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 53	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
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
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 53	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 47	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 85

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 50	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 49	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 61	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 91

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 88	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 54	99.56 53	99.14 99

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 58	99.64 25	99.35 85
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 66	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 96	98.07 184	99.27 90

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 73	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 45	96.88 60	98.16 15	99.56 53	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	97.44 156

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	99.06 107

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 87	99.41 89	98.12 141
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 92	95.95 35	99.16 16	95.13 70	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 56	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 80	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	84.21 106	94.55 54	97.25 35	95.50 102	96.23 67	99.28 111	99.09 106

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 68	97.81 36	96.73 57	99.43 85	99.42 78

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 61	95.67 92	93.33 116	99.55 58	99.12 102
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 99	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	91.45 120	94.42 36	99.76 1	86.48 50	92.88 55	98.12 31	92.62 101	99.49 2	99.32 3	95.15 208	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	78.62 110	94.80 41	95.74 46	96.11 60	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 66	91.74 76	96.96 42	93.69 126	96.58 60	98.86 138	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 72	94.64 52	96.35 56	96.02 83	95.99 75	99.34 100	97.65 152

MVS_111021_HR		95.70 43	98.16 30	92.83 57	97.57 46	99.77 15	94.78 76	88.05 55	98.61 34	82.29 87	98.85 5	94.66 51	94.63 78	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 98	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	84.57 101	95.54 49	95.87 65	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 70	84.59 100	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 99	85.29 84	99.27 13	85.89 54	93.86 48	92.70 70	97.19 39	97.70 40	96.18 69	99.49 68	99.76 36

CS-MVS		94.82 48	96.19 48	93.22 52	95.19 64	99.24 58	95.10 71	85.07 87	98.72 32	87.33 45	91.35 57	89.98 85	97.06 41	98.01 27	96.28 65	99.60 33	99.72 42

MVSTER		94.75 49	96.50 45	92.70 61	90.91 105	94.51 148	97.37 48	83.37 97	98.40 41	89.04 34	93.23 52	97.04 41	95.91 64	97.73 38	95.59 84	99.61 31	99.01 109

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 91	87.84 77	93.94 62	96.50 53	95.74 89	94.27 95	99.46 79	97.31 160
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 82	80.68 116	88.02 90	96.92 46	97.49 47	98.20 13	99.47 73	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 85	95.54 63	99.49 37	94.14 87	74.91 162	98.43 38	85.32 59	94.78 42	99.19 15	94.95 74	97.02 58	96.18 69	99.35 96	99.36 84

ETV-MVS		94.49 53	97.23 40	91.29 78	90.43 115	98.55 77	93.41 97	84.53 90	99.16 18	83.13 78	94.72 43	94.08 60	96.61 52	97.72 39	96.60 59	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 95	83.50 108	93.19 67	94.71 76	98.24 23	98.07 18	99.68 19	99.83 31

EC-MVSNet		94.33 55	96.88 42	91.36 76	90.12 122	97.70 98	95.20 70	80.27 123	98.63 33	85.97 52	93.92 47	93.85 65	97.09 40	97.54 46	96.81 55	99.49 68	99.70 45

MAR-MVS		94.18 56	95.12 60	93.09 55	98.40 36	99.17 63	94.20 86	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 90	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	92.00 130	81.51 96	82.97 111	93.88 64	95.63 69	94.24 114	94.71 90	99.09 121	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	92.13 127	87.80 41	79.67 119	88.01 91	93.14 93	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 91	81.21 98	75.96 136	91.58 78	96.21 59	96.37 72	97.10 42	99.52 63	99.54 68

EIA-MVS		93.32 60	95.32 57	90.99 82	90.45 114	98.53 80	93.46 96	84.68 89	97.56 62	81.38 97	91.04 58	87.37 94	96.39 55	97.27 49	95.73 80	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 94	88.70 49	98.09 48	88.10 39	86.96 85	75.02 136	93.08 94	97.89 30	96.90 51	99.56 53	100.00 1

PVSNet_Blended		93.30 61	93.46 82	93.10 53	95.60 61	99.38 50	93.59 94	88.70 49	98.09 48	88.10 39	86.96 85	75.02 136	93.08 94	97.89 30	96.90 51	99.56 53	100.00 1

test2506		93.08 63	93.40 84	92.70 61	92.76 80	99.20 60	94.67 79	86.82 67	92.58 123	90.81 27	86.28 90	85.24 105	91.69 110	96.85 61	96.33 63	99.45 83	97.34 159

PMMVS		93.05 64	95.40 55	90.31 89	91.41 96	97.54 104	92.62 110	83.25 99	98.08 51	79.44 108	95.18 39	88.52 89	96.43 54	95.70 90	93.88 99	98.68 154	98.91 112

LS3D		92.70 65	92.23 96	93.26 51	96.24 57	98.72 69	97.93 38	96.17 7	96.41 76	72.46 125	81.39 115	80.76 123	97.66 29	95.69 91	95.62 82	99.07 123	97.02 168

baseline1		92.67 66	93.62 78	91.55 71	91.16 100	97.15 107	93.92 92	85.97 75	94.76 99	84.07 72	87.17 81	86.89 97	94.62 79	96.72 66	95.90 78	99.57 46	96.79 172

IS_MVSNet		92.67 66	94.99 62	89.96 94	91.17 99	98.54 78	92.77 105	84.00 91	92.72 121	81.90 94	85.67 96	92.47 71	90.39 124	97.82 35	97.81 23	99.51 64	99.91 23

