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.

SED-MVS		98.87 1	99.20 2	98.48 1	99.32 12	99.85 2	99.55 8	96.20 6	99.48 3	96.78 3	98.51 15	99.99 1	99.36 2	98.98 8	97.59 29	99.67 20	99.99 3

DVP-MVS++		98.75 2	99.11 7	98.33 3	99.41 5	99.85 2	99.61 4	96.22 5	99.32 9	95.80 5	98.27 18	99.97 4	99.22 5	98.95 9	97.48 33	99.71 18	99.98 5

MSP-MVS		98.75 2	99.27 1	98.15 8	99.21 18	99.82 7	99.58 6	96.09 13	99.32 9	95.16 9	98.79 6	99.55 9	99.05 7	99.54 1	97.88 21	99.84 3	99.99 3
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
CNVR-MVS		98.73 4	99.17 5	98.22 5	99.47 4	99.85 2	99.57 7	96.23 3	99.30 11	94.90 11	98.65 10	98.93 20	99.36 2	99.46 3	98.21 11	99.81 6	99.80 33

DPE-MVS		98.69 5	99.14 6	98.16 7	99.37 8	99.82 7	99.66 3	96.26 1	99.18 17	95.02 10	98.62 12	99.98 3	98.88 12	98.90 12	97.51 32	99.75 11	99.97 8
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		98.65 6	98.87 13	98.38 2	99.30 14	99.85 2	99.14 23	96.23 3	99.51 2	97.16 1	96.01 34	99.99 1	98.90 11	98.89 13	97.88 21	99.56 52	99.98 5
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
APDe-MVS		98.60 7	98.97 10	98.18 6	99.38 7	99.78 12	99.35 15	96.14 9	99.24 14	95.66 7	98.19 20	99.01 17	98.66 18	98.77 15	97.80 24	99.86 2	99.97 8

SF-MVS		98.55 8	98.75 15	98.32 4	99.48 2	99.68 22	99.51 10	96.24 2	99.08 21	95.94 4	98.64 11	99.30 13	99.02 9	97.94 29	96.86 51	99.75 11	99.76 36

SMA-MVS		98.47 9	99.06 8	97.77 11	99.48 2	99.78 12	99.37 12	96.14 9	99.29 12	93.03 19	97.59 29	99.97 4	99.03 8	98.94 10	98.30 9	99.60 33	99.58 64
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
NCCC		98.41 10	99.18 3	97.52 15	99.36 9	99.84 6	99.55 8	96.08 15	99.33 8	91.77 24	98.79 6	99.46 11	98.59 20	99.15 7	98.07 18	99.73 14	99.64 53

SD-MVS		98.33 11	99.01 9	97.54 14	97.17 50	99.77 15	99.14 23	96.09 13	99.34 7	94.06 15	97.91 25	99.89 6	99.18 6	97.99 28	98.21 11	99.63 27	99.95 13
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
APD-MVS		98.28 12	98.69 16	97.80 10	99.31 13	99.62 28	99.31 18	96.15 8	99.19 16	93.60 16	97.28 30	98.35 27	98.72 17	98.27 21	98.22 10	99.73 14	99.89 25
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MCST-MVS		98.20 13	99.18 3	97.06 21	99.27 16	99.87 1	99.37 12	96.11 11	99.37 5	89.29 32	98.76 8	99.50 10	98.37 25	99.23 5	97.64 27	99.95 1	99.87 29

HPM-MVS++		98.16 14	98.87 13	97.32 17	99.39 6	99.70 20	99.18 21	96.10 12	99.09 20	91.14 26	98.02 23	99.89 6	98.44 23	98.75 16	97.03 46	99.67 20	99.63 56

MSLP-MVS++		98.12 15	98.23 28	97.99 9	99.28 15	99.72 17	99.59 5	95.27 28	98.61 34	94.79 12	96.11 33	97.79 36	99.27 4	96.62 67	98.96 5	99.77 9	99.80 33

HFP-MVS		98.02 16	98.55 20	97.40 16	99.11 21	99.69 21	99.41 11	95.41 26	98.79 30	91.86 23	98.61 13	98.16 29	99.02 9	97.87 33	97.40 35	99.60 33	99.35 83

TSAR-MVS + MP.		97.98 17	98.62 19	97.23 19	97.08 51	99.55 34	99.17 22	95.69 21	99.40 4	93.04 18	96.68 32	98.96 19	98.58 21	98.82 14	96.95 48	99.81 6	99.96 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SteuartSystems-ACMMP		97.86 18	98.91 11	96.64 25	98.89 27	99.79 9	99.34 16	95.20 30	98.48 36	89.91 30	98.58 14	98.69 23	96.84 48	98.92 11	98.16 15	99.66 22	99.74 39
Skip Steuart: Steuart Systems R&D Blog.
CP-MVS		97.81 19	98.26 27	97.28 18	99.00 24	99.65 24	99.10 25	95.32 27	98.38 42	92.21 22	98.33 17	97.74 37	98.50 22	97.66 42	96.55 59	99.57 46	99.48 73

ACMMPR		97.78 20	98.28 25	97.20 20	99.03 23	99.68 22	99.37 12	95.24 29	98.86 29	91.16 25	97.86 27	97.26 39	98.79 15	97.64 44	97.40 35	99.60 33	99.25 89

DeepC-MVS_fast	95.01 1	97.67 21	98.22 29	97.02 22	99.00 24	99.79 9	99.10 25	95.82 18	99.05 23	89.53 31	93.54 49	96.77 42	98.83 13	99.34 4	99.44 2	99.82 4	99.63 56
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		97.54 22	97.35 38	97.77 11	99.17 19	99.55 34	98.57 31	95.76 20	99.04 24	94.66 13	97.94 24	94.39 56	98.82 14	96.21 77	94.78 86	99.62 29	99.52 69

ACMMP_NAP		97.51 23	98.27 26	96.63 26	99.34 10	99.72 17	99.25 19	95.94 17	98.11 47	87.10 47	96.98 31	98.50 25	98.61 19	98.58 18	96.83 52	99.56 52	99.14 97

MP-MVS		97.46 24	98.30 24	96.48 27	98.93 26	99.43 43	99.20 20	95.42 25	98.43 38	87.60 43	98.19 20	98.01 35	98.09 27	98.05 26	96.67 56	99.64 25	99.35 83
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
train_agg		97.42 25	98.88 12	95.71 32	98.46 34	99.60 31	99.05 27	95.16 31	99.10 19	84.38 65	98.47 16	98.85 21	97.61 31	98.54 19	97.66 26	99.62 29	99.93 19

CPTT-MVS		97.32 26	97.60 37	96.99 23	98.29 37	99.31 55	99.04 28	94.67 35	97.99 53	93.12 17	98.03 22	98.26 28	98.77 16	96.08 80	94.26 94	98.07 182	99.27 88

X-MVS		97.20 27	98.42 23	95.77 30	99.04 22	99.64 25	98.95 30	95.10 33	98.16 45	83.97 71	98.27 18	98.08 32	97.95 28	97.89 30	97.46 34	99.58 42	99.47 74

PHI-MVS		97.09 28	98.69 16	95.22 36	97.99 42	99.59 33	97.56 44	92.16 39	98.41 40	87.11 46	98.70 9	99.42 12	96.95 44	96.88 60	98.16 15	99.56 52	99.70 45

