High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

HFP-MVS		98.48 10	98.62 12	98.32 11	99.39 17	99.33 22	99.27 10	97.42 18	98.27 8	95.25 23	98.34 11	98.83 26	99.08 1	98.26 34	98.08 26	99.48 30	99.26 35

ACMMPR		98.40 12	98.49 14	98.28 13	99.41 13	99.40 14	99.36 4	97.35 21	98.30 7	95.02 25	97.79 18	98.39 37	99.04 2	98.26 34	98.10 24	99.50 29	99.22 41

SD-MVS		98.52 8	98.77 9	98.23 15	98.15 50	99.26 27	98.79 27	97.59 15	98.52 3	96.25 15	97.99 16	99.75 6	99.01 3	98.27 33	97.97 32	99.59 7	99.63 2
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
CPTT-MVS		97.78 27	97.54 36	98.05 21	98.91 35	99.05 37	99.00 21	96.96 33	97.14 42	95.92 17	95.50 45	98.78 28	98.99 4	97.20 67	96.07 92	98.54 165	99.04 66

DVP-MVS		98.86 4	98.97 3	98.75 2	99.43 12	99.63 1	99.25 12	97.81 2	98.62 2	97.69 1	97.59 21	99.90 2	98.93 5	98.99 4	98.42 12	99.37 59	99.62 4
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
MSLP-MVS++		98.04 23	97.93 33	98.18 16	99.10 27	99.09 36	98.34 36	96.99 32	97.54 30	96.60 12	94.82 52	98.45 35	98.89 6	97.46 61	98.77 4	99.17 96	99.37 22

TSAR-MVS + MP.		98.49 9	98.78 8	98.15 19	98.14 51	99.17 33	99.34 6	97.18 29	98.44 5	95.72 19	97.84 17	99.28 12	98.87 7	99.05 1	98.05 27	99.66 2	99.60 7
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
APDe-MVS		98.87 3	98.96 4	98.77 1	99.58 2	99.53 7	99.44 1	97.81 2	98.22 11	97.33 4	98.70 6	99.33 10	98.86 8	98.96 6	98.40 14	99.63 5	99.57 9
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		98.66 7	98.89 7	98.39 9	99.60 1	99.41 13	99.00 21	97.63 12	97.78 19	95.83 18	98.33 12	99.83 4	98.85 9	98.93 8	98.56 7	99.41 51	99.40 21
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
CNVR-MVS		98.47 11	98.46 17	98.48 7	99.40 14	99.05 37	99.02 19	97.54 16	97.73 20	96.65 11	97.20 30	99.13 20	98.85 9	98.91 9	98.10 24	99.41 51	99.08 57

PGM-MVS		97.81 26	98.11 29	97.46 29	99.55 3	99.34 21	99.32 9	94.51 45	96.21 64	93.07 37	98.05 15	97.95 42	98.82 11	98.22 37	97.89 39	99.48 30	99.09 56

CP-MVS		98.32 17	98.34 23	98.29 12	99.34 20	99.30 23	99.15 14	97.35 21	97.49 32	95.58 21	97.72 19	98.62 34	98.82 11	98.29 29	97.67 47	99.51 27	99.28 30

MCST-MVS		98.20 18	98.36 20	98.01 22	99.40 14	99.05 37	99.00 21	97.62 13	97.59 29	93.70 34	97.42 28	99.30 11	98.77 13	98.39 27	97.48 52	99.59 7	99.31 29

AdaColmap		97.53 31	96.93 48	98.24 14	99.21 23	98.77 65	98.47 34	97.34 23	96.68 52	96.52 13	95.11 50	96.12 58	98.72 14	97.19 69	96.24 88	99.17 96	98.39 120

ACMMP_NAP		98.20 18	98.49 14	97.85 25	99.50 4	99.40 14	99.26 11	97.64 11	97.47 34	92.62 47	97.59 21	99.09 22	98.71 15	98.82 12	97.86 40	99.40 54	99.19 45

DeepC-MVS_fast	96.13 1	98.13 20	98.27 26	97.97 24	99.16 26	99.03 43	99.05 18	97.24 26	98.22 11	94.17 32	95.82 41	98.07 39	98.69 16	98.83 11	98.80 2	99.52 22	99.10 54
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DVP-MVS++		98.92 1	99.18 1	98.61 4	99.47 5	99.61 2	99.39 3	97.82 1	98.80 1	96.86 8	98.90 2	99.92 1	98.67 17	99.02 2	98.20 20	99.43 48	99.82 1

SED-MVS		98.90 2	99.07 2	98.69 3	99.38 18	99.61 2	99.33 8	97.80 4	98.25 9	97.60 2	98.87 4	99.89 3	98.67 17	99.02 2	98.26 18	99.36 61	99.61 6

MSP-MVS		98.73 6	98.93 5	98.50 6	99.44 11	99.57 4	99.36 4	97.65 9	98.14 13	96.51 14	98.49 8	99.65 8	98.67 17	98.60 14	98.42 12	99.40 54	99.63 2
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
HPM-MVS++		98.34 16	98.47 16	98.18 16	99.46 8	99.15 34	99.10 16	97.69 8	97.67 25	94.93 26	97.62 20	99.70 7	98.60 20	98.45 21	97.46 53	99.31 68	99.26 35

DPE-MVS		98.75 5	98.91 6	98.57 5	99.21 23	99.54 6	99.42 2	97.78 6	97.49 32	96.84 9	98.94 1	99.82 5	98.59 21	98.90 10	98.22 19	99.56 17	99.48 17
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
NCCC		98.10 21	98.05 31	98.17 18	99.38 18	99.05 37	99.00 21	97.53 17	98.04 15	95.12 24	94.80 53	99.18 18	98.58 22	98.49 18	97.78 44	99.39 56	98.98 74

MP-MVS		98.09 22	98.30 25	97.84 26	99.34 20	99.19 32	99.23 13	97.40 19	97.09 44	93.03 40	97.58 23	98.85 25	98.57 23	98.44 23	97.69 46	99.48 30	99.23 39
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
X-MVS		97.84 25	98.19 28	97.42 30	99.40 14	99.35 18	99.06 17	97.25 25	97.38 35	90.85 63	96.06 38	98.72 30	98.53 24	98.41 25	98.15 23	99.46 34	99.28 30

APD-MVS		98.36 15	98.32 24	98.41 8	99.47 5	99.26 27	99.12 15	97.77 7	96.73 50	96.12 16	97.27 29	98.88 24	98.46 25	98.47 19	98.39 15	99.52 22	99.22 41
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EC-MVSNet		96.49 50	97.63 35	95.16 64	94.75 113	98.69 71	97.39 55	88.97 126	96.34 60	92.02 53	96.04 39	96.46 52	98.21 26	98.41 25	97.96 33	99.61 6	99.55 10

train_agg		97.65 30	98.06 30	97.18 33	98.94 32	98.91 56	98.98 25	97.07 31	96.71 51	90.66 69	97.43 27	99.08 23	98.20 27	97.96 46	97.14 64	99.22 86	99.19 45

SPE-MVS-test		97.00 40	97.85 34	96.00 51	97.77 56	99.56 5	96.35 89	91.95 76	97.54 30	92.20 50	96.14 37	96.00 61	98.19 28	98.46 20	97.78 44	99.57 14	99.45 19

DeepC-MVS	94.87 4	96.76 49	96.50 54	97.05 35	98.21 49	99.28 25	98.67 28	97.38 20	97.31 36	90.36 76	89.19 104	93.58 72	98.19 28	98.31 28	98.50 8	99.51 27	99.36 23
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CS-MVS		96.87 44	97.41 40	96.24 46	97.42 61	99.48 10	97.30 56	91.83 82	97.17 40	93.02 41	94.80 53	94.45 67	98.16 30	98.61 13	97.85 41	99.69 1	99.50 13