TSAR-MVS + COLMAP		92.56 68	92.44 94	92.71 60	94.61 68	97.69 99	97.69 41	91.09 44	98.96 27	76.71 115	94.68 44	69.41 163	96.91 47	95.80 88	94.18 97	99.26 112	96.33 176

baseline		92.56 68	94.38 68	90.43 88	90.71 109	98.23 88	95.07 72	80.73 122	97.52 63	82.45 86	87.34 80	85.91 101	94.07 87	96.29 76	95.94 77	99.58 42	99.47 74

sasdasda		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

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 93	92.21 63	96.57 54	98.74 68	91.85 117	86.30 70	96.23 81	85.18 60	95.21 38	73.58 142	94.22 86	95.40 105	93.08 120	99.14 118	97.49 155

MVS_Test		92.42 73	94.43 64	90.08 93	90.69 110	98.26 85	94.78 76	80.81 121	97.27 66	78.76 109	87.06 83	84.25 109	95.84 66	97.67 41	97.56 31	99.59 37	98.93 111

MGCFI-Net		92.39 74	93.14 88	91.51 74	91.38 97	98.16 89	95.28 69	81.66 113	95.82 88	84.36 68	90.51 63	78.30 130	96.80 50	92.82 136	96.97 49	99.55 58	99.42 78

thisisatest0530		92.31 75	95.14 58	89.02 103	90.02 123	98.45 82	91.30 121	83.58 94	96.90 71	77.90 112	90.45 64	94.33 57	91.98 107	95.57 96	91.43 144	99.31 106	98.81 115

tttt0517		92.29 76	95.12 60	88.99 104	90.02 123	98.44 84	91.19 124	83.58 94	96.88 72	77.86 113	90.45 64	94.32 58	91.98 107	95.54 98	91.43 144	99.31 106	98.78 117

EPP-MVSNet		92.29 76	94.35 70	89.88 95	90.36 117	97.69 99	90.89 126	83.31 98	93.39 114	83.47 77	85.56 97	93.92 63	91.93 109	95.49 103	94.77 89	99.34 100	99.62 59

HQP-MVS		91.94 78	93.03 89	90.66 87	93.69 70	96.48 121	95.92 56	89.73 46	97.33 64	72.65 123	95.37 36	73.56 143	92.75 100	94.85 111	94.12 98	99.23 115	99.51 70

MSDG		91.93 79	90.28 124	93.85 47	97.36 48	97.12 108	95.88 58	94.07 38	94.52 103	84.13 71	76.74 129	80.89 122	92.54 102	93.97 122	93.61 110	99.14 118	95.10 185

UGNet		91.71 80	94.43 64	88.53 106	92.72 82	98.00 92	90.22 133	84.81 88	94.45 104	83.05 79	87.65 79	92.74 69	81.04 177	94.51 113	94.45 92	99.32 105	99.21 95
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 81	91.52 102	91.88 66	91.61 89	98.89 66	95.49 63	86.96 64	93.24 115	80.82 100	87.90 74	71.15 154	96.88 48	96.00 84	93.51 112	99.51 64	99.95 13

CLD-MVS		91.67 82	91.30 107	92.10 64	91.25 98	96.59 118	95.93 55	87.25 63	96.86 73	85.55 58	87.08 82	73.01 144	93.26 92	93.07 130	92.84 126	99.34 100	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 83	94.96 63	87.65 109	72.75 216	97.24 106	95.29 67	82.73 102	96.81 74	78.49 111	95.30 37	90.48 84	97.23 36	91.60 149	94.31 93	99.43 85	99.01 109

tfpn200view9		91.47 84	91.31 105	91.65 69	91.61 89	98.69 71	95.03 73	86.17 71	93.24 115	80.82 100	87.90 74	71.15 154	96.80 50	95.53 99	92.82 128	99.47 73	99.88 26

CANet_DTU		91.36 85	95.75 52	86.23 121	92.31 86	98.71 70	95.60 62	78.41 137	98.20 44	56.48 184	94.38 45	87.96 92	95.11 71	96.89 59	96.07 71	99.48 71	98.01 145

thres200		91.36 85	91.19 109	91.55 71	91.60 91	98.69 71	94.98 74	86.17 71	92.16 126	80.76 102	87.66 78	71.15 154	96.35 56	95.53 99	93.23 118	99.47 73	99.92 22

FMVSNet3		91.25 87	92.13 98	90.21 90	85.64 154	93.14 157	95.29 67	80.09 124	96.40 77	85.74 55	77.13 124	86.81 98	94.98 73	97.19 53	97.11 41	99.55 58	97.13 165

thres400		91.24 88	91.01 115	91.50 75	91.56 92	98.77 67	94.66 81	86.41 69	91.87 132	80.56 103	87.05 84	71.01 157	96.35 56	95.67 92	92.82 128	99.48 71	99.88 26

PVSNet_Blended_VisFu		91.20 89	92.89 92	89.23 101	93.41 73	98.61 76	89.80 135	85.39 81	92.84 119	82.80 81	74.21 140	91.38 80	84.64 156	97.22 51	96.04 74	99.34 100	99.93 19

DCV-MVSNet		91.15 90	92.00 99	90.17 92	90.78 107	92.23 174	93.70 93	81.17 119	95.16 94	82.98 80	89.46 68	83.31 112	93.98 89	91.79 148	92.87 123	98.41 172	99.18 97

