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.

DVP-MVS		98.75 1	99.27 1	98.15 7	99.21 16	99.82 5	99.58 4	96.09 12	99.32 9	95.16 8	98.79 6	99.55 7	99.05 4	99.54 1	97.88 21	99.84 3	99.99 3

CNVR-MVS		98.73 2	99.17 4	98.22 4	99.47 4	99.85 2	99.57 5	96.23 4	99.30 10	94.90 10	98.65 10	98.93 18	99.36 1	99.46 3	98.21 11	99.81 6	99.80 33

DPE-MVS		98.69 3	99.14 5	98.16 6	99.37 7	99.82 5	99.66 2	96.26 1	99.18 15	95.02 9	98.62 13	99.98 2	98.88 10	98.90 10	97.51 31	99.75 10	99.97 6

MSP-MVS		98.65 4	98.87 11	98.38 1	99.30 12	99.85 2	99.14 22	96.23 4	99.51 2	97.16 1	96.01 33	99.99 1	98.90 9	98.89 11	97.88 21	99.56 49	99.98 4

APDe-MVS		98.60 5	98.97 8	98.18 5	99.38 6	99.78 10	99.35 14	96.14 8	99.24 12	95.66 6	98.19 19	99.01 15	98.66 16	98.77 13	97.80 24	99.86 2	99.97 6

SF-MVS		98.55 6	98.75 13	98.32 2	99.48 1	99.68 19	99.51 7	96.24 2	99.08 19	95.94 3	98.64 11	99.30 11	99.02 6	97.94 27	96.86 49	99.75 10	99.76 36

SMA-MVS		98.47 7	99.06 6	97.77 11	99.48 1	99.78 10	99.37 11	96.14 8	99.29 11	93.03 19	97.59 27	99.97 3	99.03 5	98.94 8	98.30 9	99.60 32	99.58 62

NCCC		98.41 8	99.18 2	97.52 15	99.36 8	99.84 4	99.55 6	96.08 14	99.33 8	91.77 24	98.79 6	99.46 9	98.59 18	99.15 7	98.07 18	99.73 14	99.64 51

SD-MVS		98.33 9	99.01 7	97.54 14	97.17 50	99.77 12	99.14 22	96.09 12	99.34 7	94.06 15	97.91 24	99.89 4	99.18 3	97.99 26	98.21 11	99.63 26	99.95 12
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
APD-MVS		98.28 10	98.69 14	97.80 9	99.31 11	99.62 27	99.31 17	96.15 7	99.19 14	93.60 16	97.28 28	98.35 26	98.72 15	98.27 19	98.22 10	99.73 14	99.89 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MCST-MVS		98.20 11	99.18 2	97.06 21	99.27 14	99.87 1	99.37 11	96.11 10	99.37 5	89.29 32	98.76 8	99.50 8	98.37 24	99.23 5	97.64 27	99.95 1	99.87 28

HPM-MVS++		98.16 12	98.87 11	97.32 17	99.39 5	99.70 17	99.18 20	96.10 11	99.09 18	91.14 26	98.02 22	99.89 4	98.44 22	98.75 14	97.03 44	99.67 20	99.63 55

MSLP-MVS++		98.12 13	98.23 26	97.99 8	99.28 13	99.72 14	99.59 3	95.27 28	98.61 32	94.79 11	96.11 32	97.79 35	99.27 2	96.62 61	98.96 5	99.77 9	99.80 33

HFP-MVS		98.02 14	98.55 18	97.40 16	99.11 20	99.69 18	99.41 9	95.41 26	98.79 30	91.86 23	98.61 14	98.16 28	99.02 6	97.87 32	97.40 33	99.60 32	99.35 81

TSAR-MVS + MP.		97.98 15	98.62 17	97.23 19	97.08 51	99.55 33	99.17 21	95.69 21	99.40 4	93.04 18	96.68 30	98.96 17	98.58 19	98.82 12	96.95 46	99.81 6	99.96 9
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
zzz-MVS		97.93 16	98.05 30	97.80 9	99.20 17	99.64 23	99.40 10	95.76 19	98.01 51	94.31 14	96.54 31	98.49 24	98.58 19	98.22 22	96.23 59	99.54 59	99.23 87

SteuartSystems-ACMMP		97.86 17	98.91 9	96.64 25	98.89 26	99.79 7	99.34 15	95.20 30	98.48 34	89.91 30	98.58 15	98.69 20	96.84 45	98.92 9	98.16 15	99.66 21	99.74 39
Skip Steuart: Steuart Systems R&D Blog.
CP-MVS		97.81 18	98.26 25	97.28 18	99.00 23	99.65 22	99.10 24	95.32 27	98.38 40	92.21 22	98.33 17	97.74 36	98.50 21	97.66 41	96.55 57	99.57 44	99.48 71

ACMMPR		97.78 19	98.28 23	97.20 20	99.03 22	99.68 19	99.37 11	95.24 29	98.86 29	91.16 25	97.86 25	97.26 38	98.79 13	97.64 43	97.40 33	99.60 32	99.25 86

DeepC-MVS_fast	95.01 1	97.67 20	98.22 27	97.02 22	99.00 23	99.79 7	99.10 24	95.82 17	99.05 22	89.53 31	93.54 48	96.77 41	98.83 11	99.34 4	99.44 2	99.82 4	99.63 55
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 21	97.35 37	97.77 11	99.17 18	99.55 33	98.57 30	95.76 19	99.04 23	94.66 12	97.94 23	94.39 56	98.82 12	96.21 70	94.78 80	99.62 28	99.52 67

ACMMP_NAP		97.51 22	98.27 24	96.63 26	99.34 9	99.72 14	99.25 18	95.94 16	98.11 45	87.10 45	96.98 29	98.50 23	98.61 17	98.58 16	96.83 51	99.56 49	99.14 95

MP-MVS		97.46 23	98.30 22	96.48 27	98.93 25	99.43 43	99.20 19	95.42 25	98.43 36	87.60 42	98.19 19	98.01 34	98.09 26	98.05 25	96.67 54	99.64 24	99.35 81
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
train_agg		97.42 24	98.88 10	95.71 32	98.46 33	99.60 30	99.05 26	95.16 31	99.10 17	84.38 59	98.47 16	98.85 19	97.61 30	98.54 17	97.66 26	99.62 28	99.93 18

CPTT-MVS		97.32 25	97.60 36	96.99 23	98.29 36	99.31 54	99.04 27	94.67 35	97.99 52	93.12 17	98.03 21	98.26 27	98.77 14	96.08 73	94.26 88	98.07 173	99.27 85

X-MVS		97.20 26	98.42 21	95.77 30	99.04 21	99.64 23	98.95 29	95.10 33	98.16 43	83.97 65	98.27 18	98.08 31	97.95 27	97.89 29	97.46 32	99.58 40	99.47 72

PHI-MVS		97.09 27	98.69 14	95.22 37	97.99 42	99.59 32	97.56 43	92.16 39	98.41 38	87.11 44	98.70 9	99.42 10	96.95 41	96.88 57	98.16 15	99.56 49	99.70 44

