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 16	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 19	99.97 4	99.22 5	98.95 9	97.48 33	99.71 19	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 7	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 11	98.93 20	99.36 2	99.46 3	98.21 12	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 13	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 14	98.38 2	99.30 14	99.85 2	99.14 23	96.23 3	99.51 2	97.16 1	96.01 35	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 11	98.18 6	99.38 7	99.78 12	99.35 15	96.14 9	99.24 14	95.66 7	98.19 21	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 16	98.32 4	99.48 2	99.68 22	99.51 10	96.24 2	99.08 21	95.94 4	98.64 12	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 30	99.97 4	99.03 8	98.94 10	98.30 10	99.60 33	99.58 66
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 7	99.46 11	98.59 20	99.15 7	98.07 19	99.73 14	99.64 55

SD-MVS		98.33 11	99.01 9	97.54 14	97.17 51	99.77 15	99.14 23	96.09 13	99.34 7	94.06 15	97.91 26	99.89 6	99.18 6	97.99 28	98.21 12	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 17	97.80 10	99.31 13	99.62 29	99.31 18	96.15 8	99.19 16	93.60 16	97.28 31	98.35 28	98.72 17	98.27 21	98.22 11	99.73 14	99.89 26
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 33	98.76 9	99.50 10	98.37 25	99.23 5	97.64 27	99.95 1	99.87 30

HPM-MVS++		98.16 14	98.87 14	97.32 17	99.39 6	99.70 20	99.18 21	96.10 12	99.09 20	91.14 26	98.02 24	99.89 6	98.44 23	98.75 16	97.03 47	99.67 20	99.63 58

MSLP-MVS++		98.12 15	98.23 29	97.99 9	99.28 15	99.72 17	99.59 5	95.27 28	98.61 35	94.79 12	96.11 34	97.79 37	99.27 4	96.62 67	98.96 5	99.77 9	99.80 33

HFP-MVS		98.02 16	98.55 21	97.40 16	99.11 21	99.69 21	99.41 11	95.41 26	98.79 31	91.86 23	98.61 14	98.16 30	99.02 9	97.87 33	97.40 35	99.60 33	99.35 87

TSAR-MVS + MP.		97.98 17	98.62 20	97.23 19	97.08 52	99.55 35	99.17 22	95.69 21	99.40 4	93.04 18	96.68 33	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 12	96.64 25	98.89 27	99.79 9	99.34 16	95.20 30	98.48 37	89.91 31	98.58 15	98.69 24	96.84 50	98.92 11	98.16 16	99.66 22	99.74 39
Skip Steuart: Steuart Systems R&D Blog.
CP-MVS		97.81 19	98.26 28	97.28 18	99.00 24	99.65 25	99.10 25	95.32 27	98.38 43	92.21 22	98.33 18	97.74 38	98.50 22	97.66 42	96.55 61	99.57 46	99.48 75

ACMMPR		97.78 20	98.28 26	97.20 20	99.03 23	99.68 22	99.37 12	95.24 29	98.86 30	91.16 25	97.86 28	97.26 40	98.79 15	97.64 44	97.40 35	99.60 33	99.25 94

DeepC-MVS_fast	95.01 1	97.67 21	98.22 30	97.02 22	99.00 24	99.79 9	99.10 25	95.82 18	99.05 23	89.53 32	93.54 50	96.77 43	98.83 13	99.34 4	99.44 2	99.82 4	99.63 58
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 39	97.77 11	99.17 19	99.55 35	98.57 32	95.76 20	99.04 24	94.66 13	97.94 25	94.39 57	98.82 14	96.21 77	94.78 94	99.62 29	99.52 71

ACMMP_NAP		97.51 23	98.27 27	96.63 26	99.34 10	99.72 17	99.25 19	95.94 17	98.11 48	87.10 48	96.98 32	98.50 26	98.61 19	98.58 18	96.83 54	99.56 53	99.14 103

MP-MVS		97.46 24	98.30 25	96.48 27	98.93 26	99.43 43	99.20 20	95.42 25	98.43 39	87.60 44	98.19 21	98.01 36	98.09 27	98.05 26	96.67 58	99.64 25	99.35 87
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 13	95.71 32	98.46 34	99.60 32	99.05 27	95.16 31	99.10 19	84.38 67	98.47 17	98.85 21	97.61 31	98.54 19	97.66 26	99.62 29	99.93 19

MVS_0304		97.38 26	99.01 9	95.47 35	97.24 50	99.68 22	98.62 31	89.40 49	98.88 29	90.96 27	99.09 4	98.85 21	96.90 48	98.13 23	98.54 8	99.72 17	99.91 23

CPTT-MVS		97.32 27	97.60 38	96.99 23	98.29 37	99.31 55	99.04 28	94.67 35	97.99 54	93.12 17	98.03 23	98.26 29	98.77 16	96.08 81	94.26 102	98.07 190	99.27 93

X-MVS		97.20 28	98.42 24	95.77 30	99.04 22	99.64 26	98.95 30	95.10 33	98.16 46	83.97 75	98.27 19	98.08 33	97.95 28	97.89 30	97.46 34	99.58 42	99.47 76

PHI-MVS		97.09 29	98.69 17	95.22 37	97.99 42	99.59 34	97.56 44	92.16 39	98.41 41	87.11 47	98.70 10	99.42 12	96.95 45	96.88 60	98.16 16	99.56 53	99.70 46

DPM-MVS		97.07 30	97.99 33	96.00 29	97.25 49	99.16 65	99.67 2	95.99 16	99.08 21	85.97 53	93.00 55	98.44 27	97.47 33	99.22 6	99.62 1	99.66 22	97.44 164

PGM-MVS		97.03 31	98.14 32	95.73 31	99.34 10	99.61 31	99.34 16	89.99 45	97.70 57	87.67 43	99.44 2	96.45 46	98.44 23	97.65 43	97.09 43	99.58 42	99.06 111

PLC	94.37 2	97.03 31	96.54 45	97.60 13	98.84 28	98.64 74	98.17 36	94.99 34	99.01 25	96.80 2	93.21 54	95.64 48	97.36 34	96.37 72	94.79 93	99.41 93	98.12 149
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TSAR-MVS + ACMM		96.90 33	98.64 19	94.88 39	98.12 40	99.47 40	99.01 29	95.43 24	99.23 15	81.98 97	95.95 36	99.16 16	95.13 71	98.61 17	98.11 18	99.58 42	99.93 19

TSAR-MVS + GP.		96.47 34	98.45 23	94.17 44	92.12 87	99.29 56	97.76 40	88.05 56	99.36 6	90.26 30	97.82 29	99.21 14	97.21 37	96.78 65	96.74 56	99.63 27	99.94 16

EPNet		96.23 35	97.89 35	94.29 42	97.62 45	99.44 42	97.14 51	88.63 52	98.16 46	88.14 39	99.46 1	94.15 60	94.61 81	97.20 52	97.23 39	99.57 46	99.59 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNLPA		96.14 36	95.43 54	96.98 24	98.55 31	99.41 47	95.91 57	95.15 32	99.00 26	95.71 6	84.21 107	94.55 55	97.25 35	95.50 105	96.23 67	99.28 117	99.09 110

MVS_111021_LR		96.07 37	97.94 34	93.88 47	97.86 43	99.43 43	95.70 60	89.65 48	98.73 32	84.86 63	99.38 3	94.08 61	95.78 69	97.81 36	96.73 57	99.43 88	99.42 80

