 115	91.53 73	94.06 82	97.21 68	94.19 75	89.83 63	85.69 123	81.08 97	75.50 130	69.76 148	91.80 68	94.79 67	93.51 76	98.20 64	96.60 95

thres200		89.49 92	87.72 110	91.55 72	93.95 84	97.25 66	94.34 65	89.74 69	85.66 125	81.18 92	76.12 127	70.19 147	91.80 68	94.92 57	93.51 76	98.27 55	96.40 100

tfpn111		90.16 82	88.99 91	91.52 75	93.90 87	97.26 63	94.31 67	89.75 66	85.87 117	81.10 95	84.41 75	70.38 142	91.76 70	94.92 57	93.51 76	98.29 52	96.61 90

conf0.01		89.34 96	87.39 120	91.61 70	93.88 91	97.34 61	94.31 67	89.82 65	85.87 117	81.53 91	77.93 113	66.15 182	91.76 70	94.90 64	93.51 76	98.32 44	96.05 116

conf0.002		89.25 100	87.21 121	91.62 69	93.87 92	97.35 60	94.31 67	89.83 63	85.87 117	81.62 90	78.72 109	63.89 199	91.76 70	94.90 64	93.98 68	98.33 43	95.77 123

conf200view11		89.55 90	87.86 107	91.52 75	93.90 87	97.26 63	94.31 67	89.75 66	85.87 117	81.10 95	76.46 122	70.38 142	91.76 70	94.92 57	93.51 76	98.29 52	96.61 90

tfpn200view9		89.55 90	87.86 107	91.53 73	93.90 87	97.26 63	94.31 67	89.74 69	85.87 117	81.15 93	76.46 122	70.38 142	91.76 70	94.92 57	93.51 76	98.28 54	96.61 90

view600		89.29 98	87.48 117	91.41 77	94.10 79	97.21 68	93.96 76	89.70 72	85.67 124	80.75 99	75.29 132	69.35 149	91.70 75	94.92 57	93.23 90	98.26 59	96.69 87

thres600view7		89.28 99	87.47 118	91.39 78	94.12 78	97.25 66	93.94 79	89.74 69	85.62 127	80.63 100	75.24 135	69.33 150	91.66 76	94.92 57	93.23 90	98.27 55	96.72 86

view800		89.21 101	87.44 119	91.27 82	94.13 76	97.18 70	93.74 84	89.53 75	85.60 128	80.34 102	75.29 132	68.89 151	91.57 77	94.97 54	93.36 85	98.34 39	96.79 84

tfpn		88.67 104	86.57 124	91.12 84	94.14 75	97.15 71	93.51 89	89.37 76	85.49 129	79.91 105	75.26 134	62.24 205	91.39 78	95.00 52	93.95 69	98.41 30	96.88 81

Anonymous20240521		91.24 68	91.26 71	91.22 83	92.84 112	93.44 136	93.82 81	86.75 112	91.33 62	85.61 71	84.00 80	85.46 74	91.27 79	92.91 109	93.62 74	97.02 131	98.05 44

thres100view900		89.36 94	87.61 113	91.39 78	93.90 87	96.86 79	94.35 64	89.66 73	85.87 117	81.15 93	76.46 122	70.38 142	91.17 80	94.09 83	93.43 84	98.13 69	96.16 111

diffmvs		90.76 73	90.92 74	90.57 87	92.71 115	96.70 81	93.37 93	86.13 114	91.95 55	83.12 86	85.24 66	80.56 101	91.17 80	92.08 126	93.08 99	96.95 142	96.82 82

PMMVS		89.88 84	91.19 72	88.35 110	89.73 144	91.97 186	90.62 128	81.92 166	90.57 68	80.58 101	92.16 32	86.85 66	91.17 80	92.31 119	91.35 133	96.11 177	93.11 175

HyFIR lowres test		87.87 114	86.42 126	89.57 97	95.56 61	96.99 75	92.37 104	84.15 139	86.64 110	77.17 116	57.65 218	83.97 80	91.08 83	92.09 125	92.44 110	97.09 127	95.16 148

DELS-MVS		93.71 44	93.47 44	94.00 41	96.82 49	98.39 32	96.80 35	91.07 53	89.51 86	89.94 39	83.80 81	89.29 63	90.95 84	97.32 11	97.65 2	98.42 28	98.32 31
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
ACMH+	85.75 12	87.19 120	86.02 131	88.56 109	93.42 105	94.41 114	89.91 156	87.66 106	83.45 147	72.25 136	76.42 125	71.99 135	90.78 85	89.86 160	90.94 135	97.32 116	95.11 150

EPP-MVSNet		92.13 57	93.06 49	91.05 85	93.66 100	97.30 62	92.18 112	87.90 98	90.24 72	83.63 82	86.14 58	90.52 60	90.76 86	94.82 66	94.38 57	98.18 66	97.98 45

RPSCF		89.68 88	89.24 87	90.20 92	92.97 110	92.93 158	92.30 107	87.69 104	90.44 70	85.12 78	91.68 36	85.84 73	90.69 87	87.34 197	86.07 201	92.46 211	90.37 199

ACMH	85.51 13	87.31 119	86.59 123	88.14 115	93.96 83	94.51 110	89.00 173	87.99 95	81.58 154	70.15 162	78.41 111	71.78 136	90.60 88	91.30 136	91.99 122	97.17 121	96.58 96
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		90.77 72	92.14 62	89.17 103	93.86 94	92.81 162	93.16 95	80.22 187	90.21 73	84.67 81	89.89 43	91.38 53	90.57 89	94.94 56	92.11 118	92.52 210	93.65 168