DI_MVS_plusplus_trai		91.11 91	91.47 103	90.68 86	90.01 125	97.77 96	95.87 59	83.56 96	94.72 100	82.12 89	68.46 159	87.46 93	93.07 96	96.46 71	95.73 80	99.47 73	99.71 44

diffmvs		91.05 92	91.15 110	90.93 83	90.15 120	97.79 95	94.05 88	85.45 78	95.63 89	81.95 93	80.45 118	73.01 144	94.47 82	95.56 97	95.89 79	99.49 68	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 92	94.43 64	87.11 111	91.05 102	97.99 93	92.53 112	83.82 93	92.71 122	76.28 116	84.50 102	92.43 72	79.52 182	97.24 50	97.68 25	99.43 85	98.45 129

thres600view7		90.97 94	90.70 117	91.30 77	91.53 93	98.69 71	94.33 82	86.17 71	91.75 134	80.19 104	86.06 94	70.90 158	96.10 61	95.53 99	92.08 136	99.47 73	99.86 30

baseline2		90.91 95	94.40 67	86.84 114	87.54 145	96.83 114	89.95 134	79.22 132	96.00 85	77.04 114	88.68 69	89.73 86	88.01 145	96.35 74	93.51 112	99.29 108	99.68 49

casdiffmvs_mvg		90.83 96	90.52 121	91.20 80	90.56 111	97.67 101	94.96 75	85.45 78	90.72 140	82.03 90	76.70 130	77.08 131	94.61 80	96.57 69	95.62 82	99.57 46	99.28 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
ACMP	89.80 9	90.72 97	91.15 110	90.21 90	92.55 84	96.52 120	92.63 109	85.71 77	94.65 101	81.06 99	93.32 50	70.56 160	90.52 123	92.68 138	91.05 149	98.76 146	99.31 88
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
casdiffmvs		90.69 98	90.56 120	90.85 84	90.14 121	97.81 94	92.94 103	85.30 82	93.47 113	82.50 85	76.34 134	74.12 140	94.67 77	96.51 70	96.26 66	99.55 58	99.42 78
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 99	93.03 89	87.92 108	90.95 104	98.45 82	92.61 111	66.04 197	94.90 96	84.47 65	77.52 123	91.74 76	94.07 87	97.11 55	92.46 134	99.40 90	99.03 108

ACMM	89.40 10	90.58 100	90.02 127	91.23 79	93.30 75	94.75 144	90.69 129	88.22 54	95.20 92	82.70 83	88.54 70	71.40 152	93.48 91	93.64 127	90.94 150	98.99 129	95.72 181
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		90.49 101	91.12 113	89.75 97	84.99 157	92.73 162	93.94 89	80.09 124	96.40 77	85.74 55	77.13 124	86.81 98	94.42 83	94.12 116	93.73 101	99.35 96	96.90 169

test1		90.49 101	91.12 113	89.75 97	84.99 157	92.73 162	93.94 89	80.09 124	96.40 77	85.74 55	77.13 124	86.81 98	94.42 83	94.12 116	93.73 101	99.35 96	96.90 169

ECVR-MVS		90.37 103	88.96 136	92.01 65	92.76 80	99.20 60	94.67 79	86.82 67	92.58 123	86.71 48	68.95 158	71.46 151	91.69 110	96.85 61	96.33 63	99.45 83	97.38 158

LGP-MVS_train		90.34 104	91.63 101	88.83 105	93.31 74	96.14 127	95.49 63	85.24 85	93.91 108	68.71 137	93.96 46	71.63 149	91.12 119	93.82 124	92.79 130	99.07 123	99.16 98

test1111		90.01 105	88.67 138	91.57 70	92.68 83	99.20 60	94.25 85	86.90 66	92.03 129	85.04 61	67.79 163	71.21 153	91.12 119	96.83 63	96.34 62	99.42 88	97.28 161

EPNet_dtu		89.82 106	94.18 73	84.74 131	96.87 52	95.54 137	92.65 108	86.91 65	96.99 68	54.17 195	92.41 56	88.54 88	78.35 185	96.15 79	96.05 73	99.47 73	93.60 193
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		89.81 107	89.75 128	89.88 95	93.22 77	93.99 151	94.78 76	85.23 86	94.01 107	82.52 84	95.00 40	87.23 95	92.01 106	85.16 202	83.48 207	91.54 213	89.38 207

MDTV_nov1_ep13		89.63 108	94.38 68	84.09 138	88.76 137	97.53 105	89.37 143	68.46 195	96.95 69	70.27 132	87.88 76	93.67 66	91.04 121	93.12 128	93.83 100	96.62 202	97.68 151

UA-Net		89.56 109	93.03 89	85.52 127	92.46 85	97.55 103	91.92 115	81.91 107	85.24 167	71.39 127	83.57 107	96.56 44	76.01 196	96.81 64	97.04 46	99.46 79	94.41 188

FMVSNet2		89.51 110	89.63 129	89.38 99	84.99 157	92.73 162	93.94 89	79.28 131	93.73 110	84.28 69	69.36 157	82.32 115	94.42 83	96.16 78	96.22 68	99.35 96	96.90 169

CostFormer		89.42 111	91.67 100	86.80 116	89.99 126	96.33 123	90.75 127	64.79 199	95.17 93	83.62 76	86.20 92	82.15 117	92.96 97	89.22 172	92.94 121	98.68 154	99.65 51

FC-MVSNet-train		89.37 112	89.62 130	89.08 102	90.48 113	94.16 150	89.45 139	83.99 92	91.09 138	80.09 105	82.84 112	74.52 139	91.44 116	93.79 125	91.57 142	99.01 127	99.35 85

OPM-MVS		89.33 113	87.45 148	91.53 73	94.49 69	96.20 125	96.47 54	89.72 47	82.77 174	75.43 117	80.53 117	70.86 159	93.80 90	94.00 120	91.85 140	99.29 108	95.91 179
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test-LLR		89.31 114	93.60 80	84.30 135	88.08 141	96.98 110	88.10 148	78.00 138	94.83 97	62.43 158	84.29 104	90.96 81	89.70 130	95.63 94	92.86 124	99.51 64	99.64 53

EPMVS		89.31 114	93.70 77	84.18 137	91.10 101	98.10 90	89.17 145	62.71 203	96.24 80	70.21 134	86.46 89	92.37 73	92.79 98	91.95 146	93.59 111	99.10 120	97.19 162

Anonymous20231211		89.22 116	87.56 146	91.16 81	90.23 119	96.62 117	93.22 100	85.44 80	92.89 118	84.37 67	60.13 180	81.25 120	96.02 63	90.61 157	92.01 137	97.70 192	99.41 81

