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.

TSAR-MVS + MP.		98.49 9	98.78 8	98.15 19	98.14 50	99.17 33	99.34 6	97.18 29	98.44 5	95.72 19	97.84 16	99.28 12	98.87 7	99.05 1	98.05 26	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
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 19	99.43 47	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 8	97.60 2	98.87 4	99.89 3	98.67 17	99.02 2	98.26 17	99.36 61	99.61 6

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 20	99.90 2	98.93 5	98.99 4	98.42 11	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
SteuartSystems-ACMMP		98.38 14	98.71 10	97.99 23	99.34 20	99.46 10	99.34 6	97.33 24	97.31 35	94.25 29	98.06 13	99.17 19	98.13 31	98.98 5	98.46 9	99.55 18	99.54 11
Skip Steuart: Steuart Systems R&D Blog.
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 10	97.33 4	98.70 5	99.33 10	98.86 8	98.96 6	98.40 13	99.63 5	99.57 9
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DeepPCF-MVS	95.28 2	97.00 39	98.35 21	95.42 60	97.30 63	98.94 51	94.82 121	96.03 38	98.24 9	92.11 51	95.80 41	98.64 33	95.51 88	98.95 7	98.66 5	96.78 193	99.20 44

SMA-MVS		98.66 7	98.89 7	98.39 9	99.60 1	99.41 12	99.00 20	97.63 12	97.78 18	95.83 18	98.33 11	99.83 4	98.85 9	98.93 8	98.56 6	99.41 50	99.40 20
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 16	98.48 7	99.40 14	99.05 37	99.02 19	97.54 16	97.73 19	96.65 11	97.20 29	99.13 20	98.85 9	98.91 9	98.10 23	99.41 50	99.08 57

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 31	96.84 9	98.94 1	99.82 5	98.59 21	98.90 10	98.22 18	99.56 17	99.48 16
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DeepC-MVS_fast	96.13 1	98.13 20	98.27 25	97.97 24	99.16 26	99.03 43	99.05 18	97.24 26	98.22 10	94.17 31	95.82 40	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
ACMMP_NAP		98.20 18	98.49 13	97.85 25	99.50 4	99.40 13	99.26 11	97.64 11	97.47 33	92.62 46	97.59 20	99.09 22	98.71 15	98.82 12	97.86 40	99.40 53	99.19 45

CS-MVS		96.87 43	97.41 39	96.24 45	97.42 60	99.48 9	97.30 56	91.83 81	97.17 39	93.02 40	94.80 52	94.45 67	98.16 30	98.61 13	97.85 41	99.69 1	99.50 12

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 12	96.51 14	98.49 7	99.65 8	98.67 17	98.60 14	98.42 11	99.40 53	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
TSAR-MVS + ACMM		97.71 28	98.60 12	96.66 39	98.64 41	99.05 37	98.85 25	97.23 27	98.45 4	89.40 90	97.51 24	99.27 14	96.88 61	98.53 15	97.81 43	98.96 123	99.59 8

DELS-MVS		96.06 54	96.04 61	96.07 49	97.77 55	99.25 28	98.10 41	93.26 54	94.42 109	92.79 43	88.52 111	93.48 73	95.06 96	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
NCCC		98.10 21	98.05 30	98.17 18	99.38 18	99.05 37	99.00 20	97.53 17	98.04 14	95.12 24	94.80 52	99.18 18	98.58 22	98.49 17	97.78 44	99.39 55	98.98 74

APD-MVS		98.36 15	98.32 23	98.41 8	99.47 5	99.26 26	99.12 15	97.77 7	96.73 50	96.12 16	97.27 28	98.88 24	98.46 25	98.47 18	98.39 14	99.52 22	99.22 41
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CS-MVS-test		97.00 39	97.85 33	96.00 50	97.77 55	99.56 5	96.35 88	91.95 76	97.54 29	92.20 49	96.14 36	96.00 61	98.19 28	98.46 19	97.78 44	99.57 14	99.45 18

HPM-MVS++		98.34 16	98.47 15	98.18 16	99.46 8	99.15 34	99.10 16	97.69 8	97.67 24	94.93 26	97.62 19	99.70 7	98.60 20	98.45 20	97.46 53	99.31 68	99.26 35

IS_MVSNet		95.28 63	96.43 56	93.94 91	95.30 93	99.01 47	95.90 100	91.12 92	94.13 115	87.50 107	91.23 85	94.45 67	94.17 111	98.45 20	98.50 7	99.65 3	99.23 39

MP-MVS		98.09 22	98.30 24	97.84 26	99.34 20	99.19 32	99.23 13	97.40 19	97.09 43	93.03 39	97.58 22	98.85 25	98.57 23	98.44 22	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.
ETV-MVS		96.31 51	97.47 38	94.96 71	94.79 110	98.78 64	96.08 94	91.41 89	96.16 64	90.50 70	95.76 42	96.20 57	97.39 46	98.42 23	97.82 42	99.57 14	99.18 48

X-MVS		97.84 24	98.19 27	97.42 30	99.40 14	99.35 17	99.06 17	97.25 25	97.38 34	90.85 62	96.06 37	98.72 30	98.53 24	98.41 24	98.15 22	99.46 34	99.28 30

EC-MVSNet		96.49 49	97.63 34	95.16 64	94.75 113	98.69 71	97.39 55	88.97 121	96.34 59	92.02 52	96.04 38	96.46 51	98.21 26	98.41 24	97.96 32	99.61 6	99.55 10

MCST-MVS		98.20 18	98.36 19	98.01 22	99.40 14	99.05 37	99.00 20	97.62 13	97.59 28	93.70 33	97.42 27	99.30 11	98.77 13	98.39 26	97.48 52	99.59 7	99.31 29

DeepC-MVS	94.87 4	96.76 48	96.50 54	97.05 35	98.21 48	99.28 24	98.67 27	97.38 20	97.31 35	90.36 75	89.19 104	93.58 72	98.19 28	98.31 27	98.50 7	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
test2506		94.32 88	93.00 117	95.87 51	96.16 77	99.39 15	96.96 62	92.80 64	95.22 95	94.47 27	91.55 83	70.45 197	95.25 93	98.29 28	97.98 29	99.59 7	98.10 129

ECVR-MVS		94.14 90	92.96 118	95.52 58	96.16 77	99.39 15	96.96 62	92.80 64	95.22 95	92.38 48	81.48 156	80.31 152	95.25 93	98.29 28	97.98 29	99.59 7	98.05 130

CP-MVS		98.32 17	98.34 22	98.29 12	99.34 20	99.30 22	99.15 14	97.35 21	97.49 31	95.58 21	97.72 18	98.62 34	98.82 11	98.29 28	97.67 47	99.51 27	99.28 30

test1111		93.94 95	92.78 119	95.29 63	96.14 79	99.42 11	96.79 73	92.85 63	95.08 99	91.39 57	80.69 161	79.86 155	95.00 97	98.28 31	98.00 28	99.58 11	98.11 128

SD-MVS		98.52 8	98.77 9	98.23 15	98.15 49	99.26 26	98.79 26	97.59 15	98.52 3	96.25 15	97.99 15	99.75 6	99.01 3	98.27 32	97.97 31	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
HFP-MVS		98.48 10	98.62 11	98.32 11	99.39 17	99.33 21	99.27 10	97.42 18	98.27 7	95.25 23	98.34 10	98.83 26	99.08 1	98.26 33	98.08 25	99.48 30	99.26 35

