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.

CNVR-MVS		98.73 1	99.17 4	98.22 1	99.47 1	99.85 2	99.57 2	96.23 1	99.30 9	94.90 5	98.65 10	98.93 13	99.36 1	99.46 3	98.21 9	99.81 6	99.80 36

HSP-MVS		98.70 2	99.28 1	98.03 3	99.21 11	99.82 4	99.17 16	96.09 8	99.54 2	94.79 6	98.79 6	99.55 4	99.05 4	99.54 1	98.19 12	99.84 3	99.52 65

ESAPD		98.61 3	99.15 5	97.97 5	99.36 4	99.80 5	99.56 3	96.18 2	99.26 10	93.88 12	98.64 11	99.98 1	99.04 5	98.89 8	97.49 28	99.79 9	99.98 3

APDe-MVS		98.60 4	98.97 7	98.18 2	99.38 3	99.78 9	99.35 9	96.14 5	99.24 11	95.66 3	98.19 17	99.01 11	98.66 12	98.77 10	97.80 21	99.86 2	99.97 5

NCCC		98.41 5	99.18 2	97.52 11	99.36 4	99.84 3	99.55 4	96.08 10	99.33 8	91.77 20	98.79 6	99.46 6	98.59 14	99.15 6	98.07 17	99.73 12	99.64 50

SD-MVS		98.33 6	99.01 6	97.54 10	97.17 44	99.77 10	99.14 18	96.09 8	99.34 7	94.06 11	97.91 22	99.89 2	99.18 3	97.99 23	98.21 9	99.63 23	99.95 9

APD-MVS		98.28 7	98.69 11	97.80 6	99.31 8	99.62 23	99.31 12	96.15 4	99.19 13	93.60 13	97.28 25	98.35 20	98.72 11	98.27 16	98.22 8	99.73 12	99.89 23
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MCST-MVS		98.20 8	99.18 2	97.06 17	99.27 10	99.87 1	99.37 7	96.11 6	99.37 5	89.29 28	98.76 8	99.50 5	98.37 20	99.23 5	97.64 24	99.95 1	99.87 29

HPM-MVS++		98.16 9	98.87 10	97.32 13	99.39 2	99.70 15	99.18 15	96.10 7	99.09 15	91.14 22	98.02 20	99.89 2	98.44 18	98.75 11	97.03 41	99.67 18	99.63 54

MSLP-MVS++		98.12 10	98.23 23	97.99 4	99.28 9	99.72 12	99.59 1	95.27 22	98.61 25	94.79 6	96.11 29	97.79 29	99.27 2	96.62 52	98.96 4	99.77 10	99.80 36

HFP-MVS		98.02 11	98.55 15	97.40 12	99.11 15	99.69 16	99.41 5	95.41 20	98.79 23	91.86 19	98.61 12	98.16 22	99.02 6	97.87 27	97.40 30	99.60 28	99.35 76

TSAR-MVS + MP.		97.98 12	98.62 14	97.23 15	97.08 45	99.55 29	99.17 16	95.69 15	99.40 4	93.04 15	96.68 27	98.96 12	98.58 15	98.82 9	96.95 43	99.81 6	99.96 6

MPTG		97.93 13	98.05 27	97.80 6	99.20 12	99.64 19	99.40 6	95.76 13	98.01 44	94.31 10	96.54 28	98.49 19	98.58 15	98.22 19	96.23 52	99.54 51	99.23 83

SteuartSystems-ACMMP		97.86 14	98.91 8	96.64 21	98.89 21	99.79 6	99.34 10	95.20 24	98.48 27	89.91 26	98.58 13	98.69 15	96.84 39	98.92 7	98.16 14	99.66 19	99.74 39
Skip Steuart: Steuart Systems R&D Blog.
CP-MVS		97.81 15	98.26 22	97.28 14	99.00 18	99.65 18	99.10 19	95.32 21	98.38 33	92.21 18	98.33 15	97.74 30	98.50 17	97.66 34	96.55 51	99.57 39	99.48 70

ACMMPR		97.78 16	98.28 20	97.20 16	99.03 17	99.68 17	99.37 7	95.24 23	98.86 22	91.16 21	97.86 23	97.26 32	98.79 9	97.64 36	97.40 30	99.60 28	99.25 82

DeepC-MVS_fast	95.01 1	97.67 17	98.22 24	97.02 18	99.00 18	99.79 6	99.10 19	95.82 12	99.05 16	89.53 27	93.54 43	96.77 35	98.83 7	99.34 4	99.44 1	99.82 4	99.63 54
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		97.54 18	97.35 33	97.77 8	99.17 13	99.55 29	98.57 25	95.76 13	99.04 17	94.66 8	97.94 21	94.39 48	98.82 8	96.21 58	94.78 71	99.62 25	99.52 65

ACMMP_Plus		97.51 19	98.27 21	96.63 22	99.34 6	99.72 12	99.25 13	95.94 11	98.11 38	87.10 42	96.98 26	98.50 18	98.61 13	98.58 13	96.83 46	99.56 43	99.14 90

MP-MVS		97.46 20	98.30 19	96.48 23	98.93 20	99.43 39	99.20 14	95.42 19	98.43 29	87.60 39	98.19 17	98.01 28	98.09 22	98.05 22	96.67 49	99.64 21	99.35 76

train_agg		97.42 21	98.88 9	95.71 27	98.46 28	99.60 26	99.05 21	95.16 25	99.10 14	84.38 54	98.47 14	98.85 14	97.61 26	98.54 14	97.66 23	99.62 25	99.93 15

CPTT-MVS		97.32 22	97.60 32	96.99 19	98.29 31	99.31 50	99.04 22	94.67 29	97.99 45	93.12 14	98.03 19	98.26 21	98.77 10	96.08 62	94.26 79	98.07 184	99.27 81

X-MVS		97.20 23	98.42 18	95.77 25	99.04 16	99.64 19	98.95 24	95.10 27	98.16 36	83.97 58	98.27 16	98.08 25	97.95 23	97.89 24	97.46 29	99.58 34	99.47 71

PHI-MVS		97.09 24	98.69 11	95.22 32	97.99 37	99.59 28	97.56 38	92.16 33	98.41 31	87.11 41	98.70 9	99.42 7	96.95 35	96.88 49	98.16 14	99.56 43	99.70 44

PGM-MVS		97.03 25	98.14 26	95.73 26	99.34 6	99.61 25	99.34 10	89.99 39	97.70 48	87.67 38	99.44 2	96.45 38	98.44 18	97.65 35	97.09 38	99.58 34	99.06 99

PLC	94.37 2	97.03 25	96.54 36	97.60 9	98.84 22	98.64 69	98.17 30	94.99 28	99.01 18	96.80 1	93.21 47	95.64 40	97.36 28	96.37 55	94.79 70	99.41 80	98.12 135
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TSAR-MVS + ACMM		96.90 27	98.64 13	94.88 34	98.12 35	99.47 34	99.01 23	95.43 18	99.23 12	81.98 76	95.95 30	99.16 10	95.13 62	98.61 12	98.11 16	99.58 34	99.93 15

TSAR-MVS + GP.		96.47 28	98.45 17	94.17 39	92.12 76	99.29 51	97.76 34	88.05 50	99.36 6	90.26 25	97.82 24	99.21 8	97.21 31	96.78 51	96.74 47	99.63 23	99.94 12

EPNet		96.23 29	97.89 29	94.29 37	97.62 40	99.44 38	97.14 46	88.63 46	98.16 36	88.14 34	99.46 1	94.15 49	94.61 70	97.20 42	97.23 34	99.57 39	99.59 59
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNLPA		96.14 30	95.43 45	96.98 20	98.55 25	99.41 43	95.91 52	95.15 26	99.00 19	95.71 2	84.21 100	94.55 46	97.25 30	95.50 87	96.23 52	99.28 96	99.09 98

