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.

PGM-MVS		98.86 31	99.35 28	98.29 34	99.77 1	99.63 30	99.67 5	95.63 45	98.66 125	95.27 54	99.11 29	99.82 42	99.67 4	99.33 24	99.19 22	99.73 61	99.74 77

SMA-MVS		99.38 6	99.60 3	99.12 9	99.76 2	99.62 33	99.39 30	98.23 18	99.52 16	98.03 17	99.45 12	99.98 2	99.64 5	99.58 8	99.30 12	99.68 99	99.76 64
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
CSCG		98.90 30	98.93 53	98.85 24	99.75 3	99.72 13	99.49 22	96.58 42	99.38 25	98.05 16	98.97 38	97.87 77	99.49 18	97.78 130	98.92 42	99.78 34	99.90 7

APDe-MVS		99.49 1	99.64 1	99.32 2	99.74 4	99.74 12	99.75 1	98.34 4	99.56 11	98.72 6	99.57 8	99.97 8	99.53 15	99.65 2	99.25 16	99.84 12	99.77 58
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMP_NAP		99.05 25	99.45 14	98.58 30	99.73 5	99.60 43	99.64 8	98.28 13	99.23 48	94.57 67	99.35 17	99.97 8	99.55 13	99.63 3	98.66 58	99.70 87	99.74 77

DVP-MVS		99.45 2	99.54 7	99.35 1	99.72 6	99.76 6	99.63 12	98.37 2	99.63 8	99.03 3	98.95 40	99.98 2	99.60 7	99.60 7	99.05 30	99.74 53	99.79 45
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
DVP-MVS++		99.41 4	99.64 1	99.14 7	99.69 7	99.75 9	99.64 8	98.33 6	99.67 5	98.10 13	99.66 5	99.99 1	99.33 30	99.62 5	98.86 46	99.74 53	99.90 7

SED-MVS		99.44 3	99.58 4	99.28 3	99.69 7	99.76 6	99.62 14	98.35 3	99.51 17	99.05 2	99.60 7	99.98 2	99.28 37	99.61 6	98.83 51	99.70 87	99.77 58

HFP-MVS		99.32 8	99.53 9	99.07 13	99.69 7	99.59 45	99.63 12	98.31 9	99.56 11	97.37 26	99.27 20	99.97 8	99.70 3	99.35 22	99.24 18	99.71 79	99.76 64

HPM-MVS++		99.10 21	99.30 31	98.86 23	99.69 7	99.48 64	99.59 16	98.34 4	99.26 45	96.55 36	99.10 31	99.96 12	99.36 28	99.25 27	98.37 76	99.64 121	99.66 114

APD-MVS		99.25 12	99.38 23	99.09 11	99.69 7	99.58 48	99.56 18	98.32 8	98.85 101	97.87 19	98.91 43	99.92 28	99.30 35	99.45 15	99.38 8	99.79 31	99.58 130
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSP-MVS		99.34 7	99.52 10	99.14 7	99.68 12	99.75 9	99.64 8	98.31 9	99.44 21	98.10 13	99.28 19	99.98 2	99.30 35	99.34 23	99.05 30	99.81 23	99.79 45
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
SR-MVS					99.67 13			98.25 14				99.94 25

X-MVS		98.93 29	99.37 24	98.42 31	99.67 13	99.62 33	99.60 15	98.15 23	99.08 75	93.81 85	98.46 65	99.95 17	99.59 9	99.49 13	99.21 21	99.68 99	99.75 72

MCST-MVS		99.11 20	99.27 33	98.93 21	99.67 13	99.33 95	99.51 21	98.31 9	99.28 41	96.57 35	99.10 31	99.90 33	99.71 2	99.19 31	98.35 77	99.82 16	99.71 96

ACMMPR		99.30 9	99.54 7	99.03 16	99.66 16	99.64 27	99.68 4	98.25 14	99.56 11	97.12 30	99.19 22	99.95 17	99.72 1	99.43 16	99.25 16	99.72 69	99.77 58

SteuartSystems-ACMMP		99.20 15	99.51 11	98.83 26	99.66 16	99.66 22	99.71 3	98.12 27	99.14 66	96.62 33	99.16 24	99.98 2	99.12 49	99.63 3	99.19 22	99.78 34	99.83 29
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		99.18 16	99.32 29	99.03 16	99.65 18	99.41 79	98.87 54	98.24 17	99.14 66	98.73 5	99.11 29	99.92 28	98.92 62	99.22 28	98.84 50	99.76 41	99.56 136

CNVR-MVS		99.23 14	99.28 32	99.17 5	99.65 18	99.34 92	99.46 25	98.21 19	99.28 41	98.47 8	98.89 45	99.94 25	99.50 16	99.42 17	98.61 61	99.73 61	99.52 142

DPE-MVS		99.39 5	99.55 6	99.20 4	99.63 20	99.71 16	99.66 6	98.33 6	99.29 40	98.40 11	99.64 6	99.98 2	99.31 33	99.56 9	98.96 39	99.85 10	99.70 98
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS		99.07 23	99.36 25	98.74 27	99.63 20	99.57 50	99.66 6	98.25 14	99.00 86	95.62 46	98.97 38	99.94 25	99.54 14	99.51 12	98.79 55	99.71 79	99.73 83
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NCCC		99.05 25	99.08 42	99.02 18	99.62 22	99.38 81	99.43 29	98.21 19	99.36 31	97.66 23	97.79 83	99.90 33	99.45 22	99.17 32	98.43 71	99.77 39	99.51 147

CP-MVS		99.27 10	99.44 17	99.08 12	99.62 22	99.58 48	99.53 19	98.16 21	99.21 53	97.79 20	99.15 25	99.96 12	99.59 9	99.54 11	98.86 46	99.78 34	99.74 77

AdaColmap		99.06 24	98.98 51	99.15 6	99.60 24	99.30 98	99.38 31	98.16 21	99.02 84	98.55 7	98.71 54	99.57 56	99.58 12	99.09 37	97.84 110	99.64 121	99.36 160

CPTT-MVS		99.14 19	99.20 37	99.06 14	99.58 25	99.53 55	99.45 26	97.80 36	99.19 56	98.32 12	98.58 58	99.95 17	99.60 7	99.28 26	98.20 91	99.64 121	99.69 102

TPM-MVS					99.57 26	98.90 123	98.79 58			96.52 37	98.62 57	99.91 31	97.56 120			99.44 172	99.28 163
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
QAPM		98.62 41	99.04 48	98.13 38	99.57 26	99.48 64	99.17 38	94.78 55	99.57 10	96.16 40	96.73 106	99.80 43	99.33 30	98.79 61	99.29 14	99.75 47	99.64 121

3Dnovator	96.92 7	98.67 38	99.05 45	98.23 37	99.57 26	99.45 68	99.11 42	94.66 58	99.69 4	96.80 32	96.55 115	99.61 53	99.40 25	98.87 58	99.49 3	99.85 10	99.66 114

DeepC-MVS_fast	98.34 1	99.17 17	99.45 14	98.85 24	99.55 29	99.37 85	99.64 8	98.05 31	99.53 14	96.58 34	98.93 41	99.92 28	99.49 18	99.46 14	99.32 11	99.80 30	99.64 121
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
mPP-MVS					99.53 30							99.89 35

3Dnovator+	96.92 7	98.71 37	99.05 45	98.32 33	99.53 30	99.34 92	99.06 46	94.61 59	99.65 6	97.49 24	96.75 105	99.86 38	99.44 23	98.78 62	99.30 12	99.81 23	99.67 110

MSLP-MVS++		99.15 18	99.24 35	99.04 15	99.52 32	99.49 63	99.09 44	98.07 29	99.37 27	98.47 8	97.79 83	99.89 35	99.50 16	98.93 50	99.45 4	99.61 129	99.76 64