MSDG		90.42 77	88.25 101	92.94 59	96.67 51	94.41 114	93.96 76	92.91 37	89.59 85	86.26 63	76.74 120	80.92 100	90.43 90	92.60 116	92.08 120	97.44 115	91.41 189

conf0.05thres1000		87.90 113	85.88 135	90.26 91	93.74 97	96.39 93	92.67 100	88.94 84	80.97 161	77.71 114	70.15 157	68.40 156	90.42 91	94.46 77	93.29 89	98.09 75	97.49 64

PVSNet_BlendedMVS		92.80 50	92.44 57	93.23 52	96.02 56	97.83 48	93.74 84	90.58 56	91.86 56	90.69 34	85.87 62	82.04 93	90.01 92	96.39 34	95.26 46	98.34 39	97.81 55

PVSNet_Blended		92.80 50	92.44 57	93.23 52	96.02 56	97.83 48	93.74 84	90.58 56	91.86 56	90.69 34	85.87 62	82.04 93	90.01 92	96.39 34	95.26 46	98.34 39	97.81 55

CANet_DTU		90.74 74	92.93 51	88.19 112	94.36 72	96.61 83	94.34 65	84.66 133	90.66 66	68.75 179	90.41 42	86.89 65	89.78 94	95.46 49	94.87 52	97.25 118	95.62 128

FC-MVSNet-train		90.55 75	90.19 78	90.97 86	93.78 96	95.16 105	92.11 116	88.85 85	87.64 102	83.38 85	84.36 77	78.41 109	89.53 95	94.69 68	93.15 97	98.15 67	97.92 49

USDC		86.73 125	85.96 133	87.63 123	91.64 125	93.97 120	92.76 99	84.58 135	88.19 97	70.67 155	80.10 102	67.86 159	89.43 96	91.81 128	89.77 182	96.69 168	90.05 202

Effi-MVS+-dtu		87.51 117	88.13 103	86.77 132	91.10 132	94.90 107	90.91 126	82.67 156	83.47 146	71.55 144	81.11 100	77.04 119	89.41 97	92.65 115	91.68 129	95.00 199	96.09 114

GBi-Net		90.21 79	90.11 80	90.32 89	88.66 153	93.65 129	94.25 72	85.78 118	90.03 78	85.56 72	77.38 114	86.13 68	89.38 98	93.97 86	94.16 61	98.31 45	95.47 132

test1		90.21 79	90.11 80	90.32 89	88.66 153	93.65 129	94.25 72	85.78 118	90.03 78	85.56 72	77.38 114	86.13 68	89.38 98	93.97 86	94.16 61	98.31 45	95.47 132

FMVSNet2		89.61 89	89.14 89	90.16 93	88.66 153	93.65 129	94.25 72	85.44 125	88.57 94	84.96 80	73.53 140	83.82 81	89.38 98	94.23 80	94.68 55	98.31 45	95.47 132

FMVSNet3		90.19 81	90.06 82	90.34 88	88.69 152	93.85 121	94.58 60	85.78 118	90.03 78	85.56 72	77.38 114	86.13 68	89.22 101	93.29 106	94.36 58	98.20 64	95.40 136

IterMVS-LS		88.60 105	88.45 97	88.78 107	92.02 121	92.44 174	92.00 119	83.57 146	86.52 113	78.90 110	78.61 110	81.34 98	89.12 102	90.68 148	93.18 95	97.10 126	96.35 102
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TinyColmap		84.04 167	82.01 191	86.42 136	90.87 135	91.84 187	88.89 175	84.07 140	82.11 153	69.89 169	71.08 150	60.81 214	89.04 103	90.52 150	89.19 190	95.76 181	88.50 208

FMVSNet1		87.33 118	86.00 132	88.89 105	87.13 196	92.83 161	93.08 97	84.46 136	81.35 158	82.20 88	66.33 186	77.96 112	88.96 104	93.97 86	94.16 61	97.54 112	95.38 137

UniMVSNet_NR-MVSNet		86.80 123	85.86 136	87.89 120	88.17 159	94.07 119	90.15 147	88.51 89	84.20 141	73.45 131	72.38 148	70.30 146	88.95 105	90.25 153	92.21 115	98.12 70	97.62 61

DU-MVS		86.12 130	84.81 144	87.66 121	87.77 166	93.78 124	90.15 147	87.87 100	84.40 135	73.45 131	70.59 152	64.82 193	88.95 105	90.14 154	92.33 112	97.76 97	97.62 61

MVSTER		91.73 65	91.61 68	91.86 67	93.18 107	94.56 108	94.37 62	87.90 98	90.16 76	88.69 46	89.23 46	81.28 99	88.92 107	95.75 45	93.95 69	98.12 70	96.37 101

LTVRE_ROB	81.71 16	82.44 194	81.84 193	83.13 185	89.01 148	92.99 155	88.90 174	82.32 162	66.26 227	54.02 225	74.68 136	59.62 220	88.87 108	90.71 147	92.02 121	95.68 185	96.62 89
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
DWT-MVSNet_training		86.83 122	84.44 147	89.61 96	92.75 114	93.82 122	91.66 122	82.85 153	88.57 94	87.48 54	79.00 106	64.24 198	88.82 109	85.18 207	87.50 197	94.07 201	92.79 177

CHOSEN 1792x2688		88.57 108	87.82 109	89.44 100	95.46 64	96.89 78	93.74 84	85.87 117	89.63 84	77.42 115	61.38 213	83.31 83	88.80 110	93.44 103	93.16 96	95.37 193	96.95 78

pmmvs4		86.00 133	84.28 149	88.00 116	87.80 164	92.01 184	89.94 155	84.91 131	86.79 109	80.98 98	73.41 143	66.34 175	88.12 111	89.31 175	88.90 193	96.24 176	93.20 174

CostFormer		86.78 124	86.05 129	87.62 124	92.15 119	93.20 148	91.55 123	75.83 205	88.11 99	85.29 77	81.76 95	76.22 123	87.80 112	84.45 212	85.21 208	93.12 204	93.42 171