Effi-MVS+		88.96 117	91.13 112	86.43 119	89.12 133	97.62 102	93.15 101	75.52 156	93.90 109	66.40 142	86.23 91	70.51 161	95.03 72	95.89 85	94.28 94	99.37 92	99.51 70

SCA		88.76 118	94.29 71	82.30 154	89.33 131	96.81 115	87.68 150	61.52 208	96.95 69	64.68 148	88.35 71	94.80 50	91.58 113	92.23 140	93.21 119	98.99 129	97.70 150

test0.0.03 1		88.71 119	92.22 97	84.63 133	88.08 141	94.71 146	85.91 173	78.00 138	95.54 90	72.96 121	86.10 93	85.88 103	83.59 164	92.95 135	93.24 117	99.25 114	97.09 166

PatchmatchNet		88.67 120	94.10 74	82.34 153	89.38 130	97.72 97	87.24 156	62.18 206	97.00 67	64.79 147	87.97 73	94.43 55	91.55 114	91.21 154	92.77 131	98.90 134	97.60 154
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dps		88.66 121	90.19 125	86.88 113	89.94 127	96.48 121	89.56 137	64.08 201	94.12 106	89.00 35	83.39 109	82.56 114	90.16 127	86.81 194	89.26 168	98.53 167	98.71 119

TESTMET0.1,1		88.63 122	93.60 80	82.84 150	84.07 165	96.98 110	88.10 148	73.22 177	94.83 97	62.43 158	84.29 104	90.96 81	89.70 130	95.63 94	92.86 124	99.51 64	99.64 53

CHOSEN 1792x2688		88.63 122	89.01 134	88.19 107	94.83 65	99.21 59	92.66 107	79.85 128	92.40 125	72.18 126	56.38 200	80.22 125	90.24 125	97.64 44	97.28 38	99.37 92	99.94 16

CDS-MVSNet		88.59 124	90.13 126	86.79 117	86.98 150	95.43 138	92.03 114	81.33 117	85.54 164	74.51 120	77.07 127	85.14 106	87.03 150	93.90 123	95.18 85	98.88 136	98.67 121
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IB-MVS	84.67 14	88.34 125	90.61 119	85.70 124	92.99 79	98.62 75	78.85 199	86.07 74	94.35 105	88.64 36	85.99 95	75.69 134	68.09 209	88.21 175	91.43 144	99.55 58	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		88.25 126	93.27 87	82.38 152	83.89 166	96.86 113	87.10 160	72.80 179	94.58 102	61.85 163	83.21 110	90.65 83	89.18 134	95.43 104	92.58 133	99.46 79	99.61 60

COLMAP_ROB	84.42 15	88.24 127	87.32 149	89.32 100	95.83 59	95.82 131	92.81 104	87.68 61	92.09 128	72.64 124	72.34 148	79.96 126	88.79 136	89.54 167	89.46 164	98.16 181	92.00 199
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
IterMVS-LS		87.95 128	89.40 132	86.26 120	88.79 136	90.93 189	91.23 123	76.05 153	90.87 139	71.07 129	75.51 137	81.18 121	91.21 118	94.11 119	95.01 86	99.20 117	98.23 136
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		87.86 129	88.25 141	87.40 110	94.67 67	98.54 78	90.33 132	76.51 152	89.60 148	70.89 130	51.43 211	85.69 104	92.79 98	96.59 68	95.96 76	99.22 116	99.94 16

Vis-MVSNet		87.60 130	91.31 105	83.27 145	89.14 132	98.04 91	90.35 131	79.42 129	87.23 153	66.92 141	79.10 122	84.63 108	74.34 203	95.81 87	96.06 72	99.46 79	98.32 133
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GeoE		87.55 131	88.17 142	86.82 115	88.74 138	96.32 124	92.75 106	74.93 161	90.13 145	72.73 122	69.47 156	74.03 141	92.51 103	93.99 121	93.62 109	99.29 108	99.59 61

dmvs_re		87.43 132	87.99 143	86.77 118	84.94 161	96.19 126	91.87 116	85.95 76	91.25 137	68.58 138	81.45 114	66.04 169	89.95 129	90.91 155	91.57 142	99.37 92	98.54 125

RPMNet		87.35 133	92.41 95	81.45 158	88.85 135	96.06 128	89.42 142	59.59 215	93.57 111	61.81 164	76.48 133	91.48 79	90.18 126	96.32 75	93.37 115	98.87 137	99.59 61

tpm cat1		87.34 134	88.52 140	85.95 122	89.83 128	95.80 132	90.73 128	64.91 198	92.99 117	82.21 88	71.19 154	82.68 113	90.13 128	86.38 195	90.87 152	97.90 189	99.74 39

MS-PatchMatch		87.19 135	88.59 139	85.55 126	93.15 78	96.58 119	92.35 113	74.19 169	91.97 131	70.33 131	71.42 152	85.89 102	84.28 158	93.12 128	89.16 170	99.00 128	91.99 200

Effi-MVS+-dtu		87.18 136	90.48 122	83.32 144	86.51 151	95.76 134	91.16 125	74.28 168	90.44 144	61.31 167	86.72 88	72.68 147	91.25 117	95.01 109	93.64 104	95.45 207	99.12 102

FMVSNet5		87.06 137	89.52 131	84.20 136	79.92 203	86.57 209	87.11 159	72.37 181	96.06 83	75.41 118	84.33 103	91.76 75	91.60 112	91.51 150	91.22 147	98.77 143	85.16 212

Fast-Effi-MVS+-dtu		86.94 138	91.27 108	81.89 155	86.27 152	95.06 139	90.68 130	68.93 192	91.76 133	57.18 182	89.56 67	75.85 133	89.19 133	94.56 112	92.84 126	99.07 123	99.23 92