OpenMVS	96.23 11	97.95 58	98.45 67	97.35 56	99.52 32	99.42 77	98.91 53	94.61 59	98.87 98	92.24 114	94.61 148	99.05 64	99.10 51	98.64 73	99.05 30	99.74 53	99.51 147

PLC	97.93 2	99.02 28	98.94 52	99.11 10	99.46 34	99.24 104	99.06 46	97.96 33	99.31 37	99.16 1	97.90 81	99.79 45	99.36 28	98.71 69	98.12 95	99.65 117	99.52 142
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_111021_HR		98.59 42	99.36 25	97.68 48	99.42 35	99.61 38	98.14 91	94.81 54	99.31 37	95.00 60	99.51 10	99.79 45	99.00 59	98.94 49	98.83 51	99.69 91	99.57 135

OMC-MVS		98.84 32	99.01 50	98.65 29	99.39 36	99.23 107	99.22 35	96.70 41	99.40 24	97.77 21	97.89 82	99.80 43	99.21 38	99.02 43	98.65 59	99.57 151	99.07 178

TSAR-MVS + ACMM		98.77 34	99.45 14	97.98 43	99.37 37	99.46 66	99.44 28	98.13 26	99.65 6	92.30 112	98.91 43	99.95 17	99.05 55	99.42 17	98.95 40	99.58 147	99.82 30

MVS_111021_LR		98.67 38	99.41 22	97.81 46	99.37 37	99.53 55	98.51 67	95.52 48	99.27 43	94.85 62	99.56 9	99.69 50	99.04 56	99.36 20	98.88 45	99.60 137	99.58 130

train_agg		98.73 36	99.11 40	98.28 35	99.36 39	99.35 90	99.48 24	97.96 33	98.83 106	93.86 84	98.70 55	99.86 38	99.44 23	99.08 39	98.38 74	99.61 129	99.58 130

ACMMP		98.74 35	99.03 49	98.40 32	99.36 39	99.64 27	99.20 36	97.75 37	98.82 108	95.24 55	98.85 46	99.87 37	99.17 45	98.74 67	97.50 125	99.71 79	99.76 64
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
MAR-MVS		97.71 64	98.04 85	97.32 57	99.35 41	98.91 122	97.65 112	91.68 116	98.00 156	97.01 31	97.72 87	94.83 113	98.85 71	98.44 90	98.86 46	99.41 177	99.52 142
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
CDPH-MVS		98.41 46	99.10 41	97.61 51	99.32 42	99.36 87	99.49 22	96.15 44	98.82 108	91.82 117	98.41 66	99.66 51	99.10 51	98.93 50	98.97 38	99.75 47	99.58 130

CNLPA		99.03 27	99.05 45	99.01 19	99.27 43	99.22 108	99.03 48	97.98 32	99.34 35	99.00 4	98.25 72	99.71 49	99.31 33	98.80 60	98.82 53	99.48 166	99.17 171

MSDG		98.27 51	98.29 71	98.24 36	99.20 44	99.22 108	99.20 36	97.82 35	99.37 27	94.43 73	95.90 128	97.31 83	99.12 49	98.76 64	98.35 77	99.67 108	99.14 175

PHI-MVS		99.08 22	99.43 20	98.67 28	99.15 45	99.59 45	99.11 42	97.35 39	99.14 66	97.30 27	99.44 13	99.96 12	99.32 32	98.89 55	99.39 7	99.79 31	99.58 130

PatchMatch-RL		97.77 62	98.25 73	97.21 62	99.11 46	99.25 102	97.06 138	94.09 71	98.72 123	95.14 58	98.47 64	96.29 94	98.43 93	98.65 72	97.44 131	99.45 170	98.94 181

TAPA-MVS	97.53 5	98.41 46	98.84 57	97.91 44	99.08 47	99.33 95	99.15 39	97.13 40	99.34 35	93.20 95	97.75 85	99.19 60	99.20 39	98.66 71	98.13 94	99.66 113	99.48 151
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EPNet		98.05 55	98.86 55	97.10 64	99.02 48	99.43 75	98.47 70	94.73 56	99.05 81	95.62 46	98.93 41	97.62 81	95.48 175	98.59 81	98.55 63	99.29 186	99.84 25
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet_dtu		96.30 119	98.53 64	93.70 141	98.97 49	98.24 167	97.36 119	94.23 70	98.85 101	79.18 193	99.19 22	98.47 70	94.09 197	97.89 125	98.21 90	98.39 203	98.85 187
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
COLMAP_ROB	96.15 12	97.78 61	98.17 79	97.32 57	98.84 50	99.45 68	99.28 34	95.43 49	99.48 19	91.80 118	94.83 147	98.36 72	98.90 65	98.09 106	97.85 109	99.68 99	99.15 172
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MVS_0304		98.81 33	99.44 17	98.08 39	98.83 51	99.75 9	99.58 17	95.53 46	99.76 1	96.48 38	99.70 4	98.64 66	98.21 97	99.00 46	99.33 10	99.82 16	99.90 7

DeepPCF-MVS	97.74 3	98.34 48	99.46 13	97.04 66	98.82 52	99.33 95	96.28 154	97.47 38	99.58 9	94.70 65	98.99 37	99.85 40	97.24 128	99.55 10	99.34 9	97.73 212	99.56 136

SD-MVS		99.25 12	99.50 12	98.96 20	98.79 53	99.55 53	99.33 33	98.29 12	99.75 2	97.96 18	99.15 25	99.95 17	99.61 6	99.17 32	99.06 29	99.81 23	99.84 25
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.		99.27 10	99.57 5	98.92 22	98.78 54	99.53 55	99.72 2	98.11 28	99.73 3	97.43 25	99.15 25	99.96 12	99.59 9	99.73 1	99.07 27	99.88 4	99.82 30
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPM-MVS		98.31 50	98.53 64	98.05 40	98.76 55	98.77 130	99.13 40	98.07 29	99.10 72	94.27 78	96.70 107	99.84 41	98.70 77	97.90 124	98.11 96	99.40 179	99.28 163

PCF-MVS	97.50 6	98.18 54	98.35 70	97.99 42	98.65 56	99.36 87	98.94 52	98.14 25	98.59 127	93.62 90	96.61 111	99.76 48	99.03 57	97.77 131	97.45 130	99.57 151	98.89 186
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DeepC-MVS	97.63 4	98.33 49	98.57 62	98.04 41	98.62 57	99.65 23	99.45 26	98.15 23	99.51 17	92.80 104	95.74 135	96.44 92	99.46 21	99.37 19	99.50 2	99.78 34	99.81 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SPE-MVS-test		98.58 43	99.42 21	97.60 52	98.52 58	99.91 1	98.60 64	94.60 61	99.37 27	94.62 66	99.40 15	99.16 61	99.39 26	99.36 20	98.85 49	99.90 3	99.92 3

CANet		98.46 45	99.16 38	97.64 50	98.48 59	99.64 27	99.35 32	94.71 57	99.53 14	95.17 56	97.63 89	99.59 54	98.38 94	98.88 57	98.99 37	99.74 53	99.86 21

LS3D		97.79 60	98.25 73	97.26 61	98.40 60	99.63 30	99.53 19	98.63 1	99.25 47	88.13 137	96.93 102	94.14 123	99.19 40	99.14 35	99.23 19	99.69 91	99.42 155

CHOSEN 280x420		97.99 57	99.24 35	96.53 85	98.34 61	99.61 38	98.36 79	89.80 152	99.27 43	95.08 59	99.81 1	98.58 68	98.64 84	99.02 43	98.92 42	98.93 197	99.48 151