SteuartSystems-ACMMP		98.38 14	98.71 11	97.99 23	99.34 20	99.46 11	99.34 6	97.33 24	97.31 36	94.25 30	98.06 14	99.17 19	98.13 31	98.98 5	98.46 10	99.55 18	99.54 11
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		98.39 13	98.45 18	98.33 10	99.45 9	99.05 37	98.27 37	97.65 9	97.73 20	97.02 7	98.18 13	99.25 15	98.11 32	98.15 39	97.62 48	99.45 38	99.19 45

CSCG		97.44 33	97.18 44	97.75 27	99.47 5	99.52 8	98.55 32	95.41 40	97.69 24	95.72 19	94.29 56	95.53 63	98.10 33	96.20 108	97.38 57	99.24 80	99.62 4

3Dnovator+	93.91 7	97.23 36	97.22 41	97.24 32	98.89 36	98.85 61	98.26 38	93.25 57	97.99 16	95.56 22	90.01 100	98.03 41	98.05 34	97.91 47	98.43 11	99.44 45	99.35 24

TSAR-MVS + GP.		97.45 32	98.36 20	96.39 42	95.56 87	98.93 53	97.74 49	93.31 54	97.61 28	94.24 31	98.44 10	99.19 17	98.03 35	97.60 56	97.41 55	99.44 45	99.33 26

PLC	94.95 3	97.37 34	96.77 51	98.07 20	98.97 31	98.21 95	97.94 46	96.85 35	97.66 26	97.58 3	93.33 62	96.84 49	98.01 36	97.13 71	96.20 90	99.09 109	98.01 136
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
3Dnovator	93.79 8	97.08 38	97.20 42	96.95 38	99.09 28	99.03 43	98.20 39	93.33 53	97.99 16	93.82 33	90.61 95	96.80 50	97.82 37	97.90 48	98.78 3	99.47 33	99.26 35

LS3D		95.46 59	95.14 77	95.84 53	97.91 55	98.90 58	98.58 31	97.79 5	97.07 45	83.65 128	88.71 107	88.64 104	97.82 37	97.49 59	97.42 54	99.26 78	97.72 148

CNLPA		96.90 43	96.28 57	97.64 28	98.56 42	98.63 79	96.85 68	96.60 36	97.73 20	97.08 6	89.78 102	96.28 56	97.80 39	96.73 83	96.63 79	98.94 129	98.14 132

ACMMP		97.37 34	97.48 38	97.25 31	98.88 37	99.28 25	98.47 34	96.86 34	97.04 46	92.15 51	97.57 24	96.05 60	97.67 40	97.27 65	95.99 97	99.46 34	99.14 53
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
sasdasda		95.25 65	95.45 69	95.00 68	95.27 95	98.72 68	96.89 65	89.82 112	96.51 54	90.84 66	93.72 59	86.01 119	97.66 41	95.78 122	97.94 35	99.54 19	99.50 13

canonicalmvs		95.25 65	95.45 69	95.00 68	95.27 95	98.72 68	96.89 65	89.82 112	96.51 54	90.84 66	93.72 59	86.01 119	97.66 41	95.78 122	97.94 35	99.54 19	99.50 13

QAPM		96.78 48	97.14 45	96.36 43	99.05 29	99.14 35	98.02 43	93.26 55	97.27 38	90.84 66	91.16 87	97.31 45	97.64 43	97.70 54	98.20 20	99.33 63	99.18 48

TAPA-MVS	94.18 5	96.38 51	96.49 55	96.25 44	98.26 48	98.66 74	98.00 44	94.96 43	97.17 40	89.48 89	92.91 67	96.35 54	97.53 44	96.59 89	95.90 100	99.28 72	97.82 140
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PHI-MVS		97.78 27	98.44 19	97.02 36	98.73 38	99.25 29	98.11 40	95.54 39	96.66 53	92.79 44	98.52 7	99.38 9	97.50 45	97.84 49	98.39 15	99.45 38	99.03 67

ETV-MVS		96.31 52	97.47 39	94.96 71	94.79 110	98.78 64	96.08 97	91.41 94	96.16 65	90.50 71	95.76 43	96.20 57	97.39 46	98.42 24	97.82 42	99.57 14	99.18 48

OMC-MVS		97.00 40	96.92 49	97.09 34	98.69 39	98.66 74	97.85 47	95.02 42	98.09 14	94.47 28	93.15 63	96.90 47	97.38 47	97.16 70	96.82 76	99.13 103	97.65 149

MGCFI-Net		95.12 67	95.39 72	94.79 78	95.24 97	98.68 72	96.80 72	89.72 116	96.48 56	90.11 79	93.64 61	85.86 123	97.36 48	95.69 128	97.92 38	99.53 21	99.49 16

MAR-MVS		95.50 56	95.60 65	95.39 61	98.67 40	98.18 98	95.89 106	89.81 114	94.55 111	91.97 54	92.99 65	90.21 91	97.30 49	96.79 80	97.49 51	98.72 151	98.99 72
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
DPM-MVS		96.86 45	96.82 50	96.91 39	98.08 52	98.20 96	98.52 33	97.20 28	97.24 39	91.42 57	91.84 79	98.45 35	97.25 50	97.07 72	97.40 56	98.95 128	97.55 152

MVS_111021_LR		97.16 37	98.01 32	96.16 47	98.47 44	98.98 48	96.94 64	93.89 49	97.64 27	91.44 56	98.89 3	96.41 53	97.20 51	98.02 45	97.29 62	99.04 121	98.85 89

CDPH-MVS		96.84 46	97.49 37	96.09 48	98.92 34	98.85 61	98.61 29	95.09 41	96.00 72	87.29 112	95.45 47	97.42 44	97.16 52	97.83 50	97.94 35	99.44 45	98.92 80

thres400		93.56 109	92.43 135	94.87 75	95.40 89	98.91 56	96.70 77	92.38 67	92.93 136	88.19 106	86.69 123	77.35 172	97.13 53	96.75 82	95.85 102	99.42 50	98.56 106

thres200		93.62 107	92.54 128	94.88 73	95.36 90	98.93 53	96.75 75	92.31 68	92.84 137	88.28 104	86.99 120	77.81 171	97.13 53	96.82 77	95.92 98	99.45 38	98.49 112

tfpn200view9		93.64 106	92.57 127	94.89 72	95.33 91	98.94 51	96.82 69	92.31 68	92.63 140	88.29 102	87.21 118	78.01 168	97.12 55	96.82 77	95.85 102	99.45 38	98.56 106

thres600view7		93.49 111	92.37 138	94.79 78	95.42 88	98.93 53	96.58 81	92.31 68	93.04 134	87.88 108	86.62 125	76.94 175	97.09 56	96.82 77	95.63 108	99.45 38	98.63 102

EIA-MVS		95.50 56	96.19 59	94.69 81	94.83 109	98.88 60	95.93 103	91.50 91	94.47 112	89.43 90	93.14 64	92.72 77	97.05 57	97.82 52	97.13 65	99.43 48	99.15 51

ET-MVSNet_ETH3D		93.34 113	94.33 91	92.18 118	83.26 222	97.66 109	96.72 76	89.89 111	95.62 85	87.17 113	96.00 40	83.69 143	96.99 58	93.78 162	95.34 116	99.06 116	98.18 131

TSAR-MVS + COLMAP		94.79 73	94.51 86	95.11 65	96.50 71	97.54 110	97.99 45	94.54 44	97.81 18	85.88 119	96.73 32	81.28 156	96.99 58	96.29 103	95.21 121	98.76 150	96.73 176

