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.

aaEdge-Enhanced		99.07 1	99.22 2	98.90 1	99.39 6	99.81 9	99.36 16	96.46 1	99.30 12	99.11 2	98.75 10	99.99 1	99.23 6	98.67 17	98.11 19	99.83 4	99.93 19

MED-MVS		99.01 2	99.14 7	98.87 2	99.39 6	99.81 9	99.57 7	96.25 4	99.46 4	99.11 2	98.75 10	99.98 4	99.26 5	98.55 20	97.50 35	99.61 34	99.93 19

SED-MVS		98.87 3	99.20 3	98.48 3	99.32 14	99.85 2	99.55 9	96.20 9	99.48 3	96.78 6	98.51 18	99.99 1	99.36 2	98.98 8	97.59 31	99.67 22	99.99 3

DVP-MVS++		98.75 4	99.11 9	98.33 5	99.41 5	99.85 2	99.61 4	96.22 8	99.32 10	95.80 8	98.27 21	99.97 6	99.22 7	98.95 9	97.48 36	99.71 21	99.98 5

MSP-MVS		98.75 4	99.27 1	98.15 10	99.21 20	99.82 7	99.58 6	96.09 16	99.32 10	95.16 12	98.79 7	99.55 11	99.05 9	99.54 1	97.88 23	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 6	99.17 6	98.22 7	99.47 4	99.85 2	99.57 7	96.23 6	99.30 12	94.90 14	98.65 13	98.93 22	99.36 2	99.46 3	98.21 13	99.81 8	99.80 35

DPE-MVS		98.69 7	99.14 7	98.16 9	99.37 10	99.82 7	99.66 3	96.26 3	99.18 19	95.02 13	98.62 15	99.98 4	98.88 14	98.90 12	97.51 34	99.75 13	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 8	98.87 16	98.38 4	99.30 16	99.85 2	99.14 26	96.23 6	99.51 2	97.16 4	96.01 37	99.99 1	98.90 13	98.89 13	97.88 23	99.56 57	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 9	98.97 13	98.18 8	99.38 9	99.78 14	99.35 18	96.14 12	99.24 16	95.66 10	98.19 23	99.01 19	98.66 20	98.77 15	97.80 26	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 10	98.75 18	98.32 6	99.48 2	99.68 24	99.51 11	96.24 5	99.08 23	95.94 7	98.64 14	99.30 15	99.02 11	97.94 32	96.86 56	99.75 13	99.76 39

SMA-MVS		98.47 11	99.06 10	97.77 13	99.48 2	99.78 14	99.37 13	96.14 12	99.29 14	93.03 22	97.59 32	99.97 6	99.03 10	98.94 10	98.30 10	99.60 37	99.58 70
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 12	99.18 4	97.52 17	99.36 11	99.84 6	99.55 9	96.08 18	99.33 9	91.77 27	98.79 7	99.46 13	98.59 22	99.15 7	98.07 21	99.73 16	99.64 58

SD-MVS		98.33 13	99.01 11	97.54 16	97.17 53	99.77 17	99.14 26	96.09 16	99.34 8	94.06 18	97.91 28	99.89 8	99.18 8	97.99 31	98.21 13	99.63 29	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 14	98.69 19	97.80 12	99.31 15	99.62 31	99.31 21	96.15 11	99.19 18	93.60 19	97.28 33	98.35 30	98.72 19	98.27 24	98.22 12	99.73 16	99.89 28
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MCST-MVS		98.20 15	99.18 4	97.06 23	99.27 18	99.87 1	99.37 13	96.11 14	99.37 6	89.29 36	98.76 9	99.50 12	98.37 28	99.23 5	97.64 29	99.95 1	99.87 32

HPM-MVS++		98.16 16	98.87 16	97.32 19	99.39 6	99.70 22	99.18 24	96.10 15	99.09 22	91.14 29	98.02 26	99.89 8	98.44 26	98.75 16	97.03 50	99.67 22	99.63 61

MSLP-MVS++		98.12 17	98.23 31	97.99 11	99.28 17	99.72 19	99.59 5	95.27 31	98.61 38	94.79 15	96.11 36	97.79 39	99.27 4	96.62 71	98.96 5	99.77 11	99.80 35

HFP-MVS		98.02 18	98.55 23	97.40 18	99.11 23	99.69 23	99.41 12	95.41 29	98.79 34	91.86 26	98.61 16	98.16 32	99.02 11	97.87 36	97.40 38	99.60 37	99.35 94

TSAR-MVS + MP.		97.98 19	98.62 22	97.23 21	97.08 54	99.55 37	99.17 25	95.69 24	99.40 5	93.04 21	96.68 35	98.96 21	98.58 23	98.82 14	96.95 53	99.81 8	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 20	98.91 14	96.64 27	98.89 29	99.79 11	99.34 19	95.20 33	98.48 40	89.91 34	98.58 17	98.69 26	96.84 53	98.92 11	98.16 17	99.66 24	99.74 42
Skip Steuart: Steuart Systems R&D Blog.
CP-MVS		97.81 21	98.26 30	97.28 20	99.00 26	99.65 27	99.10 28	95.32 30	98.38 46	92.21 25	98.33 20	97.74 40	98.50 24	97.66 45	96.55 64	99.57 50	99.48 80

ACMMPR		97.78 22	98.28 28	97.20 22	99.03 25	99.68 24	99.37 13	95.24 32	98.86 33	91.16 28	97.86 30	97.26 42	98.79 17	97.64 47	97.40 38	99.60 37	99.25 102