DELS-MVS		98.19 53	98.77 59	97.52 53	98.29 62	99.71 16	99.12 41	94.58 63	98.80 111	95.38 53	96.24 120	98.24 74	97.92 109	99.06 40	99.52 1	99.82 16	99.79 45
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
CS-MVS		98.56 44	99.32 29	97.68 48	98.28 63	99.89 2	98.71 61	94.53 64	99.41 23	95.43 50	99.05 36	98.66 65	99.19 40	99.21 29	99.07 27	99.93 1	99.94 1

RPSCF		97.61 67	98.16 80	96.96 74	98.10 64	99.00 115	98.84 56	93.76 79	99.45 20	94.78 64	99.39 16	99.31 58	98.53 91	96.61 170	95.43 181	97.74 210	97.93 204

PVSNet_BlendedMVS		97.51 71	97.71 98	97.28 59	98.06 65	99.61 38	97.31 121	95.02 52	99.08 75	95.51 48	98.05 76	90.11 149	98.07 104	98.91 53	98.40 72	99.72 69	99.78 51

PVSNet_Blended		97.51 71	97.71 98	97.28 59	98.06 65	99.61 38	97.31 121	95.02 52	99.08 75	95.51 48	98.05 76	90.11 149	98.07 104	98.91 53	98.40 72	99.72 69	99.78 51

CHOSEN 1792x2688		96.41 116	96.99 130	95.74 110	98.01 67	99.72 13	97.70 110	90.78 137	99.13 70	90.03 130	87.35 206	95.36 106	98.33 95	98.59 81	98.91 44	99.59 143	99.87 18

HyFIR lowres test		95.99 127	96.56 142	95.32 116	97.99 68	99.65 23	96.54 147	88.86 161	98.44 136	89.77 133	84.14 216	97.05 87	99.03 57	98.55 83	98.19 92	99.73 61	99.86 21

OPM-MVS		96.22 121	95.85 163	96.65 80	97.75 69	98.54 149	99.00 51	95.53 46	96.88 190	89.88 131	95.95 126	86.46 176	98.07 104	97.65 140	96.63 148	99.67 108	98.83 188
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tmp_tt				82.25 218	97.73 70	88.71 227	80.18 227	68.65 230	99.15 63	86.98 146	99.47 11	85.31 185	68.35 228	87.51 223	83.81 225	91.64 227

TSAR-MVS + COLMAP		96.79 98	96.55 143	97.06 65	97.70 71	98.46 154	99.07 45	96.23 43	99.38 25	91.32 122	98.80 47	85.61 182	98.69 79	97.64 141	96.92 141	99.37 181	99.06 179

PVSNet_Blended_VisFu		97.41 74	98.49 66	96.15 96	97.49 72	99.76 6	96.02 158	93.75 81	99.26 45	93.38 94	93.73 157	99.35 57	96.47 150	98.96 47	98.46 67	99.77 39	99.90 7

MS-PatchMatch		95.99 127	97.26 119	94.51 125	97.46 73	98.76 133	97.27 123	86.97 181	99.09 73	89.83 132	93.51 161	97.78 78	96.18 156	97.53 146	95.71 178	99.35 182	98.41 194

XVS					97.42 74	99.62 33	98.59 65			93.81 85		99.95 17				99.69 91

X-MVStestdata					97.42 74	99.62 33	98.59 65			93.81 85		99.95 17				99.69 91

LGP-MVS_train		96.23 120	96.89 132	95.46 115	97.32 76	98.77 130	98.81 57	93.60 84	98.58 128	85.52 155	99.08 33	86.67 173	97.83 116	97.87 126	97.51 124	99.69 91	99.73 83

CMPMVS	70.31 18	90.74 206	91.06 214	90.36 197	97.32 76	97.43 203	92.97 202	87.82 177	93.50 221	75.34 209	83.27 218	84.90 188	92.19 212	92.64 216	91.21 220	96.50 223	94.46 221
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
HQP-MVS		96.37 117	96.58 141	96.13 98	97.31 78	98.44 156	98.45 71	95.22 50	98.86 99	88.58 135	98.33 70	87.00 168	97.67 118	97.23 157	96.56 152	99.56 154	99.62 125

ACMM	96.26 9	96.67 107	96.69 139	96.66 79	97.29 79	98.46 154	96.48 150	95.09 51	99.21 53	93.19 96	98.78 49	86.73 172	98.17 98	97.84 128	96.32 159	99.74 53	99.49 150
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		97.13 86	99.14 39	94.78 121	97.21 80	99.38 81	97.56 114	92.04 109	98.48 134	88.03 138	98.39 68	99.91 31	94.03 198	99.33 24	99.23 19	99.81 23	99.25 167

UGNet		97.66 66	99.07 44	96.01 104	97.19 81	99.65 23	97.09 136	93.39 87	99.35 33	94.40 75	98.79 48	99.59 54	94.24 195	98.04 114	98.29 86	99.73 61	99.80 37
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
TSAR-MVS + GP.		98.66 40	99.36 25	97.85 45	97.16 82	99.46 66	99.03 48	94.59 62	99.09 73	97.19 29	99.73 3	99.95 17	99.39 26	98.95 48	98.69 57	99.75 47	99.65 117

CANet_DTU		96.64 108	99.08 42	93.81 137	97.10 83	99.42 77	98.85 55	90.01 146	99.31 37	79.98 189	99.78 2	99.10 63	97.42 125	98.35 93	98.05 99	99.47 168	99.53 139

IB-MVS	93.96 15	95.02 145	96.44 153	93.36 151	97.05 84	99.28 99	90.43 212	93.39 87	98.02 155	96.02 41	94.92 146	92.07 138	83.52 221	95.38 198	95.82 175	99.72 69	99.59 129
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
ACMP	96.25 10	96.62 110	96.72 138	96.50 88	96.96 85	98.75 134	97.80 105	94.30 69	98.85 101	93.12 97	98.78 49	86.61 174	97.23 129	97.73 134	96.61 149	99.62 127	99.71 96
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test2506		97.16 84	96.68 140	97.73 47	96.95 86	99.79 4	98.48 68	94.42 66	99.17 58	97.74 22	99.15 25	80.93 210	98.89 68	99.03 41	99.09 25	99.88 4	99.62 125

ECVR-MVS		97.27 79	97.09 124	97.48 54	96.95 86	99.79 4	98.48 68	94.42 66	99.17 58	96.28 39	93.54 159	89.39 155	98.89 68	99.03 41	99.09 25	99.88 4	99.61 128

test1111		97.09 88	96.83 135	97.39 55	96.92 88	99.81 3	98.44 72	94.45 65	99.17 58	95.85 44	92.10 173	88.97 159	98.78 73	99.02 43	99.11 24	99.88 4	99.63 123

ACMH	95.42 14	95.27 142	95.96 159	94.45 127	96.83 89	98.78 129	94.72 185	91.67 117	98.95 89	86.82 148	96.42 117	83.67 193	97.00 132	97.48 148	96.68 146	99.69 91	99.76 64
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CLD-MVS		96.74 101	96.51 146	97.01 71	96.71 90	98.62 143	98.73 59	94.38 68	98.94 91	94.46 72	97.33 92	87.03 167	98.07 104	97.20 159	96.87 142	99.72 69	99.54 138
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TDRefinement		93.04 181	93.57 198	92.41 160	96.58 91	98.77 130	97.78 107	91.96 112	98.12 152	80.84 182	89.13 193	79.87 218	87.78 217	96.44 175	94.50 203	99.54 160	98.15 199

Anonymous202405211			97.40 111		96.45 92	99.54 54	98.08 97	93.79 78	98.24 148		93.55 158	94.41 119	98.88 70	98.04 114	98.24 89	99.75 47	99.76 64