DPM-MVS		97.07 29	97.99 32	96.00 29	97.25 49	99.16 65	99.67 2	95.99 16	99.08 21	85.97 52	93.00 54	98.44 26	97.47 33	99.22 6	99.62 1	99.66 22	97.44 154

PGM-MVS		97.03 30	98.14 31	95.73 31	99.34 10	99.61 30	99.34 16	89.99 45	97.70 56	87.67 42	99.44 2	96.45 45	98.44 23	97.65 43	97.09 43	99.58 42	99.06 105

PLC	94.37 2	97.03 30	96.54 44	97.60 13	98.84 28	98.64 74	98.17 36	94.99 34	99.01 25	96.80 2	93.21 53	95.64 47	97.36 34	96.37 72	94.79 85	99.41 87	98.12 139
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TSAR-MVS + ACMM		96.90 32	98.64 18	94.88 38	98.12 40	99.47 39	99.01 29	95.43 24	99.23 15	81.98 90	95.95 35	99.16 16	95.13 68	98.61 17	98.11 17	99.58 42	99.93 19

TSAR-MVS + GP.		96.47 33	98.45 22	94.17 43	92.12 87	99.29 56	97.76 40	88.05 55	99.36 6	90.26 29	97.82 28	99.21 14	97.21 37	96.78 65	96.74 54	99.63 27	99.94 16

EPNet		96.23 34	97.89 34	94.29 41	97.62 45	99.44 42	97.14 51	88.63 51	98.16 45	88.14 38	99.46 1	94.15 59	94.61 78	97.20 52	97.23 39	99.57 46	99.59 61
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNLPA		96.14 35	95.43 54	96.98 24	98.55 31	99.41 47	95.91 57	95.15 32	99.00 26	95.71 6	84.21 104	94.55 54	97.25 35	95.50 102	96.23 65	99.28 109	99.09 104

MVS_111021_LR		96.07 36	97.94 33	93.88 46	97.86 43	99.43 43	95.70 60	89.65 48	98.73 31	84.86 62	99.38 3	94.08 60	95.78 66	97.81 36	96.73 55	99.43 83	99.42 77

ACMMP		96.05 37	96.70 43	95.29 35	98.01 41	99.43 43	97.60 43	94.33 37	97.62 59	86.17 51	98.92 4	92.81 68	96.10 59	95.67 92	93.33 114	99.55 57	99.12 100
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
3Dnovator+	90.72 7	95.99 38	96.42 46	95.50 34	98.18 39	99.33 54	97.44 46	87.73 60	97.93 54	92.36 21	84.67 97	97.33 38	97.55 32	97.32 48	98.47 8	99.72 17	99.88 26

DeepPCF-MVS	94.02 3	95.92 39	98.47 21	92.95 56	97.57 46	99.79 9	91.45 118	94.42 36	99.76 1	86.48 50	92.88 55	98.12 31	92.62 99	99.49 2	99.32 3	95.15 206	99.95 13

CDPH-MVS		95.90 40	97.77 36	93.72 49	98.28 38	99.43 43	98.40 32	91.30 43	98.34 43	78.62 108	94.80 41	95.74 46	96.11 58	97.86 34	98.67 7	99.59 37	99.56 66

CSCG		95.77 41	95.35 56	96.26 28	99.13 20	99.60 31	98.14 37	91.89 42	96.57 75	92.61 20	89.65 64	91.74 76	96.96 42	93.69 126	96.58 58	98.86 136	99.63 56

OMC-MVS		95.75 42	95.84 51	95.64 33	98.52 33	99.34 53	97.15 50	92.02 41	98.94 28	90.45 28	88.31 70	94.64 52	96.35 54	96.02 83	95.99 73	99.34 98	97.65 150

MVS_111021_HR		95.70 43	98.16 30	92.83 57	97.57 46	99.77 15	94.78 74	88.05 55	98.61 34	82.29 85	98.85 5	94.66 51	94.63 76	97.80 37	97.63 28	99.64 25	99.79 35

3Dnovator	90.31 8	95.67 44	96.16 49	95.11 37	98.59 30	99.37 52	97.50 45	87.98 57	98.02 52	89.09 33	85.36 96	94.62 53	97.66 29	97.10 56	98.90 6	99.82 4	99.73 41

CANet		95.40 45	96.27 47	94.40 40	96.25 56	99.62 28	98.37 33	88.59 52	98.09 48	87.58 44	84.57 99	95.54 49	95.87 63	98.12 24	98.03 20	99.73 14	99.90 24

QAPM		95.17 46	96.05 50	94.14 44	98.55 31	99.49 37	97.41 47	87.88 58	97.72 55	84.21 68	84.59 98	95.60 48	97.21 37	97.10 56	98.19 14	99.57 46	99.65 51

CS-MVS-test		95.06 47	96.98 41	92.82 59	95.83 59	99.40 48	93.23 97	85.29 84	99.27 13	85.89 54	93.86 48	92.70 70	97.19 39	97.70 40	96.18 67	99.49 66	99.76 36

CS-MVS		94.82 48	96.19 48	93.22 52	95.19 64	99.24 58	95.10 69	85.07 87	98.72 32	87.33 45	91.35 57	89.98 85	97.06 41	98.01 27	96.28 63	99.60 33	99.72 42

MVSTER		94.75 49	96.50 45	92.70 61	90.91 103	94.51 146	97.37 48	83.37 97	98.40 41	89.04 34	93.23 52	97.04 41	95.91 62	97.73 38	95.59 82	99.61 31	99.01 107

TAPA-MVS	92.04 6	94.72 50	95.13 59	94.24 42	97.72 44	99.17 63	97.61 42	92.16 39	97.66 58	81.99 89	87.84 75	93.94 62	96.50 51	95.74 89	94.27 93	99.46 77	97.31 158
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepC-MVS	92.23 5	94.53 51	94.26 72	94.86 39	96.73 53	99.50 36	97.85 39	95.45 23	96.22 82	82.73 80	80.68 114	88.02 90	96.92 45	97.49 47	98.20 13	99.47 71	99.69 48
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CHOSEN 280x420		94.51 52	97.78 35	90.70 83	95.54 63	99.49 37	94.14 85	74.91 160	98.43 38	85.32 59	94.78 42	99.19 15	94.95 72	97.02 58	96.18 67	99.35 94	99.36 82

ETV-MVS		94.49 53	97.23 40	91.29 76	90.43 113	98.55 77	93.41 95	84.53 90	99.16 18	83.13 76	94.72 43	94.08 60	96.61 50	97.72 39	96.60 57	99.61 31	99.81 32

MVS_0304		94.35 54	95.66 53	92.83 57	94.82 66	99.46 41	98.19 35	87.75 59	97.32 65	81.83 93	83.50 106	93.19 67	94.71 74	98.24 23	98.07 18	99.68 19	99.83 31

EC-MVSNet		94.33 55	96.88 42	91.36 74	90.12 120	97.70 96	95.20 68	80.27 121	98.63 33	85.97 52	93.92 47	93.85 65	97.09 40	97.54 46	96.81 53	99.49 66	99.70 45