ACMMPR		98.40 12	98.49 13	98.28 13	99.41 13	99.40 13	99.36 4	97.35 21	98.30 6	95.02 25	97.79 17	98.39 37	99.04 2	98.26 33	98.10 23	99.50 29	99.22 41

CANet		96.84 45	97.20 41	96.42 40	97.92 53	99.24 30	98.60 29	93.51 51	97.11 42	93.07 36	91.16 86	97.24 45	96.21 74	98.24 35	98.05 26	99.22 84	99.35 24

PGM-MVS		97.81 25	98.11 28	97.46 29	99.55 3	99.34 20	99.32 9	94.51 45	96.21 63	93.07 36	98.05 14	97.95 42	98.82 11	98.22 36	97.89 39	99.48 30	99.09 56

Vis-MVSNet (Re-imp)		94.46 83	96.24 58	92.40 110	95.23 98	98.64 77	95.56 108	90.99 93	94.42 109	85.02 117	90.88 92	94.65 66	88.01 180	98.17 37	98.37 16	99.57 14	98.53 105

SF-MVS		98.39 13	98.45 17	98.33 10	99.45 9	99.05 37	98.27 37	97.65 9	97.73 19	97.02 7	98.18 12	99.25 15	98.11 32	98.15 38	97.62 48	99.45 38	99.19 45

MVS_0304		96.31 51	96.91 49	95.62 55	97.21 65	99.20 31	98.55 31	93.10 59	97.04 45	89.73 84	90.30 96	96.35 53	95.71 81	98.14 39	97.93 37	99.38 56	99.40 20

UA-Net		93.96 94	95.95 62	91.64 119	96.06 80	98.59 81	95.29 111	90.00 103	91.06 159	82.87 125	90.64 93	98.06 40	86.06 191	98.14 39	98.20 19	99.58 11	96.96 165

PVSNet_Blended_VisFu		94.77 75	95.54 67	93.87 93	96.48 72	98.97 49	94.33 130	91.84 79	94.93 101	90.37 74	85.04 137	94.99 64	90.87 158	98.12 41	97.30 60	99.30 70	99.45 18

MVS_111021_HR		97.04 38	98.20 26	95.69 54	98.44 45	99.29 23	96.59 80	93.20 57	97.70 22	89.94 82	98.46 8	96.89 47	96.71 65	98.11 42	97.95 33	99.27 74	99.01 70

PVSNet_BlendedMVS		95.41 61	95.28 73	95.57 56	97.42 60	99.02 45	95.89 102	93.10 59	96.16 64	93.12 34	91.99 74	85.27 126	94.66 102	98.09 43	97.34 58	99.24 78	99.08 57

PVSNet_Blended		95.41 61	95.28 73	95.57 56	97.42 60	99.02 45	95.89 102	93.10 59	96.16 64	93.12 34	91.99 74	85.27 126	94.66 102	98.09 43	97.34 58	99.24 78	99.08 57

MVS_111021_LR		97.16 36	98.01 31	96.16 46	98.47 43	98.98 48	96.94 64	93.89 48	97.64 26	91.44 55	98.89 3	96.41 52	97.20 51	98.02 45	97.29 62	99.04 117	98.85 89

train_agg		97.65 29	98.06 29	97.18 33	98.94 32	98.91 56	98.98 24	97.07 31	96.71 51	90.66 68	97.43 26	99.08 23	98.20 27	97.96 46	97.14 64	99.22 84	99.19 45

3Dnovator+	93.91 7	97.23 35	97.22 40	97.24 32	98.89 36	98.85 61	98.26 38	93.25 56	97.99 15	95.56 22	90.01 100	98.03 41	98.05 34	97.91 47	98.43 10	99.44 44	99.35 24

3Dnovator	93.79 8	97.08 37	97.20 41	96.95 37	99.09 28	99.03 43	98.20 39	93.33 52	97.99 15	93.82 32	90.61 94	96.80 49	97.82 37	97.90 48	98.78 3	99.47 33	99.26 35

PHI-MVS		97.78 26	98.44 18	97.02 36	98.73 38	99.25 28	98.11 40	95.54 39	96.66 53	92.79 43	98.52 6	99.38 9	97.50 45	97.84 49	98.39 14	99.45 38	99.03 67

gg-mvs-nofinetune		86.17 194	88.57 168	83.36 201	93.44 137	98.15 96	96.58 81	72.05 216	74.12 220	49.23 224	64.81 215	90.85 86	89.90 173	97.83 50	96.84 72	98.97 121	97.41 152

CDPH-MVS		96.84 45	97.49 36	96.09 47	98.92 34	98.85 61	98.61 28	95.09 41	96.00 71	87.29 108	95.45 46	97.42 43	97.16 52	97.83 50	97.94 34	99.44 44	98.92 80

EIA-MVS		95.50 56	96.19 59	94.69 81	94.83 109	98.88 60	95.93 99	91.50 88	94.47 108	89.43 88	93.14 63	92.72 77	97.05 57	97.82 52	97.13 65	99.43 47	99.15 51

FA-MVS(training)		93.94 95	95.16 76	92.53 109	94.87 108	98.57 82	95.42 110	79.49 195	95.37 87	90.98 60	86.54 124	94.26 69	95.44 90	97.80 53	95.19 118	98.97 121	98.38 116

QAPM		96.78 47	97.14 44	96.36 42	99.05 29	99.14 35	98.02 43	93.26 54	97.27 37	90.84 65	91.16 86	97.31 44	97.64 43	97.70 54	98.20 19	99.33 63	99.18 48

CHOSEN 280x420		95.46 59	97.01 45	93.66 97	97.28 64	97.98 100	96.40 86	85.39 161	96.10 68	91.07 59	96.53 32	96.34 55	95.61 85	97.65 55	96.95 69	96.21 194	97.49 149

TSAR-MVS + GP.		97.45 31	98.36 19	96.39 41	95.56 87	98.93 53	97.74 49	93.31 53	97.61 27	94.24 30	98.44 9	99.19 17	98.03 35	97.60 56	97.41 55	99.44 44	99.33 26

MVSTER		94.89 69	95.07 80	94.68 82	94.71 115	96.68 131	97.00 60	90.57 98	95.18 97	93.05 38	95.21 47	86.41 116	93.72 121	97.59 57	95.88 98	99.00 118	98.50 107

Vis-MVSNet		92.77 114	95.00 82	90.16 138	94.10 127	98.79 63	94.76 123	88.26 128	92.37 144	79.95 140	88.19 113	91.58 81	84.38 201	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
LS3D		95.46 59	95.14 77	95.84 52	97.91 54	98.90 58	98.58 30	97.79 5	97.07 44	83.65 123	88.71 107	88.64 104	97.82 37	97.49 59	97.42 54	99.26 77	97.72 143

casdiffmvs_mvg		94.55 80	94.26 92	94.88 73	94.96 105	98.51 83	97.11 58	91.82 82	94.28 112	89.20 94	86.60 122	86.85 112	96.56 69	97.47 60	97.25 63	99.64 4	98.83 91
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MSLP-MVS++		98.04 23	97.93 32	98.18 16	99.10 27	99.09 36	98.34 36	96.99 32	97.54 29	96.60 12	94.82 51	98.45 35	98.89 6	97.46 61	98.77 4	99.17 94	99.37 22

casdiffmvs		94.38 87	94.15 98	94.64 83	94.70 117	98.51 83	96.03 97	91.66 84	95.70 80	89.36 91	86.48 126	85.03 131	96.60 68	97.40 62	97.30 60	99.52 22	98.67 97
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CANet_DTU		93.92 97	96.57 53	90.83 129	95.63 85	98.39 87	96.99 61	87.38 137	96.26 61	71.97 183	96.31 34	93.02 74	94.53 105	97.38 63	96.83 73	98.49 164	97.79 136