DCV-MVSNet		97.56 69	98.36 69	96.62 84	96.44 93	98.36 163	98.37 77	91.73 115	99.11 71	94.80 63	98.36 69	96.28 95	98.60 87	98.12 103	98.44 69	99.76 41	99.87 18

ACMH+	95.51 13	95.40 138	96.00 157	94.70 122	96.33 94	98.79 127	96.79 142	91.32 127	98.77 118	87.18 145	95.60 140	85.46 183	96.97 133	97.15 160	96.59 150	99.59 143	99.65 117

Anonymous20231211		97.10 87	97.06 127	97.14 63	96.32 95	99.52 58	98.16 89	93.76 79	98.84 105	95.98 42	90.92 179	94.58 118	98.90 65	97.72 135	98.10 97	99.71 79	99.75 72

thres100view900		96.72 102	96.47 150	97.00 72	96.31 96	99.52 58	98.28 83	94.01 72	97.35 177	94.52 68	95.90 128	86.93 169	99.09 53	98.07 109	97.87 107	99.81 23	99.63 123

tfpn200view9		96.75 100	96.51 146	97.03 67	96.31 96	99.67 19	98.41 74	93.99 74	97.35 177	94.52 68	95.90 128	86.93 169	99.14 48	98.26 96	97.80 112	99.82 16	99.70 98

thres200		96.76 99	96.53 144	97.03 67	96.31 96	99.67 19	98.37 77	93.99 74	97.68 172	94.49 71	95.83 134	86.77 171	99.18 43	98.26 96	97.82 111	99.82 16	99.66 114

thres600view7		96.69 104	96.43 154	97.00 72	96.28 99	99.67 19	98.41 74	93.99 74	97.85 166	94.29 77	95.96 125	85.91 180	99.19 40	98.26 96	97.63 119	99.82 16	99.73 83

thres400		96.71 103	96.45 152	97.02 69	96.28 99	99.63 30	98.41 74	94.00 73	97.82 167	94.42 74	95.74 135	86.26 177	99.18 43	98.20 100	97.79 113	99.81 23	99.70 98

baseline1		97.58 68	98.05 84	97.02 69	96.21 101	99.45 68	97.71 109	93.71 83	98.47 135	95.75 45	98.78 49	93.20 133	98.91 63	98.52 85	98.44 69	99.81 23	99.53 139

sasdasda		97.31 76	97.81 95	96.72 76	96.20 102	99.45 68	98.21 86	91.60 118	99.22 50	95.39 51	98.48 61	90.95 143	99.16 46	97.66 137	99.05 30	99.76 41	99.90 7

canonicalmvs		97.31 76	97.81 95	96.72 76	96.20 102	99.45 68	98.21 86	91.60 118	99.22 50	95.39 51	98.48 61	90.95 143	99.16 46	97.66 137	99.05 30	99.76 41	99.90 7

MGCFI-Net		97.26 81	97.79 97	96.64 82	96.17 104	99.43 75	98.14 91	91.52 123	99.23 48	95.16 57	98.48 61	90.87 145	99.07 54	97.59 143	99.02 35	99.76 41	99.91 6

IS_MVSNet		97.86 59	98.86 55	96.68 78	96.02 105	99.72 13	98.35 80	93.37 90	98.75 122	94.01 79	96.88 104	98.40 71	98.48 92	99.09 37	99.42 5	99.83 15	99.80 37

USDC		94.26 161	94.83 173	93.59 143	96.02 105	98.44 156	97.84 102	88.65 165	98.86 99	82.73 174	94.02 154	80.56 211	96.76 139	97.28 156	96.15 166	99.55 156	98.50 192

FC-MVSNet-train		97.04 89	97.91 92	96.03 103	96.00 107	98.41 159	96.53 149	93.42 86	99.04 83	93.02 98	98.03 78	94.32 121	97.47 124	97.93 121	97.77 114	99.75 47	99.88 16

Vis-MVSNet (Re-imp)		97.40 75	98.89 54	95.66 112	95.99 108	99.62 33	97.82 103	93.22 96	98.82 108	91.40 120	96.94 101	98.56 69	95.70 167	99.14 35	99.41 6	99.79 31	99.75 72

MVSTER		97.16 84	97.71 98	96.52 86	95.97 109	98.48 152	98.63 63	92.10 108	98.68 124	95.96 43	99.23 21	91.79 139	96.87 136	98.76 64	97.37 134	99.57 151	99.68 107

baseline		97.45 73	98.70 61	95.99 105	95.89 110	99.36 87	98.29 82	91.37 126	99.21 53	92.99 99	98.40 67	96.87 89	97.96 108	98.60 79	98.60 62	99.42 176	99.86 21

TinyColmap		94.00 165	94.35 182	93.60 142	95.89 110	98.26 165	97.49 116	88.82 162	98.56 130	83.21 168	91.28 178	80.48 213	96.68 142	97.34 153	96.26 162	99.53 162	98.24 198

FA-MVS(training)		96.52 112	98.29 71	94.45 127	95.88 112	99.52 58	97.66 111	81.47 204	98.94 91	93.79 88	95.54 142	99.11 62	98.29 96	98.89 55	96.49 154	99.63 126	99.52 142

EPMVS		95.05 144	96.86 134	92.94 157	95.84 113	98.96 120	96.68 143	79.87 210	99.05 81	90.15 128	97.12 98	95.99 101	97.49 123	95.17 202	94.75 200	97.59 214	96.96 214

casdiffmvs_mvg		97.27 79	97.97 90	96.46 89	95.83 114	99.51 61	98.42 73	93.32 91	98.34 142	92.38 110	95.64 138	95.35 107	98.91 63	98.73 68	98.45 68	99.86 9	99.80 37
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PMMVS		97.52 70	98.39 68	96.51 87	95.82 115	98.73 137	97.80 105	93.05 103	98.76 119	94.39 76	99.07 34	97.03 88	98.55 89	98.31 95	97.61 120	99.43 174	99.21 170

diffmvs		96.83 96	97.33 114	96.25 93	95.76 116	99.34 92	98.06 98	93.22 96	99.43 22	92.30 112	96.90 103	89.83 154	98.55 89	98.00 118	98.14 93	99.64 121	99.70 98
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_Test		97.30 78	98.54 63	95.87 107	95.74 117	99.28 99	98.19 88	91.40 125	99.18 57	91.59 119	98.17 74	96.18 97	98.63 85	98.61 76	98.55 63	99.66 113	99.78 51

EIA-MVS		97.70 65	98.78 58	96.44 90	95.72 118	99.65 23	98.14 91	93.72 82	98.30 144	92.31 111	98.63 56	97.90 76	98.97 60	98.92 52	98.30 83	99.78 34	99.80 37

diffmvs_AUTHOR		96.68 106	97.10 123	96.19 95	95.71 119	99.37 85	97.91 100	93.19 99	99.36 31	91.97 116	95.90 128	89.02 158	98.67 82	98.01 117	98.30 83	99.68 99	99.74 77

viewmanbaseed2359cas		96.92 94	97.60 102	96.14 97	95.71 119	99.44 74	97.82 103	93.39 87	98.93 93	91.34 121	96.10 122	92.27 136	98.82 72	98.40 92	98.30 83	99.75 47	99.75 72

casdiffmvs		96.93 93	97.43 110	96.34 92	95.70 121	99.50 62	97.75 108	93.22 96	98.98 88	92.64 105	94.97 144	91.71 140	98.93 61	98.62 75	98.52 66	99.82 16	99.72 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
viewdifsd2359ckpt11		96.47 114	96.78 136	96.10 100	95.69 122	99.24 104	97.16 130	93.19 99	99.37 27	92.90 102	95.88 132	89.35 156	98.69 79	96.32 182	97.65 117	98.99 195	99.68 107

viewmsd2359difaftdt		96.47 114	96.78 136	96.11 99	95.69 122	99.24 104	97.16 130	93.19 99	99.35 33	92.93 101	95.88 132	89.34 157	98.69 79	96.31 183	97.65 117	98.99 195	99.68 107

tpmrst		93.86 170	95.88 161	91.50 179	95.69 122	98.62 143	95.64 164	79.41 213	98.80 111	83.76 164	95.63 139	96.13 98	97.25 127	92.92 214	92.31 213	97.27 217	96.74 215

