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.

SMA-MVS		98.66 5	98.89 5	98.39 8	99.60 1	99.41 7	99.00 19	97.63 9	97.78 16	95.83 16	98.33 9	99.83 2	98.85 10	98.93 6	98.56 6	99.41 41	99.40 13

APDe-MVS		98.87 1	98.96 2	98.77 1	99.58 2	99.53 4	99.44 1	97.81 1	98.22 8	97.33 3	98.70 3	99.33 8	98.86 8	98.96 4	98.40 13	99.63 3	99.57 7

PGM-MVS		97.81 23	98.11 26	97.46 27	99.55 3	99.34 14	99.32 7	94.51 43	96.21 58	93.07 35	98.05 11	97.95 39	98.82 12	98.22 29	97.89 31	99.48 22	99.09 51

ACMMP_NAP		98.20 16	98.49 11	97.85 23	99.50 4	99.40 8	99.26 10	97.64 8	97.47 30	92.62 44	97.59 17	99.09 18	98.71 16	98.82 10	97.86 33	99.40 44	99.19 40

zzz-MVS		98.43 10	98.31 21	98.57 3	99.48 5	99.40 8	99.32 7	97.62 10	97.70 19	96.67 8	96.59 29	99.09 18	98.86 8	98.65 11	97.56 40	99.45 30	99.17 43

APD-MVS		98.36 13	98.32 20	98.41 7	99.47 6	99.26 20	99.12 14	97.77 5	96.73 47	96.12 14	97.27 25	98.88 21	98.46 24	98.47 16	98.39 14	99.52 14	99.22 36
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CSCG		97.44 30	97.18 38	97.75 25	99.47 6	99.52 5	98.55 30	95.41 38	97.69 21	95.72 17	94.29 50	95.53 59	98.10 28	96.20 97	97.38 48	99.24 69	99.62 3

HPM-MVS++		98.34 14	98.47 13	98.18 14	99.46 8	99.15 28	99.10 15	97.69 6	97.67 22	94.93 24	97.62 16	99.70 5	98.60 19	98.45 17	97.46 43	99.31 58	99.26 29

SR-MVS					99.45 9			97.61 12				99.20 12

MSP-MVS		98.73 4	98.93 3	98.50 5	99.44 10	99.57 2	99.36 3	97.65 7	98.14 10	96.51 12	98.49 5	99.65 6	98.67 18	98.60 12	98.42 11	99.40 44	99.63 1

DVP-MVS		98.86 2	98.97 1	98.75 2	99.43 11	99.63 1	99.25 11	97.81 1	98.62 1	97.69 1	97.59 17	99.90 1	98.93 5	98.99 2	98.42 11	99.37 50	99.62 3

ACMMPR		98.40 11	98.49 11	98.28 11	99.41 12	99.40 8	99.36 3	97.35 19	98.30 5	95.02 23	97.79 14	98.39 34	99.04 2	98.26 26	98.10 21	99.50 21	99.22 36

X-MVS		97.84 22	98.19 25	97.42 28	99.40 13	99.35 11	99.06 16	97.25 23	97.38 31	90.85 55	96.06 33	98.72 26	98.53 23	98.41 20	98.15 20	99.46 26	99.28 24

MCST-MVS		98.20 16	98.36 16	98.01 20	99.40 13	99.05 31	99.00 19	97.62 10	97.59 26	93.70 31	97.42 24	99.30 9	98.77 14	98.39 22	97.48 42	99.59 4	99.31 23

CNVR-MVS		98.47 9	98.46 14	98.48 6	99.40 13	99.05 31	99.02 18	97.54 14	97.73 17	96.65 9	97.20 26	99.13 16	98.85 10	98.91 7	98.10 21	99.41 41	99.08 52

HFP-MVS		98.48 8	98.62 9	98.32 9	99.39 16	99.33 15	99.27 9	97.42 16	98.27 6	95.25 21	98.34 8	98.83 23	99.08 1	98.26 26	98.08 23	99.48 22	99.26 29

NCCC		98.10 19	98.05 28	98.17 16	99.38 17	99.05 31	99.00 19	97.53 15	98.04 12	95.12 22	94.80 47	99.18 14	98.58 21	98.49 15	97.78 35	99.39 46	98.98 68

MP-MVS		98.09 20	98.30 22	97.84 24	99.34 18	99.19 26	99.23 12	97.40 17	97.09 40	93.03 38	97.58 19	98.85 22	98.57 22	98.44 19	97.69 36	99.48 22	99.23 34
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		98.32 15	98.34 19	98.29 10	99.34 18	99.30 16	99.15 13	97.35 19	97.49 28	95.58 19	97.72 15	98.62 30	98.82 12	98.29 24	97.67 37	99.51 19	99.28 24

SteuartSystems-ACMMP		98.38 12	98.71 8	97.99 21	99.34 18	99.46 6	99.34 5	97.33 22	97.31 32	94.25 27	98.06 10	99.17 15	98.13 27	98.98 3	98.46 9	99.55 11	99.54 8
Skip Steuart: Steuart Systems R&D Blog.
DPE-MVS		98.75 3	98.91 4	98.57 3	99.21 21	99.54 3	99.42 2	97.78 4	97.49 28	96.84 6	98.94 1	99.82 3	98.59 20	98.90 8	98.22 17	99.56 10	99.48 10

mPP-MVS					99.21 21							98.29 35

AdaColmap		97.53 28	96.93 43	98.24 12	99.21 21	98.77 58	98.47 33	97.34 21	96.68 49	96.52 11	95.11 43	96.12 55	98.72 15	97.19 60	96.24 75	99.17 83	98.39 108

DeepC-MVS_fast	96.13 1	98.13 18	98.27 23	97.97 22	99.16 24	99.03 36	99.05 17	97.24 24	98.22 8	94.17 29	95.82 35	98.07 36	98.69 17	98.83 9	98.80 2	99.52 14	99.10 49
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSLP-MVS++		98.04 21	97.93 30	98.18 14	99.10 25	99.09 30	98.34 35	96.99 30	97.54 27	96.60 10	94.82 46	98.45 32	98.89 6	97.46 52	98.77 4	99.17 83	99.37 16

3Dnovator	93.79 8	97.08 35	97.20 36	96.95 35	99.09 26	99.03 36	98.20 37	93.33 51	97.99 13	93.82 30	90.61 87	96.80 46	97.82 32	97.90 41	98.78 3	99.47 25	99.26 29

QAPM		96.78 43	97.14 40	96.36 41	99.05 27	99.14 29	98.02 40	93.26 53	97.27 34	90.84 58	91.16 79	97.31 41	97.64 37	97.70 46	98.20 18	99.33 53	99.18 42

OpenMVS	92.33 11	95.50 52	95.22 69	95.82 49	98.98 28	98.97 43	97.67 48	93.04 61	94.64 96	89.18 85	84.44 133	94.79 61	96.79 57	97.23 57	97.61 38	99.24 69	98.88 79

