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.

DELS-MVS		98.19 52	98.77 59	97.52 52	98.29 61	99.71 15	99.12 41	94.58 62	98.80 107	95.38 52	96.24 120	98.24 74	97.92 103	99.06 40	99.52 1	99.82 16	99.79 45
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
DeepC-MVS	97.63 4	98.33 48	98.57 62	98.04 40	98.62 56	99.65 22	99.45 25	98.15 23	99.51 17	92.80 101	95.74 130	96.44 92	99.46 21	99.37 19	99.50 2	99.78 34	99.81 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator	96.92 7	98.67 37	99.05 44	98.23 37	99.57 26	99.45 68	99.11 42	94.66 57	99.69 3	96.80 32	96.55 115	99.61 53	99.40 25	98.87 57	99.49 3	99.85 10	99.66 108

MSLP-MVS++		99.15 18	99.24 34	99.04 15	99.52 32	99.49 63	99.09 44	98.07 29	99.37 27	98.47 8	97.79 82	99.89 35	99.50 16	98.93 49	99.45 4	99.61 125	99.76 63

IS_MVSNet		97.86 59	98.86 55	96.68 78	96.02 105	99.72 12	98.35 80	93.37 89	98.75 117	94.01 78	96.88 104	98.40 71	98.48 86	99.09 37	99.42 5	99.83 15	99.80 37

Vis-MVSNet (Re-imp)		97.40 75	98.89 54	95.66 106	95.99 108	99.62 33	97.82 101	93.22 93	98.82 104	91.40 116	96.94 101	98.56 69	95.70 161	99.14 35	99.41 6	99.79 31	99.75 71

PHI-MVS		99.08 22	99.43 19	98.67 28	99.15 45	99.59 45	99.11 42	97.35 39	99.14 63	97.30 27	99.44 12	99.96 12	99.32 32	98.89 54	99.39 7	99.79 31	99.58 124

APD-MVS		99.25 12	99.38 22	99.09 11	99.69 7	99.58 48	99.56 17	98.32 8	98.85 97	97.87 19	98.91 42	99.92 28	99.30 35	99.45 15	99.38 8	99.79 31	99.58 124
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepPCF-MVS	97.74 3	98.34 47	99.46 13	97.04 66	98.82 51	99.33 91	96.28 148	97.47 38	99.58 9	94.70 64	98.99 36	99.85 40	97.24 122	99.55 10	99.34 9	97.73 206	99.56 130

DeepC-MVS_fast	98.34 1	99.17 17	99.45 14	98.85 24	99.55 29	99.37 82	99.64 8	98.05 31	99.53 14	96.58 34	98.93 40	99.92 28	99.49 18	99.46 14	99.32 10	99.80 30	99.64 115
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SMA-MVS		99.38 6	99.60 3	99.12 9	99.76 2	99.62 33	99.39 29	98.23 18	99.52 16	98.03 17	99.45 11	99.98 2	99.64 5	99.58 8	99.30 11	99.68 96	99.76 63
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
3Dnovator+	96.92 7	98.71 36	99.05 44	98.32 33	99.53 30	99.34 88	99.06 46	94.61 58	99.65 5	97.49 24	96.75 105	99.86 38	99.44 23	98.78 62	99.30 11	99.81 22	99.67 104

QAPM		98.62 40	99.04 47	98.13 38	99.57 26	99.48 64	99.17 38	94.78 54	99.57 10	96.16 39	96.73 106	99.80 43	99.33 30	98.79 61	99.29 13	99.75 46	99.64 115

EC-MVSNet		98.22 51	99.44 17	96.79 75	95.62 123	99.56 51	99.01 50	92.22 100	99.17 55	94.51 69	99.41 13	99.62 52	99.49 18	99.16 34	99.26 14	99.91 2	99.94 1

APDe-MVS		99.49 1	99.64 1	99.32 2	99.74 4	99.74 11	99.75 1	98.34 4	99.56 11	98.72 6	99.57 7	99.97 8	99.53 15	99.65 2	99.25 15	99.84 12	99.77 58
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		99.30 9	99.54 7	99.03 16	99.66 16	99.64 26	99.68 4	98.25 14	99.56 11	97.12 30	99.19 21	99.95 17	99.72 1	99.43 16	99.25 15	99.72 67	99.77 58

HFP-MVS		99.32 8	99.53 9	99.07 13	99.69 7	99.59 45	99.63 12	98.31 9	99.56 11	97.37 26	99.27 19	99.97 8	99.70 3	99.35 22	99.24 17	99.71 77	99.76 63

UA-Net		97.13 86	99.14 38	94.78 115	97.21 80	99.38 79	97.56 110	92.04 103	98.48 129	88.03 132	98.39 67	99.91 31	94.03 192	99.33 24	99.23 18	99.81 22	99.25 161

LS3D		97.79 60	98.25 73	97.26 60	98.40 59	99.63 29	99.53 18	98.63 1	99.25 44	88.13 131	96.93 102	94.14 123	99.19 40	99.14 35	99.23 18	99.69 88	99.42 149

X-MVS		98.93 29	99.37 23	98.42 31	99.67 13	99.62 33	99.60 15	98.15 23	99.08 72	93.81 84	98.46 64	99.95 17	99.59 9	99.49 13	99.21 20	99.68 96	99.75 71

PGM-MVS		98.86 31	99.35 27	98.29 34	99.77 1	99.63 29	99.67 5	95.63 45	98.66 120	95.27 53	99.11 28	99.82 42	99.67 4	99.33 24	99.19 21	99.73 59	99.74 75

SteuartSystems-ACMMP		99.20 15	99.51 11	98.83 26	99.66 16	99.66 21	99.71 3	98.12 27	99.14 63	96.62 33	99.16 23	99.98 2	99.12 49	99.63 3	99.19 21	99.78 34	99.83 29
Skip Steuart: Steuart Systems R&D Blog.
test1111		97.09 88	96.83 131	97.39 54	96.92 88	99.81 3	98.44 72	94.45 64	99.17 55	95.85 43	92.10 167	88.97 153	98.78 72	99.02 43	99.11 23	99.88 4	99.63 117

test2506		97.16 84	96.68 134	97.73 46	96.95 86	99.79 4	98.48 68	94.42 65	99.17 55	97.74 22	99.15 24	80.93 204	98.89 68	99.03 41	99.09 24	99.88 4	99.62 119

ECVR-MVS		97.27 79	97.09 121	97.48 53	96.95 86	99.79 4	98.48 68	94.42 65	99.17 55	96.28 38	93.54 153	89.39 152	98.89 68	99.03 41	99.09 24	99.88 4	99.61 122

CS-MVS		98.56 43	99.32 28	97.68 47	98.28 62	99.89 2	98.71 61	94.53 63	99.41 23	95.43 49	99.05 35	98.66 66	99.19 40	99.21 29	99.07 26	99.93 1	99.94 1

TSAR-MVS + MP.		99.27 10	99.57 5	98.92 22	98.78 53	99.53 55	99.72 2	98.11 28	99.73 2	97.43 25	99.15 24	99.96 12	99.59 9	99.73 1	99.07 26	99.88 4	99.82 30
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SD-MVS		99.25 12	99.50 12	98.96 20	98.79 52	99.55 53	99.33 32	98.29 12	99.75 1	97.96 18	99.15 24	99.95 17	99.61 6	99.17 32	99.06 28	99.81 22	99.84 25
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
sasdasda		97.31 76	97.81 95	96.72 76	96.20 102	99.45 68	98.21 86	91.60 112	99.22 47	95.39 50	98.48 60	90.95 141	99.16 46	97.66 134	99.05 29	99.76 41	99.90 7