MAR-MVS		94.18 56	95.12 60	93.09 55	98.40 36	99.17 63	94.20 84	81.92 106	98.47 37	86.52 49	90.92 59	84.21 110	98.12 26	95.88 86	97.59 29	99.40 88	99.58 64
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PCF-MVS	92.56 4	93.95 57	93.82 75	94.10 45	96.07 58	99.25 57	96.82 53	95.51 22	92.00 128	81.51 94	82.97 109	93.88 64	95.63 67	94.24 114	94.71 88	99.09 119	99.70 45
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DELS-MVS		93.82 58	93.82 75	93.81 48	96.34 55	99.47 39	97.26 49	88.53 53	92.13 125	87.80 41	79.67 117	88.01 91	93.14 91	98.28 20	99.22 4	99.80 8	99.98 5
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
OpenMVS	88.43 11	93.49 59	93.62 78	93.34 50	98.46 34	99.39 49	97.00 52	87.66 62	95.37 89	81.21 96	75.96 134	91.58 78	96.21 57	96.37 72	97.10 42	99.52 61	99.54 68

EIA-MVS		93.32 60	95.32 57	90.99 80	90.45 112	98.53 80	93.46 94	84.68 89	97.56 62	81.38 95	91.04 58	87.37 94	96.39 53	97.27 49	95.73 78	99.59 37	99.76 36

PVSNet_BlendedMVS		93.30 61	93.46 82	93.10 53	95.60 61	99.38 50	93.59 92	88.70 49	98.09 48	88.10 39	86.96 83	75.02 134	93.08 92	97.89 30	96.90 49	99.56 52	100.00 1

PVSNet_Blended		93.30 61	93.46 82	93.10 53	95.60 61	99.38 50	93.59 92	88.70 49	98.09 48	88.10 39	86.96 83	75.02 134	93.08 92	97.89 30	96.90 49	99.56 52	100.00 1

test2506		93.08 63	93.40 84	92.70 61	92.76 80	99.20 60	94.67 77	86.82 67	92.58 121	90.81 27	86.28 88	85.24 105	91.69 108	96.85 61	96.33 61	99.45 81	97.34 157

PMMVS		93.05 64	95.40 55	90.31 87	91.41 95	97.54 102	92.62 108	83.25 99	98.08 51	79.44 106	95.18 39	88.52 89	96.43 52	95.70 90	93.88 97	98.68 152	98.91 110

LS3D		92.70 65	92.23 94	93.26 51	96.24 57	98.72 69	97.93 38	96.17 7	96.41 76	72.46 123	81.39 113	80.76 123	97.66 29	95.69 91	95.62 80	99.07 121	97.02 166

baseline1		92.67 66	93.62 78	91.55 70	91.16 98	97.15 105	93.92 90	85.97 75	94.76 97	84.07 70	87.17 79	86.89 97	94.62 77	96.72 66	95.90 76	99.57 46	96.79 170

IS_MVSNet		92.67 66	94.99 62	89.96 92	91.17 97	98.54 78	92.77 103	84.00 91	92.72 119	81.90 92	85.67 94	92.47 71	90.39 122	97.82 35	97.81 23	99.51 62	99.91 23

TSAR-MVS + COLMAP		92.56 68	92.44 92	92.71 60	94.61 68	97.69 97	97.69 41	91.09 44	98.96 27	76.71 113	94.68 44	69.41 161	96.91 46	95.80 88	94.18 95	99.26 110	96.33 174

baseline		92.56 68	94.38 68	90.43 86	90.71 107	98.23 87	95.07 70	80.73 120	97.52 63	82.45 84	87.34 78	85.91 101	94.07 85	96.29 76	95.94 75	99.58 42	99.47 74

canonicalmvs		92.54 70	93.28 85	91.68 67	91.44 94	98.24 86	95.45 65	81.84 110	95.98 86	84.85 63	90.69 60	78.53 128	96.96 42	92.97 132	97.06 44	99.57 46	99.47 74

PatchMatch-RL		92.54 70	92.82 91	92.21 63	96.57 54	98.74 68	91.85 115	86.30 70	96.23 81	85.18 60	95.21 38	73.58 140	94.22 84	95.40 105	93.08 118	99.14 116	97.49 153

MVS_Test		92.42 72	94.43 64	90.08 91	90.69 108	98.26 85	94.78 74	80.81 119	97.27 66	78.76 107	87.06 81	84.25 109	95.84 64	97.67 41	97.56 31	99.59 37	98.93 109

thisisatest0530		92.31 73	95.14 58	89.02 101	90.02 121	98.45 82	91.30 119	83.58 94	96.90 71	77.90 110	90.45 62	94.33 57	91.98 105	95.57 96	91.43 142	99.31 104	98.81 113

tttt0517		92.29 74	95.12 60	88.99 102	90.02 121	98.44 84	91.19 122	83.58 94	96.88 72	77.86 111	90.45 62	94.32 58	91.98 105	95.54 98	91.43 142	99.31 104	98.78 115

EPP-MVSNet		92.29 74	94.35 70	89.88 93	90.36 115	97.69 97	90.89 124	83.31 98	93.39 112	83.47 75	85.56 95	93.92 63	91.93 107	95.49 103	94.77 87	99.34 98	99.62 59

HQP-MVS		91.94 76	93.03 87	90.66 85	93.69 70	96.48 119	95.92 56	89.73 46	97.33 64	72.65 121	95.37 36	73.56 141	92.75 98	94.85 111	94.12 96	99.23 113	99.51 70

MSDG		91.93 77	90.28 122	93.85 47	97.36 48	97.12 106	95.88 58	94.07 38	94.52 101	84.13 69	76.74 127	80.89 122	92.54 100	93.97 122	93.61 108	99.14 116	95.10 183

UGNet		91.71 78	94.43 64	88.53 104	92.72 82	98.00 90	90.22 131	84.81 88	94.45 102	83.05 77	87.65 77	92.74 69	81.04 175	94.51 113	94.45 90	99.32 103	99.21 93
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
thres100view900		91.69 79	91.52 100	91.88 66	91.61 89	98.89 66	95.49 63	86.96 64	93.24 113	80.82 98	87.90 72	71.15 152	96.88 47	96.00 84	93.51 110	99.51 62	99.95 13

CLD-MVS		91.67 80	91.30 105	92.10 64	91.25 96	96.59 116	95.93 55	87.25 63	96.86 73	85.55 58	87.08 80	73.01 142	93.26 90	93.07 130	92.84 124	99.34 98	99.68 49
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ET-MVSNet_ETH3D		91.59 81	94.96 63	87.65 107	72.75 213	97.24 104	95.29 66	82.73 102	96.81 74	78.49 109	95.30 37	90.48 84	97.23 36	91.60 147	94.31 91	99.43 83	99.01 107

tfpn200view9		91.47 82	91.31 103	91.65 68	91.61 89	98.69 71	95.03 71	86.17 71	93.24 113	80.82 98	87.90 72	71.15 152	96.80 49	95.53 99	92.82 126	99.47 71	99.88 26

CANet_DTU		91.36 83	95.75 52	86.23 119	92.31 86	98.71 70	95.60 62	78.41 135	98.20 44	56.48 182	94.38 45	87.96 92	95.11 69	96.89 59	96.07 69	99.48 69	98.01 143

thres200		91.36 83	91.19 107	91.55 70	91.60 91	98.69 71	94.98 72	86.17 71	92.16 124	80.76 100	87.66 76	71.15 152	96.35 54	95.53 99	93.23 116	99.47 71	99.92 22