EPNet		93.92 43	94.40 38	93.36 50	97.89 31	96.55 85	96.08 43	92.14 41	91.65 59	89.16 42	94.07 27	90.17 62	87.78 113	95.24 50	94.97 50	97.09 127	98.15 38
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchmatchNet		85.70 135	86.65 122	84.60 169	91.79 123	93.40 140	89.27 167	73.62 213	90.19 74	72.63 134	82.74 88	81.93 95	87.64 114	84.99 208	84.29 213	92.64 208	89.00 205
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		84.51 152	83.05 170	86.20 137	87.25 192	93.28 145	90.22 144	85.40 126	79.94 174	69.78 170	67.74 167	65.15 190	87.57 115	89.12 185	90.55 146	96.97 137	95.60 129

EPMVS		85.77 134	86.24 128	85.23 157	92.76 113	93.78 124	89.91 156	73.60 214	90.19 74	74.22 126	82.18 93	78.06 111	87.55 116	85.61 206	85.38 207	93.32 203	88.48 209

tpmp4_e23		85.67 136	84.28 149	87.30 126	91.96 122	92.00 185	92.06 117	76.27 203	87.95 100	83.59 83	76.97 119	70.88 139	87.52 117	84.80 211	84.73 210	92.40 212	92.61 184

IS_MVSNet		91.87 62	93.35 46	90.14 94	94.09 80	97.73 51	93.09 96	88.12 94	88.71 92	79.98 104	84.49 74	90.63 57	87.49 118	97.07 17	96.96 12	98.07 79	97.88 54

Baseline_NR-MVSNet		85.28 141	83.42 159	87.46 125	87.77 166	90.80 200	89.90 158	87.69 104	83.93 144	74.16 127	64.72 202	66.43 172	87.48 119	90.14 154	90.83 136	97.73 100	97.11 75

COLMAP_ROB	84.39 15	87.61 116	86.03 130	89.46 99	95.54 63	94.48 111	91.77 121	90.14 60	87.16 106	75.50 122	73.41 143	76.86 121	87.33 120	90.05 159	89.76 183	96.48 170	90.46 198
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
dps		85.00 144	83.21 168	87.08 128	90.73 137	92.55 168	89.34 166	75.29 207	84.94 130	87.01 58	79.27 105	67.69 160	87.27 121	84.22 214	83.56 214	92.83 206	90.25 200

v7		84.37 160	83.23 167	85.69 148	87.34 184	93.19 149	90.32 134	83.10 152	79.88 176	69.33 173	66.33 186	65.75 183	87.06 122	90.83 143	90.38 149	96.97 137	96.26 109

v10		84.18 162	83.17 169	85.37 153	87.34 184	92.68 164	90.32 134	81.33 172	79.93 175	69.23 176	66.33 186	65.74 185	87.03 123	90.84 142	90.38 149	96.97 137	96.29 107

TranMVSNet+NR-MVSNet		85.57 138	84.41 148	86.92 129	87.67 169	93.34 142	90.31 138	88.43 91	83.07 148	70.11 165	69.99 159	65.28 188	86.96 124	89.73 162	92.27 113	98.06 81	97.17 74

v1192		83.56 179	82.35 183	84.98 164	86.84 201	92.84 159	90.01 153	82.70 154	78.54 190	66.48 195	64.88 201	62.91 201	86.91 125	90.72 146	90.25 156	96.94 145	96.32 104

v11		83.72 172	82.61 180	85.02 162	87.34 184	89.56 210	89.89 159	79.92 190	79.55 179	69.21 177	66.36 185	65.48 186	86.84 126	91.43 135	90.51 148	96.92 153	95.37 138

v1144		84.03 168	82.88 175	85.37 153	87.17 194	93.15 153	90.18 146	83.31 149	78.83 188	67.85 185	65.99 192	64.99 191	86.79 127	90.75 145	90.33 152	96.90 155	96.15 112

v1921920		83.30 183	82.09 190	84.70 167	86.59 204	92.67 165	89.82 162	82.23 163	78.32 191	65.76 199	64.64 203	62.35 204	86.78 128	90.34 152	90.02 175	97.02 131	96.31 106

v144192		83.48 181	82.23 187	84.94 165	86.65 202	92.84 159	89.63 165	82.48 159	77.87 194	67.36 189	65.33 198	63.50 200	86.51 129	89.72 163	89.99 177	97.03 130	96.35 102

v1240		82.88 189	81.66 194	84.29 173	86.46 205	92.52 172	89.06 171	81.82 168	77.16 198	65.09 203	64.17 205	61.50 210	86.36 130	90.12 156	90.13 170	96.95 142	96.04 117

tpmrst		83.72 172	83.45 157	84.03 177	92.21 118	91.66 190	88.74 176	73.58 215	88.14 98	72.67 133	77.37 117	72.11 134	86.34 131	82.94 218	82.05 219	90.63 222	89.86 203

ADS-MVSNet		84.08 166	84.95 142	83.05 189	91.53 129	91.75 189	88.16 180	70.70 223	89.96 81	69.51 172	78.83 107	76.97 120	86.29 132	84.08 215	84.60 211	92.13 216	88.48 209

v18		84.21 161	82.90 174	85.74 147	87.63 170	89.75 203	90.56 129	80.82 177	81.42 156	72.24 137	67.16 169	67.23 162	86.27 133	89.25 179	90.24 159	96.92 153	95.27 141

CR-MVSNet		85.48 139	86.29 127	84.53 171	91.08 134	92.10 178	89.18 169	73.30 218	84.75 131	71.08 150	73.12 146	77.91 113	86.27 133	91.48 132	90.75 140	96.27 175	93.94 162