MVS_111021_LR		96.07 31	97.94 28	93.88 42	97.86 38	99.43 39	95.70 55	89.65 42	98.73 24	84.86 52	99.38 3	94.08 50	95.78 59	97.81 30	96.73 48	99.43 78	99.42 73

ACMMP		96.05 32	96.70 35	95.29 31	98.01 36	99.43 39	97.60 37	94.33 31	97.62 51	86.17 45	98.92 4	92.81 57	96.10 52	95.67 76	93.33 99	99.55 48	99.12 93
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+	90.72 7	95.99 33	96.42 38	95.50 29	98.18 33	99.33 49	97.44 40	87.73 55	97.93 46	92.36 17	84.67 93	97.33 31	97.55 27	97.32 39	98.47 7	99.72 16	99.88 24

DeepPCF-MVS	94.02 3	95.92 34	98.47 16	92.95 51	97.57 41	99.79 6	91.45 110	94.42 30	99.76 1	86.48 44	92.88 49	98.12 24	92.62 88	99.49 2	99.32 2	95.15 213	99.95 9

CDPH-MVS		95.90 35	97.77 31	93.72 45	98.28 32	99.43 39	98.40 26	91.30 37	98.34 34	78.62 98	94.80 35	95.74 39	96.11 51	97.86 28	98.67 6	99.59 30	99.56 62

CSCG		95.77 36	95.35 47	96.26 24	99.13 14	99.60 26	98.14 31	91.89 36	96.57 64	92.61 16	89.65 60	91.74 64	96.96 33	93.69 117	96.58 50	98.86 126	99.63 54

OMC-MVS		95.75 37	95.84 42	95.64 28	98.52 27	99.34 48	97.15 45	92.02 35	98.94 21	90.45 24	88.31 63	94.64 44	96.35 47	96.02 65	95.99 61	99.34 89	97.65 144

MVS_111021_HR		95.70 38	98.16 25	92.83 52	97.57 41	99.77 10	94.78 68	88.05 50	98.61 25	82.29 69	98.85 5	94.66 43	94.63 69	97.80 31	97.63 25	99.64 21	99.79 38

3Dnovator	90.31 8	95.67 39	96.16 40	95.11 33	98.59 24	99.37 47	97.50 39	87.98 52	98.02 43	89.09 29	85.36 89	94.62 45	97.66 24	97.10 45	98.90 5	99.82 4	99.73 41

CANet		95.40 40	96.27 39	94.40 36	96.25 50	99.62 23	98.37 27	88.59 47	98.09 39	87.58 40	84.57 95	95.54 42	95.87 57	98.12 20	98.03 19	99.73 12	99.90 21

QAPM		95.17 41	96.05 41	94.14 40	98.55 25	99.49 32	97.41 41	87.88 53	97.72 47	84.21 56	84.59 94	95.60 41	97.21 31	97.10 45	98.19 12	99.57 39	99.65 48

MVSTER		94.75 42	96.50 37	92.70 55	90.91 103	94.51 135	97.37 43	83.37 90	98.40 32	89.04 30	93.23 46	97.04 34	95.91 55	97.73 32	95.59 65	99.61 27	99.01 100

TAPA-MVS	92.04 6	94.72 43	95.13 49	94.24 38	97.72 39	99.17 54	97.61 36	92.16 33	97.66 50	81.99 75	87.84 70	93.94 51	96.50 45	95.74 73	94.27 78	99.46 73	97.31 152
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepC-MVS	92.23 5	94.53 44	94.26 58	94.86 35	96.73 47	99.50 31	97.85 33	95.45 17	96.22 72	82.73 65	80.68 110	88.02 75	96.92 36	97.49 38	98.20 11	99.47 62	99.69 46
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CHOSEN 280x420		94.51 45	97.78 30	90.70 73	95.54 56	99.49 32	94.14 78	74.91 152	98.43 29	85.32 50	94.78 36	99.19 9	94.95 66	97.02 47	96.18 56	99.35 85	99.36 75

MVS_0304		94.35 46	95.66 44	92.83 52	94.82 58	99.46 36	98.19 29	87.75 54	97.32 56	81.83 78	83.50 102	93.19 55	94.71 68	98.24 18	98.07 17	99.68 17	99.83 32

MAR-MVS		94.18 47	95.12 50	93.09 50	98.40 30	99.17 54	94.20 77	81.92 98	98.47 28	86.52 43	90.92 56	84.21 92	98.12 21	95.88 68	97.59 26	99.40 81	99.58 61
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
PCF-MVS	92.56 4	93.95 48	93.82 62	94.10 41	96.07 52	99.25 52	96.82 48	95.51 16	92.00 118	81.51 79	82.97 105	93.88 53	95.63 61	94.24 105	94.71 73	99.09 108	99.70 44
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DELS-MVS		93.82 49	93.82 62	93.81 44	96.34 49	99.47 34	97.26 44	88.53 48	92.13 116	87.80 37	79.67 112	88.01 76	93.14 80	98.28 15	99.22 3	99.80 8	99.98 3
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
OpenMVS	88.43 11	93.49 50	93.62 65	93.34 46	98.46 28	99.39 44	97.00 47	87.66 57	95.37 80	81.21 80	75.96 127	91.58 65	96.21 50	96.37 55	97.10 37	99.52 52	99.54 64

PVSNet_BlendedMVS		93.30 51	93.46 70	93.10 48	95.60 54	99.38 45	93.59 84	88.70 44	98.09 39	88.10 35	86.96 76	75.02 121	93.08 81	97.89 24	96.90 44	99.56 43	100.00 1

PVSNet_Blended		93.30 51	93.46 70	93.10 48	95.60 54	99.38 45	93.59 84	88.70 44	98.09 39	88.10 35	86.96 76	75.02 121	93.08 81	97.89 24	96.90 44	99.56 43	100.00 1

PMMVS		93.05 53	95.40 46	90.31 79	91.41 92	97.54 94	92.62 95	83.25 92	98.08 42	79.44 95	95.18 33	88.52 74	96.43 46	95.70 74	93.88 84	98.68 154	98.91 102

conf0.002		92.80 54	93.55 69	91.93 58	91.66 78	98.85 57	95.03 62	86.42 62	93.24 103	82.20 72	92.98 48	79.35 112	96.80 40	95.83 69	94.67 75	99.48 58	99.91 19

diffmvs		92.73 55	94.75 52	90.37 77	90.81 107	98.11 79	94.69 71	80.93 109	96.91 61	82.50 68	85.28 91	92.99 56	93.84 76	94.67 102	96.19 55	99.44 77	99.12 93

LS3D		92.70 56	92.23 86	93.26 47	96.24 51	98.72 61	97.93 32	96.17 3	96.41 65	72.46 111	81.39 108	80.76 104	97.66 24	95.69 75	95.62 64	99.07 110	97.02 161

IS_MVSNet		92.67 57	94.99 51	89.96 83	91.17 96	98.54 73	92.77 90	84.00 86	92.72 112	81.90 77	85.67 87	92.47 59	90.39 107	97.82 29	97.81 20	99.51 53	99.91 19

TSAR-MVS + COLMAP		92.56 58	92.44 83	92.71 54	94.61 60	97.69 89	97.69 35	91.09 38	98.96 20	76.71 100	94.68 37	69.41 147	96.91 37	95.80 71	94.18 80	99.26 97	96.33 175

canonicalmvs		92.54 59	93.28 72	91.68 61	91.44 91	98.24 78	95.45 60	81.84 102	95.98 76	84.85 53	90.69 58	78.53 113	96.96 33	92.97 123	97.06 39	99.57 39	99.47 71