PCF-MVS	93.95 6	95.65 55	95.14 77	96.25 44	97.73 59	98.73 67	97.59 51	97.13 30	92.50 144	89.09 99	89.85 101	96.65 51	96.90 60	94.97 144	94.89 130	99.08 111	98.38 121
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TSAR-MVS + ACMM		97.71 29	98.60 13	96.66 40	98.64 41	99.05 37	98.85 26	97.23 27	98.45 4	89.40 92	97.51 25	99.27 14	96.88 61	98.53 15	97.81 43	98.96 127	99.59 8

OpenMVS	92.33 11	95.50 56	95.22 75	95.82 54	98.98 30	98.97 49	97.67 50	93.04 62	94.64 109	89.18 97	84.44 147	94.79 65	96.79 62	97.23 66	97.61 49	99.24 80	98.88 85

thres100view900		93.55 110	92.47 134	94.81 77	95.33 91	98.74 66	96.78 74	92.30 71	92.63 140	88.29 102	87.21 118	78.01 168	96.78 63	96.38 98	95.92 98	99.38 57	98.40 119

Anonymous20231211		93.49 111	92.33 139	94.84 76	94.78 112	98.00 103	96.11 95	91.85 78	94.86 106	90.91 62	74.69 187	89.18 99	96.73 64	94.82 145	95.51 112	98.67 155	99.24 38

Effi-MVS+		92.93 118	93.86 103	91.86 120	94.07 133	98.09 102	95.59 111	85.98 158	94.27 117	79.54 149	91.12 90	81.81 153	96.71 65	96.67 87	96.06 93	99.27 75	98.98 74

MVS_111021_HR		97.04 39	98.20 27	95.69 55	98.44 46	99.29 24	96.59 80	93.20 58	97.70 23	89.94 83	98.46 9	96.89 48	96.71 65	98.11 42	97.95 34	99.27 75	99.01 70

Fast-Effi-MVS+		91.87 127	92.08 142	91.62 126	92.91 149	97.21 122	94.93 122	84.60 178	93.61 127	81.49 139	83.50 152	78.95 163	96.62 67	96.55 91	96.22 89	99.16 99	98.51 110

MVS_0304		97.94 24	98.72 10	97.02 36	98.48 43	99.50 9	99.02 19	94.06 47	98.33 6	94.51 27	98.78 5	97.73 43	96.60 68	98.51 16	98.68 5	99.45 38	99.53 12

casdiffmvs		94.38 87	94.15 99	94.64 83	94.70 118	98.51 84	96.03 101	91.66 86	95.70 81	89.36 93	86.48 130	85.03 133	96.60 68	97.40 62	97.30 60	99.52 22	98.67 99
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs_mvg		94.55 80	94.26 92	94.88 73	94.96 105	98.51 84	97.11 58	91.82 83	94.28 116	89.20 96	86.60 126	86.85 112	96.56 70	97.47 60	97.25 63	99.64 4	98.83 92
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Anonymous202405211			92.18 140		95.04 104	98.20 96	96.14 94	91.79 84	93.93 120		74.60 188	88.38 107	96.48 71	95.17 140	95.82 105	99.00 122	99.15 51

MVS_Test		94.82 71	95.66 64	93.84 99	94.79 110	98.35 90	96.49 84	89.10 125	96.12 68	87.09 114	92.58 70	90.61 88	96.48 71	96.51 96	96.89 73	99.11 106	98.54 108

viewmanbaseed2359cas		94.31 89	94.25 93	94.38 88	94.72 115	98.59 81	96.09 96	91.84 79	95.35 90	87.92 107	87.86 114	85.54 124	96.45 73	96.71 84	97.04 66	99.26 78	98.67 99

ACMM	92.75 10	94.41 86	93.84 105	95.09 66	96.41 74	96.80 130	94.88 125	93.54 51	96.41 58	90.16 77	92.31 73	83.11 147	96.32 74	96.22 106	94.65 136	99.22 86	97.35 159
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OPM-MVS		93.61 108	92.43 135	95.00 68	96.94 68	97.34 117	97.78 48	94.23 46	89.64 177	85.53 120	88.70 108	82.81 149	96.28 75	96.28 104	95.00 129	99.24 80	97.22 162
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CANet		96.84 46	97.20 42	96.42 41	97.92 54	99.24 31	98.60 30	93.51 52	97.11 43	93.07 37	91.16 87	97.24 46	96.21 76	98.24 36	98.05 27	99.22 86	99.35 24

viewmacassd2359aftdt		93.65 105	93.29 117	94.07 94	94.61 120	98.51 84	96.04 100	91.75 85	93.61 127	86.56 117	84.89 142	84.41 136	96.17 77	95.97 114	97.03 67	99.28 72	98.63 102

viewmambaseed2359dif		93.92 99	93.38 115	94.54 84	94.55 122	98.15 99	96.41 86	91.47 92	95.10 102	89.58 88	86.64 124	85.10 131	96.17 77	94.08 161	95.77 106	99.09 109	98.84 91

diffmvs_AUTHOR		94.09 93	93.86 103	94.36 89	94.60 121	98.31 91	96.29 90	91.51 90	96.39 59	88.49 101	87.35 116	83.32 146	96.16 79	96.17 111	96.64 78	99.10 107	98.82 94

PMMVS		94.61 78	95.56 66	93.50 104	94.30 129	96.74 134	94.91 123	89.56 119	95.58 87	87.72 109	96.15 36	92.86 75	96.06 80	95.47 132	95.02 127	98.43 173	97.09 165

CLD-MVS		94.79 73	94.36 90	95.30 62	95.21 99	97.46 113	97.23 57	92.24 72	96.43 57	91.77 55	92.69 69	84.31 137	96.06 80	95.52 130	95.03 126	99.31 68	99.06 62
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchMatch-RL		94.69 77	94.41 88	95.02 67	97.63 60	98.15 99	94.50 133	91.99 74	95.32 92	91.31 59	95.47 46	83.44 144	96.02 82	96.56 90	95.23 120	98.69 154	96.67 177

diffmvs		94.31 89	94.21 94	94.42 87	94.64 119	98.28 92	96.36 88	91.56 87	96.77 49	88.89 100	88.97 105	84.23 138	96.01 83	96.05 113	96.41 83	99.05 120	98.79 96
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
TPM-MVS					98.94 32	98.47 87	98.04 42			92.62 47	96.51 34	98.76 29	95.94 84			98.92 131	97.55 152
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
DCV-MVSNet		94.76 76	95.12 79	94.35 90	95.10 103	95.81 164	96.46 85	89.49 120	96.33 61	90.16 77	92.55 71	90.26 90	95.83 85	95.52 130	96.03 95	99.06 116	99.33 26

HyFIR lowres test		92.03 125	91.55 150	92.58 113	97.13 66	98.72 68	94.65 130	86.54 151	93.58 129	82.56 132	67.75 216	90.47 89	95.67 86	95.87 118	95.54 111	98.91 133	98.93 79

baseline1		94.59 79	94.47 87	94.72 80	95.16 100	97.97 105	96.07 98	91.94 77	94.86 106	89.98 81	91.60 83	85.87 122	95.64 87	97.07 72	96.90 72	99.52 22	97.06 169

GeoE		92.52 123	92.64 126	92.39 116	93.96 134	97.76 107	96.01 102	85.60 163	93.23 132	83.94 125	81.56 160	84.80 134	95.63 88	96.22 106	95.83 104	99.19 94	99.07 61