PatchT		83.86 169	85.51 140	81.94 202	88.41 156	91.56 192	78.79 219	71.57 221	84.08 143	71.08 150	70.62 151	76.13 124	86.27 133	91.48 132	90.75 140	95.52 191	93.94 162

v16		84.14 163	82.86 176	85.64 150	87.61 171	89.71 205	90.36 132	80.70 179	81.36 157	71.99 141	66.91 176	67.19 163	86.23 136	89.32 173	90.25 156	96.94 145	95.29 139

v17		84.10 165	82.83 177	85.57 152	87.58 172	89.72 204	90.30 141	80.70 179	81.00 160	71.72 142	67.01 171	67.24 161	86.19 137	89.32 173	90.25 156	96.95 142	95.29 139

v8		84.45 156	83.30 166	85.80 145	87.53 174	92.95 156	90.31 138	82.46 160	80.46 167	71.43 145	66.99 172	67.16 165	86.14 138	89.26 176	90.22 165	96.94 145	96.06 115

MDTV_nov1_ep13		86.64 126	87.50 116	85.65 149	90.73 137	93.69 128	89.96 154	78.03 198	89.48 87	76.85 117	84.92 72	82.42 91	86.14 138	86.85 202	86.15 200	92.17 214	88.97 206

v1neww		84.65 149	83.34 164	86.18 139	87.53 174	93.49 133	90.32 134	85.17 128	80.57 165	71.02 153	66.93 174	67.04 168	86.13 140	89.26 176	90.23 162	96.93 148	95.88 120

v7new		84.65 149	83.34 164	86.18 139	87.53 174	93.49 133	90.32 134	85.17 128	80.57 165	71.02 153	66.93 174	67.04 168	86.13 140	89.26 176	90.23 162	96.93 148	95.88 120

v6		84.67 148	83.36 162	86.20 137	87.53 174	93.49 133	90.34 133	85.16 130	80.58 164	71.13 149	66.97 173	67.10 166	86.11 142	89.25 179	90.22 165	96.93 148	95.89 119

Fast-Effi-MVS+-dtu		86.25 127	87.70 111	84.56 170	90.37 141	93.70 127	90.54 130	78.14 196	83.50 145	65.37 202	81.59 98	75.83 125	86.09 143	91.70 130	91.70 127	96.88 160	95.84 122

v1141		84.40 157	83.00 172	86.03 141	87.41 180	93.42 137	90.28 142	85.53 122	79.58 177	70.12 164	66.62 182	66.27 179	85.94 144	89.16 182	90.19 167	96.89 157	95.73 125

divwei89l23v2f112		84.40 157	83.00 172	86.02 143	87.42 179	93.42 137	90.28 142	85.52 123	79.57 178	70.11 165	66.64 181	66.29 178	85.91 145	89.16 182	90.19 167	96.90 155	95.73 125

v1		84.40 157	83.01 171	86.03 141	87.41 180	93.42 137	90.31 138	85.52 123	79.51 180	70.13 163	66.66 180	66.40 173	85.89 146	89.15 184	90.19 167	96.89 157	95.74 124

v15		83.67 174	82.37 182	85.19 158	87.39 182	89.63 206	90.19 145	80.43 181	79.49 182	70.27 158	66.37 183	66.33 176	85.88 147	89.34 172	90.23 162	96.96 140	95.22 146

V14		83.66 175	82.38 181	85.16 159	87.37 183	89.62 207	90.15 147	80.33 183	79.51 180	70.26 159	66.30 189	66.37 174	85.87 148	89.38 169	90.24 159	96.98 136	95.22 146

v12		83.59 178	82.32 185	85.07 161	87.32 190	89.57 209	89.87 161	80.19 188	79.46 183	70.19 160	66.05 191	66.23 181	85.84 149	89.44 167	90.26 155	97.01 133	95.26 142

V9		83.61 176	82.33 184	85.11 160	87.34 184	89.59 208	90.10 150	80.25 184	79.38 184	70.17 161	66.15 190	66.33 176	85.82 150	89.41 168	90.24 159	96.99 135	95.23 145

v13		83.55 180	82.29 186	85.01 163	87.31 191	89.55 211	89.89 159	80.13 189	79.34 185	69.93 168	65.92 194	66.25 180	85.80 151	89.45 166	90.27 153	97.01 133	95.25 144

tfpnnormal		83.80 171	81.26 201	86.77 132	89.60 145	93.26 147	89.72 163	87.60 107	72.78 215	70.44 156	60.53 216	61.15 213	85.55 152	92.72 111	91.44 131	97.71 101	96.92 79

V42		84.48 154	83.36 162	85.79 146	87.14 195	93.28 145	90.03 151	83.98 141	80.30 169	71.20 148	66.90 178	67.17 164	85.55 152	89.35 170	90.27 153	96.82 163	96.27 108

PVSNet_Blended_VisFu		91.92 61	92.39 59	91.36 81	95.45 66	97.85 47	92.25 109	89.54 74	88.53 96	87.47 55	79.82 103	90.53 58	85.47 154	96.31 37	95.16 48	97.99 88	98.56 17

RPMNet		84.82 147	85.90 134	83.56 181	91.10 132	92.10 178	88.73 177	71.11 222	84.75 131	68.79 178	73.56 139	77.62 115	85.33 155	90.08 158	89.43 188	96.32 174	93.77 167

thresconf0.02		88.86 102	88.70 96	89.04 104	93.59 103	96.40 92	92.97 98	89.75 66	90.16 76	74.34 125	84.41 75	71.00 138	85.16 156	93.32 105	93.12 98	98.41 30	92.52 185

tpm cat1		84.13 164	81.99 192	86.63 134	91.74 124	91.50 193	90.68 127	75.69 206	86.12 116	85.44 76	72.39 147	70.72 140	85.16 156	80.89 224	81.56 222	91.07 220	90.71 196