DeepC-MVS_fast	95.01 1	97.67 23	98.22 32	97.02 24	99.00 26	99.79 11	99.10 28	95.82 21	99.05 25	89.53 35	93.54 52	96.77 45	98.83 15	99.34 4	99.44 2	99.82 6	99.63 61
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 24	97.35 41	97.77 13	99.17 21	99.55 37	98.57 35	95.76 23	99.04 26	94.66 16	97.94 27	94.39 59	98.82 16	96.21 84	94.78 114	99.62 32	99.52 75

ACMMP_NAP		97.51 25	98.27 29	96.63 28	99.34 12	99.72 19	99.25 22	95.94 20	98.11 51	87.10 51	96.98 34	98.50 28	98.61 21	98.58 19	96.83 57	99.56 57	99.14 116

MP-MVS		97.46 26	98.30 27	96.48 29	98.93 28	99.43 45	99.20 23	95.42 28	98.43 42	87.60 47	98.19 23	98.01 38	98.09 30	98.05 29	96.67 61	99.64 27	99.35 94
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
train_agg		97.42 27	98.88 15	95.71 34	98.46 36	99.60 34	99.05 30	95.16 34	99.10 21	84.38 78	98.47 19	98.85 23	97.61 34	98.54 21	97.66 28	99.62 32	99.93 19

MGCNet		97.38 28	99.01 11	95.47 37	97.24 52	99.68 24	98.62 34	89.40 52	98.88 32	90.96 30	99.09 4	98.85 23	96.90 51	98.13 26	98.54 8	99.72 19	99.91 25

CPTT-MVS		97.32 29	97.60 40	96.99 25	98.29 39	99.31 57	99.04 31	94.67 38	97.99 57	93.12 20	98.03 25	98.26 31	98.77 18	96.08 89	94.26 123	98.07 214	99.27 101

X-MVS		97.20 30	98.42 26	95.77 32	99.04 24	99.64 28	98.95 33	95.10 36	98.16 49	83.97 87	98.27 21	98.08 35	97.95 31	97.89 33	97.46 37	99.58 46	99.47 81

PHI-MVS		97.09 31	98.69 19	95.22 39	97.99 44	99.59 36	97.56 47	92.16 42	98.41 44	87.11 50	98.70 12	99.42 14	96.95 48	96.88 63	98.16 17	99.56 57	99.70 49

DPM-MVS		97.07 32	97.99 35	96.00 31	97.25 51	99.16 67	99.67 2	95.99 19	99.08 23	85.97 60	93.00 57	98.44 29	97.47 36	99.22 6	99.62 1	99.66 24	97.44 190

PGM-MVS		97.03 33	98.14 34	95.73 33	99.34 12	99.61 33	99.34 19	89.99 48	97.70 60	87.67 46	99.44 2	96.45 48	98.44 26	97.65 46	97.09 46	99.58 46	99.06 127

PLC	94.37 2	97.03 33	96.54 48	97.60 15	98.84 30	98.64 77	98.17 39	94.99 37	99.01 28	96.80 5	93.21 56	95.64 50	97.36 37	96.37 78	94.79 113	99.41 101	98.12 173
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TSAR-MVS + ACMM		96.90 35	98.64 21	94.88 41	98.12 42	99.47 42	99.01 32	95.43 27	99.23 17	81.98 116	95.95 38	99.16 18	95.13 75	98.61 18	98.11 19	99.58 46	99.93 19

TSAR-MVS + GP.		96.47 36	98.45 25	94.17 46	92.12 89	99.29 58	97.76 43	88.05 59	99.36 7	90.26 33	97.82 31	99.21 16	97.21 40	96.78 68	96.74 59	99.63 29	99.94 16

EPNet		96.23 37	97.89 37	94.29 44	97.62 47	99.44 44	97.14 55	88.63 55	98.16 49	88.14 42	99.46 1	94.15 62	94.61 93	97.20 55	97.23 42	99.57 50	99.59 67
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNLPA		96.14 38	95.43 57	96.98 26	98.55 33	99.41 49	95.91 61	95.15 35	99.00 29	95.71 9	84.21 116	94.55 57	97.25 38	95.50 113	96.23 70	99.28 137	99.09 126

MVS_111021_LR		96.07 39	97.94 36	93.88 49	97.86 45	99.43 45	95.70 64	89.65 51	98.73 35	84.86 72	99.38 3	94.08 63	95.78 72	97.81 39	96.73 60	99.43 95	99.42 87

ACMMP		96.05 40	96.70 47	95.29 38	98.01 43	99.43 45	97.60 46	94.33 40	97.62 63	86.17 55	98.92 5	92.81 70	96.10 65	95.67 102	93.33 144	99.55 62	99.12 120
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 41	96.42 50	95.50 36	98.18 41	99.33 56	97.44 50	87.73 63	97.93 58	92.36 24	84.67 107	97.33 41	97.55 35	97.32 51	98.47 9	99.72 19	99.88 29

DeepPCF-MVS	94.02 3	95.92 42	98.47 24	92.95 59	97.57 48	99.79 11	91.45 153	94.42 39	99.76 1	86.48 54	92.88 58	98.12 34	92.62 133	99.49 2	99.32 3	95.15 254	99.95 13

CDPH-MVS		95.90 43	97.77 39	93.72 52	98.28 40	99.43 45	98.40 36	91.30 46	98.34 47	78.62 145	94.80 44	95.74 49	96.11 64	97.86 37	98.67 7	99.59 41	99.56 72

CSCG		95.77 44	95.35 59	96.26 30	99.13 22	99.60 34	98.14 40	91.89 45	96.57 78	92.61 23	89.65 70	91.74 78	96.96 45	93.69 148	96.58 63	98.86 167	99.63 61