Fast-Effi-MVS+		86.94 138	87.88 145	85.84 123	86.99 149	95.80 132	91.24 122	73.48 176	92.75 120	69.22 135	72.70 146	65.71 170	94.84 75	94.98 110	94.71 90	99.26 112	98.48 128

tpmrst		86.78 140	90.29 123	82.69 151	90.55 112	96.95 112	88.49 147	62.58 204	95.09 95	63.52 154	76.67 132	84.00 111	92.05 105	87.93 178	91.89 139	98.98 131	99.50 72

CR-MVSNet		86.73 141	91.47 103	81.20 161	88.56 139	96.06 128	89.43 140	61.37 209	93.57 111	60.81 169	72.89 145	88.85 87	88.13 143	96.03 81	93.64 104	98.89 135	99.22 93

ADS-MVSNet		86.68 142	90.79 116	81.88 156	90.38 116	96.81 115	86.90 161	60.50 213	96.01 84	63.93 151	81.67 113	84.72 107	90.78 122	87.03 188	91.67 141	98.77 143	97.63 153

FMVSNet1		85.85 143	84.91 159	86.96 112	82.70 171	91.39 183	91.54 119	77.45 144	85.29 166	79.56 107	60.70 177	72.68 147	92.37 104	94.12 116	93.73 101	98.12 182	96.44 173

FC-MVSNet-test		85.51 144	89.08 133	81.35 159	85.31 156	93.35 153	87.65 151	77.55 143	90.01 146	64.07 150	79.63 120	81.83 119	74.94 200	92.08 143	90.83 154	98.55 164	95.81 180

ACMH	85.22 13	85.40 145	85.73 156	85.02 129	91.76 88	94.46 149	84.97 179	81.54 115	85.18 168	65.22 146	76.92 128	64.22 171	88.58 139	90.17 159	90.25 160	98.03 185	98.90 113
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		85.35 146	86.00 155	84.59 134	84.97 160	95.57 136	88.98 146	77.29 147	81.44 179	71.36 128	71.48 151	75.00 138	87.03 150	91.92 147	92.21 135	97.92 188	94.40 189

ACMH+	85.62 12	85.27 147	84.96 158	85.64 125	90.84 106	94.78 143	87.46 153	81.30 118	86.94 154	67.35 140	74.56 139	64.09 172	88.70 137	88.14 176	89.00 171	98.22 180	97.19 162

USDC		85.11 148	85.35 157	84.83 130	89.45 129	94.93 142	92.98 102	77.30 146	90.53 142	61.80 165	76.69 131	59.62 182	88.90 135	92.78 137	90.79 156	98.53 167	92.12 197

IterMVS		85.02 149	88.98 135	80.41 167	87.03 148	90.34 197	89.78 136	69.45 189	89.77 147	54.04 196	73.71 142	82.05 118	83.44 167	95.11 107	93.64 104	98.75 147	98.22 138
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		84.91 150	88.90 137	80.25 170	87.04 147	90.27 198	89.23 144	69.25 191	89.17 149	54.04 196	73.65 143	82.22 116	83.23 172	95.11 107	93.63 108	98.73 148	98.23 136

PatchT		84.89 151	90.67 118	78.13 190	87.83 144	94.99 141	72.46 211	60.22 214	91.74 135	60.81 169	72.16 149	86.95 96	88.13 143	96.03 81	93.64 104	99.36 95	99.22 93

pmmvs4		84.88 152	84.67 160	85.13 128	82.80 170	92.37 167	87.29 154	79.08 133	90.51 143	74.94 119	70.37 155	62.49 175	88.17 142	92.01 145	88.51 176	98.49 170	96.44 173

CVMVSNet		84.01 153	86.91 150	80.61 165	88.39 140	93.29 154	86.06 169	82.29 104	83.13 172	54.29 192	72.68 147	79.59 127	75.11 199	91.23 153	92.91 122	97.54 196	95.58 182

tpm		83.97 154	87.97 144	79.31 180	87.35 146	93.21 156	86.00 171	61.90 207	90.69 141	54.01 198	79.42 121	75.61 135	88.65 138	87.18 186	90.48 158	97.95 187	99.21 95

GA-MVS		83.83 155	86.63 151	80.58 166	85.40 155	94.73 145	87.27 155	78.76 136	86.49 156	49.57 206	74.21 140	67.67 166	83.38 168	95.28 106	90.92 151	99.08 122	97.09 166

UniMVSNet_NR-MVSNet		83.83 155	83.70 163	83.98 139	81.41 181	92.56 166	86.54 164	82.96 100	85.98 161	66.27 143	66.16 167	63.63 173	87.78 147	87.65 181	90.81 155	98.94 132	99.13 100

UniMVSNet (Re)		83.28 157	83.16 164	83.42 143	81.93 176	93.12 158	86.27 167	80.83 120	85.88 162	68.23 139	64.56 170	60.58 177	84.25 159	89.13 173	89.44 166	99.04 126	99.40 82

thisisatest0515		83.17 158	86.49 152	79.30 181	82.04 174	93.12 158	78.70 200	77.92 140	86.43 157	63.05 155	74.91 138	73.01 144	75.56 198	92.10 142	88.05 189	98.50 169	97.76 149

TinyColmap		83.03 159	82.24 168	83.95 140	88.88 134	93.22 155	89.48 138	76.89 149	87.53 152	62.12 160	68.46 159	55.03 198	88.43 141	90.87 156	89.65 162	97.89 190	90.91 203

testgi		82.88 160	86.14 154	79.08 183	86.05 153	92.20 175	81.23 196	74.77 164	88.70 150	57.63 181	86.73 87	61.53 176	76.83 193	90.33 158	89.43 167	97.99 186	94.05 190

DU-MVS		82.87 161	82.16 169	83.70 142	80.77 190	92.24 171	86.54 164	81.91 107	86.41 158	66.27 143	63.95 171	55.66 196	87.78 147	86.83 191	90.86 153	98.94 132	99.13 100

