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.

SMA-MVS		98.66 7	98.89 7	98.39 9	99.60 1	99.41 12	99.00 20	97.63 12	97.78 18	95.83 18	98.33 11	99.83 4	98.85 9	98.93 8	98.56 6	99.41 49	99.40 19
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
APDe-MVS		98.87 3	98.96 4	98.77 1	99.58 2	99.53 7	99.44 1	97.81 2	98.22 10	97.33 4	98.70 5	99.33 10	98.86 8	98.96 6	98.40 13	99.63 5	99.57 9
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
PGM-MVS		97.81 25	98.11 28	97.46 29	99.55 3	99.34 20	99.32 9	94.51 45	96.21 62	93.07 36	98.05 14	97.95 42	98.82 11	98.22 36	97.89 38	99.48 29	99.09 55

ACMMP_NAP		98.20 18	98.49 13	97.85 25	99.50 4	99.40 13	99.26 11	97.64 11	97.47 33	92.62 46	97.59 20	99.09 22	98.71 15	98.82 12	97.86 39	99.40 52	99.19 44

DVP-MVS++		98.92 1	99.18 1	98.61 4	99.47 5	99.61 2	99.39 3	97.82 1	98.80 1	96.86 8	98.90 2	99.92 1	98.67 17	99.02 2	98.20 19	99.43 46	99.82 1

APD-MVS		98.36 15	98.32 23	98.41 8	99.47 5	99.26 26	99.12 15	97.77 7	96.73 50	96.12 16	97.27 28	98.88 24	98.46 25	98.47 18	98.39 14	99.52 21	99.22 40
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CSCG		97.44 32	97.18 43	97.75 27	99.47 5	99.52 8	98.55 31	95.41 40	97.69 23	95.72 19	94.29 55	95.53 63	98.10 33	96.20 107	97.38 56	99.24 77	99.62 4

HPM-MVS++		98.34 16	98.47 15	98.18 16	99.46 8	99.15 34	99.10 16	97.69 8	97.67 24	94.93 26	97.62 19	99.70 7	98.60 20	98.45 20	97.46 52	99.31 67	99.26 34

SF-MVS		98.39 13	98.45 17	98.33 10	99.45 9	99.05 37	98.27 37	97.65 9	97.73 19	97.02 7	98.18 12	99.25 15	98.11 32	98.15 38	97.62 47	99.45 37	99.19 44

SR-MVS					99.45 9			97.61 14				99.20 16

MSP-MVS		98.73 6	98.93 5	98.50 6	99.44 11	99.57 4	99.36 4	97.65 9	98.14 12	96.51 14	98.49 7	99.65 8	98.67 17	98.60 14	98.42 11	99.40 52	99.63 2
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DVP-MVS		98.86 4	98.97 3	98.75 2	99.43 12	99.63 1	99.25 12	97.81 2	98.62 2	97.69 1	97.59 20	99.90 2	98.93 5	98.99 4	98.42 11	99.37 58	99.62 4
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
ACMMPR		98.40 12	98.49 13	98.28 13	99.41 13	99.40 13	99.36 4	97.35 21	98.30 6	95.02 25	97.79 17	98.39 37	99.04 2	98.26 33	98.10 23	99.50 28	99.22 40

X-MVS		97.84 24	98.19 27	97.42 30	99.40 14	99.35 17	99.06 17	97.25 25	97.38 34	90.85 62	96.06 37	98.72 30	98.53 24	98.41 24	98.15 22	99.46 33	99.28 29

MCST-MVS		98.20 18	98.36 19	98.01 22	99.40 14	99.05 37	99.00 20	97.62 13	97.59 28	93.70 33	97.42 27	99.30 11	98.77 13	98.39 26	97.48 51	99.59 7	99.31 28

CNVR-MVS		98.47 11	98.46 16	98.48 7	99.40 14	99.05 37	99.02 19	97.54 16	97.73 19	96.65 11	97.20 29	99.13 20	98.85 9	98.91 9	98.10 23	99.41 49	99.08 56

HFP-MVS		98.48 10	98.62 11	98.32 11	99.39 17	99.33 21	99.27 10	97.42 18	98.27 7	95.25 23	98.34 10	98.83 26	99.08 1	98.26 33	98.08 25	99.48 29	99.26 34

SED-MVS		98.90 2	99.07 2	98.69 3	99.38 18	99.61 2	99.33 8	97.80 4	98.25 8	97.60 2	98.87 4	99.89 3	98.67 17	99.02 2	98.26 17	99.36 60	99.61 6

NCCC		98.10 21	98.05 30	98.17 18	99.38 18	99.05 37	99.00 20	97.53 17	98.04 14	95.12 24	94.80 52	99.18 18	98.58 22	98.49 17	97.78 43	99.39 54	98.98 73

MP-MVS		98.09 22	98.30 24	97.84 26	99.34 20	99.19 32	99.23 13	97.40 19	97.09 43	93.03 39	97.58 22	98.85 25	98.57 23	98.44 22	97.69 45	99.48 29	99.23 38
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		98.32 17	98.34 22	98.29 12	99.34 20	99.30 22	99.15 14	97.35 21	97.49 31	95.58 21	97.72 18	98.62 34	98.82 11	98.29 28	97.67 46	99.51 26	99.28 29

SteuartSystems-ACMMP		98.38 14	98.71 10	97.99 23	99.34 20	99.46 10	99.34 6	97.33 24	97.31 35	94.25 29	98.06 13	99.17 19	98.13 31	98.98 5	98.46 9	99.55 18	99.54 11
Skip Steuart: Steuart Systems R&D Blog.
DPE-MVS		98.75 5	98.91 6	98.57 5	99.21 23	99.54 6	99.42 2	97.78 6	97.49 31	96.84 9	98.94 1	99.82 5	98.59 21	98.90 10	98.22 18	99.56 17	99.48 15
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
mPP-MVS					99.21 23							98.29 38

AdaColmap		97.53 30	96.93 47	98.24 14	99.21 23	98.77 65	98.47 34	97.34 23	96.68 52	96.52 13	95.11 49	96.12 58	98.72 14	97.19 69	96.24 84	99.17 93	98.39 114

DeepC-MVS_fast	96.13 1	98.13 20	98.27 25	97.97 24	99.16 26	99.03 43	99.05 18	97.24 26	98.22 10	94.17 31	95.82 40	98.07 39	98.69 16	98.83 11	98.80 2	99.52 21	99.10 53
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSLP-MVS++		98.04 23	97.93 32	98.18 16	99.10 27	99.09 36	98.34 36	96.99 32	97.54 29	96.60 12	94.82 51	98.45 35	98.89 6	97.46 61	98.77 4	99.17 93	99.37 21

3Dnovator	93.79 8	97.08 37	97.20 41	96.95 37	99.09 28	99.03 43	98.20 39	93.33 52	97.99 15	93.82 32	90.61 93	96.80 49	97.82 37	97.90 48	98.78 3	99.47 32	99.26 34

QAPM		96.78 47	97.14 44	96.36 42	99.05 29	99.14 35	98.02 43	93.26 54	97.27 37	90.84 65	91.16 85	97.31 44	97.64 43	97.70 54	98.20 19	99.33 62	99.18 47

OpenMVS	92.33 11	95.50 56	95.22 74	95.82 53	98.98 30	98.97 49	97.67 50	93.04 62	94.64 104	89.18 94	84.44 141	94.79 65	96.79 61	97.23 66	97.61 48	99.24 77	98.88 84