FMVSNet3		91.25 85	92.13 96	90.21 88	85.64 152	93.14 155	95.29 66	80.09 122	96.40 77	85.74 55	77.13 122	86.81 98	94.98 71	97.19 53	97.11 41	99.55 57	97.13 163

thres400		91.24 86	91.01 113	91.50 73	91.56 92	98.77 67	94.66 79	86.41 69	91.87 130	80.56 101	87.05 82	71.01 155	96.35 54	95.67 92	92.82 126	99.48 69	99.88 26

PVSNet_Blended_VisFu		91.20 87	92.89 90	89.23 99	93.41 73	98.61 76	89.80 133	85.39 81	92.84 117	82.80 79	74.21 138	91.38 80	84.64 154	97.22 51	96.04 72	99.34 98	99.93 19

DCV-MVSNet		91.15 88	92.00 97	90.17 90	90.78 105	92.23 172	93.70 91	81.17 117	95.16 92	82.98 78	89.46 66	83.31 112	93.98 87	91.79 146	92.87 121	98.41 170	99.18 95

DI_MVS_plusplus_trai		91.11 89	91.47 101	90.68 84	90.01 123	97.77 94	95.87 59	83.56 96	94.72 98	82.12 87	68.46 157	87.46 93	93.07 94	96.46 71	95.73 78	99.47 71	99.71 44

diffmvs		91.05 90	91.15 108	90.93 81	90.15 118	97.79 93	94.05 86	85.45 78	95.63 87	81.95 91	80.45 116	73.01 142	94.47 80	95.56 97	95.89 77	99.49 66	99.72 42
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Vis-MVSNet (Re-imp)		91.05 90	94.43 64	87.11 109	91.05 100	97.99 91	92.53 110	83.82 93	92.71 120	76.28 114	84.50 100	92.43 72	79.52 180	97.24 50	97.68 25	99.43 83	98.45 127

thres600view7		90.97 92	90.70 115	91.30 75	91.53 93	98.69 71	94.33 80	86.17 71	91.75 132	80.19 102	86.06 92	70.90 156	96.10 59	95.53 99	92.08 134	99.47 71	99.86 30

baseline2		90.91 93	94.40 67	86.84 112	87.54 143	96.83 112	89.95 132	79.22 130	96.00 85	77.04 112	88.68 67	89.73 86	88.01 143	96.35 74	93.51 110	99.29 106	99.68 49

casdiffmvs_mvg		90.83 94	90.52 119	91.20 78	90.56 109	97.67 99	94.96 73	85.45 78	90.72 138	82.03 88	76.70 128	77.08 129	94.61 78	96.57 69	95.62 80	99.57 46	99.28 87
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ACMP	89.80 9	90.72 95	91.15 108	90.21 88	92.55 84	96.52 118	92.63 107	85.71 77	94.65 99	81.06 97	93.32 50	70.56 158	90.52 121	92.68 136	91.05 147	98.76 144	99.31 86
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
casdiffmvs		90.69 96	90.56 118	90.85 82	90.14 119	97.81 92	92.94 101	85.30 82	93.47 111	82.50 83	76.34 132	74.12 138	94.67 75	96.51 70	96.26 64	99.55 57	99.42 77
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
FA-MVS(training)		90.67 97	93.03 87	87.92 106	90.95 102	98.45 82	92.61 109	66.04 194	94.90 94	84.47 64	77.52 121	91.74 76	94.07 85	97.11 55	92.46 132	99.40 88	99.03 106

ACMM	89.40 10	90.58 98	90.02 125	91.23 77	93.30 75	94.75 142	90.69 127	88.22 54	95.20 90	82.70 81	88.54 68	71.40 150	93.48 89	93.64 127	90.94 148	98.99 127	95.72 179
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		90.49 99	91.12 111	89.75 95	84.99 155	92.73 160	93.94 87	80.09 122	96.40 77	85.74 55	77.13 122	86.81 98	94.42 81	94.12 116	93.73 99	99.35 94	96.90 167

test1		90.49 99	91.12 111	89.75 95	84.99 155	92.73 160	93.94 87	80.09 122	96.40 77	85.74 55	77.13 122	86.81 98	94.42 81	94.12 116	93.73 99	99.35 94	96.90 167

ECVR-MVS		90.37 101	88.96 134	92.01 65	92.76 80	99.20 60	94.67 77	86.82 67	92.58 121	86.71 48	68.95 156	71.46 149	91.69 108	96.85 61	96.33 61	99.45 81	97.38 156

LGP-MVS_train		90.34 102	91.63 99	88.83 103	93.31 74	96.14 125	95.49 63	85.24 85	93.91 106	68.71 135	93.96 46	71.63 147	91.12 117	93.82 124	92.79 128	99.07 121	99.16 96

test1111		90.01 103	88.67 136	91.57 69	92.68 83	99.20 60	94.25 83	86.90 66	92.03 127	85.04 61	67.79 161	71.21 151	91.12 117	96.83 63	96.34 60	99.42 86	97.28 159

EPNet_dtu		89.82 104	94.18 73	84.74 129	96.87 52	95.54 135	92.65 106	86.91 65	96.99 68	54.17 193	92.41 56	88.54 88	78.35 183	96.15 79	96.05 71	99.47 71	93.60 191
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		89.81 105	89.75 126	89.88 93	93.22 77	93.99 149	94.78 74	85.23 86	94.01 105	82.52 82	95.00 40	87.23 95	92.01 104	85.16 200	83.48 205	91.54 211	89.38 205

MDTV_nov1_ep13		89.63 106	94.38 68	84.09 136	88.76 135	97.53 103	89.37 141	68.46 192	96.95 69	70.27 130	87.88 74	93.67 66	91.04 119	93.12 128	93.83 98	96.62 200	97.68 149

UA-Net		89.56 107	93.03 87	85.52 125	92.46 85	97.55 101	91.92 113	81.91 107	85.24 165	71.39 125	83.57 105	96.56 44	76.01 194	96.81 64	97.04 45	99.46 77	94.41 186

FMVSNet2		89.51 108	89.63 127	89.38 97	84.99 155	92.73 160	93.94 87	79.28 129	93.73 108	84.28 67	69.36 155	82.32 115	94.42 81	96.16 78	96.22 66	99.35 94	96.90 167

CostFormer		89.42 109	91.67 98	86.80 114	89.99 124	96.33 121	90.75 125	64.79 196	95.17 91	83.62 74	86.20 90	82.15 117	92.96 95	89.22 170	92.94 119	98.68 152	99.65 51

FC-MVSNet-train		89.37 110	89.62 128	89.08 100	90.48 111	94.16 148	89.45 137	83.99 92	91.09 136	80.09 103	82.84 110	74.52 137	91.44 114	93.79 125	91.57 140	99.01 125	99.35 83

OPM-MVS		89.33 111	87.45 146	91.53 72	94.49 69	96.20 123	96.47 54	89.72 47	82.77 172	75.43 115	80.53 115	70.86 157	93.80 88	94.00 120	91.85 138	99.29 106	95.91 177
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test-LLR		89.31 112	93.60 80	84.30 133	88.08 139	96.98 108	88.10 146	78.00 136	94.83 95	62.43 156	84.29 102	90.96 81	89.70 128	95.63 94	92.86 122	99.51 62	99.64 53

EPMVS		89.31 112	93.70 77	84.18 135	91.10 99	98.10 88	89.17 143	62.71 200	96.24 80	70.21 132	86.46 87	92.37 73	92.79 96	91.95 144	93.59 109	99.10 118	97.19 160