MIMVSNet		82.87 161	86.17 153	79.02 184	77.23 211	92.88 161	84.88 180	60.62 212	86.72 155	64.16 149	73.58 144	71.48 150	88.51 140	94.14 115	93.50 114	98.72 150	90.87 204

NR-MVSNet		82.37 163	81.95 171	82.85 149	82.56 173	92.24 171	87.49 152	81.91 107	86.41 158	65.51 145	63.95 171	52.93 207	80.80 179	89.41 169	89.61 163	98.85 139	99.10 105

Baseline_NR-MVSNet		82.08 164	80.64 178	83.77 141	80.77 190	88.50 204	86.88 162	81.71 112	85.58 163	68.80 136	58.20 192	57.75 188	86.16 152	86.83 191	88.68 173	98.33 177	98.90 113

TranMVSNet+NR-MVSNet		82.07 165	81.36 174	82.90 148	80.43 196	91.39 183	87.16 158	82.75 101	84.28 170	62.98 156	62.28 176	56.01 195	85.30 155	86.06 197	90.69 157	98.80 140	98.80 116

pm-mvs1		81.68 166	81.70 172	81.65 157	82.61 172	92.26 170	85.54 177	78.95 134	76.29 201	63.81 152	58.43 191	66.33 168	80.63 180	92.30 139	89.93 161	98.37 176	96.39 175

TDRefinement		81.49 167	80.08 184	83.13 147	91.02 103	94.53 147	91.66 118	82.43 103	81.70 177	62.12 160	62.30 175	59.32 183	73.93 204	87.31 184	85.29 200	97.61 193	90.14 205

anonymousdsp		81.29 168	84.52 162	77.52 192	79.83 204	92.62 165	82.61 191	70.88 186	80.76 183	50.82 203	68.35 161	68.76 164	82.45 175	93.00 131	89.45 165	98.55 164	98.69 120

gg-mvs-nofinetune		81.27 169	84.65 161	77.32 193	87.96 143	98.48 81	95.64 61	56.36 218	59.35 220	32.80 224	47.96 215	92.11 74	91.49 115	98.12 24	97.00 48	99.65 24	99.56 66

tfpnnormal		81.11 170	79.33 192	83.19 146	84.23 163	92.29 169	86.76 163	82.27 105	72.67 207	62.02 162	56.10 202	53.86 204	85.35 154	92.06 144	89.23 169	98.49 170	99.11 104

UniMVSNet_ETH3D		80.95 171	77.71 200	84.74 131	84.45 162	93.11 160	86.45 166	79.97 127	75.21 203	70.22 133	51.24 212	50.26 213	89.55 132	84.47 204	91.12 148	97.81 191	98.53 126

V42		80.88 172	80.74 176	81.05 162	81.21 184	92.01 177	85.96 172	77.75 142	81.62 178	59.73 176	59.93 183	58.35 187	82.98 174	86.90 190	88.06 188	98.69 153	98.32 133

v2v482		80.86 173	80.52 182	81.25 160	80.79 189	91.85 178	85.68 175	78.78 135	81.05 180	58.09 179	60.46 178	56.08 193	85.45 153	87.27 185	88.53 175	98.73 148	98.38 132

v8		80.61 174	80.61 180	80.62 164	81.51 179	91.00 188	86.06 169	74.07 172	81.78 176	59.93 175	60.10 182	58.42 186	83.35 169	86.99 189	88.11 186	98.79 141	97.83 147

pmmvs5		80.48 175	81.43 173	79.36 179	81.50 180	92.24 171	82.07 194	74.08 171	78.10 194	55.86 187	67.72 164	54.35 201	83.91 163	92.97 132	88.65 174	98.77 143	96.01 177

v10		80.38 176	80.73 177	79.96 172	81.22 183	90.40 196	86.11 168	71.63 183	82.42 175	57.65 180	58.74 189	57.47 189	84.44 157	89.75 163	88.28 179	98.71 151	98.06 144

v1144		80.36 177	80.63 179	80.05 171	80.86 188	91.56 181	85.78 174	75.22 158	80.73 184	55.83 188	58.51 190	56.99 191	83.93 162	89.79 162	88.25 180	98.68 154	98.56 124

SixPastTwentyTwo		80.28 178	82.06 170	78.21 189	81.89 178	92.35 168	77.72 201	74.48 165	83.04 173	54.22 193	76.06 135	56.40 192	83.55 165	86.83 191	84.83 202	97.38 197	94.93 186

CP-MVSNet		79.90 179	79.49 189	80.38 168	80.72 192	90.83 190	82.98 188	75.17 159	79.70 189	61.39 166	59.74 184	51.98 210	83.31 170	87.37 183	88.38 177	98.71 151	98.45 129

v1192		79.84 180	80.05 186	79.61 175	80.49 195	91.04 187	85.56 176	74.37 167	80.73 184	54.35 191	57.07 197	54.54 200	84.23 160	89.94 160	88.38 177	98.63 158	98.61 122

WR-MVS_H		79.76 181	80.07 185	79.40 178	81.25 182	91.73 180	82.77 189	74.82 163	79.02 193	62.55 157	59.41 186	57.32 190	76.27 195	87.61 182	87.30 194	98.78 142	98.09 142

WR-MVS		79.67 182	80.25 183	79.00 185	80.65 193	91.16 185	83.31 186	76.57 151	80.97 181	60.50 174	59.20 187	58.66 185	74.38 202	85.85 199	87.76 191	98.61 159	98.14 139

v148		79.66 183	79.13 194	80.27 169	81.02 186	91.76 179	81.90 195	79.32 130	79.24 191	63.79 153	58.07 194	54.34 202	77.17 191	84.42 205	87.52 193	98.40 173	98.59 123