ACMMP		96.05 38	96.70 44	95.29 36	98.01 41	99.43 43	97.60 43	94.33 37	97.62 60	86.17 52	98.92 5	92.81 68	96.10 62	95.67 94	93.33 122	99.55 58	99.12 106
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 39	96.42 47	95.50 34	98.18 39	99.33 54	97.44 46	87.73 60	97.93 55	92.36 21	84.67 100	97.33 39	97.55 32	97.32 48	98.47 9	99.72 17	99.88 27

DeepPCF-MVS	94.02 3	95.92 40	98.47 22	92.95 57	97.57 46	99.79 9	91.45 126	94.42 36	99.76 1	86.48 51	92.88 56	98.12 32	92.62 109	99.49 2	99.32 3	95.15 217	99.95 13

CDPH-MVS		95.90 41	97.77 37	93.72 50	98.28 38	99.43 43	98.40 33	91.30 43	98.34 44	78.62 117	94.80 42	95.74 47	96.11 61	97.86 34	98.67 7	99.59 37	99.56 68

CSCG		95.77 42	95.35 56	96.26 28	99.13 20	99.60 32	98.14 37	91.89 42	96.57 75	92.61 20	89.65 67	91.74 76	96.96 42	93.69 131	96.58 60	98.86 144	99.63 58

OMC-MVS		95.75 43	95.84 52	95.64 33	98.52 33	99.34 53	97.15 50	92.02 41	98.94 28	90.45 29	88.31 73	94.64 53	96.35 57	96.02 84	95.99 77	99.34 105	97.65 160

MVS_111021_HR		95.70 44	98.16 31	92.83 58	97.57 46	99.77 15	94.78 76	88.05 56	98.61 35	82.29 92	98.85 6	94.66 52	94.63 79	97.80 37	97.63 28	99.64 25	99.79 35

3Dnovator	90.31 8	95.67 45	96.16 50	95.11 38	98.59 30	99.37 52	97.50 45	87.98 58	98.02 53	89.09 34	85.36 99	94.62 54	97.66 29	97.10 56	98.90 6	99.82 4	99.73 42

CANet		95.40 46	96.27 48	94.40 41	96.25 57	99.62 29	98.37 34	88.59 53	98.09 49	87.58 45	84.57 102	95.54 50	95.87 66	98.12 24	98.03 20	99.73 14	99.90 25

QAPM		95.17 47	96.05 51	94.14 45	98.55 31	99.49 38	97.41 47	87.88 59	97.72 56	84.21 71	84.59 101	95.60 49	97.21 37	97.10 56	98.19 15	99.57 46	99.65 53

SPE-MVS-test		95.06 48	96.98 42	92.82 59	95.83 60	99.40 48	93.23 103	85.29 88	99.27 13	85.89 55	93.86 49	92.70 70	97.19 39	97.70 40	96.18 70	99.49 68	99.76 36

CS-MVS		94.82 49	96.19 49	93.22 53	95.19 65	99.24 58	95.10 71	85.07 93	98.72 33	87.33 46	91.35 58	89.98 85	97.06 41	98.01 27	96.28 65	99.60 33	99.72 43

MVSTER		94.75 50	96.50 46	92.70 61	90.91 105	94.51 156	97.37 48	83.37 105	98.40 42	89.04 35	93.23 53	97.04 42	95.91 65	97.73 38	95.59 89	99.61 31	99.01 115

TAPA-MVS	92.04 6	94.72 51	95.13 59	94.24 43	97.72 44	99.17 63	97.61 42	92.16 39	97.66 59	81.99 96	87.84 78	93.94 63	96.50 54	95.74 91	94.27 101	99.46 79	97.31 168
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepC-MVS	92.23 5	94.53 52	94.26 72	94.86 40	96.73 54	99.50 37	97.85 39	95.45 23	96.22 82	82.73 85	80.68 116	88.02 90	96.92 46	97.49 47	98.20 14	99.47 73	99.69 49
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 53	97.78 36	90.70 87	95.54 64	99.49 38	94.14 87	74.91 170	98.43 39	85.32 60	94.78 43	99.19 15	94.95 76	97.02 58	96.18 70	99.35 101	99.36 86

ETV-MVS		94.49 54	97.23 41	91.29 78	90.43 115	98.55 77	93.41 100	84.53 98	99.16 18	83.13 80	94.72 44	94.08 61	96.61 53	97.72 39	96.60 59	99.61 31	99.81 32

EC-MVSNet		94.33 55	96.88 43	91.36 76	90.12 123	97.70 103	95.20 70	80.27 131	98.63 34	85.97 53	93.92 48	93.85 66	97.09 40	97.54 46	96.81 55	99.49 68	99.70 46

MAR-MVS		94.18 56	95.12 60	93.09 56	98.40 36	99.17 63	94.20 86	81.92 114	98.47 38	86.52 50	90.92 60	84.21 110	98.12 26	95.88 88	97.59 29	99.40 94	99.58 66
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 46	96.07 59	99.25 57	96.82 53	95.51 22	92.00 137	81.51 100	82.97 111	93.88 65	95.63 70	94.24 118	94.71 96	99.09 127	99.70 46
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 49	96.34 56	99.47 40	97.26 49	88.53 54	92.13 134	87.80 42	79.67 120	88.01 91	93.14 99	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 51	98.46 34	99.39 49	97.00 52	87.66 62	95.37 91	81.21 102	75.96 143	91.58 78	96.21 60	96.37 72	97.10 42	99.52 63	99.54 70

EIA-MVS		93.32 60	95.32 57	90.99 83	90.45 114	98.53 80	93.46 98	84.68 97	97.56 63	81.38 101	91.04 59	87.37 94	96.39 56	97.27 49	95.73 85	99.59 37	99.76 36

PVSNet_BlendedMVS		93.30 61	93.46 82	93.10 54	95.60 62	99.38 50	93.59 96	88.70 50	98.09 49	88.10 40	86.96 86	75.02 137	93.08 100	97.89 30	96.90 51	99.56 53	100.00 1

PVSNet_Blended		93.30 61	93.46 82	93.10 54	95.60 62	99.38 50	93.59 96	88.70 50	98.09 49	88.10 40	86.96 86	75.02 137	93.08 100	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	92.58 127	90.81 28	86.28 91	85.24 105	91.69 118	96.85 61	96.33 63	99.45 83	97.34 167

PMMVS		93.05 64	95.40 55	90.31 94	91.41 96	97.54 110	92.62 116	83.25 107	98.08 52	79.44 115	95.18 40	88.52 89	96.43 55	95.70 92	93.88 105	98.68 160	98.91 118

LS3D		92.70 65	92.23 96	93.26 52	96.24 58	98.72 69	97.93 38	96.17 7	96.41 76	72.46 133	81.39 115	80.76 123	97.66 29	95.69 93	95.62 87	99.07 129	97.02 176

baseline1		92.67 66	93.62 78	91.55 71	91.16 100	97.15 113	93.92 93	85.97 75	94.76 99	84.07 73	87.17 82	86.89 97	94.62 80	96.72 66	95.90 80	99.57 46	96.79 180