DPM-MVS		97.07 28	97.99 31	96.00 29	97.25 49	99.16 60	99.67 1	95.99 15	99.08 19	85.97 49	93.00 53	98.44 25	97.47 32	99.22 6	99.62 1	99.66 21	97.44 149

PGM-MVS		97.03 29	98.14 29	95.73 31	99.34 9	99.61 29	99.34 15	89.99 45	97.70 55	87.67 41	99.44 2	96.45 44	98.44 22	97.65 42	97.09 41	99.58 40	99.06 103

PLC	94.37 2	97.03 29	96.54 42	97.60 13	98.84 27	98.64 69	98.17 35	94.99 34	99.01 25	96.80 2	93.21 52	95.64 46	97.36 33	96.37 65	94.79 79	99.41 81	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 31	98.64 16	94.88 39	98.12 40	99.47 38	99.01 28	95.43 24	99.23 13	81.98 83	95.95 34	99.16 14	95.13 66	98.61 15	98.11 17	99.58 40	99.93 18

TSAR-MVS + GP.		96.47 32	98.45 20	94.17 44	92.12 82	99.29 55	97.76 39	88.05 56	99.36 6	90.26 29	97.82 26	99.21 12	97.21 37	96.78 59	96.74 52	99.63 26	99.94 15

xxxxxxxxxxxxxcwj		96.27 33	94.51 61	98.32 2	99.48 1	99.68 19	99.51 7	96.24 2	99.08 19	95.94 3	98.64 11	69.64 152	99.02 6	97.94 27	96.86 49	99.75 10	99.76 36

EPNet		96.23 34	97.89 33	94.29 42	97.62 45	99.44 42	97.14 51	88.63 52	98.16 43	88.14 37	99.46 1	94.15 59	94.61 76	97.20 50	97.23 37	99.57 44	99.59 60
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNLPA		96.14 35	95.43 51	96.98 24	98.55 30	99.41 47	95.91 57	95.15 32	99.00 26	95.71 5	84.21 101	94.55 54	97.25 35	95.50 95	96.23 59	99.28 100	99.09 102

MVS_111021_LR		96.07 36	97.94 32	93.88 47	97.86 43	99.43 43	95.70 60	89.65 48	98.73 31	84.86 56	99.38 3	94.08 60	95.78 64	97.81 35	96.73 53	99.43 78	99.42 75

ACMMP		96.05 37	96.70 41	95.29 36	98.01 41	99.43 43	97.60 42	94.33 37	97.62 58	86.17 48	98.92 4	92.81 67	96.10 57	95.67 85	93.33 107	99.55 54	99.12 98
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 38	96.42 44	95.50 34	98.18 38	99.33 53	97.44 45	87.73 61	97.93 53	92.36 21	84.67 94	97.33 37	97.55 31	97.32 46	98.47 8	99.72 18	99.88 25

DeepPCF-MVS	94.02 3	95.92 39	98.47 19	92.95 56	97.57 46	99.79 7	91.45 109	94.42 36	99.76 1	86.48 47	92.88 54	98.12 30	92.62 95	99.49 2	99.32 3	95.15 196	99.95 12

CDPH-MVS		95.90 40	97.77 35	93.72 50	98.28 37	99.43 43	98.40 31	91.30 43	98.34 41	78.62 101	94.80 40	95.74 45	96.11 56	97.86 33	98.67 7	99.59 35	99.56 64

CSCG		95.77 41	95.35 53	96.26 28	99.13 19	99.60 30	98.14 36	91.89 42	96.57 74	92.61 20	89.65 62	91.74 74	96.96 39	93.69 118	96.58 56	98.86 127	99.63 55

OMC-MVS		95.75 42	95.84 48	95.64 33	98.52 32	99.34 52	97.15 50	92.02 41	98.94 28	90.45 28	88.31 68	94.64 51	96.35 52	96.02 76	95.99 68	99.34 90	97.65 145

MVS_111021_HR		95.70 43	98.16 28	92.83 57	97.57 46	99.77 12	94.78 72	88.05 56	98.61 32	82.29 79	98.85 5	94.66 50	94.63 74	97.80 36	97.63 28	99.64 24	99.79 35

3Dnovator	90.31 8	95.67 44	96.16 46	95.11 38	98.59 29	99.37 51	97.50 44	87.98 58	98.02 50	89.09 33	85.36 93	94.62 52	97.66 28	97.10 53	98.90 6	99.82 4	99.73 41

CANet		95.40 45	96.27 45	94.40 41	96.25 56	99.62 27	98.37 32	88.59 53	98.09 46	87.58 43	84.57 96	95.54 48	95.87 61	98.12 23	98.03 20	99.73 14	99.90 23

QAPM		95.17 46	96.05 47	94.14 45	98.55 30	99.49 36	97.41 46	87.88 59	97.72 54	84.21 62	84.59 95	95.60 47	97.21 37	97.10 53	98.19 14	99.57 44	99.65 49

MVSTER		94.75 47	96.50 43	92.70 60	90.91 97	94.51 137	97.37 48	83.37 91	98.40 39	89.04 34	93.23 51	97.04 40	95.91 60	97.73 37	95.59 76	99.61 30	99.01 104

TAPA-MVS	92.04 6	94.72 48	95.13 56	94.24 43	97.72 44	99.17 58	97.61 41	92.16 39	97.66 57	81.99 82	87.84 73	93.94 62	96.50 49	95.74 82	94.27 87	99.46 74	97.31 150
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CS-MVS		94.60 49	97.10 40	91.67 65	90.73 100	98.52 76	95.51 63	83.30 93	99.02 24	84.42 58	94.12 46	94.58 53	96.52 48	97.70 39	96.12 63	99.55 54	99.64 51

DeepC-MVS	92.23 5	94.53 50	94.26 70	94.86 40	96.73 53	99.50 35	97.85 38	95.45 23	96.22 81	82.73 74	80.68 110	88.02 86	96.92 42	97.49 45	98.20 13	99.47 68	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 51	97.78 34	90.70 78	95.54 62	99.49 36	94.14 80	74.91 152	98.43 36	85.32 54	94.78 41	99.19 13	94.95 70	97.02 55	96.18 62	99.35 86	99.36 80

ETV-MVS		94.49 52	97.23 39	91.29 72	90.43 107	98.55 72	93.41 90	84.53 84	99.16 16	83.13 70	94.72 42	94.08 60	96.61 47	97.72 38	96.60 55	99.61 30	99.81 32

MVS_0304		94.35 53	95.66 50	92.83 57	94.82 64	99.46 40	98.19 34	87.75 60	97.32 64	81.83 86	83.50 103	93.19 66	94.71 72	98.24 21	98.07 18	99.68 19	99.83 30

