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.

ESAPD		97.65 2	97.98 1	97.27 4	99.12 3	99.14 2	98.66 2	96.80 1	95.74 16	93.46 13	97.72 2	99.48 1	96.76 13	97.77 3	96.92 13	98.83 4	99.07 6

APDe-MVS		97.79 1	97.96 2	97.60 1	99.20 1	99.10 3	98.88 1	96.68 2	96.81 3	94.64 4	97.84 1	98.02 8	97.24 2	97.74 5	97.02 10	98.97 1	99.16 2

APD-MVS		97.12 9	97.05 14	97.19 5	99.04 7	98.63 16	98.45 5	96.54 3	94.81 32	93.50 11	96.10 14	97.40 17	96.81 10	97.05 17	96.82 15	98.80 5	98.56 17
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HPM-MVS++		97.22 8	97.40 9	97.01 7	99.08 5	98.55 21	98.19 12	96.48 4	96.02 14	93.28 16	96.26 12	98.71 5	96.76 13	97.30 12	96.25 30	98.30 47	98.68 11

HSP-MVS		97.51 3	97.70 5	97.29 3	99.00 11	99.17 1	98.61 3	96.41 5	95.88 15	94.34 7	97.72 2	99.04 4	96.93 8	97.29 13	95.90 36	98.45 23	98.94 9

SMA-MVS		97.42 4	97.82 3	96.95 9	99.18 2	99.05 5	98.10 17	96.31 6	96.28 10	92.94 19	95.50 21	99.21 2	96.69 16	97.96 2	97.67 2	98.50 15	99.06 7

NCCC		96.75 16	96.67 21	96.85 13	99.03 9	98.44 29	98.15 14	96.28 7	96.32 8	92.39 22	92.16 31	97.55 15	96.68 17	97.32 10	96.65 18	98.55 12	98.26 32

SD-MVS		97.35 5	97.73 4	96.90 11	97.35 39	98.66 11	97.85 23	96.25 8	96.86 2	94.54 5	96.75 8	99.13 3	96.99 5	96.94 20	96.58 19	98.39 33	99.20 1

CNVR-MVS		97.30 7	97.41 8	97.18 6	99.02 10	98.60 18	98.15 14	96.24 9	96.12 12	94.10 8	95.54 20	97.99 9	96.99 5	97.97 1	97.17 6	98.57 11	98.50 23

MCST-MVS		96.83 15	97.06 13	96.57 16	98.88 17	98.47 27	98.02 19	96.16 10	95.58 20	90.96 30	95.78 18	97.84 11	96.46 20	97.00 19	96.17 32	98.94 3	98.55 22

ACMMP_Plus		96.93 13	97.27 11	96.53 20	99.06 6	98.95 6	98.24 11	96.06 11	95.66 18	90.96 30	95.63 19	97.71 12	96.53 18	97.66 7	96.68 16	98.30 47	98.61 16

zzz-MVS		96.98 12	96.68 20	97.33 2	99.09 4	98.71 10	98.43 6	96.01 12	96.11 13	95.19 3	92.89 29	97.32 18	96.84 9	97.20 14	96.09 33	98.44 24	98.46 27

SteuartSystems-ACMMP		97.10 11	97.49 7	96.65 15	98.97 13	98.95 6	98.43 6	95.96 13	95.12 25	91.46 25	96.85 6	97.60 14	96.37 22	97.76 4	97.16 7	98.68 6	98.97 8
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS	92.10 3	95.22 31	94.77 35	95.75 27	97.77 33	98.54 22	97.63 25	95.96 13	95.07 28	88.85 42	85.35 61	91.85 47	95.82 25	96.88 22	97.10 8	98.44 24	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
AdaColmap		95.02 33	93.71 42	96.54 19	98.51 22	97.76 49	96.69 35	95.94 15	93.72 41	93.50 11	89.01 46	90.53 57	96.49 19	94.51 74	93.76 69	98.07 77	96.69 83

DeepC-MVS_fast	93.32 1	96.48 19	96.42 24	96.56 17	98.70 21	98.31 33	97.97 20	95.76 16	96.31 9	92.01 24	91.43 36	95.42 34	96.46 20	97.65 8	97.69 1	98.49 17	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
HFP-MVS		97.11 10	97.19 12	97.00 8	98.97 13	98.73 9	98.37 9	95.69 17	96.60 5	93.28 16	96.87 5	96.64 23	97.27 1	96.64 25	96.33 28	98.44 24	98.56 17

LS3D		91.97 58	90.98 71	93.12 54	97.03 46	97.09 71	95.33 53	95.59 18	92.47 50	79.26 103	81.60 93	82.77 84	94.39 37	94.28 77	94.23 57	97.14 122	94.45 152

MP-MVS		96.56 18	96.72 19	96.37 21	98.93 16	98.48 25	98.04 18	95.55 19	94.32 36	90.95 32	95.88 17	97.02 20	96.29 23	96.77 23	96.01 35	98.47 18	98.56 17
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMPR		96.92 14	96.96 15	96.87 12	98.99 12	98.78 8	98.38 8	95.52 20	96.57 6	92.81 21	96.06 15	95.90 30	97.07 4	96.60 27	96.34 27	98.46 20	98.42 28

CP-MVS		96.68 17	96.59 23	96.77 14	98.85 18	98.58 19	98.18 13	95.51 21	95.34 22	92.94 19	95.21 24	96.25 25	96.79 12	96.44 32	95.77 38	98.35 36	98.56 17

TSAR-MVS + ACMM		96.19 20	97.39 10	94.78 33	97.70 35	98.41 30	97.72 24	95.49 22	96.47 7	86.66 58	96.35 10	97.85 10	93.99 43	97.19 15	96.37 24	97.12 123	99.13 3

TSAR-MVS + MP.		97.31 6	97.64 6	96.92 10	97.28 41	98.56 20	98.61 3	95.48 23	96.72 4	94.03 10	96.73 9	98.29 6	97.15 3	97.61 9	96.42 22	98.96 2	99.13 3

PCF-MVS	90.19 8	92.98 48	92.07 62	94.04 39	96.39 52	97.87 44	96.03 43	95.47 24	87.16 100	85.09 75	84.81 69	93.21 42	93.46 51	91.98 120	91.98 117	97.78 93	97.51 60
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVS		96.07 23	96.33 25	95.77 26	98.94 15	98.66 11	97.94 21	95.41 25	95.12 25	88.03 46	93.00 28	96.06 26	95.85 24	96.65 24	96.35 25	98.47 18	98.48 24

train_agg		96.15 22	96.64 22	95.58 30	98.44 23	98.03 40	98.14 16	95.40 26	93.90 40	87.72 50	96.26 12	98.10 7	95.75 26	96.25 37	95.45 43	98.01 83	98.47 25

CPTT-MVS		95.54 28	95.07 33	96.10 22	97.88 31	97.98 43	97.92 22	94.86 27	94.56 35	92.16 23	91.01 38	95.71 31	96.97 7	94.56 72	93.50 78	96.81 158	98.14 39

ACMMP		95.54 28	95.49 31	95.61 29	98.27 26	98.53 23	97.16 30	94.86 27	94.88 31	89.34 38	95.36 23	91.74 48	95.50 29	95.51 47	94.16 59	98.50 15	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
MSLP-MVS++		96.05 24	95.63 28	96.55 18	98.33 25	98.17 36	96.94 32	94.61 29	94.70 34	94.37 6	89.20 45	95.96 29	96.81 10	95.57 46	97.33 5	98.24 59	98.47 25