PLC	94.95 3	97.37 31	96.77 47	98.07 18	98.97 29	98.21 81	97.94 43	96.85 33	97.66 23	97.58 2	93.33 55	96.84 45	98.01 31	97.13 62	96.20 77	99.09 95	98.01 120
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
train_agg		97.65 27	98.06 27	97.18 31	98.94 30	98.91 50	98.98 23	97.07 29	96.71 48	90.66 61	97.43 23	99.08 20	98.20 25	97.96 39	97.14 54	99.22 75	99.19 40

CDPH-MVS		96.84 41	97.49 33	96.09 45	98.92 31	98.85 55	98.61 27	95.09 39	96.00 65	87.29 98	95.45 40	97.42 40	97.16 47	97.83 43	97.94 28	99.44 36	98.92 74

CPTT-MVS		97.78 24	97.54 31	98.05 19	98.91 32	99.05 31	99.00 19	96.96 31	97.14 37	95.92 15	95.50 38	98.78 25	98.99 4	97.20 58	96.07 79	98.54 148	99.04 60

3Dnovator+	93.91 7	97.23 33	97.22 35	97.24 30	98.89 33	98.85 55	98.26 36	93.25 55	97.99 13	95.56 20	90.01 93	98.03 38	98.05 29	97.91 40	98.43 10	99.44 36	99.35 18

ACMMP		97.37 31	97.48 34	97.25 29	98.88 34	99.28 18	98.47 33	96.86 32	97.04 42	92.15 45	97.57 20	96.05 57	97.67 35	97.27 56	95.99 84	99.46 26	99.14 48
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
PHI-MVS		97.78 24	98.44 15	97.02 34	98.73 35	99.25 22	98.11 38	95.54 37	96.66 50	92.79 41	98.52 4	99.38 7	97.50 40	97.84 42	98.39 14	99.45 30	99.03 61

OMC-MVS		97.00 37	96.92 44	97.09 32	98.69 36	98.66 66	97.85 44	95.02 40	98.09 11	94.47 25	93.15 56	96.90 43	97.38 41	97.16 61	96.82 64	99.13 90	97.65 133

MAR-MVS		95.50 52	95.60 61	95.39 56	98.67 37	98.18 84	95.89 91	89.81 101	94.55 98	91.97 47	92.99 58	90.21 85	97.30 43	96.79 71	97.49 41	98.72 134	98.99 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
TSAR-MVS + ACMM		97.71 26	98.60 10	96.66 38	98.64 38	99.05 31	98.85 24	97.23 25	98.45 3	89.40 81	97.51 21	99.27 11	96.88 56	98.53 13	97.81 34	98.96 111	99.59 6

abl_6				96.82 37	98.60 39	98.74 60	97.74 46	93.73 47	96.25 56	94.37 26	94.55 49	98.60 31	97.25 44			99.27 64	98.61 93

CNLPA		96.90 39	96.28 53	97.64 26	98.56 40	98.63 71	96.85 61	96.60 34	97.73 17	97.08 5	89.78 95	96.28 54	97.80 34	96.73 74	96.63 66	98.94 113	98.14 119

EPNet		96.27 48	96.97 42	95.46 54	98.47 41	98.28 78	97.41 51	93.67 48	95.86 71	92.86 40	97.51 21	93.79 65	91.76 131	97.03 65	97.03 56	98.61 144	99.28 24
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_111021_LR		97.16 34	98.01 29	96.16 44	98.47 41	98.98 41	96.94 58	93.89 46	97.64 24	91.44 49	98.89 2	96.41 50	97.20 46	98.02 37	97.29 53	99.04 106	98.85 83

MVS_111021_HR		97.04 36	98.20 24	95.69 50	98.44 43	99.29 17	96.59 71	93.20 56	97.70 19	89.94 73	98.46 6	96.89 44	96.71 60	98.11 34	97.95 27	99.27 64	99.01 64

MSDG		94.82 65	93.73 97	96.09 45	98.34 44	97.43 99	97.06 55	96.05 35	95.84 72	90.56 62	86.30 122	89.10 95	95.55 79	96.13 100	95.61 94	99.00 107	95.73 169

TAPA-MVS	94.18 5	96.38 46	96.49 51	96.25 42	98.26 45	98.66 66	98.00 41	94.96 41	97.17 36	89.48 78	92.91 60	96.35 51	97.53 39	96.59 79	95.90 87	99.28 62	97.82 124
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepC-MVS	94.87 4	96.76 44	96.50 50	97.05 33	98.21 46	99.28 18	98.67 26	97.38 18	97.31 32	90.36 67	89.19 97	93.58 66	98.19 26	98.31 23	98.50 7	99.51 19	99.36 17
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SD-MVS		98.52 6	98.77 7	98.23 13	98.15 47	99.26 20	98.79 25	97.59 13	98.52 2	96.25 13	97.99 12	99.75 4	99.01 3	98.27 25	97.97 26	99.59 4	99.63 1
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
TSAR-MVS + MP.		98.49 7	98.78 6	98.15 17	98.14 48	99.17 27	99.34 5	97.18 27	98.44 4	95.72 17	97.84 13	99.28 10	98.87 7	99.05 1	98.05 24	99.66 1	99.60 5
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPM-MVS		96.86 40	96.82 46	96.91 36	98.08 49	98.20 82	98.52 32	97.20 26	97.24 35	91.42 50	91.84 72	98.45 32	97.25 44	97.07 63	97.40 47	98.95 112	97.55 136

EPNet_dtu		92.45 107	95.02 74	89.46 136	98.02 50	95.47 158	94.79 110	92.62 62	94.97 91	70.11 182	94.76 48	92.61 72	84.07 191	95.94 103	95.56 95	97.15 177	95.82 168
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet		96.84 41	97.20 36	96.42 39	97.92 51	99.24 24	98.60 28	93.51 50	97.11 38	93.07 35	91.16 79	97.24 42	96.21 68	98.24 28	98.05 24	99.22 75	99.35 18

LS3D		95.46 55	95.14 70	95.84 48	97.91 52	98.90 52	98.58 29	97.79 3	97.07 41	83.65 112	88.71 101	88.64 98	97.82 32	97.49 51	97.42 45	99.26 68	97.72 132

DELS-MVS		96.06 50	96.04 57	96.07 47	97.77 53	99.25 22	98.10 39	93.26 53	94.42 100	92.79 41	88.52 105	93.48 67	95.06 85	98.51 14	98.83 1	99.45 30	99.28 24
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
COLMAP_ROB	90.49 14	93.27 100	92.71 109	93.93 83	97.75 54	97.44 98	96.07 85	93.17 57	95.40 81	83.86 110	83.76 137	88.72 97	93.87 104	94.25 141	94.11 135	98.87 119	95.28 175
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PCF-MVS	93.95 6	95.65 51	95.14 70	96.25 42	97.73 55	98.73 62	97.59 49	97.13 28	92.50 129	89.09 87	89.85 94	96.65 47	96.90 55	94.97 129	94.89 113	99.08 96	98.38 109
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PatchMatch-RL		94.69 71	94.41 81	95.02 62	97.63 56	98.15 85	94.50 116	91.99 70	95.32 83	91.31 51	95.47 39	83.44 131	96.02 71	96.56 80	95.23 105	98.69 137	96.67 160