DVP-MVS		99.45 2	99.54 7	99.35 1	99.72 6	99.76 6	99.63 12	98.37 2	99.63 7	99.03 3	98.95 39	99.98 2	99.60 7	99.60 7	99.05 29	99.74 51	99.79 45
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
MSP-MVS		99.34 7	99.52 10	99.14 7	99.68 12	99.75 9	99.64 8	98.31 9	99.44 21	98.10 13	99.28 18	99.98 2	99.30 35	99.34 23	99.05 29	99.81 22	99.79 45
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
canonicalmvs		97.31 76	97.81 95	96.72 76	96.20 102	99.45 68	98.21 86	91.60 112	99.22 47	95.39 50	98.48 60	90.95 141	99.16 46	97.66 134	99.05 29	99.76 41	99.90 7

OpenMVS	96.23 11	97.95 58	98.45 67	97.35 55	99.52 32	99.42 75	98.91 53	94.61 58	98.87 94	92.24 111	94.61 143	99.05 64	99.10 51	98.64 73	99.05 29	99.74 51	99.51 141

MGCFI-Net		97.26 81	97.79 97	96.64 82	96.17 104	99.43 73	98.14 90	91.52 117	99.23 45	95.16 56	98.48 60	90.87 143	99.07 54	97.59 140	99.02 34	99.76 41	99.91 6

Vis-MVSNet		96.16 117	98.22 77	93.75 132	95.33 136	99.70 17	97.27 119	90.85 128	98.30 138	85.51 150	95.72 132	96.45 90	93.69 198	98.70 70	99.00 35	99.84 12	99.69 98
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CANet		98.46 44	99.16 37	97.64 49	98.48 58	99.64 26	99.35 31	94.71 56	99.53 14	95.17 55	97.63 88	99.59 54	98.38 88	98.88 56	98.99 36	99.74 51	99.86 21

CDPH-MVS		98.41 45	99.10 40	97.61 50	99.32 42	99.36 83	99.49 21	96.15 44	98.82 104	91.82 113	98.41 65	99.66 51	99.10 51	98.93 49	98.97 37	99.75 46	99.58 124

DPE-MVS		99.39 5	99.55 6	99.20 4	99.63 20	99.71 15	99.66 6	98.33 6	99.29 37	98.40 11	99.64 5	99.98 2	99.31 33	99.56 9	98.96 38	99.85 10	99.70 94
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TSAR-MVS + ACMM		98.77 33	99.45 14	97.98 42	99.37 37	99.46 66	99.44 27	98.13 26	99.65 5	92.30 109	98.91 42	99.95 17	99.05 55	99.42 17	98.95 39	99.58 143	99.82 30

EPP-MVSNet		97.75 63	98.71 60	96.63 83	95.68 121	99.56 51	97.51 111	93.10 96	99.22 47	94.99 60	97.18 97	97.30 84	98.65 77	98.83 58	98.93 40	99.84 12	99.92 3

CHOSEN 280x420		97.99 57	99.24 34	96.53 85	98.34 60	99.61 38	98.36 79	89.80 146	99.27 40	95.08 58	99.81 1	98.58 68	98.64 78	99.02 43	98.92 41	98.93 191	99.48 145

CSCG		98.90 30	98.93 53	98.85 24	99.75 3	99.72 12	99.49 21	96.58 42	99.38 25	98.05 16	98.97 37	97.87 77	99.49 18	97.78 127	98.92 41	99.78 34	99.90 7

CHOSEN 1792x2688		96.41 110	96.99 126	95.74 104	98.01 67	99.72 12	97.70 107	90.78 131	99.13 67	90.03 124	87.35 200	95.36 106	98.33 89	98.59 81	98.91 43	99.59 139	99.87 18

MVS_111021_LR		98.67 37	99.41 21	97.81 45	99.37 37	99.53 55	98.51 67	95.52 47	99.27 40	94.85 61	99.56 8	99.69 50	99.04 56	99.36 20	98.88 44	99.60 133	99.58 124

MVS_0304		98.14 54	99.03 48	97.10 63	98.05 66	99.63 29	99.27 34	94.33 68	99.63 7	93.06 97	97.32 92	99.05 64	98.09 97	98.82 59	98.87 45	99.81 22	99.89 12

DVP-MVS++		99.41 4	99.64 1	99.14 7	99.69 7	99.75 9	99.64 8	98.33 6	99.67 4	98.10 13	99.66 4	99.99 1	99.33 30	99.62 5	98.86 46	99.74 51	99.90 7

CP-MVS		99.27 10	99.44 17	99.08 12	99.62 22	99.58 48	99.53 18	98.16 21	99.21 50	97.79 20	99.15 24	99.96 12	99.59 9	99.54 11	98.86 46	99.78 34	99.74 75

MAR-MVS		97.71 64	98.04 85	97.32 56	99.35 41	98.91 116	97.65 109	91.68 110	98.00 150	97.01 31	97.72 86	94.83 113	98.85 71	98.44 90	98.86 46	99.41 173	99.52 136
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
CS-MVS-test		98.58 42	99.42 20	97.60 51	98.52 57	99.91 1	98.60 64	94.60 60	99.37 27	94.62 65	99.40 14	99.16 61	99.39 26	99.36 20	98.85 49	99.90 3	99.92 3

SF-MVS		99.18 16	99.32 28	99.03 16	99.65 18	99.41 77	98.87 54	98.24 17	99.14 63	98.73 5	99.11 28	99.92 28	98.92 62	99.22 28	98.84 50	99.76 41	99.56 130

SED-MVS		99.44 3	99.58 4	99.28 3	99.69 7	99.76 6	99.62 14	98.35 3	99.51 17	99.05 2	99.60 6	99.98 2	99.28 37	99.61 6	98.83 51	99.70 85	99.77 58

MVS_111021_HR		98.59 41	99.36 24	97.68 47	99.42 35	99.61 38	98.14 90	94.81 53	99.31 34	95.00 59	99.51 9	99.79 45	99.00 59	98.94 48	98.83 51	99.69 88	99.57 129

CNLPA		99.03 27	99.05 44	99.01 19	99.27 43	99.22 102	99.03 48	97.98 32	99.34 32	99.00 4	98.25 71	99.71 49	99.31 33	98.80 60	98.82 53	99.48 162	99.17 165

FMVSNet2		96.64 105	97.50 103	95.63 107	93.81 156	97.98 166	98.09 93	90.87 127	98.99 84	93.48 91	93.17 160	95.25 108	97.89 104	98.63 74	98.80 54	99.68 96	99.67 104

MP-MVS		99.07 23	99.36 24	98.74 27	99.63 20	99.57 50	99.66 6	98.25 14	99.00 83	95.62 45	98.97 37	99.94 25	99.54 14	99.51 12	98.79 55	99.71 77	99.73 79
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ETV-MVS		98.05 55	99.25 33	96.65 80	95.61 124	99.61 38	98.26 85	93.52 85	98.90 93	93.74 88	99.32 17	99.20 59	98.90 65	99.21 29	98.72 56	99.87 8	99.79 45

TSAR-MVS + GP.		98.66 39	99.36 24	97.85 44	97.16 82	99.46 66	99.03 48	94.59 61	99.09 70	97.19 29	99.73 3	99.95 17	99.39 26	98.95 47	98.69 57	99.75 46	99.65 111