UniMVSNet (Re)		86.22 128	85.46 141	87.11 127	88.34 157	94.42 113	89.65 164	87.10 110	84.39 137	74.61 124	70.41 155	68.10 157	85.10 158	91.17 138	91.79 125	97.84 93	97.94 47

MS-PatchMatch		87.63 115	87.61 113	87.65 122	93.95 84	94.09 118	92.60 102	81.52 171	86.64 110	76.41 120	73.46 142	85.94 71	85.01 159	92.23 123	90.00 176	96.43 172	90.93 195

tfpn_ndepth		89.72 87	89.91 83	89.49 98	93.56 104	96.67 82	92.34 106	89.25 79	90.85 64	78.68 111	84.25 78	77.39 117	84.84 160	93.58 97	92.76 107	98.30 48	93.90 164

pmmvs5		83.37 182	82.68 178	84.18 175	87.13 196	93.18 150	86.74 192	82.08 164	76.48 202	67.28 190	71.26 149	62.70 203	84.71 161	90.77 144	90.12 173	97.15 122	94.24 158

anonymousdsp		84.51 152	85.85 137	82.95 190	86.30 206	93.51 132	85.77 202	80.38 182	78.25 193	63.42 208	73.51 141	72.20 133	84.64 162	93.21 108	92.16 117	97.19 120	98.14 39

test-mter		86.09 132	88.38 98	83.43 183	87.89 163	92.61 166	86.89 191	77.11 201	84.30 138	68.62 181	82.57 90	82.45 90	84.34 163	92.40 118	90.11 174	95.74 182	94.21 160

test-LLR		86.88 121	88.28 99	85.24 156	91.22 130	92.07 180	87.41 186	83.62 144	84.58 133	69.33 173	83.00 83	82.79 86	84.24 164	92.26 120	89.81 180	95.64 186	93.44 169

TESTMET0.1,1		86.11 131	88.28 99	83.59 180	87.80 164	92.07 180	87.41 186	77.12 200	84.58 133	69.33 173	83.00 83	82.79 86	84.24 164	92.26 120	89.81 180	95.64 186	93.44 169

tfpnview11		88.80 103	89.21 88	88.31 111	93.70 99	96.24 97	92.35 105	89.11 80	89.90 82	72.14 138	85.12 68	73.93 127	84.20 166	93.75 92	92.85 104	98.38 35	92.68 183

tpm		83.16 184	83.64 154	82.60 197	90.75 136	91.05 196	88.49 178	73.99 211	82.36 151	67.08 192	78.10 112	68.79 152	84.17 167	85.95 205	85.96 203	91.09 219	93.23 173

GA-MVS		85.08 143	85.65 138	84.42 172	89.77 143	94.25 116	89.26 168	84.62 134	81.19 159	62.25 210	75.72 129	68.44 155	84.14 168	93.57 99	91.68 129	96.49 169	94.71 153

gm-plane-assit		77.65 211	78.50 209	76.66 213	87.96 162	85.43 223	64.70 232	74.50 209	64.15 229	51.26 227	61.32 214	58.17 222	84.11 169	95.16 51	93.83 71	97.45 114	91.41 189

SixPastTwentyTwo		83.12 186	83.44 158	82.74 194	87.71 168	93.11 154	82.30 212	82.33 161	79.24 186	64.33 205	78.77 108	62.75 202	84.11 169	88.11 190	87.89 195	95.70 184	94.21 160

tfpn_n400		88.58 106	88.91 93	88.19 112	93.63 101	96.34 95	92.22 110	89.04 81	87.37 104	72.14 138	85.12 68	73.93 127	84.04 171	93.65 93	93.20 93	98.09 75	92.77 178

tfpnconf		88.58 106	88.91 93	88.19 112	93.63 101	96.34 95	92.22 110	89.04 81	87.37 104	72.14 138	85.12 68	73.93 127	84.04 171	93.65 93	93.20 93	98.09 75	92.77 178

gg-mvs-nofinetune		81.83 199	83.58 155	79.80 207	91.57 126	96.54 86	93.79 82	68.80 227	62.71 230	43.01 236	55.28 222	85.06 77	83.65 173	96.13 40	94.86 53	97.98 90	94.46 155

FMVSNet5		84.47 155	84.72 145	84.18 175	83.30 219	88.43 214	88.09 181	79.42 193	84.25 139	74.14 128	73.15 145	78.74 107	83.65 173	91.19 137	91.19 134	96.46 171	86.07 215

tfpn1000		89.30 97	89.72 85	88.81 106	93.83 95	96.50 87	91.53 124	88.74 87	91.20 63	76.74 118	84.96 71	75.44 126	83.50 175	93.63 95	92.42 111	98.51 14	93.88 165

IterMVS		85.25 142	86.49 125	83.80 178	90.42 140	90.77 201	90.02 152	78.04 197	84.10 142	66.27 197	77.28 118	78.41 109	83.01 176	90.88 141	89.72 184	95.04 197	94.24 158
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PM-MVS		80.29 205	79.30 207	81.45 205	81.91 221	88.23 215	82.61 210	79.01 194	79.99 173	67.15 191	69.07 162	51.39 226	82.92 177	87.55 196	85.59 204	95.08 196	93.28 172

NR-MVSNet		85.46 140	84.54 146	86.52 135	88.33 158	93.78 124	90.45 131	87.87 100	84.40 135	71.61 143	70.59 152	62.09 208	82.79 178	91.75 129	91.75 126	98.10 74	97.44 66

v52		82.11 196	81.50 198	82.82 193	84.59 216	92.51 173	85.96 201	80.24 185	76.38 205	66.83 194	63.12 208	64.62 196	82.56 179	87.70 193	89.55 185	96.73 166	96.61 90