PVSNet_BlendedMVS		95.41 57	95.28 67	95.57 52	97.42 57	99.02 38	95.89 91	93.10 58	96.16 59	93.12 33	91.99 68	85.27 117	94.66 90	98.09 35	97.34 49	99.24 69	99.08 52

PVSNet_Blended		95.41 57	95.28 67	95.57 52	97.42 57	99.02 38	95.89 91	93.10 58	96.16 59	93.12 33	91.99 68	85.27 117	94.66 90	98.09 35	97.34 49	99.24 69	99.08 52

DeepPCF-MVS	95.28 2	97.00 37	98.35 18	95.42 55	97.30 59	98.94 45	94.82 109	96.03 36	98.24 7	92.11 46	95.80 36	98.64 29	95.51 80	98.95 5	98.66 5	96.78 180	99.20 39

CHOSEN 280x420		95.46 55	97.01 41	93.66 88	97.28 60	97.98 89	96.40 77	85.39 150	96.10 62	91.07 53	96.53 30	96.34 53	95.61 77	97.65 47	96.95 59	96.21 182	97.49 137

MVS_0304		96.31 47	96.91 45	95.62 51	97.21 61	99.20 25	98.55 30	93.10 58	97.04 42	89.73 75	90.30 89	96.35 51	95.71 74	98.14 31	97.93 30	99.38 47	99.40 13

CHOSEN 1792x2688		92.66 105	92.49 114	92.85 99	97.13 62	98.89 53	95.90 89	88.50 117	95.32 83	83.31 113	71.99 186	88.96 96	94.10 101	96.69 75	96.49 68	98.15 161	99.10 49

HyFIR lowres test		92.03 108	91.55 132	92.58 100	97.13 62	98.72 63	94.65 113	86.54 135	93.58 115	82.56 116	67.75 197	90.47 83	95.67 75	95.87 105	95.54 96	98.91 116	98.93 73

OPM-MVS		93.61 93	92.43 118	95.00 63	96.94 64	97.34 100	97.78 45	94.23 44	89.64 160	85.53 105	88.70 102	82.81 134	96.28 67	96.28 94	95.00 112	99.24 69	97.22 145

XVS					96.60 65	99.35 11	96.82 62			90.85 55		98.72 26				99.46 26

X-MVStestdata					96.60 65	99.35 11	96.82 62			90.85 55		98.72 26				99.46 26

TSAR-MVS + COLMAP		94.79 67	94.51 79	95.11 60	96.50 67	97.54 94	97.99 42	94.54 42	97.81 15	85.88 104	96.73 28	81.28 141	96.99 53	96.29 93	95.21 106	98.76 133	96.73 159

PVSNet_Blended_VisFu		94.77 69	95.54 63	93.87 84	96.48 68	98.97 43	94.33 118	91.84 73	94.93 92	90.37 66	85.04 128	94.99 60	90.87 146	98.12 33	97.30 51	99.30 60	99.45 12

LGP-MVS_train		94.12 82	94.62 77	93.53 89	96.44 69	97.54 94	97.40 52	91.84 73	94.66 95	81.09 125	95.70 37	83.36 132	95.10 84	96.36 91	95.71 92	99.32 55	99.03 61

HQP-MVS		94.43 77	94.57 78	94.27 79	96.41 70	97.23 103	96.89 59	93.98 45	95.94 68	83.68 111	95.01 44	84.46 123	95.58 78	95.47 117	94.85 117	99.07 98	99.00 65

ACMM	92.75 10	94.41 79	93.84 95	95.09 61	96.41 70	96.80 113	94.88 108	93.54 49	96.41 53	90.16 68	92.31 66	83.11 133	96.32 66	96.22 96	94.65 119	99.22 75	97.35 142
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
RPSCF		94.05 83	94.00 91	94.12 81	96.20 72	96.41 127	96.61 70	91.54 79	95.83 73	89.73 75	96.94 27	92.80 70	95.35 83	91.63 178	90.44 182	95.27 193	93.94 185

UA-Net		93.96 85	95.95 58	91.64 108	96.06 73	98.59 73	95.29 99	90.00 96	91.06 148	82.87 114	90.64 86	98.06 37	86.06 178	98.14 31	98.20 18	99.58 7	96.96 153

UGNet		94.92 62	96.63 48	92.93 98	96.03 74	98.63 71	94.53 115	91.52 80	96.23 57	90.03 70	92.87 61	96.10 56	86.28 177	96.68 76	96.60 67	99.16 86	99.32 22
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
ACMP	92.88 9	94.43 77	94.38 82	94.50 75	96.01 75	97.69 92	95.85 94	92.09 69	95.74 74	89.12 86	95.14 42	82.62 136	94.77 86	95.73 111	94.67 118	99.14 89	99.06 56
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IB-MVS	89.56 15	91.71 113	92.50 113	90.79 120	95.94 76	98.44 75	87.05 190	91.38 82	93.15 118	92.98 39	84.78 129	85.14 120	78.27 196	92.47 167	94.44 130	99.10 94	99.08 52
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
MS-PatchMatch		91.82 111	92.51 112	91.02 114	95.83 77	96.88 108	95.05 102	84.55 163	93.85 110	82.01 118	82.51 143	91.71 74	90.52 153	95.07 127	93.03 157	98.13 162	94.52 177

CANet_DTU		93.92 86	96.57 49	90.83 118	95.63 78	98.39 76	96.99 57	87.38 127	96.26 55	71.97 171	96.31 31	93.02 68	94.53 93	97.38 54	96.83 63	98.49 151	97.79 125

ACMH	90.77 13	91.51 118	91.63 131	91.38 111	95.62 79	96.87 110	91.76 163	89.66 103	91.58 143	78.67 134	86.73 113	78.12 149	93.77 107	94.59 132	94.54 126	98.78 131	98.98 68
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TSAR-MVS + GP.		97.45 29	98.36 16	96.39 40	95.56 80	98.93 47	97.74 46	93.31 52	97.61 25	94.24 28	98.44 7	99.19 13	98.03 30	97.60 48	97.41 46	99.44 36	99.33 20

thres600view7		93.49 96	92.37 121	94.79 70	95.42 81	98.93 47	96.58 72	92.31 64	93.04 119	87.88 94	86.62 115	76.94 156	97.09 51	96.82 68	95.63 93	99.45 30	98.63 92

thres400		93.56 94	92.43 118	94.87 67	95.40 82	98.91 50	96.70 68	92.38 63	92.93 121	88.19 93	86.69 114	77.35 154	97.13 48	96.75 73	95.85 89	99.42 40	98.56 95

thres200		93.62 92	92.54 111	94.88 66	95.36 83	98.93 47	96.75 66	92.31 64	92.84 122	88.28 91	86.99 111	77.81 153	97.13 48	96.82 68	95.92 85	99.45 30	98.49 101

thres100view900		93.55 95	92.47 117	94.81 69	95.33 84	98.74 60	96.78 65	92.30 67	92.63 125	88.29 89	87.21 109	78.01 151	96.78 58	96.38 88	95.92 85	99.38 47	98.40 107

tfpn200view9		93.64 91	92.57 110	94.89 65	95.33 84	98.94 45	96.82 62	92.31 64	92.63 125	88.29 89	87.21 109	78.01 151	97.12 50	96.82 68	95.85 89	99.45 30	98.56 95