ADS-MVSNet		94.65 153	97.04 128	91.88 175	95.68 125	98.99 117	95.89 159	79.03 217	99.15 63	85.81 153	96.96 100	98.21 75	97.10 130	94.48 210	94.24 204	97.74 210	97.21 210

EPP-MVSNet		97.75 63	98.71 60	96.63 83	95.68 125	99.56 51	97.51 115	93.10 102	99.22 50	94.99 61	97.18 97	97.30 84	98.65 83	98.83 59	98.93 41	99.84 12	99.92 3

viewmacassd2359aftdt		96.50 113	97.01 129	95.91 106	95.65 127	99.45 68	97.65 112	93.31 92	98.36 140	90.30 127	94.48 151	90.82 146	98.77 74	97.91 122	98.26 87	99.76 41	99.77 58

viewmambaseed2359dif		96.82 97	97.19 121	96.39 91	95.64 128	99.38 81	98.15 90	93.24 93	98.78 117	92.85 103	95.93 127	91.24 142	98.75 76	97.41 149	97.86 108	99.70 87	99.74 77

EC-MVSNet		98.22 52	99.44 17	96.79 75	95.62 129	99.56 51	99.01 50	92.22 106	99.17 58	94.51 70	99.41 14	99.62 52	99.49 18	99.16 34	99.26 15	99.91 2	99.94 1

ETV-MVS		98.05 55	99.25 34	96.65 80	95.61 130	99.61 38	98.26 85	93.52 85	98.90 97	93.74 89	99.32 18	99.20 59	98.90 65	99.21 29	98.72 56	99.87 8	99.79 45

DI_MVS_pp		96.90 95	97.49 105	96.21 94	95.61 130	99.40 80	98.72 60	92.11 107	99.14 66	92.98 100	93.08 169	95.14 109	98.13 102	98.05 113	97.91 105	99.74 53	99.73 83

thisisatest0530		97.23 82	98.25 73	96.05 101	95.60 132	99.59 45	96.96 140	93.23 94	99.17 58	92.60 107	98.75 52	96.19 96	98.17 98	98.19 101	96.10 167	99.72 69	99.77 58

tttt0517		97.23 82	98.24 76	96.04 102	95.60 132	99.60 43	96.94 141	93.23 94	99.15 63	92.56 108	98.74 53	96.12 99	98.17 98	98.21 99	96.10 167	99.73 61	99.78 51

SCA		94.95 146	97.44 109	92.04 167	95.55 134	99.16 110	96.26 155	79.30 214	99.02 84	85.73 154	98.18 73	97.13 86	97.69 117	96.03 191	94.91 195	97.69 213	97.65 206

dps		94.63 154	95.31 169	93.84 136	95.53 135	98.71 138	96.54 147	80.12 209	97.81 169	97.21 28	96.98 99	92.37 134	96.34 153	92.46 217	91.77 217	97.26 218	97.08 212

PatchmatchNet		94.70 151	97.08 126	91.92 172	95.53 135	98.85 125	95.77 161	79.54 212	98.95 89	85.98 151	98.52 59	96.45 90	97.39 126	95.32 199	94.09 205	97.32 216	97.38 209
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test-LLR		95.50 136	97.32 115	93.37 150	95.49 137	98.74 135	96.44 152	90.82 135	98.18 149	82.75 172	96.60 112	94.67 116	95.54 173	98.09 106	96.00 169	99.20 189	98.93 182

test0.0.03 1		96.69 104	98.12 82	95.01 119	95.49 137	98.99 117	95.86 160	90.82 135	98.38 138	92.54 109	96.66 109	97.33 82	95.75 165	97.75 133	98.34 79	99.60 137	99.40 158

CostFormer		94.25 162	94.88 172	93.51 147	95.43 139	98.34 164	96.21 156	80.64 207	97.94 161	94.01 79	98.30 71	86.20 179	97.52 121	92.71 215	92.69 211	97.23 219	98.02 202

MDTV_nov1_ep13		95.57 134	97.48 106	93.35 152	95.43 139	98.97 119	97.19 129	83.72 202	98.92 96	87.91 140	97.75 85	96.12 99	97.88 113	96.84 169	95.64 179	97.96 208	98.10 200

tpm cat1		94.06 163	94.90 171	93.06 155	95.42 141	98.52 151	96.64 145	80.67 206	97.82 167	92.63 106	93.39 163	95.00 111	96.06 160	91.36 221	91.58 219	96.98 220	96.66 217

Vis-MVSNet		96.16 123	98.22 77	93.75 138	95.33 142	99.70 18	97.27 123	90.85 134	98.30 144	85.51 156	95.72 137	96.45 90	93.69 204	98.70 70	99.00 36	99.84 12	99.69 102
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CVMVSNet		95.33 141	97.09 124	93.27 153	95.23 143	98.39 161	95.49 167	92.58 105	97.71 171	83.00 171	94.44 152	93.28 131	93.92 201	97.79 129	98.54 65	99.41 177	99.45 153

IterMVS-LS		96.12 124	97.48 106	94.53 124	95.19 144	97.56 197	97.15 132	89.19 159	99.08 75	88.23 136	94.97 144	94.73 115	97.84 115	97.86 127	98.26 87	99.60 137	99.88 16
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+		95.81 130	97.31 118	94.06 133	95.09 145	99.35 90	97.24 126	88.22 170	98.54 131	85.38 157	98.52 59	88.68 160	98.70 77	98.32 94	97.93 102	99.74 53	99.84 25

testgi		95.67 133	97.48 106	93.56 144	95.07 146	99.00 115	95.33 171	88.47 167	98.80 111	86.90 147	97.30 93	92.33 135	95.97 162	97.66 137	97.91 105	99.60 137	99.38 159

GeoE		95.98 129	97.24 120	94.51 125	95.02 147	99.38 81	98.02 99	87.86 176	98.37 139	87.86 141	92.99 171	93.54 128	98.56 88	98.61 76	97.92 103	99.73 61	99.85 24

RPMNet		94.66 152	97.16 122	91.75 176	94.98 148	98.59 146	97.00 139	78.37 221	97.98 157	83.78 162	96.27 119	94.09 126	96.91 135	97.36 152	96.73 144	99.48 166	99.09 177

CR-MVSNet		94.57 158	97.34 113	91.33 183	94.90 149	98.59 146	97.15 132	79.14 215	97.98 157	80.42 185	96.59 114	93.50 130	96.85 137	98.10 104	97.49 126	99.50 165	99.15 172

gg-mvs-nofinetune		90.85 205	94.14 184	87.02 210	94.89 150	99.25 102	98.64 62	76.29 225	88.24 226	57.50 230	79.93 222	95.45 105	95.18 184	98.77 63	98.07 98	99.62 127	99.24 168

IterMVS-SCA-FT		94.89 148	97.87 93	91.42 180	94.86 151	97.70 183	97.24 126	84.88 196	98.93 93	75.74 205	94.26 153	98.25 73	96.69 141	98.52 85	97.68 116	99.10 193	99.73 83