MAR-MVS		94.18 54	95.12 57	93.09 55	98.40 35	99.17 58	94.20 79	81.92 101	98.47 35	86.52 46	90.92 57	84.21 105	98.12 25	95.88 79	97.59 29	99.40 82	99.58 62
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 55	93.82 73	94.10 46	96.07 58	99.25 56	96.82 53	95.51 22	92.00 123	81.51 87	82.97 106	93.88 64	95.63 65	94.24 107	94.71 82	99.09 110	99.70 44
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DELS-MVS		93.82 56	93.82 73	93.81 49	96.34 55	99.47 38	97.26 49	88.53 54	92.13 121	87.80 40	79.67 113	88.01 87	93.14 87	98.28 18	99.22 4	99.80 8	99.98 4
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 57	93.62 76	93.34 51	98.46 33	99.39 48	97.00 52	87.66 63	95.37 88	81.21 89	75.96 128	91.58 75	96.21 55	96.37 65	97.10 40	99.52 60	99.54 66

EIA-MVS		93.32 58	95.32 54	90.99 75	90.45 106	98.53 75	93.46 89	84.68 83	97.56 61	81.38 88	91.04 56	87.37 90	96.39 51	97.27 47	95.73 73	99.59 35	99.76 36

PVSNet_BlendedMVS		93.30 59	93.46 80	93.10 53	95.60 60	99.38 49	93.59 87	88.70 50	98.09 46	88.10 38	86.96 81	75.02 128	93.08 88	97.89 29	96.90 47	99.56 49	100.00 1

PVSNet_Blended		93.30 59	93.46 80	93.10 53	95.60 60	99.38 49	93.59 87	88.70 50	98.09 46	88.10 38	86.96 81	75.02 128	93.08 88	97.89 29	96.90 47	99.56 49	100.00 1

PMMVS		93.05 61	95.40 52	90.31 82	91.41 90	97.54 95	92.62 101	83.25 94	98.08 49	79.44 99	95.18 38	88.52 85	96.43 50	95.70 83	93.88 91	98.68 143	98.91 107

LS3D		92.70 62	92.23 90	93.26 52	96.24 57	98.72 64	97.93 37	96.17 6	96.41 75	72.46 115	81.39 109	80.76 118	97.66 28	95.69 84	95.62 75	99.07 112	97.02 157

baseline1		92.67 63	93.62 76	91.55 67	91.16 93	97.15 98	93.92 85	85.97 73	94.76 95	84.07 64	87.17 77	86.89 93	94.62 75	96.72 60	95.90 71	99.57 44	96.79 161

IS_MVSNet		92.67 63	94.99 59	89.96 87	91.17 92	98.54 73	92.77 97	84.00 85	92.72 117	81.90 85	85.67 91	92.47 69	90.39 114	97.82 34	97.81 23	99.51 61	99.91 22

TSAR-MVS + COLMAP		92.56 65	92.44 88	92.71 59	94.61 66	97.69 91	97.69 40	91.09 44	98.96 27	76.71 106	94.68 43	69.41 153	96.91 43	95.80 81	94.18 89	99.26 101	96.33 165

baseline		92.56 65	94.38 66	90.43 81	90.71 102	98.23 82	95.07 69	80.73 115	97.52 62	82.45 78	87.34 76	85.91 97	94.07 82	96.29 69	95.94 70	99.58 40	99.47 72

canonicalmvs		92.54 67	93.28 82	91.68 64	91.44 89	98.24 81	95.45 66	81.84 105	95.98 85	84.85 57	90.69 58	78.53 123	96.96 39	92.97 124	97.06 42	99.57 44	99.47 72

PatchMatch-RL		92.54 67	92.82 87	92.21 61	96.57 54	98.74 63	91.85 106	86.30 68	96.23 80	85.18 55	95.21 37	73.58 133	94.22 81	95.40 98	93.08 111	99.14 107	97.49 148

MVS_Test		92.42 69	94.43 62	90.08 86	90.69 103	98.26 80	94.78 72	80.81 114	97.27 65	78.76 100	87.06 79	84.25 104	95.84 62	97.67 40	97.56 30	99.59 35	98.93 106

thisisatest0530		92.31 70	95.14 55	89.02 96	90.02 114	98.45 78	91.30 110	83.58 88	96.90 70	77.90 103	90.45 60	94.33 57	91.98 100	95.57 89	91.43 133	99.31 96	98.81 110

tttt0517		92.29 71	95.12 57	88.99 97	90.02 114	98.44 79	91.19 113	83.58 88	96.88 71	77.86 104	90.45 60	94.32 58	91.98 100	95.54 91	91.43 133	99.31 96	98.78 112

EPP-MVSNet		92.29 71	94.35 68	89.88 88	90.36 109	97.69 91	90.89 115	83.31 92	93.39 110	83.47 69	85.56 92	93.92 63	91.93 102	95.49 96	94.77 81	99.34 90	99.62 58

HQP-MVS		91.94 73	93.03 84	90.66 80	93.69 68	96.48 112	95.92 56	89.73 46	97.33 63	72.65 113	95.37 35	73.56 134	92.75 94	94.85 104	94.12 90	99.23 104	99.51 68

MSDG		91.93 74	90.28 117	93.85 48	97.36 48	97.12 99	95.88 58	94.07 38	94.52 99	84.13 63	76.74 122	80.89 117	92.54 96	93.97 114	93.61 101	99.14 107	95.10 173

UGNet		91.71 75	94.43 62	88.53 99	92.72 78	98.00 85	90.22 122	84.81 82	94.45 100	83.05 71	87.65 75	92.74 68	81.04 166	94.51 106	94.45 84	99.32 95	99.21 91
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 76	91.52 96	91.88 63	91.61 84	98.89 61	95.49 64	86.96 65	93.24 111	80.82 91	87.90 70	71.15 143	96.88 44	96.00 77	93.51 103	99.51 61	99.95 12

CLD-MVS		91.67 77	91.30 101	92.10 62	91.25 91	96.59 109	95.93 55	87.25 64	96.86 72	85.55 53	87.08 78	73.01 135	93.26 86	93.07 122	92.84 117	99.34 90	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
ET-MVSNet_ETH3D		91.59 78	94.96 60	87.65 101	72.75 203	97.24 97	95.29 67	82.73 97	96.81 73	78.49 102	95.30 36	90.48 81	97.23 36	91.60 139	94.31 85	99.43 78	99.01 104

tfpn200view9		91.47 79	91.31 99	91.65 66	91.61 84	98.69 66	95.03 70	86.17 69	93.24 111	80.82 91	87.90 70	71.15 143	96.80 46	95.53 92	92.82 119	99.47 68	99.88 25

CANet_DTU		91.36 80	95.75 49	86.23 111	92.31 81	98.71 65	95.60 62	78.41 129	98.20 42	56.48 173	94.38 45	87.96 88	95.11 67	96.89 56	96.07 64	99.48 66	98.01 139

thres200		91.36 80	91.19 103	91.55 67	91.60 86	98.69 66	94.98 71	86.17 69	92.16 120	80.76 93	87.66 74	71.15 143	96.35 52	95.53 92	93.23 109	99.47 68	99.92 21