IS_MVSNet		95.28 59	96.43 52	93.94 82	95.30 86	99.01 40	95.90 89	91.12 84	94.13 106	87.50 97	91.23 78	94.45 63	94.17 99	98.45 17	98.50 7	99.65 2	99.23 34

CMPMVS	65.18 17	84.76 187	83.10 193	86.69 177	95.29 87	95.05 170	88.37 185	85.51 149	80.27 201	71.31 175	68.37 195	73.85 168	85.25 182	87.72 194	87.75 192	94.38 201	88.70 201
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
canonicalmvs		95.25 61	95.45 65	95.00 63	95.27 88	98.72 63	96.89 59	89.82 100	96.51 51	90.84 58	93.72 54	86.01 112	97.66 36	95.78 109	97.94 28	99.54 13	99.50 9

Vis-MVSNet (Re-imp)		94.46 76	96.24 54	92.40 101	95.23 89	98.64 69	95.56 97	90.99 85	94.42 100	85.02 107	90.88 85	94.65 62	88.01 168	98.17 30	98.37 16	99.57 9	98.53 98

CLD-MVS		94.79 67	94.36 83	95.30 57	95.21 90	97.46 97	97.23 53	92.24 68	96.43 52	91.77 48	92.69 62	84.31 124	96.06 69	95.52 115	95.03 109	99.31 58	99.06 56
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
baseline1		94.59 73	94.47 80	94.72 71	95.16 91	97.97 90	96.07 85	91.94 71	94.86 93	89.98 71	91.60 76	85.87 114	95.64 76	97.07 63	96.90 60	99.52 14	97.06 152

TDRefinement		89.07 151	88.15 160	90.14 129	95.16 91	96.88 108	95.55 98	90.20 94	89.68 159	76.42 147	76.67 160	74.30 166	84.85 185	93.11 157	91.91 175	98.64 143	94.47 178

ACMH+	90.88 12	91.41 119	91.13 135	91.74 107	95.11 93	96.95 107	93.13 135	89.48 107	92.42 131	79.93 129	85.13 127	78.02 150	93.82 106	93.49 152	93.88 141	98.94 113	97.99 121

DCV-MVSNet		94.76 70	95.12 72	94.35 78	95.10 94	95.81 147	96.46 76	89.49 106	96.33 54	90.16 68	92.55 64	90.26 84	95.83 73	95.52 115	96.03 82	99.06 101	99.33 20

Anonymous202405211			92.18 123		95.04 95	98.20 82	96.14 83	91.79 75	93.93 107		74.60 169	88.38 101	96.48 64	95.17 125	95.82 91	99.00 107	99.15 45

CS-MVS		96.23 49	97.15 39	95.16 58	95.01 96	98.98 41	97.13 54	90.68 89	96.00 65	91.21 52	94.03 51	96.48 48	97.35 42	98.00 38	97.43 44	99.55 11	99.15 45

FC-MVSNet-train		93.85 87	93.91 92	93.78 86	94.94 97	96.79 116	94.29 119	91.13 83	93.84 111	88.26 92	90.40 88	85.23 119	94.65 92	96.54 82	95.31 102	99.38 47	99.28 24

EPP-MVSNet		95.27 60	96.18 56	94.20 80	94.88 98	98.64 69	94.97 104	90.70 88	95.34 82	89.67 77	91.66 75	93.84 64	95.42 82	97.32 55	97.00 57	99.58 7	99.47 11

EIA-MVS		95.50 52	96.19 55	94.69 72	94.83 99	98.88 54	95.93 88	91.50 81	94.47 99	89.43 79	93.14 57	92.72 71	97.05 52	97.82 45	97.13 55	99.43 39	99.15 45

ETV-MVS		96.42 45	97.53 32	95.13 59	94.80 100	98.77 58	96.40 77	91.63 77	97.10 39	90.75 60	94.90 45	96.42 49	97.61 38	98.40 21	97.87 32	99.59 4	99.25 32

MVS_Test		94.82 65	95.66 60	93.84 85	94.79 101	98.35 77	96.49 75	89.10 111	96.12 61	87.09 100	92.58 63	90.61 82	96.48 64	96.51 86	96.89 61	99.11 93	98.54 97

Anonymous20231211		93.49 96	92.33 122	94.84 68	94.78 102	98.00 88	96.11 84	91.85 72	94.86 93	90.91 54	74.69 168	89.18 93	96.73 59	94.82 130	95.51 97	98.67 138	99.24 33

baseline		94.83 64	95.82 59	93.68 87	94.75 103	97.80 91	96.51 74	88.53 116	97.02 44	89.34 83	92.93 59	92.18 73	94.69 89	95.78 109	96.08 78	98.27 159	98.97 72

MVSTER		94.89 63	95.07 73	94.68 73	94.71 104	96.68 119	97.00 56	90.57 91	95.18 89	93.05 37	95.21 41	86.41 109	93.72 108	97.59 49	95.88 88	99.00 107	98.50 100

EPMVS		90.88 125	92.12 124	89.44 137	94.71 104	97.24 102	93.55 126	76.81 189	95.89 69	81.77 120	91.49 77	86.47 108	93.87 104	90.21 185	90.07 184	95.92 184	93.49 191

casdiffmvs		94.38 80	94.15 90	94.64 74	94.70 106	98.51 74	96.03 87	91.66 76	95.70 75	89.36 82	86.48 117	85.03 122	96.60 63	97.40 53	97.30 51	99.52 14	98.67 90

diffmvs		94.31 81	94.21 85	94.42 77	94.64 107	98.28 78	96.36 79	91.56 78	96.77 46	88.89 88	88.97 99	84.23 125	96.01 72	96.05 101	96.41 70	99.05 105	98.79 87

DWT-MVSNet_training		91.30 120	89.73 146	93.13 97	94.64 107	96.87 110	94.93 105	86.17 140	94.22 104	93.18 32	89.11 98	73.28 172	93.59 111	88.00 193	90.73 180	96.26 181	95.87 166

DI_MVS_plusplus_trai		94.01 84	93.63 99	94.44 76	94.54 109	98.26 80	97.51 50	90.63 90	95.88 70	89.34 83	80.54 150	89.36 90	95.48 81	96.33 92	96.27 74	99.17 83	98.78 88

thisisatest0530		94.54 74	95.47 64	93.46 91	94.51 110	98.65 68	94.66 112	90.72 86	95.69 77	86.90 101	93.80 52	89.44 89	94.74 87	96.98 67	94.86 114	99.19 82	98.85 83

tttt0517		94.52 75	95.44 66	93.44 92	94.51 110	98.68 65	94.61 114	90.72 86	95.61 79	86.84 102	93.78 53	89.26 92	94.74 87	97.02 66	94.86 114	99.20 81	98.87 81