PLC	94.95 3	97.37 33	96.77 51	98.07 20	98.97 31	98.21 91	97.94 46	96.85 35	97.66 25	97.58 3	93.33 60	96.84 48	98.01 36	97.13 71	96.20 86	99.09 105	98.01 130
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TPM-MVS					98.94 32	98.47 84	98.04 42			92.62 46	96.51 33	98.76 29	95.94 78			98.92 126	97.55 146
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
train_agg		97.65 29	98.06 29	97.18 33	98.94 32	98.91 56	98.98 24	97.07 31	96.71 51	90.66 68	97.43 26	99.08 23	98.20 27	97.96 46	97.14 63	99.22 83	99.19 44

CDPH-MVS		96.84 45	97.49 36	96.09 47	98.92 34	98.85 61	98.61 28	95.09 41	96.00 70	87.29 107	95.45 46	97.42 43	97.16 51	97.83 50	97.94 34	99.44 43	98.92 79

CPTT-MVS		97.78 26	97.54 35	98.05 21	98.91 35	99.05 37	99.00 20	96.96 33	97.14 41	95.92 17	95.50 44	98.78 28	98.99 4	97.20 67	96.07 88	98.54 160	99.04 65

3Dnovator+	93.91 7	97.23 35	97.22 40	97.24 32	98.89 36	98.85 61	98.26 38	93.25 56	97.99 15	95.56 22	90.01 99	98.03 41	98.05 34	97.91 47	98.43 10	99.44 43	99.35 23

ACMMP		97.37 33	97.48 37	97.25 31	98.88 37	99.28 24	98.47 34	96.86 34	97.04 45	92.15 50	97.57 23	96.05 60	97.67 40	97.27 65	95.99 93	99.46 33	99.14 52
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
PHI-MVS		97.78 26	98.44 18	97.02 36	98.73 38	99.25 28	98.11 40	95.54 39	96.66 53	92.79 43	98.52 6	99.38 9	97.50 45	97.84 49	98.39 14	99.45 37	99.03 66

OMC-MVS		97.00 39	96.92 48	97.09 34	98.69 39	98.66 73	97.85 47	95.02 42	98.09 13	94.47 27	93.15 61	96.90 46	97.38 47	97.16 70	96.82 73	99.13 100	97.65 143

MAR-MVS		95.50 56	95.60 65	95.39 61	98.67 40	98.18 94	95.89 101	89.81 109	94.55 106	91.97 53	92.99 63	90.21 91	97.30 48	96.79 80	97.49 50	98.72 146	98.99 71
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
TSAR-MVS + ACMM		97.71 28	98.60 12	96.66 39	98.64 41	99.05 37	98.85 25	97.23 27	98.45 4	89.40 89	97.51 24	99.27 14	96.88 60	98.53 15	97.81 42	98.96 122	99.59 8

CNLPA		96.90 42	96.28 57	97.64 28	98.56 42	98.63 78	96.85 68	96.60 36	97.73 19	97.08 6	89.78 101	96.28 56	97.80 39	96.73 83	96.63 75	98.94 124	98.14 126

EPNet		96.27 53	96.97 46	95.46 59	98.47 43	98.28 88	97.41 53	93.67 49	95.86 75	92.86 42	97.51 24	93.79 71	91.76 142	97.03 74	97.03 65	98.61 156	99.28 29
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_111021_LR		97.16 36	98.01 31	96.16 46	98.47 43	98.98 48	96.94 64	93.89 48	97.64 26	91.44 55	98.89 3	96.41 52	97.20 50	98.02 45	97.29 61	99.04 116	98.85 88

MVS_111021_HR		97.04 38	98.20 26	95.69 54	98.44 45	99.29 23	96.59 79	93.20 57	97.70 22	89.94 81	98.46 8	96.89 47	96.71 64	98.11 42	97.95 33	99.27 73	99.01 69

MSDG		94.82 70	93.73 104	96.09 47	98.34 46	97.43 110	97.06 59	96.05 37	95.84 76	90.56 69	86.30 130	89.10 101	95.55 86	96.13 110	95.61 104	99.00 117	95.73 179

TAPA-MVS	94.18 5	96.38 50	96.49 55	96.25 43	98.26 47	98.66 73	98.00 44	94.96 43	97.17 39	89.48 86	92.91 65	96.35 53	97.53 44	96.59 88	95.90 96	99.28 71	97.82 134
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepC-MVS	94.87 4	96.76 48	96.50 54	97.05 35	98.21 48	99.28 24	98.67 27	97.38 20	97.31 35	90.36 75	89.19 103	93.58 72	98.19 28	98.31 27	98.50 7	99.51 26	99.36 22
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SD-MVS		98.52 8	98.77 9	98.23 15	98.15 49	99.26 26	98.79 26	97.59 15	98.52 3	96.25 15	97.99 15	99.75 6	99.01 3	98.27 32	97.97 31	99.59 7	99.63 2
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
TSAR-MVS + MP.		98.49 9	98.78 8	98.15 19	98.14 50	99.17 33	99.34 6	97.18 29	98.44 5	95.72 19	97.84 16	99.28 12	98.87 7	99.05 1	98.05 26	99.66 2	99.60 7
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPM-MVS		96.86 44	96.82 50	96.91 38	98.08 51	98.20 92	98.52 33	97.20 28	97.24 38	91.42 56	91.84 77	98.45 35	97.25 49	97.07 72	97.40 55	98.95 123	97.55 146

EPNet_dtu		92.45 118	95.02 80	89.46 146	98.02 52	95.47 169	94.79 121	92.62 66	94.97 99	70.11 193	94.76 54	92.61 78	84.07 203	95.94 113	95.56 105	97.15 189	95.82 178
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet		96.84 45	97.20 41	96.42 40	97.92 53	99.24 30	98.60 29	93.51 51	97.11 42	93.07 36	91.16 85	97.24 45	96.21 73	98.24 35	98.05 26	99.22 83	99.35 23

LS3D		95.46 59	95.14 76	95.84 52	97.91 54	98.90 58	98.58 30	97.79 5	97.07 44	83.65 122	88.71 106	88.64 104	97.82 37	97.49 59	97.42 53	99.26 76	97.72 142

CS-MVS-test		97.00 39	97.85 33	96.00 50	97.77 55	99.56 5	96.35 87	91.95 76	97.54 29	92.20 49	96.14 36	96.00 61	98.19 28	98.46 19	97.78 43	99.57 14	99.45 17

DELS-MVS		96.06 54	96.04 61	96.07 49	97.77 55	99.25 28	98.10 41	93.26 54	94.42 108	92.79 43	88.52 110	93.48 73	95.06 95	98.51 16	98.83 1	99.45 37	99.28 29
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
COLMAP_ROB	90.49 14	93.27 110	92.71 119	93.93 91	97.75 57	97.44 109	96.07 94	93.17 58	95.40 85	83.86 120	83.76 145	88.72 103	93.87 115	94.25 153	94.11 147	98.87 131	95.28 185
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PCF-MVS	93.95 6	95.65 55	95.14 76	96.25 43	97.73 58	98.73 67	97.59 51	97.13 30	92.50 138	89.09 96	89.85 100	96.65 50	96.90 59	94.97 140	94.89 125	99.08 106	98.38 115
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PatchMatch-RL		94.69 76	94.41 87	95.02 67	97.63 59	98.15 95	94.50 127	91.99 74	95.32 88	91.31 58	95.47 45	83.44 140	96.02 76	96.56 89	95.23 115	98.69 149	96.67 171