EPP-MVSNet		95.27 64	96.18 60	94.20 89	94.88 107	98.64 77	94.97 117	90.70 96	95.34 88	89.67 86	91.66 81	93.84 70	95.42 91	97.32 64	97.00 67	99.58 11	99.47 17

ACMMP		97.37 33	97.48 37	97.25 31	98.88 37	99.28 24	98.47 34	96.86 34	97.04 45	92.15 50	97.57 23	96.05 60	97.67 40	97.27 65	95.99 94	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
OpenMVS	92.33 11	95.50 56	95.22 75	95.82 53	98.98 30	98.97 49	97.67 50	93.04 62	94.64 105	89.18 95	84.44 142	94.79 65	96.79 62	97.23 66	97.61 49	99.24 78	98.88 85

gm-plane-assit		83.26 204	85.29 201	80.89 204	89.52 182	89.89 215	70.26 221	78.24 197	77.11 218	58.01 221	74.16 188	66.90 211	90.63 164	97.20 67	96.05 91	98.66 154	95.68 181

CPTT-MVS		97.78 26	97.54 35	98.05 21	98.91 35	99.05 37	99.00 20	96.96 33	97.14 41	95.92 17	95.50 44	98.78 28	98.99 4	97.20 67	96.07 89	98.54 161	99.04 66

AdaColmap		97.53 30	96.93 47	98.24 14	99.21 23	98.77 65	98.47 34	97.34 23	96.68 52	96.52 13	95.11 49	96.12 58	98.72 14	97.19 69	96.24 85	99.17 94	98.39 115

OMC-MVS		97.00 39	96.92 48	97.09 34	98.69 39	98.66 74	97.85 47	95.02 42	98.09 13	94.47 27	93.15 62	96.90 46	97.38 47	97.16 70	96.82 74	99.13 101	97.65 144

PLC	94.95 3	97.37 33	96.77 51	98.07 20	98.97 31	98.21 92	97.94 46	96.85 35	97.66 25	97.58 3	93.33 61	96.84 48	98.01 36	97.13 71	96.20 87	99.09 106	98.01 131
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DPM-MVS		96.86 44	96.82 50	96.91 38	98.08 51	98.20 93	98.52 33	97.20 28	97.24 38	91.42 56	91.84 78	98.45 35	97.25 50	97.07 72	97.40 56	98.95 124	97.55 147

baseline1		94.59 79	94.47 87	94.72 80	95.16 100	97.97 101	96.07 95	91.94 77	94.86 102	89.98 80	91.60 82	85.87 122	95.64 83	97.07 72	96.90 70	99.52 22	97.06 164

EPNet		96.27 53	96.97 46	95.46 59	98.47 43	98.28 89	97.41 53	93.67 49	95.86 76	92.86 42	97.51 24	93.79 71	91.76 143	97.03 74	97.03 66	98.61 157	99.28 30
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tttt0517		94.52 82	95.44 71	93.44 101	94.51 120	98.68 72	94.61 126	90.72 94	95.61 84	86.84 112	93.78 57	89.26 98	94.74 99	97.02 75	94.86 127	99.20 91	98.87 87

thisisatest0530		94.54 81	95.47 68	93.46 100	94.51 120	98.65 76	94.66 124	90.72 94	95.69 82	86.90 111	93.80 56	89.44 95	94.74 99	96.98 76	94.86 127	99.19 92	98.85 89

tfpn200view9		93.64 102	92.57 122	94.89 72	95.33 91	98.94 51	96.82 69	92.31 68	92.63 135	88.29 99	87.21 115	78.01 163	97.12 55	96.82 77	95.85 99	99.45 38	98.56 102

thres600view7		93.49 107	92.37 133	94.79 78	95.42 88	98.93 53	96.58 81	92.31 68	93.04 129	87.88 104	86.62 121	76.94 170	97.09 56	96.82 77	95.63 104	99.45 38	98.63 99

thres200		93.62 103	92.54 123	94.88 73	95.36 90	98.93 53	96.75 75	92.31 68	92.84 132	88.28 101	86.99 117	77.81 166	97.13 53	96.82 77	95.92 95	99.45 38	98.49 108

MAR-MVS		95.50 56	95.60 65	95.39 61	98.67 40	98.18 95	95.89 102	89.81 109	94.55 107	91.97 53	92.99 64	90.21 91	97.30 49	96.79 80	97.49 51	98.72 147	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
FMVSNet2		93.30 110	93.36 113	93.22 105	91.34 159	95.86 155	96.22 89	88.24 129	95.15 98	89.92 83	81.64 154	89.36 96	94.40 108	96.77 81	96.98 68	99.21 88	97.79 136

thres400		93.56 105	92.43 130	94.87 75	95.40 89	98.91 56	96.70 77	92.38 67	92.93 131	88.19 103	86.69 120	77.35 167	97.13 53	96.75 82	95.85 99	99.42 49	98.56 102

CNLPA		96.90 42	96.28 57	97.64 28	98.56 42	98.63 79	96.85 68	96.60 36	97.73 19	97.08 6	89.78 102	96.28 56	97.80 39	96.73 83	96.63 76	98.94 125	98.14 127

CHOSEN 1792x2688		92.66 116	92.49 126	92.85 107	97.13 66	98.89 59	95.90 100	88.50 127	95.32 89	83.31 124	71.99 200	88.96 102	94.10 113	96.69 84	96.49 78	98.15 174	99.10 54

UGNet		94.92 68	96.63 52	92.93 106	96.03 81	98.63 79	94.53 127	91.52 87	96.23 62	90.03 79	92.87 67	96.10 59	86.28 190	96.68 85	96.60 77	99.16 97	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
Effi-MVS+		92.93 113	93.86 102	91.86 115	94.07 128	98.09 98	95.59 107	85.98 153	94.27 113	79.54 144	91.12 89	81.81 148	96.71 65	96.67 86	96.06 90	99.27 74	98.98 74

Effi-MVS+-dtu		91.78 125	93.59 109	89.68 146	92.44 150	97.11 118	94.40 129	84.94 169	92.43 140	75.48 165	91.09 90	83.75 139	93.55 124	96.61 87	95.47 109	97.24 189	98.67 97

TAPA-MVS	94.18 5	96.38 50	96.49 55	96.25 43	98.26 47	98.66 74	98.00 44	94.96 43	97.17 39	89.48 87	92.91 66	96.35 53	97.53 44	96.59 88	95.90 97	99.28 72	97.82 135
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchMatch-RL		94.69 77	94.41 88	95.02 67	97.63 59	98.15 96	94.50 128	91.99 74	95.32 89	91.31 58	95.47 45	83.44 141	96.02 77	96.56 89	95.23 116	98.69 150	96.67 172