ADS-MVSNet		89.80 140	91.33 134	88.00 158	94.43 112	96.71 118	92.29 151	74.95 199	96.07 63	77.39 139	88.67 103	86.09 111	93.26 116	88.44 191	89.57 187	95.68 187	93.81 188

tpmrst		88.86 155	89.62 147	87.97 159	94.33 113	95.98 137	92.62 143	76.36 192	94.62 97	76.94 143	85.98 123	82.80 135	92.80 121	86.90 197	87.15 195	94.77 198	93.93 186

PMMVS		94.61 72	95.56 62	93.50 90	94.30 114	96.74 117	94.91 107	89.56 105	95.58 80	87.72 95	96.15 32	92.86 69	96.06 69	95.47 117	95.02 110	98.43 156	97.09 148

CostFormer		90.69 126	90.48 143	90.93 116	94.18 115	96.08 135	94.03 121	78.20 185	93.47 116	89.96 72	90.97 84	80.30 142	93.72 108	87.66 196	88.75 189	95.51 190	96.12 164

USDC		90.69 126	90.52 142	90.88 117	94.17 116	96.43 126	95.82 95	86.76 133	93.92 108	76.27 149	86.49 116	74.30 166	93.67 110	95.04 128	93.36 150	98.61 144	94.13 182

Vis-MVSNet		92.77 103	95.00 75	90.16 127	94.10 117	98.79 57	94.76 111	88.26 118	92.37 134	79.95 128	88.19 107	91.58 75	84.38 188	97.59 49	97.58 39	99.52 14	98.91 77
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		92.93 102	93.86 94	91.86 104	94.07 118	98.09 87	95.59 96	85.98 143	94.27 103	79.54 132	91.12 82	81.81 138	96.71 60	96.67 77	96.06 80	99.27 64	98.98 68

IterMVS-LS		92.56 106	93.18 106	91.84 105	93.90 119	94.97 172	94.99 103	86.20 139	94.18 105	82.68 115	85.81 124	87.36 105	94.43 94	95.31 121	96.02 83	98.87 119	98.60 94
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dps		90.11 138	89.37 151	90.98 115	93.89 120	96.21 132	93.49 128	77.61 187	91.95 140	92.74 43	88.85 100	78.77 148	92.37 124	87.71 195	87.71 193	95.80 186	94.38 180

tpm cat1		88.90 153	87.78 169	90.22 126	93.88 121	95.39 161	93.79 124	78.11 186	92.55 128	89.43 79	81.31 146	79.84 144	91.40 134	84.95 198	86.34 198	94.68 200	94.09 183

PatchmatchNet		90.56 128	92.49 114	88.31 149	93.83 122	96.86 112	92.42 147	76.50 191	95.96 67	78.31 135	91.96 70	89.66 88	93.48 113	90.04 187	89.20 188	95.32 191	93.73 189
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TinyColmap		89.42 143	88.58 155	90.40 124	93.80 123	95.45 159	93.96 123	86.54 135	92.24 137	76.49 146	80.83 148	70.44 184	93.37 114	94.45 136	93.30 153	98.26 160	93.37 192

SCA		90.92 124	93.04 108	88.45 146	93.72 124	97.33 101	92.77 139	76.08 194	96.02 64	78.26 136	91.96 70	90.86 79	93.99 103	90.98 182	90.04 185	95.88 185	94.06 184

RPMNet		90.19 135	92.03 127	88.05 155	93.46 125	95.95 140	93.41 129	74.59 200	92.40 132	75.91 151	84.22 134	86.41 109	92.49 122	94.42 137	93.85 143	98.44 154	96.96 153

gg-mvs-nofinetune		86.17 182	88.57 156	83.36 189	93.44 126	98.15 85	96.58 72	72.05 203	74.12 206	49.23 209	64.81 201	90.85 80	89.90 161	97.83 43	96.84 62	98.97 110	97.41 140

MDTV_nov1_ep13		91.57 116	93.18 106	89.70 133	93.39 127	96.97 106	93.53 127	80.91 180	95.70 75	81.86 119	92.40 65	89.93 86	93.25 117	91.97 176	90.80 179	95.25 194	94.46 179

CR-MVSNet		90.16 136	91.96 128	88.06 154	93.32 128	95.95 140	93.36 131	75.99 195	92.40 132	75.19 157	83.18 139	85.37 116	92.05 126	95.21 123	94.56 124	98.47 153	97.08 150

test-LLR		91.62 115	93.56 102	89.35 139	93.31 129	96.57 122	92.02 159	87.06 131	92.34 135	75.05 160	90.20 90	88.64 98	90.93 142	96.19 98	94.07 136	97.75 171	96.90 156

test0.0.03 1		91.97 109	93.91 92	89.72 132	93.31 129	96.40 128	91.34 168	87.06 131	93.86 109	81.67 121	91.15 81	89.16 94	86.02 179	95.08 126	95.09 107	98.91 116	96.64 162

CVMVSNet		89.77 141	91.66 130	87.56 168	93.21 131	95.45 159	91.94 162	89.22 109	89.62 161	69.34 188	83.99 136	85.90 113	84.81 186	94.30 140	95.28 103	96.85 179	97.09 148

PatchT		89.13 150	91.71 129	86.11 182	92.92 132	95.59 154	83.64 197	75.09 198	91.87 141	75.19 157	82.63 142	85.06 121	92.05 126	95.21 123	94.56 124	97.76 170	97.08 150

Fast-Effi-MVS+		91.87 110	92.08 125	91.62 110	92.91 133	97.21 104	94.93 105	84.60 161	93.61 114	81.49 123	83.50 138	78.95 146	96.62 62	96.55 81	96.22 76	99.16 86	98.51 99

IterMVS-SCA-FT		90.24 133	92.48 116	87.63 165	92.85 134	94.30 188	93.79 124	81.47 179	92.66 124	69.95 183	84.66 131	88.38 101	89.99 159	95.39 120	94.34 131	97.74 173	97.63 134

baseline2		93.01 101	94.17 88	91.64 108	92.83 135	97.49 96	93.40 130	87.53 125	93.67 113	86.07 103	91.83 73	86.58 106	91.36 135	96.38 88	95.06 108	98.67 138	98.20 117

IterMVS		90.20 134	92.43 118	87.61 166	92.82 136	94.31 187	94.11 120	81.54 177	92.97 120	69.90 184	84.71 130	88.16 104	89.96 160	95.25 122	94.17 134	97.31 175	97.46 138
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		92.77 103	93.60 100	91.80 106	92.63 137	96.80 113	95.24 100	89.14 110	90.30 157	84.58 108	86.76 112	90.65 81	90.42 154	95.89 104	96.49 68	98.79 130	98.32 113
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm		87.95 163	89.44 150	86.21 181	92.53 138	94.62 182	91.40 166	76.36 192	91.46 144	69.80 186	87.43 108	75.14 161	91.55 133	89.85 189	90.60 181	95.61 188	96.96 153