ACMMP_NAP		99.05 25	99.45 14	98.58 30	99.73 5	99.60 43	99.64 8	98.28 13	99.23 45	94.57 66	99.35 16	99.97 8	99.55 13	99.63 3	98.66 58	99.70 85	99.74 75

OMC-MVS		98.84 32	99.01 50	98.65 29	99.39 36	99.23 101	99.22 35	96.70 41	99.40 24	97.77 21	97.89 81	99.80 43	99.21 38	99.02 43	98.65 59	99.57 147	99.07 172

FMVSNet3		97.02 90	98.12 82	95.73 105	93.59 162	97.98 166	98.34 81	91.32 121	98.80 107	93.92 80	97.21 94	95.94 102	97.63 113	98.61 76	98.62 60	99.61 125	99.65 111

CNVR-MVS		99.23 14	99.28 31	99.17 5	99.65 18	99.34 88	99.46 24	98.21 19	99.28 38	98.47 8	98.89 44	99.94 25	99.50 16	99.42 17	98.61 61	99.73 59	99.52 136

baseline		97.45 73	98.70 61	95.99 100	95.89 110	99.36 83	98.29 82	91.37 120	99.21 50	92.99 99	98.40 66	96.87 89	97.96 102	98.60 79	98.60 62	99.42 172	99.86 21

MVS_Test		97.30 78	98.54 63	95.87 101	95.74 117	99.28 95	98.19 88	91.40 119	99.18 54	91.59 115	98.17 73	96.18 97	98.63 79	98.61 76	98.55 63	99.66 109	99.78 51

EPNet		98.05 55	98.86 55	97.10 63	99.02 48	99.43 73	98.47 70	94.73 55	99.05 78	95.62 45	98.93 40	97.62 81	95.48 169	98.59 81	98.55 63	99.29 182	99.84 25
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		95.33 135	97.09 121	93.27 147	95.23 137	98.39 155	95.49 161	92.58 99	97.71 165	83.00 165	94.44 146	93.28 131	93.92 195	97.79 126	98.54 65	99.41 173	99.45 147

casdiffmvs		96.93 93	97.43 109	96.34 91	95.70 119	99.50 62	97.75 105	93.22 93	98.98 85	92.64 102	94.97 139	91.71 139	98.93 61	98.62 75	98.52 66	99.82 16	99.72 89
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_Blended_VisFu		97.41 74	98.49 66	96.15 94	97.49 72	99.76 6	96.02 152	93.75 81	99.26 42	93.38 93	93.73 151	99.35 57	96.47 144	98.96 46	98.46 67	99.77 39	99.90 7

casdiffmvs_mvg		97.27 79	97.97 90	96.46 89	95.83 114	99.51 61	98.42 73	93.32 90	98.34 136	92.38 107	95.64 133	95.35 107	98.91 63	98.73 68	98.45 68	99.86 9	99.80 37
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DCV-MVSNet		97.56 69	98.36 69	96.62 84	96.44 93	98.36 157	98.37 77	91.73 109	99.11 68	94.80 62	98.36 68	96.28 95	98.60 81	98.12 102	98.44 69	99.76 41	99.87 18

baseline1		97.58 68	98.05 84	97.02 69	96.21 101	99.45 68	97.71 106	93.71 83	98.47 130	95.75 44	98.78 48	93.20 133	98.91 63	98.52 85	98.44 69	99.81 22	99.53 133

NCCC		99.05 25	99.08 41	99.02 18	99.62 22	99.38 79	99.43 28	98.21 19	99.36 30	97.66 23	97.79 82	99.90 33	99.45 22	99.17 32	98.43 71	99.77 39	99.51 141

PVSNet_BlendedMVS		97.51 71	97.71 98	97.28 58	98.06 64	99.61 38	97.31 117	95.02 51	99.08 72	95.51 47	98.05 75	90.11 146	98.07 98	98.91 52	98.40 72	99.72 67	99.78 51

PVSNet_Blended		97.51 71	97.71 98	97.28 58	98.06 64	99.61 38	97.31 117	95.02 51	99.08 72	95.51 47	98.05 75	90.11 146	98.07 98	98.91 52	98.40 72	99.72 67	99.78 51

train_agg		98.73 35	99.11 39	98.28 35	99.36 39	99.35 86	99.48 23	97.96 33	98.83 102	93.86 83	98.70 54	99.86 38	99.44 23	99.08 39	98.38 74	99.61 125	99.58 124

CDS-MVSNet		96.59 108	98.02 87	94.92 114	94.45 149	98.96 114	97.46 113	91.75 108	97.86 159	90.07 123	96.02 123	97.25 85	96.21 148	98.04 113	98.38 74	99.60 133	99.65 111
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HPM-MVS++		99.10 21	99.30 30	98.86 23	99.69 7	99.48 64	99.59 16	98.34 4	99.26 42	96.55 36	99.10 30	99.96 12	99.36 28	99.25 27	98.37 76	99.64 117	99.66 108

MCST-MVS		99.11 20	99.27 32	98.93 21	99.67 13	99.33 91	99.51 20	98.31 9	99.28 38	96.57 35	99.10 30	99.90 33	99.71 2	99.19 31	98.35 77	99.82 16	99.71 92

MSDG		98.27 50	98.29 71	98.24 36	99.20 44	99.22 102	99.20 36	97.82 35	99.37 27	94.43 72	95.90 126	97.31 83	99.12 49	98.76 64	98.35 77	99.67 104	99.14 169

test0.0.03 1		96.69 102	98.12 82	95.01 113	95.49 131	98.99 111	95.86 154	90.82 129	98.38 133	92.54 106	96.66 109	97.33 82	95.75 159	97.75 130	98.34 79	99.60 133	99.40 152

GBi-Net		96.98 91	98.00 88	95.78 102	93.81 156	97.98 166	98.09 93	91.32 121	98.80 107	93.92 80	97.21 94	95.94 102	97.89 104	98.07 108	98.34 79	99.68 96	99.67 104

test1		96.98 91	98.00 88	95.78 102	93.81 156	97.98 166	98.09 93	91.32 121	98.80 107	93.92 80	97.21 94	95.94 102	97.89 104	98.07 108	98.34 79	99.68 96	99.67 104

FMVSNet1		95.77 125	96.41 149	95.03 112	93.42 164	97.86 173	97.11 129	89.89 143	98.53 127	92.00 112	89.17 185	93.23 132	98.15 94	98.07 108	98.34 79	99.61 125	99.69 98

EIA-MVS		97.70 65	98.78 58	96.44 90	95.72 118	99.65 22	98.14 90	93.72 82	98.30 138	92.31 108	98.63 55	97.90 76	98.97 60	98.92 51	98.30 83	99.78 34	99.80 37

UGNet		97.66 66	99.07 43	96.01 99	97.19 81	99.65 22	97.09 130	93.39 87	99.35 31	94.40 74	98.79 47	99.59 54	94.24 189	98.04 113	98.29 84	99.73 59	99.80 37
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
IterMVS-LS		96.12 118	97.48 105	94.53 118	95.19 138	97.56 191	97.15 126	89.19 153	99.08 72	88.23 130	94.97 139	94.73 115	97.84 109	97.86 124	98.26 85	99.60 133	99.88 16
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Anonymous202405211			97.40 110		96.45 92	99.54 54	98.08 96	93.79 78	98.24 142		93.55 152	94.41 119	98.88 70	98.04 113	98.24 86	99.75 46	99.76 63