FMVSNet3		91.25 82	92.13 92	90.21 83	85.64 144	93.14 146	95.29 67	80.09 116	96.40 76	85.74 50	77.13 117	86.81 94	94.98 69	97.19 51	97.11 39	99.55 54	97.13 154

thres400		91.24 83	91.01 109	91.50 70	91.56 87	98.77 62	94.66 75	86.41 67	91.87 125	80.56 94	87.05 80	71.01 146	96.35 52	95.67 85	92.82 119	99.48 66	99.88 25

PVSNet_Blended_VisFu		91.20 84	92.89 86	89.23 94	93.41 71	98.61 71	89.80 124	85.39 77	92.84 115	82.80 73	74.21 132	91.38 77	84.64 145	97.22 49	96.04 67	99.34 90	99.93 18

DCV-MVSNet		91.15 85	92.00 93	90.17 85	90.78 99	92.23 163	93.70 86	81.17 112	95.16 91	82.98 72	89.46 64	83.31 107	93.98 83	91.79 138	92.87 114	98.41 161	99.18 93

DI_MVS_plusplus_trai		91.11 86	91.47 97	90.68 79	90.01 116	97.77 89	95.87 59	83.56 90	94.72 96	82.12 81	68.46 149	87.46 89	93.07 90	96.46 64	95.73 73	99.47 68	99.71 43

diffmvs		91.05 87	91.15 104	90.93 76	90.15 112	97.79 88	94.05 81	85.45 75	95.63 86	81.95 84	80.45 112	73.01 135	94.47 77	95.56 90	95.89 72	99.49 65	99.72 42

Vis-MVSNet (Re-imp)		91.05 87	94.43 62	87.11 103	91.05 95	97.99 86	92.53 102	83.82 87	92.71 118	76.28 107	84.50 97	92.43 70	79.52 171	97.24 48	97.68 25	99.43 78	98.45 123

thres600view7		90.97 89	90.70 111	91.30 71	91.53 88	98.69 66	94.33 76	86.17 69	91.75 127	80.19 95	86.06 89	70.90 147	96.10 57	95.53 92	92.08 126	99.47 68	99.86 29

baseline2		90.91 90	94.40 65	86.84 106	87.54 135	96.83 105	89.95 123	79.22 124	96.00 84	77.04 105	88.68 65	89.73 82	88.01 134	96.35 67	93.51 103	99.29 98	99.68 47

ACMP	89.80 9	90.72 91	91.15 104	90.21 83	92.55 79	96.52 111	92.63 100	85.71 74	94.65 97	81.06 90	93.32 49	70.56 149	90.52 113	92.68 128	91.05 138	98.76 135	99.31 84
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
casdiffmvs		90.69 92	90.56 114	90.85 77	90.14 113	97.81 87	92.94 95	85.30 78	93.47 109	82.50 77	76.34 126	74.12 132	94.67 73	96.51 63	96.26 58	99.55 54	99.42 75

ACMM	89.40 10	90.58 93	90.02 120	91.23 73	93.30 73	94.75 133	90.69 118	88.22 55	95.20 89	82.70 75	88.54 66	71.40 142	93.48 85	93.64 119	90.94 139	98.99 118	95.72 170
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		90.49 94	91.12 107	89.75 90	84.99 147	92.73 151	93.94 82	80.09 116	96.40 76	85.74 50	77.13 117	86.81 94	94.42 78	94.12 109	93.73 93	99.35 86	96.90 158

test1		90.49 94	91.12 107	89.75 90	84.99 147	92.73 151	93.94 82	80.09 116	96.40 76	85.74 50	77.13 117	86.81 94	94.42 78	94.12 109	93.73 93	99.35 86	96.90 158

LGP-MVS_train		90.34 96	91.63 95	88.83 98	93.31 72	96.14 116	95.49 64	85.24 80	93.91 104	68.71 127	93.96 47	71.63 140	91.12 110	93.82 116	92.79 121	99.07 112	99.16 94

EPNet_dtu		89.82 97	94.18 71	84.74 121	96.87 52	95.54 126	92.65 99	86.91 66	96.99 67	54.17 184	92.41 55	88.54 84	78.35 174	96.15 72	96.05 66	99.47 68	93.60 181
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		89.81 98	89.75 121	89.88 88	93.22 75	93.99 140	94.78 72	85.23 81	94.01 103	82.52 76	95.00 39	87.23 91	92.01 99	85.16 190	83.48 195	91.54 201	89.38 195

MDTV_nov1_ep13		89.63 99	94.38 66	84.09 128	88.76 128	97.53 96	89.37 132	68.46 184	96.95 68	70.27 122	87.88 72	93.67 65	91.04 111	93.12 120	93.83 92	96.62 190	97.68 144

UA-Net		89.56 100	93.03 84	85.52 117	92.46 80	97.55 94	91.92 105	81.91 102	85.24 156	71.39 117	83.57 102	96.56 43	76.01 183	96.81 58	97.04 43	99.46 74	94.41 176

FMVSNet2		89.51 101	89.63 122	89.38 92	84.99 147	92.73 151	93.94 82	79.28 123	93.73 106	84.28 61	69.36 148	82.32 110	94.42 78	96.16 71	96.22 61	99.35 86	96.90 158

CostFormer		89.42 102	91.67 94	86.80 107	89.99 117	96.33 114	90.75 116	64.79 187	95.17 90	83.62 68	86.20 87	82.15 112	92.96 91	89.22 160	92.94 112	98.68 143	99.65 49

FC-MVSNet-train		89.37 103	89.62 123	89.08 95	90.48 105	94.16 139	89.45 128	83.99 86	91.09 130	80.09 96	82.84 107	74.52 131	91.44 107	93.79 117	91.57 132	99.01 116	99.35 81

OPM-MVS		89.33 104	87.45 137	91.53 69	94.49 67	96.20 115	96.47 54	89.72 47	82.77 162	75.43 108	80.53 111	70.86 148	93.80 84	94.00 113	91.85 130	99.29 98	95.91 168

test-LLR		89.31 105	93.60 78	84.30 125	88.08 131	96.98 101	88.10 137	78.00 130	94.83 93	62.43 147	84.29 99	90.96 78	89.70 119	95.63 87	92.86 115	99.51 61	99.64 51

EPMVS		89.31 105	93.70 75	84.18 127	91.10 94	98.10 83	89.17 134	62.71 191	96.24 79	70.21 124	86.46 85	92.37 71	92.79 92	91.95 136	93.59 102	99.10 109	97.19 151

Anonymous20231211		89.22 107	87.56 135	91.16 74	90.23 111	96.62 108	93.22 92	85.44 76	92.89 114	84.37 60	60.13 167	81.25 115	96.02 59	90.61 145	92.01 127	97.70 181	99.41 77

Effi-MVS+		88.96 108	91.13 106	86.43 109	89.12 124	97.62 93	93.15 93	75.52 147	93.90 105	66.40 131	86.23 86	70.51 150	95.03 68	95.89 78	94.28 86	99.37 83	99.51 68