PLC	90.69 4	94.32 40	92.99 49	95.87 25	97.91 29	96.49 84	95.95 46	94.12 30	94.94 29	94.09 9	85.90 57	90.77 54	95.58 28	94.52 73	93.32 83	97.55 108	95.00 147
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DeepPCF-MVS	92.65 2	95.50 30	96.96 15	93.79 46	96.44 51	98.21 34	93.51 85	94.08 31	96.94 1	89.29 39	93.08 27	96.77 22	93.82 47	97.68 6	97.40 4	95.59 183	98.65 12

CSCG		95.68 27	95.46 32	95.93 24	98.71 20	99.07 4	97.13 31	93.55 32	95.48 21	93.35 15	90.61 40	93.82 40	95.16 31	94.60 71	95.57 41	97.70 100	99.08 5

CNLPA		93.69 45	92.50 54	95.06 32	97.11 44	97.36 56	93.88 77	93.30 33	95.64 19	93.44 14	80.32 97	90.73 55	94.99 33	93.58 95	93.33 82	97.67 103	96.57 93

CDPH-MVS		94.80 37	95.50 30	93.98 42	98.34 24	98.06 39	97.41 26	93.23 34	92.81 47	82.98 82	92.51 30	94.82 36	93.53 49	96.08 40	96.30 29	98.42 27	97.94 45

PHI-MVS		95.86 25	96.93 18	94.61 37	97.60 37	98.65 15	96.49 36	93.13 35	94.07 38	87.91 49	97.12 4	97.17 19	93.90 46	96.46 30	96.93 12	98.64 8	98.10 43

MSDG		90.42 74	88.25 98	92.94 57	96.67 50	94.41 109	93.96 73	92.91 36	89.59 81	86.26 60	76.74 116	80.92 95	90.43 83	92.60 111	92.08 114	97.44 113	91.41 185

PGM-MVS		96.16 21	96.33 25	95.95 23	99.04 7	98.63 16	98.32 10	92.76 37	93.42 43	90.49 35	96.30 11	95.31 35	96.71 15	96.46 30	96.02 34	98.38 34	98.19 36

OPM-MVS		91.08 67	89.34 83	93.11 55	96.18 54	96.13 95	96.39 38	92.39 38	82.97 143	81.74 84	82.55 87	80.20 96	93.97 45	94.62 69	93.23 85	98.00 84	95.73 121

OMC-MVS		94.49 39	94.36 39	94.64 36	97.17 43	97.73 50	95.49 51	92.25 39	96.18 11	90.34 36	88.51 47	92.88 44	94.90 34	94.92 56	94.17 58	97.69 101	96.15 108

EPNet		93.92 43	94.40 38	93.36 49	97.89 30	96.55 81	96.08 42	92.14 40	91.65 56	89.16 40	94.07 26	90.17 61	87.78 107	95.24 49	94.97 49	97.09 125	98.15 38
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP-MVS		92.39 54	92.49 55	92.29 61	95.65 59	95.94 96	95.64 50	92.12 41	92.46 51	79.65 101	91.97 33	82.68 85	92.92 56	93.47 100	92.77 99	97.74 96	98.12 41

TSAR-MVS + COLMAP		92.39 54	92.31 59	92.47 59	95.35 67	96.46 85	96.13 41	92.04 42	95.33 23	80.11 98	94.95 25	77.35 112	94.05 42	94.49 75	93.08 94	97.15 120	94.53 150

CANet		94.85 34	94.92 34	94.78 33	97.25 42	98.52 24	97.20 28	91.81 43	93.25 44	91.06 29	86.29 53	94.46 38	92.99 54	97.02 18	96.68 16	98.34 38	98.20 35

abl_6				94.78 33	97.46 38	97.99 42	95.76 47	91.80 44	93.72 41	91.25 27	91.33 37	96.47 24	94.28 40			98.14 67	97.39 65

TSAR-MVS + GP.		95.86 25	96.95 17	94.60 38	94.07 79	98.11 38	96.30 39	91.76 45	95.67 17	91.07 28	96.82 7	97.69 13	95.71 27	95.96 41	95.75 39	98.68 6	98.63 13

TAPA-MVS	90.35 6	93.69 45	93.52 43	93.90 43	96.89 47	97.62 53	96.15 40	91.67 46	94.94 29	85.97 61	87.72 50	91.96 46	94.40 36	93.76 89	93.06 96	98.30 47	95.58 126
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
3Dnovator+	90.56 5	95.06 32	94.56 37	95.65 28	98.11 27	98.15 37	97.19 29	91.59 47	95.11 27	93.23 18	81.99 90	94.71 37	95.43 30	96.48 29	96.88 14	98.35 36	98.63 13

ACMM	88.76 10	91.70 64	90.43 73	93.19 52	95.56 60	95.14 101	93.35 88	91.48 48	92.26 52	87.12 54	84.02 76	79.34 99	93.99 43	94.07 83	92.68 101	97.62 107	95.50 127
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS_0304		94.30 41	94.68 36	93.86 45	96.33 53	98.48 25	97.41 26	91.20 49	92.75 48	86.96 56	86.03 56	93.81 41	92.64 58	96.89 21	96.54 21	98.61 10	98.24 33

MVS_111021_HR		94.84 35	95.91 27	93.60 47	97.35 39	98.46 28	95.08 54	91.19 50	94.18 37	85.97 61	95.38 22	92.56 45	93.61 48	96.61 26	96.25 30	98.40 31	97.92 47

MVS_111021_LR		94.84 35	95.57 29	94.00 40	97.11 44	97.72 52	94.88 57	91.16 51	95.24 24	88.74 43	96.03 16	91.52 51	94.33 39	95.96 41	95.01 48	97.79 92	97.49 61

DELS-MVS		93.71 44	93.47 44	94.00 40	96.82 48	98.39 31	96.80 34	91.07 52	89.51 82	89.94 37	83.80 77	89.29 63	90.95 77	97.32 10	97.65 3	98.42 27	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
3Dnovator	90.28 7	94.70 38	94.34 40	95.11 31	98.06 28	98.21 34	96.89 33	91.03 53	94.72 33	91.45 26	82.87 81	93.10 43	94.61 35	96.24 38	97.08 9	98.63 9	98.16 37

QAPM		94.13 42	94.33 41	93.90 43	97.82 32	98.37 32	96.47 37	90.89 54	92.73 49	85.63 67	85.35 61	93.87 39	94.17 41	95.71 45	95.90 36	98.40 31	98.42 28

PVSNet_BlendedMVS		92.80 49	92.44 56	93.23 50	96.02 55	97.83 47	93.74 80	90.58 55	91.86 53	90.69 33	85.87 59	82.04 89	90.01 86	96.39 33	95.26 45	98.34 38	97.81 52

PVSNet_Blended		92.80 49	92.44 56	93.23 50	96.02 55	97.83 47	93.74 80	90.58 55	91.86 53	90.69 33	85.87 59	82.04 89	90.01 86	96.39 33	95.26 45	98.34 38	97.81 52

OpenMVS	88.18 11	92.51 52	91.61 67	93.55 48	97.74 34	98.02 41	95.66 49	90.46 57	89.14 84	86.50 59	75.80 124	90.38 60	92.69 57	94.99 52	95.30 44	98.27 54	97.63 57