CHOSEN 280x420		95.46 59	97.01 46	93.66 102	97.28 65	97.98 104	96.40 87	85.39 166	96.10 69	91.07 60	96.53 33	96.34 55	95.61 89	97.65 55	96.95 71	96.21 199	97.49 154

viewmsd2359difaftdt		93.27 115	92.72 124	93.91 97	94.46 126	97.42 116	94.91 123	91.42 93	95.69 83	89.59 87	87.34 117	82.90 148	95.60 90	92.62 181	94.62 138	97.49 192	98.44 113

HQP-MVS		94.43 84	94.57 85	94.27 91	96.41 74	97.23 121	96.89 65	93.98 48	95.94 74	83.68 127	95.01 51	84.46 135	95.58 91	95.47 132	94.85 134	99.07 113	99.00 71

MSDG		94.82 71	93.73 107	96.09 48	98.34 47	97.43 115	97.06 59	96.05 37	95.84 78	90.56 70	86.30 135	89.10 101	95.55 92	96.13 112	95.61 109	99.00 122	95.73 185

DeepPCF-MVS	95.28 2	97.00 40	98.35 22	95.42 60	97.30 64	98.94 51	94.82 126	96.03 38	98.24 10	92.11 52	95.80 42	98.64 33	95.51 93	98.95 7	98.66 6	96.78 198	99.20 44

DI_MVS_pp		94.01 95	93.63 109	94.44 86	94.54 123	98.26 94	97.51 52	90.63 102	95.88 76	89.34 94	80.54 168	89.36 96	95.48 94	96.33 102	96.27 87	99.17 96	98.78 97

FA-MVS(training)		93.94 97	95.16 76	92.53 114	94.87 108	98.57 83	95.42 114	79.49 200	95.37 89	90.98 61	86.54 128	94.26 69	95.44 95	97.80 53	95.19 122	98.97 125	98.38 121

EPP-MVSNet		95.27 64	96.18 60	94.20 92	94.88 107	98.64 77	94.97 121	90.70 101	95.34 91	89.67 86	91.66 82	93.84 70	95.42 96	97.32 64	97.00 69	99.58 11	99.47 18

RPSCF		94.05 94	94.00 100	94.12 93	96.20 76	96.41 144	96.61 79	91.54 88	95.83 79	89.73 85	96.94 31	92.80 76	95.35 97	91.63 197	90.44 199	95.27 211	93.94 202

test2506		94.32 88	93.00 121	95.87 52	96.16 77	99.39 16	96.96 62	92.80 64	95.22 98	94.47 28	91.55 84	70.45 202	95.25 98	98.29 29	97.98 30	99.59 7	98.10 134

ECVR-MVS		94.14 91	92.96 122	95.52 58	96.16 77	99.39 16	96.96 62	92.80 64	95.22 98	92.38 49	81.48 161	80.31 157	95.25 98	98.29 29	97.98 30	99.59 7	98.05 135

LGP-MVS_train		94.12 92	94.62 84	93.53 103	96.44 73	97.54 110	97.40 54	91.84 79	94.66 108	81.09 141	95.70 44	83.36 145	95.10 100	96.36 101	95.71 107	99.32 65	99.03 67

DELS-MVS		96.06 54	96.04 61	96.07 50	97.77 56	99.25 29	98.10 41	93.26 55	94.42 113	92.79 44	88.52 111	93.48 73	95.06 101	98.51 16	98.83 1	99.45 38	99.28 30
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
test1111		93.94 97	92.78 123	95.29 63	96.14 79	99.42 12	96.79 73	92.85 63	95.08 103	91.39 58	80.69 166	79.86 160	95.00 102	98.28 32	98.00 29	99.58 11	98.11 133

ACMP	92.88 9	94.43 84	94.38 89	94.50 85	96.01 82	97.69 108	95.85 109	92.09 73	95.74 80	89.12 98	95.14 49	82.62 151	94.77 103	95.73 125	94.67 135	99.14 102	99.06 62
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
thisisatest0530		94.54 81	95.47 68	93.46 105	94.51 124	98.65 76	94.66 129	90.72 99	95.69 83	86.90 115	93.80 57	89.44 95	94.74 104	96.98 76	94.86 131	99.19 94	98.85 89

tttt0517		94.52 82	95.44 71	93.44 106	94.51 124	98.68 72	94.61 131	90.72 99	95.61 86	86.84 116	93.78 58	89.26 98	94.74 104	97.02 75	94.86 131	99.20 93	98.87 87

baseline		94.83 70	95.82 63	93.68 101	94.75 113	97.80 106	96.51 83	88.53 131	97.02 47	89.34 94	92.93 66	92.18 79	94.69 106	95.78 122	96.08 91	98.27 176	98.97 78

PVSNet_BlendedMVS		95.41 61	95.28 73	95.57 56	97.42 61	99.02 45	95.89 106	93.10 60	96.16 65	93.12 35	91.99 75	85.27 127	94.66 107	98.09 43	97.34 58	99.24 80	99.08 57

PVSNet_Blended		95.41 61	95.28 73	95.57 56	97.42 61	99.02 45	95.89 106	93.10 60	96.16 65	93.12 35	91.99 75	85.27 127	94.66 107	98.09 43	97.34 58	99.24 80	99.08 57

FC-MVSNet-train		93.85 101	93.91 101	93.78 100	94.94 106	96.79 133	94.29 136	91.13 96	93.84 124	88.26 105	90.40 96	85.23 129	94.65 109	96.54 92	95.31 117	99.38 57	99.28 30

CANet_DTU		93.92 99	96.57 53	90.83 134	95.63 85	98.39 89	96.99 61	87.38 142	96.26 62	71.97 188	96.31 35	93.02 74	94.53 110	97.38 63	96.83 75	98.49 168	97.79 141

FMVSNet1		91.54 135	90.93 156	92.26 117	90.35 174	95.27 182	95.22 117	87.16 145	91.37 161	87.62 110	75.45 182	83.84 141	94.43 111	96.52 93	96.30 84	98.82 140	97.74 147

IterMVS-LS		92.56 122	93.18 118	91.84 121	93.90 135	94.97 189	94.99 120	86.20 155	94.18 118	82.68 131	85.81 137	87.36 111	94.43 111	95.31 136	96.02 96	98.87 136	98.60 105
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GBi-Net		93.81 102	94.18 95	93.38 107	91.34 164	95.86 160	96.22 91	88.68 128	95.23 95	90.40 72	86.39 131	91.16 82	94.40 113	96.52 93	96.30 84	99.21 90	97.79 141

test1		93.81 102	94.18 95	93.38 107	91.34 164	95.86 160	96.22 91	88.68 128	95.23 95	90.40 72	86.39 131	91.16 82	94.40 113	96.52 93	96.30 84	99.21 90	97.79 141

FMVSNet2		93.30 114	93.36 116	93.22 110	91.34 164	95.86 160	96.22 91	88.24 134	95.15 101	89.92 84	81.64 159	89.36 96	94.40 113	96.77 81	96.98 70	99.21 90	97.79 141

IS_MVSNet		95.28 63	96.43 56	93.94 95	95.30 93	99.01 47	95.90 104	91.12 97	94.13 119	87.50 111	91.23 86	94.45 67	94.17 116	98.45 21	98.50 8	99.65 3	99.23 39

FMVSNet3		93.79 104	94.17 97	93.35 109	91.21 167	95.99 153	96.62 78	88.68 128	95.23 95	90.40 72	86.39 131	91.16 82	94.11 117	95.96 115	96.67 77	99.07 113	97.79 141

CHOSEN 1792x2688		92.66 121	92.49 131	92.85 112	97.13 66	98.89 59	95.90 104	88.50 132	95.32 92	83.31 129	71.99 205	88.96 102	94.10 118	96.69 85	96.49 81	98.15 178	99.10 54