SCA		88.76 109	94.29 69	82.30 144	89.33 122	96.81 106	87.68 139	61.52 196	96.95 68	64.68 137	88.35 67	94.80 49	91.58 104	92.23 130	93.21 110	98.99 118	97.70 143

test0.0.03 1		88.71 110	92.22 91	84.63 123	88.08 131	94.71 135	85.91 162	78.00 130	95.54 87	72.96 112	86.10 88	85.88 99	83.59 153	92.95 126	93.24 108	99.25 103	97.09 155

PatchmatchNet		88.67 111	94.10 72	82.34 143	89.38 121	97.72 90	87.24 145	62.18 194	97.00 66	64.79 136	87.97 69	94.43 55	91.55 105	91.21 143	92.77 122	98.90 123	97.60 147
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dps		88.66 112	90.19 118	86.88 105	89.94 118	96.48 112	89.56 126	64.08 189	94.12 102	89.00 35	83.39 104	82.56 109	90.16 117	86.81 182	89.26 157	98.53 156	98.71 114

TESTMET0.1,1		88.63 113	93.60 78	82.84 140	84.07 154	96.98 101	88.10 137	73.22 166	94.83 93	62.43 147	84.29 99	90.96 78	89.70 119	95.63 87	92.86 115	99.51 61	99.64 51

CHOSEN 1792x2688		88.63 113	89.01 127	88.19 100	94.83 63	99.21 57	92.66 98	79.85 120	92.40 119	72.18 116	56.38 187	80.22 120	90.24 115	97.64 43	97.28 36	99.37 83	99.94 15

CDS-MVSNet		88.59 115	90.13 119	86.79 108	86.98 140	95.43 127	92.03 104	81.33 110	85.54 153	74.51 111	77.07 120	85.14 101	87.03 139	93.90 115	95.18 77	98.88 125	98.67 116
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IB-MVS	84.67 14	88.34 116	90.61 113	85.70 114	92.99 77	98.62 70	78.85 188	86.07 72	94.35 101	88.64 36	85.99 90	75.69 126	68.09 196	88.21 163	91.43 133	99.55 54	99.96 9
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
test-mter		88.25 117	93.27 83	82.38 142	83.89 155	96.86 104	87.10 149	72.80 168	94.58 98	61.85 152	83.21 105	90.65 80	89.18 123	95.43 97	92.58 124	99.46 74	99.61 59

COLMAP_ROB	84.42 15	88.24 118	87.32 138	89.32 93	95.83 59	95.82 120	92.81 96	87.68 62	92.09 122	72.64 114	72.34 140	79.96 121	88.79 125	89.54 155	89.46 153	98.16 170	92.00 187
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
IterMVS-LS		87.95 119	89.40 125	86.26 110	88.79 127	90.93 178	91.23 112	76.05 144	90.87 131	71.07 119	75.51 129	81.18 116	91.21 109	94.11 112	95.01 78	99.20 106	98.23 130
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		87.86 120	88.25 132	87.40 102	94.67 65	98.54 73	90.33 121	76.51 143	89.60 138	70.89 120	51.43 198	85.69 100	92.79 92	96.59 62	95.96 69	99.22 105	99.94 15

Vis-MVSNet		87.60 121	91.31 99	83.27 135	89.14 123	98.04 84	90.35 120	79.42 121	87.23 143	66.92 130	79.10 116	84.63 103	74.34 190	95.81 80	96.06 65	99.46 74	98.32 127
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
RPMNet		87.35 122	92.41 89	81.45 148	88.85 126	96.06 117	89.42 131	59.59 203	93.57 107	61.81 153	76.48 125	91.48 76	90.18 116	96.32 68	93.37 106	98.87 126	99.59 60

tpm cat1		87.34 123	88.52 131	85.95 112	89.83 119	95.80 121	90.73 117	64.91 186	92.99 113	82.21 80	71.19 146	82.68 108	90.13 118	86.38 183	90.87 141	97.90 178	99.74 39

MS-PatchMatch		87.19 124	88.59 130	85.55 116	93.15 76	96.58 110	92.35 103	74.19 159	91.97 124	70.33 121	71.42 144	85.89 98	84.28 147	93.12 120	89.16 159	99.00 117	91.99 188

Effi-MVS+-dtu		87.18 125	90.48 115	83.32 134	86.51 141	95.76 123	91.16 114	74.28 158	90.44 135	61.31 156	86.72 84	72.68 138	91.25 108	95.01 102	93.64 96	95.45 195	99.12 98

FMVSNet5		87.06 126	89.52 124	84.20 126	79.92 191	86.57 197	87.11 148	72.37 170	96.06 82	75.41 109	84.33 98	91.76 73	91.60 103	91.51 140	91.22 136	98.77 132	85.16 200

Fast-Effi-MVS+-dtu		86.94 127	91.27 102	81.89 145	86.27 142	95.06 128	90.68 119	68.93 181	91.76 126	57.18 171	89.56 63	75.85 125	89.19 122	94.56 105	92.84 117	99.07 112	99.23 87

Fast-Effi-MVS+		86.94 127	87.88 134	85.84 113	86.99 139	95.80 121	91.24 111	73.48 165	92.75 116	69.22 125	72.70 138	65.71 159	94.84 71	94.98 103	94.71 82	99.26 101	98.48 122

tpmrst		86.78 129	90.29 116	82.69 141	90.55 104	96.95 103	88.49 136	62.58 192	95.09 92	63.52 143	76.67 124	84.00 106	92.05 98	87.93 166	91.89 129	98.98 120	99.50 70

CR-MVSNet		86.73 130	91.47 97	81.20 151	88.56 129	96.06 117	89.43 129	61.37 197	93.57 107	60.81 158	72.89 137	88.85 83	88.13 132	96.03 74	93.64 96	98.89 124	99.22 89

ADS-MVSNet		86.68 131	90.79 110	81.88 146	90.38 108	96.81 106	86.90 150	60.50 201	96.01 83	63.93 140	81.67 108	84.72 102	90.78 112	87.03 176	91.67 131	98.77 132	97.63 146

FMVSNet1		85.85 132	84.91 148	86.96 104	82.70 160	91.39 172	91.54 108	77.45 135	85.29 155	79.56 98	60.70 164	72.68 138	92.37 97	94.12 109	93.73 93	98.12 171	96.44 162

FC-MVSNet-test		85.51 133	89.08 126	81.35 149	85.31 146	93.35 142	87.65 140	77.55 134	90.01 136	64.07 139	79.63 114	81.83 114	74.94 187	92.08 133	90.83 143	98.55 153	95.81 169

ACMH	85.22 13	85.40 134	85.73 145	85.02 119	91.76 83	94.46 138	84.97 168	81.54 108	85.18 157	65.22 135	76.92 121	64.22 160	88.58 128	90.17 147	90.25 149	98.03 174	98.90 108
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		85.35 135	86.00 144	84.59 124	84.97 150	95.57 125	88.98 135	77.29 138	81.44 167	71.36 118	71.48 143	75.00 130	87.03 139	91.92 137	92.21 125	97.92 177	94.40 177

ACMH+	85.62 12	85.27 136	84.96 147	85.64 115	90.84 98	94.78 132	87.46 142	81.30 111	86.94 144	67.35 129	74.56 131	64.09 161	88.70 126	88.14 164	89.00 160	98.22 169	97.19 151