EPNet_dtu		88.32 106	90.61 72	85.64 145	96.79 49	92.27 171	92.03 112	90.31 58	89.05 85	65.44 197	89.43 43	85.90 72	74.22 205	92.76 107	92.09 113	95.02 193	92.76 176
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
COLMAP_ROB	84.39 15	87.61 111	86.03 125	89.46 94	95.54 62	94.48 106	91.77 115	90.14 59	87.16 100	75.50 117	73.41 139	76.86 115	87.33 114	90.05 154	89.76 178	96.48 164	90.46 195
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMP	89.13 9	92.03 57	91.70 66	92.41 60	94.92 68	96.44 87	93.95 75	89.96 60	91.81 55	85.48 71	90.97 39	79.12 100	92.42 60	93.28 105	92.55 102	97.76 94	97.74 55
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		91.83 61	92.04 63	91.58 67	95.46 63	96.18 94	95.97 45	89.85 61	90.45 65	77.76 107	91.92 34	80.07 97	92.34 61	94.27 78	93.47 79	98.11 71	97.90 50

conf0.002		89.25 95	87.21 116	91.62 65	93.87 90	97.35 57	94.31 64	89.83 62	85.87 111	81.62 85	78.72 105	63.89 194	91.76 65	94.90 63	93.98 65	98.33 42	95.77 119

thres400		89.40 88	87.58 110	91.53 69	94.06 80	97.21 65	94.19 72	89.83 62	85.69 117	81.08 92	75.50 126	69.76 142	91.80 63	94.79 66	93.51 72	98.20 63	96.60 91

conf0.01		89.34 91	87.39 115	91.61 66	93.88 89	97.34 58	94.31 64	89.82 64	85.87 111	81.53 86	77.93 109	66.15 177	91.76 65	94.90 63	93.51 72	98.32 43	96.05 112

tfpn111		90.16 79	88.99 88	91.52 71	93.90 85	97.26 60	94.31 64	89.75 65	85.87 111	81.10 90	84.41 72	70.38 136	91.76 65	94.92 56	93.51 72	98.29 51	96.61 86

thresconf0.02		88.86 97	88.70 93	89.04 99	93.59 100	96.40 88	92.97 92	89.75 65	90.16 72	74.34 120	84.41 72	71.00 132	85.16 150	93.32 103	93.12 93	98.41 29	92.52 181

conf200view11		89.55 85	87.86 102	91.52 71	93.90 85	97.26 60	94.31 64	89.75 65	85.87 111	81.10 90	76.46 118	70.38 136	91.76 65	94.92 56	93.51 72	98.29 51	96.61 86

tfpn200view9		89.55 85	87.86 102	91.53 69	93.90 85	97.26 60	94.31 64	89.74 68	85.87 111	81.15 88	76.46 118	70.38 136	91.76 65	94.92 56	93.51 72	98.28 53	96.61 86

thres600view7		89.28 94	87.47 113	91.39 74	94.12 76	97.25 63	93.94 76	89.74 68	85.62 121	80.63 95	75.24 131	69.33 144	91.66 71	94.92 56	93.23 85	98.27 54	96.72 81

thres200		89.49 87	87.72 105	91.55 68	93.95 82	97.25 63	94.34 62	89.74 68	85.66 119	81.18 87	76.12 123	70.19 141	91.80 63	94.92 56	93.51 72	98.27 54	96.40 96

view600		89.29 93	87.48 112	91.41 73	94.10 77	97.21 65	93.96 73	89.70 71	85.67 118	80.75 94	75.29 128	69.35 143	91.70 70	94.92 56	93.23 85	98.26 58	96.69 83

thres100view900		89.36 89	87.61 108	91.39 74	93.90 85	96.86 76	94.35 61	89.66 72	85.87 111	81.15 88	76.46 118	70.38 136	91.17 74	94.09 82	93.43 80	98.13 68	96.16 107

PVSNet_Blended_VisFu		91.92 59	92.39 58	91.36 77	95.45 65	97.85 46	92.25 103	89.54 73	88.53 91	87.47 52	79.82 99	90.53 57	85.47 148	96.31 36	95.16 47	97.99 85	98.56 17

view800		89.21 96	87.44 114	91.27 78	94.13 74	97.18 67	93.74 80	89.53 74	85.60 122	80.34 97	75.29 128	68.89 145	91.57 72	94.97 53	93.36 81	98.34 38	96.79 79

tfpn		88.67 99	86.57 119	91.12 79	94.14 73	97.15 68	93.51 85	89.37 75	85.49 123	79.91 100	75.26 130	62.24 200	91.39 73	95.00 51	93.95 66	98.41 29	96.88 77

tfpn_ndepth		89.72 82	89.91 80	89.49 93	93.56 101	96.67 78	92.34 100	89.25 76	90.85 60	78.68 106	84.25 75	77.39 111	84.84 154	93.58 95	92.76 100	98.30 47	93.90 160

tfpnview11		88.80 98	89.21 85	88.31 106	93.70 96	96.24 92	92.35 99	89.11 77	89.90 78	72.14 133	85.12 64	73.93 121	84.20 160	93.75 90	92.85 97	98.38 34	92.68 179

tfpn_n400		88.58 101	88.91 90	88.19 107	93.63 98	96.34 90	92.22 104	89.04 78	87.37 98	72.14 133	85.12 64	73.93 121	84.04 165	93.65 91	93.20 88	98.09 73	92.77 174

tfpnconf		88.58 101	88.91 90	88.19 107	93.63 98	96.34 90	92.22 104	89.04 78	87.37 98	72.14 133	85.12 64	73.93 121	84.04 165	93.65 91	93.20 88	98.09 73	92.77 174

UGNet		91.52 65	93.41 45	89.32 96	94.13 74	97.15 68	91.83 114	89.01 80	90.62 63	85.86 64	86.83 51	91.73 49	77.40 197	94.68 68	94.43 54	97.71 98	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
conf0.05thres1000		87.90 108	85.88 130	90.26 86	93.74 94	96.39 89	92.67 94	88.94 81	80.97 155	77.71 109	70.15 153	68.40 151	90.42 84	94.46 76	93.29 84	98.09 73	97.49 61

FC-MVSNet-train		90.55 72	90.19 75	90.97 81	93.78 93	95.16 100	92.11 110	88.85 82	87.64 96	83.38 81	84.36 74	78.41 103	89.53 89	94.69 67	93.15 92	98.15 66	97.92 47

CLD-MVS		92.50 53	91.96 64	93.13 53	93.93 84	96.24 92	95.69 48	88.77 83	92.92 46	89.01 41	88.19 49	81.74 92	93.13 53	93.63 93	93.08 94	98.23 60	97.91 49
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tfpn1000		89.30 92	89.72 82	88.81 101	93.83 92	96.50 83	91.53 118	88.74 84	91.20 59	76.74 113	84.96 67	75.44 120	83.50 169	93.63 93	92.42 105	98.51 13	93.88 161

PatchMatch-RL		90.30 75	88.93 89	91.89 63	95.41 66	95.68 97	90.94 119	88.67 85	89.80 79	86.95 57	85.90 57	72.51 125	92.46 59	93.56 98	92.18 110	96.93 143	92.89 172

UniMVSNet_NR-MVSNet		86.80 118	85.86 131	87.89 115	88.17 153	94.07 114	90.15 141	88.51 86	84.20 135	73.45 126	72.38 144	70.30 140	88.95 99	90.25 148	92.21 109	98.12 69	97.62 58

DI_MVS_plusplus_trai		91.05 68	90.15 76	92.11 62	92.67 111	96.61 79	96.03 43	88.44 87	90.25 67	85.92 63	73.73 134	84.89 76	91.92 62	94.17 81	94.07 63	97.68 102	97.31 67

TranMVSNet+NR-MVSNet		85.57 133	84.41 143	86.92 124	87.67 163	93.34 137	90.31 132	88.43 88	83.07 142	70.11 161	69.99 155	65.28 183	86.96 118	89.73 157	92.27 107	98.06 79	97.17 70

UA-Net		90.81 69	92.58 53	88.74 103	94.87 69	97.44 55	92.61 95	88.22 89	82.35 146	78.93 104	85.20 63	95.61 32	79.56 190	96.52 28	96.57 20	98.23 60	94.37 153