IS_MVSNet		92.67 66	94.99 62	89.96 99	91.17 99	98.54 78	92.77 111	84.00 99	92.72 125	81.90 99	85.67 97	92.47 71	90.39 132	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	97.69 104	97.69 41	91.09 44	98.96 27	76.71 123	94.68 45	69.41 171	96.91 47	95.80 90	94.18 103	99.26 118	96.33 184

baseline		92.56 68	94.38 68	90.43 92	90.71 109	98.23 88	95.07 72	80.73 130	97.52 64	82.45 89	87.34 81	85.91 101	94.07 92	96.29 76	95.94 79	99.58 42	99.47 76

sasdasda		92.54 70	93.28 85	91.68 67	91.44 94	98.24 86	95.45 65	81.84 118	95.98 86	84.85 64	90.69 61	78.53 128	96.96 42	92.97 137	97.06 44	99.57 46	99.47 76

canonicalmvs		92.54 70	93.28 85	91.68 67	91.44 94	98.24 86	95.45 65	81.84 118	95.98 86	84.85 64	90.69 61	78.53 128	96.96 42	92.97 137	97.06 44	99.57 46	99.47 76

PatchMatch-RL		92.54 70	92.82 93	92.21 63	96.57 55	98.74 68	91.85 123	86.30 70	96.23 81	85.18 61	95.21 39	73.58 146	94.22 91	95.40 108	93.08 126	99.14 124	97.49 163

MVS_Test		92.42 73	94.43 64	90.08 98	90.69 110	98.26 85	94.78 76	80.81 129	97.27 66	78.76 116	87.06 84	84.25 109	95.84 67	97.67 41	97.56 31	99.59 37	98.93 117

MGCFI-Net		92.39 74	93.14 88	91.51 74	91.38 97	98.16 89	95.28 69	81.66 121	95.82 88	84.36 69	90.51 64	78.30 130	96.80 51	92.82 141	96.97 49	99.55 58	99.42 80

thisisatest0530		92.31 75	95.14 58	89.02 111	90.02 125	98.45 82	91.30 127	83.58 102	96.90 71	77.90 119	90.45 65	94.33 58	91.98 115	95.57 98	91.43 150	99.31 112	98.81 121

tttt0517		92.29 76	95.12 60	88.99 112	90.02 125	98.44 84	91.19 130	83.58 102	96.88 72	77.86 120	90.45 65	94.32 59	91.98 115	95.54 101	91.43 150	99.31 112	98.78 123

EPP-MVSNet		92.29 76	94.35 70	89.88 100	90.36 117	97.69 104	90.89 134	83.31 106	93.39 115	83.47 79	85.56 98	93.92 64	91.93 117	95.49 106	94.77 95	99.34 105	99.62 61

HQP-MVS		91.94 78	93.03 89	90.66 89	93.69 70	96.48 127	95.92 56	89.73 46	97.33 65	72.65 131	95.37 37	73.56 147	92.75 108	94.85 114	94.12 104	99.23 121	99.51 72

MSDG		91.93 79	90.28 127	93.85 48	97.36 48	97.12 114	95.88 58	94.07 38	94.52 103	84.13 72	76.74 136	80.89 122	92.54 110	93.97 126	93.61 116	99.14 124	95.10 193

UGNet		91.71 80	94.43 64	88.53 114	92.72 82	98.00 94	90.22 141	84.81 96	94.45 105	83.05 81	87.65 80	92.74 69	81.04 185	94.51 117	94.45 98	99.32 111	99.21 98
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	93.24 116	80.82 105	87.90 75	71.15 160	96.88 49	96.00 85	93.51 118	99.51 64	99.95 13

CLD-MVS		91.67 82	91.30 107	92.10 64	91.25 98	96.59 124	95.93 55	87.25 63	96.86 73	85.55 59	87.08 83	73.01 149	93.26 98	93.07 135	92.84 132	99.34 105	99.68 50
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	87.65 117	72.75 224	97.24 112	95.29 67	82.73 110	96.81 74	78.49 118	95.30 38	90.48 84	97.23 36	91.60 154	94.31 99	99.43 88	99.01 115

tfpn200view9		91.47 84	91.31 105	91.65 69	91.61 89	98.69 71	95.03 73	86.17 71	93.24 116	80.82 105	87.90 75	71.15 160	96.80 51	95.53 102	92.82 134	99.47 73	99.88 27

CANet_DTU		91.36 85	95.75 53	86.23 129	92.31 86	98.71 70	95.60 62	78.41 145	98.20 45	56.48 192	94.38 46	87.96 92	95.11 72	96.89 59	96.07 72	99.48 71	98.01 153

thres200		91.36 85	91.19 109	91.55 71	91.60 91	98.69 71	94.98 74	86.17 71	92.16 133	80.76 107	87.66 79	71.15 160	96.35 57	95.53 102	93.23 124	99.47 73	99.92 22

FMVSNet3		91.25 87	92.13 98	90.21 95	85.64 162	93.14 165	95.29 67	80.09 132	96.40 77	85.74 56	77.13 130	86.81 98	94.98 75	97.19 53	97.11 41	99.55 58	97.13 173

thres400		91.24 88	91.01 116	91.50 75	91.56 92	98.77 67	94.66 81	86.41 69	91.87 139	80.56 108	87.05 85	71.01 163	96.35 57	95.67 94	92.82 134	99.48 71	99.88 27

PVSNet_Blended_VisFu		91.20 89	92.89 92	89.23 109	93.41 73	98.61 76	89.80 143	85.39 84	92.84 122	82.80 84	74.21 147	91.38 80	84.64 164	97.22 51	96.04 75	99.34 105	99.93 19

viewcassd2359sk11		91.16 90	91.10 115	91.23 79	89.96 130	97.99 95	93.45 99	85.49 79	92.46 130	84.03 74	80.13 119	75.86 133	94.99 74	95.98 86	96.00 76	99.44 86	99.29 91

DCV-MVSNet		91.15 91	92.00 99	90.17 97	90.78 107	92.23 182	93.70 95	81.17 127	95.16 94	82.98 82	89.46 69	83.31 112	93.98 94	91.79 153	92.87 129	98.41 178	99.18 101

DI_MVS_pp		91.11 92	91.47 103	90.68 88	90.01 127	97.77 99	95.87 59	83.56 104	94.72 100	82.12 94	68.46 167	87.46 93	93.07 102	96.46 71	95.73 85	99.47 73	99.71 45

diffmvs		91.05 93	91.15 110	90.93 85	90.15 121	97.79 98	94.05 88	85.45 81	95.63 89	81.95 98	80.45 118	73.01 149	94.47 84	95.56 99	95.89 81	99.49 68	99.72 43
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 93	94.43 64	87.11 119	91.05 102	97.99 95	92.53 118	83.82 101	92.71 126	76.28 124	84.50 103	92.43 72	79.52 190	97.24 50	97.68 25	99.43 88	98.45 137

thres600view7		90.97 95	90.70 119	91.30 77	91.53 93	98.69 71	94.33 82	86.17 71	91.75 141	80.19 109	86.06 95	70.90 164	96.10 62	95.53 102	92.08 142	99.47 73	99.86 31

baseline2		90.91 96	94.40 67	86.84 122	87.54 153	96.83 120	89.95 142	79.22 140	96.00 85	77.04 122	88.68 70	89.73 86	88.01 153	96.35 74	93.51 118	99.29 114	99.68 50

casdiffmvs_mvg		90.83 97	90.52 123	91.20 81	90.56 111	97.67 106	94.96 75	85.45 81	90.72 147	82.03 95	76.70 137	77.08 131	94.61 81	96.57 69	95.62 87	99.57 46	99.28 92
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ACMP	89.80 9	90.72 98	91.15 110	90.21 95	92.55 84	96.52 126	92.63 115	85.71 77	94.65 101	81.06 104	93.32 51	70.56 168	90.52 131	92.68 143	91.05 157	98.76 152	99.31 90
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
casdiffmvs		90.69 99	90.56 122	90.85 86	90.14 122	97.81 97	92.94 108	85.30 85	93.47 114	82.50 88	76.34 141	74.12 144	94.67 78	96.51 70	96.26 66	99.55 58	99.42 80
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)		90.67 100	93.03 89	87.92 116	90.95 104	98.45 82	92.61 117	66.04 206	94.90 96	84.47 66	77.52 129	91.74 76	94.07 92	97.11 55	92.46 140	99.40 94	99.03 112

viewmanbaseed2359cas		90.60 101	90.74 118	90.44 91	90.21 120	98.01 93	93.39 102	85.57 78	92.53 129	79.63 113	78.77 124	74.90 140	94.37 89	95.55 100	96.19 69	99.45 83	99.20 100