PatchMatch-RL		92.54 59	92.82 79	92.21 56	96.57 48	98.74 60	91.85 106	86.30 65	96.23 71	85.18 51	95.21 32	73.58 125	94.22 74	95.40 90	93.08 103	99.14 103	97.49 150

MVS_Test		92.42 61	94.43 53	90.08 82	90.69 108	98.26 77	94.78 68	80.81 111	97.27 57	78.76 97	87.06 74	84.25 91	95.84 58	97.67 33	97.56 27	99.59 30	98.93 101

conf0.01		92.41 62	92.86 78	91.90 59	91.65 79	98.84 58	95.03 62	86.38 64	93.24 103	82.03 74	91.90 55	77.54 115	96.80 40	95.78 72	92.82 111	99.48 58	99.90 21

EPP-MVSNet		92.29 63	94.35 57	89.88 84	90.36 112	97.69 89	90.89 114	83.31 91	93.39 102	83.47 62	85.56 88	93.92 52	91.93 95	95.49 88	94.77 72	99.34 89	99.62 57

tfpn_ndepth		92.26 64	93.84 61	90.42 76	91.45 90	97.91 85	92.73 91	85.80 74	96.69 63	82.22 70	91.92 54	83.42 94	90.76 105	95.51 86	93.28 100	99.58 34	98.14 131

thresconf0.02		92.16 65	95.16 48	88.67 95	91.10 97	97.63 91	92.93 88	86.58 61	96.29 69	73.55 107	94.67 38	88.63 72	88.29 124	96.14 61	95.40 66	99.58 34	97.33 151

DWT-MVSNet_training		92.09 66	93.58 68	90.35 78	91.27 93	97.94 84	92.05 101	78.82 124	97.40 54	88.83 32	87.91 65	86.76 84	91.99 94	90.03 142	95.25 67	99.13 105	99.73 41

tfpn111		91.99 67	92.28 85	91.65 62	91.61 80	98.69 63	95.03 62	86.17 66	93.24 103	80.82 82	94.67 38	71.15 133	96.80 40	95.53 80	92.82 111	99.47 62	99.88 24

HQP-MVS		91.94 68	93.03 75	90.66 75	93.69 62	96.48 109	95.92 51	89.73 40	97.33 55	72.65 109	95.37 31	73.56 126	92.75 87	94.85 99	94.12 81	99.23 100	99.51 67

MSDG		91.93 69	90.28 114	93.85 43	97.36 43	97.12 100	95.88 53	94.07 32	94.52 90	84.13 57	76.74 122	80.89 103	92.54 89	93.97 113	93.61 94	99.14 103	95.10 186

UGNet		91.71 70	94.43 53	88.53 96	92.72 72	98.00 82	90.22 121	84.81 84	94.45 91	83.05 63	87.65 72	92.74 58	81.04 177	94.51 104	94.45 76	99.32 94	99.21 87
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
thres100view900		91.69 71	91.52 92	91.88 60	91.61 80	98.89 56	95.49 58	86.96 59	93.24 103	80.82 82	87.90 66	71.15 133	96.88 38	96.00 66	93.51 96	99.51 53	99.95 9

CLD-MVS		91.67 72	91.30 98	92.10 57	91.25 95	96.59 106	95.93 50	87.25 58	96.86 62	85.55 49	87.08 73	73.01 127	93.26 79	93.07 121	92.84 108	99.34 89	99.68 47
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tfpn1000		91.48 73	93.17 74	89.51 88	91.27 93	97.71 88	92.08 100	85.28 81	96.13 73	80.20 88	90.77 57	82.52 97	88.64 120	95.17 95	92.35 121	99.56 43	97.52 149

conf200view11		91.47 74	91.31 95	91.65 62	91.61 80	98.69 63	95.03 62	86.17 66	93.24 103	80.82 82	87.90 66	71.15 133	96.80 40	95.53 80	92.82 111	99.47 62	99.88 24

tfpn200view9		91.47 74	91.31 95	91.65 62	91.61 80	98.69 63	95.03 62	86.17 66	93.24 103	80.82 82	87.90 66	71.15 133	96.80 40	95.53 80	92.82 111	99.47 62	99.88 24

CANet_DTU		91.36 76	95.75 43	86.23 109	92.31 75	98.71 62	95.60 57	78.41 128	98.20 35	56.48 187	94.38 41	87.96 77	95.11 63	96.89 48	96.07 57	99.48 58	98.01 139

thres200		91.36 76	91.19 100	91.55 65	91.60 84	98.69 63	94.98 67	86.17 66	92.16 115	80.76 86	87.66 71	71.15 133	96.35 47	95.53 80	93.23 102	99.47 62	99.92 18

tfpn		91.26 78	91.55 91	90.92 72	91.47 89	98.50 75	93.85 83	85.72 75	91.40 126	79.30 96	84.78 92	77.33 116	95.70 60	95.29 92	93.73 86	99.47 62	99.82 34

FMVSNet3		91.25 79	92.13 88	90.21 80	85.64 142	93.14 144	95.29 61	80.09 112	96.40 66	85.74 46	77.13 116	86.81 81	94.98 65	97.19 43	97.11 36	99.55 48	97.13 156

thres400		91.24 80	91.01 105	91.50 67	91.56 85	98.77 59	94.66 72	86.41 63	91.87 120	80.56 87	87.05 75	71.01 138	96.35 47	95.67 76	92.82 111	99.48 58	99.88 24

PVSNet_Blended_VisFu		91.20 81	92.89 77	89.23 91	93.41 65	98.61 71	89.80 122	85.39 80	92.84 110	82.80 64	74.21 132	91.38 67	84.64 141	97.22 41	96.04 60	99.34 89	99.93 15

DI_MVS_plusplus_trai		91.11 82	91.47 93	90.68 74	90.01 114	97.77 86	95.87 54	83.56 89	94.72 87	82.12 73	68.46 149	87.46 78	93.07 83	96.46 54	95.73 63	99.47 62	99.71 43

Vis-MVSNet (Re-imp)		91.05 83	94.43 53	87.11 102	91.05 99	97.99 83	92.53 96	83.82 88	92.71 113	76.28 101	84.50 96	92.43 60	79.52 183	97.24 40	97.68 22	99.43 78	98.45 116

view600		90.97 84	90.70 107	91.30 68	91.53 86	98.69 63	94.33 73	86.17 66	91.75 122	80.19 89	86.06 84	70.90 139	96.10 52	95.53 80	92.08 124	99.47 62	99.86 30

thres600view7		90.97 84	90.70 107	91.30 68	91.53 86	98.69 63	94.33 73	86.17 66	91.75 122	80.19 89	86.06 84	70.90 139	96.10 52	95.53 80	92.08 124	99.47 62	99.86 30

view800		90.79 86	90.54 111	91.09 71	91.50 88	98.58 72	94.09 79	85.92 73	91.57 125	79.68 92	85.29 90	70.72 142	95.91 55	95.40 90	92.39 120	99.47 62	99.83 32

ACMP	89.80 9	90.72 87	91.15 101	90.21 80	92.55 73	96.52 108	92.63 94	85.71 76	94.65 88	81.06 81	93.32 44	70.56 143	90.52 106	92.68 127	91.05 133	98.76 135	99.31 80
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	89.40 10	90.58 88	90.02 117	91.23 70	93.30 67	94.75 131	90.69 117	88.22 49	95.20 81	82.70 66	88.54 62	71.40 132	93.48 78	93.64 118	90.94 134	98.99 118	95.72 183
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		90.49 89	91.12 103	89.75 86	84.99 145	92.73 147	93.94 80	80.09 112	96.40 66	85.74 46	77.13 116	86.81 81	94.42 71	94.12 107	93.73 86	99.35 85	96.90 165

test1		90.49 89	91.12 103	89.75 86	84.99 145	92.73 147	93.94 80	80.09 112	96.40 66	85.74 46	77.13 116	86.81 81	94.42 71	94.12 107	93.73 86	99.35 85	96.90 165

tfpnview11		90.36 91	92.74 80	87.59 98	90.93 102	97.30 99	92.28 98	85.63 77	95.88 77	70.44 117	92.30 51	79.50 109	86.76 134	95.26 94	92.83 110	99.51 53	96.09 176