OMC-MVS		95.75 45	95.84 55	95.64 35	98.52 35	99.34 55	97.15 54	92.02 44	98.94 31	90.45 32	88.31 76	94.64 55	96.35 60	96.02 92	95.99 81	99.34 120	97.65 186

MVS_111021_HR		95.70 46	98.16 33	92.83 61	97.57 48	99.77 17	94.78 80	88.05 59	98.61 38	82.29 111	98.85 6	94.66 54	94.63 89	97.80 40	97.63 30	99.64 27	99.79 37

3Dnovator	90.31 8	95.67 47	96.16 53	95.11 40	98.59 32	99.37 54	97.50 48	87.98 61	98.02 56	89.09 37	85.36 106	94.62 56	97.66 32	97.10 59	98.90 6	99.82 6	99.73 45

CANet		95.40 48	96.27 51	94.40 43	96.25 59	99.62 31	98.37 37	88.59 56	98.09 52	87.58 48	84.57 110	95.54 52	95.87 69	98.12 27	98.03 22	99.73 16	99.90 27

QAPM		95.17 49	96.05 54	94.14 47	98.55 33	99.49 40	97.41 51	87.88 62	97.72 59	84.21 82	84.59 109	95.60 51	97.21 40	97.10 59	98.19 16	99.57 50	99.65 56

SPE-MVS-test		95.06 50	96.98 44	92.82 62	95.83 62	99.40 50	93.23 122	85.29 102	99.27 15	85.89 64	93.86 51	92.70 72	97.19 42	97.70 43	96.18 73	99.49 72	99.76 39

MVSMamba_PlusPlus		94.95 51	96.76 46	92.84 60	90.79 109	98.88 69	97.50 48	87.29 66	99.03 27	83.39 93	92.88 58	90.90 85	98.45 25	98.32 22	98.30 10	99.63 29	98.98 133

CS-MVS		94.82 52	96.19 52	93.22 55	95.19 67	99.24 60	95.10 75	85.07 112	98.72 36	87.33 49	91.35 61	89.98 88	97.06 44	98.01 30	96.28 68	99.60 37	99.72 46

MVSTER		94.75 53	96.50 49	92.70 64	90.91 107	94.51 177	97.37 52	83.37 130	98.40 45	89.04 38	93.23 55	97.04 44	95.91 68	97.73 41	95.59 104	99.61 34	99.01 131

TAPA-MVS	92.04 6	94.72 54	95.13 62	94.24 45	97.72 46	99.17 65	97.61 45	92.16 42	97.66 62	81.99 115	87.84 81	93.94 65	96.50 57	95.74 99	94.27 122	99.46 83	97.31 194
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepC-MVS	92.23 5	94.53 55	94.26 75	94.86 42	96.73 56	99.50 39	97.85 42	95.45 26	96.22 87	82.73 101	80.68 128	88.02 93	96.92 49	97.49 50	98.20 15	99.47 77	99.69 52
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 56	97.78 38	90.70 100	95.54 66	99.49 40	94.14 93	74.91 205	98.43 42	85.32 69	94.78 45	99.19 17	94.95 82	97.02 61	96.18 73	99.35 116	99.36 93

ETV-MVS		94.49 57	97.23 43	91.29 84	90.43 118	98.55 80	93.41 112	84.53 121	99.16 20	83.13 95	94.72 46	94.08 63	96.61 56	97.72 42	96.60 62	99.61 34	99.81 34

EC-MVSNet		94.33 58	96.88 45	91.36 81	90.12 132	97.70 120	95.20 74	80.27 157	98.63 37	85.97 60	93.92 50	93.85 68	97.09 43	97.54 49	96.81 58	99.49 72	99.70 49

MAR-MVS		94.18 59	95.12 63	93.09 58	98.40 38	99.17 65	94.20 92	81.92 139	98.47 41	86.52 53	90.92 63	84.21 113	98.12 29	95.88 96	97.59 31	99.40 103	99.58 70
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 60	93.82 78	94.10 48	96.07 61	99.25 59	96.82 57	95.51 25	92.00 152	81.51 121	82.97 120	93.88 67	95.63 73	94.24 131	94.71 116	99.09 149	99.70 49
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DELS-MVS		93.82 61	93.82 78	93.81 51	96.34 58	99.47 42	97.26 53	88.53 57	92.13 149	87.80 45	79.67 134	88.01 94	93.14 122	98.28 23	99.22 4	99.80 10	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 62	93.62 81	93.34 53	98.46 36	99.39 51	97.00 56	87.66 65	95.37 98	81.21 125	75.96 163	91.58 80	96.21 63	96.37 78	97.10 45	99.52 67	99.54 74

EIA-MVS		93.32 63	95.32 60	90.99 90	90.45 117	98.53 83	93.46 110	84.68 117	97.56 66	81.38 122	91.04 62	87.37 97	96.39 59	97.27 52	95.73 93	99.59 41	99.76 39

PVSNet_BlendedMVS		93.30 64	93.46 85	93.10 56	95.60 64	99.38 52	93.59 105	88.70 53	98.09 52	88.10 43	86.96 89	75.02 153	93.08 123	97.89 33	96.90 54	99.56 57	100.00 1

PVSNet_Blended		93.30 64	93.46 85	93.10 56	95.60 64	99.38 52	93.59 105	88.70 53	98.09 52	88.10 43	86.96 89	75.02 153	93.08 123	97.89 33	96.90 54	99.56 57	100.00 1

test2506		93.08 66	93.40 87	92.70 64	92.76 82	99.20 62	94.67 83	86.82 71	92.58 142	90.81 31	86.28 94	85.24 108	91.69 143	96.85 64	96.33 66	99.45 89	97.34 193