IterMVS		94.81 150	97.71 98	91.42 180	94.83 152	97.63 190	97.38 118	85.08 193	98.93 93	75.67 206	94.02 154	97.64 79	96.66 144	98.45 88	97.60 121	98.90 198	99.72 93
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchT		93.96 167	97.36 112	90.00 199	94.76 153	98.65 141	90.11 215	78.57 220	97.96 160	80.42 185	96.07 123	94.10 125	96.85 137	98.10 104	97.49 126	99.26 187	99.15 172

baseline2		96.36 118	97.82 94	94.65 123	94.60 154	99.09 113	96.45 151	89.63 154	98.36 140	91.29 123	97.60 90	94.13 124	96.37 151	98.45 88	97.70 115	99.54 160	99.41 156

CDS-MVSNet		96.59 111	98.02 87	94.92 120	94.45 155	98.96 120	97.46 117	91.75 114	97.86 165	90.07 129	96.02 124	97.25 85	96.21 154	98.04 114	98.38 74	99.60 137	99.65 117
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm		92.38 197	94.79 174	89.56 203	94.30 156	97.50 200	94.24 197	78.97 218	97.72 170	74.93 210	97.97 80	82.91 199	96.60 146	93.65 213	94.81 199	98.33 204	98.98 180

Fast-Effi-MVS+		95.38 139	96.52 145	94.05 134	94.15 157	99.14 112	97.24 126	86.79 182	98.53 132	87.62 143	94.51 149	87.06 166	98.76 75	98.60 79	98.04 100	99.72 69	99.77 58

Effi-MVS+-dtu		95.74 132	98.04 85	93.06 155	93.92 158	99.16 110	97.90 101	88.16 172	99.07 80	82.02 177	98.02 79	94.32 121	96.74 140	98.53 84	97.56 122	99.61 129	99.62 125

UniMVSNet_ETH3D		93.15 178	92.33 211	94.11 132	93.91 159	98.61 145	94.81 182	90.98 132	97.06 186	87.51 144	82.27 220	76.33 226	97.87 114	94.79 208	97.47 129	99.56 154	99.81 35

Fast-Effi-MVS+-dtu		95.38 139	98.20 78	92.09 166	93.91 159	98.87 124	97.35 120	85.01 195	99.08 75	81.09 181	98.10 75	96.36 93	95.62 170	98.43 91	97.03 138	99.55 156	99.50 149

TAMVS		95.53 135	96.50 148	94.39 129	93.86 161	99.03 114	96.67 144	89.55 156	97.33 179	90.64 125	93.02 170	91.58 141	96.21 154	97.72 135	97.43 132	99.43 174	99.36 160

GBi-Net		96.98 91	98.00 88	95.78 108	93.81 162	97.98 172	98.09 94	91.32 127	98.80 111	93.92 81	97.21 94	95.94 102	97.89 110	98.07 109	98.34 79	99.68 99	99.67 110

test1		96.98 91	98.00 88	95.78 108	93.81 162	97.98 172	98.09 94	91.32 127	98.80 111	93.92 81	97.21 94	95.94 102	97.89 110	98.07 109	98.34 79	99.68 99	99.67 110

FMVSNet2		96.64 108	97.50 104	95.63 113	93.81 162	97.98 172	98.09 94	90.87 133	98.99 87	93.48 92	93.17 166	95.25 108	97.89 110	98.63 74	98.80 54	99.68 99	99.67 110

MVS-HIRNet		92.51 191	95.97 158	88.48 207	93.73 165	98.37 162	90.33 213	75.36 227	98.32 143	77.78 199	89.15 192	94.87 112	95.14 185	97.62 142	96.39 157	98.51 200	97.11 211

GA-MVS		93.93 168	96.31 156	91.16 187	93.61 166	98.79 127	95.39 170	90.69 140	98.25 147	73.28 214	96.15 121	88.42 161	94.39 193	97.76 132	95.35 183	99.58 147	99.45 153

FC-MVSNet-test		96.07 125	97.94 91	93.89 135	93.60 167	98.67 140	96.62 146	90.30 145	98.76 119	88.62 134	95.57 141	97.63 80	94.48 191	97.97 119	97.48 128	99.71 79	99.52 142

FMVSNet3		97.02 90	98.12 82	95.73 111	93.59 168	97.98 172	98.34 81	91.32 127	98.80 111	93.92 81	97.21 94	95.94 102	97.63 119	98.61 76	98.62 60	99.61 129	99.65 117

dmvs_re		96.02 126	96.49 149	95.47 114	93.49 169	99.26 101	97.25 125	93.82 77	97.51 174	90.43 126	97.52 91	87.93 162	98.12 103	96.86 167	96.59 150	99.73 61	99.76 64

FMVSNet1		95.77 131	96.41 155	95.03 118	93.42 170	97.86 179	97.11 135	89.89 149	98.53 132	92.00 115	89.17 191	93.23 132	98.15 101	98.07 109	98.34 79	99.61 129	99.69 102

tfpnnormal		93.85 171	94.12 186	93.54 146	93.22 171	98.24 167	95.45 168	91.96 112	94.61 217	83.91 160	90.74 181	81.75 207	97.04 131	97.49 147	96.16 165	99.68 99	99.84 25

TransMVSNet (Re)		93.45 174	94.08 187	92.72 159	92.83 172	97.62 193	94.94 176	91.54 122	95.65 214	83.06 170	88.93 194	83.53 194	94.25 194	97.41 149	97.03 138	99.67 108	98.40 197

LTVRE_ROB	93.20 16	92.84 183	94.92 170	90.43 196	92.83 172	98.63 142	97.08 137	87.87 175	97.91 162	68.42 223	93.54 159	79.46 220	96.62 145	97.55 145	97.40 133	99.74 53	99.92 3
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
TESTMET0.1,1		94.95 146	97.32 115	92.20 164	92.62 174	98.74 135	96.44 152	86.67 184	98.18 149	82.75 172	96.60 112	94.67 116	95.54 173	98.09 106	96.00 169	99.20 189	98.93 182

pm-mvs1		94.27 160	95.57 165	92.75 158	92.58 175	98.13 170	94.87 180	90.71 139	96.70 196	83.78 162	89.94 187	89.85 153	94.96 188	97.58 144	97.07 137	99.61 129	99.72 93

NR-MVSNet		94.01 164	94.51 179	93.44 148	92.56 176	97.77 180	95.67 162	91.57 120	97.17 183	85.84 152	93.13 167	80.53 212	95.29 181	97.01 164	96.17 164	99.69 91	99.75 72

EG-PatchMatch MVS		92.45 192	93.92 193	90.72 193	92.56 176	98.43 158	94.88 179	84.54 198	97.18 182	79.55 191	86.12 213	83.23 197	93.15 208	97.22 158	96.00 169	99.67 108	99.27 166

pmnet_mix02		92.44 193	94.68 176	89.83 202	92.46 178	97.65 189	89.92 217	90.49 142	98.76 119	73.05 216	91.78 174	90.08 151	94.86 189	94.53 209	91.94 216	98.21 206	98.01 203

test-mter		94.86 149	97.32 115	92.00 169	92.41 179	98.82 126	96.18 157	86.35 188	98.05 154	82.28 175	96.48 116	94.39 120	95.46 177	98.17 102	96.20 163	99.32 184	99.13 176

our_test_3					92.30 180	97.58 195	90.09 216

pmmvs4		95.09 143	95.90 160	94.14 131	92.29 181	97.70 183	95.45 168	90.31 143	98.60 126	90.70 124	93.25 164	89.90 152	96.67 143	97.13 161	95.42 182	99.44 172	99.28 163

FMVSNet5		95.42 137	96.47 150	94.20 130	92.26 182	95.99 218	95.66 163	87.15 180	97.87 164	93.46 93	96.68 108	93.79 127	97.52 121	97.10 163	97.21 136	99.11 192	96.62 218