LTVRE_ROB	79.45 16	79.66 183	80.55 181	78.61 187	83.01 169	92.19 176	87.18 157	73.69 175	71.70 210	43.22 219	71.22 153	50.85 211	87.82 146	89.47 168	90.43 159	96.75 200	98.00 146
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		79.61 185	79.77 187	79.41 177	80.28 197	91.06 186	84.87 181	73.86 173	79.65 190	55.38 189	57.76 195	55.20 197	83.46 166	88.42 174	87.89 190	98.61 159	98.42 131

v1921920		79.55 186	79.77 187	79.30 181	80.24 198	90.77 192	85.37 178	73.75 174	80.38 186	53.78 199	56.89 199	54.18 203	84.05 161	89.55 166	88.13 185	98.59 161	98.52 127

TransMVSNet (Re)		79.51 187	78.36 196	80.84 163	83.17 167	89.72 200	84.22 184	81.45 116	73.98 206	60.79 172	57.20 196	56.05 194	77.11 192	89.88 161	88.86 172	98.30 179	92.83 195

MVS-HIRNet		79.34 188	82.56 165	75.57 198	84.11 164	95.02 140	75.03 208	57.28 217	85.50 165	55.88 186	53.00 208	70.51 161	83.05 173	92.12 141	91.96 138	98.09 183	89.83 206

PS-CasMVS		79.06 189	78.58 195	79.63 174	80.59 194	90.55 194	82.54 192	75.04 160	77.76 195	58.84 177	58.16 193	50.11 215	82.09 176	87.05 187	88.18 183	98.66 157	98.27 135

v1240		78.97 190	79.27 193	78.63 186	80.04 199	90.61 193	84.25 183	72.95 178	79.22 192	52.70 201	56.22 201	52.88 209	83.28 171	89.60 165	88.20 182	98.56 163	98.14 139

pmnet_mix02		78.91 191	81.17 175	76.28 197	81.91 177	90.82 191	74.25 209	77.87 141	86.17 160	49.04 207	67.97 162	62.93 174	77.40 189	82.75 210	82.11 209	97.18 198	95.42 183

MDTV_nov1_ep13_2view		78.83 192	82.35 166	74.73 201	78.65 206	91.51 182	79.18 198	62.52 205	84.51 169	52.51 202	67.49 165	67.29 167	78.90 183	85.52 201	86.34 197	96.62 202	93.76 191

PEN-MVS		78.80 193	78.13 198	79.58 176	80.03 200	89.67 201	83.61 185	75.83 154	77.71 197	58.41 178	60.11 181	50.00 216	81.02 178	84.08 206	88.14 184	98.59 161	97.18 164

EG-PatchMatch MVS		78.32 194	79.42 191	77.03 195	83.03 168	93.77 152	84.47 182	69.26 190	75.85 202	53.69 200	55.68 203	60.23 180	73.20 205	89.69 164	88.22 181	98.55 164	92.54 196

DTE-MVSNet		77.92 195	77.42 201	78.51 188	79.34 205	89.00 203	83.05 187	75.60 155	76.89 199	56.58 183	59.63 185	50.31 212	78.09 188	82.57 211	87.56 192	98.38 174	95.95 178

v7n		77.71 196	78.25 197	77.09 194	78.49 207	90.55 194	82.15 193	71.11 185	76.79 200	54.18 194	55.63 204	50.20 214	78.28 186	89.36 171	87.15 195	98.33 177	98.07 143

gm-plane-assit		77.20 197	82.26 167	71.30 204	81.10 185	82.00 217	54.33 222	64.41 200	63.80 219	40.93 221	59.04 188	76.57 132	87.30 149	98.26 22	97.36 37	99.74 13	98.76 118

N_pmnet		76.83 198	77.97 199	75.50 199	80.96 187	88.23 206	72.81 210	76.83 150	80.87 182	50.55 204	56.94 198	60.09 181	75.70 197	83.28 208	84.23 204	96.14 206	92.12 197

pmmvs6		76.79 199	75.69 206	78.09 191	79.95 202	89.57 202	80.92 197	74.46 166	64.79 217	60.74 173	45.71 217	60.55 178	78.37 184	88.04 177	86.00 198	94.07 210	95.15 184

CMPMVS	58.73 17	76.78 200	74.27 207	79.70 173	93.26 76	95.58 135	82.74 190	77.44 145	71.46 213	56.29 185	53.58 207	59.13 184	77.33 190	79.20 212	79.71 212	91.14 215	81.24 215
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		76.76 201	79.47 190	73.60 202	79.99 201	87.47 207	77.39 202	75.43 157	77.62 198	47.83 210	64.78 169	60.44 179	64.80 210	86.28 196	86.53 196	96.17 205	93.19 194

PM-MVS		75.81 202	76.11 205	75.46 200	73.81 213	85.48 211	76.42 204	70.57 187	80.05 188	54.75 190	62.33 174	39.56 223	80.59 181	87.71 180	82.81 208	96.61 204	94.81 187

pmmvs-eth3d		75.17 203	74.09 208	76.43 196	72.92 214	84.49 213	76.61 203	72.42 180	74.33 204	61.28 168	54.71 206	39.42 224	78.20 187	87.77 179	84.25 203	97.17 199	93.63 192

Anonymous20231206		74.59 204	77.00 202	71.78 203	77.89 210	87.45 208	75.14 207	72.29 182	77.76 195	46.65 212	52.14 209	52.93 207	61.10 213	89.37 170	88.09 187	97.59 194	91.30 202

test20.03		72.81 205	76.24 204	68.80 207	78.31 208	85.40 212	71.04 212	71.20 184	71.85 209	43.40 218	65.31 168	54.71 199	51.27 216	85.92 198	84.18 205	97.58 195	86.35 211

test_method		71.90 206	76.72 203	66.28 212	60.87 222	78.37 219	69.75 216	49.81 223	83.44 171	49.63 205	47.13 216	53.23 206	76.38 194	91.32 152	85.76 199	91.22 214	97.77 148

new_pmnet		71.86 207	73.67 209	69.75 206	72.56 217	84.20 214	70.95 214	66.81 196	80.34 187	43.62 217	51.60 210	53.81 205	71.24 207	82.91 209	80.93 210	93.35 212	81.92 214