Fast-Effi-MVS+		91.87 122	92.08 137	91.62 121	92.91 144	97.21 117	94.93 118	84.60 173	93.61 123	81.49 134	83.50 147	78.95 158	96.62 67	96.55 90	96.22 86	99.16 97	98.51 106

FC-MVSNet-train		93.85 98	93.91 100	93.78 95	94.94 106	96.79 128	94.29 131	91.13 91	93.84 120	88.26 102	90.40 95	85.23 128	94.65 104	96.54 91	95.31 113	99.38 56	99.28 30

GBi-Net		93.81 99	94.18 94	93.38 102	91.34 159	95.86 155	96.22 89	88.68 123	95.23 92	90.40 71	86.39 127	91.16 82	94.40 108	96.52 92	96.30 81	99.21 88	97.79 136

test1		93.81 99	94.18 94	93.38 102	91.34 159	95.86 155	96.22 89	88.68 123	95.23 92	90.40 71	86.39 127	91.16 82	94.40 108	96.52 92	96.30 81	99.21 88	97.79 136

FMVSNet1		91.54 130	90.93 151	92.26 112	90.35 169	95.27 177	95.22 113	87.16 140	91.37 156	87.62 106	75.45 177	83.84 138	94.43 106	96.52 92	96.30 81	98.82 136	97.74 142

MVS_Test		94.82 71	95.66 64	93.84 94	94.79 110	98.35 88	96.49 84	89.10 120	96.12 67	87.09 110	92.58 69	90.61 88	96.48 70	96.51 95	96.89 71	99.11 104	98.54 104

GG-mvs-BLEND		66.17 216	94.91 83	32.63 221	1.32 230	96.64 132	91.40 178	0.85 227	94.39 111	2.20 231	90.15 99	95.70 62	2.27 227	96.39 96	95.44 110	97.78 182	95.68 181

thres100view900		93.55 106	92.47 129	94.81 77	95.33 91	98.74 66	96.78 74	92.30 71	92.63 135	88.29 99	87.21 115	78.01 163	96.78 63	96.38 97	95.92 95	99.38 56	98.40 114

baseline2		93.01 112	94.17 96	91.64 119	92.83 146	97.49 108	93.40 142	87.53 135	93.67 122	86.07 113	91.83 79	86.58 113	91.36 147	96.38 97	95.06 121	98.67 151	98.20 125

test-mter		90.95 135	93.54 112	87.93 171	90.28 170	96.80 125	91.44 177	82.68 185	92.15 149	74.37 176	89.57 103	88.23 109	90.88 157	96.37 99	94.31 145	97.93 181	97.37 153

LGP-MVS_train		94.12 91	94.62 84	93.53 98	96.44 73	97.54 106	97.40 54	91.84 79	94.66 104	81.09 136	95.70 43	83.36 142	95.10 95	96.36 100	95.71 103	99.32 65	99.03 67

DI_MVS_plusplus_trai		94.01 93	93.63 107	94.44 85	94.54 119	98.26 91	97.51 52	90.63 97	95.88 75	89.34 92	80.54 163	89.36 96	95.48 89	96.33 101	96.27 84	99.17 94	98.78 95

TSAR-MVS + COLMAP		94.79 73	94.51 86	95.11 65	96.50 71	97.54 106	97.99 45	94.54 44	97.81 17	85.88 114	96.73 31	81.28 151	96.99 58	96.29 102	95.21 117	98.76 146	96.73 171

OPM-MVS		93.61 104	92.43 130	95.00 68	96.94 68	97.34 112	97.78 48	94.23 46	89.64 172	85.53 115	88.70 108	82.81 144	96.28 73	96.28 103	95.00 125	99.24 78	97.22 157
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
anonymousdsp		88.90 165	91.00 150	86.44 190	88.74 201	95.97 150	90.40 190	82.86 183	88.77 179	67.33 203	81.18 158	81.44 150	90.22 169	96.23 104	94.27 146	99.12 103	99.16 50

GeoE		92.52 118	92.64 121	92.39 111	93.96 129	97.76 103	96.01 98	85.60 158	93.23 127	83.94 120	81.56 155	84.80 132	95.63 84	96.22 105	95.83 101	99.19 92	99.07 61

ACMM	92.75 10	94.41 86	93.84 103	95.09 66	96.41 74	96.80 125	94.88 120	93.54 50	96.41 58	90.16 76	92.31 72	83.11 143	96.32 72	96.22 105	94.65 132	99.22 84	97.35 154
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CSCG		97.44 32	97.18 43	97.75 27	99.47 5	99.52 8	98.55 31	95.41 40	97.69 23	95.72 19	94.29 55	95.53 63	98.10 33	96.20 107	97.38 57	99.24 78	99.62 4

test-LLR		91.62 128	93.56 110	89.35 150	93.31 140	96.57 134	92.02 171	87.06 141	92.34 145	75.05 172	90.20 97	88.64 104	90.93 154	96.19 108	94.07 149	97.75 184	96.90 168

TESTMET0.1,1		91.07 134	93.56 110	88.17 161	90.43 166	96.57 134	92.02 171	82.83 184	92.34 145	75.05 172	90.20 97	88.64 104	90.93 154	96.19 108	94.07 149	97.75 184	96.90 168

MSDG		94.82 71	93.73 105	96.09 47	98.34 46	97.43 111	97.06 59	96.05 37	95.84 77	90.56 69	86.30 131	89.10 101	95.55 87	96.13 110	95.61 105	99.00 118	95.73 180

diffmvs		94.31 89	94.21 93	94.42 86	94.64 118	98.28 89	96.36 87	91.56 85	96.77 49	88.89 98	88.97 105	84.23 135	96.01 78	96.05 111	96.41 80	99.05 116	98.79 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
FMVSNet3		93.79 101	94.17 96	93.35 104	91.21 162	95.99 148	96.62 78	88.68 123	95.23 92	90.40 71	86.39 127	91.16 82	94.11 112	95.96 112	96.67 75	99.07 109	97.79 136

EPNet_dtu		92.45 119	95.02 81	89.46 147	98.02 52	95.47 170	94.79 122	92.62 66	94.97 100	70.11 194	94.76 54	92.61 78	84.07 204	95.94 113	95.56 106	97.15 190	95.82 179
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CDS-MVSNet		92.77 114	93.60 108	91.80 117	92.63 148	96.80 125	95.24 112	89.14 119	90.30 169	84.58 118	86.76 118	90.65 87	90.42 166	95.89 114	96.49 78	98.79 143	98.32 121
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HyFIR lowres test		92.03 120	91.55 145	92.58 108	97.13 66	98.72 68	94.65 125	86.54 146	93.58 124	82.56 127	67.75 211	90.47 89	95.67 82	95.87 115	95.54 107	98.91 129	98.93 79

GA-MVS		89.28 158	90.75 154	87.57 178	91.77 154	96.48 136	92.29 163	87.58 134	90.61 166	65.77 205	84.48 141	76.84 171	89.46 174	95.84 116	93.68 159	98.52 162	97.34 155