UniMVSNet (Re)		94.58 157	95.34 167	93.71 140	92.25 183	98.08 171	94.97 175	91.29 131	97.03 188	87.94 139	93.97 156	86.25 178	96.07 159	96.27 185	95.97 172	99.72 69	99.79 45

SixPastTwentyTwo		93.44 175	95.32 168	91.24 185	92.11 184	98.40 160	92.77 203	88.64 166	98.09 153	77.83 198	93.51 161	85.74 181	96.52 149	96.91 166	94.89 198	99.59 143	99.73 83

v8		92.87 182	93.87 195	91.72 178	92.05 185	97.50 200	94.79 183	88.20 171	96.85 192	80.11 188	90.01 186	82.86 201	95.48 175	95.15 203	94.90 196	99.66 113	99.80 37

thisisatest0515		94.61 155	96.89 132	91.95 171	92.00 186	98.47 153	92.01 207	90.73 138	98.18 149	83.96 159	94.51 149	95.13 110	93.38 205	97.38 151	94.74 201	99.61 129	99.79 45

WR-MVS_H		93.54 173	94.67 177	92.22 162	91.95 187	97.91 177	94.58 191	88.75 163	96.64 197	83.88 161	90.66 183	85.13 186	94.40 192	96.54 174	95.91 174	99.73 61	99.89 13

V42		93.05 180	93.90 194	92.04 167	91.91 188	97.66 187	94.91 177	89.91 148	96.85 192	80.58 184	89.66 188	83.43 196	95.37 179	95.03 206	94.90 196	99.59 143	99.78 51

EU-MVSNet		92.80 185	94.76 175	90.51 194	91.88 189	96.74 215	92.48 205	88.69 164	96.21 203	79.00 194	91.51 175	87.82 163	91.83 213	95.87 195	96.27 160	99.21 188	98.92 185

N_pmnet		92.21 201	94.60 178	89.42 204	91.88 189	97.38 206	89.15 219	89.74 153	97.89 163	73.75 212	87.94 203	92.23 137	93.85 202	96.10 189	93.20 210	98.15 207	97.43 208

UniMVSNet_NR-MVSNet		94.59 156	95.47 166	93.55 145	91.85 191	97.89 178	95.03 173	92.00 110	97.33 179	86.12 149	93.19 165	87.29 165	96.60 146	96.12 188	96.70 145	99.72 69	99.80 37

pmmvs6		91.90 203	92.53 210	91.17 186	91.81 192	97.63 190	93.23 200	88.37 169	93.43 222	80.61 183	77.32 224	87.47 164	94.12 196	96.58 172	95.72 177	98.88 199	99.53 139

v10		92.79 186	94.06 188	91.31 184	91.78 193	97.29 209	94.87 180	86.10 189	96.97 189	79.82 190	88.16 200	84.56 190	95.63 169	96.33 181	95.31 184	99.65 117	99.80 37

MIMVSNet		94.49 159	97.59 103	90.87 192	91.74 194	98.70 139	94.68 187	78.73 219	97.98 157	83.71 165	97.71 88	94.81 114	96.96 134	97.97 119	97.92 103	99.40 179	98.04 201

v1144		92.81 184	94.03 189	91.40 182	91.68 195	97.60 194	94.73 184	88.40 168	96.71 195	78.48 196	88.14 201	84.46 191	95.45 178	96.31 183	95.22 187	99.65 117	99.76 64

DU-MVS		93.98 166	94.44 181	93.44 148	91.66 196	97.77 180	95.03 173	91.57 120	97.17 183	86.12 149	93.13 167	81.13 209	96.60 146	95.10 204	97.01 140	99.67 108	99.80 37

Baseline_NR-MVSNet		93.87 169	93.98 191	93.75 138	91.66 196	97.02 210	95.53 166	91.52 123	97.16 185	87.77 142	87.93 204	83.69 192	96.35 152	95.10 204	97.23 135	99.68 99	99.73 83

CP-MVSNet		93.25 177	94.00 190	92.38 161	91.65 198	97.56 197	94.38 194	89.20 158	96.05 208	83.16 169	89.51 189	81.97 205	96.16 158	96.43 176	96.56 152	99.71 79	99.89 13

v148		92.36 199	92.88 206	91.75 176	91.63 199	97.66 187	92.64 204	90.55 141	96.09 206	83.34 167	88.19 199	80.00 215	92.74 209	93.98 212	94.58 202	99.58 147	99.69 102

PS-CasMVS		92.72 188	93.36 202	91.98 170	91.62 200	97.52 199	94.13 198	88.98 160	95.94 211	81.51 180	87.35 206	79.95 217	95.91 163	96.37 178	96.49 154	99.70 87	99.89 13

v2v482		92.77 187	93.52 201	91.90 174	91.59 201	97.63 190	94.57 192	90.31 143	96.80 194	79.22 192	88.74 196	81.55 208	96.04 161	95.26 200	94.97 194	99.66 113	99.69 102

v1192		92.43 195	93.61 197	91.05 188	91.53 202	97.43 203	94.61 190	87.99 174	96.60 198	76.72 201	87.11 208	82.74 202	95.85 164	96.35 180	95.30 185	99.60 137	99.74 77

WR-MVS		93.43 176	94.48 180	92.21 163	91.52 203	97.69 185	94.66 189	89.98 147	96.86 191	83.43 166	90.12 185	85.03 187	93.94 200	96.02 192	95.82 175	99.71 79	99.82 30

v144192		92.38 197	93.55 200	91.00 189	91.44 204	97.47 202	94.27 195	87.41 179	96.52 200	78.03 197	87.50 205	82.65 203	95.32 180	95.82 196	95.15 189	99.55 156	99.78 51

pmmvs5		92.71 190	94.27 183	90.90 191	91.42 205	97.74 182	93.23 200	86.66 185	95.99 210	78.96 195	91.45 176	83.44 195	95.55 172	97.30 155	95.05 192	99.58 147	98.93 182

v1921920		92.36 199	93.57 198	90.94 190	91.39 206	97.39 205	94.70 186	87.63 178	96.60 198	76.63 202	86.98 209	82.89 200	95.75 165	96.26 186	95.14 190	99.55 156	99.73 83

gm-plane-assit		89.44 212	92.82 209	85.49 214	91.37 207	95.34 221	79.55 229	82.12 203	91.68 225	64.79 227	87.98 202	80.26 214	95.66 168	98.51 87	97.56 122	99.45 170	98.41 194

v1240		91.99 202	93.33 203	90.44 195	91.29 208	97.30 208	94.25 196	86.79 182	96.43 201	75.49 208	86.34 212	81.85 206	95.29 181	96.42 177	95.22 187	99.52 163	99.73 83

PEN-MVS		92.72 188	93.20 204	92.15 165	91.29 208	97.31 207	94.67 188	89.81 150	96.19 204	81.83 178	88.58 197	79.06 221	95.61 171	95.21 201	96.27 160	99.72 69	99.82 30

TranMVSNet+NR-MVSNet		93.67 172	94.14 184	93.13 154	91.28 210	97.58 195	95.60 165	91.97 111	97.06 186	84.05 158	90.64 184	82.22 204	96.17 157	94.94 207	96.78 143	99.69 91	99.78 51

anonymousdsp		93.12 179	95.86 162	89.93 201	91.09 211	98.25 166	95.12 172	85.08 193	97.44 176	73.30 213	90.89 180	90.78 147	95.25 183	97.91 122	95.96 173	99.71 79	99.82 30

MDTV_nov1_ep13_2view		92.44 193	95.66 164	88.68 205	91.05 212	97.92 176	92.17 206	79.64 211	98.83 106	76.20 203	91.45 176	93.51 129	95.04 186	95.68 197	93.70 208	97.96 208	98.53 191