USDC		85.11 137	85.35 146	84.83 120	89.45 120	94.93 131	92.98 94	77.30 137	90.53 133	61.80 154	76.69 123	59.62 170	88.90 124	92.78 127	90.79 145	98.53 156	92.12 185

IterMVS		85.02 138	88.98 128	80.41 157	87.03 138	90.34 185	89.78 125	69.45 178	89.77 137	54.04 185	73.71 134	82.05 113	83.44 156	95.11 100	93.64 96	98.75 136	98.22 132
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		84.91 139	88.90 129	80.25 160	87.04 137	90.27 186	89.23 133	69.25 180	89.17 139	54.04 185	73.65 135	82.22 111	83.23 161	95.11 100	93.63 100	98.73 137	98.23 130

PatchT		84.89 140	90.67 112	78.13 180	87.83 134	94.99 130	72.46 199	60.22 202	91.74 128	60.81 158	72.16 141	86.95 92	88.13 132	96.03 74	93.64 96	99.36 85	99.22 89

pmmvs4		84.88 141	84.67 149	85.13 118	82.80 159	92.37 156	87.29 143	79.08 125	90.51 134	74.94 110	70.37 147	62.49 163	88.17 131	92.01 135	88.51 165	98.49 159	96.44 162

CVMVSNet		84.01 142	86.91 139	80.61 155	88.39 130	93.29 143	86.06 158	82.29 99	83.13 160	54.29 181	72.68 139	79.59 122	75.11 186	91.23 142	92.91 113	97.54 185	95.58 171

tpm		83.97 143	87.97 133	79.31 170	87.35 136	93.21 145	86.00 160	61.90 195	90.69 132	54.01 187	79.42 115	75.61 127	88.65 127	87.18 174	90.48 147	97.95 176	99.21 91

GA-MVS		83.83 144	86.63 140	80.58 156	85.40 145	94.73 134	87.27 144	78.76 128	86.49 146	49.57 194	74.21 132	67.67 156	83.38 157	95.28 99	90.92 140	99.08 111	97.09 155

UniMVSNet_NR-MVSNet		83.83 144	83.70 152	83.98 129	81.41 169	92.56 155	86.54 153	82.96 95	85.98 150	66.27 132	66.16 155	63.63 162	87.78 136	87.65 169	90.81 144	98.94 121	99.13 96

UniMVSNet (Re)		83.28 146	83.16 153	83.42 133	81.93 165	93.12 147	86.27 156	80.83 113	85.88 151	68.23 128	64.56 158	60.58 165	84.25 148	89.13 161	89.44 155	99.04 115	99.40 78

thisisatest0515		83.17 147	86.49 141	79.30 171	82.04 163	93.12 147	78.70 189	77.92 132	86.43 147	63.05 144	74.91 130	73.01 135	75.56 185	92.10 132	88.05 178	98.50 158	97.76 142

TinyColmap		83.03 148	82.24 157	83.95 130	88.88 125	93.22 144	89.48 127	76.89 140	87.53 142	62.12 149	68.46 149	55.03 186	88.43 130	90.87 144	89.65 151	97.89 179	90.91 191

testgi		82.88 149	86.14 143	79.08 173	86.05 143	92.20 164	81.23 185	74.77 154	88.70 140	57.63 170	86.73 83	61.53 164	76.83 181	90.33 146	89.43 156	97.99 175	94.05 178

DU-MVS		82.87 150	82.16 158	83.70 132	80.77 178	92.24 160	86.54 153	81.91 102	86.41 148	66.27 132	63.95 159	55.66 184	87.78 136	86.83 179	90.86 142	98.94 121	99.13 96

MIMVSNet		82.87 150	86.17 142	79.02 174	77.23 199	92.88 150	84.88 169	60.62 200	86.72 145	64.16 138	73.58 136	71.48 141	88.51 129	94.14 108	93.50 105	98.72 139	90.87 192

NR-MVSNet		82.37 152	81.95 160	82.85 139	82.56 162	92.24 160	87.49 141	81.91 102	86.41 148	65.51 134	63.95 159	52.93 194	80.80 168	89.41 157	89.61 152	98.85 128	99.10 101

Baseline_NR-MVSNet		82.08 153	80.64 166	83.77 131	80.77 178	88.50 192	86.88 151	81.71 106	85.58 152	68.80 126	58.20 179	57.75 176	86.16 141	86.83 179	88.68 162	98.33 166	98.90 108

TranMVSNet+NR-MVSNet		82.07 154	81.36 163	82.90 138	80.43 184	91.39 172	87.16 147	82.75 96	84.28 159	62.98 145	62.28 163	56.01 183	85.30 144	86.06 185	90.69 146	98.80 129	98.80 111

pm-mvs1		81.68 155	81.70 161	81.65 147	82.61 161	92.26 159	85.54 166	78.95 126	76.29 189	63.81 141	58.43 178	66.33 158	80.63 169	92.30 129	89.93 150	98.37 165	96.39 164

TDRefinement		81.49 156	80.08 172	83.13 137	91.02 96	94.53 136	91.66 107	82.43 98	81.70 165	62.12 149	62.30 162	59.32 171	73.93 191	87.31 172	85.29 188	97.61 182	90.14 193

anonymousdsp		81.29 157	84.52 151	77.52 182	79.83 192	92.62 154	82.61 180	70.88 175	80.76 171	50.82 192	68.35 151	68.76 154	82.45 164	93.00 123	89.45 154	98.55 153	98.69 115

gg-mvs-nofinetune		81.27 158	84.65 150	77.32 183	87.96 133	98.48 77	95.64 61	56.36 206	59.35 207	32.80 210	47.96 202	92.11 72	91.49 106	98.12 23	97.00 45	99.65 23	99.56 64

tfpnnormal		81.11 159	79.33 180	83.19 136	84.23 152	92.29 158	86.76 152	82.27 100	72.67 195	62.02 151	56.10 189	53.86 192	85.35 143	92.06 134	89.23 158	98.49 159	99.11 100

UniMVSNet_ETH3D		80.95 160	77.71 188	84.74 121	84.45 151	93.11 149	86.45 155	79.97 119	75.21 191	70.22 123	51.24 199	50.26 200	89.55 121	84.47 192	91.12 137	97.81 180	98.53 120