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

PVSNet_BlendedMVS		95.41 61	95.28 72	95.57 56	97.42 60	99.02 45	95.89 101	93.10 59	96.16 63	93.12 34	91.99 73	85.27 125	94.66 101	98.09 43	97.34 57	99.24 77	99.08 56

PVSNet_Blended		95.41 61	95.28 72	95.57 56	97.42 60	99.02 45	95.89 101	93.10 59	96.16 63	93.12 34	91.99 73	85.27 125	94.66 101	98.09 43	97.34 57	99.24 77	99.08 56

DeepPCF-MVS	95.28 2	97.00 39	98.35 21	95.42 60	97.30 63	98.94 51	94.82 120	96.03 38	98.24 9	92.11 51	95.80 41	98.64 33	95.51 87	98.95 7	98.66 5	96.78 192	99.20 43

CHOSEN 280x420		95.46 59	97.01 45	93.66 96	97.28 64	97.98 99	96.40 85	85.39 160	96.10 67	91.07 59	96.53 32	96.34 55	95.61 84	97.65 55	96.95 68	96.21 193	97.49 148

MVS_0304		96.31 51	96.91 49	95.62 55	97.21 65	99.20 31	98.55 31	93.10 59	97.04 45	89.73 83	90.30 95	96.35 53	95.71 80	98.14 39	97.93 37	99.38 55	99.40 19

CHOSEN 1792x2688		92.66 115	92.49 125	92.85 106	97.13 66	98.89 59	95.90 99	88.50 126	95.32 88	83.31 123	71.99 199	88.96 102	94.10 112	96.69 84	96.49 77	98.15 173	99.10 53

HyFIR lowres test		92.03 119	91.55 144	92.58 107	97.13 66	98.72 68	94.65 124	86.54 145	93.58 123	82.56 126	67.75 210	90.47 89	95.67 81	95.87 115	95.54 106	98.91 128	98.93 78

OPM-MVS		93.61 103	92.43 129	95.00 68	96.94 68	97.34 111	97.78 48	94.23 46	89.64 171	85.53 114	88.70 107	82.81 143	96.28 72	96.28 103	95.00 124	99.24 77	97.22 156
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
XVS					96.60 69	99.35 17	96.82 69			90.85 62		98.72 30				99.46 33

X-MVStestdata					96.60 69	99.35 17	96.82 69			90.85 62		98.72 30				99.46 33

TSAR-MVS + COLMAP		94.79 72	94.51 85	95.11 65	96.50 71	97.54 105	97.99 45	94.54 44	97.81 17	85.88 113	96.73 31	81.28 150	96.99 57	96.29 102	95.21 116	98.76 145	96.73 170

PVSNet_Blended_VisFu		94.77 74	95.54 67	93.87 92	96.48 72	98.97 49	94.33 129	91.84 79	94.93 100	90.37 74	85.04 136	94.99 64	90.87 157	98.12 41	97.30 59	99.30 69	99.45 17

LGP-MVS_train		94.12 90	94.62 83	93.53 97	96.44 73	97.54 105	97.40 54	91.84 79	94.66 103	81.09 135	95.70 43	83.36 141	95.10 94	96.36 100	95.71 102	99.32 64	99.03 66

HQP-MVS		94.43 83	94.57 84	94.27 87	96.41 74	97.23 115	96.89 65	93.98 47	95.94 72	83.68 121	95.01 50	84.46 132	95.58 85	95.47 128	94.85 129	99.07 108	99.00 70

ACMM	92.75 10	94.41 85	93.84 102	95.09 66	96.41 74	96.80 124	94.88 119	93.54 50	96.41 57	90.16 76	92.31 71	83.11 142	96.32 71	96.22 105	94.65 131	99.22 83	97.35 153
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
RPSCF		94.05 91	94.00 98	94.12 89	96.20 76	96.41 138	96.61 78	91.54 86	95.83 77	89.73 83	96.94 30	92.80 76	95.35 91	91.63 191	90.44 193	95.27 205	93.94 196

test2506		94.32 87	93.00 116	95.87 51	96.16 77	99.39 15	96.96 62	92.80 64	95.22 94	94.47 27	91.55 82	70.45 196	95.25 92	98.29 28	97.98 29	99.59 7	98.10 128

ECVR-MVS		94.14 89	92.96 117	95.52 58	96.16 77	99.39 15	96.96 62	92.80 64	95.22 94	92.38 48	81.48 155	80.31 151	95.25 92	98.29 28	97.98 29	99.59 7	98.05 129

test1111		93.94 94	92.78 118	95.29 63	96.14 79	99.42 11	96.79 72	92.85 63	95.08 98	91.39 57	80.69 160	79.86 154	95.00 96	98.28 31	98.00 28	99.58 11	98.11 127

UA-Net		93.96 93	95.95 62	91.64 118	96.06 80	98.59 80	95.29 110	90.00 103	91.06 158	82.87 124	90.64 92	98.06 40	86.06 190	98.14 39	98.20 19	99.58 11	96.96 164

UGNet		94.92 67	96.63 52	92.93 105	96.03 81	98.63 78	94.53 126	91.52 87	96.23 61	90.03 78	92.87 66	96.10 59	86.28 189	96.68 85	96.60 76	99.16 96	99.32 27
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
ACMP	92.88 9	94.43 83	94.38 88	94.50 83	96.01 82	97.69 103	95.85 104	92.09 73	95.74 78	89.12 95	95.14 48	82.62 145	94.77 97	95.73 122	94.67 130	99.14 99	99.06 61
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IB-MVS	89.56 15	91.71 125	92.50 124	90.79 130	95.94 83	98.44 85	87.05 201	91.38 90	93.15 127	92.98 41	84.78 137	85.14 128	78.27 208	92.47 179	94.44 142	99.10 104	99.08 56
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
MS-PatchMatch		91.82 123	92.51 123	91.02 124	95.83 84	96.88 120	95.05 114	84.55 174	93.85 118	82.01 128	82.51 151	91.71 80	90.52 164	95.07 138	93.03 169	98.13 174	94.52 187

CANet_DTU		93.92 96	96.57 53	90.83 128	95.63 85	98.39 86	96.99 61	87.38 136	96.26 60	71.97 182	96.31 34	93.02 74	94.53 104	97.38 63	96.83 72	98.49 163	97.79 135

ACMH	90.77 13	91.51 130	91.63 142	91.38 121	95.62 86	96.87 122	91.76 174	89.66 111	91.58 153	78.67 145	86.73 118	78.12 160	93.77 119	94.59 144	94.54 138	98.78 143	98.98 73
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TSAR-MVS + GP.		97.45 31	98.36 19	96.39 41	95.56 87	98.93 53	97.74 49	93.31 53	97.61 27	94.24 30	98.44 9	99.19 17	98.03 35	97.60 56	97.41 54	99.44 43	99.33 25

thres600view7		93.49 106	92.37 132	94.79 78	95.42 88	98.93 53	96.58 80	92.31 68	93.04 128	87.88 103	86.62 120	76.94 169	97.09 55	96.82 77	95.63 103	99.45 37	98.63 98

thres400		93.56 104	92.43 129	94.87 75	95.40 89	98.91 56	96.70 76	92.38 67	92.93 130	88.19 102	86.69 119	77.35 166	97.13 52	96.75 82	95.85 98	99.42 48	98.56 101

thres200		93.62 102	92.54 122	94.88 73	95.36 90	98.93 53	96.75 74	92.31 68	92.84 131	88.28 100	86.99 116	77.81 165	97.13 52	96.82 77	95.92 94	99.45 37	98.49 107

thres100view900		93.55 105	92.47 128	94.81 77	95.33 91	98.74 66	96.78 73	92.30 71	92.63 134	88.29 98	87.21 114	78.01 162	96.78 62	96.38 97	95.92 94	99.38 55	98.40 113

tfpn200view9		93.64 101	92.57 121	94.89 72	95.33 91	98.94 51	96.82 69	92.31 68	92.63 134	88.29 98	87.21 114	78.01 162	97.12 54	96.82 77	95.85 98	99.45 37	98.56 101