MIMVSNet		88.99 164	91.07 149	86.57 189	86.78 210	95.62 163	91.20 183	75.40 210	90.65 165	76.57 157	84.05 144	82.44 147	91.01 153	95.84 116	95.38 111	98.48 165	93.50 202

pm-mvs1		89.19 161	89.02 164	89.38 149	90.40 167	95.74 162	92.05 169	88.10 131	86.13 198	77.70 149	73.72 191	79.44 157	88.97 177	95.81 118	94.51 141	99.08 107	97.78 141

sasdasda		95.25 65	95.45 69	95.00 68	95.27 95	98.72 68	96.89 65	89.82 107	96.51 54	90.84 65	93.72 58	86.01 119	97.66 41	95.78 119	97.94 34	99.54 19	99.50 12

canonicalmvs		95.25 65	95.45 69	95.00 68	95.27 95	98.72 68	96.89 65	89.82 107	96.51 54	90.84 65	93.72 58	86.01 119	97.66 41	95.78 119	97.94 34	99.54 19	99.50 12

baseline		94.83 70	95.82 63	93.68 96	94.75 113	97.80 102	96.51 83	88.53 126	97.02 47	89.34 92	92.93 65	92.18 79	94.69 101	95.78 119	96.08 88	98.27 172	98.97 78

Fast-Effi-MVS+-dtu		91.19 133	93.64 106	88.33 159	92.19 152	96.46 137	93.99 134	81.52 190	92.59 137	71.82 184	92.17 73	85.54 124	91.68 144	95.73 122	94.64 133	98.80 141	98.34 118

ACMP	92.88 9	94.43 84	94.38 89	94.50 84	96.01 82	97.69 104	95.85 105	92.09 73	95.74 79	89.12 96	95.14 48	82.62 146	94.77 98	95.73 122	94.67 131	99.14 100	99.06 62
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
FC-MVSNet-test		91.63 127	93.82 104	89.08 151	92.02 153	96.40 140	93.26 145	87.26 138	93.72 121	77.26 152	88.61 110	89.86 93	85.50 194	95.72 124	95.02 123	99.16 97	97.44 151

MGCFI-Net		95.12 67	95.39 72	94.79 78	95.24 97	98.68 72	96.80 72	89.72 111	96.48 56	90.11 78	93.64 60	85.86 123	97.36 48	95.69 125	97.92 38	99.53 21	99.49 15

tfpnnormal		88.50 168	87.01 190	90.23 136	91.36 158	95.78 161	92.74 152	90.09 102	83.65 207	76.33 160	71.46 203	69.58 203	91.84 141	95.54 126	94.02 151	99.06 112	99.03 67

DCV-MVSNet		94.76 76	95.12 79	94.35 87	95.10 103	95.81 159	96.46 85	89.49 115	96.33 60	90.16 76	92.55 70	90.26 90	95.83 80	95.52 127	96.03 92	99.06 112	99.33 26

CLD-MVS		94.79 73	94.36 90	95.30 62	95.21 99	97.46 109	97.23 57	92.24 72	96.43 57	91.77 54	92.69 68	84.31 134	96.06 75	95.52 127	95.03 122	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
HQP-MVS		94.43 84	94.57 85	94.27 88	96.41 74	97.23 116	96.89 65	93.98 47	95.94 73	83.68 122	95.01 50	84.46 133	95.58 86	95.47 129	94.85 130	99.07 109	99.00 71

PMMVS		94.61 78	95.56 66	93.50 99	94.30 124	96.74 129	94.91 119	89.56 114	95.58 85	87.72 105	96.15 35	92.86 75	96.06 75	95.47 129	95.02 123	98.43 169	97.09 160

thisisatest0515		90.12 149	92.06 138	87.85 172	90.03 173	96.17 145	87.83 199	87.45 136	91.71 153	77.15 153	85.40 135	84.01 137	85.74 193	95.41 131	93.30 166	98.88 131	98.43 110

IterMVS-SCA-FT		90.24 145	92.48 128	87.63 176	92.85 145	94.30 200	93.79 136	81.47 191	92.66 134	69.95 195	84.66 140	88.38 107	89.99 171	95.39 132	94.34 144	97.74 186	97.63 145

IterMVS-LS		92.56 117	93.18 114	91.84 116	93.90 130	94.97 184	94.99 116	86.20 150	94.18 114	82.68 126	85.81 133	87.36 111	94.43 106	95.31 133	96.02 93	98.87 132	98.60 101
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS		90.20 146	92.43 130	87.61 177	92.82 147	94.31 199	94.11 132	81.54 189	92.97 130	69.90 196	84.71 139	88.16 110	89.96 172	95.25 134	94.17 147	97.31 188	97.46 150
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CR-MVSNet		90.16 148	91.96 140	88.06 165	93.32 139	95.95 152	93.36 143	75.99 208	92.40 142	75.19 169	83.18 148	85.37 125	92.05 138	95.21 135	94.56 137	98.47 166	97.08 162

PatchT		89.13 162	91.71 141	86.11 193	92.92 143	95.59 166	83.64 210	75.09 211	91.87 151	75.19 169	82.63 151	85.06 130	92.05 138	95.21 135	94.56 137	97.76 183	97.08 162

Anonymous202405211			92.18 135		95.04 104	98.20 93	96.14 92	91.79 83	93.93 116		74.60 183	88.38 107	96.48 70	95.17 137	95.82 102	99.00 118	99.15 51

test0.0.03 1		91.97 121	93.91 100	89.72 143	93.31 140	96.40 140	91.34 180	87.06 141	93.86 118	81.67 132	91.15 88	89.16 100	86.02 192	95.08 138	95.09 119	98.91 129	96.64 174

MS-PatchMatch		91.82 124	92.51 124	91.02 125	95.83 84	96.88 121	95.05 115	84.55 175	93.85 119	82.01 129	82.51 152	91.71 80	90.52 165	95.07 139	93.03 170	98.13 175	94.52 188

USDC		90.69 138	90.52 155	90.88 128	94.17 126	96.43 138	95.82 106	86.76 143	93.92 117	76.27 161	86.49 125	74.30 180	93.67 123	95.04 140	93.36 163	98.61 157	94.13 193

PCF-MVS	93.95 6	95.65 55	95.14 77	96.25 43	97.73 58	98.73 67	97.59 51	97.13 30	92.50 139	89.09 97	89.85 101	96.65 50	96.90 60	94.97 141	94.89 126	99.08 107	98.38 116
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Anonymous20231211		93.49 107	92.33 134	94.84 76	94.78 112	98.00 99	96.11 93	91.85 78	94.86 102	90.91 61	74.69 182	89.18 99	96.73 64	94.82 142	95.51 108	98.67 151	99.24 38

dmvs_re		91.84 123	91.60 144	92.12 114	91.60 155	97.26 114	95.14 114	91.96 75	91.02 160	80.98 137	86.56 123	77.96 165	93.84 118	94.71 143	95.08 120	99.22 84	98.62 100

FMVSNet5		90.36 143	90.93 151	89.70 144	87.99 204	92.25 209	92.03 170	83.51 179	92.20 148	84.13 119	85.59 134	86.48 114	92.43 135	94.61 144	94.52 140	98.13 175	90.85 210