EPNet_dtu		96.30 113	98.53 64	93.70 135	98.97 49	98.24 161	97.36 115	94.23 70	98.85 97	79.18 187	99.19 21	98.47 70	94.09 191	97.89 122	98.21 87	98.39 197	98.85 181
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CPTT-MVS		99.14 19	99.20 36	99.06 14	99.58 25	99.53 55	99.45 25	97.80 36	99.19 53	98.32 12	98.58 57	99.95 17	99.60 7	99.28 26	98.20 88	99.64 117	99.69 98

HyFIR lowres test		95.99 121	96.56 136	95.32 110	97.99 68	99.65 22	96.54 141	88.86 155	98.44 131	89.77 127	84.14 210	97.05 87	99.03 57	98.55 83	98.19 89	99.73 59	99.86 21

diffmvs		96.83 95	97.33 113	96.25 92	95.76 116	99.34 88	98.06 97	93.22 93	99.43 22	92.30 109	96.90 103	89.83 151	98.55 83	98.00 116	98.14 90	99.64 117	99.70 94
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
TAPA-MVS	97.53 5	98.41 45	98.84 57	97.91 43	99.08 47	99.33 91	99.15 39	97.13 40	99.34 32	93.20 94	97.75 84	99.19 60	99.20 39	98.66 71	98.13 91	99.66 109	99.48 145
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PLC	97.93 2	99.02 28	98.94 52	99.11 10	99.46 34	99.24 100	99.06 46	97.96 33	99.31 34	99.16 1	97.90 80	99.79 45	99.36 28	98.71 69	98.12 92	99.65 113	99.52 136
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DPM-MVS		98.31 49	98.53 64	98.05 39	98.76 54	98.77 124	99.13 40	98.07 29	99.10 69	94.27 77	96.70 107	99.84 41	98.70 74	97.90 121	98.11 93	99.40 175	99.28 157

Anonymous20231211		97.10 87	97.06 124	97.14 62	96.32 95	99.52 58	98.16 89	93.76 79	98.84 101	95.98 41	90.92 173	94.58 118	98.90 65	97.72 132	98.10 94	99.71 77	99.75 71

gg-mvs-nofinetune		90.85 199	94.14 178	87.02 204	94.89 144	99.25 98	98.64 62	76.29 219	88.24 220	57.50 224	79.93 216	95.45 105	95.18 178	98.77 63	98.07 95	99.62 123	99.24 162

CANet_DTU		96.64 105	99.08 41	93.81 131	97.10 83	99.42 75	98.85 55	90.01 140	99.31 34	79.98 183	99.78 2	99.10 63	97.42 119	98.35 92	98.05 96	99.47 164	99.53 133

Fast-Effi-MVS+		95.38 133	96.52 139	94.05 128	94.15 151	99.14 106	97.24 122	86.79 176	98.53 127	87.62 137	94.51 144	87.06 160	98.76 73	98.60 79	98.04 97	99.72 67	99.77 58

GG-mvs-BLEND		69.11 217	98.13 81	35.26 221	3.49 231	98.20 163	94.89 172	2.38 227	98.42 132	5.82 232	96.37 118	98.60 67	5.97 227	98.75 66	97.98 98	99.01 190	98.61 183

Effi-MVS+		95.81 124	97.31 117	94.06 127	95.09 139	99.35 86	97.24 122	88.22 164	98.54 126	85.38 151	98.52 58	88.68 154	98.70 74	98.32 93	97.93 99	99.74 51	99.84 25

GeoE		95.98 123	97.24 119	94.51 119	95.02 141	99.38 79	98.02 98	87.86 170	98.37 134	87.86 135	92.99 165	93.54 128	98.56 82	98.61 76	97.92 100	99.73 59	99.85 24

MIMVSNet		94.49 153	97.59 102	90.87 186	91.74 188	98.70 133	94.68 181	78.73 213	97.98 151	83.71 159	97.71 87	94.81 114	96.96 128	97.97 117	97.92 100	99.40 175	98.04 195

DI_MVS_plusplus_trai		96.90 94	97.49 104	96.21 93	95.61 124	99.40 78	98.72 60	92.11 101	99.14 63	92.98 100	93.08 163	95.14 109	98.13 95	98.05 112	97.91 102	99.74 51	99.73 79

testgi		95.67 127	97.48 105	93.56 138	95.07 140	99.00 109	95.33 165	88.47 161	98.80 107	86.90 141	97.30 93	92.33 135	95.97 156	97.66 134	97.91 102	99.60 133	99.38 153

thres100view900		96.72 100	96.47 144	97.00 72	96.31 96	99.52 58	98.28 83	94.01 72	97.35 171	94.52 67	95.90 126	86.93 163	99.09 53	98.07 108	97.87 104	99.81 22	99.63 117

COLMAP_ROB	96.15 12	97.78 61	98.17 79	97.32 56	98.84 50	99.45 68	99.28 33	95.43 48	99.48 19	91.80 114	94.83 142	98.36 72	98.90 65	98.09 105	97.85 105	99.68 96	99.15 166
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AdaColmap		99.06 24	98.98 51	99.15 6	99.60 24	99.30 94	99.38 30	98.16 21	99.02 81	98.55 7	98.71 53	99.57 56	99.58 12	99.09 37	97.84 106	99.64 117	99.36 154

thres200		96.76 97	96.53 138	97.03 67	96.31 96	99.67 18	98.37 77	93.99 74	97.68 166	94.49 70	95.83 129	86.77 165	99.18 43	98.26 95	97.82 107	99.82 16	99.66 108

tfpn200view9		96.75 98	96.51 140	97.03 67	96.31 96	99.67 18	98.41 74	93.99 74	97.35 171	94.52 67	95.90 126	86.93 163	99.14 48	98.26 95	97.80 108	99.82 16	99.70 94

thres400		96.71 101	96.45 146	97.02 69	96.28 99	99.63 29	98.41 74	94.00 73	97.82 161	94.42 73	95.74 130	86.26 171	99.18 43	98.20 99	97.79 109	99.81 22	99.70 94

FC-MVSNet-train		97.04 89	97.91 92	96.03 98	96.00 107	98.41 153	96.53 143	93.42 86	99.04 80	93.02 98	98.03 77	94.32 121	97.47 118	97.93 119	97.77 110	99.75 46	99.88 16

baseline2		96.36 112	97.82 94	94.65 117	94.60 148	99.09 107	96.45 145	89.63 148	98.36 135	91.29 118	97.60 89	94.13 124	96.37 145	98.45 88	97.70 111	99.54 156	99.41 150

IterMVS-SCA-FT		94.89 142	97.87 93	91.42 174	94.86 145	97.70 177	97.24 122	84.88 190	98.93 90	75.74 199	94.26 147	98.25 73	96.69 135	98.52 85	97.68 112	99.10 189	99.73 79

thres600view7		96.69 102	96.43 148	97.00 72	96.28 99	99.67 18	98.41 74	93.99 74	97.85 160	94.29 76	95.96 124	85.91 174	99.19 40	98.26 95	97.63 113	99.82 16	99.73 79

PMMVS		97.52 70	98.39 68	96.51 87	95.82 115	98.73 131	97.80 102	93.05 97	98.76 114	94.39 75	99.07 33	97.03 88	98.55 83	98.31 94	97.61 114	99.43 170	99.21 164

IterMVS		94.81 144	97.71 98	91.42 174	94.83 146	97.63 184	97.38 114	85.08 187	98.93 90	75.67 200	94.02 148	97.64 79	96.66 138	98.45 88	97.60 115	98.90 192	99.72 89
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		95.74 126	98.04 85	93.06 149	93.92 152	99.16 104	97.90 99	88.16 166	99.07 77	82.02 171	98.02 78	94.32 121	96.74 134	98.53 84	97.56 116	99.61 125	99.62 119

gm-plane-assit		89.44 206	92.82 203	85.49 208	91.37 201	95.34 215	79.55 223	82.12 197	91.68 219	64.79 221	87.98 196	80.26 208	95.66 162	98.51 87	97.56 116	99.45 166	98.41 188