PMMVS		93.05 67	95.40 58	90.31 112	91.41 98	97.54 130	92.62 142	83.25 132	98.08 55	79.44 143	95.18 42	88.52 92	96.43 58	95.70 100	93.88 127	98.68 183	98.91 135

LS3D		92.70 68	92.23 99	93.26 54	96.24 60	98.72 72	97.93 41	96.17 10	96.41 80	72.46 161	81.39 125	80.76 126	97.66 32	95.69 101	95.62 101	99.07 151	97.02 202

baseline1		92.67 69	93.62 81	91.55 74	91.16 102	97.15 134	93.92 99	85.97 79	94.76 107	84.07 84	87.17 85	86.89 100	94.62 92	96.72 69	95.90 85	99.57 50	96.79 206

IS_MVSNet		92.67 69	94.99 65	89.96 119	91.17 101	98.54 81	92.77 133	84.00 124	92.72 140	81.90 118	85.67 103	92.47 73	90.39 157	97.82 38	97.81 25	99.51 68	99.91 25

TSAR-MVS + COLMAP		92.56 71	92.44 97	92.71 63	94.61 70	97.69 121	97.69 44	91.09 47	98.96 30	76.71 151	94.68 47	69.41 199	96.91 50	95.80 98	94.18 124	99.26 140	96.33 210

baseline		92.56 71	94.38 71	90.43 109	90.71 112	98.23 91	95.07 76	80.73 156	97.52 67	82.45 106	87.34 84	85.91 104	94.07 109	96.29 83	95.94 83	99.58 46	99.47 81

sasdasda		92.54 73	93.28 88	91.68 70	91.44 96	98.24 89	95.45 69	81.84 143	95.98 92	84.85 73	90.69 64	78.53 131	96.96 45	92.97 157	97.06 47	99.57 50	99.47 81

canonicalmvs		92.54 73	93.28 88	91.68 70	91.44 96	98.24 89	95.45 69	81.84 143	95.98 92	84.85 73	90.69 64	78.53 131	96.96 45	92.97 157	97.06 47	99.57 50	99.47 81

PatchMatch-RL		92.54 73	92.82 96	92.21 66	96.57 57	98.74 71	91.85 150	86.30 74	96.23 86	85.18 70	95.21 41	73.58 168	94.22 106	95.40 116	93.08 148	99.14 146	97.49 189

MVS_Test		92.42 76	94.43 67	90.08 118	90.69 113	98.26 88	94.78 80	80.81 155	97.27 69	78.76 144	87.06 87	84.25 112	95.84 70	97.67 44	97.56 33	99.59 41	98.93 134

MGCFI-Net		92.39 77	93.14 91	91.51 77	91.38 99	98.16 92	95.28 73	81.66 146	95.82 95	84.36 80	90.51 67	78.30 133	96.80 54	92.82 161	96.97 52	99.55 62	99.42 87

thisisatest0530		92.31 78	95.14 61	89.02 135	90.02 135	98.45 85	91.30 154	83.58 127	96.90 74	77.90 147	90.45 68	94.33 60	91.98 139	95.57 106	91.43 175	99.31 127	98.81 139

tttt0517		92.29 79	95.12 63	88.99 136	90.02 135	98.44 87	91.19 158	83.58 127	96.88 75	77.86 148	90.45 68	94.32 61	91.98 139	95.54 109	91.43 175	99.31 127	98.78 142

EPP-MVSNet		92.29 79	94.35 73	89.88 121	90.36 120	97.69 121	90.89 162	83.31 131	93.39 126	83.47 92	85.56 104	93.92 66	91.93 141	95.49 114	94.77 115	99.34 120	99.62 64

HQP-MVS		91.94 81	93.03 92	90.66 102	93.69 72	96.48 148	95.92 60	89.73 49	97.33 68	72.65 159	95.37 39	73.56 169	92.75 132	94.85 124	94.12 125	99.23 143	99.51 76

MSDG		91.93 82	90.28 139	93.85 50	97.36 50	97.12 135	95.88 62	94.07 41	94.52 112	84.13 83	76.74 156	80.89 125	92.54 134	93.97 141	93.61 138	99.14 146	95.10 226

UGNet		91.71 83	94.43 67	88.53 138	92.72 84	98.00 98	90.22 169	84.81 115	94.45 114	83.05 96	87.65 83	92.74 71	81.04 219	94.51 129	94.45 119	99.32 126	99.21 108
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 84	91.52 106	91.88 69	91.61 91	98.89 68	95.49 67	86.96 68	93.24 127	80.82 130	87.90 78	71.15 188	96.88 52	96.00 93	93.51 140	99.51 68	99.95 13

E2		91.67 85	91.90 103	91.41 79	90.00 138	98.06 94	93.59 105	85.55 83	93.75 120	84.70 75	82.50 122	77.16 134	95.17 74	96.33 81	96.16 75	99.46 83	99.35 94

CLD-MVS		91.67 85	91.30 112	92.10 67	91.25 100	96.59 145	95.93 59	87.25 67	96.86 76	85.55 68	87.08 86	73.01 175	93.26 121	93.07 155	92.84 154	99.34 120	99.68 53
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 87	94.96 66	87.65 141	72.75 252	97.24 133	95.29 71	82.73 135	96.81 77	78.49 146	95.30 40	90.48 87	97.23 39	91.60 176	94.31 120	99.43 95	99.01 131

tfpn200view9		91.47 88	91.31 110	91.65 72	91.61 91	98.69 74	95.03 77	86.17 75	93.24 127	80.82 130	87.90 78	71.15 188	96.80 54	95.53 110	92.82 156	99.47 77	99.88 29