ACMM	89.40 10	90.58 102	90.02 131	91.23 79	93.30 75	94.75 152	90.69 137	88.22 55	95.20 92	82.70 86	88.54 71	71.40 158	93.48 97	93.64 132	90.94 158	98.99 135	95.72 189
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		90.49 103	91.12 113	89.75 103	84.99 165	92.73 170	93.94 90	80.09 132	96.40 77	85.74 56	77.13 130	86.81 98	94.42 85	94.12 120	93.73 107	99.35 101	96.90 177

test1		90.49 103	91.12 113	89.75 103	84.99 165	92.73 170	93.94 90	80.09 132	96.40 77	85.74 56	77.13 130	86.81 98	94.42 85	94.12 120	93.73 107	99.35 101	96.90 177

viewdifsd2359ckpt13		90.44 105	90.52 123	90.35 93	89.94 132	98.06 91	92.84 109	85.47 80	92.33 132	79.93 111	77.99 125	74.39 142	94.49 83	96.09 80	95.76 84	99.44 86	99.03 112

diffmvs_AUTHOR		90.43 106	90.26 128	90.64 90	90.00 128	97.72 101	93.72 94	85.18 91	94.49 104	81.20 103	77.72 126	71.57 155	94.30 90	94.78 115	95.85 82	99.42 91	99.66 52

ECVR-MVS		90.37 107	88.96 141	92.01 65	92.76 80	99.20 60	94.67 79	86.82 67	92.58 127	86.71 49	68.95 166	71.46 157	91.69 118	96.85 61	96.33 63	99.45 83	97.38 166

LGP-MVS_train		90.34 108	91.63 101	88.83 113	93.31 74	96.14 133	95.49 63	85.24 89	93.91 109	68.71 145	93.96 47	71.63 154	91.12 127	93.82 128	92.79 136	99.07 129	99.16 102

viewmambaseed2359dif		90.29 109	89.69 133	90.98 84	90.03 124	97.61 108	93.96 89	85.18 91	93.22 118	82.97 83	76.79 135	74.32 143	94.41 88	91.14 160	95.02 91	99.33 110	99.74 39

test1111		90.01 110	88.67 144	91.57 70	92.68 83	99.20 60	94.25 85	86.90 66	92.03 136	85.04 62	67.79 171	71.21 159	91.12 127	96.83 63	96.34 62	99.42 91	97.28 169

EPNet_dtu		89.82 111	94.18 73	84.74 139	96.87 53	95.54 145	92.65 114	86.91 65	96.99 68	54.17 203	92.41 57	88.54 88	78.35 193	96.15 79	96.05 74	99.47 73	93.60 201
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		89.81 112	89.75 132	89.88 100	93.22 77	93.99 159	94.78 76	85.23 90	94.01 108	82.52 87	95.00 41	87.23 95	92.01 114	85.16 211	83.48 216	91.54 222	89.38 215

MDTV_nov1_ep13		89.63 113	94.38 68	84.09 146	88.76 145	97.53 111	89.37 151	68.46 204	96.95 69	70.27 140	87.88 77	93.67 67	91.04 129	93.12 133	93.83 106	96.62 208	97.68 159

UA-Net		89.56 114	93.03 89	85.52 135	92.46 85	97.55 109	91.92 121	81.91 115	85.24 175	71.39 135	83.57 108	96.56 45	76.01 204	96.81 64	97.04 46	99.46 79	94.41 196

FMVSNet2		89.51 115	89.63 134	89.38 107	84.99 165	92.73 170	93.94 90	79.28 139	93.73 111	84.28 70	69.36 165	82.32 115	94.42 85	96.16 78	96.22 68	99.35 101	96.90 177

CostFormer		89.42 116	91.67 100	86.80 124	89.99 129	96.33 129	90.75 135	64.79 208	95.17 93	83.62 78	86.20 93	82.15 117	92.96 103	89.22 178	92.94 127	98.68 160	99.65 53

FC-MVSNet-train		89.37 117	89.62 135	89.08 110	90.48 113	94.16 158	89.45 147	83.99 100	91.09 145	80.09 110	82.84 112	74.52 141	91.44 124	93.79 129	91.57 148	99.01 133	99.35 87

OPM-MVS		89.33 118	87.45 156	91.53 73	94.49 69	96.20 131	96.47 54	89.72 47	82.77 182	75.43 125	80.53 117	70.86 166	93.80 95	94.00 124	91.85 146	99.29 114	95.91 187
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
viewmacassd2359aftdt		89.31 119	88.92 142	89.76 102	89.95 131	97.76 100	93.06 106	85.30 85	88.99 157	77.33 121	73.96 149	73.12 148	93.55 96	93.79 129	95.80 83	99.36 99	99.02 114

test-LLR		89.31 119	93.60 80	84.30 143	88.08 149	96.98 116	88.10 156	78.00 146	94.83 97	62.43 166	84.29 105	90.96 81	89.70 138	95.63 96	92.86 130	99.51 64	99.64 55

EPMVS		89.31 119	93.70 77	84.18 145	91.10 101	98.10 90	89.17 153	62.71 212	96.24 80	70.21 142	86.46 90	92.37 73	92.79 106	91.95 151	93.59 117	99.10 126	97.19 170

Anonymous20231211		89.22 122	87.56 154	91.16 82	90.23 119	96.62 123	93.22 104	85.44 83	92.89 120	84.37 68	60.13 188	81.25 120	96.02 64	90.61 163	92.01 143	97.70 198	99.41 83

Effi-MVS+		88.96 123	91.13 112	86.43 127	89.12 141	97.62 107	93.15 105	75.52 164	93.90 110	66.40 150	86.23 92	70.51 169	95.03 73	95.89 87	94.28 100	99.37 96	99.51 72

SCA		88.76 124	94.29 71	82.30 162	89.33 139	96.81 121	87.68 158	61.52 217	96.95 69	64.68 156	88.35 72	94.80 51	91.58 121	92.23 145	93.21 125	98.99 135	97.70 158

test0.0.03 1		88.71 125	92.22 97	84.63 141	88.08 149	94.71 154	85.91 181	78.00 146	95.54 90	72.96 129	86.10 94	85.88 103	83.59 172	92.95 140	93.24 123	99.25 120	97.09 174