IB-MVS	85.10 14	87.98 107	87.97 101	87.99 112	94.55 70	96.86 76	84.52 200	88.21 90	86.48 109	88.54 45	74.41 133	77.74 108	74.10 207	89.65 160	92.85 97	98.06 79	97.80 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
IS_MVSNet		91.87 60	93.35 46	90.14 89	94.09 78	97.73 50	93.09 90	88.12 91	88.71 87	79.98 99	84.49 70	90.63 56	87.49 112	97.07 16	96.96 11	98.07 77	97.88 51

ACMH	85.51 13	87.31 114	86.59 118	88.14 110	93.96 81	94.51 105	89.00 167	87.99 92	81.58 148	70.15 158	78.41 107	71.78 130	90.60 81	91.30 130	91.99 116	97.17 119	96.58 92
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MAR-MVS		92.71 51	92.63 52	92.79 58	97.70 35	97.15 68	93.75 79	87.98 93	90.71 61	85.76 66	86.28 54	86.38 67	94.35 38	94.95 54	95.49 42	97.22 117	97.44 63
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
Vis-MVSNet (Re-imp)		90.54 73	92.76 51	87.94 113	93.73 95	96.94 74	92.17 108	87.91 94	88.77 86	76.12 116	83.68 78	90.80 53	79.49 191	96.34 35	96.35 25	98.21 62	96.46 94

MVSTER		91.73 63	91.61 67	91.86 64	93.18 104	94.56 103	94.37 60	87.90 95	90.16 72	88.69 44	89.23 44	81.28 94	88.92 101	95.75 44	93.95 66	98.12 69	96.37 97

EPP-MVSNet		92.13 56	93.06 48	91.05 80	93.66 97	97.30 59	92.18 106	87.90 95	90.24 68	83.63 78	86.14 55	90.52 59	90.76 79	94.82 65	94.38 55	98.18 65	97.98 44

DU-MVS		86.12 125	84.81 139	87.66 116	87.77 160	93.78 120	90.15 141	87.87 97	84.40 129	73.45 126	70.59 148	64.82 188	88.95 99	90.14 149	92.33 106	97.76 94	97.62 58

NR-MVSNet		85.46 135	84.54 141	86.52 130	88.33 152	93.78 120	90.45 125	87.87 97	84.40 129	71.61 139	70.59 148	62.09 203	82.79 172	91.75 123	91.75 120	98.10 72	97.44 63

canonicalmvs		93.08 47	93.09 47	93.07 56	94.24 72	97.86 45	95.45 52	87.86 99	94.00 39	87.47 52	88.32 48	82.37 88	95.13 32	93.96 88	96.41 23	98.27 54	98.73 10

TDRefinement		84.97 140	83.39 155	86.81 126	92.97 106	94.12 112	92.18 106	87.77 100	82.78 144	71.31 143	68.43 160	68.07 153	81.10 186	89.70 159	89.03 187	95.55 185	91.62 183

Baseline_NR-MVSNet		85.28 136	83.42 154	87.46 120	87.77 160	90.80 195	89.90 152	87.69 101	83.93 138	74.16 122	64.72 198	66.43 167	87.48 113	90.14 149	90.83 131	97.73 97	97.11 71

RPSCF		89.68 83	89.24 84	90.20 87	92.97 106	92.93 153	92.30 101	87.69 101	90.44 66	85.12 74	91.68 35	85.84 73	90.69 80	87.34 192	86.07 196	92.46 206	90.37 196

ACMH+	85.75 12	87.19 115	86.02 126	88.56 104	93.42 102	94.41 109	89.91 150	87.66 103	83.45 141	72.25 131	76.42 121	71.99 129	90.78 78	89.86 155	90.94 130	97.32 114	95.11 146

tfpnnormal		83.80 167	81.26 196	86.77 127	89.60 139	93.26 142	89.72 157	87.60 104	72.78 210	70.44 152	60.53 211	61.15 208	85.55 146	92.72 108	91.44 126	97.71 98	96.92 75

TransMVSNet (Re)		82.67 187	80.93 199	84.69 163	88.71 145	91.50 188	87.90 176	87.15 105	71.54 216	68.24 179	63.69 201	64.67 190	78.51 193	91.65 125	90.73 137	97.64 105	92.73 178

UniMVSNet (Re)		86.22 123	85.46 136	87.11 122	88.34 151	94.42 108	89.65 158	87.10 106	84.39 131	74.61 119	70.41 151	68.10 152	85.10 152	91.17 132	91.79 119	97.84 90	97.94 45

MVS_Test		91.81 62	92.19 60	91.37 76	93.24 103	96.95 73	94.43 59	86.25 107	91.45 58	83.45 80	86.31 52	85.15 74	92.93 55	93.99 84	94.71 52	97.92 88	96.77 80

diffmvs		91.35 66	91.81 65	90.82 82	92.80 108	95.62 98	93.74 80	86.04 108	93.17 45	85.82 65	84.48 71	89.74 62	90.23 85	90.49 146	92.45 103	96.29 169	96.72 81

CDS-MVSNet		88.34 105	88.71 92	87.90 114	90.70 133	94.54 104	92.38 97	86.02 109	80.37 162	79.42 102	79.30 100	83.43 79	82.04 178	93.39 102	94.01 64	96.86 156	95.93 114
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Vis-MVSNet		89.36 89	91.49 69	86.88 125	92.10 114	97.60 54	92.16 109	85.89 110	84.21 134	75.20 118	82.58 85	87.13 64	77.40 197	95.90 43	95.63 40	98.51 13	97.36 66
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CHOSEN 1792x2688		88.57 103	87.82 104	89.44 95	95.46 63	96.89 75	93.74 80	85.87 111	89.63 80	77.42 110	61.38 208	83.31 80	88.80 104	93.44 101	93.16 91	95.37 188	96.95 74

GBi-Net		90.21 76	90.11 77	90.32 84	88.66 147	93.65 125	94.25 69	85.78 112	90.03 74	85.56 68	77.38 110	86.13 68	89.38 92	93.97 85	94.16 59	98.31 44	95.47 128

test1		90.21 76	90.11 77	90.32 84	88.66 147	93.65 125	94.25 69	85.78 112	90.03 74	85.56 68	77.38 110	86.13 68	89.38 92	93.97 85	94.16 59	98.31 44	95.47 128

FMVSNet3		90.19 78	90.06 79	90.34 83	88.69 146	93.85 117	94.58 58	85.78 112	90.03 74	85.56 68	77.38 110	86.13 68	89.22 95	93.29 104	94.36 56	98.20 63	95.40 132

v148		83.61 172	82.10 184	85.37 148	87.34 178	92.94 152	87.48 179	85.72 115	78.92 182	73.87 124	65.71 193	64.69 189	81.78 182	87.82 186	89.35 184	96.01 173	95.26 138

v1141		84.40 152	83.00 167	86.03 136	87.41 174	93.42 132	90.28 136	85.53 116	79.58 171	70.12 160	66.62 179	66.27 174	85.94 138	89.16 177	90.19 162	96.89 151	95.73 121

divwei89l23v2f112		84.40 152	83.00 167	86.02 138	87.42 173	93.42 132	90.28 136	85.52 117	79.57 172	70.11 161	66.64 178	66.29 173	85.91 139	89.16 177	90.19 162	96.90 149	95.73 121

v1		84.40 152	83.01 166	86.03 136	87.41 174	93.42 132	90.31 132	85.52 117	79.51 175	70.13 159	66.66 177	66.40 168	85.89 140	89.15 179	90.19 162	96.89 151	95.74 120