LGP-MVS_train		90.34 92	91.63 90	88.83 94	93.31 66	96.14 113	95.49 58	85.24 82	93.91 95	68.71 125	93.96 42	71.63 130	91.12 102	93.82 115	92.79 117	99.07 110	99.16 89

tfpn_n400		90.13 93	92.47 81	87.40 99	90.89 104	97.37 97	92.05 101	85.47 78	93.43 100	70.44 117	92.30 51	79.50 109	86.50 135	94.84 100	93.93 82	99.07 110	95.91 179

tfpnconf		90.13 93	92.47 81	87.40 99	90.89 104	97.37 97	92.05 101	85.47 78	93.43 100	70.44 117	92.30 51	79.50 109	86.50 135	94.84 100	93.93 82	99.07 110	95.91 179

EPNet_dtu		89.82 95	94.18 59	84.74 120	96.87 46	95.54 124	92.65 93	86.91 60	96.99 59	54.17 200	92.41 50	88.54 73	78.35 189	96.15 60	96.05 59	99.47 62	93.60 194
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		89.81 96	89.75 119	89.88 84	93.22 69	93.99 138	94.78 68	85.23 83	94.01 94	82.52 67	95.00 34	87.23 79	92.01 93	85.16 204	83.48 212	91.54 219	89.38 210

MDTV_nov1_ep13		89.63 97	94.38 56	84.09 126	88.76 126	97.53 95	89.37 130	68.46 196	96.95 60	70.27 121	87.88 69	93.67 54	91.04 103	93.12 119	93.83 85	96.62 205	97.68 143

UA-Net		89.56 98	93.03 75	85.52 116	92.46 74	97.55 93	91.92 105	81.91 99	85.24 152	71.39 113	83.57 101	96.56 37	76.01 198	96.81 50	97.04 40	99.46 73	94.41 189

FMVSNet2		89.51 99	89.63 120	89.38 89	84.99 145	92.73 147	93.94 80	79.28 118	93.73 97	84.28 55	69.36 148	82.32 98	94.42 71	96.16 59	96.22 54	99.35 85	96.90 165

CostFormer		89.42 100	91.67 89	86.80 105	89.99 115	96.33 111	90.75 115	64.79 203	95.17 82	83.62 61	86.20 82	82.15 99	92.96 84	89.22 156	92.94 104	98.68 154	99.65 48

FC-MVSNet-train		89.37 101	89.62 121	89.08 93	90.48 110	94.16 137	89.45 126	83.99 87	91.09 127	80.09 91	82.84 106	74.52 124	91.44 99	93.79 116	91.57 130	99.01 116	99.35 76

OPM-MVS		89.33 102	87.45 134	91.53 66	94.49 61	96.20 112	96.47 49	89.72 41	82.77 163	75.43 102	80.53 111	70.86 141	93.80 77	94.00 111	91.85 128	99.29 95	95.91 179

test-LLR		89.31 103	93.60 66	84.30 123	88.08 129	96.98 101	88.10 134	78.00 131	94.83 84	62.43 143	84.29 98	90.96 68	89.70 112	95.63 78	92.86 106	99.51 53	99.64 50

EPMVS		89.31 103	93.70 64	84.18 125	91.10 97	98.10 80	89.17 131	62.71 207	96.24 70	70.21 122	86.46 80	92.37 61	92.79 85	91.95 133	93.59 95	99.10 107	97.19 153

Effi-MVS+		88.96 105	91.13 102	86.43 107	89.12 122	97.62 92	93.15 86	75.52 147	93.90 96	66.40 129	86.23 81	70.51 144	95.03 64	95.89 67	94.28 77	99.37 82	99.51 67

test0.0.03 1		88.71 106	92.22 87	84.63 121	88.08 129	94.71 133	85.91 168	78.00 131	95.54 79	72.96 108	86.10 83	85.88 86	83.59 151	92.95 125	93.24 101	99.25 99	97.09 157

PatchmatchNet		88.67 107	94.10 60	82.34 141	89.38 120	97.72 87	87.24 141	62.18 212	97.00 58	64.79 134	87.97 64	94.43 47	91.55 97	91.21 137	92.77 118	98.90 122	97.60 146
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dps		88.66 108	90.19 115	86.88 104	89.94 116	96.48 109	89.56 124	64.08 205	94.12 93	89.00 31	83.39 103	82.56 96	90.16 110	86.81 190	89.26 152	98.53 170	98.71 107

TESTMET0.1,1		88.63 109	93.60 66	82.84 138	84.07 151	96.98 101	88.10 134	73.22 168	94.83 84	62.43 143	84.29 98	90.96 68	89.70 112	95.63 78	92.86 106	99.51 53	99.64 50

CHOSEN 1792x2688		88.63 109	89.01 125	88.19 97	94.83 57	99.21 53	92.66 92	79.85 115	92.40 114	72.18 112	56.38 201	80.22 105	90.24 108	97.64 36	97.28 33	99.37 82	99.94 12

CDS-MVSNet		88.59 111	90.13 116	86.79 106	86.98 136	95.43 125	92.03 104	81.33 107	85.54 149	74.51 105	77.07 119	85.14 88	87.03 132	93.90 114	95.18 68	98.88 124	98.67 109
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IB-MVS	84.67 14	88.34 112	90.61 110	85.70 113	92.99 71	98.62 70	78.85 201	86.07 72	94.35 92	88.64 33	85.99 86	75.69 119	68.09 211	88.21 159	91.43 131	99.55 48	99.96 6
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
conf0.05thres1000		88.28 113	87.54 133	89.15 92	91.00 101	97.50 96	92.18 99	84.70 85	85.15 154	73.91 106	73.77 134	70.50 146	94.01 75	93.99 112	92.21 122	99.11 106	99.64 50

test-mter		88.25 114	93.27 73	82.38 140	83.89 152	96.86 104	87.10 145	72.80 170	94.58 89	61.85 148	83.21 104	90.65 70	89.18 115	95.43 89	92.58 119	99.46 73	99.61 58

COLMAP_ROB	84.42 15	88.24 115	87.32 135	89.32 90	95.83 53	95.82 117	92.81 89	87.68 56	92.09 117	72.64 110	72.34 140	79.96 107	88.79 117	89.54 151	89.46 148	98.16 181	92.00 200
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tpmp4_e23		88.10 116	90.02 117	85.86 111	89.94 116	95.73 121	91.83 107	64.92 201	94.79 86	78.25 99	81.03 109	78.34 114	92.33 91	88.10 161	92.82 111	97.90 191	99.34 79

IterMVS-LS		87.95 117	89.40 123	86.26 108	88.79 125	90.93 180	91.23 112	76.05 144	90.87 128	71.07 115	75.51 129	81.18 102	91.21 101	94.11 110	95.01 69	99.20 102	98.23 129
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		87.86 118	88.25 129	87.40 99	94.67 59	98.54 73	90.33 120	76.51 143	89.60 135	70.89 116	51.43 216	85.69 87	92.79 85	96.59 53	95.96 62	99.22 101	99.94 12