DTE-MVSNet		92.42 196	92.85 207	91.91 173	90.87 213	96.97 211	94.53 193	89.81 150	95.86 213	81.59 179	88.83 195	77.88 224	95.01 187	94.34 211	96.35 158	99.64 121	99.73 83

v7n		91.61 204	92.95 205	90.04 198	90.56 214	97.69 185	93.74 199	85.59 191	95.89 212	76.95 200	86.60 211	78.60 223	93.76 203	97.01 164	94.99 193	99.65 117	99.87 18

test20.03		90.65 208	93.71 196	87.09 209	90.44 215	96.24 216	89.74 218	85.46 192	95.59 215	72.99 217	90.68 182	85.33 184	84.41 220	95.94 194	95.10 191	99.52 163	97.06 213

FPMVS		83.82 218	84.61 221	82.90 217	90.39 216	90.71 226	90.85 211	84.10 201	95.47 216	65.15 225	83.44 217	74.46 227	75.48 223	81.63 225	79.42 227	91.42 228	87.14 227

Anonymous20231206		90.70 207	93.93 192	86.92 211	90.21 217	96.79 213	90.30 214	86.61 186	96.05 208	69.25 221	88.46 198	84.86 189	85.86 219	97.11 162	96.47 156	99.30 185	97.80 205

new_pmnet		90.45 209	92.84 208	87.66 208	88.96 218	96.16 217	88.71 220	84.66 197	97.56 173	71.91 220	85.60 214	86.58 175	93.28 206	96.07 190	93.54 209	98.46 201	94.39 222

WB-MVS		81.36 220	89.93 217	71.35 223	88.65 219	87.85 229	71.46 231	88.12 173	96.23 202	32.21 235	92.61 172	83.00 198	56.27 230	91.92 220	89.43 221	91.39 229	88.49 226

ET-MVSNet_ETH3D		96.17 122	96.99 130	95.21 117	88.53 220	98.54 149	98.28 83	92.61 104	98.85 101	93.60 91	99.06 35	90.39 148	98.63 85	95.98 193	96.68 146	99.61 129	99.41 156

PM-MVS		89.55 211	90.30 216	88.67 206	87.06 221	95.60 219	90.88 210	84.51 199	96.14 205	75.75 204	86.89 210	63.47 232	94.64 190	96.85 168	93.89 206	99.17 191	99.29 162

pmmvs-eth3d		89.81 210	89.65 218	90.00 199	86.94 222	95.38 220	91.08 208	86.39 187	94.57 218	82.27 176	83.03 219	64.94 229	93.96 199	96.57 173	93.82 207	99.35 182	99.24 168

new-patchmatchnet		86.12 217	87.30 220	84.74 215	86.92 223	95.19 223	83.57 226	84.42 200	92.67 223	65.66 224	80.32 221	64.72 230	89.41 215	92.33 219	89.21 222	98.43 202	96.69 216

pmmvs3		88.19 214	91.27 213	84.60 216	85.60 224	93.66 224	85.68 224	81.13 205	92.36 224	63.66 229	89.51 189	77.10 225	93.22 207	96.37 178	92.40 212	98.30 205	97.46 207

Gipuma		81.40 219	81.78 222	80.96 220	83.21 225	85.61 231	79.73 228	76.25 226	97.33 179	64.21 228	55.32 228	55.55 233	86.04 218	92.43 218	92.20 215	96.32 224	93.99 223
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet-bldmvs		87.84 215	89.22 219	86.23 212	81.74 226	96.77 214	83.74 225	89.57 155	94.50 219	72.83 218	96.64 110	64.47 231	92.71 210	81.43 226	92.28 214	96.81 221	98.47 193

MIMVSNet1		88.61 213	90.68 215	86.19 213	81.56 227	95.30 222	87.78 221	85.98 190	94.19 220	72.30 219	78.84 223	78.90 222	90.06 214	96.59 171	95.47 180	99.46 169	95.49 220

PMVS	72.60 17	76.39 222	77.66 225	74.92 221	81.04 228	69.37 235	68.47 232	80.54 208	85.39 227	65.07 226	73.52 225	72.91 228	65.67 229	80.35 227	76.81 228	88.71 230	85.25 230
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ambc			80.99 223		80.04 229	90.84 225	90.91 209		96.09 206	74.18 211	62.81 227	30.59 238	82.44 222	96.25 187	91.77 217	95.91 225	98.56 190

PMMVS2		77.26 221	79.47 224	74.70 222	76.00 230	88.37 228	74.22 230	76.34 224	78.31 228	54.13 231	69.96 226	52.50 234	70.14 227	84.83 224	88.71 223	97.35 215	93.58 224

test_method		87.27 216	91.58 212	82.25 218	75.65 231	87.52 230	86.81 223	72.60 228	97.51 174	73.20 215	85.07 215	79.97 216	88.69 216	97.31 154	95.24 186	96.53 222	98.41 194

EMVS		68.12 225	68.11 227	68.14 225	75.51 232	71.76 233	55.38 235	77.20 223	77.78 229	37.79 234	53.59 229	43.61 235	74.72 224	67.05 230	76.70 229	88.27 232	86.24 228

E-PMN		68.30 224	68.43 226	68.15 224	74.70 233	71.56 234	55.64 234	77.24 222	77.48 230	39.46 233	51.95 231	41.68 236	73.28 225	70.65 229	79.51 226	88.61 231	86.20 229

MVE	67.97 19	65.53 226	67.43 228	63.31 226	59.33 234	74.20 232	53.09 236	70.43 229	66.27 231	43.13 232	45.98 232	30.62 237	70.65 226	79.34 228	86.30 224	83.25 233	89.33 225
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		31.24 227	40.15 229	20.86 228	12.61 235	17.99 236	25.16 237	13.30 231	48.42 232	24.82 236	53.07 230	30.13 239	28.47 231	42.73 231	37.65 230	20.79 234	51.04 231

test123		26.75 228	34.25 230	18.01 229	7.93 236	17.18 237	24.85 238	12.36 232	44.83 233	16.52 237	41.80 233	18.10 240	28.29 232	33.08 232	34.79 231	18.10 235	49.95 232

GG-mvs-BLEND		69.11 223	98.13 81	35.26 227	3.49 237	98.20 169	94.89 178	2.38 233	98.42 137	5.82 238	96.37 118	98.60 67	5.97 233	98.75 66	97.98 101	99.01 194	98.61 189

uanet_test		0.00 229	0.00 231	0.00 230	0.00 238	0.00 238	0.00 239	0.00 234	0.00 234	0.00 239	0.00 234	0.00 241	0.00 234	0.00 233	0.00 232	0.00 236	0.00 233

sosnet-low-res		0.00 229	0.00 231	0.00 230	0.00 238	0.00 238	0.00 239	0.00 234	0.00 234	0.00 239	0.00 234	0.00 241	0.00 234	0.00 233	0.00 232	0.00 236	0.00 233

sosnet		0.00 229	0.00 231	0.00 230	0.00 238	0.00 238	0.00 239	0.00 234	0.00 234	0.00 239	0.00 234	0.00 241	0.00 234	0.00 233	0.00 232	0.00 236	0.00 233

RE-MVS-def										69.05 222

9.14												99.79 45

MTAPA										98.09 15		99.97 8

MTMP										98.46 10		99.96 12

Patchmatch-RL test							66.86 233

NP-MVS									98.57 129

Patchmtry						98.59 146	97.15 132	79.14 215		80.42 185

DeepMVS_CX						96.85 212	87.43 222	89.27 157	98.30 144	75.55 207	95.05 143	79.47 219	92.62 211	89.48 222		95.18 226	95.96 219

High-resolution multi-view benchmark