Anonymous20231211		89.22 114	87.56 144	91.16 79	90.23 117	96.62 115	93.22 98	85.44 80	92.89 116	84.37 66	60.13 177	81.25 120	96.02 61	90.61 155	92.01 135	97.70 190	99.41 79

Effi-MVS+		88.96 115	91.13 110	86.43 117	89.12 131	97.62 100	93.15 99	75.52 154	93.90 107	66.40 140	86.23 89	70.51 159	95.03 70	95.89 85	94.28 92	99.37 90	99.51 70

SCA		88.76 116	94.29 71	82.30 152	89.33 129	96.81 113	87.68 148	61.52 205	96.95 69	64.68 146	88.35 69	94.80 50	91.58 111	92.23 138	93.21 117	98.99 127	97.70 148

test0.0.03 1		88.71 117	92.22 95	84.63 131	88.08 139	94.71 144	85.91 171	78.00 136	95.54 88	72.96 119	86.10 91	85.88 103	83.59 162	92.95 134	93.24 115	99.25 112	97.09 164

PatchmatchNet		88.67 118	94.10 74	82.34 151	89.38 128	97.72 95	87.24 154	62.18 203	97.00 67	64.79 145	87.97 71	94.43 55	91.55 112	91.21 152	92.77 129	98.90 132	97.60 152
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dps		88.66 119	90.19 123	86.88 111	89.94 125	96.48 119	89.56 135	64.08 198	94.12 104	89.00 35	83.39 107	82.56 114	90.16 125	86.81 192	89.26 166	98.53 165	98.71 117

TESTMET0.1,1		88.63 120	93.60 80	82.84 148	84.07 163	96.98 108	88.10 146	73.22 174	94.83 95	62.43 156	84.29 102	90.96 81	89.70 128	95.63 94	92.86 122	99.51 62	99.64 53

CHOSEN 1792x2688		88.63 120	89.01 132	88.19 105	94.83 65	99.21 59	92.66 105	79.85 126	92.40 123	72.18 124	56.38 197	80.22 125	90.24 123	97.64 44	97.28 38	99.37 90	99.94 16

CDS-MVSNet		88.59 122	90.13 124	86.79 115	86.98 148	95.43 136	92.03 112	81.33 115	85.54 162	74.51 118	77.07 125	85.14 106	87.03 148	93.90 123	95.18 83	98.88 134	98.67 119
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IB-MVS	84.67 14	88.34 123	90.61 117	85.70 122	92.99 79	98.62 75	78.85 197	86.07 74	94.35 103	88.64 36	85.99 93	75.69 132	68.09 207	88.21 173	91.43 142	99.55 57	99.96 10
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
test-mter		88.25 124	93.27 86	82.38 150	83.89 164	96.86 111	87.10 158	72.80 176	94.58 100	61.85 161	83.21 108	90.65 83	89.18 132	95.43 104	92.58 131	99.46 77	99.61 60

COLMAP_ROB	84.42 15	88.24 125	87.32 147	89.32 98	95.83 59	95.82 129	92.81 102	87.68 61	92.09 126	72.64 122	72.34 146	79.96 126	88.79 134	89.54 165	89.46 162	98.16 179	92.00 197
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
IterMVS-LS		87.95 126	89.40 130	86.26 118	88.79 134	90.93 187	91.23 121	76.05 151	90.87 137	71.07 127	75.51 135	81.18 121	91.21 116	94.11 119	95.01 84	99.20 115	98.23 134
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		87.86 127	88.25 139	87.40 108	94.67 67	98.54 78	90.33 130	76.51 150	89.60 146	70.89 128	51.43 208	85.69 104	92.79 96	96.59 68	95.96 74	99.22 114	99.94 16

Vis-MVSNet		87.60 128	91.31 103	83.27 143	89.14 130	98.04 89	90.35 129	79.42 127	87.23 151	66.92 139	79.10 120	84.63 108	74.34 201	95.81 87	96.06 70	99.46 77	98.32 131
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GeoE		87.55 129	88.17 140	86.82 113	88.74 136	96.32 122	92.75 104	74.93 159	90.13 143	72.73 120	69.47 154	74.03 139	92.51 101	93.99 121	93.62 107	99.29 106	99.59 61

dmvs_re		87.43 130	87.99 141	86.77 116	84.94 159	96.19 124	91.87 114	85.95 76	91.25 135	68.58 136	81.45 112	66.04 167	89.95 127	90.91 153	91.57 140	99.37 90	98.54 123

RPMNet		87.35 131	92.41 93	81.45 156	88.85 133	96.06 126	89.42 140	59.59 212	93.57 109	61.81 162	76.48 131	91.48 79	90.18 124	96.32 75	93.37 113	98.87 135	99.59 61

tpm cat1		87.34 132	88.52 138	85.95 120	89.83 126	95.80 130	90.73 126	64.91 195	92.99 115	82.21 86	71.19 152	82.68 113	90.13 126	86.38 193	90.87 150	97.90 187	99.74 39

MS-PatchMatch		87.19 133	88.59 137	85.55 124	93.15 78	96.58 117	92.35 111	74.19 167	91.97 129	70.33 129	71.42 150	85.89 102	84.28 156	93.12 128	89.16 168	99.00 126	91.99 198

Effi-MVS+-dtu		87.18 134	90.48 120	83.32 142	86.51 149	95.76 132	91.16 123	74.28 166	90.44 142	61.31 165	86.72 86	72.68 145	91.25 115	95.01 109	93.64 102	95.45 205	99.12 100

FMVSNet5		87.06 135	89.52 129	84.20 134	79.92 201	86.57 207	87.11 157	72.37 178	96.06 83	75.41 116	84.33 101	91.76 75	91.60 110	91.51 148	91.22 145	98.77 141	85.16 210

Fast-Effi-MVS+-dtu		86.94 136	91.27 106	81.89 153	86.27 150	95.06 137	90.68 128	68.93 189	91.76 131	57.18 180	89.56 65	75.85 131	89.19 131	94.56 112	92.84 124	99.07 121	99.23 90

Fast-Effi-MVS+		86.94 136	87.88 143	85.84 121	86.99 147	95.80 130	91.24 120	73.48 173	92.75 118	69.22 133	72.70 144	65.71 168	94.84 73	94.98 110	94.71 88	99.26 110	98.48 126

tpmrst		86.78 138	90.29 121	82.69 149	90.55 110	96.95 110	88.49 145	62.58 201	95.09 93	63.52 152	76.67 130	84.00 111	92.05 103	87.93 176	91.89 137	98.98 129	99.50 72

CR-MVSNet		86.73 139	91.47 101	81.20 159	88.56 137	96.06 126	89.43 138	61.37 206	93.57 109	60.81 167	72.89 143	88.85 87	88.13 141	96.03 81	93.64 102	98.89 133	99.22 91

ADS-MVSNet		86.68 140	90.79 114	81.88 154	90.38 114	96.81 113	86.90 159	60.50 210	96.01 84	63.93 149	81.67 111	84.72 107	90.78 120	87.03 186	91.67 139	98.77 141	97.63 151

FMVSNet1		85.85 141	84.91 157	86.96 110	82.70 169	91.39 181	91.54 117	77.45 142	85.29 164	79.56 105	60.70 174	72.68 145	92.37 102	94.12 116	93.73 99	98.12 180	96.44 171