IS_MVSNet		95.28 63	96.43 56	93.94 90	95.30 93	99.01 47	95.90 99	91.12 92	94.13 114	87.50 106	91.23 84	94.45 67	94.17 110	98.45 20	98.50 7	99.65 3	99.23 38

CMPMVS	65.18 17	84.76 199	83.10 205	86.69 187	95.29 94	95.05 181	88.37 196	85.51 159	80.27 214	71.31 186	68.37 208	73.85 181	85.25 194	87.72 206	87.75 203	94.38 213	88.70 213
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
sasdasda		95.25 65	95.45 69	95.00 68	95.27 95	98.72 68	96.89 65	89.82 107	96.51 54	90.84 65	93.72 58	86.01 119	97.66 41	95.78 119	97.94 34	99.54 19	99.50 12

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

Vis-MVSNet (Re-imp)		94.46 82	96.24 58	92.40 109	95.23 97	98.64 76	95.56 107	90.99 93	94.42 108	85.02 116	90.88 91	94.65 66	88.01 179	98.17 37	98.37 16	99.57 14	98.53 104

CLD-MVS		94.79 72	94.36 89	95.30 62	95.21 98	97.46 108	97.23 57	92.24 72	96.43 56	91.77 54	92.69 67	84.31 133	96.06 74	95.52 126	95.03 121	99.31 67	99.06 61
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
baseline1		94.59 78	94.47 86	94.72 79	95.16 99	97.97 100	96.07 94	91.94 77	94.86 101	89.98 79	91.60 81	85.87 122	95.64 82	97.07 72	96.90 69	99.52 21	97.06 163

TDRefinement		89.07 162	88.15 171	90.14 139	95.16 99	96.88 120	95.55 108	90.20 101	89.68 170	76.42 158	76.67 173	74.30 179	84.85 197	93.11 169	91.91 187	98.64 155	94.47 188

ACMH+	90.88 12	91.41 131	91.13 147	91.74 117	95.11 101	96.95 119	93.13 146	89.48 115	92.42 140	79.93 140	85.13 135	78.02 161	93.82 118	93.49 164	93.88 153	98.94 124	97.99 131

DCV-MVSNet		94.76 75	95.12 78	94.35 86	95.10 102	95.81 158	96.46 84	89.49 114	96.33 59	90.16 76	92.55 69	90.26 90	95.83 79	95.52 126	96.03 91	99.06 111	99.33 25

Anonymous202405211			92.18 134		95.04 103	98.20 92	96.14 91	91.79 83	93.93 115		74.60 182	88.38 107	96.48 69	95.17 136	95.82 101	99.00 117	99.15 50

casdiffmvs_mvg		94.55 79	94.26 91	94.88 73	94.96 104	98.51 82	97.11 58	91.82 82	94.28 111	89.20 93	86.60 121	86.85 112	96.56 68	97.47 60	97.25 62	99.64 4	98.83 90
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
FC-MVSNet-train		93.85 97	93.91 99	93.78 94	94.94 105	96.79 127	94.29 130	91.13 91	93.84 119	88.26 101	90.40 94	85.23 127	94.65 103	96.54 91	95.31 112	99.38 55	99.28 29

EPP-MVSNet		95.27 64	96.18 60	94.20 88	94.88 106	98.64 76	94.97 116	90.70 96	95.34 87	89.67 85	91.66 80	93.84 70	95.42 90	97.32 64	97.00 66	99.58 11	99.47 16

FA-MVS(training)		93.94 94	95.16 75	92.53 108	94.87 107	98.57 81	95.42 109	79.49 194	95.37 86	90.98 60	86.54 123	94.26 69	95.44 89	97.80 53	95.19 117	98.97 120	98.38 115

EIA-MVS		95.50 56	96.19 59	94.69 80	94.83 108	98.88 60	95.93 98	91.50 88	94.47 107	89.43 87	93.14 62	92.72 77	97.05 56	97.82 52	97.13 64	99.43 46	99.15 50

ETV-MVS		96.31 51	97.47 38	94.96 71	94.79 109	98.78 64	96.08 93	91.41 89	96.16 63	90.50 70	95.76 42	96.20 57	97.39 46	98.42 23	97.82 41	99.57 14	99.18 47

MVS_Test		94.82 70	95.66 64	93.84 93	94.79 109	98.35 87	96.49 83	89.10 119	96.12 66	87.09 109	92.58 68	90.61 88	96.48 69	96.51 95	96.89 70	99.11 103	98.54 103

Anonymous20231211		93.49 106	92.33 133	94.84 76	94.78 111	98.00 98	96.11 92	91.85 78	94.86 101	90.91 61	74.69 181	89.18 99	96.73 63	94.82 141	95.51 107	98.67 150	99.24 37

baseline		94.83 69	95.82 63	93.68 95	94.75 112	97.80 101	96.51 82	88.53 125	97.02 47	89.34 91	92.93 64	92.18 79	94.69 100	95.78 119	96.08 87	98.27 171	98.97 77

EC-MVSNet		96.49 49	97.63 34	95.16 64	94.75 112	98.69 71	97.39 55	88.97 120	96.34 58	92.02 52	96.04 38	96.46 51	98.21 26	98.41 24	97.96 32	99.61 6	99.55 10

MVSTER		94.89 68	95.07 79	94.68 81	94.71 114	96.68 130	97.00 60	90.57 98	95.18 96	93.05 38	95.21 47	86.41 116	93.72 120	97.59 57	95.88 97	99.00 117	98.50 106

EPMVS		90.88 136	92.12 135	89.44 147	94.71 114	97.24 114	93.55 137	76.81 201	95.89 73	81.77 130	91.49 83	86.47 115	93.87 115	90.21 198	90.07 195	95.92 196	93.49 202

casdiffmvs		94.38 86	94.15 97	94.64 82	94.70 116	98.51 82	96.03 96	91.66 84	95.70 79	89.36 90	86.48 125	85.03 130	96.60 67	97.40 62	97.30 59	99.52 21	98.67 96
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs		94.31 88	94.21 92	94.42 85	94.64 117	98.28 88	96.36 86	91.56 85	96.77 49	88.89 97	88.97 104	84.23 134	96.01 77	96.05 111	96.41 79	99.05 115	98.79 93
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DI_MVS_plusplus_trai		94.01 92	93.63 106	94.44 84	94.54 118	98.26 90	97.51 52	90.63 97	95.88 74	89.34 91	80.54 162	89.36 96	95.48 88	96.33 101	96.27 83	99.17 93	98.78 94

thisisatest0530		94.54 80	95.47 68	93.46 99	94.51 119	98.65 75	94.66 123	90.72 94	95.69 81	86.90 110	93.80 56	89.44 95	94.74 98	96.98 76	94.86 126	99.19 91	98.85 88

tttt0517		94.52 81	95.44 71	93.44 100	94.51 119	98.68 72	94.61 125	90.72 94	95.61 83	86.84 111	93.78 57	89.26 98	94.74 98	97.02 75	94.86 126	99.20 90	98.87 86