Effi-MVS+-dtu		91.78 112	93.59 101	89.68 135	92.44 139	97.11 105	94.40 117	84.94 157	92.43 130	75.48 153	91.09 83	83.75 129	93.55 112	96.61 78	95.47 98	97.24 176	98.67 90

testgi		89.42 143	91.50 133	87.00 175	92.40 140	95.59 154	89.15 184	85.27 154	92.78 123	72.42 169	91.75 74	76.00 159	84.09 190	94.38 138	93.82 145	98.65 142	96.15 163

Fast-Effi-MVS+-dtu		91.19 121	93.64 98	88.33 148	92.19 141	96.46 125	93.99 122	81.52 178	92.59 127	71.82 172	92.17 67	85.54 115	91.68 132	95.73 111	94.64 120	98.80 128	98.34 110

FC-MVSNet-test		91.63 114	93.82 96	89.08 140	92.02 142	96.40 128	93.26 133	87.26 128	93.72 112	77.26 140	88.61 104	89.86 87	85.50 181	95.72 113	95.02 110	99.16 86	97.44 139

GA-MVS		89.28 146	90.75 141	87.57 167	91.77 143	96.48 124	92.29 151	87.58 124	90.61 154	65.77 193	84.48 132	76.84 157	89.46 162	95.84 106	93.68 146	98.52 149	97.34 143

UniMVSNet_ETH3D		88.47 157	86.00 186	91.35 112	91.55 144	96.29 130	92.53 144	88.81 112	85.58 189	82.33 117	67.63 198	66.87 197	94.04 102	91.49 179	95.24 104	98.84 122	98.92 74

TAMVS		90.54 130	90.87 140	90.16 127	91.48 145	96.61 121	93.26 133	86.08 141	87.71 175	81.66 122	83.11 141	84.04 126	90.42 154	94.54 133	94.60 121	98.04 166	95.48 173

tfpnnormal		88.50 156	87.01 177	90.23 125	91.36 146	95.78 149	92.74 140	90.09 95	83.65 194	76.33 148	71.46 189	69.58 189	91.84 129	95.54 114	94.02 138	99.06 101	99.03 61

GBi-Net		93.81 88	94.18 86	93.38 93	91.34 147	95.86 143	96.22 80	88.68 113	95.23 86	90.40 63	86.39 118	91.16 76	94.40 96	96.52 83	96.30 71	99.21 78	97.79 125

test1		93.81 88	94.18 86	93.38 93	91.34 147	95.86 143	96.22 80	88.68 113	95.23 86	90.40 63	86.39 118	91.16 76	94.40 96	96.52 83	96.30 71	99.21 78	97.79 125

FMVSNet2		93.30 99	93.36 105	93.22 96	91.34 147	95.86 143	96.22 80	88.24 119	95.15 90	89.92 74	81.64 145	89.36 90	94.40 96	96.77 72	96.98 58	99.21 78	97.79 125

FMVSNet3		93.79 90	94.17 88	93.35 95	91.21 150	95.99 136	96.62 69	88.68 113	95.23 86	90.40 63	86.39 118	91.16 76	94.11 100	95.96 102	96.67 65	99.07 98	97.79 125

TransMVSNet (Re)		87.73 169	86.79 179	88.83 142	90.76 151	94.40 185	91.33 169	89.62 104	84.73 191	75.41 155	72.73 182	71.41 180	86.80 174	94.53 134	93.93 140	99.06 101	95.83 167

LTVRE_ROB	87.32 16	87.55 170	88.25 159	86.73 176	90.66 152	95.80 148	93.05 136	84.77 158	83.35 195	60.32 202	83.12 140	67.39 195	93.32 115	94.36 139	94.86 114	98.28 158	98.87 81
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
EG-PatchMatch MVS		86.68 178	87.24 174	86.02 183	90.58 153	96.26 131	91.08 172	81.59 176	84.96 190	69.80 186	71.35 190	75.08 163	84.23 189	94.24 142	93.35 151	98.82 123	95.46 174

TESTMET0.1,1		91.07 122	93.56 102	88.17 150	90.43 154	96.57 122	92.02 159	82.83 172	92.34 135	75.05 160	90.20 90	88.64 98	90.93 142	96.19 98	94.07 136	97.75 171	96.90 156

pm-mvs1		89.19 149	89.02 152	89.38 138	90.40 155	95.74 150	92.05 157	88.10 121	86.13 185	77.70 137	73.72 177	79.44 145	88.97 165	95.81 108	94.51 128	99.08 96	97.78 130

NR-MVSNet		89.34 145	88.66 154	90.13 130	90.40 155	95.61 152	93.04 137	89.91 97	91.22 146	78.96 133	77.72 158	68.90 192	89.16 164	94.24 142	93.95 139	99.32 55	98.99 66

FMVSNet1		91.54 117	90.93 138	92.26 102	90.35 157	95.27 165	95.22 101	87.16 130	91.37 145	87.62 96	75.45 163	83.84 128	94.43 94	96.52 83	96.30 71	98.82 123	97.74 131

test-mter		90.95 123	93.54 104	87.93 160	90.28 158	96.80 113	91.44 165	82.68 173	92.15 139	74.37 164	89.57 96	88.23 103	90.88 145	96.37 90	94.31 132	97.93 168	97.37 141

pmmvs4		90.55 129	89.91 145	91.30 113	90.26 159	94.95 173	92.73 141	87.94 122	93.44 117	85.35 106	82.28 144	76.09 158	93.02 120	93.56 150	92.26 173	98.51 150	96.77 158

MVS-HIRNet		85.36 185	86.89 178	83.57 188	90.13 160	94.51 183	83.57 198	72.61 202	88.27 172	71.22 176	68.97 193	81.81 138	88.91 166	93.08 158	91.94 174	94.97 197	89.64 200

thisisatest0515		90.12 137	92.06 126	87.85 161	90.03 161	96.17 133	87.83 187	87.45 126	91.71 142	77.15 141	85.40 126	84.01 127	85.74 180	95.41 119	93.30 153	98.88 118	98.43 103

SixPastTwentyTwo		88.37 158	89.47 149	87.08 173	90.01 162	95.93 142	87.41 188	85.32 151	90.26 158	70.26 180	86.34 121	71.95 177	90.93 142	92.89 162	91.72 176	98.55 147	97.22 145

UniMVSNet (Re)		90.03 139	89.61 148	90.51 123	89.97 163	96.12 134	92.32 149	89.26 108	90.99 149	80.95 126	78.25 157	75.08 163	91.14 138	93.78 145	93.87 142	99.41 41	99.21 38

our_test_3					89.78 164	93.84 190	85.59 193

UniMVSNet_NR-MVSNet		90.35 132	89.96 144	90.80 119	89.66 165	95.83 146	92.48 145	90.53 92	90.96 150	79.57 130	79.33 154	77.14 155	93.21 118	92.91 161	94.50 129	99.37 50	99.05 58

v8		88.21 161	87.94 166	88.51 145	89.62 166	95.01 171	92.31 150	84.99 156	88.94 163	74.70 162	75.03 165	73.51 170	90.67 150	92.11 172	92.74 165	98.80 128	98.24 115