FC-MVSNet-test		85.51 142	89.08 131	81.35 157	85.31 154	93.35 151	87.65 149	77.55 141	90.01 144	64.07 148	79.63 118	81.83 119	74.94 198	92.08 141	90.83 152	98.55 162	95.81 178

ACMH	85.22 13	85.40 143	85.73 154	85.02 127	91.76 88	94.46 147	84.97 177	81.54 113	85.18 166	65.22 144	76.92 126	64.22 169	88.58 137	90.17 157	90.25 158	98.03 183	98.90 111
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		85.35 144	86.00 153	84.59 132	84.97 158	95.57 134	88.98 144	77.29 145	81.44 177	71.36 126	71.48 149	75.00 136	87.03 148	91.92 145	92.21 133	97.92 186	94.40 187

ACMH+	85.62 12	85.27 145	84.96 156	85.64 123	90.84 104	94.78 141	87.46 151	81.30 116	86.94 152	67.35 138	74.56 137	64.09 170	88.70 135	88.14 174	89.00 169	98.22 178	97.19 160

USDC		85.11 146	85.35 155	84.83 128	89.45 127	94.93 140	92.98 100	77.30 144	90.53 140	61.80 163	76.69 129	59.62 180	88.90 133	92.78 135	90.79 154	98.53 165	92.12 195

IterMVS		85.02 147	88.98 133	80.41 165	87.03 146	90.34 195	89.78 134	69.45 186	89.77 145	54.04 194	73.71 140	82.05 118	83.44 165	95.11 107	93.64 102	98.75 145	98.22 136
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		84.91 148	88.90 135	80.25 168	87.04 145	90.27 196	89.23 142	69.25 188	89.17 147	54.04 194	73.65 141	82.22 116	83.23 170	95.11 107	93.63 106	98.73 146	98.23 134

PatchT		84.89 149	90.67 116	78.13 188	87.83 142	94.99 139	72.46 209	60.22 211	91.74 133	60.81 167	72.16 147	86.95 96	88.13 141	96.03 81	93.64 102	99.36 93	99.22 91

pmmvs4		84.88 150	84.67 158	85.13 126	82.80 168	92.37 165	87.29 152	79.08 131	90.51 141	74.94 117	70.37 153	62.49 173	88.17 140	92.01 143	88.51 174	98.49 168	96.44 171

CVMVSNet		84.01 151	86.91 148	80.61 163	88.39 138	93.29 152	86.06 167	82.29 104	83.13 170	54.29 190	72.68 145	79.59 127	75.11 197	91.23 151	92.91 120	97.54 194	95.58 180

tpm		83.97 152	87.97 142	79.31 178	87.35 144	93.21 154	86.00 169	61.90 204	90.69 139	54.01 196	79.42 119	75.61 133	88.65 136	87.18 184	90.48 156	97.95 185	99.21 93

GA-MVS		83.83 153	86.63 149	80.58 164	85.40 153	94.73 143	87.27 153	78.76 134	86.49 154	49.57 204	74.21 138	67.67 164	83.38 166	95.28 106	90.92 149	99.08 120	97.09 164

UniMVSNet_NR-MVSNet		83.83 153	83.70 161	83.98 137	81.41 179	92.56 164	86.54 162	82.96 100	85.98 159	66.27 141	66.16 165	63.63 171	87.78 145	87.65 179	90.81 153	98.94 130	99.13 98

UniMVSNet (Re)		83.28 155	83.16 162	83.42 141	81.93 174	93.12 156	86.27 165	80.83 118	85.88 160	68.23 137	64.56 168	60.58 175	84.25 157	89.13 171	89.44 164	99.04 124	99.40 80

thisisatest0515		83.17 156	86.49 150	79.30 179	82.04 172	93.12 156	78.70 198	77.92 138	86.43 155	63.05 153	74.91 136	73.01 142	75.56 196	92.10 140	88.05 187	98.50 167	97.76 147

TinyColmap		83.03 157	82.24 166	83.95 138	88.88 132	93.22 153	89.48 136	76.89 147	87.53 150	62.12 158	68.46 157	55.03 196	88.43 139	90.87 154	89.65 160	97.89 188	90.91 201

testgi		82.88 158	86.14 152	79.08 181	86.05 151	92.20 173	81.23 194	74.77 162	88.70 148	57.63 179	86.73 85	61.53 174	76.83 191	90.33 156	89.43 165	97.99 184	94.05 188

DU-MVS		82.87 159	82.16 167	83.70 140	80.77 188	92.24 169	86.54 162	81.91 107	86.41 156	66.27 141	63.95 169	55.66 194	87.78 145	86.83 189	90.86 151	98.94 130	99.13 98

MIMVSNet		82.87 159	86.17 151	79.02 182	77.23 209	92.88 159	84.88 178	60.62 209	86.72 153	64.16 147	73.58 142	71.48 148	88.51 138	94.14 115	93.50 112	98.72 148	90.87 202

NR-MVSNet		82.37 161	81.95 169	82.85 147	82.56 171	92.24 169	87.49 150	81.91 107	86.41 156	65.51 143	63.95 169	52.93 205	80.80 177	89.41 167	89.61 161	98.85 137	99.10 103

Baseline_NR-MVSNet		82.08 162	80.64 176	83.77 139	80.77 188	88.50 202	86.88 160	81.71 111	85.58 161	68.80 134	58.20 189	57.75 186	86.16 150	86.83 189	88.68 171	98.33 175	98.90 111

TranMVSNet+NR-MVSNet		82.07 163	81.36 172	82.90 146	80.43 194	91.39 181	87.16 156	82.75 101	84.28 168	62.98 154	62.28 173	56.01 193	85.30 153	86.06 195	90.69 155	98.80 138	98.80 114

pm-mvs1		81.68 164	81.70 170	81.65 155	82.61 170	92.26 168	85.54 175	78.95 132	76.29 199	63.81 150	58.43 188	66.33 166	80.63 178	92.30 137	89.93 159	98.37 174	96.39 173

TDRefinement		81.49 165	80.08 182	83.13 145	91.02 101	94.53 145	91.66 116	82.43 103	81.70 175	62.12 158	62.30 172	59.32 181	73.93 202	87.31 182	85.29 198	97.61 191	90.14 203

anonymousdsp		81.29 166	84.52 160	77.52 190	79.83 202	92.62 163	82.61 189	70.88 183	80.76 181	50.82 201	68.35 159	68.76 162	82.45 173	93.00 131	89.45 163	98.55 162	98.69 118

gg-mvs-nofinetune		81.27 167	84.65 159	77.32 191	87.96 141	98.48 81	95.64 61	56.36 215	59.35 217	32.80 222	47.96 212	92.11 74	91.49 113	98.12 24	97.00 47	99.65 24	99.56 66

tfpnnormal		81.11 168	79.33 190	83.19 144	84.23 161	92.29 167	86.76 161	82.27 105	72.67 205	62.02 160	56.10 199	53.86 202	85.35 152	92.06 142	89.23 167	98.49 168	99.11 102

UniMVSNet_ETH3D		80.95 169	77.71 198	84.74 129	84.45 160	93.11 158	86.45 164	79.97 125	75.21 201	70.22 131	51.24 209	50.26 211	89.55 130	84.47 202	91.12 146	97.81 189	98.53 124