FMVSNet2		89.61 84	89.14 86	90.16 88	88.66 147	93.65 125	94.25 69	85.44 119	88.57 89	84.96 76	73.53 136	83.82 78	89.38 92	94.23 79	94.68 53	98.31 44	95.47 128

v2v482		84.51 147	83.05 165	86.20 132	87.25 186	93.28 140	90.22 138	85.40 120	79.94 168	69.78 166	67.74 163	65.15 185	87.57 109	89.12 180	90.55 141	96.97 134	95.60 125

pm-mvs1		84.55 146	83.46 151	85.82 139	88.16 155	93.39 136	89.05 166	85.36 121	74.03 208	72.43 130	65.08 196	71.11 131	82.30 177	93.48 99	91.70 121	97.64 105	95.43 131

v1neww		84.65 144	83.34 159	86.18 134	87.53 168	93.49 129	90.32 128	85.17 122	80.57 159	71.02 149	66.93 171	67.04 163	86.13 134	89.26 171	90.23 157	96.93 143	95.88 116

v7new		84.65 144	83.34 159	86.18 134	87.53 168	93.49 129	90.32 128	85.17 122	80.57 159	71.02 149	66.93 171	67.04 163	86.13 134	89.26 171	90.23 157	96.93 143	95.88 116

v6		84.67 143	83.36 157	86.20 132	87.53 168	93.49 129	90.34 127	85.16 124	80.58 158	71.13 145	66.97 170	67.10 161	86.11 136	89.25 174	90.22 160	96.93 143	95.89 115

pmmvs4		86.00 128	84.28 145	88.00 111	87.80 158	92.01 179	89.94 149	84.91 125	86.79 103	80.98 93	73.41 139	66.34 170	88.12 105	89.31 170	88.90 188	96.24 171	93.20 170

FC-MVSNet-test		86.15 124	89.10 87	82.71 191	89.83 136	93.18 145	87.88 177	84.69 126	86.54 106	62.18 207	82.39 88	83.31 80	74.18 206	92.52 112	91.86 118	97.50 110	93.88 161

CANet_DTU		90.74 71	92.93 50	88.19 107	94.36 71	96.61 79	94.34 62	84.66 127	90.66 62	68.75 175	90.41 41	86.89 65	89.78 88	95.46 48	94.87 50	97.25 116	95.62 124

GA-MVS		85.08 138	85.65 133	84.42 167	89.77 137	94.25 111	89.26 162	84.62 128	81.19 153	62.25 206	75.72 125	68.44 150	84.14 162	93.57 97	91.68 123	96.49 163	94.71 149

USDC		86.73 120	85.96 128	87.63 118	91.64 119	93.97 115	92.76 93	84.58 129	88.19 92	70.67 151	80.10 98	67.86 154	89.43 90	91.81 122	89.77 177	96.69 162	90.05 199

FMVSNet1		87.33 113	86.00 127	88.89 100	87.13 190	92.83 156	93.08 91	84.46 130	81.35 152	82.20 83	66.33 183	77.96 106	88.96 98	93.97 85	94.16 59	97.54 109	95.38 133

Effi-MVS+		89.79 81	89.83 81	89.74 90	92.98 105	96.45 86	93.48 87	84.24 131	87.62 97	76.45 114	81.76 91	77.56 110	93.48 50	94.61 70	93.59 71	97.82 91	97.22 68

WR-MVS		83.14 181	83.38 156	82.87 187	87.55 167	93.29 139	86.36 190	84.21 132	80.05 166	66.41 192	66.91 173	66.92 165	75.66 203	88.96 182	90.56 140	97.05 127	96.96 73

HyFIR lowres test		87.87 109	86.42 121	89.57 92	95.56 60	96.99 72	92.37 98	84.15 133	86.64 104	77.17 111	57.65 213	83.97 77	91.08 76	92.09 119	92.44 104	97.09 125	95.16 144

TinyColmap		84.04 163	82.01 186	86.42 131	90.87 129	91.84 182	88.89 169	84.07 134	82.11 147	69.89 165	71.08 146	60.81 209	89.04 97	90.52 144	89.19 185	95.76 176	88.50 205

V42		84.48 149	83.36 157	85.79 141	87.14 189	93.28 140	90.03 145	83.98 135	80.30 163	71.20 144	66.90 175	67.17 159	85.55 146	89.35 165	90.27 148	96.82 157	96.27 104

PEN-MVS		82.49 189	81.58 190	83.56 177	86.93 193	92.05 178	86.71 187	83.84 136	76.94 195	64.68 200	67.24 165	60.11 212	81.17 185	87.78 187	90.70 138	98.02 82	96.21 106

DTE-MVSNet		81.76 196	81.04 197	82.60 193	86.63 197	91.48 190	85.97 193	83.70 137	76.45 199	62.44 205	67.16 166	59.98 213	78.98 192	87.15 194	89.93 173	97.88 89	95.12 145

Anonymous20240521		84.09 161	84.41 143	83.72 174	86.43 200	93.88 116	85.39 199	83.67 138	79.53 174	71.84 137	67.72 164	68.63 148	77.52 196	91.90 121	91.53 125	97.45 111	97.71 56

test-LLR		86.88 116	88.28 96	85.24 151	91.22 124	92.07 175	87.41 180	83.62 139	84.58 127	69.33 169	83.00 79	82.79 82	84.24 158	92.26 115	89.81 175	95.64 181	93.44 165

test0.0.03 1		85.58 132	87.69 107	83.11 182	91.22 124	92.54 164	85.60 198	83.62 139	85.66 119	67.84 182	82.79 83	79.70 98	73.51 209	91.15 133	90.79 132	96.88 154	91.23 188

IterMVS-LS		88.60 100	88.45 94	88.78 102	92.02 115	92.44 169	92.00 113	83.57 141	86.52 107	78.90 105	78.61 106	81.34 93	89.12 96	90.68 142	93.18 90	97.10 124	96.35 98
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CP-MVSNet		83.11 183	82.15 183	84.23 169	87.20 187	92.70 158	86.42 189	83.53 142	77.83 190	67.67 183	66.89 176	60.53 211	82.47 175	89.23 176	90.65 139	98.08 76	97.20 69

PS-CasMVS		82.53 188	81.54 191	83.68 175	87.08 192	92.54 164	86.20 191	83.46 143	76.46 198	65.73 196	65.71 193	59.41 216	81.61 183	89.06 181	90.55 141	98.03 81	97.07 72

v1144		84.03 164	82.88 170	85.37 148	87.17 188	93.15 148	90.18 140	83.31 144	78.83 183	67.85 181	65.99 189	64.99 186	86.79 121	90.75 139	90.33 147	96.90 149	96.15 108

CVMVSNet		83.83 166	85.53 134	81.85 199	89.60 139	90.92 192	87.81 178	83.21 145	80.11 165	60.16 211	76.47 117	78.57 102	76.79 199	89.76 156	90.13 165	93.51 197	92.75 177

WR-MVS_H		82.86 186	82.66 174	83.10 183	87.44 172	93.33 138	85.71 197	83.20 146	77.36 192	68.20 180	66.37 180	65.23 184	76.05 202	89.35 165	90.13 165	97.99 85	96.89 76

v7		84.37 155	83.23 162	85.69 143	87.34 178	93.19 144	90.32 128	83.10 147	79.88 170	69.33 169	66.33 183	65.75 178	87.06 116	90.83 137	90.38 144	96.97 134	96.26 105

DWT-MVSNet_training		86.83 117	84.44 142	89.61 91	92.75 110	93.82 118	91.66 116	82.85 148	88.57 89	87.48 51	79.00 102	64.24 193	88.82 103	85.18 202	87.50 192	94.07 196	92.79 173

v1192		83.56 175	82.35 178	84.98 159	86.84 195	92.84 154	90.01 147	82.70 149	78.54 185	66.48 191	64.88 197	62.91 196	86.91 119	90.72 140	90.25 151	96.94 140	96.32 100

Fast-Effi-MVS+		88.56 104	87.99 100	89.22 97	91.56 121	95.21 99	92.29 102	82.69 150	86.82 102	77.73 108	76.24 122	73.39 124	93.36 52	94.22 80	93.64 70	97.65 104	96.43 95