WR-MVS_H		87.93 164	87.85 167	88.03 157	89.62 166	95.58 156	90.47 177	85.55 148	87.20 180	76.83 144	74.42 172	72.67 175	86.37 176	93.22 156	93.04 156	99.33 53	98.83 85

pmmvs5		87.83 168	88.09 161	87.51 170	89.59 168	95.48 157	89.75 182	84.73 159	86.07 187	71.44 174	80.57 149	70.09 187	90.74 149	94.47 135	92.87 161	98.82 123	97.10 147

gm-plane-assit		83.26 191	85.29 188	80.89 192	89.52 169	89.89 202	70.26 207	78.24 184	77.11 204	58.01 206	74.16 174	66.90 196	90.63 152	97.20 58	96.05 81	98.66 141	95.68 170

v10		88.00 162	87.96 164	88.05 155	89.44 170	94.68 179	92.36 148	83.35 168	89.37 162	72.96 168	73.98 175	72.79 174	91.35 136	93.59 147	92.88 160	98.81 126	98.42 105

V42		88.31 159	87.95 165	88.73 143	89.44 170	95.34 162	92.23 153	87.21 129	88.83 165	74.49 163	74.89 167	73.43 171	90.41 156	92.08 174	92.77 164	98.60 146	98.33 111

v148		87.51 171	86.79 179	88.36 147	89.39 172	95.21 167	89.84 181	88.20 120	87.61 177	77.56 138	73.38 180	70.32 186	86.80 174	90.70 183	92.31 171	98.37 157	97.98 123

CP-MVSNet		87.89 167	87.27 173	88.62 144	89.30 173	95.06 169	90.60 176	85.78 145	87.43 179	75.98 150	74.60 169	68.14 194	90.76 147	93.07 159	93.60 147	99.30 60	98.98 68

v1144		87.92 166	87.79 168	88.07 152	89.27 174	95.15 168	92.17 154	85.62 147	88.52 169	71.52 173	73.80 176	72.40 176	91.06 140	93.54 151	92.80 162	98.81 126	98.33 111

DU-MVS		89.67 142	88.84 153	90.63 122	89.26 175	95.61 152	92.48 145	89.91 97	91.22 146	79.57 130	77.72 158	71.18 181	93.21 118	92.53 165	94.57 123	99.35 52	99.05 58

WR-MVS		87.93 164	88.09 161	87.75 162	89.26 175	95.28 163	90.81 174	86.69 134	88.90 164	75.29 156	74.31 173	73.72 169	85.19 184	92.26 168	93.32 152	99.27 64	98.81 86

Baseline_NR-MVSNet		89.27 147	88.01 163	90.73 121	89.26 175	93.71 191	92.71 142	89.78 102	90.73 151	81.28 124	73.53 178	72.85 173	92.30 125	92.53 165	93.84 144	99.07 98	98.88 79

N_pmnet		84.80 186	85.10 190	84.45 186	89.25 178	92.86 194	84.04 196	86.21 137	88.78 166	66.73 192	72.41 185	74.87 165	85.21 183	88.32 192	86.45 196	95.30 192	92.04 194

v2v482		88.25 160	87.71 170	88.88 141	89.23 179	95.28 163	92.10 155	87.89 123	88.69 168	73.31 167	75.32 164	71.64 178	91.89 128	92.10 173	92.92 159	98.86 121	97.99 121

PS-CasMVS		87.33 174	86.68 182	88.10 151	89.22 180	94.93 174	90.35 179	85.70 146	86.44 184	74.01 165	73.43 179	66.59 200	90.04 158	92.92 160	93.52 148	99.28 62	98.91 77

TranMVSNet+NR-MVSNet		89.23 148	88.48 157	90.11 131	89.07 181	95.25 166	92.91 138	90.43 93	90.31 156	77.10 142	76.62 161	71.57 179	91.83 130	92.12 171	94.59 122	99.32 55	98.92 74

v1192		87.51 171	87.31 172	87.74 163	89.04 182	94.87 177	92.07 156	85.03 155	88.49 170	70.32 179	72.65 183	70.35 185	91.21 137	93.59 147	92.80 162	98.78 131	98.42 105

v144192		87.40 173	87.20 175	87.64 164	88.89 183	94.88 176	91.65 164	84.70 160	87.80 174	71.17 177	73.20 181	70.91 182	90.75 148	92.69 163	92.49 168	98.71 135	98.43 103

PEN-MVS		87.22 176	86.50 184	88.07 152	88.88 184	94.44 184	90.99 173	86.21 137	86.53 183	73.66 166	74.97 166	66.56 201	89.42 163	91.20 181	93.48 149	99.24 69	98.31 114

v1921920		87.31 175	87.13 176	87.52 169	88.87 185	94.72 178	91.96 161	84.59 162	88.28 171	69.86 185	72.50 184	70.03 188	91.10 139	93.33 154	92.61 167	98.71 135	98.44 102

pmmvs6		85.98 183	84.89 191	87.25 172	88.83 186	94.35 186	89.36 183	85.30 153	78.51 203	75.44 154	62.71 202	75.41 160	87.65 170	93.58 149	92.40 170	96.89 178	97.29 144

v1240		86.89 177	86.75 181	87.06 174	88.75 187	94.65 181	91.30 170	84.05 164	87.49 178	68.94 189	71.96 187	68.86 193	90.65 151	93.33 154	92.72 166	98.67 138	98.24 115

anonymousdsp		88.90 153	91.00 137	86.44 179	88.74 188	95.97 138	90.40 178	82.86 171	88.77 167	67.33 191	81.18 147	81.44 140	90.22 157	96.23 95	94.27 133	99.12 92	99.16 44

EU-MVSNet		85.62 184	87.65 171	83.24 190	88.54 189	92.77 195	87.12 189	85.32 151	86.71 181	64.54 195	78.52 156	75.11 162	78.35 195	92.25 169	92.28 172	95.58 189	95.93 165

DTE-MVSNet		86.67 179	86.09 185	87.35 171	88.45 190	94.08 189	90.65 175	86.05 142	86.13 185	72.19 170	74.58 171	66.77 199	87.61 171	90.31 184	93.12 155	99.13 90	97.62 135

FMVSNet5		90.36 131	90.93 138	89.70 133	87.99 191	92.25 196	92.03 158	83.51 167	92.20 138	84.13 109	85.59 125	86.48 107	92.43 123	94.61 131	94.52 127	98.13 162	90.85 197

v7n		86.43 180	86.52 183	86.33 180	87.91 192	94.93 174	90.15 180	83.05 169	86.57 182	70.21 181	71.48 188	66.78 198	87.72 169	94.19 144	92.96 158	98.92 115	98.76 89

test20.03		82.92 192	85.52 187	79.90 195	87.75 193	91.84 197	82.80 199	82.99 170	82.65 199	60.32 202	78.90 155	70.50 183	67.10 202	92.05 175	90.89 178	98.44 154	91.80 195