UniMVSNet_ETH3D		88.47 174	86.00 204	91.35 128	91.55 161	96.29 147	92.53 161	88.81 127	85.58 207	82.33 133	67.63 217	66.87 217	94.04 119	91.49 198	95.24 119	98.84 139	98.92 80

SCA		90.92 141	93.04 120	88.45 162	93.72 140	97.33 118	92.77 156	76.08 212	96.02 71	78.26 153	91.96 77	90.86 85	93.99 120	90.98 201	90.04 202	95.88 203	94.06 201

EPMVS		90.88 142	92.12 141	89.44 153	94.71 116	97.24 120	93.55 143	76.81 207	95.89 75	81.77 136	91.49 85	86.47 115	93.87 121	90.21 204	90.07 201	95.92 202	93.49 208

COLMAP_ROB	90.49 14	93.27 115	92.71 125	93.93 96	97.75 58	97.44 114	96.07 98	93.17 59	95.40 88	83.86 126	83.76 151	88.72 103	93.87 121	94.25 157	94.11 153	98.87 136	95.28 191
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
dmvs_re		91.84 128	91.60 149	92.12 119	91.60 160	97.26 119	95.14 118	91.96 75	91.02 165	80.98 142	86.56 127	77.96 170	93.84 123	94.71 146	95.08 124	99.22 86	98.62 104

ACMH+	90.88 12	91.41 137	91.13 153	91.74 123	95.11 102	96.95 125	93.13 152	89.48 121	92.42 146	79.93 146	85.13 140	78.02 167	93.82 124	93.49 169	93.88 159	98.94 129	97.99 137

ACMH	90.77 13	91.51 136	91.63 148	91.38 127	95.62 86	96.87 128	91.76 180	89.66 117	91.58 159	78.67 151	86.73 122	78.12 166	93.77 125	94.59 148	94.54 144	98.78 148	98.98 74
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CostFormer		90.69 143	90.48 161	90.93 132	94.18 130	96.08 152	94.03 138	78.20 203	93.47 130	89.96 82	90.97 92	80.30 158	93.72 126	87.66 214	88.75 206	95.51 208	96.12 181

MVSTER		94.89 69	95.07 80	94.68 82	94.71 116	96.68 136	97.00 60	90.57 103	95.18 100	93.05 39	95.21 48	86.41 116	93.72 126	97.59 57	95.88 101	99.00 122	98.50 111

USDC		90.69 143	90.52 160	90.88 133	94.17 131	96.43 143	95.82 110	86.76 148	93.92 121	76.27 166	86.49 129	74.30 185	93.67 128	95.04 143	93.36 168	98.61 161	94.13 198

Effi-MVS+-dtu		91.78 130	93.59 111	89.68 151	92.44 155	97.11 123	94.40 134	84.94 174	92.43 145	75.48 170	91.09 91	83.75 142	93.55 129	96.61 88	95.47 113	97.24 194	98.67 99

PatchmatchNet		90.56 145	92.49 131	88.31 165	93.83 138	96.86 129	92.42 164	76.50 209	95.96 73	78.31 152	91.96 77	89.66 94	93.48 130	90.04 206	89.20 205	95.32 209	93.73 206
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TinyColmap		89.42 160	88.58 172	90.40 140	93.80 139	95.45 176	93.96 140	86.54 151	92.24 152	76.49 163	80.83 164	70.44 203	93.37 131	94.45 152	93.30 171	98.26 177	93.37 209

LTVRE_ROB	87.32 16	87.55 187	88.25 176	86.73 192	90.66 169	95.80 165	93.05 153	84.77 175	83.35 213	60.32 222	83.12 154	67.39 215	93.32 132	94.36 155	94.86 131	98.28 175	98.87 87
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
ADS-MVSNet		89.80 157	91.33 152	88.00 174	94.43 127	96.71 135	92.29 168	74.95 217	96.07 70	77.39 156	88.67 109	86.09 118	93.26 133	88.44 210	89.57 204	95.68 205	93.81 205

MDTV_nov1_ep13		91.57 134	93.18 118	89.70 149	93.39 143	96.97 124	93.53 144	80.91 197	95.70 81	81.86 135	92.40 72	89.93 92	93.25 134	91.97 194	90.80 197	95.25 212	94.46 195

UniMVSNet_NR-MVSNet		90.35 149	89.96 162	90.80 135	89.66 183	95.83 163	92.48 162	90.53 104	90.96 167	79.57 147	79.33 172	77.14 173	93.21 135	92.91 178	94.50 147	99.37 59	99.05 64

DU-MVS		89.67 159	88.84 170	90.63 138	89.26 193	95.61 169	92.48 162	89.91 109	91.22 162	79.57 147	77.72 176	71.18 199	93.21 135	92.53 183	94.57 141	99.35 62	99.05 64

pmmvs4		90.55 146	89.91 163	91.30 129	90.26 176	94.95 190	92.73 158	87.94 137	93.44 131	85.35 121	82.28 158	76.09 177	93.02 137	93.56 167	92.26 191	98.51 167	96.77 175

tpmrst		88.86 172	89.62 164	87.97 175	94.33 128	95.98 154	92.62 160	76.36 210	94.62 110	76.94 160	85.98 136	82.80 150	92.80 138	86.90 216	87.15 212	94.77 216	93.93 203

RPMNet		90.19 152	92.03 144	88.05 171	93.46 141	95.95 157	93.41 146	74.59 218	92.40 147	75.91 168	84.22 148	86.41 116	92.49 139	94.42 153	93.85 161	98.44 171	96.96 170

FMVSNet5		90.36 148	90.93 156	89.70 149	87.99 209	92.25 214	92.03 175	83.51 184	92.20 153	84.13 124	85.59 138	86.48 114	92.43 140	94.61 147	94.52 145	98.13 179	90.85 215

dps		90.11 155	89.37 168	90.98 131	93.89 136	96.21 149	93.49 145	77.61 205	91.95 155	92.74 46	88.85 106	78.77 165	92.37 141	87.71 213	87.71 210	95.80 204	94.38 196

Baseline_NR-MVSNet		89.27 164	88.01 180	90.73 137	89.26 193	93.71 209	92.71 159	89.78 115	90.73 168	81.28 140	73.53 197	72.85 191	92.30 142	92.53 183	93.84 162	99.07 113	98.88 85

CR-MVSNet		90.16 153	91.96 145	88.06 170	93.32 144	95.95 157	93.36 148	75.99 213	92.40 147	75.19 174	83.18 153	85.37 126	92.05 143	95.21 138	94.56 142	98.47 170	97.08 167

PatchT		89.13 167	91.71 146	86.11 198	92.92 148	95.59 171	83.64 215	75.09 216	91.87 156	75.19 174	82.63 156	85.06 132	92.05 143	95.21 138	94.56 142	97.76 187	97.08 167

v2v482		88.25 177	87.71 187	88.88 157	89.23 197	95.28 180	92.10 172	87.89 138	88.69 185	73.31 184	75.32 183	71.64 196	91.89 145	92.10 191	92.92 177	98.86 138	97.99 137

tfpnnormal		88.50 173	87.01 195	90.23 141	91.36 163	95.78 166	92.74 157	90.09 107	83.65 212	76.33 165	71.46 208	69.58 208	91.84 146	95.54 129	94.02 156	99.06 116	99.03 67

TranMVSNet+NR-MVSNet		89.23 165	88.48 174	90.11 147	89.07 199	95.25 183	92.91 155	90.43 105	90.31 173	77.10 159	76.62 180	71.57 197	91.83 147	92.12 189	94.59 140	99.32 65	98.92 80