LGP-MVS_train		96.23 114	96.89 128	95.46 109	97.32 76	98.77 124	98.81 57	93.60 84	98.58 123	85.52 149	99.08 32	86.67 167	97.83 110	97.87 123	97.51 118	99.69 88	99.73 79

ACMMP		98.74 34	99.03 48	98.40 32	99.36 39	99.64 26	99.20 36	97.75 37	98.82 104	95.24 54	98.85 45	99.87 37	99.17 45	98.74 67	97.50 119	99.71 77	99.76 63
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
CR-MVSNet		94.57 152	97.34 112	91.33 177	94.90 143	98.59 140	97.15 126	79.14 209	97.98 151	80.42 179	96.59 114	93.50 130	96.85 131	98.10 103	97.49 120	99.50 161	99.15 166

PatchT		93.96 161	97.36 111	90.00 193	94.76 147	98.65 135	90.11 209	78.57 214	97.96 154	80.42 179	96.07 122	94.10 125	96.85 131	98.10 103	97.49 120	99.26 183	99.15 166

FC-MVSNet-test		96.07 119	97.94 91	93.89 129	93.60 161	98.67 134	96.62 140	90.30 139	98.76 114	88.62 128	95.57 136	97.63 80	94.48 185	97.97 117	97.48 122	99.71 77	99.52 136

UniMVSNet_ETH3D		93.15 172	92.33 205	94.11 126	93.91 153	98.61 139	94.81 176	90.98 126	97.06 180	87.51 138	82.27 214	76.33 220	97.87 108	94.79 202	97.47 123	99.56 150	99.81 35

PCF-MVS	97.50 6	98.18 53	98.35 70	97.99 41	98.65 55	99.36 83	98.94 52	98.14 25	98.59 122	93.62 89	96.61 111	99.76 48	99.03 57	97.77 128	97.45 124	99.57 147	98.89 180
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PatchMatch-RL		97.77 62	98.25 73	97.21 61	99.11 46	99.25 98	97.06 132	94.09 71	98.72 118	95.14 57	98.47 63	96.29 94	98.43 87	98.65 72	97.44 125	99.45 166	98.94 175

TAMVS		95.53 129	96.50 142	94.39 123	93.86 155	99.03 108	96.67 138	89.55 150	97.33 173	90.64 120	93.02 164	91.58 140	96.21 148	97.72 132	97.43 126	99.43 170	99.36 154

LTVRE_ROB	93.20 16	92.84 177	94.92 164	90.43 190	92.83 166	98.63 136	97.08 131	87.87 169	97.91 156	68.42 217	93.54 153	79.46 214	96.62 139	97.55 142	97.40 127	99.74 51	99.92 3
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
MVSTER		97.16 84	97.71 98	96.52 86	95.97 109	98.48 146	98.63 63	92.10 102	98.68 119	95.96 42	99.23 20	91.79 138	96.87 130	98.76 64	97.37 128	99.57 147	99.68 103

Baseline_NR-MVSNet		93.87 163	93.98 185	93.75 132	91.66 190	97.02 204	95.53 160	91.52 117	97.16 179	87.77 136	87.93 198	83.69 186	96.35 146	95.10 198	97.23 129	99.68 96	99.73 79

FMVSNet5		95.42 131	96.47 144	94.20 124	92.26 176	95.99 212	95.66 157	87.15 174	97.87 158	93.46 92	96.68 108	93.79 127	97.52 115	97.10 159	97.21 130	99.11 188	96.62 212

pm-mvs1		94.27 154	95.57 159	92.75 152	92.58 169	98.13 164	94.87 174	90.71 133	96.70 190	83.78 156	89.94 181	89.85 150	94.96 182	97.58 141	97.07 131	99.61 125	99.72 89

Fast-Effi-MVS+-dtu		95.38 133	98.20 78	92.09 160	93.91 153	98.87 118	97.35 116	85.01 189	99.08 72	81.09 175	98.10 74	96.36 93	95.62 164	98.43 91	97.03 132	99.55 152	99.50 143

TransMVSNet (Re)		93.45 168	94.08 181	92.72 153	92.83 166	97.62 187	94.94 170	91.54 116	95.65 208	83.06 164	88.93 188	83.53 188	94.25 188	97.41 146	97.03 132	99.67 104	98.40 191

DU-MVS		93.98 160	94.44 175	93.44 142	91.66 190	97.77 174	95.03 167	91.57 114	97.17 177	86.12 143	93.13 161	81.13 203	96.60 140	95.10 198	97.01 134	99.67 104	99.80 37

TSAR-MVS + COLMAP		96.79 96	96.55 137	97.06 65	97.70 71	98.46 148	99.07 45	96.23 43	99.38 25	91.32 117	98.80 46	85.61 176	98.69 76	97.64 138	96.92 135	99.37 177	99.06 173

CLD-MVS		96.74 99	96.51 140	97.01 71	96.71 90	98.62 137	98.73 59	94.38 67	98.94 88	94.46 71	97.33 91	87.03 161	98.07 98	97.20 155	96.87 136	99.72 67	99.54 132
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TranMVSNet+NR-MVSNet		93.67 166	94.14 178	93.13 148	91.28 204	97.58 189	95.60 159	91.97 105	97.06 180	84.05 152	90.64 178	82.22 198	96.17 151	94.94 201	96.78 137	99.69 88	99.78 51

RPMNet		94.66 146	97.16 120	91.75 170	94.98 142	98.59 140	97.00 133	78.37 215	97.98 151	83.78 156	96.27 119	94.09 126	96.91 129	97.36 148	96.73 138	99.48 162	99.09 171

UniMVSNet_NR-MVSNet		94.59 150	95.47 160	93.55 139	91.85 185	97.89 172	95.03 167	92.00 104	97.33 173	86.12 143	93.19 159	87.29 159	96.60 140	96.12 182	96.70 139	99.72 67	99.80 37

ET-MVSNet_ETH3D		96.17 116	96.99 126	95.21 111	88.53 214	98.54 143	98.28 83	92.61 98	98.85 97	93.60 90	99.06 34	90.39 145	98.63 79	95.98 187	96.68 140	99.61 125	99.41 150

ACMH	95.42 14	95.27 136	95.96 153	94.45 121	96.83 89	98.78 123	94.72 179	91.67 111	98.95 86	86.82 142	96.42 117	83.67 187	97.00 126	97.48 145	96.68 140	99.69 88	99.76 63
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OPM-MVS		96.22 115	95.85 157	96.65 80	97.75 69	98.54 143	99.00 51	95.53 46	96.88 184	89.88 125	95.95 125	86.46 170	98.07 98	97.65 137	96.63 142	99.67 104	98.83 182
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMP	96.25 10	96.62 107	96.72 132	96.50 88	96.96 85	98.75 128	97.80 102	94.30 69	98.85 97	93.12 96	98.78 48	86.61 168	97.23 123	97.73 131	96.61 143	99.62 123	99.71 92
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
dmvs_re		96.02 120	96.49 143	95.47 108	93.49 163	99.26 97	97.25 121	93.82 77	97.51 168	90.43 121	97.52 90	87.93 156	98.12 96	96.86 163	96.59 144	99.73 59	99.76 63