V42		80.88 161	80.74 164	81.05 152	81.21 172	92.01 166	85.96 161	77.75 133	81.62 166	59.73 165	59.93 170	58.35 175	82.98 163	86.90 178	88.06 177	98.69 142	98.32 127

v2v482		80.86 162	80.52 170	81.25 150	80.79 177	91.85 167	85.68 164	78.78 127	81.05 168	58.09 168	60.46 165	56.08 181	85.45 142	87.27 173	88.53 164	98.73 137	98.38 126

v8		80.61 163	80.61 168	80.62 154	81.51 167	91.00 177	86.06 158	74.07 161	81.78 164	59.93 164	60.10 169	58.42 174	83.35 158	86.99 177	88.11 175	98.79 130	97.83 141

pmmvs5		80.48 164	81.43 162	79.36 169	81.50 168	92.24 160	82.07 183	74.08 160	78.10 182	55.86 176	67.72 152	54.35 189	83.91 152	92.97 124	88.65 163	98.77 132	96.01 166

v10		80.38 165	80.73 165	79.96 162	81.22 171	90.40 184	86.11 157	71.63 172	82.42 163	57.65 169	58.74 176	57.47 177	84.44 146	89.75 151	88.28 168	98.71 140	98.06 138

v1144		80.36 166	80.63 167	80.05 161	80.86 176	91.56 170	85.78 163	75.22 149	80.73 172	55.83 177	58.51 177	56.99 179	83.93 151	89.79 150	88.25 169	98.68 143	98.56 119

SixPastTwentyTwo		80.28 167	82.06 159	78.21 179	81.89 166	92.35 157	77.72 190	74.48 155	83.04 161	54.22 182	76.06 127	56.40 180	83.55 154	86.83 179	84.83 190	97.38 186	94.93 174

CP-MVSNet		79.90 168	79.49 177	80.38 158	80.72 180	90.83 179	82.98 177	75.17 150	79.70 177	61.39 155	59.74 171	51.98 197	83.31 159	87.37 171	88.38 166	98.71 140	98.45 123

v1192		79.84 169	80.05 174	79.61 165	80.49 183	91.04 176	85.56 165	74.37 157	80.73 172	54.35 180	57.07 184	54.54 188	84.23 149	89.94 148	88.38 166	98.63 147	98.61 117

WR-MVS_H		79.76 170	80.07 173	79.40 168	81.25 170	91.73 169	82.77 178	74.82 153	79.02 181	62.55 146	59.41 173	57.32 178	76.27 182	87.61 170	87.30 183	98.78 131	98.09 136

WR-MVS		79.67 171	80.25 171	79.00 175	80.65 181	91.16 174	83.31 175	76.57 142	80.97 169	60.50 163	59.20 174	58.66 173	74.38 189	85.85 187	87.76 180	98.61 148	98.14 133

v148		79.66 172	79.13 182	80.27 159	81.02 174	91.76 168	81.90 184	79.32 122	79.24 179	63.79 142	58.07 181	54.34 190	77.17 179	84.42 193	87.52 182	98.40 162	98.59 118

LTVRE_ROB	79.45 16	79.66 172	80.55 169	78.61 177	83.01 158	92.19 165	87.18 146	73.69 164	71.70 198	43.22 205	71.22 145	50.85 198	87.82 135	89.47 156	90.43 148	96.75 188	98.00 140
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		79.61 174	79.77 175	79.41 167	80.28 185	91.06 175	84.87 170	73.86 162	79.65 178	55.38 178	57.76 182	55.20 185	83.46 155	88.42 162	87.89 179	98.61 148	98.42 125

v1921920		79.55 175	79.77 175	79.30 171	80.24 186	90.77 180	85.37 167	73.75 163	80.38 174	53.78 188	56.89 186	54.18 191	84.05 150	89.55 154	88.13 174	98.59 150	98.52 121

TransMVSNet (Re)		79.51 176	78.36 184	80.84 153	83.17 156	89.72 188	84.22 173	81.45 109	73.98 194	60.79 161	57.20 183	56.05 182	77.11 180	89.88 149	88.86 161	98.30 168	92.83 183

MVS-HIRNet		79.34 177	82.56 154	75.57 187	84.11 153	95.02 129	75.03 197	57.28 205	85.50 154	55.88 175	53.00 195	70.51 150	83.05 162	92.12 131	91.96 128	98.09 172	89.83 194

PS-CasMVS		79.06 178	78.58 183	79.63 164	80.59 182	90.55 182	82.54 181	75.04 151	77.76 183	58.84 166	58.16 180	50.11 202	82.09 165	87.05 175	88.18 172	98.66 146	98.27 129

v1240		78.97 179	79.27 181	78.63 176	80.04 187	90.61 181	84.25 172	72.95 167	79.22 180	52.70 190	56.22 188	52.88 196	83.28 160	89.60 153	88.20 171	98.56 152	98.14 133

MDTV_nov1_ep13_2view		78.83 180	82.35 155	74.73 190	78.65 194	91.51 171	79.18 187	62.52 193	84.51 158	52.51 191	67.49 153	67.29 157	78.90 172	85.52 189	86.34 186	96.62 190	93.76 179

PEN-MVS		78.80 181	78.13 186	79.58 166	80.03 188	89.67 189	83.61 174	75.83 145	77.71 185	58.41 167	60.11 168	50.00 203	81.02 167	84.08 194	88.14 173	98.59 150	97.18 153

EG-PatchMatch MVS		78.32 182	79.42 179	77.03 185	83.03 157	93.77 141	84.47 171	69.26 179	75.85 190	53.69 189	55.68 190	60.23 168	73.20 192	89.69 152	88.22 170	98.55 153	92.54 184

DTE-MVSNet		77.92 183	77.42 189	78.51 178	79.34 193	89.00 191	83.05 176	75.60 146	76.89 187	56.58 172	59.63 172	50.31 199	78.09 177	82.57 198	87.56 181	98.38 163	95.95 167

v7n		77.71 184	78.25 185	77.09 184	78.49 195	90.55 182	82.15 182	71.11 174	76.79 188	54.18 183	55.63 191	50.20 201	78.28 175	89.36 159	87.15 184	98.33 166	98.07 137

gm-plane-assit		77.20 185	82.26 156	71.30 193	81.10 173	82.00 205	54.33 209	64.41 188	63.80 206	40.93 207	59.04 175	76.57 124	87.30 138	98.26 20	97.36 35	99.74 13	98.76 113

N_pmnet		76.83 186	77.97 187	75.50 188	80.96 175	88.23 194	72.81 198	76.83 141	80.87 170	50.55 193	56.94 185	60.09 169	75.70 184	83.28 196	84.23 192	96.14 194	92.12 185

pmmvs6		76.79 187	75.69 193	78.09 181	79.95 190	89.57 190	80.92 186	74.46 156	64.79 204	60.74 162	45.71 203	60.55 166	78.37 173	88.04 165	86.00 187	94.07 198	95.15 172

CMPMVS	58.73 17	76.78 188	74.27 194	79.70 163	93.26 74	95.58 124	82.74 179	77.44 136	71.46 201	56.29 174	53.58 194	59.13 172	77.33 178	79.20 199	79.71 199	91.14 202	81.24 203
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		76.76 189	79.47 178	73.60 191	79.99 189	87.47 195	77.39 191	75.43 148	77.62 186	47.83 197	64.78 157	60.44 167	64.80 197	86.28 184	86.53 185	96.17 193	93.19 182

PM-MVS		75.81 190	76.11 192	75.46 189	73.81 200	85.48 199	76.42 193	70.57 176	80.05 176	54.75 179	62.33 161	39.56 209	80.59 170	87.71 168	82.81 196	96.61 192	94.81 175

pmmvs-eth3d		75.17 191	74.09 195	76.43 186	72.92 201	84.49 201	76.61 192	72.42 169	74.33 192	61.28 157	54.71 193	39.42 210	78.20 176	87.77 167	84.25 191	97.17 187	93.63 180