MDTV_nov1_ep13_2view		86.30 181	88.27 158	84.01 187	87.71 194	94.67 180	88.08 186	76.78 190	90.59 155	68.66 190	80.46 151	80.12 143	87.58 172	89.95 188	88.20 191	95.25 194	93.90 187

Anonymous20231206		83.84 190	85.19 189	82.26 191	87.38 195	92.87 193	85.49 194	83.65 166	86.07 187	63.44 198	68.42 194	69.01 191	75.45 199	93.34 153	92.44 169	98.12 164	94.20 181

FPMVS		75.84 198	74.59 200	77.29 199	86.92 196	83.89 207	85.01 195	80.05 182	82.91 197	60.61 201	65.25 200	60.41 204	63.86 203	75.60 203	73.60 205	87.29 207	80.47 205

MIMVSNet		88.99 152	91.07 136	86.57 178	86.78 197	95.62 151	91.20 171	75.40 197	90.65 153	76.57 145	84.05 135	82.44 137	91.01 141	95.84 106	95.38 100	98.48 152	93.50 190

tmp_tt				66.88 202	86.07 198	73.86 209	68.22 208	33.38 210	96.88 45	80.67 127	88.23 106	78.82 147	49.78 207	82.68 201	77.47 203	83.19 209

PM-MVS		84.72 188	84.47 192	85.03 185	84.67 199	91.57 198	86.27 192	82.31 175	87.65 176	70.62 178	76.54 162	56.41 208	88.75 167	92.59 164	89.85 186	97.54 174	96.66 161

pmmvs-eth3d		84.33 189	82.94 194	85.96 184	84.16 200	90.94 199	86.55 191	83.79 165	84.25 192	75.85 152	70.64 191	56.43 207	87.44 173	92.20 170	90.41 183	97.97 167	95.68 170

new-patchmatchnet		78.49 197	78.19 199	78.84 197	84.13 201	90.06 201	77.11 206	80.39 181	79.57 202	59.64 205	66.01 199	55.65 209	75.62 198	84.55 199	80.70 201	96.14 183	90.77 198

new_pmnet		81.53 193	82.68 195	80.20 193	83.47 202	89.47 203	82.21 201	78.36 183	87.86 173	60.14 204	67.90 196	69.43 190	82.03 193	89.22 190	87.47 194	94.99 196	87.39 202

ET-MVSNet_ETH3D		93.34 98	94.33 84	92.18 103	83.26 203	97.66 93	96.72 67	89.89 99	95.62 78	87.17 99	96.00 34	83.69 130	96.99 53	93.78 145	95.34 101	99.06 101	98.18 118

pmmvs3		79.16 196	80.12 198	78.05 198	79.36 204	86.59 205	78.13 205	73.87 201	76.42 205	57.51 207	70.59 192	57.02 206	84.66 187	90.10 186	88.32 190	94.75 199	91.77 196

PMVS	63.12 18	67.27 200	66.39 203	68.30 201	77.98 205	60.24 211	59.53 211	76.82 188	66.65 207	60.74 200	54.39 204	59.82 205	51.24 206	73.92 206	70.52 206	83.48 208	79.17 207
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MDA-MVSNet-bldmvs		80.11 194	80.24 197	79.94 194	77.01 206	93.21 192	78.86 204	85.94 144	82.71 198	60.86 199	79.71 153	51.77 210	83.71 192	75.60 203	86.37 197	93.28 202	92.35 193

ambc			73.83 201		76.23 207	85.13 206	82.27 200		84.16 193	65.58 194	52.82 205	23.31 215	73.55 200	91.41 180	85.26 200	92.97 203	94.70 176

Gipuma		68.35 199	66.71 202	70.27 200	74.16 208	68.78 210	63.93 210	71.77 204	83.34 196	54.57 208	34.37 207	31.88 212	68.69 201	83.30 200	85.53 199	88.48 206	79.78 206
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		80.03 195	80.93 196	78.97 196	72.46 209	90.73 200	80.81 202	82.44 174	80.39 200	63.64 197	57.57 203	64.93 202	76.37 197	91.66 177	91.55 177	98.07 165	89.70 199

PMMVS2		64.36 202	65.94 204	62.52 203	67.37 210	77.44 208	64.39 209	69.32 208	61.47 208	34.59 210	46.09 206	41.03 211	48.02 209	74.56 205	78.23 202	91.43 204	82.76 204

EMVS		49.98 204	46.76 207	53.74 205	64.96 211	51.29 213	37.81 213	69.35 207	51.83 209	22.69 213	29.57 209	25.06 213	57.28 204	44.81 209	56.11 208	70.32 211	68.64 210

E-PMN		50.67 203	47.85 206	53.96 204	64.13 212	50.98 214	38.06 212	69.51 206	51.40 210	24.60 212	29.46 210	24.39 214	56.07 205	48.17 208	59.70 207	71.40 210	70.84 209

MVE	50.86 19	49.54 205	51.43 205	47.33 206	44.14 213	59.20 212	36.45 214	60.59 209	41.47 211	31.14 211	29.58 208	17.06 216	48.52 208	62.22 207	74.63 204	63.12 212	75.87 208
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		12.09 206	16.94 208	6.42 208	3.15 214	6.08 215	9.51 216	3.84 211	21.46 212	5.31 214	27.49 211	6.76 217	10.89 210	17.06 210	15.01 209	5.84 213	24.75 211

GG-mvs-BLEND		66.17 201	94.91 76	32.63 207	1.32 215	96.64 120	91.40 166	0.85 213	94.39 102	2.20 215	90.15 92	95.70 58	2.27 212	96.39 87	95.44 99	97.78 169	95.68 170

test123		9.58 207	13.53 209	4.97 209	1.31 216	5.47 216	8.32 217	2.95 212	18.14 213	2.03 216	20.82 212	2.34 218	10.60 211	10.00 211	14.16 210	4.60 214	23.77 212

sosnet-low-res		0.00 208	0.00 210	0.00 210	0.00 217	0.00 217	0.00 218	0.00 214	0.00 214	0.00 217	0.00 213	0.00 219	0.00 213	0.00 212	0.00 211	0.00 215	0.00 213

sosnet		0.00 208	0.00 210	0.00 210	0.00 217	0.00 217	0.00 218	0.00 214	0.00 214	0.00 217	0.00 213	0.00 219	0.00 213	0.00 212	0.00 211	0.00 215	0.00 213

test_part1																	99.38 15

MTAPA										96.83 7		99.12 17

MTMP										97.18 4		98.83 23

Patchmatch-RL test							34.61 215

NP-MVS									95.32 83

Patchmtry						95.96 139	93.36 131	75.99 195		75.19 157

DeepMVS_CX						86.86 204	79.50 203	70.43 205	90.73 151	63.66 196	80.36 152	60.83 203	79.68 194	76.23 202		89.46 205	86.53 203

High-resolution multi-view benchmark