EPNet		96.27 53	96.97 47	95.46 59	98.47 44	98.28 92	97.41 53	93.67 50	95.86 77	92.86 43	97.51 25	93.79 71	91.76 148	97.03 74	97.03 67	98.61 161	99.28 30
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Fast-Effi-MVS+-dtu		91.19 138	93.64 108	88.33 164	92.19 157	96.46 142	93.99 139	81.52 195	92.59 142	71.82 189	92.17 74	85.54 124	91.68 149	95.73 125	94.64 137	98.80 145	98.34 123

tpm		87.95 180	89.44 167	86.21 197	92.53 154	94.62 199	91.40 183	76.36 210	91.46 160	69.80 203	87.43 115	75.14 180	91.55 150	89.85 208	90.60 198	95.61 206	96.96 170

tpm cat1		88.90 170	87.78 186	90.22 142	93.88 137	95.39 178	93.79 141	78.11 204	92.55 143	89.43 90	81.31 162	79.84 161	91.40 151	84.95 217	86.34 215	94.68 218	94.09 199

baseline2		93.01 117	94.17 97	91.64 124	92.83 151	97.49 112	93.40 147	87.53 140	93.67 126	86.07 118	91.83 80	86.58 113	91.36 152	96.38 98	95.06 125	98.67 155	98.20 130

v10		88.00 179	87.96 181	88.05 171	89.44 188	94.68 196	92.36 165	83.35 185	89.37 179	72.96 185	73.98 194	72.79 192	91.35 153	93.59 164	92.88 178	98.81 143	98.42 117

v1192		87.51 188	87.31 189	87.74 179	89.04 200	94.87 194	92.07 173	85.03 171	88.49 187	70.32 196	72.65 202	70.35 204	91.21 154	93.59 164	92.80 180	98.78 148	98.42 117

UniMVSNet (Re)		90.03 156	89.61 165	90.51 139	89.97 180	96.12 151	92.32 166	89.26 122	90.99 166	80.95 143	78.25 175	75.08 182	91.14 155	93.78 162	93.87 160	99.41 51	99.21 43

v1921920		87.31 192	87.13 193	87.52 185	88.87 203	94.72 195	91.96 178	84.59 179	88.28 188	69.86 202	72.50 203	70.03 207	91.10 156	93.33 171	92.61 185	98.71 152	98.44 113

v1144		87.92 183	87.79 185	88.07 168	89.27 192	95.15 185	92.17 171	85.62 162	88.52 186	71.52 190	73.80 195	72.40 194	91.06 157	93.54 168	92.80 180	98.81 143	98.33 124

MIMVSNet		88.99 169	91.07 154	86.57 194	86.78 215	95.62 168	91.20 188	75.40 215	90.65 170	76.57 162	84.05 149	82.44 152	91.01 158	95.84 119	95.38 115	98.48 169	93.50 207

test-LLR		91.62 133	93.56 112	89.35 155	93.31 145	96.57 139	92.02 176	87.06 146	92.34 150	75.05 177	90.20 97	88.64 104	90.93 159	96.19 109	94.07 154	97.75 188	96.90 173

TESTMET0.1,1		91.07 139	93.56 112	88.17 166	90.43 171	96.57 139	92.02 176	82.83 189	92.34 150	75.05 177	90.20 97	88.64 104	90.93 159	96.19 109	94.07 154	97.75 188	96.90 173

SixPastTwentyTwo		88.37 175	89.47 166	87.08 189	90.01 179	95.93 159	87.41 205	85.32 167	90.26 175	70.26 197	86.34 134	71.95 195	90.93 159	92.89 179	91.72 194	98.55 164	97.22 162

test-mter		90.95 140	93.54 114	87.93 176	90.28 175	96.80 130	91.44 182	82.68 190	92.15 154	74.37 181	89.57 103	88.23 109	90.88 162	96.37 100	94.31 150	97.93 185	97.37 158

PVSNet_Blended_VisFu		94.77 75	95.54 67	93.87 98	96.48 72	98.97 49	94.33 135	91.84 79	94.93 105	90.37 75	85.04 141	94.99 64	90.87 163	98.12 41	97.30 60	99.30 70	99.45 19

CP-MVSNet		87.89 184	87.27 190	88.62 160	89.30 191	95.06 186	90.60 193	85.78 160	87.43 197	75.98 167	74.60 188	68.14 214	90.76 164	93.07 176	93.60 165	99.30 70	98.98 74

v144192		87.40 190	87.20 192	87.64 180	88.89 201	94.88 193	91.65 181	84.70 177	87.80 192	71.17 194	73.20 200	70.91 200	90.75 165	92.69 180	92.49 186	98.71 152	98.43 115

pmmvs5		87.83 185	88.09 178	87.51 186	89.59 186	95.48 174	89.75 199	84.73 176	86.07 205	71.44 191	80.57 167	70.09 206	90.74 166	94.47 151	92.87 179	98.82 140	97.10 164

v8		88.21 178	87.94 183	88.51 161	89.62 184	95.01 188	92.31 167	84.99 172	88.94 180	74.70 179	75.03 184	73.51 189	90.67 167	92.11 190	92.74 183	98.80 145	98.24 128

v1240		86.89 194	86.75 199	87.06 190	88.75 205	94.65 198	91.30 187	84.05 181	87.49 196	68.94 206	71.96 206	68.86 212	90.65 168	93.33 171	92.72 184	98.67 155	98.24 128

gm-plane-assit		83.26 209	85.29 206	80.89 209	89.52 187	89.89 220	70.26 226	78.24 202	77.11 223	58.01 226	74.16 193	66.90 216	90.63 169	97.20 67	96.05 94	98.66 158	95.68 186

MS-PatchMatch		91.82 129	92.51 129	91.02 130	95.83 84	96.88 126	95.05 119	84.55 180	93.85 123	82.01 134	82.51 157	91.71 80	90.52 170	95.07 142	93.03 175	98.13 179	94.52 193

CDS-MVSNet		92.77 119	93.60 110	91.80 122	92.63 153	96.80 130	95.24 116	89.14 124	90.30 174	84.58 123	86.76 121	90.65 87	90.42 171	95.89 117	96.49 81	98.79 147	98.32 126
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		90.54 147	90.87 158	90.16 143	91.48 162	96.61 138	93.26 150	86.08 156	87.71 193	81.66 138	83.11 155	84.04 139	90.42 171	94.54 149	94.60 139	98.04 183	95.48 189

V42		88.31 176	87.95 182	88.73 159	89.44 188	95.34 179	92.23 170	87.21 144	88.83 182	74.49 180	74.89 186	73.43 190	90.41 173	92.08 192	92.77 182	98.60 163	98.33 124

anonymousdsp		88.90 170	91.00 155	86.44 195	88.74 206	95.97 155	90.40 195	82.86 188	88.77 184	67.33 208	81.18 163	81.44 155	90.22 174	96.23 105	94.27 151	99.12 105	99.16 50

PS-CasMVS		87.33 191	86.68 200	88.10 167	89.22 198	94.93 191	90.35 196	85.70 161	86.44 202	74.01 182	73.43 198	66.59 220	90.04 175	92.92 177	93.52 166	99.28 72	98.91 83

IterMVS-SCA-FT		90.24 150	92.48 133	87.63 181	92.85 150	94.30 205	93.79 141	81.47 196	92.66 139	69.95 200	84.66 145	88.38 107	89.99 176	95.39 135	94.34 149	97.74 190	97.63 150

IterMVS		90.20 151	92.43 135	87.61 182	92.82 152	94.31 204	94.11 137	81.54 194	92.97 135	69.90 201	84.71 144	88.16 110	89.96 177	95.25 137	94.17 152	97.31 193	97.46 155
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
gg-mvs-nofinetune		86.17 199	88.57 173	83.36 206	93.44 142	98.15 99	96.58 81	72.05 221	74.12 225	49.23 229	64.81 220	90.85 86	89.90 178	97.83 50	96.84 74	98.97 125	97.41 157