Casviewmamba		91.42 89	91.45 109	91.38 80	90.16 128	97.86 104	94.22 91	85.43 93	95.30 99	84.02 86	79.08 138	76.50 138	95.02 79	96.70 70	95.91 84	99.45 89	98.80 140

CANet_DTU		91.36 90	95.75 56	86.23 154	92.31 88	98.71 73	95.60 66	78.41 172	98.20 48	56.48 227	94.38 48	87.96 95	95.11 76	96.89 62	96.07 76	99.48 75	98.01 178

thres200		91.36 90	91.19 114	91.55 74	91.60 93	98.69 74	94.98 78	86.17 75	92.16 148	80.76 134	87.66 82	71.15 188	96.35 60	95.53 110	93.23 146	99.47 77	99.92 24

FMVSNet3		91.25 92	92.13 101	90.21 113	85.64 183	93.14 191	95.29 71	80.09 158	96.40 81	85.74 65	77.13 149	86.81 101	94.98 81	97.19 56	97.11 44	99.55 62	97.13 199

thres400		91.24 93	91.01 123	91.50 78	91.56 94	98.77 70	94.66 85	86.41 73	91.87 154	80.56 135	87.05 88	71.01 191	96.35 60	95.67 102	92.82 156	99.48 75	99.88 29

PVSNet_Blended_VisFu		91.20 94	92.89 95	89.23 133	93.41 75	98.61 79	89.80 171	85.39 96	92.84 137	82.80 100	74.21 173	91.38 82	84.64 193	97.22 54	96.04 79	99.34 120	99.93 19

viewcassd2359sk11		91.16 95	91.10 121	91.23 85	89.96 141	97.99 99	93.45 111	85.49 85	92.46 145	84.03 85	80.13 131	75.86 146	94.99 80	95.98 94	96.00 80	99.44 93	99.29 99

DCV-MVSNet		91.15 96	92.00 102	90.17 117	90.78 110	92.23 208	93.70 102	81.17 152	95.16 102	82.98 97	89.46 72	83.31 115	93.98 114	91.79 175	92.87 151	98.41 201	99.18 112

DI_MVS_pp		91.11 97	91.47 107	90.68 101	90.01 137	97.77 110	95.87 63	83.56 129	94.72 108	82.12 113	68.46 196	87.46 96	93.07 125	96.46 77	95.73 93	99.47 77	99.71 48

hybridcas		91.06 98	90.80 126	91.36 81	90.05 133	97.82 105	94.34 86	85.32 98	94.61 110	84.46 77	77.14 148	75.28 151	94.53 95	96.52 74	95.42 106	99.46 83	98.84 138

diffmvs		91.05 99	91.15 115	90.93 93	90.15 129	97.79 107	94.05 94	85.45 90	95.63 96	81.95 117	80.45 130	73.01 175	94.47 97	95.56 107	95.89 86	99.49 72	99.72 46
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 99	94.43 67	87.11 143	91.05 104	97.99 99	92.53 144	83.82 126	92.71 141	76.28 152	84.50 111	92.43 74	79.52 224	97.24 53	97.68 27	99.43 95	98.45 158

thres600view7		90.97 101	90.70 130	91.30 83	91.53 95	98.69 74	94.33 87	86.17 75	91.75 156	80.19 137	86.06 98	70.90 192	96.10 65	95.53 110	92.08 167	99.47 77	99.86 33

viewdifsd2359ckpt09		90.94 102	91.04 122	90.82 96	89.85 147	97.92 103	93.33 119	85.35 97	92.89 134	81.87 119	79.68 133	75.67 149	95.08 77	96.17 85	95.76 91	99.42 98	99.20 110

baseline2		90.91 103	94.40 70	86.84 146	87.54 174	96.83 141	89.95 170	79.22 166	96.00 91	77.04 150	88.68 73	89.73 89	88.01 178	96.35 80	93.51 140	99.29 130	99.68 53

hybrid		90.90 104	91.12 118	90.63 106	90.26 125	97.66 125	92.76 134	85.22 107	96.34 84	82.32 110	81.25 126	73.73 167	93.86 115	93.70 147	95.69 97	99.38 106	99.50 78

onestephybrid01		90.88 105	90.91 125	90.85 94	90.31 123	97.71 118	92.72 138	85.46 89	95.97 94	83.61 91	78.95 139	75.19 152	93.62 118	93.08 154	95.68 98	99.37 108	99.79 37

hybridnocas07		90.86 106	90.94 124	90.76 99	90.34 121	97.71 118	93.08 127	85.27 103	96.19 88	82.38 109	81.06 127	73.79 166	94.13 108	92.73 163	95.63 100	99.38 106	99.43 86

casdiffmvs_mvg		90.83 107	90.52 134	91.20 87	90.56 114	97.67 124	94.96 79	85.45 90	90.72 166	82.03 114	76.70 157	77.08 135	94.61 93	96.57 73	95.62 101	99.57 50	99.28 100
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewmamba		90.78 108	90.80 126	90.77 98	90.34 121	97.68 123	93.09 126	85.20 108	96.57 78	81.81 120	80.08 132	74.29 163	94.77 87	92.01 171	95.53 105	99.39 105	99.44 85

ACMP	89.80 9	90.72 109	91.15 115	90.21 113	92.55 86	96.52 147	92.63 141	85.71 81	94.65 109	81.06 127	93.32 53	70.56 196	90.52 156	92.68 164	91.05 182	98.76 175	99.31 98
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
casdiffmvs		90.69 110	90.56 133	90.85 94	90.14 130	97.81 106	92.94 130	85.30 99	93.47 124	82.50 105	76.34 161	74.12 164	94.67 88	96.51 75	96.26 69	99.55 62	99.42 87
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
FA-MVS(training)		90.67 111	93.03 92	87.92 140	90.95 106	98.45 85	92.61 143	66.04 243	94.90 104	84.47 76	77.52 147	91.74 78	94.07 109	97.11 58	92.46 164	99.40 103	99.03 128

viewmanbaseed2359cas		90.60 112	90.74 129	90.44 108	90.21 127	98.01 97	93.39 114	85.57 82	92.53 144	79.63 141	78.77 140	74.90 156	94.37 104	95.55 108	96.19 72	99.45 89	99.20 110