PatchmatchNet		88.67 126	94.10 74	82.34 161	89.38 138	97.72 101	87.24 164	62.18 215	97.00 67	64.79 155	87.97 74	94.43 56	91.55 122	91.21 159	92.77 137	98.90 140	97.60 162
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dps		88.66 127	90.19 129	86.88 121	89.94 132	96.48 127	89.56 145	64.08 210	94.12 107	89.00 36	83.39 109	82.56 114	90.16 135	86.81 202	89.26 176	98.53 173	98.71 125

TESTMET0.1,1		88.63 128	93.60 80	82.84 158	84.07 173	96.98 116	88.10 156	73.22 185	94.83 97	62.43 166	84.29 105	90.96 81	89.70 138	95.63 96	92.86 130	99.51 64	99.64 55

CHOSEN 1792x2688		88.63 128	89.01 139	88.19 115	94.83 66	99.21 59	92.66 113	79.85 136	92.40 131	72.18 134	56.38 208	80.22 125	90.24 133	97.64 44	97.28 38	99.37 96	99.94 16

CDS-MVSNet		88.59 130	90.13 130	86.79 125	86.98 158	95.43 146	92.03 120	81.33 125	85.54 172	74.51 128	77.07 133	85.14 106	87.03 158	93.90 127	95.18 90	98.88 142	98.67 127
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
viewdifsd2359ckpt11		88.57 131	87.77 152	89.51 105	89.74 135	95.73 141	91.01 132	85.05 94	92.88 121	82.40 91	77.72 126	70.86 166	92.86 104	87.17 193	91.36 154	95.98 214	98.64 128

viewmsd2359difaftdt		88.57 131	87.76 153	89.51 105	89.74 135	95.73 141	91.01 132	85.05 94	92.79 123	82.43 90	77.72 126	70.90 164	92.85 105	87.16 194	91.37 153	95.98 214	98.64 128

IB-MVS	84.67 14	88.34 133	90.61 121	85.70 132	92.99 79	98.62 75	78.85 207	86.07 74	94.35 106	88.64 37	85.99 96	75.69 135	68.09 217	88.21 181	91.43 150	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		88.25 134	93.27 87	82.38 160	83.89 174	96.86 119	87.10 168	72.80 187	94.58 102	61.85 171	83.21 110	90.65 83	89.18 142	95.43 107	92.58 139	99.46 79	99.61 62

COLMAP_ROB	84.42 15	88.24 135	87.32 157	89.32 108	95.83 60	95.82 137	92.81 110	87.68 61	92.09 135	72.64 132	72.34 156	79.96 126	88.79 144	89.54 173	89.46 172	98.16 187	92.00 207
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
IterMVS-LS		87.95 136	89.40 137	86.26 128	88.79 144	90.93 197	91.23 129	76.05 161	90.87 146	71.07 137	75.51 144	81.18 121	91.21 126	94.11 123	95.01 92	99.20 123	98.23 144
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		87.86 137	88.25 147	87.40 118	94.67 67	98.54 78	90.33 140	76.51 160	89.60 155	70.89 138	51.43 219	85.69 104	92.79 106	96.59 68	95.96 78	99.22 122	99.94 16

Vis-MVSNet		87.60 138	91.31 105	83.27 153	89.14 140	98.04 92	90.35 139	79.42 137	87.23 161	66.92 149	79.10 123	84.63 108	74.34 211	95.81 89	96.06 73	99.46 79	98.32 141
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GeoE		87.55 139	88.17 148	86.82 123	88.74 146	96.32 130	92.75 112	74.93 169	90.13 152	72.73 130	69.47 164	74.03 145	92.51 111	93.99 125	93.62 115	99.29 114	99.59 63

dmvs_re		87.43 140	87.99 149	86.77 126	84.94 169	96.19 132	91.87 122	85.95 76	91.25 144	68.58 146	81.45 114	66.04 177	89.95 137	90.91 161	91.57 148	99.37 96	98.54 133

RPMNet		87.35 141	92.41 95	81.45 166	88.85 143	96.06 134	89.42 150	59.59 224	93.57 112	61.81 172	76.48 140	91.48 79	90.18 134	96.32 75	93.37 121	98.87 143	99.59 63

tpm cat1		87.34 142	88.52 146	85.95 130	89.83 134	95.80 138	90.73 136	64.91 207	92.99 119	82.21 93	71.19 162	82.68 113	90.13 136	86.38 203	90.87 160	97.90 195	99.74 39

MS-PatchMatch		87.19 143	88.59 145	85.55 134	93.15 78	96.58 125	92.35 119	74.19 177	91.97 138	70.33 139	71.42 160	85.89 102	84.28 166	93.12 133	89.16 178	99.00 134	91.99 208

Effi-MVS+-dtu		87.18 144	90.48 125	83.32 152	86.51 159	95.76 140	91.16 131	74.28 176	90.44 151	61.31 175	86.72 89	72.68 152	91.25 125	95.01 112	93.64 110	95.45 216	99.12 106

FMVSNet5		87.06 145	89.52 136	84.20 144	79.92 211	86.57 217	87.11 167	72.37 189	96.06 83	75.41 126	84.33 104	91.76 75	91.60 120	91.51 155	91.22 155	98.77 149	85.16 221

Fast-Effi-MVS+-dtu		86.94 146	91.27 108	81.89 163	86.27 160	95.06 147	90.68 138	68.93 201	91.76 140	57.18 190	89.56 68	75.85 134	89.19 141	94.56 116	92.84 132	99.07 129	99.23 95

Fast-Effi-MVS+		86.94 146	87.88 151	85.84 131	86.99 157	95.80 138	91.24 128	73.48 184	92.75 124	69.22 143	72.70 154	65.71 178	94.84 77	94.98 113	94.71 96	99.26 118	98.48 136

tpmrst		86.78 148	90.29 126	82.69 159	90.55 112	96.95 118	88.49 155	62.58 213	95.09 95	63.52 162	76.67 139	84.00 111	92.05 113	87.93 184	91.89 145	98.98 137	99.50 74

CR-MVSNet		86.73 149	91.47 103	81.20 169	88.56 147	96.06 134	89.43 148	61.37 218	93.57 112	60.81 177	72.89 153	88.85 87	88.13 151	96.03 82	93.64 110	98.89 141	99.22 96

ADS-MVSNet		86.68 150	90.79 117	81.88 164	90.38 116	96.81 121	86.90 169	60.50 222	96.01 84	63.93 159	81.67 113	84.72 107	90.78 130	87.03 196	91.67 147	98.77 149	97.63 161

FMVSNet1		85.85 151	84.91 167	86.96 120	82.70 179	91.39 191	91.54 125	77.45 152	85.29 174	79.56 114	60.70 185	72.68 152	92.37 112	94.12 120	93.73 107	98.12 188	96.44 181

FC-MVSNet-test		85.51 152	89.08 138	81.35 167	85.31 164	93.35 161	87.65 159	77.55 151	90.01 153	64.07 158	79.63 121	81.83 119	74.94 208	92.08 148	90.83 162	98.55 170	95.81 188

ACMH	85.22 13	85.40 153	85.73 164	85.02 137	91.76 88	94.46 157	84.97 187	81.54 123	85.18 176	65.22 154	76.92 134	64.22 179	88.58 147	90.17 165	90.25 168	98.03 191	98.90 119
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		85.35 154	86.00 163	84.59 142	84.97 168	95.57 144	88.98 154	77.29 155	81.44 187	71.36 136	71.48 159	75.00 139	87.03 158	91.92 152	92.21 141	97.92 194	94.40 197