MDA-MVSNet-bldmvs		69.61 208	70.36 211	68.74 208	62.88 220	88.50 204	65.40 219	77.01 148	71.60 212	43.93 214	66.71 166	35.33 226	72.47 206	61.01 219	80.63 211	90.73 216	88.75 209

pmmvs3		69.04 209	70.75 210	67.04 210	66.83 218	78.54 218	64.99 220	60.92 211	64.67 218	40.61 222	55.08 205	40.29 222	74.89 201	83.76 207	84.01 206	93.98 211	88.88 208

MIMVSNet1		68.63 210	70.24 212	66.76 211	56.86 224	83.26 215	67.93 217	70.26 188	68.05 215	46.80 211	40.44 218	48.15 217	62.01 211	84.96 203	84.86 201	96.69 201	81.93 213

GG-mvs-BLEND		67.99 211	97.35 38	33.72 220	1.22 230	99.72 17	98.30 34	0.57 227	97.61 61	1.18 231	93.26 51	96.63 43	1.74 227	97.15 54	97.14 40	99.34 100	99.96 10

new-patchmatchnet		67.66 212	68.07 213	67.18 209	72.85 215	82.86 216	63.09 221	68.61 194	66.60 216	42.64 220	49.28 213	38.68 225	61.21 212	75.84 213	75.22 214	94.67 209	88.00 210

FPMVS		63.27 213	61.31 216	65.57 213	78.25 209	74.42 222	75.23 206	68.92 193	72.33 208	43.87 215	49.01 214	43.94 220	48.64 218	61.15 218	58.81 220	78.51 222	69.49 220

WB-MVS		56.28 214	63.25 215	48.16 217	75.24 212	65.97 223	39.91 226	74.13 170	69.25 214	10.01 229	62.67 173	44.05 219	20.71 226	70.43 216	69.57 216	68.94 224	60.78 225

Gipuma		54.59 215	53.98 217	55.30 214	59.03 223	52.63 225	47.17 224	56.08 219	71.68 211	37.54 223	20.90 224	19.00 228	52.33 215	71.69 215	75.20 215	79.64 221	66.79 221
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	49.05 18	51.88 216	50.56 219	53.42 215	64.21 219	43.30 227	42.64 225	62.93 202	50.56 221	43.72 216	37.44 219	42.95 221	35.05 221	58.76 221	54.58 221	71.95 223	66.33 222
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		50.69 217	52.33 218	48.78 216	51.24 225	64.81 224	47.91 223	53.79 222	44.95 222	21.75 225	29.98 222	25.90 227	31.98 223	59.95 220	65.37 218	86.00 219	75.36 218

E-PMN		37.15 218	34.82 221	39.86 218	47.53 227	35.42 229	23.79 228	55.26 220	35.18 225	14.12 227	17.38 227	14.13 230	39.73 220	32.24 223	46.98 222	58.76 225	62.39 224

EMVS		36.45 219	33.63 222	39.74 219	48.47 226	35.73 228	23.59 229	55.11 221	35.61 224	12.88 228	17.49 225	14.62 229	41.04 219	29.33 224	43.00 223	57.32 226	59.62 226

MVE	42.40 19	36.00 220	38.65 220	32.92 221	29.16 228	46.17 226	22.61 230	44.21 224	26.44 227	18.88 226	17.41 226	9.36 232	32.29 222	45.75 222	61.38 219	50.35 227	64.03 223
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		21.55 221	30.91 223	10.62 222	2.78 229	11.66 230	18.51 231	4.82 225	38.21 223	4.06 230	36.35 220	4.47 233	26.81 224	23.27 225	27.11 224	6.75 228	75.30 219

test123		16.81 222	24.80 224	7.48 223	0.82 231	8.38 231	11.92 232	2.60 226	28.96 226	1.12 232	28.39 223	1.26 234	24.51 225	8.93 226	22.19 225	3.90 229	75.49 217

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					99.50 1	99.78 12	99.69 1			88.49 37	97.88 26	98.84 22	99.42 1			99.76 10	97.44 156
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										46.54 213

9.14												99.73 8

SR-MVS					99.27 16			95.82 18				99.00 18

Anonymous202405211			87.54 147		90.72 108	97.10 109	93.40 98	85.30 82	91.41 136		60.23 179	80.69 124	95.80 67	91.33 151	92.60 132	98.38 174	99.40 82

our_test_3					81.94 175	90.26 199	75.39 205

ambc			64.61 214		61.80 221	75.31 221	71.00 213		74.16 205	48.83 208	36.02 221	13.22 231	58.66 214	85.80 200	76.26 213	88.01 217	91.53 201

MTAPA										94.58 14		98.56 24

MTMP										95.24 8		98.13 30

Patchmatch-RL test							37.05 227

tmp_tt				71.24 205	90.29 118	76.39 220	65.81 218	59.43 216	97.62 59	79.65 106	90.60 62	68.71 165	49.71 217	72.71 214	65.70 217	82.54 220

XVS					93.63 71	99.64 25	94.32 83			83.97 73		98.08 32				99.59 37

X-MVStestdata					93.63 71	99.64 25	94.32 83			83.97 73		98.08 32				99.59 37

mPP-MVS					98.66 29							97.11 40

NP-MVS									97.69 57

Patchmtry						95.86 130	89.43 140	61.37 209		60.81 169

DeepMVS_CX						85.88 210	69.83 215	81.56 114	87.99 151	48.22 209	71.85 150	45.52 218	68.67 208	63.21 217		86.64 218	80.03 216

High-resolution multi-view benchmark