ADS-MVSNet		89.80 151	91.33 146	88.00 168	94.43 121	96.71 129	92.29 162	74.95 211	96.07 68	77.39 150	88.67 108	86.09 118	93.26 127	88.44 204	89.57 198	95.68 199	93.81 199

tpmrst		88.86 166	89.62 158	87.97 169	94.33 122	95.98 148	92.62 154	76.36 204	94.62 105	76.94 154	85.98 131	82.80 144	92.80 132	86.90 210	87.15 206	94.77 210	93.93 197

PMMVS		94.61 77	95.56 66	93.50 98	94.30 123	96.74 128	94.91 118	89.56 113	95.58 84	87.72 104	96.15 35	92.86 75	96.06 74	95.47 128	95.02 122	98.43 168	97.09 159

CostFormer		90.69 137	90.48 155	90.93 126	94.18 124	96.08 146	94.03 132	78.20 197	93.47 124	89.96 80	90.97 90	80.30 152	93.72 120	87.66 208	88.75 200	95.51 202	96.12 175

USDC		90.69 137	90.52 154	90.88 127	94.17 125	96.43 137	95.82 105	86.76 142	93.92 116	76.27 160	86.49 124	74.30 179	93.67 122	95.04 139	93.36 162	98.61 156	94.13 192

Vis-MVSNet		92.77 113	95.00 81	90.16 137	94.10 126	98.79 63	94.76 122	88.26 127	92.37 143	79.95 139	88.19 112	91.58 81	84.38 200	97.59 57	97.58 49	99.52 21	98.91 82
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		92.93 112	93.86 101	91.86 114	94.07 127	98.09 97	95.59 106	85.98 152	94.27 112	79.54 143	91.12 88	81.81 147	96.71 64	96.67 86	96.06 89	99.27 73	98.98 73

GeoE		92.52 117	92.64 120	92.39 110	93.96 128	97.76 102	96.01 97	85.60 157	93.23 126	83.94 119	81.56 154	84.80 131	95.63 83	96.22 105	95.83 100	99.19 91	99.07 60

IterMVS-LS		92.56 116	93.18 113	91.84 115	93.90 129	94.97 183	94.99 115	86.20 149	94.18 113	82.68 125	85.81 132	87.36 111	94.43 105	95.31 132	96.02 92	98.87 131	98.60 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dps		90.11 149	89.37 162	90.98 125	93.89 130	96.21 143	93.49 139	77.61 199	91.95 149	92.74 45	88.85 105	78.77 159	92.37 135	87.71 207	87.71 204	95.80 198	94.38 190

tpm cat1		88.90 164	87.78 180	90.22 136	93.88 131	95.39 172	93.79 135	78.11 198	92.55 137	89.43 87	81.31 156	79.84 155	91.40 145	84.95 211	86.34 209	94.68 212	94.09 193

PatchmatchNet		90.56 139	92.49 125	88.31 159	93.83 132	96.86 123	92.42 158	76.50 203	95.96 71	78.31 146	91.96 75	89.66 94	93.48 124	90.04 200	89.20 199	95.32 203	93.73 200
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TinyColmap		89.42 154	88.58 166	90.40 134	93.80 133	95.45 170	93.96 134	86.54 145	92.24 146	76.49 157	80.83 158	70.44 197	93.37 125	94.45 148	93.30 165	98.26 172	93.37 203

SCA		90.92 135	93.04 115	88.45 156	93.72 134	97.33 112	92.77 150	76.08 206	96.02 69	78.26 147	91.96 75	90.86 85	93.99 114	90.98 195	90.04 196	95.88 197	94.06 195

RPMNet		90.19 146	92.03 138	88.05 165	93.46 135	95.95 151	93.41 140	74.59 212	92.40 141	75.91 162	84.22 142	86.41 116	92.49 133	94.42 149	93.85 155	98.44 166	96.96 164

gg-mvs-nofinetune		86.17 193	88.57 167	83.36 200	93.44 136	98.15 95	96.58 80	72.05 215	74.12 219	49.23 223	64.81 214	90.85 86	89.90 172	97.83 50	96.84 71	98.97 120	97.41 151

MDTV_nov1_ep13		91.57 128	93.18 113	89.70 143	93.39 137	96.97 118	93.53 138	80.91 191	95.70 79	81.86 129	92.40 70	89.93 92	93.25 128	91.97 188	90.80 191	95.25 206	94.46 189

CR-MVSNet		90.16 147	91.96 139	88.06 164	93.32 138	95.95 151	93.36 142	75.99 207	92.40 141	75.19 168	83.18 147	85.37 124	92.05 137	95.21 134	94.56 136	98.47 165	97.08 161

test-LLR		91.62 127	93.56 109	89.35 149	93.31 139	96.57 133	92.02 170	87.06 140	92.34 144	75.05 171	90.20 96	88.64 104	90.93 153	96.19 108	94.07 148	97.75 183	96.90 167

test0.0.03 1		91.97 120	93.91 99	89.72 142	93.31 139	96.40 139	91.34 179	87.06 140	93.86 117	81.67 131	91.15 87	89.16 100	86.02 191	95.08 137	95.09 118	98.91 128	96.64 173

CVMVSNet		89.77 152	91.66 141	87.56 178	93.21 141	95.45 170	91.94 173	89.22 117	89.62 172	69.34 199	83.99 144	85.90 121	84.81 198	94.30 152	95.28 113	96.85 191	97.09 159

PatchT		89.13 161	91.71 140	86.11 192	92.92 142	95.59 165	83.64 209	75.09 210	91.87 150	75.19 168	82.63 150	85.06 129	92.05 137	95.21 134	94.56 136	97.76 182	97.08 161

Fast-Effi-MVS+		91.87 121	92.08 136	91.62 120	92.91 143	97.21 116	94.93 117	84.60 172	93.61 122	81.49 133	83.50 146	78.95 157	96.62 66	96.55 90	96.22 85	99.16 96	98.51 105

IterMVS-SCA-FT		90.24 144	92.48 127	87.63 175	92.85 144	94.30 199	93.79 135	81.47 190	92.66 133	69.95 194	84.66 139	88.38 107	89.99 170	95.39 131	94.34 143	97.74 185	97.63 144

baseline2		93.01 111	94.17 95	91.64 118	92.83 145	97.49 107	93.40 141	87.53 134	93.67 121	86.07 112	91.83 78	86.58 113	91.36 146	96.38 97	95.06 120	98.67 150	98.20 124

IterMVS		90.20 145	92.43 129	87.61 176	92.82 146	94.31 198	94.11 131	81.54 188	92.97 129	69.90 195	84.71 138	88.16 110	89.96 171	95.25 133	94.17 146	97.31 187	97.46 149
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		92.77 113	93.60 107	91.80 116	92.63 147	96.80 124	95.24 111	89.14 118	90.30 168	84.58 117	86.76 117	90.65 87	90.42 165	95.89 114	96.49 77	98.79 142	98.32 120
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm		87.95 174	89.44 161	86.21 191	92.53 148	94.62 193	91.40 177	76.36 204	91.46 154	69.80 197	87.43 113	75.14 174	91.55 144	89.85 202	90.60 192	95.61 200	96.96 164