V42		80.88 170	80.74 174	81.05 160	81.21 182	92.01 175	85.96 170	77.75 140	81.62 176	59.73 174	59.93 180	58.35 185	82.98 172	86.90 188	88.06 186	98.69 151	98.32 131

v2v482		80.86 171	80.52 180	81.25 158	80.79 187	91.85 176	85.68 173	78.78 133	81.05 178	58.09 177	60.46 175	56.08 191	85.45 151	87.27 183	88.53 173	98.73 146	98.38 130

v8		80.61 172	80.61 178	80.62 162	81.51 177	91.00 186	86.06 167	74.07 169	81.78 174	59.93 173	60.10 179	58.42 184	83.35 167	86.99 187	88.11 184	98.79 139	97.83 145

pmmvs5		80.48 173	81.43 171	79.36 177	81.50 178	92.24 169	82.07 192	74.08 168	78.10 192	55.86 185	67.72 162	54.35 199	83.91 161	92.97 132	88.65 172	98.77 141	96.01 175

v10		80.38 174	80.73 175	79.96 170	81.22 181	90.40 194	86.11 166	71.63 180	82.42 173	57.65 178	58.74 186	57.47 187	84.44 155	89.75 161	88.28 177	98.71 149	98.06 142

v1144		80.36 175	80.63 177	80.05 169	80.86 186	91.56 179	85.78 172	75.22 156	80.73 182	55.83 186	58.51 187	56.99 189	83.93 160	89.79 160	88.25 178	98.68 152	98.56 122

SixPastTwentyTwo		80.28 176	82.06 168	78.21 187	81.89 176	92.35 166	77.72 199	74.48 163	83.04 171	54.22 191	76.06 133	56.40 190	83.55 163	86.83 189	84.83 200	97.38 195	94.93 184

CP-MVSNet		79.90 177	79.49 187	80.38 166	80.72 190	90.83 188	82.98 186	75.17 157	79.70 187	61.39 164	59.74 181	51.98 208	83.31 168	87.37 181	88.38 175	98.71 149	98.45 127

v1192		79.84 178	80.05 184	79.61 173	80.49 193	91.04 185	85.56 174	74.37 165	80.73 182	54.35 189	57.07 194	54.54 198	84.23 158	89.94 158	88.38 175	98.63 156	98.61 120

WR-MVS_H		79.76 179	80.07 183	79.40 176	81.25 180	91.73 178	82.77 187	74.82 161	79.02 191	62.55 155	59.41 183	57.32 188	76.27 193	87.61 180	87.30 192	98.78 140	98.09 140

WR-MVS		79.67 180	80.25 181	79.00 183	80.65 191	91.16 183	83.31 184	76.57 149	80.97 179	60.50 172	59.20 184	58.66 183	74.38 200	85.85 197	87.76 189	98.61 157	98.14 137

v148		79.66 181	79.13 192	80.27 167	81.02 184	91.76 177	81.90 193	79.32 128	79.24 189	63.79 151	58.07 191	54.34 200	77.17 189	84.42 203	87.52 191	98.40 171	98.59 121

LTVRE_ROB	79.45 16	79.66 181	80.55 179	78.61 185	83.01 167	92.19 174	87.18 155	73.69 172	71.70 208	43.22 217	71.22 151	50.85 209	87.82 144	89.47 166	90.43 157	96.75 198	98.00 144
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
v144192		79.61 183	79.77 185	79.41 175	80.28 195	91.06 184	84.87 179	73.86 170	79.65 188	55.38 187	57.76 192	55.20 195	83.46 164	88.42 172	87.89 188	98.61 157	98.42 129

v1921920		79.55 184	79.77 185	79.30 179	80.24 196	90.77 190	85.37 176	73.75 171	80.38 184	53.78 197	56.89 196	54.18 201	84.05 159	89.55 164	88.13 183	98.59 159	98.52 125

TransMVSNet (Re)		79.51 185	78.36 194	80.84 161	83.17 165	89.72 198	84.22 182	81.45 114	73.98 204	60.79 170	57.20 193	56.05 192	77.11 190	89.88 159	88.86 170	98.30 177	92.83 193

MVS-HIRNet		79.34 186	82.56 163	75.57 196	84.11 162	95.02 138	75.03 206	57.28 214	85.50 163	55.88 184	53.00 205	70.51 159	83.05 171	92.12 139	91.96 136	98.09 181	89.83 204

PS-CasMVS		79.06 187	78.58 193	79.63 172	80.59 192	90.55 192	82.54 190	75.04 158	77.76 193	58.84 175	58.16 190	50.11 213	82.09 174	87.05 185	88.18 181	98.66 155	98.27 133

v1240		78.97 188	79.27 191	78.63 184	80.04 197	90.61 191	84.25 181	72.95 175	79.22 190	52.70 199	56.22 198	52.88 207	83.28 169	89.60 163	88.20 180	98.56 161	98.14 137

pmnet_mix02		78.91 189	81.17 173	76.28 195	81.91 175	90.82 189	74.25 207	77.87 139	86.17 158	49.04 205	67.97 160	62.93 172	77.40 187	82.75 208	82.11 207	97.18 196	95.42 181

MDTV_nov1_ep13_2view		78.83 190	82.35 164	74.73 199	78.65 204	91.51 180	79.18 196	62.52 202	84.51 167	52.51 200	67.49 163	67.29 165	78.90 181	85.52 199	86.34 195	96.62 200	93.76 189

PEN-MVS		78.80 191	78.13 196	79.58 174	80.03 198	89.67 199	83.61 183	75.83 152	77.71 195	58.41 176	60.11 178	50.00 214	81.02 176	84.08 204	88.14 182	98.59 159	97.18 162

EG-PatchMatch MVS		78.32 192	79.42 189	77.03 193	83.03 166	93.77 150	84.47 180	69.26 187	75.85 200	53.69 198	55.68 200	60.23 178	73.20 203	89.69 162	88.22 179	98.55 162	92.54 194

DTE-MVSNet		77.92 193	77.42 199	78.51 186	79.34 203	89.00 201	83.05 185	75.60 153	76.89 197	56.58 181	59.63 182	50.31 210	78.09 186	82.57 209	87.56 190	98.38 172	95.95 176

v7n		77.71 194	78.25 195	77.09 192	78.49 205	90.55 192	82.15 191	71.11 182	76.79 198	54.18 192	55.63 201	50.20 212	78.28 184	89.36 169	87.15 193	98.33 175	98.07 141

gm-plane-assit		77.20 195	82.26 165	71.30 202	81.10 183	82.00 215	54.33 220	64.41 197	63.80 216	40.93 219	59.04 185	76.57 130	87.30 147	98.26 22	97.36 37	99.74 13	98.76 116

N_pmnet		76.83 196	77.97 197	75.50 197	80.96 185	88.23 204	72.81 208	76.83 148	80.87 180	50.55 202	56.94 195	60.09 179	75.70 195	83.28 206	84.23 202	96.14 204	92.12 195

pmmvs6		76.79 197	75.69 204	78.09 189	79.95 200	89.57 200	80.92 195	74.46 164	64.79 214	60.74 171	45.71 214	60.55 176	78.37 182	88.04 175	86.00 196	94.07 208	95.15 182

CMPMVS	58.73 17	76.78 198	74.27 205	79.70 171	93.26 76	95.58 133	82.74 188	77.44 143	71.46 211	56.29 183	53.58 204	59.13 182	77.33 188	79.20 210	79.71 210	91.14 213	81.24 213
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		76.76 199	79.47 188	73.60 200	79.99 199	87.47 205	77.39 200	75.43 155	77.62 196	47.83 208	64.78 167	60.44 177	64.80 208	86.28 194	86.53 194	96.17 203	93.19 192