Vis-MVSNet		87.60 119	91.31 95	83.27 133	89.14 121	98.04 81	90.35 119	79.42 116	87.23 139	66.92 128	79.10 115	84.63 90	74.34 204	95.81 70	96.06 58	99.46 73	98.32 125
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
RPMNet		87.35 120	92.41 84	81.45 145	88.85 124	96.06 114	89.42 129	59.59 221	93.57 98	61.81 149	76.48 125	91.48 66	90.18 109	96.32 57	93.37 98	98.87 125	99.59 59

tpm cat1		87.34 121	88.52 128	85.95 110	89.83 118	95.80 118	90.73 116	64.91 202	92.99 109	82.21 71	71.19 146	82.68 95	90.13 111	86.38 194	90.87 136	97.90 191	99.74 39

MS-PatchMatch		87.19 122	88.59 127	85.55 115	93.15 70	96.58 107	92.35 97	74.19 160	91.97 119	70.33 120	71.42 144	85.89 85	84.28 144	93.12 119	89.16 154	99.00 117	91.99 201

Effi-MVS+-dtu		87.18 123	90.48 112	83.32 132	86.51 138	95.76 120	91.16 113	74.28 159	90.44 132	61.31 152	86.72 79	72.68 128	91.25 100	95.01 97	93.64 90	95.45 212	99.12 93

FMVSNet5		87.06 124	89.52 122	84.20 124	79.92 202	86.57 202	87.11 144	72.37 172	96.06 74	75.41 103	84.33 97	91.76 63	91.60 96	91.51 135	91.22 132	98.77 132	85.16 217

Fast-Effi-MVS+-dtu		86.94 125	91.27 99	81.89 142	86.27 139	95.06 126	90.68 118	68.93 193	91.76 121	57.18 185	89.56 61	75.85 118	89.19 114	94.56 103	92.84 108	99.07 110	99.23 83

Fast-Effi-MVS+		86.94 125	87.88 131	85.84 112	86.99 135	95.80 118	91.24 111	73.48 166	92.75 111	69.22 123	72.70 138	65.71 154	94.84 67	94.98 98	94.71 73	99.26 97	98.48 115

tpmrst		86.78 127	90.29 113	82.69 139	90.55 109	96.95 103	88.49 133	62.58 208	95.09 83	63.52 140	76.67 124	84.00 93	92.05 92	87.93 163	91.89 127	98.98 119	99.50 69

CR-MVSNet		86.73 128	91.47 93	81.20 151	88.56 127	96.06 114	89.43 127	61.37 215	93.57 98	60.81 154	72.89 137	88.85 71	88.13 126	96.03 63	93.64 90	98.89 123	99.22 85

ADS-MVSNet		86.68 129	90.79 106	81.88 143	90.38 111	96.81 105	86.90 146	60.50 219	96.01 75	63.93 137	81.67 107	84.72 89	90.78 104	87.03 176	91.67 129	98.77 132	97.63 145

FMVSNet1		85.85 130	84.91 144	86.96 103	82.70 157	91.39 174	91.54 109	77.45 135	85.29 151	79.56 94	60.70 164	72.68 128	92.37 90	94.12 107	93.73 86	98.12 182	96.44 172

FC-MVSNet-test		85.51 131	89.08 124	81.35 146	85.31 144	93.35 140	87.65 136	77.55 134	90.01 133	64.07 136	79.63 113	81.83 101	74.94 201	92.08 130	90.83 138	98.55 167	95.81 182

ACMH	85.22 13	85.40 132	85.73 141	85.02 118	91.76 77	94.46 136	84.97 179	81.54 105	85.18 153	65.22 133	76.92 121	64.22 155	88.58 121	90.17 140	90.25 144	98.03 185	98.90 103
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		85.35 133	86.00 140	84.59 122	84.97 148	95.57 123	88.98 132	77.29 138	81.44 174	71.36 114	71.48 143	75.00 123	87.03 132	91.92 134	92.21 122	97.92 190	94.40 190

ACMH+	85.62 12	85.27 134	84.96 143	85.64 114	90.84 106	94.78 130	87.46 138	81.30 108	86.94 140	67.35 127	74.56 131	64.09 156	88.70 118	88.14 160	89.00 155	98.22 180	97.19 153

USDC		85.11 135	85.35 142	84.83 119	89.45 119	94.93 129	92.98 87	77.30 137	90.53 130	61.80 150	76.69 123	59.62 165	88.90 116	92.78 126	90.79 140	98.53 170	92.12 198

IterMVS		85.02 136	88.98 126	80.41 161	87.03 134	90.34 190	89.78 123	69.45 189	89.77 134	54.04 201	73.71 135	82.05 100	83.44 156	95.11 96	93.64 90	98.75 140	98.22 130
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchT		84.89 137	90.67 109	78.13 191	87.83 132	94.99 128	72.46 213	60.22 220	91.74 124	60.81 154	72.16 141	86.95 80	88.13 126	96.03 63	93.64 90	99.36 84	99.22 85

pmmvs4		84.88 138	84.67 145	85.13 117	82.80 156	92.37 152	87.29 139	79.08 119	90.51 131	74.94 104	70.37 147	62.49 158	88.17 125	92.01 132	88.51 161	98.49 172	96.44 172

CVMVSNet		84.01 139	86.91 136	80.61 158	88.39 128	93.29 141	86.06 158	82.29 96	83.13 160	54.29 197	72.68 139	79.59 108	75.11 200	91.23 136	92.91 105	97.54 198	95.58 184

tpm		83.97 140	87.97 130	79.31 180	87.35 133	93.21 143	86.00 163	61.90 213	90.69 129	54.01 202	79.42 114	75.61 120	88.65 119	87.18 171	90.48 142	97.95 189	99.21 87

GA-MVS		83.83 141	86.63 137	80.58 159	85.40 143	94.73 132	87.27 140	78.76 126	86.49 142	49.57 210	74.21 132	67.67 150	83.38 158	95.28 93	90.92 135	99.08 109	97.09 157

UniMVSNet_NR-MVSNet		83.83 141	83.70 148	83.98 127	81.41 170	92.56 151	86.54 150	82.96 93	85.98 145	66.27 130	66.16 155	63.63 157	87.78 129	87.65 166	90.81 139	98.94 120	99.13 91

UniMVSNet (Re)		83.28 143	83.16 149	83.42 131	81.93 160	93.12 145	86.27 152	80.83 110	85.88 146	68.23 126	64.56 158	60.58 160	84.25 145	89.13 157	89.44 150	99.04 115	99.40 74

TinyColmap		83.03 144	82.24 153	83.95 128	88.88 123	93.22 142	89.48 125	76.89 140	87.53 138	62.12 145	68.46 149	55.03 200	88.43 123	90.87 138	89.65 146	97.89 193	90.91 205

testgi		82.88 145	86.14 139	79.08 184	86.05 140	92.20 159	81.23 198	74.77 155	88.70 136	57.63 183	86.73 78	61.53 159	76.83 196	90.33 139	89.43 151	97.99 186	94.05 191

DU-MVS		82.87 146	82.16 154	83.70 130	80.77 189	92.24 156	86.54 150	81.91 99	86.41 143	66.27 130	63.95 159	55.66 198	87.78 129	86.83 187	90.86 137	98.94 120	99.13 91

MIMVSNet		82.87 146	86.17 138	79.02 185	77.23 213	92.88 146	84.88 180	60.62 218	86.72 141	64.16 135	73.58 136	71.48 131	88.51 122	94.14 106	93.50 97	98.72 145	90.87 206

NR-MVSNet		82.37 148	81.95 156	82.85 137	82.56 159	92.24 156	87.49 137	81.91 99	86.41 143	65.51 132	63.95 159	52.93 210	80.80 179	89.41 153	89.61 147	98.85 127	99.10 97

Baseline_NR-MVSNet		82.08 149	80.64 166	83.77 129	80.77 189	88.50 197	86.88 147	81.71 103	85.58 148	68.80 124	58.20 193	57.75 177	86.16 137	86.83 187	88.68 158	98.33 177	98.90 103