ACMH	90.77 13	91.51 131	91.63 143	91.38 122	95.62 86	96.87 123	91.76 175	89.66 112	91.58 154	78.67 146	86.73 119	78.12 161	93.77 120	94.59 145	94.54 139	98.78 144	98.98 74
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		90.54 142	90.87 153	90.16 138	91.48 157	96.61 133	93.26 145	86.08 151	87.71 188	81.66 133	83.11 150	84.04 136	90.42 166	94.54 146	94.60 134	98.04 179	95.48 184

TransMVSNet (Re)		87.73 181	86.79 192	88.83 153	90.76 163	94.40 197	91.33 181	89.62 113	84.73 204	75.41 167	72.73 196	71.41 193	86.80 186	94.53 147	93.93 153	99.06 112	95.83 178

pmmvs5		87.83 180	88.09 173	87.51 181	89.59 181	95.48 169	89.75 194	84.73 171	86.07 200	71.44 186	80.57 162	70.09 201	90.74 161	94.47 148	92.87 174	98.82 136	97.10 159

TinyColmap		89.42 155	88.58 167	90.40 135	93.80 134	95.45 171	93.96 135	86.54 146	92.24 147	76.49 158	80.83 159	70.44 198	93.37 126	94.45 149	93.30 166	98.26 173	93.37 204

RPMNet		90.19 147	92.03 139	88.05 166	93.46 136	95.95 152	93.41 141	74.59 213	92.40 142	75.91 163	84.22 143	86.41 116	92.49 134	94.42 150	93.85 156	98.44 167	96.96 165

testgi		89.42 155	91.50 146	87.00 186	92.40 151	95.59 166	89.15 196	85.27 165	92.78 133	72.42 181	91.75 80	76.00 173	84.09 203	94.38 151	93.82 158	98.65 155	96.15 175

LTVRE_ROB	87.32 16	87.55 182	88.25 171	86.73 187	90.66 164	95.80 160	93.05 148	84.77 170	83.35 208	60.32 217	83.12 149	67.39 210	93.32 127	94.36 152	94.86 127	98.28 171	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
CVMVSNet		89.77 153	91.66 142	87.56 179	93.21 142	95.45 171	91.94 174	89.22 118	89.62 173	69.34 200	83.99 145	85.90 121	84.81 199	94.30 153	95.28 114	96.85 192	97.09 160

COLMAP_ROB	90.49 14	93.27 111	92.71 120	93.93 92	97.75 57	97.44 110	96.07 95	93.17 58	95.40 86	83.86 121	83.76 146	88.72 103	93.87 116	94.25 154	94.11 148	98.87 132	95.28 186
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
NR-MVSNet		89.34 157	88.66 166	90.13 141	90.40 167	95.61 164	93.04 149	89.91 104	91.22 157	78.96 145	77.72 171	68.90 206	89.16 176	94.24 155	93.95 152	99.32 65	98.99 72

EG-PatchMatch MVS		86.68 190	87.24 186	86.02 194	90.58 165	96.26 143	91.08 184	81.59 188	84.96 203	69.80 198	71.35 204	75.08 177	84.23 202	94.24 155	93.35 164	98.82 136	95.46 185

v7n		86.43 192	86.52 196	86.33 191	87.91 205	94.93 186	90.15 192	83.05 181	86.57 195	70.21 193	71.48 202	66.78 213	87.72 181	94.19 157	92.96 171	98.92 127	98.76 96

ET-MVSNet_ETH3D		93.34 109	94.33 91	92.18 113	83.26 217	97.66 105	96.72 76	89.89 106	95.62 83	87.17 109	96.00 39	83.69 140	96.99 58	93.78 158	95.34 112	99.06 112	98.18 126

UniMVSNet (Re)		90.03 151	89.61 160	90.51 134	89.97 175	96.12 146	92.32 161	89.26 117	90.99 161	80.95 138	78.25 170	75.08 177	91.14 150	93.78 158	93.87 155	99.41 50	99.21 43

v1192		87.51 183	87.31 184	87.74 174	89.04 195	94.87 189	92.07 168	85.03 166	88.49 182	70.32 191	72.65 197	70.35 199	91.21 149	93.59 160	92.80 175	98.78 144	98.42 112

v10		88.00 174	87.96 176	88.05 166	89.44 183	94.68 191	92.36 160	83.35 180	89.37 174	72.96 180	73.98 189	72.79 187	91.35 148	93.59 160	92.88 173	98.81 139	98.42 112

pmmvs6		85.98 196	84.89 204	87.25 183	88.83 199	94.35 198	89.36 195	85.30 164	78.51 217	75.44 166	62.71 217	75.41 174	87.65 182	93.58 162	92.40 183	96.89 191	97.29 156

pmmvs4		90.55 141	89.91 158	91.30 124	90.26 171	94.95 185	92.73 153	87.94 132	93.44 126	85.35 116	82.28 153	76.09 172	93.02 132	93.56 163	92.26 186	98.51 163	96.77 170

v1144		87.92 178	87.79 180	88.07 163	89.27 187	95.15 180	92.17 166	85.62 157	88.52 181	71.52 185	73.80 190	72.40 189	91.06 152	93.54 164	92.80 175	98.81 139	98.33 119

ACMH+	90.88 12	91.41 132	91.13 148	91.74 118	95.11 102	96.95 120	93.13 147	89.48 116	92.42 141	79.93 141	85.13 136	78.02 162	93.82 119	93.49 165	93.88 154	98.94 125	97.99 132

Anonymous20231206		83.84 203	85.19 202	82.26 203	87.38 208	92.87 206	85.49 206	83.65 178	86.07 200	63.44 212	68.42 208	69.01 205	75.45 212	93.34 166	92.44 182	98.12 177	94.20 192

v1921920		87.31 187	87.13 188	87.52 180	88.87 198	94.72 190	91.96 173	84.59 174	88.28 183	69.86 197	72.50 198	70.03 202	91.10 151	93.33 167	92.61 180	98.71 148	98.44 109

v1240		86.89 189	86.75 194	87.06 185	88.75 200	94.65 193	91.30 182	84.05 176	87.49 191	68.94 201	71.96 201	68.86 207	90.65 163	93.33 167	92.72 179	98.67 151	98.24 123

WR-MVS_H		87.93 176	87.85 179	88.03 168	89.62 179	95.58 168	90.47 189	85.55 159	87.20 193	76.83 156	74.42 186	72.67 188	86.37 189	93.22 169	93.04 169	99.33 63	98.83 91

TDRefinement		89.07 163	88.15 172	90.14 140	95.16 100	96.88 121	95.55 109	90.20 101	89.68 171	76.42 159	76.67 174	74.30 180	84.85 198	93.11 170	91.91 188	98.64 156	94.47 189

MVS-HIRNet		85.36 198	86.89 191	83.57 200	90.13 172	94.51 195	83.57 211	72.61 215	88.27 184	71.22 188	68.97 207	81.81 148	88.91 178	93.08 171	91.94 187	94.97 210	89.64 213

CP-MVSNet		87.89 179	87.27 185	88.62 155	89.30 186	95.06 181	90.60 188	85.78 155	87.43 192	75.98 162	74.60 183	68.14 209	90.76 159	93.07 172	93.60 160	99.30 70	98.98 74