PM-MVS		75.81 200	76.11 203	75.46 198	73.81 210	85.48 209	76.42 202	70.57 184	80.05 186	54.75 188	62.33 171	39.56 220	80.59 179	87.71 178	82.81 206	96.61 202	94.81 185

pmmvs-eth3d		75.17 201	74.09 206	76.43 194	72.92 211	84.49 211	76.61 201	72.42 177	74.33 202	61.28 166	54.71 203	39.42 221	78.20 185	87.77 177	84.25 201	97.17 197	93.63 190

Anonymous20231206		74.59 202	77.00 200	71.78 201	77.89 208	87.45 206	75.14 205	72.29 179	77.76 193	46.65 210	52.14 206	52.93 205	61.10 211	89.37 168	88.09 185	97.59 192	91.30 200

test20.03		72.81 203	76.24 202	68.80 205	78.31 206	85.40 210	71.04 210	71.20 181	71.85 207	43.40 216	65.31 166	54.71 197	51.27 214	85.92 196	84.18 203	97.58 193	86.35 209

test_method		71.90 204	76.72 201	66.28 210	60.87 219	78.37 217	69.75 214	49.81 220	83.44 169	49.63 203	47.13 213	53.23 204	76.38 192	91.32 150	85.76 197	91.22 212	97.77 146

new_pmnet		71.86 205	73.67 207	69.75 204	72.56 214	84.20 212	70.95 212	66.81 193	80.34 185	43.62 215	51.60 207	53.81 203	71.24 205	82.91 207	80.93 208	93.35 210	81.92 212

MDA-MVSNet-bldmvs		69.61 206	70.36 209	68.74 206	62.88 217	88.50 202	65.40 217	77.01 146	71.60 210	43.93 212	66.71 164	35.33 223	72.47 204	61.01 216	80.63 209	90.73 214	88.75 207

pmmvs3		69.04 207	70.75 208	67.04 208	66.83 215	78.54 216	64.99 218	60.92 208	64.67 215	40.61 220	55.08 202	40.29 219	74.89 199	83.76 205	84.01 204	93.98 209	88.88 206

MIMVSNet1		68.63 208	70.24 210	66.76 209	56.86 221	83.26 213	67.93 215	70.26 185	68.05 212	46.80 209	40.44 215	48.15 215	62.01 209	84.96 201	84.86 199	96.69 199	81.93 211

GG-mvs-BLEND		67.99 209	97.35 38	33.72 217	1.22 227	99.72 17	98.30 34	0.57 224	97.61 61	1.18 228	93.26 51	96.63 43	1.74 224	97.15 54	97.14 40	99.34 98	99.96 10

new-patchmatchnet		67.66 210	68.07 211	67.18 207	72.85 212	82.86 214	63.09 219	68.61 191	66.60 213	42.64 218	49.28 210	38.68 222	61.21 210	75.84 211	75.22 212	94.67 207	88.00 208

FPMVS		63.27 211	61.31 213	65.57 211	78.25 207	74.42 220	75.23 204	68.92 190	72.33 206	43.87 213	49.01 211	43.94 217	48.64 216	61.15 215	58.81 217	78.51 220	69.49 218

Gipuma		54.59 212	53.98 214	55.30 212	59.03 220	52.63 222	47.17 222	56.08 216	71.68 209	37.54 221	20.90 221	19.00 225	52.33 213	71.69 213	75.20 213	79.64 219	66.79 219
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	49.05 18	51.88 213	50.56 216	53.42 213	64.21 216	43.30 224	42.64 223	62.93 199	50.56 218	43.72 214	37.44 216	42.95 218	35.05 219	58.76 218	54.58 218	71.95 221	66.33 220
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		50.69 214	52.33 215	48.78 214	51.24 222	64.81 221	47.91 221	53.79 219	44.95 219	21.75 223	29.98 219	25.90 224	31.98 221	59.95 217	65.37 215	86.00 217	75.36 216

E-PMN		37.15 215	34.82 218	39.86 215	47.53 224	35.42 226	23.79 225	55.26 217	35.18 222	14.12 225	17.38 224	14.13 227	39.73 218	32.24 220	46.98 219	58.76 222	62.39 222

EMVS		36.45 216	33.63 219	39.74 216	48.47 223	35.73 225	23.59 226	55.11 218	35.61 221	12.88 226	17.49 222	14.62 226	41.04 217	29.33 221	43.00 220	57.32 223	59.62 223

MVE	42.40 19	36.00 217	38.65 217	32.92 218	29.16 225	46.17 223	22.61 227	44.21 221	26.44 224	18.88 224	17.41 223	9.36 229	32.29 220	45.75 219	61.38 216	50.35 224	64.03 221
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		21.55 218	30.91 220	10.62 219	2.78 226	11.66 227	18.51 228	4.82 222	38.21 220	4.06 227	36.35 217	4.47 230	26.81 222	23.27 222	27.11 221	6.75 225	75.30 217

test123		16.81 219	24.80 221	7.48 220	0.82 228	8.38 228	11.92 229	2.60 223	28.96 223	1.12 229	28.39 220	1.26 231	24.51 223	8.93 223	22.19 222	3.90 226	75.49 215

uanet_test		0.00 220	0.00 222	0.00 221	0.00 229	0.00 229	0.00 230	0.00 225	0.00 225	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet-low-res		0.00 220	0.00 222	0.00 221	0.00 229	0.00 229	0.00 230	0.00 225	0.00 225	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet		0.00 220	0.00 222	0.00 221	0.00 229	0.00 229	0.00 230	0.00 225	0.00 225	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

TPM-MVS					99.50 1	99.78 12	99.69 1			88.49 37	97.88 26	98.84 22	99.42 1			99.76 10	97.44 154

RE-MVS-def										46.54 211

9.14												99.73 8

SR-MVS					99.27 16			95.82 18				99.00 18

Anonymous202405211			87.54 145		90.72 106	97.10 107	93.40 96	85.30 82	91.41 134		60.23 176	80.69 124	95.80 65	91.33 149	92.60 130	98.38 172	99.40 80

our_test_3					81.94 173	90.26 197	75.39 203

ambc			64.61 212		61.80 218	75.31 219	71.00 211		74.16 203	48.83 206	36.02 218	13.22 228	58.66 212	85.80 198	76.26 211	88.01 215	91.53 199

MTAPA										94.58 14		98.56 24

MTMP										95.24 8		98.13 30

Patchmatch-RL test							37.05 224

tmp_tt				71.24 203	90.29 116	76.39 218	65.81 216	59.43 213	97.62 59	79.65 104	90.60 61	68.71 163	49.71 215	72.71 212	65.70 214	82.54 218

XVS					93.63 71	99.64 25	94.32 81			83.97 71		98.08 32				99.59 37

X-MVStestdata					93.63 71	99.64 25	94.32 81			83.97 71		98.08 32				99.59 37

mPP-MVS					98.66 29							97.11 40

NP-MVS									97.69 57

Patchmtry						95.86 128	89.43 138	61.37 206		60.81 167

DeepMVS_CX						85.88 208	69.83 213	81.56 112	87.99 149	48.22 207	71.85 148	45.52 216	68.67 206	63.21 214		86.64 216	80.03 214

High-resolution multi-view benchmark