TranMVSNet+NR-MVSNet		82.07 150	81.36 159	82.90 136	80.43 195	91.39 174	87.16 143	82.75 94	84.28 158	62.98 141	62.28 163	56.01 195	85.30 140	86.06 197	90.69 141	98.80 128	98.80 105

pm-mvs1		81.68 151	81.70 157	81.65 144	82.61 158	92.26 155	85.54 175	78.95 120	76.29 206	63.81 138	58.43 192	66.33 153	80.63 180	92.30 128	89.93 145	98.37 176	96.39 174

testpf		81.62 152	87.82 132	74.38 206	85.88 141	89.26 195	74.45 211	48.92 231	95.87 78	60.31 162	76.95 120	80.17 106	80.07 182	85.72 201	88.77 157	96.67 204	98.01 139

TDRefinement		81.49 153	80.08 175	83.13 135	91.02 100	94.53 134	91.66 108	82.43 95	81.70 171	62.12 145	62.30 162	59.32 166	73.93 205	87.31 169	85.29 204	97.61 195	90.14 207

anonymousdsp		81.29 154	84.52 147	77.52 193	79.83 203	92.62 150	82.61 191	70.88 179	80.76 178	50.82 207	68.35 151	68.76 148	82.45 175	93.00 122	89.45 149	98.55 167	98.69 108

gg-mvs-nofinetune		81.27 155	84.65 146	77.32 194	87.96 131	98.48 76	95.64 56	56.36 226	59.35 224	32.80 231	47.96 219	92.11 62	91.49 98	98.12 20	97.00 42	99.65 20	99.56 62

tfpnnormal		81.11 156	79.33 188	83.19 134	84.23 149	92.29 154	86.76 148	82.27 97	72.67 212	62.02 147	56.10 203	53.86 208	85.35 139	92.06 131	89.23 153	98.49 172	99.11 96

v6		81.06 157	80.87 161	81.28 147	81.47 169	92.12 163	86.14 154	78.42 127	81.99 169	59.68 166	60.14 168	58.36 173	83.22 164	86.99 180	88.14 176	98.76 135	98.32 125

v1neww		81.04 158	80.86 162	81.25 148	81.48 167	92.14 161	86.06 158	78.41 128	82.02 167	59.43 168	60.09 172	58.30 175	83.37 159	87.02 178	88.15 174	98.76 135	98.33 123

v7new		81.04 158	80.86 162	81.25 148	81.48 167	92.14 161	86.06 158	78.41 128	82.02 167	59.43 168	60.09 172	58.30 175	83.37 159	87.02 178	88.15 174	98.76 135	98.33 123

V42		80.88 160	80.74 164	81.05 152	81.21 174	92.01 167	85.96 164	77.75 133	81.62 172	59.73 165	59.93 174	58.35 174	82.98 166	86.90 184	88.06 185	98.69 152	98.32 125

v2v482		80.86 161	80.52 170	81.25 148	80.79 188	91.85 168	85.68 173	78.78 125	81.05 175	58.09 180	60.46 165	56.08 193	85.45 138	87.27 170	88.53 160	98.73 144	98.38 119

v7		80.74 162	80.95 160	80.50 160	81.23 172	91.58 171	86.12 155	74.83 153	82.30 166	57.64 182	58.74 188	57.45 181	84.48 142	89.75 146	88.27 166	98.72 145	98.57 112

v1141		80.70 163	80.42 171	81.02 154	81.14 175	92.03 165	85.94 166	78.92 122	80.59 182	58.40 178	59.32 179	57.41 184	82.97 167	87.10 172	88.16 172	98.72 145	98.37 120

divwei89l23v2f112		80.69 164	80.42 171	81.02 154	81.13 176	92.04 164	85.95 165	78.92 122	80.45 184	58.43 176	59.34 178	57.46 180	82.92 168	87.09 173	88.16 172	98.75 140	98.36 122

v1		80.69 164	80.38 173	81.05 152	81.13 176	92.02 166	86.02 162	78.93 121	80.32 190	58.65 172	59.29 180	57.45 181	82.83 171	87.07 174	88.14 176	98.74 143	98.37 120

v8		80.61 166	80.61 168	80.62 157	81.51 165	91.00 179	86.06 158	74.07 162	81.78 170	59.93 164	60.10 171	58.42 172	83.35 161	86.99 180	88.11 181	98.79 129	97.83 142

pmmvs5		80.48 167	81.43 158	79.36 178	81.50 166	92.24 156	82.07 194	74.08 161	78.10 197	55.86 190	67.72 152	54.35 205	83.91 150	92.97 123	88.65 159	98.77 132	96.01 177

v10		80.38 168	80.73 165	79.96 168	81.22 173	90.40 189	86.11 156	71.63 174	82.42 165	57.65 181	58.74 188	57.47 179	84.44 143	89.75 146	88.28 165	98.71 149	98.06 138

v1144		80.36 169	80.63 167	80.05 166	80.86 187	91.56 172	85.78 172	75.22 149	80.73 179	55.83 191	58.51 191	56.99 191	83.93 149	89.79 145	88.25 167	98.68 154	98.56 113

SixPastTwentyTwo		80.28 170	82.06 155	78.21 190	81.89 161	92.35 153	77.72 203	74.48 156	83.04 162	54.22 198	76.06 126	56.40 192	83.55 152	86.83 187	84.83 207	97.38 199	94.93 187

v18		80.16 171	80.01 179	80.34 163	81.72 162	85.71 204	86.58 149	70.68 180	83.23 159	60.78 158	60.39 166	58.50 171	83.49 153	87.03 176	88.19 170	98.79 129	97.06 159

v16		80.03 172	79.95 180	80.13 165	81.64 163	85.63 206	86.17 153	70.42 183	83.12 161	60.34 161	60.11 169	58.61 169	83.45 155	86.98 182	88.12 180	98.75 140	97.05 160

v17		79.95 173	79.87 181	80.05 166	81.55 164	85.65 205	86.10 157	70.44 182	82.59 164	60.02 163	60.26 167	58.53 170	83.41 157	86.98 182	88.09 182	98.76 135	97.02 161

CP-MVSNet		79.90 174	79.49 185	80.38 162	80.72 191	90.83 181	82.98 188	75.17 150	79.70 192	61.39 151	59.74 175	51.98 213	83.31 162	87.37 168	88.38 163	98.71 149	98.45 116

v1192		79.84 175	80.05 178	79.61 171	80.49 194	91.04 178	85.56 174	74.37 158	80.73 179	54.35 196	57.07 198	54.54 204	84.23 146	89.94 143	88.38 163	98.63 161	98.61 110

WR-MVS_H		79.76 176	80.07 176	79.40 176	81.25 171	91.73 170	82.77 189	74.82 154	79.02 196	62.55 142	59.41 177	57.32 187	76.27 197	87.61 167	87.30 196	98.78 131	98.09 136

WR-MVS		79.67 177	80.25 174	79.00 186	80.65 192	91.16 176	83.31 186	76.57 142	80.97 176	60.50 160	59.20 181	58.66 168	74.38 203	85.85 199	87.76 191	98.61 162	98.14 131

v148		79.66 178	79.13 193	80.27 164	81.02 180	91.76 169	81.90 195	79.32 117	79.24 194	63.79 139	58.07 195	54.34 206	77.17 194	84.42 206	87.52 195	98.40 174	98.59 111