Effi-MVS+-dtu		91.78 124	93.59 108	89.68 145	92.44 149	97.11 117	94.40 128	84.94 168	92.43 139	75.48 164	91.09 89	83.75 138	93.55 123	96.61 87	95.47 108	97.24 188	98.67 96

testgi		89.42 154	91.50 145	87.00 185	92.40 150	95.59 165	89.15 195	85.27 164	92.78 132	72.42 180	91.75 79	76.00 172	84.09 202	94.38 150	93.82 157	98.65 154	96.15 174

Fast-Effi-MVS+-dtu		91.19 132	93.64 105	88.33 158	92.19 151	96.46 136	93.99 133	81.52 189	92.59 136	71.82 183	92.17 72	85.54 123	91.68 143	95.73 122	94.64 132	98.80 140	98.34 117

FC-MVSNet-test		91.63 126	93.82 103	89.08 150	92.02 152	96.40 139	93.26 144	87.26 137	93.72 120	77.26 151	88.61 109	89.86 93	85.50 193	95.72 124	95.02 122	99.16 96	97.44 150

GA-MVS		89.28 157	90.75 153	87.57 177	91.77 153	96.48 135	92.29 162	87.58 133	90.61 165	65.77 204	84.48 140	76.84 170	89.46 173	95.84 116	93.68 158	98.52 161	97.34 154

dmvs_re		91.84 122	91.60 143	92.12 113	91.60 154	97.26 113	95.14 113	91.96 75	91.02 159	80.98 136	86.56 122	77.96 164	93.84 117	94.71 142	95.08 119	99.22 83	98.62 99

UniMVSNet_ETH3D		88.47 168	86.00 198	91.35 122	91.55 155	96.29 141	92.53 155	88.81 121	85.58 201	82.33 127	67.63 211	66.87 211	94.04 113	91.49 192	95.24 114	98.84 134	98.92 79

TAMVS		90.54 141	90.87 152	90.16 137	91.48 156	96.61 132	93.26 144	86.08 150	87.71 187	81.66 132	83.11 149	84.04 135	90.42 165	94.54 145	94.60 133	98.04 178	95.48 183

tfpnnormal		88.50 167	87.01 189	90.23 135	91.36 157	95.78 160	92.74 151	90.09 102	83.65 206	76.33 159	71.46 202	69.58 202	91.84 140	95.54 125	94.02 150	99.06 111	99.03 66

GBi-Net		93.81 98	94.18 93	93.38 101	91.34 158	95.86 154	96.22 88	88.68 122	95.23 91	90.40 71	86.39 126	91.16 82	94.40 107	96.52 92	96.30 80	99.21 87	97.79 135

test1		93.81 98	94.18 93	93.38 101	91.34 158	95.86 154	96.22 88	88.68 122	95.23 91	90.40 71	86.39 126	91.16 82	94.40 107	96.52 92	96.30 80	99.21 87	97.79 135

FMVSNet2		93.30 109	93.36 112	93.22 104	91.34 158	95.86 154	96.22 88	88.24 128	95.15 97	89.92 82	81.64 153	89.36 96	94.40 107	96.77 81	96.98 67	99.21 87	97.79 135

FMVSNet3		93.79 100	94.17 95	93.35 103	91.21 161	95.99 147	96.62 77	88.68 122	95.23 91	90.40 71	86.39 126	91.16 82	94.11 111	95.96 112	96.67 74	99.07 108	97.79 135

TransMVSNet (Re)		87.73 180	86.79 191	88.83 152	90.76 162	94.40 196	91.33 180	89.62 112	84.73 203	75.41 166	72.73 195	71.41 192	86.80 185	94.53 146	93.93 152	99.06 111	95.83 177

LTVRE_ROB	87.32 16	87.55 181	88.25 170	86.73 186	90.66 163	95.80 159	93.05 147	84.77 169	83.35 207	60.32 216	83.12 148	67.39 209	93.32 126	94.36 151	94.86 126	98.28 170	98.87 86
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
EG-PatchMatch MVS		86.68 189	87.24 185	86.02 193	90.58 164	96.26 142	91.08 183	81.59 187	84.96 202	69.80 197	71.35 203	75.08 176	84.23 201	94.24 154	93.35 163	98.82 135	95.46 184

TESTMET0.1,1		91.07 133	93.56 109	88.17 160	90.43 165	96.57 133	92.02 170	82.83 183	92.34 144	75.05 171	90.20 96	88.64 104	90.93 153	96.19 108	94.07 148	97.75 183	96.90 167

pm-mvs1		89.19 160	89.02 163	89.38 148	90.40 166	95.74 161	92.05 168	88.10 130	86.13 197	77.70 148	73.72 190	79.44 156	88.97 176	95.81 118	94.51 140	99.08 106	97.78 140

NR-MVSNet		89.34 156	88.66 165	90.13 140	90.40 166	95.61 163	93.04 148	89.91 104	91.22 156	78.96 144	77.72 170	68.90 205	89.16 175	94.24 154	93.95 151	99.32 64	98.99 71

FMVSNet1		91.54 129	90.93 150	92.26 111	90.35 168	95.27 176	95.22 112	87.16 139	91.37 155	87.62 105	75.45 176	83.84 137	94.43 105	96.52 92	96.30 80	98.82 135	97.74 141

test-mter		90.95 134	93.54 111	87.93 170	90.28 169	96.80 124	91.44 176	82.68 184	92.15 148	74.37 175	89.57 102	88.23 109	90.88 156	96.37 99	94.31 144	97.93 180	97.37 152

pmmvs4		90.55 140	89.91 157	91.30 123	90.26 170	94.95 184	92.73 152	87.94 131	93.44 125	85.35 115	82.28 152	76.09 171	93.02 131	93.56 162	92.26 185	98.51 162	96.77 169

MVS-HIRNet		85.36 197	86.89 190	83.57 199	90.13 171	94.51 194	83.57 210	72.61 214	88.27 183	71.22 187	68.97 206	81.81 147	88.91 177	93.08 170	91.94 186	94.97 209	89.64 212

thisisatest0515		90.12 148	92.06 137	87.85 171	90.03 172	96.17 144	87.83 198	87.45 135	91.71 152	77.15 152	85.40 134	84.01 136	85.74 192	95.41 130	93.30 165	98.88 130	98.43 109

SixPastTwentyTwo		88.37 169	89.47 160	87.08 183	90.01 173	95.93 153	87.41 199	85.32 161	90.26 169	70.26 191	86.34 129	71.95 189	90.93 153	92.89 174	91.72 188	98.55 159	97.22 156

UniMVSNet (Re)		90.03 150	89.61 159	90.51 133	89.97 174	96.12 145	92.32 160	89.26 116	90.99 160	80.95 137	78.25 169	75.08 176	91.14 149	93.78 157	93.87 154	99.41 49	99.21 42

pmnet_mix02		86.12 194	87.12 188	84.96 196	89.82 175	94.12 200	84.88 207	86.63 144	91.78 151	65.60 205	80.76 159	76.98 168	86.61 187	87.29 209	84.80 212	96.21 193	94.09 193

our_test_3					89.78 176	93.84 202	85.59 204

UniMVSNet_NR-MVSNet		90.35 143	89.96 156	90.80 129	89.66 177	95.83 157	92.48 156	90.53 99	90.96 161	79.57 141	79.33 166	77.14 167	93.21 129	92.91 173	94.50 141	99.37 58	99.05 63

v8		88.21 172	87.94 177	88.51 155	89.62 178	95.01 182	92.31 161	84.99 166	88.94 174	74.70 173	75.03 178	73.51 183	90.67 161	92.11 184	92.74 177	98.80 140	98.24 122