Effi-MVS+-dtu		87.51 112	88.13 99	86.77 127	91.10 126	94.90 102	90.91 120	82.67 151	83.47 140	71.55 140	81.11 96	77.04 113	89.41 91	92.65 110	91.68 123	95.00 194	96.09 110

MDA-MVSNet-bldmvs		73.81 213	72.56 217	75.28 211	72.52 230	88.87 208	74.95 218	82.67 151	71.57 214	55.02 218	65.96 190	42.84 232	76.11 201	70.61 229	81.47 219	90.38 220	86.59 210

pmmvs6		80.90 199	78.77 203	83.38 180	85.84 202	91.61 186	86.01 192	82.54 153	64.17 223	70.43 153	54.14 221	67.06 162	80.73 187	90.50 145	89.17 186	94.74 195	94.75 148

v144192		83.48 177	82.23 182	84.94 160	86.65 196	92.84 154	89.63 159	82.48 154	77.87 189	67.36 185	65.33 195	63.50 195	86.51 123	89.72 158	89.99 172	97.03 128	96.35 98

v8		84.45 151	83.30 161	85.80 140	87.53 168	92.95 151	90.31 132	82.46 155	80.46 161	71.43 141	66.99 169	67.16 160	86.14 132	89.26 171	90.22 160	96.94 140	96.06 111

SixPastTwentyTwo		83.12 182	83.44 153	82.74 190	87.71 162	93.11 149	82.30 207	82.33 156	79.24 181	64.33 201	78.77 104	62.75 197	84.11 163	88.11 185	87.89 190	95.70 179	94.21 156

LTVRE_ROB	81.71 16	82.44 190	81.84 188	83.13 181	89.01 142	92.99 150	88.90 168	82.32 157	66.26 222	54.02 221	74.68 132	59.62 215	88.87 102	90.71 141	92.02 115	95.68 180	96.62 85
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
v1921920		83.30 179	82.09 185	84.70 162	86.59 198	92.67 160	89.82 156	82.23 158	78.32 186	65.76 195	64.64 199	62.35 199	86.78 122	90.34 147	90.02 170	97.02 129	96.31 102

pmmvs5		83.37 178	82.68 173	84.18 170	87.13 190	93.18 145	86.74 186	82.08 159	76.48 197	67.28 186	71.26 145	62.70 198	84.71 155	90.77 138	90.12 168	97.15 120	94.24 154

v748		81.57 198	80.68 200	82.60 193	85.55 205	92.07 175	83.57 202	82.06 160	74.64 207	69.97 163	63.11 204	61.46 206	78.09 194	87.30 193	89.88 174	96.37 167	96.32 100

PMMVS		89.88 80	91.19 70	88.35 105	89.73 138	91.97 181	90.62 122	81.92 161	90.57 64	80.58 96	92.16 31	86.85 66	91.17 74	92.31 114	91.35 128	96.11 172	93.11 171

N_pmnet		77.55 208	76.68 210	78.56 206	85.43 207	87.30 215	78.84 213	81.88 162	78.30 187	60.61 210	61.46 207	62.15 202	74.03 208	82.04 214	80.69 221	90.59 219	84.81 216

v1240		82.88 185	81.66 189	84.29 168	86.46 199	92.52 167	89.06 165	81.82 163	77.16 193	65.09 199	64.17 200	61.50 205	86.36 124	90.12 151	90.13 165	96.95 138	96.04 113

testgi		81.94 194	84.09 147	79.43 204	89.53 141	90.83 194	82.49 206	81.75 164	80.59 157	59.46 213	82.82 82	65.75 178	67.97 211	90.10 152	89.52 182	95.39 187	89.03 201

CMPMVS	61.19 17	79.86 202	77.46 209	82.66 192	91.54 122	91.82 183	83.25 203	81.57 165	70.51 218	68.64 176	59.89 212	66.77 166	79.63 189	84.00 211	84.30 207	91.34 213	84.89 215
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MS-PatchMatch		87.63 110	87.61 108	87.65 117	93.95 82	94.09 113	92.60 96	81.52 166	86.64 104	76.41 115	73.46 138	85.94 71	85.01 153	92.23 117	90.00 171	96.43 166	90.93 191

v10		84.18 157	83.17 164	85.37 148	87.34 178	92.68 159	90.32 128	81.33 167	79.93 169	69.23 172	66.33 183	65.74 180	87.03 117	90.84 136	90.38 144	96.97 134	96.29 103

pmmvs-eth3d		79.78 203	77.58 207	82.34 196	81.57 217	87.46 214	82.92 204	81.28 168	75.33 206	71.34 142	61.88 206	52.41 220	81.59 184	87.56 190	86.90 194	95.36 189	91.48 184

TAMVS		84.94 141	84.95 137	84.93 161	88.82 143	93.18 145	88.44 173	81.28 168	77.16 193	73.76 125	75.43 127	76.57 116	82.04 178	90.59 143	90.79 132	95.22 190	90.94 190

Anonymous20231206		78.09 206	78.11 206	78.07 208	85.19 208	89.17 207	80.99 209	81.24 170	75.46 205	58.25 215	54.78 220	59.90 214	66.73 215	88.94 183	88.26 189	96.01 173	90.25 197

v7n		82.25 191	81.54 191	83.07 184	85.55 205	92.58 162	86.68 188	81.10 171	76.54 196	65.97 194	62.91 205	60.56 210	82.36 176	91.07 134	90.35 146	96.77 159	96.80 78

v18		84.21 156	82.90 169	85.74 142	87.63 164	89.75 198	90.56 123	80.82 172	81.42 150	72.24 132	67.16 166	67.23 157	86.27 127	89.25 174	90.24 154	96.92 147	95.27 137

test20.03		76.41 210	78.49 205	73.98 212	85.64 204	87.50 213	75.89 216	80.71 173	70.84 217	51.07 225	68.06 162	61.40 207	54.99 226	88.28 184	87.20 193	95.58 184	86.15 211

v17		84.10 160	82.83 172	85.57 147	87.58 166	89.72 199	90.30 135	80.70 174	81.00 154	71.72 138	67.01 168	67.24 156	86.19 131	89.32 168	90.25 151	96.95 138	95.29 135

v16		84.14 158	82.86 171	85.64 145	87.61 165	89.71 200	90.36 126	80.70 174	81.36 151	71.99 136	66.91 173	67.19 158	86.23 130	89.32 168	90.25 151	96.94 140	95.29 135

v15		83.67 170	82.37 177	85.19 153	87.39 176	89.63 201	90.19 139	80.43 176	79.49 177	70.27 154	66.37 180	66.33 171	85.88 141	89.34 167	90.23 157	96.96 137	95.22 142

anonymousdsp		84.51 147	85.85 132	82.95 186	86.30 201	93.51 128	85.77 196	80.38 177	78.25 188	63.42 204	73.51 137	72.20 127	84.64 156	93.21 106	92.16 111	97.19 118	98.14 39

V14		83.66 171	82.38 176	85.16 154	87.37 177	89.62 202	90.15 141	80.33 178	79.51 175	70.26 155	66.30 186	66.37 169	85.87 142	89.38 164	90.24 154	96.98 133	95.22 142

V9		83.61 172	82.33 179	85.11 155	87.34 178	89.59 203	90.10 144	80.25 179	79.38 179	70.17 157	66.15 187	66.33 171	85.82 144	89.41 163	90.24 154	96.99 132	95.23 141

v52		82.11 192	81.50 193	82.82 189	84.59 211	92.51 168	85.96 195	80.24 180	76.38 200	66.83 190	63.12 203	64.62 191	82.56 173	87.70 188	89.55 180	96.73 160	96.61 86