ACMH+	95.51 13	95.40 132	96.00 151	94.70 116	96.33 94	98.79 121	96.79 136	91.32 121	98.77 113	87.18 139	95.60 135	85.46 177	96.97 127	97.15 156	96.59 144	99.59 139	99.65 111

CP-MVSNet		93.25 171	94.00 184	92.38 155	91.65 192	97.56 191	94.38 188	89.20 152	96.05 202	83.16 163	89.51 183	81.97 199	96.16 152	96.43 172	96.56 146	99.71 77	99.89 12

HQP-MVS		96.37 111	96.58 135	96.13 95	97.31 78	98.44 150	98.45 71	95.22 49	98.86 95	88.58 129	98.33 69	87.00 162	97.67 112	97.23 153	96.56 146	99.56 150	99.62 119

FA-MVS(training)		96.52 109	98.29 71	94.45 121	95.88 112	99.52 58	97.66 108	81.47 198	98.94 88	93.79 87	95.54 137	99.11 62	98.29 90	98.89 54	96.49 148	99.63 122	99.52 136

PS-CasMVS		92.72 182	93.36 196	91.98 164	91.62 194	97.52 193	94.13 192	88.98 154	95.94 205	81.51 174	87.35 200	79.95 211	95.91 157	96.37 174	96.49 148	99.70 85	99.89 12

Anonymous20231206		90.70 201	93.93 186	86.92 205	90.21 211	96.79 207	90.30 208	86.61 180	96.05 202	69.25 215	88.46 192	84.86 183	85.86 213	97.11 158	96.47 150	99.30 181	97.80 199

MVS-HIRNet		92.51 185	95.97 152	88.48 201	93.73 159	98.37 156	90.33 207	75.36 221	98.32 137	77.78 193	89.15 186	94.87 112	95.14 179	97.62 139	96.39 151	98.51 194	97.11 205

DTE-MVSNet		92.42 190	92.85 201	91.91 167	90.87 207	96.97 205	94.53 187	89.81 144	95.86 207	81.59 173	88.83 189	77.88 218	95.01 181	94.34 205	96.35 152	99.64 117	99.73 79

ACMM	96.26 9	96.67 104	96.69 133	96.66 79	97.29 79	98.46 148	96.48 144	95.09 50	99.21 50	93.19 95	98.78 48	86.73 166	98.17 91	97.84 125	96.32 153	99.74 51	99.49 144
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EU-MVSNet		92.80 179	94.76 169	90.51 188	91.88 183	96.74 209	92.48 199	88.69 158	96.21 197	79.00 188	91.51 169	87.82 157	91.83 207	95.87 189	96.27 154	99.21 184	98.92 179

PEN-MVS		92.72 182	93.20 198	92.15 159	91.29 202	97.31 201	94.67 182	89.81 144	96.19 198	81.83 172	88.58 191	79.06 215	95.61 165	95.21 195	96.27 154	99.72 67	99.82 30

TinyColmap		94.00 159	94.35 176	93.60 136	95.89 110	98.26 159	97.49 112	88.82 156	98.56 125	83.21 162	91.28 172	80.48 207	96.68 136	97.34 149	96.26 156	99.53 158	98.24 192

test-mter		94.86 143	97.32 114	92.00 163	92.41 173	98.82 120	96.18 151	86.35 182	98.05 148	82.28 169	96.48 116	94.39 120	95.46 171	98.17 101	96.20 157	99.32 180	99.13 170

NR-MVSNet		94.01 158	94.51 173	93.44 142	92.56 170	97.77 174	95.67 156	91.57 114	97.17 177	85.84 146	93.13 161	80.53 206	95.29 175	97.01 160	96.17 158	99.69 88	99.75 71

tfpnnormal		93.85 165	94.12 180	93.54 140	93.22 165	98.24 161	95.45 162	91.96 106	94.61 211	83.91 154	90.74 175	81.75 201	97.04 125	97.49 144	96.16 159	99.68 96	99.84 25

USDC		94.26 155	94.83 167	93.59 137	96.02 105	98.44 150	97.84 100	88.65 159	98.86 95	82.73 168	94.02 148	80.56 205	96.76 133	97.28 152	96.15 160	99.55 152	98.50 186

thisisatest0530		97.23 82	98.25 73	96.05 96	95.60 126	99.59 45	96.96 134	93.23 91	99.17 55	92.60 104	98.75 51	96.19 96	98.17 91	98.19 100	96.10 161	99.72 67	99.77 58

tttt0517		97.23 82	98.24 76	96.04 97	95.60 126	99.60 43	96.94 135	93.23 91	99.15 60	92.56 105	98.74 52	96.12 99	98.17 91	98.21 98	96.10 161	99.73 59	99.78 51

test-LLR		95.50 130	97.32 114	93.37 144	95.49 131	98.74 129	96.44 146	90.82 129	98.18 143	82.75 166	96.60 112	94.67 116	95.54 167	98.09 105	96.00 163	99.20 185	98.93 176

TESTMET0.1,1		94.95 140	97.32 114	92.20 158	92.62 168	98.74 129	96.44 146	86.67 178	98.18 143	82.75 166	96.60 112	94.67 116	95.54 167	98.09 105	96.00 163	99.20 185	98.93 176

EG-PatchMatch MVS		92.45 186	93.92 187	90.72 187	92.56 170	98.43 152	94.88 173	84.54 192	97.18 176	79.55 185	86.12 207	83.23 191	93.15 202	97.22 154	96.00 163	99.67 104	99.27 160

UniMVSNet (Re)		94.58 151	95.34 161	93.71 134	92.25 177	98.08 165	94.97 169	91.29 125	97.03 182	87.94 133	93.97 150	86.25 172	96.07 153	96.27 179	95.97 166	99.72 67	99.79 45

anonymousdsp		93.12 173	95.86 156	89.93 195	91.09 205	98.25 160	95.12 166	85.08 187	97.44 170	73.30 207	90.89 174	90.78 144	95.25 177	97.91 120	95.96 167	99.71 77	99.82 30

WR-MVS_H		93.54 167	94.67 171	92.22 156	91.95 181	97.91 171	94.58 185	88.75 157	96.64 191	83.88 155	90.66 177	85.13 180	94.40 186	96.54 170	95.91 168	99.73 59	99.89 12

WR-MVS		93.43 170	94.48 174	92.21 157	91.52 197	97.69 179	94.66 183	89.98 141	96.86 185	83.43 160	90.12 179	85.03 181	93.94 194	96.02 186	95.82 169	99.71 77	99.82 30

IB-MVS	93.96 15	95.02 139	96.44 147	93.36 145	97.05 84	99.28 95	90.43 206	93.39 87	98.02 149	96.02 40	94.92 141	92.07 137	83.52 215	95.38 192	95.82 169	99.72 67	99.59 123
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
pmmvs6		91.90 197	92.53 204	91.17 180	91.81 186	97.63 184	93.23 194	88.37 163	93.43 216	80.61 177	77.32 218	87.47 158	94.12 190	96.58 168	95.72 171	98.88 193	99.53 133

MS-PatchMatch		95.99 121	97.26 118	94.51 119	97.46 73	98.76 127	97.27 119	86.97 175	99.09 70	89.83 126	93.51 155	97.78 78	96.18 150	97.53 143	95.71 172	99.35 178	98.41 188

MDTV_nov1_ep13		95.57 128	97.48 105	93.35 146	95.43 133	98.97 113	97.19 125	83.72 196	98.92 92	87.91 134	97.75 84	96.12 99	97.88 107	96.84 165	95.64 173	97.96 202	98.10 194