E3new		90.58 113	90.21 142	91.01 89	89.89 146	97.93 101	93.35 118	85.40 95	90.82 165	83.22 94	77.64 145	74.60 158	94.80 85	95.38 118	95.85 87	99.37 108	99.23 103

ACMM	89.40 10	90.58 113	90.02 145	91.23 85	93.30 77	94.75 173	90.69 165	88.22 58	95.20 100	82.70 102	88.54 74	71.40 186	93.48 120	93.64 149	90.94 183	98.99 157	95.72 221
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
E3		90.56 115	90.22 141	90.96 92	89.90 145	97.93 101	93.37 115	85.41 94	90.85 164	82.94 99	77.63 146	74.66 157	94.78 86	95.39 117	95.84 89	99.37 108	99.23 103

GBi-Net		90.49 116	91.12 118	89.75 124	84.99 187	92.73 196	93.94 96	80.09 158	96.40 81	85.74 65	77.13 149	86.81 101	94.42 98	94.12 135	93.73 129	99.35 116	96.90 203

test1		90.49 116	91.12 118	89.75 124	84.99 187	92.73 196	93.94 96	80.09 158	96.40 81	85.74 65	77.13 149	86.81 101	94.42 98	94.12 135	93.73 129	99.35 116	96.90 203

viewdifsd2359ckpt13		90.44 118	90.52 134	90.35 111	89.94 143	98.06 94	92.84 131	85.47 86	92.33 147	79.93 139	77.99 141	74.39 161	94.49 96	96.09 88	95.76 91	99.44 93	99.03 128

diffmvs_AUTHOR		90.43 119	90.26 140	90.64 103	90.00 138	97.72 116	93.72 101	85.18 109	94.49 113	81.20 126	77.72 142	71.57 183	94.30 105	94.78 125	95.85 87	99.42 98	99.66 55

viewdifsd2359ckpt07		90.42 120	90.45 137	90.39 110	90.14 130	97.76 112	93.31 120	85.51 84	91.60 158	80.95 128	77.01 153	76.13 145	93.04 126	96.50 76	95.66 99	99.41 101	98.48 156

ECVR-MVS		90.37 121	88.96 162	92.01 68	92.76 82	99.20 62	94.67 83	86.82 71	92.58 142	86.71 52	68.95 195	71.46 185	91.69 143	96.85 64	96.33 66	99.45 89	97.38 192

LGP-MVS_train		90.34 122	91.63 105	88.83 137	93.31 76	96.14 154	95.49 67	85.24 105	93.91 118	68.71 173	93.96 49	71.63 182	91.12 152	93.82 144	92.79 158	99.07 151	99.16 115

viewmambaseed2359dif		90.29 123	89.69 147	90.98 91	90.03 134	97.61 127	93.96 95	85.18 109	93.22 129	82.97 98	76.79 155	74.32 162	94.41 101	91.14 182	95.02 110	99.33 125	99.74 42

test1111		90.01 124	88.67 167	91.57 73	92.68 85	99.20 62	94.25 90	86.90 70	92.03 151	85.04 71	67.79 200	71.21 187	91.12 152	96.83 66	96.34 65	99.42 98	97.28 195

dtuplus		89.91 125	89.13 157	90.82 96	89.75 152	97.55 128	93.24 121	85.10 111	91.04 162	82.64 103	75.46 165	74.51 160	94.91 83	90.75 185	94.99 112	99.29 130	99.60 66

E5new		89.87 126	89.21 155	90.64 103	89.76 149	97.78 108	93.37 115	85.47 86	88.83 178	81.32 123	75.36 166	73.09 172	94.63 89	94.54 127	95.71 95	99.29 130	99.17 113

E5		89.87 126	89.21 155	90.64 103	89.76 149	97.78 108	93.37 115	85.47 86	88.83 178	81.32 123	75.36 166	73.09 172	94.63 89	94.54 127	95.71 95	99.29 130	99.17 113

EPNet_dtu		89.82 128	94.18 76	84.74 164	96.87 55	95.54 166	92.65 140	86.91 69	96.99 71	54.17 238	92.41 60	88.54 91	78.35 227	96.15 87	96.05 78	99.47 77	93.60 235
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		89.81 129	89.75 146	89.88 121	93.22 79	93.99 184	94.78 80	85.23 106	94.01 117	82.52 104	95.00 43	87.23 98	92.01 138	85.16 238	83.48 243	91.54 259	89.38 252

E4		89.71 130	89.07 159	90.45 107	89.76 149	97.77 110	93.15 124	85.26 104	88.58 183	80.86 129	74.87 169	73.08 174	94.38 103	94.44 130	95.61 103	99.29 130	99.14 116

MDTV_nov1_ep13		89.63 131	94.38 71	84.09 172	88.76 166	97.53 131	89.37 179	68.46 241	96.95 72	70.27 168	87.88 80	93.67 69	91.04 154	93.12 152	93.83 128	96.62 236	97.68 185

UA-Net		89.56 132	93.03 92	85.52 160	92.46 87	97.55 128	91.92 148	81.91 140	85.24 202	71.39 163	83.57 117	96.56 47	76.01 238	96.81 67	97.04 49	99.46 83	94.41 230