ACMH+	85.62 12	85.27 155	84.96 166	85.64 133	90.84 106	94.78 151	87.46 161	81.30 126	86.94 162	67.35 148	74.56 146	64.09 180	88.70 145	88.14 182	89.00 179	98.22 186	97.19 170

USDC		85.11 156	85.35 165	84.83 138	89.45 137	94.93 150	92.98 107	77.30 154	90.53 149	61.80 173	76.69 138	59.62 190	88.90 143	92.78 142	90.79 164	98.53 173	92.12 205

IterMVS		85.02 157	88.98 140	80.41 175	87.03 156	90.34 205	89.78 144	69.45 198	89.77 154	54.04 204	73.71 150	82.05 118	83.44 175	95.11 110	93.64 110	98.75 153	98.22 146
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		84.91 158	88.90 143	80.25 178	87.04 155	90.27 206	89.23 152	69.25 200	89.17 156	54.04 204	73.65 151	82.22 116	83.23 180	95.11 110	93.63 114	98.73 154	98.23 144

PatchT		84.89 159	90.67 120	78.13 198	87.83 152	94.99 149	72.46 219	60.22 223	91.74 142	60.81 177	72.16 157	86.95 96	88.13 151	96.03 82	93.64 110	99.36 99	99.22 96

pmmvs4		84.88 160	84.67 168	85.13 136	82.80 178	92.37 175	87.29 162	79.08 141	90.51 150	74.94 127	70.37 163	62.49 183	88.17 150	92.01 150	88.51 184	98.49 176	96.44 181

CVMVSNet		84.01 161	86.91 158	80.61 173	88.39 148	93.29 162	86.06 177	82.29 112	83.13 180	54.29 200	72.68 155	79.59 127	75.11 207	91.23 158	92.91 128	97.54 202	95.58 190

tpm		83.97 162	87.97 150	79.31 188	87.35 154	93.21 164	86.00 179	61.90 216	90.69 148	54.01 206	79.42 122	75.61 136	88.65 146	87.18 192	90.48 166	97.95 193	99.21 98

GA-MVS		83.83 163	86.63 159	80.58 174	85.40 163	94.73 153	87.27 163	78.76 144	86.49 164	49.57 214	74.21 147	67.67 174	83.38 176	95.28 109	90.92 159	99.08 128	97.09 174

UniMVSNet_NR-MVSNet		83.83 163	83.70 171	83.98 147	81.41 189	92.56 174	86.54 172	82.96 108	85.98 169	66.27 151	66.16 175	63.63 181	87.78 155	87.65 187	90.81 163	98.94 138	99.13 104

UniMVSNet (Re)		83.28 165	83.16 172	83.42 151	81.93 184	93.12 166	86.27 175	80.83 128	85.88 170	68.23 147	64.56 178	60.58 185	84.25 167	89.13 179	89.44 174	99.04 132	99.40 84

thisisatest0515		83.17 166	86.49 160	79.30 189	82.04 182	93.12 166	78.70 208	77.92 148	86.43 165	63.05 163	74.91 145	73.01 149	75.56 206	92.10 147	88.05 197	98.50 175	97.76 157

TinyColmap		83.03 167	82.24 176	83.95 148	88.88 142	93.22 163	89.48 146	76.89 157	87.53 160	62.12 168	68.46 167	55.03 206	88.43 149	90.87 162	89.65 170	97.89 196	90.91 211

testgi		82.88 168	86.14 162	79.08 191	86.05 161	92.20 183	81.23 204	74.77 172	88.70 158	57.63 189	86.73 88	61.53 184	76.83 201	90.33 164	89.43 175	97.99 192	94.05 198

DU-MVS		82.87 169	82.16 177	83.70 150	80.77 198	92.24 179	86.54 172	81.91 115	86.41 166	66.27 151	63.95 179	55.66 204	87.78 155	86.83 199	90.86 161	98.94 138	99.13 104

MIMVSNet		82.87 169	86.17 161	79.02 192	77.23 219	92.88 169	84.88 188	60.62 221	86.72 163	64.16 157	73.58 152	71.48 156	88.51 148	94.14 119	93.50 120	98.72 156	90.87 212

NR-MVSNet		82.37 171	81.95 179	82.85 157	82.56 181	92.24 179	87.49 160	81.91 115	86.41 166	65.51 153	63.95 179	52.93 215	80.80 187	89.41 175	89.61 171	98.85 145	99.10 109

Baseline_NR-MVSNet		82.08 172	80.64 186	83.77 149	80.77 198	88.50 212	86.88 170	81.71 120	85.58 171	68.80 144	58.20 200	57.75 196	86.16 160	86.83 199	88.68 181	98.33 183	98.90 119

TranMVSNet+NR-MVSNet		82.07 173	81.36 182	82.90 156	80.43 204	91.39 191	87.16 166	82.75 109	84.28 178	62.98 164	62.28 184	56.01 203	85.30 163	86.06 205	90.69 165	98.80 146	98.80 122

pm-mvs1		81.68 174	81.70 180	81.65 165	82.61 180	92.26 178	85.54 185	78.95 142	76.29 209	63.81 160	58.43 199	66.33 176	80.63 188	92.30 144	89.93 169	98.37 182	96.39 183

TDRefinement		81.49 175	80.08 192	83.13 155	91.02 103	94.53 155	91.66 124	82.43 111	81.70 185	62.12 168	62.30 183	59.32 191	73.93 212	87.31 190	85.29 208	97.61 199	90.14 213

anonymousdsp		81.29 176	84.52 170	77.52 200	79.83 212	92.62 173	82.61 199	70.88 194	80.76 191	50.82 211	68.35 169	68.76 172	82.45 183	93.00 136	89.45 173	98.55 170	98.69 126

gg-mvs-nofinetune		81.27 177	84.65 169	77.32 201	87.96 151	98.48 81	95.64 61	56.36 227	59.35 229	32.80 233	47.96 223	92.11 74	91.49 123	98.12 24	97.00 48	99.65 24	99.56 68

tfpnnormal		81.11 178	79.33 200	83.19 154	84.23 171	92.29 177	86.76 171	82.27 113	72.67 215	62.02 170	56.10 210	53.86 212	85.35 162	92.06 149	89.23 177	98.49 176	99.11 108

UniMVSNet_ETH3D		80.95 179	77.71 208	84.74 139	84.45 170	93.11 168	86.45 174	79.97 135	75.21 211	70.22 141	51.24 220	50.26 221	89.55 140	84.47 213	91.12 156	97.81 197	98.53 134

V42		80.88 180	80.74 184	81.05 170	81.21 192	92.01 185	85.96 180	77.75 150	81.62 186	59.73 184	59.93 191	58.35 195	82.98 182	86.90 198	88.06 196	98.69 159	98.32 141

v2v482		80.86 181	80.52 190	81.25 168	80.79 197	91.85 186	85.68 183	78.78 143	81.05 188	58.09 187	60.46 186	56.08 201	85.45 161	87.27 191	88.53 183	98.73 154	98.38 140

v8		80.61 182	80.61 188	80.62 172	81.51 187	91.00 196	86.06 177	74.07 180	81.78 184	59.93 183	60.10 190	58.42 194	83.35 177	86.99 197	88.11 194	98.79 147	97.83 155

pmmvs5		80.48 183	81.43 181	79.36 187	81.50 188	92.24 179	82.07 202	74.08 179	78.10 202	55.86 195	67.72 172	54.35 209	83.91 171	92.97 137	88.65 182	98.77 149	96.01 185

v10		80.38 184	80.73 185	79.96 180	81.22 191	90.40 204	86.11 176	71.63 191	82.42 183	57.65 188	58.74 197	57.47 197	84.44 165	89.75 169	88.28 187	98.71 157	98.06 152

v1144		80.36 185	80.63 187	80.05 179	80.86 196	91.56 189	85.78 182	75.22 166	80.73 192	55.83 196	58.51 198	56.99 199	83.93 170	89.79 168	88.25 188	98.68 160	98.56 132