MIMVSNet1		88.61 207	90.68 209	86.19 207	81.56 221	95.30 216	87.78 215	85.98 184	94.19 214	72.30 213	78.84 217	78.90 216	90.06 208	96.59 167	95.47 174	99.46 165	95.49 214

RPSCF		97.61 67	98.16 80	96.96 74	98.10 63	99.00 109	98.84 56	93.76 79	99.45 20	94.78 63	99.39 15	99.31 58	98.53 85	96.61 166	95.43 175	97.74 204	97.93 198

pmmvs4		95.09 137	95.90 154	94.14 125	92.29 175	97.70 177	95.45 162	90.31 137	98.60 121	90.70 119	93.25 158	89.90 149	96.67 137	97.13 157	95.42 176	99.44 168	99.28 157

GA-MVS		93.93 162	96.31 150	91.16 181	93.61 160	98.79 121	95.39 164	90.69 134	98.25 141	73.28 208	96.15 121	88.42 155	94.39 187	97.76 129	95.35 177	99.58 143	99.45 147

v10		92.79 180	94.06 182	91.31 178	91.78 187	97.29 203	94.87 174	86.10 183	96.97 183	79.82 184	88.16 194	84.56 184	95.63 163	96.33 177	95.31 178	99.65 113	99.80 37

v1192		92.43 189	93.61 191	91.05 182	91.53 196	97.43 197	94.61 184	87.99 168	96.60 192	76.72 195	87.11 202	82.74 196	95.85 158	96.35 176	95.30 179	99.60 133	99.74 75

test_method		87.27 210	91.58 206	82.25 212	75.65 225	87.52 224	86.81 217	72.60 222	97.51 168	73.20 209	85.07 209	79.97 210	88.69 210	97.31 150	95.24 180	96.53 216	98.41 188

v1144		92.81 178	94.03 183	91.40 176	91.68 189	97.60 188	94.73 178	88.40 162	96.71 189	78.48 190	88.14 195	84.46 185	95.45 172	96.31 178	95.22 181	99.65 113	99.76 63

v1240		91.99 196	93.33 197	90.44 189	91.29 202	97.30 202	94.25 190	86.79 176	96.43 195	75.49 202	86.34 206	81.85 200	95.29 175	96.42 173	95.22 181	99.52 159	99.73 79

v144192		92.38 191	93.55 194	91.00 183	91.44 198	97.47 196	94.27 189	87.41 173	96.52 194	78.03 191	87.50 199	82.65 197	95.32 174	95.82 190	95.15 183	99.55 152	99.78 51

v1921920		92.36 193	93.57 192	90.94 184	91.39 200	97.39 199	94.70 180	87.63 172	96.60 192	76.63 196	86.98 203	82.89 194	95.75 159	96.26 180	95.14 184	99.55 152	99.73 79

test20.03		90.65 202	93.71 190	87.09 203	90.44 209	96.24 210	89.74 212	85.46 186	95.59 209	72.99 211	90.68 176	85.33 178	84.41 214	95.94 188	95.10 185	99.52 159	97.06 207

pmmvs5		92.71 184	94.27 177	90.90 185	91.42 199	97.74 176	93.23 194	86.66 179	95.99 204	78.96 189	91.45 170	83.44 189	95.55 166	97.30 151	95.05 186	99.58 143	98.93 176

v7n		91.61 198	92.95 199	90.04 192	90.56 208	97.69 179	93.74 193	85.59 185	95.89 206	76.95 194	86.60 205	78.60 217	93.76 197	97.01 160	94.99 187	99.65 113	99.87 18

v2v482		92.77 181	93.52 195	91.90 168	91.59 195	97.63 184	94.57 186	90.31 137	96.80 188	79.22 186	88.74 190	81.55 202	96.04 155	95.26 194	94.97 188	99.66 109	99.69 98

SCA		94.95 140	97.44 108	92.04 161	95.55 128	99.16 104	96.26 149	79.30 208	99.02 81	85.73 148	98.18 72	97.13 86	97.69 111	96.03 185	94.91 189	97.69 207	97.65 200

v8		92.87 176	93.87 189	91.72 172	92.05 179	97.50 194	94.79 177	88.20 165	96.85 186	80.11 182	90.01 180	82.86 195	95.48 169	95.15 197	94.90 190	99.66 109	99.80 37

V42		93.05 174	93.90 188	92.04 161	91.91 182	97.66 181	94.91 171	89.91 142	96.85 186	80.58 178	89.66 182	83.43 190	95.37 173	95.03 200	94.90 190	99.59 139	99.78 51

SixPastTwentyTwo		93.44 169	95.32 162	91.24 179	92.11 178	98.40 154	92.77 197	88.64 160	98.09 147	77.83 192	93.51 155	85.74 175	96.52 143	96.91 162	94.89 192	99.59 139	99.73 79

tpm		92.38 191	94.79 168	89.56 197	94.30 150	97.50 194	94.24 191	78.97 212	97.72 164	74.93 204	97.97 79	82.91 193	96.60 140	93.65 207	94.81 193	98.33 198	98.98 174

EPMVS		95.05 138	96.86 130	92.94 151	95.84 113	98.96 114	96.68 137	79.87 204	99.05 78	90.15 122	97.12 98	95.99 101	97.49 117	95.17 196	94.75 194	97.59 208	96.96 208

thisisatest0515		94.61 149	96.89 128	91.95 165	92.00 180	98.47 147	92.01 201	90.73 132	98.18 143	83.96 153	94.51 144	95.13 110	93.38 199	97.38 147	94.74 195	99.61 125	99.79 45

v148		92.36 193	92.88 200	91.75 170	91.63 193	97.66 181	92.64 198	90.55 135	96.09 200	83.34 161	88.19 193	80.00 209	92.74 203	93.98 206	94.58 196	99.58 143	99.69 98

TDRefinement		93.04 175	93.57 192	92.41 154	96.58 91	98.77 124	97.78 104	91.96 106	98.12 146	80.84 176	89.13 187	79.87 212	87.78 211	96.44 171	94.50 197	99.54 156	98.15 193

ADS-MVSNet		94.65 147	97.04 125	91.88 169	95.68 121	98.99 111	95.89 153	79.03 211	99.15 60	85.81 147	96.96 100	98.21 75	97.10 124	94.48 204	94.24 198	97.74 204	97.21 204

PatchmatchNet		94.70 145	97.08 123	91.92 166	95.53 129	98.85 119	95.77 155	79.54 206	98.95 86	85.98 145	98.52 58	96.45 90	97.39 120	95.32 193	94.09 199	97.32 210	97.38 203
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PM-MVS		89.55 205	90.30 210	88.67 200	87.06 215	95.60 213	90.88 204	84.51 193	96.14 199	75.75 198	86.89 204	63.47 226	94.64 184	96.85 164	93.89 200	99.17 187	99.29 156

pmmvs-eth3d		89.81 204	89.65 212	90.00 193	86.94 216	95.38 214	91.08 202	86.39 181	94.57 212	82.27 170	83.03 213	64.94 223	93.96 193	96.57 169	93.82 201	99.35 178	99.24 162

MDTV_nov1_ep13_2view		92.44 187	95.66 158	88.68 199	91.05 206	97.92 170	92.17 200	79.64 205	98.83 102	76.20 197	91.45 170	93.51 129	95.04 180	95.68 191	93.70 202	97.96 202	98.53 185

new_pmnet		90.45 203	92.84 202	87.66 202	88.96 212	96.16 211	88.71 214	84.66 191	97.56 167	71.91 214	85.60 208	86.58 169	93.28 200	96.07 184	93.54 203	98.46 195	94.39 216