Anonymous20231206		74.59 192	77.00 190	71.78 192	77.89 198	87.45 196	75.14 196	72.29 171	77.76 183	46.65 199	52.14 196	52.93 194	61.10 200	89.37 158	88.09 176	97.59 183	91.30 190

test20.03		72.81 193	76.24 191	68.80 196	78.31 196	85.40 200	71.04 200	71.20 173	71.85 197	43.40 204	65.31 156	54.71 187	51.27 203	85.92 186	84.18 193	97.58 184	86.35 199

new_pmnet		71.86 194	73.67 196	69.75 195	72.56 204	84.20 202	70.95 202	66.81 185	80.34 175	43.62 203	51.60 197	53.81 193	71.24 194	82.91 197	80.93 197	93.35 200	81.92 202

MDA-MVSNet-bldmvs		69.61 195	70.36 198	68.74 197	62.88 207	88.50 192	65.40 206	77.01 139	71.60 200	43.93 200	66.71 154	35.33 212	72.47 193	61.01 205	80.63 198	90.73 203	88.75 197

pmmvs3		69.04 196	70.75 197	67.04 199	66.83 205	78.54 206	64.99 207	60.92 199	64.67 205	40.61 208	55.08 192	40.29 208	74.89 188	83.76 195	84.01 194	93.98 199	88.88 196

MIMVSNet1		68.63 197	70.24 199	66.76 200	56.86 210	83.26 203	67.93 204	70.26 177	68.05 202	46.80 198	40.44 204	48.15 204	62.01 198	84.96 191	84.86 189	96.69 189	81.93 201

GG-mvs-BLEND		67.99 198	97.35 37	33.72 207	1.22 216	99.72 14	98.30 33	0.57 214	97.61 60	1.18 216	93.26 50	96.63 42	1.74 213	97.15 52	97.14 38	99.34 90	99.96 9

new-patchmatchnet		67.66 199	68.07 200	67.18 198	72.85 202	82.86 204	63.09 208	68.61 183	66.60 203	42.64 206	49.28 200	38.68 211	61.21 199	75.84 200	75.22 201	94.67 197	88.00 198

FPMVS		63.27 200	61.31 202	65.57 201	78.25 197	74.42 209	75.23 195	68.92 182	72.33 196	43.87 201	49.01 201	43.94 206	48.64 205	61.15 204	58.81 206	78.51 209	69.49 208

Gipuma		54.59 201	53.98 203	55.30 202	59.03 209	52.63 211	47.17 211	56.08 207	71.68 199	37.54 209	20.90 210	19.00 214	52.33 202	71.69 202	75.20 202	79.64 208	66.79 209
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	49.05 18	51.88 202	50.56 205	53.42 203	64.21 206	43.30 213	42.64 212	62.93 190	50.56 208	43.72 202	37.44 205	42.95 207	35.05 208	58.76 207	54.58 207	71.95 210	66.33 210
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		50.69 203	52.33 204	48.78 204	51.24 211	64.81 210	47.91 210	53.79 210	44.95 209	21.75 211	29.98 208	25.90 213	31.98 210	59.95 206	65.37 204	86.00 206	75.36 206

E-PMN		37.15 204	34.82 207	39.86 205	47.53 213	35.42 215	23.79 214	55.26 208	35.18 212	14.12 213	17.38 213	14.13 216	39.73 207	32.24 209	46.98 208	58.76 211	62.39 212

EMVS		36.45 205	33.63 208	39.74 206	48.47 212	35.73 214	23.59 215	55.11 209	35.61 211	12.88 214	17.49 211	14.62 215	41.04 206	29.33 210	43.00 209	57.32 212	59.62 213

MVE	42.40 19	36.00 206	38.65 206	32.92 208	29.16 214	46.17 212	22.61 216	44.21 211	26.44 214	18.88 212	17.41 212	9.36 218	32.29 209	45.75 208	61.38 205	50.35 213	64.03 211
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		21.55 207	30.91 209	10.62 209	2.78 215	11.66 216	18.51 217	4.82 212	38.21 210	4.06 215	36.35 206	4.47 219	26.81 211	23.27 211	27.11 210	6.75 214	75.30 207

test123		16.81 208	24.80 210	7.48 210	0.82 217	8.38 217	11.92 218	2.60 213	28.96 213	1.12 217	28.39 209	1.26 220	24.51 212	8.93 212	22.19 211	3.90 215	75.49 205

uanet_test		0.00 209	0.00 211	0.00 211	0.00 218	0.00 218	0.00 219	0.00 215	0.00 215	0.00 218	0.00 214	0.00 221	0.00 214	0.00 213	0.00 212	0.00 216	0.00 214

sosnet-low-res		0.00 209	0.00 211	0.00 211	0.00 218	0.00 218	0.00 219	0.00 215	0.00 215	0.00 218	0.00 214	0.00 221	0.00 214	0.00 213	0.00 212	0.00 216	0.00 214

sosnet		0.00 209	0.00 211	0.00 211	0.00 218	0.00 218	0.00 219	0.00 215	0.00 215	0.00 218	0.00 214	0.00 221	0.00 214	0.00 213	0.00 212	0.00 216	0.00 214

9.14												99.73 6

SR-MVS					99.27 14			95.82 17				99.00 16

Anonymous202405211			87.54 136		90.72 101	97.10 100	93.40 91	85.30 78	91.41 129		60.23 166	80.69 119	95.80 63	91.33 141	92.60 123	98.38 163	99.40 78

our_test_3					81.94 164	90.26 187	75.39 194

test_part1																	99.97 6

ambc			64.61 201		61.80 208	75.31 208	71.00 201		74.16 193	48.83 195	36.02 207	13.22 217	58.66 201	85.80 188	76.26 200	88.01 204	91.53 189

MTAPA										94.58 13		98.56 22

MTMP										95.24 7		98.13 29

Patchmatch-RL test							37.05 213

tmp_tt				71.24 194	90.29 110	76.39 207	65.81 205	59.43 204	97.62 58	79.65 97	90.60 59	68.71 155	49.71 204	72.71 201	65.70 203	82.54 207

XVS					93.63 69	99.64 23	94.32 77			83.97 65		98.08 31				99.59 35

X-MVStestdata					93.63 69	99.64 23	94.32 77			83.97 65		98.08 31				99.59 35

abl_6				95.40 35	98.18 38	99.45 41	97.39 47	89.27 49	99.48 3	90.52 27	94.52 44	98.63 21	97.32 34			99.73 14	99.82 31

mPP-MVS					98.66 28							97.11 39

NP-MVS									97.69 56

Patchmtry						95.86 119	89.43 129	61.37 197		60.81 158

DeepMVS_CX						85.88 198	69.83 203	81.56 107	87.99 141	48.22 196	71.85 142	45.52 205	68.67 195	63.21 203		86.64 205	80.03 204

High-resolution multi-view benchmark