PS-CasMVS		87.33 186	86.68 195	88.10 162	89.22 193	94.93 186	90.35 191	85.70 156	86.44 197	74.01 177	73.43 193	66.59 215	90.04 170	92.92 173	93.52 161	99.28 72	98.91 83

UniMVSNet_NR-MVSNet		90.35 144	89.96 157	90.80 130	89.66 178	95.83 158	92.48 157	90.53 99	90.96 162	79.57 142	79.33 167	77.14 168	93.21 130	92.91 174	94.50 142	99.37 59	99.05 64

SixPastTwentyTwo		88.37 170	89.47 161	87.08 184	90.01 174	95.93 154	87.41 200	85.32 162	90.26 170	70.26 192	86.34 130	71.95 190	90.93 154	92.89 175	91.72 189	98.55 160	97.22 157

v144192		87.40 185	87.20 187	87.64 175	88.89 196	94.88 188	91.65 176	84.70 172	87.80 187	71.17 189	73.20 195	70.91 195	90.75 160	92.69 176	92.49 181	98.71 148	98.43 110

PM-MVS		84.72 201	84.47 205	85.03 196	84.67 213	91.57 211	86.27 204	82.31 187	87.65 189	70.62 190	76.54 176	56.41 224	88.75 179	92.59 177	89.85 198	97.54 187	96.66 173

DU-MVS		89.67 154	88.84 165	90.63 133	89.26 188	95.61 164	92.48 157	89.91 104	91.22 157	79.57 142	77.72 171	71.18 194	93.21 130	92.53 178	94.57 136	99.35 62	99.05 64

Baseline_NR-MVSNet		89.27 159	88.01 175	90.73 132	89.26 188	93.71 204	92.71 154	89.78 110	90.73 163	81.28 135	73.53 192	72.85 186	92.30 137	92.53 178	93.84 157	99.07 109	98.88 85

IB-MVS	89.56 15	91.71 126	92.50 125	90.79 131	95.94 83	98.44 86	87.05 202	91.38 90	93.15 128	92.98 41	84.78 138	85.14 129	78.27 209	92.47 180	94.44 143	99.10 105	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
WR-MVS		87.93 176	88.09 173	87.75 173	89.26 188	95.28 175	90.81 186	86.69 144	88.90 176	75.29 168	74.31 187	73.72 183	85.19 197	92.26 181	93.32 165	99.27 74	98.81 93

EU-MVSNet		85.62 197	87.65 183	83.24 202	88.54 202	92.77 208	87.12 201	85.32 162	86.71 194	64.54 208	78.52 169	75.11 176	78.35 208	92.25 182	92.28 185	95.58 202	95.93 177

pmmvs-eth3d		84.33 202	82.94 207	85.96 195	84.16 214	90.94 212	86.55 203	83.79 177	84.25 205	75.85 164	70.64 205	56.43 223	87.44 185	92.20 183	90.41 195	97.97 180	95.68 181

TranMVSNet+NR-MVSNet		89.23 160	88.48 169	90.11 142	89.07 194	95.25 178	92.91 150	90.43 100	90.31 168	77.10 154	76.62 175	71.57 192	91.83 142	92.12 184	94.59 135	99.32 65	98.92 80

v8		88.21 173	87.94 178	88.51 156	89.62 179	95.01 183	92.31 162	84.99 167	88.94 175	74.70 174	75.03 179	73.51 184	90.67 162	92.11 185	92.74 178	98.80 141	98.24 123

v2v482		88.25 172	87.71 182	88.88 152	89.23 192	95.28 175	92.10 167	87.89 133	88.69 180	73.31 179	75.32 178	71.64 191	91.89 140	92.10 186	92.92 172	98.86 134	97.99 132

V42		88.31 171	87.95 177	88.73 154	89.44 183	95.34 174	92.23 165	87.21 139	88.83 177	74.49 175	74.89 181	73.43 185	90.41 168	92.08 187	92.77 177	98.60 159	98.33 119

test20.03		82.92 205	85.52 200	79.90 207	87.75 206	91.84 210	82.80 212	82.99 182	82.65 212	60.32 217	78.90 168	70.50 196	67.10 216	92.05 188	90.89 191	98.44 167	91.80 208

MDTV_nov1_ep13		91.57 129	93.18 114	89.70 144	93.39 138	96.97 119	93.53 139	80.91 192	95.70 80	81.86 130	92.40 71	89.93 92	93.25 129	91.97 189	90.80 192	95.25 207	94.46 190

test_method		72.96 212	78.68 212	66.28 215	50.17 227	64.90 225	75.45 220	50.90 223	87.89 185	62.54 213	62.98 216	68.34 208	70.45 214	91.90 190	82.41 214	88.19 220	92.35 205

MIMVSNet1		80.03 208	80.93 209	78.97 208	72.46 223	90.73 213	80.81 215	82.44 186	80.39 214	63.64 210	57.57 218	64.93 217	76.37 210	91.66 191	91.55 190	98.07 178	89.70 212

RPSCF		94.05 92	94.00 99	94.12 90	96.20 76	96.41 139	96.61 79	91.54 86	95.83 78	89.73 84	96.94 30	92.80 76	95.35 92	91.63 192	90.44 194	95.27 206	93.94 197

UniMVSNet_ETH3D		88.47 169	86.00 199	91.35 123	91.55 156	96.29 142	92.53 156	88.81 122	85.58 202	82.33 128	67.63 212	66.87 212	94.04 114	91.49 193	95.24 115	98.84 135	98.92 80

ambc			73.83 216		76.23 221	85.13 219	82.27 213		84.16 206	65.58 207	52.82 220	23.31 231	73.55 213	91.41 194	85.26 212	92.97 216	94.70 187

PEN-MVS		87.22 188	86.50 197	88.07 163	88.88 197	94.44 196	90.99 185	86.21 148	86.53 196	73.66 178	74.97 180	66.56 216	89.42 175	91.20 195	93.48 162	99.24 78	98.31 122

SCA		90.92 136	93.04 116	88.45 157	93.72 135	97.33 113	92.77 151	76.08 207	96.02 70	78.26 148	91.96 76	90.86 85	93.99 115	90.98 196	90.04 197	95.88 198	94.06 196

v148		87.51 183	86.79 192	88.36 158	89.39 185	95.21 179	89.84 193	88.20 130	87.61 190	77.56 150	73.38 194	70.32 200	86.80 186	90.70 197	92.31 184	98.37 170	97.98 134

DTE-MVSNet		86.67 191	86.09 198	87.35 182	88.45 203	94.08 202	90.65 187	86.05 152	86.13 198	72.19 182	74.58 185	66.77 214	87.61 183	90.31 198	93.12 168	99.13 101	97.62 146

EPMVS		90.88 137	92.12 136	89.44 148	94.71 115	97.24 115	93.55 138	76.81 202	95.89 74	81.77 131	91.49 84	86.47 115	93.87 116	90.21 199	90.07 196	95.92 197	93.49 203

pmmvs3		79.16 209	80.12 211	78.05 210	79.36 218	86.59 218	78.13 218	73.87 214	76.42 219	57.51 222	70.59 206	57.02 222	84.66 200	90.10 200	88.32 202	94.75 212	91.77 209