N_pmnet		92.21 195	94.60 172	89.42 198	91.88 183	97.38 200	89.15 213	89.74 147	97.89 157	73.75 206	87.94 197	92.23 136	93.85 196	96.10 183	93.20 204	98.15 201	97.43 202

CostFormer		94.25 156	94.88 166	93.51 141	95.43 133	98.34 158	96.21 150	80.64 201	97.94 155	94.01 78	98.30 70	86.20 173	97.52 115	92.71 209	92.69 205	97.23 213	98.02 196

pmmvs3		88.19 208	91.27 207	84.60 210	85.60 218	93.66 218	85.68 218	81.13 199	92.36 218	63.66 223	89.51 183	77.10 219	93.22 201	96.37 174	92.40 206	98.30 199	97.46 201

tpmrst		93.86 164	95.88 155	91.50 173	95.69 120	98.62 137	95.64 158	79.41 207	98.80 107	83.76 158	95.63 134	96.13 98	97.25 121	92.92 208	92.31 207	97.27 211	96.74 209

MDA-MVSNet-bldmvs		87.84 209	89.22 213	86.23 206	81.74 220	96.77 208	83.74 219	89.57 149	94.50 213	72.83 212	96.64 110	64.47 225	92.71 204	81.43 220	92.28 208	96.81 215	98.47 187

Gipuma		81.40 213	81.78 216	80.96 214	83.21 219	85.61 225	79.73 222	76.25 220	97.33 173	64.21 222	55.32 222	55.55 227	86.04 212	92.43 212	92.20 209	96.32 218	93.99 217
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmnet_mix02		92.44 187	94.68 170	89.83 196	92.46 172	97.65 183	89.92 211	90.49 136	98.76 114	73.05 210	91.78 168	90.08 148	94.86 183	94.53 203	91.94 210	98.21 200	98.01 197

ambc			80.99 217		80.04 223	90.84 219	90.91 203		96.09 200	74.18 205	62.81 221	30.59 232	82.44 216	96.25 181	91.77 211	95.91 219	98.56 184

dps		94.63 148	95.31 163	93.84 130	95.53 129	98.71 132	96.54 141	80.12 203	97.81 163	97.21 28	96.98 99	92.37 134	96.34 147	92.46 211	91.77 211	97.26 212	97.08 206

tpm cat1		94.06 157	94.90 165	93.06 149	95.42 135	98.52 145	96.64 139	80.67 200	97.82 161	92.63 103	93.39 157	95.00 111	96.06 154	91.36 215	91.58 213	96.98 214	96.66 211

CMPMVS	70.31 18	90.74 200	91.06 208	90.36 191	97.32 76	97.43 197	92.97 196	87.82 171	93.50 215	75.34 203	83.27 212	84.90 182	92.19 206	92.64 210	91.21 214	96.50 217	94.46 215
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WB-MVS		81.36 214	89.93 211	71.35 217	88.65 213	87.85 223	71.46 225	88.12 167	96.23 196	32.21 229	92.61 166	83.00 192	56.27 224	91.92 214	89.43 215	91.39 223	88.49 220

new-patchmatchnet		86.12 211	87.30 214	84.74 209	86.92 217	95.19 217	83.57 220	84.42 194	92.67 217	65.66 218	80.32 215	64.72 224	89.41 209	92.33 213	89.21 216	98.43 196	96.69 210

PMMVS2		77.26 215	79.47 218	74.70 216	76.00 224	88.37 222	74.22 224	76.34 218	78.31 222	54.13 225	69.96 220	52.50 228	70.14 221	84.83 218	88.71 217	97.35 209	93.58 218

MVE	67.97 19	65.53 220	67.43 222	63.31 220	59.33 228	74.20 226	53.09 230	70.43 223	66.27 225	43.13 226	45.98 226	30.62 231	70.65 220	79.34 222	86.30 218	83.25 227	89.33 219
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				82.25 212	97.73 70	88.71 221	80.18 221	68.65 224	99.15 60	86.98 140	99.47 10	85.31 179	68.35 222	87.51 217	83.81 219	91.64 221

E-PMN		68.30 218	68.43 220	68.15 218	74.70 227	71.56 228	55.64 228	77.24 216	77.48 224	39.46 227	51.95 225	41.68 230	73.28 219	70.65 223	79.51 220	88.61 225	86.20 223

FPMVS		83.82 212	84.61 215	82.90 211	90.39 210	90.71 220	90.85 205	84.10 195	95.47 210	65.15 219	83.44 211	74.46 221	75.48 217	81.63 219	79.42 221	91.42 222	87.14 221

PMVS	72.60 17	76.39 216	77.66 219	74.92 215	81.04 222	69.37 229	68.47 226	80.54 202	85.39 221	65.07 220	73.52 219	72.91 222	65.67 223	80.35 221	76.81 222	88.71 224	85.25 224
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		68.12 219	68.11 221	68.14 219	75.51 226	71.76 227	55.38 229	77.20 217	77.78 223	37.79 228	53.59 223	43.61 229	74.72 218	67.05 224	76.70 223	88.27 226	86.24 222

testmvs		31.24 221	40.15 223	20.86 222	12.61 229	17.99 230	25.16 231	13.30 225	48.42 226	24.82 230	53.07 224	30.13 233	28.47 225	42.73 225	37.65 224	20.79 228	51.04 225

test123		26.75 222	34.25 224	18.01 223	7.93 230	17.18 231	24.85 232	12.36 226	44.83 227	16.52 231	41.80 227	18.10 234	28.29 226	33.08 226	34.79 225	18.10 229	49.95 226

uanet_test		0.00 223	0.00 225	0.00 224	0.00 232	0.00 232	0.00 233	0.00 228	0.00 228	0.00 233	0.00 228	0.00 235	0.00 228	0.00 227	0.00 226	0.00 230	0.00 227

sosnet-low-res		0.00 223	0.00 225	0.00 224	0.00 232	0.00 232	0.00 233	0.00 228	0.00 228	0.00 233	0.00 228	0.00 235	0.00 228	0.00 227	0.00 226	0.00 230	0.00 227

sosnet		0.00 223	0.00 225	0.00 224	0.00 232	0.00 232	0.00 233	0.00 228	0.00 228	0.00 233	0.00 228	0.00 235	0.00 228	0.00 227	0.00 226	0.00 230	0.00 227

TPM-MVS					99.57 26	98.90 117	98.79 58			96.52 37	98.62 56	99.91 31	97.56 114			99.44 168	99.28 157
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										69.05 216

9.14												99.79 45

SR-MVS					99.67 13			98.25 14				99.94 25

our_test_3					92.30 174	97.58 189	90.09 210

MTAPA										98.09 15		99.97 8

MTMP										98.46 10		99.96 12

Patchmatch-RL test							66.86 227

XVS					97.42 74	99.62 33	98.59 65			93.81 84		99.95 17				99.69 88

X-MVStestdata					97.42 74	99.62 33	98.59 65			93.81 84		99.95 17				99.69 88

mPP-MVS					99.53 30							99.89 35

NP-MVS									98.57 124

Patchmtry						98.59 140	97.15 126	79.14 209		80.42 179

DeepMVS_CX						96.85 206	87.43 216	89.27 151	98.30 138	75.55 201	95.05 138	79.47 213	92.62 205	89.48 216		95.18 220	95.96 213

High-resolution multi-view benchmark