WR-MVS_H		87.93 175	87.85 178	88.03 167	89.62 178	95.58 167	90.47 188	85.55 158	87.20 192	76.83 155	74.42 185	72.67 187	86.37 188	93.22 168	93.04 168	99.33 62	98.83 90

pmmvs5		87.83 179	88.09 172	87.51 180	89.59 180	95.48 168	89.75 193	84.73 170	86.07 199	71.44 185	80.57 161	70.09 200	90.74 160	94.47 147	92.87 173	98.82 135	97.10 158

gm-plane-assit		83.26 203	85.29 200	80.89 203	89.52 181	89.89 214	70.26 220	78.24 196	77.11 217	58.01 220	74.16 187	66.90 210	90.63 163	97.20 67	96.05 90	98.66 153	95.68 180

v10		88.00 173	87.96 175	88.05 165	89.44 182	94.68 190	92.36 159	83.35 179	89.37 173	72.96 179	73.98 188	72.79 186	91.35 147	93.59 159	92.88 172	98.81 138	98.42 111

V42		88.31 170	87.95 176	88.73 153	89.44 182	95.34 173	92.23 164	87.21 138	88.83 176	74.49 174	74.89 180	73.43 184	90.41 167	92.08 186	92.77 176	98.60 158	98.33 118

v148		87.51 182	86.79 191	88.36 157	89.39 184	95.21 178	89.84 192	88.20 129	87.61 189	77.56 149	73.38 193	70.32 199	86.80 185	90.70 196	92.31 183	98.37 169	97.98 133

CP-MVSNet		87.89 178	87.27 184	88.62 154	89.30 185	95.06 180	90.60 187	85.78 154	87.43 191	75.98 161	74.60 182	68.14 208	90.76 158	93.07 171	93.60 159	99.30 69	98.98 73

v1144		87.92 177	87.79 179	88.07 162	89.27 186	95.15 179	92.17 165	85.62 156	88.52 180	71.52 184	73.80 189	72.40 188	91.06 151	93.54 163	92.80 174	98.81 138	98.33 118

DU-MVS		89.67 153	88.84 164	90.63 132	89.26 187	95.61 163	92.48 156	89.91 104	91.22 156	79.57 141	77.72 170	71.18 193	93.21 129	92.53 177	94.57 135	99.35 61	99.05 63

WR-MVS		87.93 175	88.09 172	87.75 172	89.26 187	95.28 174	90.81 185	86.69 143	88.90 175	75.29 167	74.31 186	73.72 182	85.19 196	92.26 180	93.32 164	99.27 73	98.81 92

Baseline_NR-MVSNet		89.27 158	88.01 174	90.73 131	89.26 187	93.71 203	92.71 153	89.78 110	90.73 162	81.28 134	73.53 191	72.85 185	92.30 136	92.53 177	93.84 156	99.07 108	98.88 84

N_pmnet		84.80 198	85.10 202	84.45 197	89.25 190	92.86 206	84.04 208	86.21 147	88.78 177	66.73 203	72.41 198	74.87 178	85.21 195	88.32 205	86.45 207	95.30 204	92.04 206

v2v482		88.25 171	87.71 181	88.88 151	89.23 191	95.28 174	92.10 166	87.89 132	88.69 179	73.31 178	75.32 177	71.64 190	91.89 139	92.10 185	92.92 171	98.86 133	97.99 131

PS-CasMVS		87.33 185	86.68 194	88.10 161	89.22 192	94.93 185	90.35 190	85.70 155	86.44 196	74.01 176	73.43 192	66.59 214	90.04 169	92.92 172	93.52 160	99.28 71	98.91 82

TranMVSNet+NR-MVSNet		89.23 159	88.48 168	90.11 141	89.07 193	95.25 177	92.91 149	90.43 100	90.31 167	77.10 153	76.62 174	71.57 191	91.83 141	92.12 183	94.59 134	99.32 64	98.92 79

v1192		87.51 182	87.31 183	87.74 173	89.04 194	94.87 188	92.07 167	85.03 165	88.49 181	70.32 190	72.65 196	70.35 198	91.21 148	93.59 159	92.80 174	98.78 143	98.42 111

v144192		87.40 184	87.20 186	87.64 174	88.89 195	94.88 187	91.65 175	84.70 171	87.80 186	71.17 188	73.20 194	70.91 194	90.75 159	92.69 175	92.49 180	98.71 147	98.43 109

PEN-MVS		87.22 187	86.50 196	88.07 162	88.88 196	94.44 195	90.99 184	86.21 147	86.53 195	73.66 177	74.97 179	66.56 215	89.42 174	91.20 194	93.48 161	99.24 77	98.31 121

v1921920		87.31 186	87.13 187	87.52 179	88.87 197	94.72 189	91.96 172	84.59 173	88.28 182	69.86 196	72.50 197	70.03 201	91.10 150	93.33 166	92.61 179	98.71 147	98.44 108

pmmvs6		85.98 195	84.89 203	87.25 182	88.83 198	94.35 197	89.36 194	85.30 163	78.51 216	75.44 165	62.71 216	75.41 173	87.65 181	93.58 161	92.40 182	96.89 190	97.29 155

v1240		86.89 188	86.75 193	87.06 184	88.75 199	94.65 192	91.30 181	84.05 175	87.49 190	68.94 200	71.96 200	68.86 206	90.65 162	93.33 166	92.72 178	98.67 150	98.24 122

anonymousdsp		88.90 164	91.00 149	86.44 189	88.74 200	95.97 149	90.40 189	82.86 182	88.77 178	67.33 202	81.18 157	81.44 149	90.22 168	96.23 104	94.27 145	99.12 102	99.16 49

EU-MVSNet		85.62 196	87.65 182	83.24 201	88.54 201	92.77 207	87.12 200	85.32 161	86.71 193	64.54 207	78.52 168	75.11 175	78.35 207	92.25 181	92.28 184	95.58 201	95.93 176

DTE-MVSNet		86.67 190	86.09 197	87.35 181	88.45 202	94.08 201	90.65 186	86.05 151	86.13 197	72.19 181	74.58 184	66.77 213	87.61 182	90.31 197	93.12 167	99.13 100	97.62 145

FMVSNet5		90.36 142	90.93 150	89.70 143	87.99 203	92.25 208	92.03 169	83.51 178	92.20 147	84.13 118	85.59 133	86.48 114	92.43 134	94.61 143	94.52 139	98.13 174	90.85 209

v7n		86.43 191	86.52 195	86.33 190	87.91 204	94.93 185	90.15 191	83.05 180	86.57 194	70.21 192	71.48 201	66.78 212	87.72 180	94.19 156	92.96 170	98.92 126	98.76 95

test20.03		82.92 204	85.52 199	79.90 206	87.75 205	91.84 209	82.80 211	82.99 181	82.65 211	60.32 216	78.90 167	70.50 195	67.10 215	92.05 187	90.89 190	98.44 166	91.80 207

MDTV_nov1_ep13_2view		86.30 192	88.27 169	84.01 198	87.71 206	94.67 191	88.08 197	76.78 202	90.59 166	68.66 201	80.46 163	80.12 153	87.58 183	89.95 201	88.20 202	95.25 206	93.90 198