V4		82.11 196	81.49 199	82.83 192	84.60 215	92.53 171	85.97 199	80.24 185	76.35 206	66.87 193	63.17 207	64.55 197	82.54 180	87.70 193	89.55 185	96.73 166	96.61 90

CP-MVSNet		83.11 187	82.15 188	84.23 174	87.20 193	92.70 163	86.42 195	83.53 147	77.83 195	67.67 187	66.89 179	60.53 216	82.47 181	89.23 181	90.65 144	98.08 78	97.20 73

v7n		82.25 195	81.54 196	83.07 188	85.55 210	92.58 167	86.68 194	81.10 176	76.54 201	65.97 198	62.91 210	60.56 215	82.36 182	91.07 140	90.35 151	96.77 165	96.80 83

pm-mvs1		84.55 151	83.46 156	85.82 144	88.16 161	93.39 141	89.05 172	85.36 127	74.03 213	72.43 135	65.08 199	71.11 137	82.30 183	93.48 101	91.70 127	97.64 108	95.43 135

CDS-MVSNet		88.34 110	88.71 95	87.90 119	90.70 139	94.54 109	92.38 103	86.02 115	80.37 168	79.42 107	79.30 104	83.43 82	82.04 184	93.39 104	94.01 67	96.86 162	95.93 118
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		84.94 146	84.95 142	84.93 166	88.82 149	93.18 150	88.44 179	81.28 173	77.16 198	73.76 130	75.43 131	76.57 122	82.04 184	90.59 149	90.79 137	95.22 195	90.94 194

MVS-HIRNet		78.16 209	77.57 213	78.83 209	85.83 208	87.76 217	76.67 220	70.22 224	75.82 209	67.39 188	55.61 221	70.52 141	81.96 186	86.67 203	85.06 209	90.93 221	81.58 224

MIMVSNet		82.97 188	84.00 152	81.77 204	82.23 220	92.25 177	87.40 188	72.73 220	81.48 155	69.55 171	68.79 163	72.42 132	81.82 187	92.23 123	92.25 114	96.89 157	88.61 207

v148		83.61 176	82.10 189	85.37 153	87.34 184	92.94 157	87.48 185	85.72 121	78.92 187	73.87 129	65.71 196	64.69 194	81.78 188	87.82 191	89.35 189	96.01 178	95.26 142

PS-CasMVS		82.53 192	81.54 196	83.68 179	87.08 198	92.54 169	86.20 197	83.46 148	76.46 203	65.73 200	65.71 196	59.41 221	81.61 189	89.06 186	90.55 146	98.03 83	97.07 76

pmmvs-eth3d		79.78 207	77.58 212	82.34 200	81.57 222	87.46 219	82.92 209	81.28 173	75.33 211	71.34 146	61.88 211	52.41 225	81.59 190	87.56 195	86.90 199	95.36 194	91.48 188

PEN-MVS		82.49 193	81.58 195	83.56 181	86.93 199	92.05 183	86.71 193	83.84 142	76.94 200	64.68 204	67.24 168	60.11 217	81.17 191	87.78 192	90.70 143	98.02 85	96.21 110

TDRefinement		84.97 145	83.39 160	86.81 131	92.97 110	94.12 117	92.18 112	87.77 103	82.78 150	71.31 147	68.43 164	68.07 158	81.10 192	89.70 164	89.03 192	95.55 190	91.62 187

pmmvs6		80.90 203	78.77 208	83.38 184	85.84 207	91.61 191	86.01 198	82.54 158	64.17 228	70.43 157	54.14 226	67.06 167	80.73 193	90.50 151	89.17 191	94.74 200	94.75 152

MDTV_nov1_ep13_2view		80.43 204	80.94 203	79.84 206	84.82 214	90.87 198	84.23 206	73.80 212	80.28 170	64.33 205	70.05 158	68.77 153	79.67 194	84.83 210	83.50 215	92.17 214	88.25 211

CMPMVS	61.19 17	79.86 206	77.46 214	82.66 196	91.54 128	91.82 188	83.25 208	81.57 170	70.51 223	68.64 180	59.89 217	66.77 171	79.63 195	84.00 216	84.30 212	91.34 218	84.89 218
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UA-Net		90.81 71	92.58 54	88.74 108	94.87 70	97.44 57	92.61 101	88.22 92	82.35 152	78.93 109	85.20 67	95.61 33	79.56 196	96.52 29	96.57 21	98.23 61	94.37 157

Vis-MVSNet (Re-imp)		90.54 76	92.76 52	87.94 118	93.73 98	96.94 77	92.17 114	87.91 97	88.77 91	76.12 121	83.68 82	90.80 54	79.49 197	96.34 36	96.35 26	98.21 63	96.46 98

DTE-MVSNet		81.76 200	81.04 202	82.60 197	86.63 203	91.48 195	85.97 199	83.70 143	76.45 204	62.44 209	67.16 169	59.98 218	78.98 198	87.15 199	89.93 178	97.88 92	95.12 149

TransMVSNet (Re)		82.67 191	80.93 204	84.69 168	88.71 151	91.50 193	87.90 182	87.15 109	71.54 221	68.24 183	63.69 206	64.67 195	78.51 199	91.65 131	90.73 142	97.64 108	92.73 182

v748		81.57 202	80.68 205	82.60 197	85.55 210	92.07 180	83.57 207	82.06 165	74.64 212	69.97 167	63.11 209	61.46 211	78.09 200	87.30 198	89.88 179	96.37 173	96.32 104

LP		77.28 213	76.57 216	78.12 211	88.17 159	88.06 216	80.85 216	68.35 230	80.78 162	61.49 213	57.59 219	61.80 209	77.59 201	81.45 223	82.34 218	92.25 213	83.96 221