SixPastTwentyTwo		80.28 186	82.06 178	78.21 197	81.89 186	92.35 176	77.72 209	74.48 173	83.04 181	54.22 201	76.06 142	56.40 200	83.55 173	86.83 199	84.83 210	97.38 203	94.93 194

CP-MVSNet		79.90 187	79.49 197	80.38 176	80.72 200	90.83 198	82.98 196	75.17 167	79.70 197	61.39 174	59.74 192	51.98 218	83.31 178	87.37 189	88.38 185	98.71 157	98.45 137

v1192		79.84 188	80.05 194	79.61 183	80.49 203	91.04 195	85.56 184	74.37 175	80.73 192	54.35 199	57.07 205	54.54 208	84.23 168	89.94 166	88.38 185	98.63 164	98.61 130

WR-MVS_H		79.76 189	80.07 193	79.40 186	81.25 190	91.73 188	82.77 197	74.82 171	79.02 201	62.55 165	59.41 194	57.32 198	76.27 203	87.61 188	87.30 202	98.78 148	98.09 150

WR-MVS		79.67 190	80.25 191	79.00 193	80.65 201	91.16 193	83.31 194	76.57 159	80.97 189	60.50 182	59.20 195	58.66 193	74.38 210	85.85 207	87.76 199	98.61 165	98.14 147

v148		79.66 191	79.13 202	80.27 177	81.02 194	91.76 187	81.90 203	79.32 138	79.24 199	63.79 161	58.07 202	54.34 210	77.17 199	84.42 214	87.52 201	98.40 179	98.59 131

LTVRE_ROB	79.45 16	79.66 191	80.55 189	78.61 195	83.01 177	92.19 184	87.18 165	73.69 183	71.70 218	43.22 227	71.22 161	50.85 219	87.82 154	89.47 174	90.43 167	96.75 206	98.00 154
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		79.61 193	79.77 195	79.41 185	80.28 205	91.06 194	84.87 189	73.86 181	79.65 198	55.38 197	57.76 203	55.20 205	83.46 174	88.42 180	87.89 198	98.61 165	98.42 139

v1921920		79.55 194	79.77 195	79.30 189	80.24 206	90.77 200	85.37 186	73.75 182	80.38 194	53.78 207	56.89 207	54.18 211	84.05 169	89.55 172	88.13 193	98.59 167	98.52 135

TransMVSNet (Re)		79.51 195	78.36 204	80.84 171	83.17 175	89.72 208	84.22 192	81.45 124	73.98 214	60.79 180	57.20 204	56.05 202	77.11 200	89.88 167	88.86 180	98.30 185	92.83 203

MVS-HIRNet		79.34 196	82.56 173	75.57 206	84.11 172	95.02 148	75.03 216	57.28 226	85.50 173	55.88 194	53.00 216	70.51 169	83.05 181	92.12 146	91.96 144	98.09 189	89.83 214

PS-CasMVS		79.06 197	78.58 203	79.63 182	80.59 202	90.55 202	82.54 200	75.04 168	77.76 203	58.84 185	58.16 201	50.11 223	82.09 184	87.05 195	88.18 191	98.66 163	98.27 143

v1240		78.97 198	79.27 201	78.63 194	80.04 207	90.61 201	84.25 191	72.95 186	79.22 200	52.70 209	56.22 209	52.88 217	83.28 179	89.60 171	88.20 190	98.56 169	98.14 147

pmnet_mix02		78.91 199	81.17 183	76.28 205	81.91 185	90.82 199	74.25 217	77.87 149	86.17 168	49.04 215	67.97 170	62.93 182	77.40 197	82.75 219	82.11 218	97.18 204	95.42 191

MDTV_nov1_ep13_2view		78.83 200	82.35 174	74.73 209	78.65 214	91.51 190	79.18 206	62.52 214	84.51 177	52.51 210	67.49 173	67.29 175	78.90 191	85.52 209	86.34 205	96.62 208	93.76 199

PEN-MVS		78.80 201	78.13 206	79.58 184	80.03 208	89.67 209	83.61 193	75.83 162	77.71 205	58.41 186	60.11 189	50.00 224	81.02 186	84.08 215	88.14 192	98.59 167	97.18 172

EG-PatchMatch MVS		78.32 202	79.42 199	77.03 203	83.03 176	93.77 160	84.47 190	69.26 199	75.85 210	53.69 208	55.68 211	60.23 188	73.20 213	89.69 170	88.22 189	98.55 170	92.54 204

DTE-MVSNet		77.92 203	77.42 209	78.51 196	79.34 213	89.00 211	83.05 195	75.60 163	76.89 207	56.58 191	59.63 193	50.31 220	78.09 196	82.57 220	87.56 200	98.38 180	95.95 186

v7n		77.71 204	78.25 205	77.09 202	78.49 215	90.55 202	82.15 201	71.11 193	76.79 208	54.18 202	55.63 212	50.20 222	78.28 194	89.36 177	87.15 203	98.33 183	98.07 151

gm-plane-assit		77.20 205	82.26 175	71.30 212	81.10 193	82.00 225	54.33 231	64.41 209	63.80 228	40.93 230	59.04 196	76.57 132	87.30 157	98.26 22	97.36 37	99.74 13	98.76 124

N_pmnet		76.83 206	77.97 207	75.50 207	80.96 195	88.23 214	72.81 218	76.83 158	80.87 190	50.55 212	56.94 206	60.09 189	75.70 205	83.28 217	84.23 212	96.14 212	92.12 205

pmmvs6		76.79 207	75.69 214	78.09 199	79.95 210	89.57 210	80.92 205	74.46 174	64.79 226	60.74 181	45.71 225	60.55 186	78.37 192	88.04 183	86.00 206	94.07 219	95.15 192

CMPMVS	58.73 17	76.78 208	74.27 215	79.70 181	93.26 76	95.58 143	82.74 198	77.44 153	71.46 221	56.29 193	53.58 215	59.13 192	77.33 198	79.20 221	79.71 221	91.14 224	81.24 224
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		76.76 209	79.47 198	73.60 210	79.99 209	87.47 215	77.39 210	75.43 165	77.62 206	47.83 218	64.78 177	60.44 187	64.80 218	86.28 204	86.53 204	96.17 211	93.19 202

PM-MVS		75.81 210	76.11 213	75.46 208	73.81 221	85.48 219	76.42 212	70.57 195	80.05 196	54.75 198	62.33 182	39.56 232	80.59 189	87.71 186	82.81 217	96.61 210	94.81 195

pmmvs-eth3d		75.17 211	74.09 216	76.43 204	72.92 222	84.49 221	76.61 211	72.42 188	74.33 212	61.28 176	54.71 214	39.42 233	78.20 195	87.77 185	84.25 211	97.17 205	93.63 200