V4		82.11 192	81.49 194	82.83 188	84.60 210	92.53 166	85.97 193	80.24 180	76.35 201	66.87 189	63.17 202	64.55 192	82.54 174	87.70 188	89.55 180	96.73 160	96.61 86

CHOSEN 280x420		90.77 70	92.14 61	89.17 98	93.86 91	92.81 157	93.16 89	80.22 182	90.21 69	84.67 77	89.89 42	91.38 52	90.57 82	94.94 55	92.11 112	92.52 205	93.65 164

v12		83.59 174	82.32 180	85.07 156	87.32 184	89.57 204	89.87 155	80.19 183	79.46 178	70.19 156	66.05 188	66.23 176	85.84 143	89.44 162	90.26 150	97.01 130	95.26 138

v13		83.55 176	82.29 181	85.01 158	87.31 185	89.55 206	89.89 153	80.13 184	79.34 180	69.93 164	65.92 191	66.25 175	85.80 145	89.45 161	90.27 148	97.01 130	95.25 140

v11		83.72 168	82.61 175	85.02 157	87.34 178	89.56 205	89.89 153	79.92 185	79.55 173	69.21 173	66.36 182	65.48 181	86.84 120	91.43 129	90.51 143	96.92 147	95.37 134

MIMVSNet1		73.19 215	73.70 215	72.60 218	65.42 234	86.69 217	75.56 217	79.65 186	67.87 221	55.30 217	45.24 229	56.41 218	63.79 218	86.98 195	87.66 191	95.85 175	85.04 214

EU-MVSNet		78.43 204	80.25 201	76.30 210	83.81 213	87.27 216	80.99 209	79.52 187	76.01 202	54.12 220	70.44 150	64.87 187	67.40 214	86.23 199	85.54 201	91.95 212	91.41 185

FMVSNet5		84.47 150	84.72 140	84.18 170	83.30 214	88.43 209	88.09 175	79.42 188	84.25 133	74.14 123	73.15 141	78.74 101	83.65 167	91.19 131	91.19 129	96.46 165	86.07 212

PM-MVS		80.29 201	79.30 202	81.45 201	81.91 216	88.23 210	82.61 205	79.01 189	79.99 167	67.15 187	69.07 158	51.39 221	82.92 171	87.55 191	85.59 199	95.08 191	93.28 168

EG-PatchMatch MVS		81.70 197	81.31 195	82.15 197	88.75 144	93.81 119	87.14 183	78.89 190	71.57 214	64.12 203	61.20 210	68.46 149	76.73 200	91.48 126	90.77 134	97.28 115	91.90 182

Anonymous20231211		69.76 220	67.18 221	72.76 215	78.31 221	83.47 220	74.12 219	78.37 191	51.44 232	52.48 222	36.04 231	45.46 231	62.33 220	80.49 220	82.43 213	90.96 216	90.93 191

Fast-Effi-MVS+-dtu		86.25 122	87.70 106	84.56 165	90.37 135	93.70 123	90.54 124	78.14 192	83.50 139	65.37 198	81.59 94	75.83 119	86.09 137	91.70 124	91.70 121	96.88 154	95.84 118

IterMVS		85.25 137	86.49 120	83.80 173	90.42 134	90.77 196	90.02 146	78.04 193	84.10 136	66.27 193	77.28 114	78.41 103	83.01 170	90.88 135	89.72 179	95.04 192	94.24 154
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MDTV_nov1_ep13		86.64 121	87.50 111	85.65 144	90.73 131	93.69 124	89.96 148	78.03 194	89.48 83	76.85 112	84.92 68	82.42 87	86.14 132	86.85 197	86.15 195	92.17 209	88.97 203

new-patchmatchnet		72.32 216	71.09 218	73.74 213	81.17 219	84.86 219	72.21 225	77.48 195	68.32 220	54.89 219	55.10 218	49.31 225	63.68 219	79.30 221	76.46 226	93.03 200	84.32 217

TESTMET0.1,1		86.11 126	88.28 96	83.59 176	87.80 158	92.07 175	87.41 180	77.12 196	84.58 127	69.33 169	83.00 79	82.79 82	84.24 158	92.26 115	89.81 175	95.64 181	93.44 165

test-mter		86.09 127	88.38 95	83.43 179	87.89 157	92.61 161	86.89 185	77.11 197	84.30 132	68.62 177	82.57 86	82.45 86	84.34 157	92.40 113	90.11 169	95.74 177	94.21 156

FPMVS		69.87 219	67.10 222	73.10 214	84.09 212	78.35 227	79.40 212	76.41 198	71.92 212	57.71 216	54.06 222	50.04 223	56.72 224	71.19 228	68.70 229	84.25 229	75.43 226

tpmp4_e23		85.67 131	84.28 145	87.30 121	91.96 116	92.00 180	92.06 111	76.27 199	87.95 95	83.59 79	76.97 115	70.88 133	87.52 111	84.80 206	84.73 205	92.40 207	92.61 180

test2356		73.82 212	74.82 214	72.66 217	81.25 218	80.70 224	73.47 222	75.91 200	72.55 211	48.73 228	68.14 161	50.74 222	63.96 217	84.44 208	85.57 200	92.63 204	81.60 220

CostFormer		86.78 119	86.05 124	87.62 119	92.15 113	93.20 143	91.55 117	75.83 201	88.11 94	85.29 73	81.76 91	76.22 117	87.80 106	84.45 207	85.21 203	93.12 199	93.42 167

tpm cat1		84.13 159	81.99 187	86.63 129	91.74 118	91.50 188	90.68 121	75.69 202	86.12 110	85.44 72	72.39 143	70.72 134	85.16 150	80.89 219	81.56 218	91.07 215	90.71 193

dps		85.00 139	83.21 163	87.08 123	90.73 131	92.55 163	89.34 160	75.29 203	84.94 124	87.01 55	79.27 101	67.69 155	87.27 115	84.22 209	83.56 209	92.83 201	90.25 197

testus		73.65 214	74.92 213	72.17 219	80.93 220	81.11 222	73.02 224	75.23 204	73.23 209	48.77 227	69.38 157	46.10 230	62.28 221	84.84 204	86.01 197	92.77 202	83.75 219

gm-plane-assit		77.65 207	78.50 204	76.66 209	87.96 156	85.43 218	64.70 228	74.50 205	64.15 224	51.26 224	61.32 209	58.17 217	84.11 163	95.16 50	93.83 68	97.45 111	91.41 185

PMVS	56.77 18	61.27 225	58.64 227	64.35 225	75.66 226	54.60 237	53.62 235	74.23 206	53.69 229	58.37 214	44.27 230	49.38 224	44.16 231	69.51 230	65.35 231	80.07 231	73.66 227
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tpm		83.16 180	83.64 149	82.60 193	90.75 130	91.05 191	88.49 172	73.99 207	82.36 145	67.08 188	78.10 108	68.79 146	84.17 161	85.95 200	85.96 198	91.09 214	93.23 169

MDTV_nov1_ep13_2view		80.43 200	80.94 198	79.84 202	84.82 209	90.87 193	84.23 201	73.80 208	80.28 164	64.33 201	70.05 154	68.77 147	79.67 188	84.83 205	83.50 210	92.17 209	88.25 208