LTVRE_ROB	79.45 16	79.66 178	80.55 169	78.61 188	83.01 155	92.19 160	87.18 142	73.69 165	71.70 215	43.22 221	71.22 145	50.85 214	87.82 128	89.47 152	90.43 143	96.75 202	98.00 141
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
v144192		79.61 180	79.77 182	79.41 175	80.28 196	91.06 177	84.87 181	73.86 163	79.65 193	55.38 192	57.76 196	55.20 199	83.46 154	88.42 158	87.89 188	98.61 162	98.42 118

v1921920		79.55 181	79.77 182	79.30 181	80.24 197	90.77 182	85.37 178	73.75 164	80.38 187	53.78 203	56.89 200	54.18 207	84.05 147	89.55 150	88.13 179	98.59 164	98.52 114

v11		79.54 182	79.71 184	79.35 179	80.96 182	85.36 213	85.81 171	69.10 192	81.49 173	57.63 183	58.90 186	57.07 190	83.94 148	90.09 141	88.08 184	98.66 159	96.97 163

TransMVSNet (Re)		79.51 183	78.36 198	80.84 156	83.17 153	89.72 192	84.22 184	81.45 106	73.98 210	60.79 157	57.20 197	56.05 194	77.11 195	89.88 144	88.86 156	98.30 179	92.83 196

v15		79.35 184	79.20 190	79.54 173	81.08 179	85.48 207	85.92 167	70.02 185	80.60 181	58.63 173	59.14 183	57.40 185	82.87 170	86.89 185	87.95 186	98.70 151	96.92 164

MVS-HIRNet		79.34 185	82.56 150	75.57 201	84.11 150	95.02 127	75.03 210	57.28 224	85.50 150	55.88 189	53.00 213	70.51 144	83.05 165	92.12 129	91.96 126	98.09 183	89.83 209

V14		79.33 186	79.18 191	79.51 174	81.00 181	85.46 209	85.88 169	69.79 186	80.52 183	58.76 171	59.16 182	57.52 178	82.91 169	86.86 186	87.90 187	98.72 145	96.87 169

V9		79.23 187	79.09 194	79.39 177	80.95 184	85.40 210	85.85 170	69.63 187	80.42 185	58.45 175	58.94 185	57.42 183	82.77 172	86.79 191	87.85 189	98.69 152	96.83 170

v12		79.16 188	79.04 195	79.30 181	80.88 185	85.37 212	85.45 177	69.52 188	80.39 186	58.57 174	58.90 186	57.17 189	82.68 174	86.76 192	87.82 190	98.68 154	96.88 168

v13		79.09 189	78.98 196	79.22 183	80.88 185	85.34 214	85.50 176	69.40 190	80.36 188	58.14 179	58.62 190	57.30 188	82.70 173	86.72 193	87.75 192	98.67 158	96.76 171

PS-CasMVS		79.06 190	78.58 197	79.63 170	80.59 193	90.55 186	82.54 192	75.04 151	77.76 198	58.84 170	58.16 194	50.11 218	82.09 176	87.05 175	88.18 171	98.66 159	98.27 128

v1240		78.97 191	79.27 189	78.63 187	80.04 198	90.61 184	84.25 183	72.95 169	79.22 195	52.70 205	56.22 202	52.88 212	83.28 163	89.60 149	88.20 169	98.56 166	98.14 131

MDTV_nov1_ep13_2view		78.83 192	82.35 151	74.73 205	78.65 206	91.51 173	79.18 200	62.52 209	84.51 156	52.51 206	67.49 153	67.29 151	78.90 187	85.52 202	86.34 200	96.62 205	93.76 192

PEN-MVS		78.80 193	78.13 200	79.58 172	80.03 199	89.67 193	83.61 185	75.83 145	77.71 200	58.41 177	60.11 169	50.00 219	81.02 178	84.08 207	88.14 176	98.59 164	97.18 155

EG-PatchMatch MVS		78.32 194	79.42 187	77.03 198	83.03 154	93.77 139	84.47 182	69.26 191	75.85 207	53.69 204	55.68 206	60.23 163	73.20 206	89.69 148	88.22 168	98.55 167	92.54 197

DTE-MVSNet		77.92 195	77.42 204	78.51 189	79.34 204	89.00 196	83.05 187	75.60 146	76.89 202	56.58 186	59.63 176	50.31 216	78.09 192	82.57 214	87.56 194	98.38 175	95.95 178

v7n		77.71 196	78.25 199	77.09 197	78.49 207	90.55 186	82.15 193	71.11 178	76.79 203	54.18 199	55.63 207	50.20 217	78.28 190	89.36 155	87.15 197	98.33 177	98.07 137

v52		77.69 197	78.04 201	77.29 195	77.79 212	90.54 188	81.76 196	71.62 176	76.52 204	55.34 194	55.70 205	55.91 196	79.27 185	84.02 208	86.03 201	97.96 188	97.56 147

V4		77.67 198	78.01 202	77.28 196	77.82 211	90.56 185	81.70 197	71.63 174	76.33 205	55.38 192	55.74 204	55.83 197	79.20 186	84.02 208	86.01 202	97.97 187	97.55 148

gm-plane-assit		77.20 199	82.26 152	71.30 209	81.10 178	82.00 219	54.33 226	64.41 204	63.80 223	40.93 224	59.04 184	76.57 117	87.30 131	98.26 17	97.36 32	99.74 11	98.76 106

LP		77.20 199	79.14 192	74.92 204	86.71 137	90.62 183	77.97 202	57.87 223	85.88 146	50.75 208	55.29 208	66.34 152	79.39 184	80.75 215	85.03 205	96.86 201	90.09 208

N_pmnet		76.83 201	77.97 203	75.50 202	80.96 182	88.23 199	72.81 212	76.83 141	80.87 177	50.55 209	56.94 199	60.09 164	75.70 199	83.28 212	84.23 209	96.14 209	92.12 198

pmmvs6		76.79 202	75.69 210	78.09 192	79.95 201	89.57 194	80.92 199	74.46 157	64.79 221	60.74 159	45.71 222	60.55 161	78.37 188	88.04 162	86.00 203	94.07 215	95.15 185

CMPMVS	58.73 17	76.78 203	74.27 211	79.70 169	93.26 68	95.58 122	82.74 190	77.44 136	71.46 218	56.29 188	53.58 212	59.13 167	77.33 193	79.20 216	79.71 217	91.14 222	81.24 220
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		76.76 204	79.47 186	73.60 207	79.99 200	87.47 200	77.39 204	75.43 148	77.62 201	47.83 213	64.78 157	60.44 162	64.80 212	86.28 195	86.53 199	96.17 208	93.19 195

v748		76.68 205	76.82 207	76.51 199	78.70 205	90.06 191	77.12 205	73.40 167	73.32 211	59.57 167	55.00 210	50.71 215	72.48 207	83.71 211	86.78 198	97.81 194	98.13 134

PM-MVS		75.81 206	76.11 209	75.46 203	73.81 214	85.48 207	76.42 207	70.57 181	80.05 191	54.75 195	62.33 161	39.56 226	80.59 181	87.71 165	82.81 213	96.61 207	94.81 188

pmmvs-eth3d		75.17 207	74.09 212	76.43 200	72.92 217	84.49 215	76.61 206	72.42 171	74.33 208	61.28 153	54.71 211	39.42 227	78.20 191	87.77 164	84.25 208	97.17 200	93.63 193

Anonymous20231206		74.59 208	77.00 206	71.78 208	77.89 210	87.45 201	75.14 209	72.29 173	77.76 198	46.65 215	52.14 214	52.93 210	61.10 217	89.37 154	88.09 182	97.59 196	91.30 203

test2356		74.04 209	80.07 176	67.01 217	73.77 215	80.65 220	67.82 219	66.87 199	84.93 155	37.70 228	75.45 130	57.40 185	60.26 218	86.28 195	88.47 162	95.64 211	87.33 214

test20.03		72.81 210	76.24 208	68.80 212	78.31 208	85.40 210	71.04 214	71.20 177	71.85 214	43.40 220	65.31 156	54.71 203	51.27 223	85.92 198	84.18 210	97.58 197	86.35 216

testus		72.50 211	77.19 205	67.04 215	73.69 216	80.06 221	67.65 220	68.24 197	84.46 157	37.48 230	75.90 128	40.07 225	59.40 219	85.45 203	87.69 193	95.76 210	86.70 215

new_pmnet		71.86 212	73.67 213	69.75 211	72.56 219	84.20 216	70.95 216	66.81 200	80.34 189	43.62 219	51.60 215	53.81 209	71.24 209	82.91 213	80.93 214	93.35 217	81.92 219