E6new		89.53 133	88.95 163	90.21 113	89.75 152	97.74 114	92.76 134	84.66 118	88.63 181	80.77 132	74.83 170	72.74 178	94.07 109	94.20 132	95.39 107	99.27 138	99.10 123

E6		89.53 133	88.95 163	90.21 113	89.75 152	97.74 114	92.76 134	84.66 118	88.63 181	80.77 132	74.83 170	72.74 178	94.07 109	94.20 132	95.39 107	99.27 138	99.10 123

FMVSNet2		89.51 135	89.63 148	89.38 130	84.99 187	92.73 196	93.94 96	79.28 165	93.73 121	84.28 81	69.36 193	82.32 118	94.42 98	96.16 86	96.22 71	99.35 116	96.90 203

0.3-1-1-0.015		89.49 136	89.45 151	89.53 126	81.16 216	94.36 181	93.56 108	84.71 116	93.21 130	86.01 56	85.38 105	76.34 139	94.39 102	85.97 231	92.53 163	97.35 230	98.35 162

CostFormer		89.42 137	91.67 104	86.80 148	89.99 140	96.33 150	90.75 163	64.79 245	95.17 101	83.62 90	86.20 96	82.15 120	92.96 127	89.22 201	92.94 149	98.68 183	99.65 56

0.4-1-1-0.2		89.40 138	89.35 153	89.46 129	81.13 217	94.37 180	93.62 104	84.58 120	93.20 131	85.95 62	84.67 107	76.32 143	94.14 107	85.99 230	92.56 162	97.36 229	98.35 162

FC-MVSNet-train		89.37 139	89.62 149	89.08 134	90.48 116	94.16 183	89.45 175	83.99 125	91.09 161	80.09 138	82.84 121	74.52 159	91.44 149	93.79 145	91.57 173	99.01 155	99.35 94

OPM-MVS		89.33 140	87.45 179	91.53 76	94.49 71	96.20 152	96.47 58	89.72 50	82.77 209	75.43 153	80.53 129	70.86 194	93.80 116	94.00 139	91.85 171	99.29 130	95.91 215
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
viewmacassd2359aftdt		89.31 141	88.92 165	89.76 123	89.95 142	97.76 112	93.06 128	85.30 99	88.99 177	77.33 149	73.96 175	73.12 171	93.55 119	93.79 145	95.80 90	99.36 114	99.02 130

test-LLR		89.31 141	93.60 83	84.30 168	88.08 170	96.98 137	88.10 185	78.00 173	94.83 105	62.43 200	84.29 114	90.96 83	89.70 163	95.63 104	92.86 152	99.51 68	99.64 58

EPMVS		89.31 141	93.70 80	84.18 170	91.10 103	98.10 93	89.17 181	62.71 249	96.24 85	70.21 170	86.46 93	92.37 75	92.79 130	91.95 173	93.59 139	99.10 148	97.19 196

0.4-1-1-0.1		89.28 144	89.22 154	89.36 131	81.12 218	94.34 182	93.49 109	84.24 123	93.17 132	85.92 63	84.41 112	76.32 143	94.04 113	85.88 233	92.10 166	97.33 231	98.32 164

Anonymous20231211		89.22 145	87.56 177	91.16 88	90.23 126	96.62 144	93.22 123	85.44 92	92.89 134	84.37 79	60.13 223	81.25 123	96.02 67	90.61 186	92.01 168	97.70 222	99.41 90

Effi-MVS+		88.96 146	91.13 117	86.43 152	89.12 162	97.62 126	93.15 124	75.52 199	93.90 119	66.40 179	86.23 95	70.51 197	95.03 78	95.89 95	94.28 121	99.37 108	99.51 76

SCA		88.76 147	94.29 74	82.30 191	89.33 160	96.81 142	87.68 187	61.52 255	96.95 72	64.68 190	88.35 75	94.80 53	91.58 146	92.23 166	93.21 147	98.99 157	97.70 184

test0.0.03 1		88.71 148	92.22 100	84.63 166	88.08 170	94.71 175	85.91 210	78.00 173	95.54 97	72.96 157	86.10 97	85.88 106	83.59 205	92.95 160	93.24 145	99.25 142	97.09 200

PatchmatchNet		88.67 149	94.10 77	82.34 190	89.38 159	97.72 116	87.24 193	62.18 253	97.00 70	64.79 189	87.97 77	94.43 58	91.55 147	91.21 181	92.77 159	98.90 162	97.60 188
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dps		88.66 150	90.19 143	86.88 145	89.94 143	96.48 148	89.56 173	64.08 247	94.12 116	89.00 39	83.39 118	82.56 117	90.16 160	86.81 226	89.26 202	98.53 196	98.71 144

TESTMET0.1,1		88.63 151	93.60 83	82.84 185	84.07 195	96.98 137	88.10 185	73.22 220	94.83 105	62.43 200	84.29 114	90.96 83	89.70 163	95.63 104	92.86 152	99.51 68	99.64 58

CHOSEN 1792x2688		88.63 151	89.01 160	88.19 139	94.83 68	99.21 61	92.66 139	79.85 162	92.40 146	72.18 162	56.38 243	80.22 128	90.24 158	97.64 47	97.28 41	99.37 108	99.94 16