PatchmatchNet		90.56 140	92.49 126	88.31 160	93.83 133	96.86 124	92.42 159	76.50 204	95.96 72	78.31 147	91.96 76	89.66 94	93.48 125	90.04 201	89.20 200	95.32 204	93.73 201
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13_2view		86.30 193	88.27 170	84.01 199	87.71 207	94.67 192	88.08 198	76.78 203	90.59 167	68.66 202	80.46 164	80.12 154	87.58 184	89.95 202	88.20 203	95.25 207	93.90 199

tpm		87.95 175	89.44 162	86.21 192	92.53 149	94.62 194	91.40 178	76.36 205	91.46 155	69.80 198	87.43 114	75.14 175	91.55 145	89.85 203	90.60 193	95.61 201	96.96 165

new_pmnet		81.53 206	82.68 208	80.20 205	83.47 216	89.47 216	82.21 214	78.36 196	87.86 186	60.14 219	67.90 210	69.43 204	82.03 206	89.22 204	87.47 206	94.99 209	87.39 215

ADS-MVSNet		89.80 152	91.33 147	88.00 169	94.43 122	96.71 130	92.29 163	74.95 212	96.07 69	77.39 151	88.67 109	86.09 118	93.26 128	88.44 205	89.57 199	95.68 200	93.81 200

N_pmnet		84.80 199	85.10 203	84.45 198	89.25 191	92.86 207	84.04 209	86.21 148	88.78 178	66.73 204	72.41 199	74.87 179	85.21 196	88.32 206	86.45 208	95.30 205	92.04 207

CMPMVS	65.18 17	84.76 200	83.10 206	86.69 188	95.29 94	95.05 182	88.37 197	85.51 160	80.27 215	71.31 187	68.37 209	73.85 182	85.25 195	87.72 207	87.75 204	94.38 214	88.70 214
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
dps		90.11 150	89.37 163	90.98 126	93.89 131	96.21 144	93.49 140	77.61 200	91.95 150	92.74 45	88.85 106	78.77 160	92.37 136	87.71 208	87.71 205	95.80 199	94.38 191

CostFormer		90.69 138	90.48 156	90.93 127	94.18 125	96.08 147	94.03 133	78.20 198	93.47 125	89.96 81	90.97 91	80.30 153	93.72 121	87.66 209	88.75 201	95.51 203	96.12 176

pmnet_mix02		86.12 195	87.12 189	84.96 197	89.82 176	94.12 201	84.88 208	86.63 145	91.78 152	65.60 206	80.76 160	76.98 169	86.61 188	87.29 210	84.80 213	96.21 194	94.09 194

tpmrst		88.86 167	89.62 159	87.97 170	94.33 123	95.98 149	92.62 155	76.36 205	94.62 106	76.94 155	85.98 132	82.80 145	92.80 133	86.90 211	87.15 207	94.77 211	93.93 198

tpm cat1		88.90 165	87.78 181	90.22 137	93.88 132	95.39 173	93.79 136	78.11 199	92.55 138	89.43 88	81.31 157	79.84 156	91.40 146	84.95 212	86.34 210	94.68 213	94.09 194

new-patchmatchnet		78.49 210	78.19 213	78.84 209	84.13 215	90.06 214	77.11 219	80.39 193	79.57 216	59.64 220	66.01 213	55.65 225	75.62 211	84.55 213	80.70 216	96.14 196	90.77 211

Gipuma		68.35 214	66.71 217	70.27 212	74.16 222	68.78 224	63.93 224	71.77 217	83.34 209	54.57 223	34.37 222	31.88 228	68.69 215	83.30 214	85.53 211	88.48 219	79.78 219
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt				66.88 214	86.07 211	73.86 223	68.22 222	33.38 224	96.88 48	80.67 139	88.23 112	78.82 159	49.78 221	82.68 215	77.47 218	83.19 223

WB-MVS		69.22 213	76.91 214	60.24 217	85.80 212	79.37 221	56.86 226	84.96 168	81.50 213	18.16 229	76.85 173	61.07 218	34.23 224	82.46 216	81.81 215	81.43 224	75.31 222

DeepMVS_CX						86.86 217	79.50 216	70.43 218	90.73 163	63.66 209	80.36 165	60.83 219	79.68 207	76.23 217		89.46 218	86.53 216

MDA-MVSNet-bldmvs		80.11 207	80.24 210	79.94 206	77.01 220	93.21 205	78.86 217	85.94 154	82.71 211	60.86 214	79.71 166	51.77 226	83.71 205	75.60 218	86.37 209	93.28 215	92.35 205

FPMVS		75.84 211	74.59 215	77.29 211	86.92 209	83.89 220	85.01 207	80.05 194	82.91 210	60.61 216	65.25 214	60.41 220	63.86 217	75.60 218	73.60 220	87.29 221	80.47 218

PMMVS2		64.36 217	65.94 219	62.52 216	67.37 224	77.44 222	64.39 223	69.32 221	61.47 222	34.59 225	46.09 221	41.03 227	48.02 223	74.56 220	78.23 217	91.43 217	82.76 217

PMVS	63.12 18	67.27 215	66.39 218	68.30 213	77.98 219	60.24 226	59.53 225	76.82 201	66.65 221	60.74 215	54.39 219	59.82 221	51.24 220	73.92 221	70.52 221	83.48 222	79.17 220
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	50.86 19	49.54 220	51.43 220	47.33 220	44.14 228	59.20 227	36.45 229	60.59 222	41.47 225	31.14 226	29.58 223	17.06 232	48.52 222	62.22 222	74.63 219	63.12 227	75.87 221
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		50.67 218	47.85 221	53.96 218	64.13 226	50.98 229	38.06 227	69.51 219	51.40 224	24.60 227	29.46 225	24.39 230	56.07 219	48.17 223	59.70 222	71.40 225	70.84 223

EMVS		49.98 219	46.76 222	53.74 219	64.96 225	51.29 228	37.81 228	69.35 220	51.83 223	22.69 228	29.57 224	25.06 229	57.28 218	44.81 224	56.11 223	70.32 226	68.64 224

testmvs		12.09 221	16.94 223	6.42 222	3.15 229	6.08 230	9.51 231	3.84 225	21.46 226	5.31 230	27.49 226	6.76 233	10.89 225	17.06 225	15.01 224	5.84 228	24.75 225

test123		9.58 222	13.53 224	4.97 223	1.31 231	5.47 231	8.32 232	2.95 226	18.14 227	2.03 232	20.82 227	2.34 234	10.60 226	10.00 226	14.16 225	4.60 229	23.77 226

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					98.94 32	98.47 85	98.04 42			92.62 46	96.51 33	98.76 29	95.94 79			98.92 127	97.55 147
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										63.50 211

9.14												99.28 12

SR-MVS					99.45 9			97.61 14				99.20 16

our_test_3					89.78 177	93.84 203	85.59 205

MTAPA										96.83 10		99.12 21

MTMP										97.18 5		98.83 26

Patchmatch-RL test							34.61 230

XVS					96.60 69	99.35 17	96.82 69			90.85 62		98.72 30				99.46 34

X-MVStestdata					96.60 69	99.35 17	96.82 69			90.85 62		98.72 30				99.46 34

mPP-MVS					99.21 23							98.29 38

NP-MVS									95.32 89

Patchmtry						95.96 151	93.36 143	75.99 208		75.19 169

High-resolution multi-view benchmark