GA-MVS		89.28 163	90.75 159	87.57 183	91.77 159	96.48 141	92.29 168	87.58 139	90.61 171	65.77 210	84.48 146	76.84 176	89.46 179	95.84 119	93.68 164	98.52 166	97.34 160

PEN-MVS		87.22 193	86.50 202	88.07 168	88.88 202	94.44 201	90.99 190	86.21 153	86.53 201	73.66 183	74.97 185	66.56 221	89.42 180	91.20 200	93.48 167	99.24 80	98.31 127

NR-MVSNet		89.34 162	88.66 171	90.13 146	90.40 172	95.61 169	93.04 154	89.91 109	91.22 162	78.96 150	77.72 176	68.90 211	89.16 181	94.24 158	93.95 157	99.32 65	98.99 72

pm-mvs1		89.19 166	89.02 169	89.38 154	90.40 172	95.74 167	92.05 174	88.10 136	86.13 203	77.70 154	73.72 196	79.44 162	88.97 182	95.81 121	94.51 146	99.08 111	97.78 146

MVS-HIRNet		85.36 203	86.89 196	83.57 205	90.13 177	94.51 200	83.57 216	72.61 220	88.27 189	71.22 193	68.97 212	81.81 153	88.91 183	93.08 175	91.94 192	94.97 215	89.64 218

PM-MVS		84.72 206	84.47 210	85.03 201	84.67 218	91.57 216	86.27 209	82.31 192	87.65 194	70.62 195	76.54 181	56.41 229	88.75 184	92.59 182	89.85 203	97.54 191	96.66 178

Vis-MVSNet (Re-imp)		94.46 83	96.24 58	92.40 115	95.23 98	98.64 77	95.56 112	90.99 98	94.42 113	85.02 122	90.88 93	94.65 66	88.01 185	98.17 38	98.37 17	99.57 14	98.53 109

v7n		86.43 197	86.52 201	86.33 196	87.91 210	94.93 191	90.15 197	83.05 186	86.57 200	70.21 198	71.48 207	66.78 218	87.72 186	94.19 160	92.96 176	98.92 131	98.76 98

pmmvs6		85.98 201	84.89 209	87.25 188	88.83 204	94.35 203	89.36 200	85.30 169	78.51 222	75.44 171	62.71 222	75.41 179	87.65 187	93.58 166	92.40 188	96.89 196	97.29 161

DTE-MVSNet		86.67 196	86.09 203	87.35 187	88.45 208	94.08 207	90.65 192	86.05 157	86.13 203	72.19 187	74.58 190	66.77 219	87.61 188	90.31 203	93.12 173	99.13 103	97.62 151

MDTV_nov1_ep13_2view		86.30 198	88.27 175	84.01 204	87.71 212	94.67 197	88.08 203	76.78 208	90.59 172	68.66 207	80.46 169	80.12 159	87.58 189	89.95 207	88.20 208	95.25 212	93.90 204

pmmvs-eth3d		84.33 207	82.94 212	85.96 200	84.16 219	90.94 217	86.55 208	83.79 182	84.25 210	75.85 169	70.64 210	56.43 228	87.44 190	92.20 188	90.41 200	97.97 184	95.68 186

v148		87.51 188	86.79 197	88.36 163	89.39 190	95.21 184	89.84 198	88.20 135	87.61 195	77.56 155	73.38 199	70.32 205	86.80 191	90.70 202	92.31 189	98.37 174	97.98 139

TransMVSNet (Re)		87.73 186	86.79 197	88.83 158	90.76 168	94.40 202	91.33 186	89.62 118	84.73 209	75.41 172	72.73 201	71.41 198	86.80 191	94.53 150	93.93 158	99.06 116	95.83 183

pmnet_mix02		86.12 200	87.12 194	84.96 202	89.82 181	94.12 206	84.88 213	86.63 150	91.78 157	65.60 211	80.76 165	76.98 174	86.61 193	87.29 215	84.80 218	96.21 199	94.09 199

WR-MVS_H		87.93 181	87.85 184	88.03 173	89.62 184	95.58 173	90.47 194	85.55 164	87.20 198	76.83 161	74.42 191	72.67 193	86.37 194	93.22 173	93.04 174	99.33 63	98.83 92

UGNet		94.92 68	96.63 52	92.93 111	96.03 81	98.63 79	94.53 132	91.52 89	96.23 63	90.03 80	92.87 68	96.10 59	86.28 195	96.68 86	96.60 80	99.16 99	99.32 28
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
UA-Net		93.96 96	95.95 62	91.64 124	96.06 80	98.59 81	95.29 115	90.00 108	91.06 164	82.87 130	90.64 94	98.06 40	86.06 196	98.14 40	98.20 20	99.58 11	96.96 170

test0.0.03 1		91.97 126	93.91 101	89.72 148	93.31 145	96.40 145	91.34 185	87.06 146	93.86 122	81.67 137	91.15 89	89.16 100	86.02 197	95.08 141	95.09 123	98.91 133	96.64 179

thisisatest0515		90.12 154	92.06 143	87.85 177	90.03 178	96.17 150	87.83 204	87.45 141	91.71 158	77.15 158	85.40 139	84.01 140	85.74 198	95.41 134	93.30 171	98.88 135	98.43 115

FC-MVSNet-test		91.63 132	93.82 106	89.08 156	92.02 158	96.40 145	93.26 150	87.26 143	93.72 125	77.26 157	88.61 110	89.86 93	85.50 199	95.72 127	95.02 127	99.16 99	97.44 156

CMPMVS	65.18 17	84.76 205	83.10 211	86.69 193	95.29 94	95.05 187	88.37 202	85.51 165	80.27 220	71.31 192	68.37 214	73.85 187	85.25 200	87.72 212	87.75 209	94.38 219	88.70 219
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
N_pmnet		84.80 204	85.10 208	84.45 203	89.25 196	92.86 212	84.04 214	86.21 153	88.78 183	66.73 209	72.41 204	74.87 184	85.21 201	88.32 211	86.45 213	95.30 210	92.04 212

WR-MVS		87.93 181	88.09 178	87.75 178	89.26 193	95.28 180	90.81 191	86.69 149	88.90 181	75.29 173	74.31 192	73.72 188	85.19 202	92.26 186	93.32 170	99.27 75	98.81 95

TDRefinement		89.07 168	88.15 177	90.14 145	95.16 100	96.88 126	95.55 113	90.20 106	89.68 176	76.42 164	76.67 179	74.30 185	84.85 203	93.11 174	91.91 193	98.64 160	94.47 194

CVMVSNet		89.77 158	91.66 147	87.56 184	93.21 147	95.45 176	91.94 179	89.22 123	89.62 178	69.34 205	83.99 150	85.90 121	84.81 204	94.30 156	95.28 118	96.85 197	97.09 165

pmmvs3		79.16 214	80.12 216	78.05 215	79.36 223	86.59 223	78.13 223	73.87 219	76.42 224	57.51 227	70.59 211	57.02 227	84.66 205	90.10 205	88.32 207	94.75 217	91.77 214

Vis-MVSNet		92.77 119	95.00 82	90.16 143	94.10 132	98.79 63	94.76 128	88.26 133	92.37 149	79.95 145	88.19 113	91.58 81	84.38 206	97.59 57	97.58 50	99.52 22	98.91 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EG-PatchMatch MVS		86.68 195	87.24 191	86.02 199	90.58 170	96.26 148	91.08 189	81.59 193	84.96 208	69.80 203	71.35 209	75.08 182	84.23 207	94.24 158	93.35 169	98.82 140	95.46 190

testgi		89.42 160	91.50 151	87.00 191	92.40 156	95.59 171	89.15 201	85.27 170	92.78 138	72.42 186	91.75 81	76.00 178	84.09 208	94.38 154	93.82 163	98.65 159	96.15 180