PatchmatchNet		85.70 130	86.65 117	84.60 164	91.79 117	93.40 135	89.27 161	73.62 209	90.19 70	72.63 129	82.74 84	81.93 91	87.64 108	84.99 203	84.29 208	92.64 203	89.00 202
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		85.77 129	86.24 123	85.23 152	92.76 109	93.78 120	89.91 150	73.60 210	90.19 70	74.22 121	82.18 89	78.06 105	87.55 110	85.61 201	85.38 202	93.32 198	88.48 206

tpmrst		83.72 168	83.45 152	84.03 172	92.21 112	91.66 185	88.74 170	73.58 211	88.14 93	72.67 128	77.37 113	72.11 128	86.34 125	82.94 213	82.05 215	90.63 218	89.86 200

testmv		65.29 222	65.25 224	65.34 223	77.73 222	75.55 230	58.75 231	73.56 212	53.22 230	38.47 234	49.33 223	38.30 233	53.38 227	79.13 222	81.65 216	90.15 221	79.58 223

test1235678		65.29 222	65.24 225	65.34 223	77.73 222	75.54 231	58.75 231	73.56 212	53.19 231	38.47 234	49.32 224	38.28 234	53.38 227	79.13 222	81.65 216	90.15 221	79.57 224

CR-MVSNet		85.48 134	86.29 122	84.53 166	91.08 128	92.10 173	89.18 163	73.30 214	84.75 125	71.08 146	73.12 142	77.91 107	86.27 127	91.48 126	90.75 135	96.27 170	93.94 158

Patchmtry						92.39 170	89.18 163	73.30 214		71.08 146

MIMVSNet		82.97 184	84.00 148	81.77 200	82.23 215	92.25 172	87.40 182	72.73 216	81.48 149	69.55 167	68.79 159	72.42 126	81.82 181	92.23 117	92.25 108	96.89 151	88.61 204

PatchT		83.86 165	85.51 135	81.94 198	88.41 150	91.56 187	78.79 214	71.57 217	84.08 137	71.08 146	70.62 147	76.13 118	86.27 127	91.48 126	90.75 135	95.52 186	93.94 158

RPMNet		84.82 142	85.90 129	83.56 177	91.10 126	92.10 173	88.73 171	71.11 218	84.75 125	68.79 174	73.56 135	77.62 109	85.33 149	90.08 153	89.43 183	96.32 168	93.77 163

ADS-MVSNet		84.08 162	84.95 137	83.05 185	91.53 123	91.75 184	88.16 174	70.70 219	89.96 77	69.51 168	78.83 103	76.97 114	86.29 126	84.08 210	84.60 206	92.13 211	88.48 206

MVS-HIRNet		78.16 205	77.57 208	78.83 205	85.83 203	87.76 212	76.67 215	70.22 220	75.82 204	67.39 184	55.61 216	70.52 135	81.96 180	86.67 198	85.06 204	90.93 217	81.58 221

test12356		60.37 226	61.08 226	59.53 227	72.42 231	70.09 233	57.72 233	69.53 221	51.31 233	36.05 236	47.32 225	32.04 235	36.19 232	74.15 227	80.35 222	85.27 228	72.29 229

new_pmnet		72.29 217	73.25 216	71.16 221	75.35 227	81.38 221	73.72 221	69.27 222	75.97 203	49.84 226	56.27 215	56.12 219	69.08 210	81.73 215	80.86 220	89.72 224	80.44 222

gg-mvs-nofinetune		81.83 195	83.58 150	79.80 203	91.57 120	96.54 82	93.79 78	68.80 223	62.71 225	43.01 233	55.28 217	85.06 75	83.65 167	96.13 39	94.86 51	97.98 87	94.46 151

no-one		49.70 229	49.06 230	50.46 230	65.32 235	67.46 234	38.16 238	68.73 224	34.38 237	22.88 238	24.40 233	22.99 237	28.55 235	51.41 233	70.93 227	79.08 233	71.81 230

Gipuma		58.52 227	56.17 228	61.27 226	67.14 233	58.06 236	52.16 236	68.40 225	69.00 219	45.02 232	22.79 234	20.57 239	55.11 225	76.27 225	79.33 224	79.80 232	67.16 232
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LP		77.28 209	76.57 211	78.12 207	88.17 153	88.06 211	80.85 211	68.35 226	80.78 156	61.49 209	57.59 214	61.80 204	77.59 195	81.45 218	82.34 214	92.25 208	83.96 218

pmmvs3		71.13 218	71.06 219	71.21 220	73.54 229	80.19 225	71.69 226	64.86 227	62.04 226	52.10 223	54.92 219	48.00 228	75.03 204	83.75 212	83.24 211	90.04 223	85.27 213

1111		66.22 221	66.42 223	65.98 222	75.69 224	76.42 228	58.90 229	63.25 228	57.86 227	48.33 229	45.46 227	49.13 226	61.32 222	81.57 216	82.80 212	88.38 226	71.69 231

.test1245		48.95 230	46.78 231	51.48 228	75.69 224	76.42 228	58.90 229	63.25 228	57.86 227	48.33 229	45.46 227	49.13 226	61.32 222	81.57 216	5.58 235	1.40 239	11.42 237

E-PMN		40.00 231	35.74 233	44.98 232	57.69 238	39.15 241	28.05 239	62.70 230	35.52 236	17.78 240	20.90 235	14.36 241	44.47 230	35.89 235	47.86 233	59.15 236	56.47 234

EMVS		39.04 233	34.32 234	44.54 233	58.25 237	39.35 240	27.61 240	62.55 231	35.99 235	16.40 241	20.04 237	14.77 240	44.80 229	33.12 236	44.10 234	57.61 237	52.89 235

PMMVS2		53.68 228	55.72 229	51.30 229	58.84 236	67.02 235	54.23 234	60.97 232	47.50 234	19.42 239	34.81 232	31.97 236	30.88 234	65.84 231	69.99 228	83.47 230	72.92 228

testpf		74.66 211	76.34 212	72.71 216	87.34 178	80.91 223	73.15 223	60.30 233	78.73 184	61.68 208	69.83 156	62.22 201	67.48 212	76.83 224	78.17 225	86.28 227	87.68 209

MVE	39.81 19	39.52 232	41.58 232	37.11 234	33.93 239	49.06 238	26.45 241	54.22 234	29.46 238	24.15 237	20.77 236	10.60 242	34.42 233	51.12 234	65.27 232	49.49 238	64.81 233
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX						71.82 232	68.37 227	48.05 235	77.38 191	46.88 231	65.77 192	47.03 229	67.48 212	64.27 232		76.89 234	76.72 225

tmp_tt				50.24 231	68.55 232	46.86 239	48.90 237	18.28 236	86.51 108	68.32 178	70.19 152	65.33 182	26.69 236	74.37 226	66.80 230	70.72 235

testmvs		4.35 234	6.54 235	1.79 236	0.60 240	1.82 242	3.06 243	0.95 237	7.22 239	0.88 243	12.38 238	1.25 243	3.87 238	6.09 237	5.58 235	1.40 239	11.42 237

test123		3.48 235	5.31 236	1.34 237	0.20 242	1.52 243	2.17 244	0.58 238	6.13 240	0.31 244	9.85 239	0.31 244	3.90 237	2.65 238	5.28 237	0.87 241	11.46 236

GG-mvs-BLEND		62.84 224	90.21 74	30.91 235	0.57 241	94.45 107	86.99 184	0.34 239	88.71 87	0.98 242	81.55 95	91.58 50	0.86 239	92.66 109	91.43 127	95.73 178	91.11 189

sosnet-low-res		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

sosnet		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

our_test_3					86.93 193	89.77 197	81.61 208

ambc			67.96 220		73.69 228	79.79 226	73.82 220		71.61 213	59.80 212	46.00 226	20.79 238	66.15 216	86.92 196	80.11 223	89.13 225	90.50 194

MTAPA										95.36 2		97.46 16

MTMP										95.70 1		96.90 21

Patchmatch-RL test							18.47 242

XVS					95.68 57	98.66 11	94.96 55			88.03 46		96.06 26				98.46 20

X-MVStestdata					95.68 57	98.66 11	94.96 55			88.03 46		96.06 26				98.46 20

mPP-MVS					98.76 19							95.49 33

NP-MVS									91.63 57

High-resolution multi-view benchmark