MDA-MVSNet-bldmvs		69.61 213	70.36 215	68.74 213	62.88 228	88.50 197	65.40 223	77.01 139	71.60 217	43.93 216	66.71 154	35.33 229	72.47 208	61.01 228	80.63 215	90.73 223	88.75 212

pmmvs3		69.04 214	70.75 214	67.04 215	66.83 223	78.54 222	64.99 224	60.92 217	64.67 222	40.61 225	55.08 209	40.29 224	74.89 202	83.76 210	84.01 211	93.98 216	88.88 211

MIMVSNet1		68.63 215	70.24 216	66.76 218	56.86 231	83.26 217	67.93 218	70.26 184	68.05 219	46.80 214	40.44 224	48.15 220	62.01 215	84.96 205	84.86 206	96.69 203	81.93 218

GG-mvs-BLEND		67.99 216	97.35 33	33.72 232	1.22 238	99.72 12	98.30 28	0.57 237	97.61 53	1.18 241	93.26 45	96.63 36	1.74 237	97.15 44	97.14 35	99.34 89	99.96 6

new-patchmatchnet		67.66 217	68.07 217	67.18 214	72.85 218	82.86 218	63.09 225	68.61 195	66.60 220	42.64 223	49.28 217	38.68 228	61.21 216	75.84 219	75.22 223	94.67 214	88.00 213

Anonymous20231211		63.52 218	62.24 222	65.02 220	68.68 220	78.21 223	65.79 222	68.17 198	49.86 231	42.89 222	29.67 231	34.65 230	55.41 221	75.07 220	76.98 221	89.18 225	91.26 204

FPMVS		63.27 219	61.31 223	65.57 219	78.25 209	74.42 226	75.23 208	68.92 194	72.33 213	43.87 217	49.01 218	43.94 222	48.64 225	61.15 227	58.81 229	78.51 231	69.49 229

1111		61.69 220	63.75 219	59.29 221	64.35 224	70.45 227	48.44 230	48.86 232	55.76 225	39.40 226	39.25 225	54.73 201	62.55 213	77.84 217	80.37 216	92.16 218	67.84 230

testmv		60.16 221	62.42 220	57.53 222	67.85 221	69.87 229	48.47 228	62.44 210	54.75 227	29.08 232	46.99 220	31.77 231	45.97 226	74.85 221	79.08 219	91.39 220	79.62 222

test1235678		60.16 221	62.41 221	57.53 222	67.85 221	69.86 230	48.47 228	62.43 211	54.73 228	29.08 232	46.99 220	31.76 232	45.97 226	74.84 222	79.07 220	91.39 220	79.61 223

test12356		57.24 223	59.66 224	54.43 225	64.26 226	66.14 231	49.96 227	61.73 214	54.37 229	28.80 234	44.89 223	25.68 234	32.36 231	70.23 225	79.19 218	89.46 224	77.11 224

Gipuma		54.59 224	53.98 226	55.30 224	59.03 230	52.63 234	47.17 233	56.08 227	71.68 216	37.54 229	20.90 233	19.00 235	52.33 222	71.69 224	75.20 224	79.64 230	66.79 231
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	49.05 18	51.88 225	50.56 228	53.42 226	64.21 227	43.30 236	42.64 234	62.93 206	50.56 230	43.72 218	37.44 227	42.95 223	35.05 230	58.76 230	54.58 230	71.95 233	66.33 232
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
.test1245		51.60 226	57.21 225	45.06 228	64.35 224	70.45 227	48.44 230	48.86 232	55.76 225	39.40 226	39.25 225	54.73 201	62.55 213	77.84 217	27.11 233	6.75 237	75.30 227

PMMVS2		50.69 227	52.33 227	48.78 227	51.24 233	64.81 232	47.91 232	53.79 230	44.95 232	21.75 235	29.98 230	25.90 233	31.98 233	59.95 229	65.37 226	86.00 228	75.36 226

no-one		41.64 228	41.19 229	42.16 229	52.35 232	58.34 233	27.46 236	57.21 225	28.41 237	21.09 236	19.65 234	17.04 236	21.39 236	39.74 232	61.20 228	73.45 232	63.95 234

E-PMN		37.15 229	34.82 231	39.86 230	47.53 235	35.42 238	23.79 237	55.26 228	35.18 235	14.12 238	17.38 237	14.13 238	39.73 229	32.24 233	46.98 231	58.76 234	62.39 235

EMVS		36.45 230	33.63 232	39.74 231	48.47 234	35.73 237	23.59 238	55.11 229	35.61 234	12.88 239	17.49 235	14.62 237	41.04 228	29.33 234	43.00 232	57.32 235	59.62 236

MVE	42.40 19	36.00 231	38.65 230	32.92 233	29.16 236	46.17 235	22.61 239	44.21 234	26.44 238	18.88 237	17.41 236	9.36 240	32.29 232	45.75 231	61.38 227	50.35 236	64.03 233
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		21.55 232	30.91 233	10.62 234	2.78 237	11.66 239	18.51 240	4.82 235	38.21 233	4.06 240	36.35 228	4.47 241	26.81 234	23.27 235	27.11 233	6.75 237	75.30 227

test123		16.81 233	24.80 234	7.48 235	0.82 239	8.38 240	11.92 241	2.60 236	28.96 236	1.12 242	28.39 232	1.26 242	24.51 235	8.93 236	22.19 235	3.90 239	75.49 225

sosnet-low-res		0.00 234	0.00 235	0.00 236	0.00 240	0.00 241	0.00 242	0.00 238	0.00 239	0.00 243	0.00 238	0.00 243	0.00 238	0.00 237	0.00 236	0.00 240	0.00 237

sosnet		0.00 234	0.00 235	0.00 236	0.00 240	0.00 241	0.00 242	0.00 238	0.00 239	0.00 243	0.00 238	0.00 243	0.00 238	0.00 237	0.00 236	0.00 240	0.00 237

ambc			64.61 218		61.80 229	75.31 225	71.00 215		74.16 209	48.83 211	36.02 229	13.22 239	58.66 220	85.80 200	76.26 222	88.01 226	91.53 202

MTAPA										94.58 9		98.56 17

MTMP										95.24 4		98.13 23

Patchmatch-RL test							37.05 235

tmp_tt				71.24 210	90.29 113	76.39 224	65.81 221	59.43 222	97.62 51	79.65 93	90.60 59	68.71 149	49.71 224	72.71 223	65.70 225	82.54 229

XVS					93.63 63	99.64 19	94.32 75			83.97 58		98.08 25				99.59 30

X-MVStestdata					93.63 63	99.64 19	94.32 75			83.97 58		98.08 25				99.59 30

abl_6				95.40 30	98.18 33	99.45 37	97.39 42	89.27 43	99.48 3	90.52 23	94.52 40	98.63 16	97.32 29			99.73 12	99.82 34

mPP-MVS					98.66 23							97.11 33

NP-MVS									97.69 49

Patchmtry						95.86 116	89.43 127	61.37 215		60.81 154

DeepMVS_CX						85.88 203	69.83 217	81.56 104	87.99 137	48.22 212	71.85 142	45.52 221	68.67 210	63.21 226		86.64 227	80.03 221

High-resolution multi-view benchmark