UGNet		91.52 67	93.41 45	89.32 101	94.13 76	97.15 71	91.83 120	89.01 83	90.62 67	85.86 68	86.83 54	91.73 50	77.40 202	94.68 69	94.43 56	97.71 101	98.40 30
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
Vis-MVSNet		89.36 94	91.49 70	86.88 130	92.10 120	97.60 56	92.16 115	85.89 116	84.21 140	75.20 123	82.58 89	87.13 64	77.40 202	95.90 44	95.63 41	98.51 14	97.36 69
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CVMVSNet		83.83 170	85.53 139	81.85 203	89.60 145	90.92 197	87.81 184	83.21 150	80.11 171	60.16 215	76.47 121	78.57 108	76.79 204	89.76 161	90.13 170	93.51 202	92.75 181

EG-PatchMatch MVS		81.70 201	81.31 200	82.15 201	88.75 150	93.81 123	87.14 189	78.89 195	71.57 219	64.12 207	61.20 215	68.46 154	76.73 205	91.48 132	90.77 139	97.28 117	91.90 186

MDA-MVSNet-bldmvs		73.81 217	72.56 222	75.28 215	72.52 234	88.87 213	74.95 223	82.67 156	71.57 219	55.02 222	65.96 193	42.84 236	76.11 206	70.61 233	81.47 223	90.38 224	86.59 213

WR-MVS_H		82.86 190	82.66 179	83.10 187	87.44 178	93.33 143	85.71 203	83.20 151	77.36 197	68.20 184	66.37 183	65.23 189	76.05 207	89.35 170	90.13 170	97.99 88	96.89 80

WR-MVS		83.14 185	83.38 161	82.87 191	87.55 173	93.29 144	86.36 196	84.21 138	80.05 172	66.41 196	66.91 176	66.92 170	75.66 208	88.96 187	90.56 145	97.05 129	96.96 77

pmmvs3		71.13 222	71.06 224	71.21 223	73.54 233	80.19 229	71.69 230	64.86 231	62.04 231	52.10 226	54.92 224	48.00 233	75.03 209	83.75 217	83.24 216	90.04 227	85.27 216

EPNet_dtu		88.32 111	90.61 75	85.64 150	96.79 50	92.27 176	92.03 118	90.31 59	89.05 89	65.44 201	89.43 45	85.90 72	74.22 210	92.76 110	92.09 119	95.02 198	92.76 180
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FC-MVSNet-test		86.15 129	89.10 90	82.71 195	89.83 142	93.18 150	87.88 183	84.69 132	86.54 112	62.18 211	82.39 92	83.31 83	74.18 211	92.52 117	91.86 124	97.50 113	93.88 165

IB-MVS	85.10 14	87.98 112	87.97 106	87.99 117	94.55 71	96.86 79	84.52 205	88.21 93	86.48 115	88.54 47	74.41 137	77.74 114	74.10 212	89.65 165	92.85 104	98.06 81	97.80 57
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
N_pmnet		77.55 212	76.68 215	78.56 210	85.43 212	87.30 220	78.84 218	81.88 167	78.30 192	60.61 214	61.46 212	62.15 207	74.03 213	82.04 219	80.69 225	90.59 223	84.81 219

test0.0.03 1		85.58 137	87.69 112	83.11 186	91.22 130	92.54 169	85.60 204	83.62 144	85.66 125	67.84 186	82.79 87	79.70 104	73.51 214	91.15 139	90.79 137	96.88 160	91.23 192

new_pmnet		72.29 221	73.25 221	71.16 224	75.35 231	81.38 225	73.72 225	69.27 226	75.97 208	49.84 229	56.27 220	56.12 224	69.08 215	81.73 220	80.86 224	89.72 228	80.44 225

testgi		81.94 198	84.09 151	79.43 208	89.53 147	90.83 199	82.49 211	81.75 169	80.59 163	59.46 217	82.82 86	65.75 183	67.97 216	90.10 157	89.52 187	95.39 192	89.03 204

testpf		74.66 215	76.34 217	72.71 219	87.34 184	80.91 227	73.15 227	60.30 237	78.73 189	61.68 212	69.83 160	62.22 206	67.48 217	76.83 228	78.17 229	86.28 231	87.68 212

DeepMVS_CX						71.82 236	68.37 231	48.05 239	77.38 196	46.88 234	65.77 195	47.03 234	67.48 217	64.27 236		76.89 238	76.72 228

EU-MVSNet		78.43 208	80.25 206	76.30 214	83.81 218	87.27 221	80.99 214	79.52 192	76.01 207	54.12 224	70.44 154	64.87 192	67.40 219	86.23 204	85.54 206	91.95 217	91.41 189

Anonymous20231206		78.09 210	78.11 211	78.07 212	85.19 213	89.17 212	80.99 214	81.24 175	75.46 210	58.25 219	54.78 225	59.90 219	66.73 220	88.94 188	88.26 194	96.01 178	90.25 200

ambc			67.96 225		73.69 232	79.79 230	73.82 224		71.61 218	59.80 216	46.00 231	20.79 242	66.15 221	86.92 201	80.11 227	89.13 229	90.50 197

test2356		73.82 216	74.82 219	72.66 220	81.25 223	80.70 228	73.47 226	75.91 204	72.55 216	48.73 231	68.14 165	50.74 227	63.96 222	84.44 213	85.57 205	92.63 209	81.60 223

MIMVSNet1		73.19 219	73.70 220	72.60 221	65.42 238	86.69 222	75.56 222	79.65 191	67.87 226	55.30 221	45.24 234	56.41 223	63.79 223	86.98 200	87.66 196	95.85 180	85.04 217

new-patchmatchnet		72.32 220	71.09 223	73.74 217	81.17 224	84.86 224	72.21 229	77.48 199	68.32 225	54.89 223	55.10 223	49.31 230	63.68 224	79.30 225	76.46 230	93.03 205	84.32 220

testus		73.65 218	74.92 218	72.17 222	80.93 225	81.11 226	73.02 228	75.23 208	73.23 214	48.77 230	69.38 161	46.10 235	62.28 225	84.84 209	86.01 202	92.77 207	83.75 222