Anonymous20231206		83.84 202	85.19 201	82.26 202	87.38 207	92.87 205	85.49 205	83.65 177	86.07 199	63.44 211	68.42 207	69.01 204	75.45 211	93.34 165	92.44 181	98.12 176	94.20 191

FPMVS		75.84 210	74.59 214	77.29 210	86.92 208	83.89 219	85.01 206	80.05 193	82.91 209	60.61 215	65.25 213	60.41 219	63.86 216	75.60 217	73.60 219	87.29 220	80.47 217

MIMVSNet		88.99 163	91.07 148	86.57 188	86.78 209	95.62 162	91.20 182	75.40 209	90.65 164	76.57 156	84.05 143	82.44 146	91.01 152	95.84 116	95.38 110	98.48 164	93.50 201

tmp_tt				66.88 213	86.07 210	73.86 222	68.22 221	33.38 223	96.88 48	80.67 138	88.23 111	78.82 158	49.78 220	82.68 214	77.47 217	83.19 222

WB-MVS		69.22 212	76.91 213	60.24 216	85.80 211	79.37 220	56.86 225	84.96 167	81.50 212	18.16 228	76.85 172	61.07 217	34.23 223	82.46 215	81.81 214	81.43 223	75.31 221

PM-MVS		84.72 200	84.47 204	85.03 195	84.67 212	91.57 210	86.27 203	82.31 186	87.65 188	70.62 189	76.54 175	56.41 223	88.75 178	92.59 176	89.85 197	97.54 186	96.66 172

pmmvs-eth3d		84.33 201	82.94 206	85.96 194	84.16 213	90.94 211	86.55 202	83.79 176	84.25 204	75.85 163	70.64 204	56.43 222	87.44 184	92.20 182	90.41 194	97.97 179	95.68 180

new-patchmatchnet		78.49 209	78.19 212	78.84 208	84.13 214	90.06 213	77.11 218	80.39 192	79.57 215	59.64 219	66.01 212	55.65 224	75.62 210	84.55 212	80.70 215	96.14 195	90.77 210

new_pmnet		81.53 205	82.68 207	80.20 204	83.47 215	89.47 215	82.21 213	78.36 195	87.86 185	60.14 218	67.90 209	69.43 203	82.03 205	89.22 203	87.47 205	94.99 208	87.39 214

ET-MVSNet_ETH3D		93.34 108	94.33 90	92.18 112	83.26 216	97.66 104	96.72 75	89.89 106	95.62 82	87.17 108	96.00 39	83.69 139	96.99 57	93.78 157	95.34 111	99.06 111	98.18 125

pmmvs3		79.16 208	80.12 210	78.05 209	79.36 217	86.59 217	78.13 217	73.87 213	76.42 218	57.51 221	70.59 205	57.02 221	84.66 199	90.10 199	88.32 201	94.75 211	91.77 208

PMVS	63.12 18	67.27 214	66.39 217	68.30 212	77.98 218	60.24 225	59.53 224	76.82 200	66.65 220	60.74 214	54.39 218	59.82 220	51.24 219	73.92 220	70.52 220	83.48 221	79.17 219
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MDA-MVSNet-bldmvs		80.11 206	80.24 209	79.94 205	77.01 219	93.21 204	78.86 216	85.94 153	82.71 210	60.86 213	79.71 165	51.77 225	83.71 204	75.60 217	86.37 208	93.28 214	92.35 204

ambc			73.83 215		76.23 220	85.13 218	82.27 212		84.16 205	65.58 206	52.82 219	23.31 230	73.55 212	91.41 193	85.26 211	92.97 215	94.70 186

Gipuma		68.35 213	66.71 216	70.27 211	74.16 221	68.78 223	63.93 223	71.77 216	83.34 208	54.57 222	34.37 221	31.88 227	68.69 214	83.30 213	85.53 210	88.48 218	79.78 218
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		80.03 207	80.93 208	78.97 207	72.46 222	90.73 212	80.81 214	82.44 185	80.39 213	63.64 209	57.57 217	64.93 216	76.37 209	91.66 190	91.55 189	98.07 177	89.70 211

PMMVS2		64.36 216	65.94 218	62.52 215	67.37 223	77.44 221	64.39 222	69.32 220	61.47 221	34.59 224	46.09 220	41.03 226	48.02 222	74.56 219	78.23 216	91.43 216	82.76 216

EMVS		49.98 218	46.76 221	53.74 218	64.96 224	51.29 227	37.81 227	69.35 219	51.83 222	22.69 227	29.57 223	25.06 228	57.28 217	44.81 223	56.11 222	70.32 225	68.64 223

E-PMN		50.67 217	47.85 220	53.96 217	64.13 225	50.98 228	38.06 226	69.51 218	51.40 223	24.60 226	29.46 224	24.39 229	56.07 218	48.17 222	59.70 221	71.40 224	70.84 222

test_method		72.96 211	78.68 211	66.28 214	50.17 226	64.90 224	75.45 219	50.90 222	87.89 184	62.54 212	62.98 215	68.34 207	70.45 213	91.90 189	82.41 213	88.19 219	92.35 204

MVE	50.86 19	49.54 219	51.43 219	47.33 219	44.14 227	59.20 226	36.45 228	60.59 221	41.47 224	31.14 225	29.58 222	17.06 231	48.52 221	62.22 221	74.63 218	63.12 226	75.87 220
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		12.09 220	16.94 222	6.42 221	3.15 228	6.08 229	9.51 230	3.84 224	21.46 225	5.31 229	27.49 225	6.76 232	10.89 224	17.06 224	15.01 223	5.84 227	24.75 224

GG-mvs-BLEND		66.17 215	94.91 82	32.63 220	1.32 229	96.64 131	91.40 177	0.85 226	94.39 110	2.20 230	90.15 98	95.70 62	2.27 226	96.39 96	95.44 109	97.78 181	95.68 180

test123		9.58 221	13.53 223	4.97 222	1.31 230	5.47 230	8.32 231	2.95 225	18.14 226	2.03 231	20.82 226	2.34 233	10.60 225	10.00 225	14.16 224	4.60 228	23.77 225

uanet_test		0.00 222	0.00 224	0.00 223	0.00 231	0.00 231	0.00 232	0.00 227	0.00 227	0.00 232	0.00 227	0.00 234	0.00 227	0.00 226	0.00 225	0.00 229	0.00 226

sosnet-low-res		0.00 222	0.00 224	0.00 223	0.00 231	0.00 231	0.00 232	0.00 227	0.00 227	0.00 232	0.00 227	0.00 234	0.00 227	0.00 226	0.00 225	0.00 229	0.00 226

sosnet		0.00 222	0.00 224	0.00 223	0.00 231	0.00 231	0.00 232	0.00 227	0.00 227	0.00 232	0.00 227	0.00 234	0.00 227	0.00 226	0.00 225	0.00 229	0.00 226

RE-MVS-def										63.50 210

9.14												99.28 12

MTAPA										96.83 10		99.12 21

MTMP										97.18 5		98.83 26

Patchmatch-RL test							34.61 229

NP-MVS									95.32 88

Patchmtry						95.96 150	93.36 142	75.99 207		75.19 168

DeepMVS_CX						86.86 216	79.50 215	70.43 217	90.73 162	63.66 208	80.36 164	60.83 218	79.68 206	76.23 216		89.46 217	86.53 215

High-resolution multi-view benchmark