Anonymous20231206		74.59 212	77.00 210	71.78 211	77.89 218	87.45 216	75.14 215	72.29 190	77.76 203	46.65 220	52.14 217	52.93 215	61.10 222	89.37 176	88.09 195	97.59 200	91.30 210

test20.03		72.81 213	76.24 212	68.80 215	78.31 216	85.40 220	71.04 220	71.20 192	71.85 217	43.40 226	65.31 176	54.71 207	51.27 225	85.92 206	84.18 213	97.58 201	86.35 220

test_method		71.90 214	76.72 211	66.28 220	60.87 230	78.37 228	69.75 224	49.81 232	83.44 179	49.63 213	47.13 224	53.23 214	76.38 202	91.32 157	85.76 207	91.22 223	97.77 156

new_pmnet		71.86 215	73.67 217	69.75 214	72.56 225	84.20 222	70.95 222	66.81 205	80.34 195	43.62 225	51.60 218	53.81 213	71.24 215	82.91 218	80.93 219	93.35 221	81.92 223

MDA-MVSNet-bldmvs		69.61 216	70.36 219	68.74 216	62.88 228	88.50 212	65.40 228	77.01 156	71.60 220	43.93 222	66.71 174	35.33 235	72.47 214	61.01 228	80.63 220	90.73 225	88.75 217

pmmvs3		69.04 217	70.75 218	67.04 218	66.83 226	78.54 227	64.99 229	60.92 220	64.67 227	40.61 231	55.08 213	40.29 231	74.89 209	83.76 216	84.01 214	93.98 220	88.88 216

MIMVSNet1		68.63 218	70.24 220	66.76 219	56.86 232	83.26 223	67.93 226	70.26 197	68.05 223	46.80 219	40.44 227	48.15 225	62.01 220	84.96 212	84.86 209	96.69 207	81.93 222

FE-MVSNET		68.01 219	70.02 221	65.66 221	53.56 233	81.28 226	68.74 225	70.37 196	67.27 224	42.26 229	42.17 226	42.41 230	62.95 219	85.18 210	83.97 215	96.09 213	87.90 219

GG-mvs-BLEND		67.99 220	97.35 39	33.72 229	1.22 239	99.72 17	98.30 35	0.57 236	97.61 62	1.18 240	93.26 52	96.63 44	1.74 236	97.15 54	97.14 40	99.34 105	99.96 10

new-patchmatchnet		67.66 221	68.07 222	67.18 217	72.85 223	82.86 224	63.09 230	68.61 203	66.60 225	42.64 228	49.28 221	38.68 234	61.21 221	75.84 222	75.22 223	94.67 218	88.00 218

FPMVS		63.27 222	61.31 225	65.57 222	78.25 217	74.42 231	75.23 214	68.92 202	72.33 216	43.87 223	49.01 222	43.94 228	48.64 227	61.15 227	58.81 229	78.51 231	69.49 229

WB-MVS		56.28 223	63.25 224	48.16 226	75.24 220	65.97 232	39.91 235	74.13 178	69.25 222	10.01 238	62.67 181	44.05 227	20.71 235	70.43 225	69.57 225	68.94 233	60.78 234

Gipuma		54.59 224	53.98 226	55.30 223	59.03 231	52.63 234	47.17 233	56.08 228	71.68 219	37.54 232	20.90 233	19.00 237	52.33 224	71.69 224	75.20 224	79.64 230	66.79 230
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	49.05 18	51.88 225	50.56 228	53.42 224	64.21 227	43.30 236	42.64 234	62.93 211	50.56 230	43.72 224	37.44 228	42.95 229	35.05 230	58.76 230	54.58 230	71.95 232	66.33 231
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		50.69 226	52.33 227	48.78 225	51.24 234	64.81 233	47.91 232	53.79 231	44.95 231	21.75 234	29.98 231	25.90 236	31.98 232	59.95 229	65.37 227	86.00 228	75.36 227

E-PMN		37.15 227	34.82 230	39.86 227	47.53 236	35.42 238	23.79 237	55.26 229	35.18 234	14.12 236	17.38 236	14.13 239	39.73 229	32.24 232	46.98 231	58.76 234	62.39 233

EMVS		36.45 228	33.63 231	39.74 228	48.47 235	35.73 237	23.59 238	55.11 230	35.61 233	12.88 237	17.49 234	14.62 238	41.04 228	29.33 233	43.00 232	57.32 235	59.62 235

MVE	42.40 19	36.00 229	38.65 229	32.92 230	29.16 237	46.17 235	22.61 239	44.21 233	26.44 236	18.88 235	17.41 235	9.36 241	32.29 231	45.75 231	61.38 228	50.35 236	64.03 232
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		21.55 230	30.91 232	10.62 231	2.78 238	11.66 239	18.51 240	4.82 234	38.21 232	4.06 239	36.35 229	4.47 242	26.81 233	23.27 234	27.11 233	6.75 237	75.30 228

test123		16.81 231	24.80 233	7.48 232	0.82 240	8.38 240	11.92 241	2.60 235	28.96 235	1.12 241	28.39 232	1.26 243	24.51 234	8.93 235	22.19 234	3.90 238	75.49 226

uanet_test		0.00 232	0.00 234	0.00 233	0.00 241	0.00 241	0.00 242	0.00 237	0.00 237	0.00 242	0.00 237	0.00 244	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

sosnet-low-res		0.00 232	0.00 234	0.00 233	0.00 241	0.00 241	0.00 242	0.00 237	0.00 237	0.00 242	0.00 237	0.00 244	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

sosnet		0.00 232	0.00 234	0.00 233	0.00 241	0.00 241	0.00 242	0.00 237	0.00 237	0.00 242	0.00 237	0.00 244	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

TPM-MVS					99.50 1	99.78 12	99.69 1			88.49 38	97.88 27	98.84 23	99.42 1			99.76 10	97.44 164
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										46.54 221

9.14												99.73 8

SR-MVS					99.27 16			95.82 18				99.00 18

Anonymous202405211			87.54 155		90.72 108	97.10 115	93.40 101	85.30 85	91.41 143		60.23 187	80.69 124	95.80 68	91.33 156	92.60 138	98.38 180	99.40 84

our_test_3					81.94 183	90.26 207	75.39 213

ambc			64.61 223		61.80 229	75.31 230	71.00 221		74.16 213	48.83 216	36.02 230	13.22 240	58.66 223	85.80 208	76.26 222	88.01 226	91.53 209

MTAPA										94.58 14		98.56 25

MTMP										95.24 8		98.13 31

Patchmatch-RL test							37.05 236

tmp_tt				71.24 213	90.29 118	76.39 229	65.81 227	59.43 225	97.62 60	79.65 112	90.60 63	68.71 173	49.71 226	72.71 223	65.70 226	82.54 229

XVS					93.63 71	99.64 26	94.32 83			83.97 75		98.08 33				99.59 37

X-MVStestdata					93.63 71	99.64 26	94.32 83			83.97 75		98.08 33				99.59 37

mPP-MVS					98.66 29							97.11 41

NP-MVS									97.69 58

Patchmtry						95.86 136	89.43 148	61.37 218		60.81 177

DeepMVS_CX						85.88 218	69.83 223	81.56 122	87.99 159	48.22 217	71.85 158	45.52 226	68.67 216	63.21 226		86.64 227	80.03 225

High-resolution multi-view benchmark