1111		66.22 224	66.42 227	65.98 225	75.69 228	76.42 232	58.90 233	63.25 232	57.86 232	48.33 232	45.46 232	49.13 231	61.32 226	81.57 221	82.80 217	88.38 230	71.69 234

.test1245		48.95 233	46.78 235	51.48 231	75.69 228	76.42 232	58.90 233	63.25 232	57.86 232	48.33 232	45.46 232	49.13 231	61.32 226	81.57 221	5.58 239	1.40 243	11.42 240

FPMVS		69.87 223	67.10 226	73.10 218	84.09 217	78.35 231	79.40 217	76.41 202	71.92 217	57.71 220	54.06 227	50.04 228	56.72 228	71.19 232	68.70 233	84.25 233	75.43 229

Gipuma		58.52 230	56.17 232	61.27 229	67.14 237	58.06 240	52.16 240	68.40 229	69.00 224	45.02 235	22.79 238	20.57 243	55.11 229	76.27 229	79.33 228	79.80 236	67.16 235
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test20.03		76.41 214	78.49 210	73.98 216	85.64 209	87.50 218	75.89 221	80.71 178	70.84 222	51.07 228	68.06 166	61.40 212	54.99 230	88.28 189	87.20 198	95.58 189	86.15 214

testmv		65.29 225	65.25 228	65.34 226	77.73 226	75.55 234	58.75 235	73.56 216	53.22 235	38.47 237	49.33 228	38.30 237	53.38 231	79.13 226	81.65 220	90.15 225	79.58 226

test1235678		65.29 225	65.24 229	65.34 226	77.73 226	75.54 235	58.75 235	73.56 216	53.19 236	38.47 237	49.32 229	38.28 238	53.38 231	79.13 226	81.65 220	90.15 225	79.57 227

EMVS		39.04 236	34.32 238	44.54 236	58.25 241	39.35 244	27.61 244	62.55 235	35.99 239	16.40 244	20.04 241	14.77 244	44.80 233	33.12 240	44.10 238	57.61 241	52.89 238

E-PMN		40.00 234	35.74 237	44.98 235	57.69 242	39.15 245	28.05 243	62.70 234	35.52 240	17.78 243	20.90 239	14.36 245	44.47 234	35.89 239	47.86 237	59.15 240	56.47 237

PMVS	56.77 18	61.27 228	58.64 231	64.35 228	75.66 230	54.60 241	53.62 239	74.23 210	53.69 234	58.37 218	44.27 235	49.38 229	44.16 235	69.51 234	65.35 235	80.07 235	73.66 230
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test12356		60.37 229	61.08 230	59.53 230	72.42 235	70.09 237	57.72 237	69.53 225	51.31 237	36.05 239	47.32 230	32.04 239	36.19 236	74.15 231	80.35 226	85.27 232	72.29 232

MVE	39.81 19	39.52 235	41.58 236	37.11 237	33.93 243	49.06 242	26.45 245	54.22 238	29.46 242	24.15 240	20.77 240	10.60 246	34.42 237	51.12 238	65.27 236	49.49 242	64.81 236
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		53.68 231	55.72 233	51.30 232	58.84 240	67.02 239	54.23 238	60.97 236	47.50 238	19.42 242	34.81 236	31.97 240	30.88 238	65.84 235	69.99 232	83.47 234	72.92 231

no-one		49.70 232	49.06 234	50.46 233	65.32 239	67.46 238	38.16 242	68.73 228	34.38 241	22.88 241	24.40 237	22.99 241	28.55 239	51.41 237	70.93 231	79.08 237	71.81 233

tmp_tt				50.24 234	68.55 236	46.86 243	48.90 241	18.28 240	86.51 114	68.32 182	70.19 156	65.33 187	26.69 240	74.37 230	66.80 234	70.72 239

test123		3.48 238	5.31 240	1.34 240	0.20 246	1.52 247	2.17 248	0.58 242	6.13 244	0.31 247	9.85 243	0.31 248	3.90 241	2.65 242	5.28 241	0.87 245	11.46 239

testmvs		4.35 237	6.54 239	1.79 239	0.60 244	1.82 246	3.06 247	0.95 241	7.22 243	0.88 246	12.38 242	1.25 247	3.87 242	6.09 241	5.58 239	1.40 243	11.42 240

GG-mvs-BLEND		62.84 227	90.21 77	30.91 238	0.57 245	94.45 112	86.99 190	0.34 243	88.71 92	0.98 245	81.55 99	91.58 51	0.86 243	92.66 114	91.43 132	95.73 183	91.11 193

sosnet-low-res		0.00 239	0.00 241	0.00 241	0.00 247	0.00 248	0.00 249	0.00 244	0.00 245	0.00 248	0.00 244	0.00 249	0.00 244	0.00 243	0.00 242	0.00 246	0.00 242

sosnet		0.00 239	0.00 241	0.00 241	0.00 247	0.00 248	0.00 249	0.00 244	0.00 245	0.00 248	0.00 244	0.00 249	0.00 244	0.00 243	0.00 242	0.00 246	0.00 242

our_test_3					86.93 199	89.77 202	81.61 213

MTAPA										95.36 2		97.46 17

MTMP										95.70 1		96.90 22

Patchmatch-RL test							18.47 246

XVS					95.68 58	98.66 12	94.96 57			88.03 49		96.06 27				98.46 21

X-MVStestdata					95.68 58	98.66 12	94.96 57			88.03 49		96.06 27				98.46 21

mPP-MVS					98.76 20							95.49 34

NP-MVS									91.63 60

Patchmtry						92.39 175	89.18 169	73.30 218		71.08 150

High-resolution multi-view benchmark