EPNet_dtu		92.45 124	95.02 81	89.46 152	98.02 53	95.47 175	94.79 127	92.62 66	94.97 104	70.11 199	94.76 55	92.61 78	84.07 209	95.94 116	95.56 110	97.15 195	95.82 184
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MDA-MVSNet-bldmvs		80.11 212	80.24 215	79.94 211	77.01 225	93.21 210	78.86 222	85.94 159	82.71 216	60.86 219	79.71 171	51.77 231	83.71 210	75.60 223	86.37 214	93.28 220	92.35 210

new_pmnet		81.53 211	82.68 213	80.20 210	83.47 221	89.47 221	82.21 219	78.36 201	87.86 191	60.14 224	67.90 215	69.43 209	82.03 211	89.22 209	87.47 211	94.99 214	87.39 220

DeepMVS_CX						86.86 222	79.50 221	70.43 223	90.73 168	63.66 214	80.36 170	60.83 224	79.68 212	76.23 222		89.46 223	86.53 221

EU-MVSNet		85.62 202	87.65 188	83.24 207	88.54 207	92.77 213	87.12 206	85.32 167	86.71 199	64.54 213	78.52 174	75.11 181	78.35 213	92.25 187	92.28 190	95.58 207	95.93 182

IB-MVS	89.56 15	91.71 131	92.50 130	90.79 136	95.94 83	98.44 88	87.05 207	91.38 95	93.15 133	92.98 42	84.78 143	85.14 130	78.27 214	92.47 185	94.44 148	99.10 107	99.08 57
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
MIMVSNet1		80.03 213	80.93 214	78.97 213	72.46 228	90.73 218	80.81 220	82.44 191	80.39 219	63.64 215	57.57 223	64.93 222	76.37 215	91.66 196	91.55 195	98.07 182	89.70 217

new-patchmatchnet		78.49 215	78.19 218	78.84 214	84.13 220	90.06 219	77.11 224	80.39 198	79.57 221	59.64 225	66.01 218	55.65 230	75.62 216	84.55 218	80.70 221	96.14 201	90.77 216

Anonymous20231206		83.84 208	85.19 207	82.26 208	87.38 213	92.87 211	85.49 211	83.65 183	86.07 205	63.44 217	68.42 213	69.01 210	75.45 217	93.34 170	92.44 187	98.12 181	94.20 197

ambc			73.83 221		76.23 226	85.13 224	82.27 218		84.16 211	65.58 212	52.82 225	23.31 236	73.55 218	91.41 199	85.26 217	92.97 221	94.70 192

test_method		72.96 217	78.68 217	66.28 220	50.17 232	64.90 230	75.45 225	50.90 228	87.89 190	62.54 218	62.98 221	68.34 213	70.45 219	91.90 195	82.41 219	88.19 225	92.35 210

Gipuma		68.35 219	66.71 222	70.27 217	74.16 227	68.78 229	63.93 229	71.77 222	83.34 214	54.57 228	34.37 227	31.88 233	68.69 220	83.30 219	85.53 216	88.48 224	79.78 224
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test20.03		82.92 210	85.52 205	79.90 212	87.75 211	91.84 215	82.80 217	82.99 187	82.65 217	60.32 222	78.90 173	70.50 201	67.10 221	92.05 193	90.89 196	98.44 171	91.80 213

FPMVS		75.84 216	74.59 220	77.29 216	86.92 214	83.89 225	85.01 212	80.05 199	82.91 215	60.61 221	65.25 219	60.41 225	63.86 222	75.60 223	73.60 225	87.29 226	80.47 223

EMVS		49.98 224	46.76 227	53.74 224	64.96 230	51.29 233	37.81 233	69.35 225	51.83 228	22.69 233	29.57 229	25.06 234	57.28 223	44.81 229	56.11 228	70.32 231	68.64 229

E-PMN		50.67 223	47.85 226	53.96 223	64.13 231	50.98 234	38.06 232	69.51 224	51.40 229	24.60 232	29.46 230	24.39 235	56.07 224	48.17 228	59.70 227	71.40 230	70.84 228

PMVS	63.12 18	67.27 220	66.39 223	68.30 218	77.98 224	60.24 231	59.53 230	76.82 206	66.65 226	60.74 220	54.39 224	59.82 226	51.24 225	73.92 226	70.52 226	83.48 227	79.17 225
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt				66.88 219	86.07 216	73.86 228	68.22 227	33.38 229	96.88 48	80.67 144	88.23 112	78.82 164	49.78 226	82.68 220	77.47 223	83.19 228

MVE	50.86 19	49.54 225	51.43 225	47.33 225	44.14 233	59.20 232	36.45 234	60.59 227	41.47 230	31.14 231	29.58 228	17.06 237	48.52 227	62.22 227	74.63 224	63.12 232	75.87 226
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		64.36 222	65.94 224	62.52 221	67.37 229	77.44 227	64.39 228	69.32 226	61.47 227	34.59 230	46.09 226	41.03 232	48.02 228	74.56 225	78.23 222	91.43 222	82.76 222

WB-MVS		69.22 218	76.91 219	60.24 222	85.80 217	79.37 226	56.86 231	84.96 173	81.50 218	18.16 234	76.85 178	61.07 223	34.23 229	82.46 221	81.81 220	81.43 229	75.31 227

testmvs		12.09 226	16.94 228	6.42 227	3.15 234	6.08 235	9.51 236	3.84 230	21.46 231	5.31 235	27.49 231	6.76 238	10.89 230	17.06 230	15.01 229	5.84 233	24.75 230

test123		9.58 227	13.53 229	4.97 228	1.31 236	5.47 236	8.32 237	2.95 231	18.14 232	2.03 237	20.82 232	2.34 239	10.60 231	10.00 231	14.16 230	4.60 234	23.77 231

GG-mvs-BLEND		66.17 221	94.91 83	32.63 226	1.32 235	96.64 137	91.40 183	0.85 232	94.39 115	2.20 236	90.15 99	95.70 62	2.27 232	96.39 97	95.44 114	97.78 186	95.68 186

uanet_test		0.00 228	0.00 230	0.00 229	0.00 237	0.00 237	0.00 238	0.00 233	0.00 233	0.00 238	0.00 233	0.00 240	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

sosnet-low-res		0.00 228	0.00 230	0.00 229	0.00 237	0.00 237	0.00 238	0.00 233	0.00 233	0.00 238	0.00 233	0.00 240	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

sosnet		0.00 228	0.00 230	0.00 229	0.00 237	0.00 237	0.00 238	0.00 233	0.00 233	0.00 238	0.00 233	0.00 240	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

RE-MVS-def										63.50 216

9.14												99.28 12

SR-MVS					99.45 9			97.61 14				99.20 16

our_test_3					89.78 182	93.84 208	85.59 210

MTAPA										96.83 10		99.12 21

MTMP										97.18 5		98.83 26

Patchmatch-RL test							34.61 235

XVS					96.60 69	99.35 18	96.82 69			90.85 63		98.72 30				99.46 34

X-MVStestdata					96.60 69	99.35 18	96.82 69			90.85 63		98.72 30				99.46 34

mPP-MVS					99.21 23							98.29 38

NP-MVS									95.32 92

Patchmtry						95.96 156	93.36 148	75.99 213		75.19 174

High-resolution multi-view benchmark