CDS-MVSNet		88.59 153	90.13 144	86.79 149	86.98 179	95.43 167	92.03 146	81.33 150	85.54 199	74.51 156	77.07 152	85.14 109	87.03 183	93.90 142	95.18 109	98.88 165	98.67 147
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
viewdifsd2359ckpt11		88.57 154	87.77 175	89.51 127	89.74 155	95.73 162	91.01 160	85.05 113	92.88 136	82.40 108	77.72 142	70.86 194	92.86 128	87.17 217	91.36 179	95.98 250	98.64 148

viewmsd2359difaftdt		88.57 154	87.76 176	89.51 127	89.74 155	95.73 162	91.01 160	85.05 113	92.79 138	82.43 107	77.72 142	70.90 192	92.85 129	87.16 218	91.37 178	95.98 250	98.64 148

casdiffseed414692147		88.35 156	86.99 182	89.95 120	89.67 157	97.32 132	92.02 147	84.43 122	87.86 185	80.50 136	69.80 191	67.01 204	93.79 117	93.52 150	94.70 118	99.30 129	98.70 145

IB-MVS	84.67 14	88.34 157	90.61 132	85.70 157	92.99 81	98.62 78	78.85 242	86.07 78	94.35 115	88.64 40	85.99 99	75.69 148	68.09 253	88.21 205	91.43 175	99.55 62	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		88.25 158	93.27 90	82.38 189	83.89 196	96.86 140	87.10 197	72.80 222	94.58 111	61.85 205	83.21 119	90.65 86	89.18 167	95.43 115	92.58 161	99.46 83	99.61 65

COLMAP_ROB	84.42 15	88.24 159	87.32 181	89.32 132	95.83 62	95.82 158	92.81 132	87.68 64	92.09 150	72.64 160	72.34 182	79.96 129	88.79 169	89.54 196	89.46 198	98.16 211	92.00 241
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
IterMVS-LS		87.95 160	89.40 152	86.26 153	88.79 165	90.93 223	91.23 156	76.05 196	90.87 163	71.07 165	75.51 164	81.18 124	91.21 151	94.11 138	95.01 111	99.20 145	98.23 168
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		87.86 161	88.25 170	87.40 142	94.67 69	98.54 81	90.33 168	76.51 195	89.60 175	70.89 166	51.43 254	85.69 107	92.79 130	96.59 72	95.96 82	99.22 144	99.94 16

Vis-MVSNet		87.60 162	91.31 110	83.27 180	89.14 161	98.04 96	90.35 167	79.42 163	87.23 187	66.92 177	79.10 137	84.63 111	74.34 245	95.81 97	96.06 77	99.46 83	98.32 164
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GeoE		87.55 163	88.17 171	86.82 147	88.74 167	96.32 151	92.75 137	74.93 204	90.13 171	72.73 158	69.47 192	74.03 165	92.51 135	93.99 140	93.62 137	99.29 130	99.59 67

dmvs_re		87.43 164	87.99 172	86.77 150	84.94 191	96.19 153	91.87 149	85.95 80	91.25 160	68.58 174	81.45 124	66.04 207	89.95 162	90.91 183	91.57 173	99.37 108	98.54 153

RPMNet		87.35 165	92.41 98	81.45 195	88.85 164	96.06 155	89.42 178	59.59 262	93.57 122	61.81 206	76.48 160	91.48 81	90.18 159	96.32 82	93.37 143	98.87 166	99.59 67

tpm cat1		87.34 166	88.52 169	85.95 155	89.83 148	95.80 159	90.73 164	64.91 244	92.99 133	82.21 112	71.19 188	82.68 116	90.13 161	86.38 227	90.87 185	97.90 219	99.74 42

MS-PatchMatch		87.19 167	88.59 168	85.55 159	93.15 80	96.58 146	92.35 145	74.19 212	91.97 153	70.33 167	71.42 186	85.89 105	84.28 195	93.12 152	89.16 204	99.00 156	91.99 242

Effi-MVS+-dtu		87.18 168	90.48 136	83.32 179	86.51 180	95.76 161	91.16 159	74.28 211	90.44 170	61.31 209	86.72 92	72.68 180	91.25 150	95.01 122	93.64 132	95.45 252	99.12 120

FMVSNet5		87.06 169	89.52 150	84.20 169	79.92 237	86.57 252	87.11 196	72.37 224	96.06 89	75.41 154	84.33 113	91.76 77	91.60 145	91.51 177	91.22 180	98.77 172	85.16 258

Fast-Effi-MVS+-dtu		86.94 170	91.27 113	81.89 192	86.27 181	95.06 168	90.68 166	68.93 238	91.76 155	57.18 225	89.56 71	75.85 147	89.19 166	94.56 126	92.84 154	99.07 151	99.23 103

Fast-Effi-MVS+		86.94 170	87.88 174	85.84 156	86.99 178	95.80 159	91.24 155	73.48 219	92.75 139	69.22 171	72.70 180	65.71 208	94.84 84	94.98 123	94.71 116	99.26 140	98.48 156

tpmrst		86.78 172	90.29 138	82.69 186	90.55 115	96.95 139	88.49 183	62.58 250	95.09 103	63.52 196	76.67 159	84.00 114	92.05 137	87.93 208	91.89 170	98.98 159	99.50 78

CR-MVSNet		86.73 173	91.47 107	81.20 198	88.56 168	96.06 155	89.43 176	61.37 256	93.57 122	60.81 211	72.89 179	88.85 90	88.13 176	96.03 90	93.64 132	98.89 164	99.22 106

ADS-MVSNet		86.68 174	90.79 128	81.88 193	90.38 119	96.81 142	86.90 198	60.50 260	96.01 90	63.93 193	81.67 123	84.72 110	90.78 155	87.03 220	91.67 172	98.77 172	97.63 187

blend_shiyan4		86.12 175	85.60 191	86.72 151	81.42 211	88.06 241	93.87 100	77.81 181	93.43 125	86.01 56	85.86 100	76.34 139	84.87 190	81.26 248	78.21 250	96.36 239	96.04 211

dtuonly		85.87 176	87.38 180	84.10 171	85.14 186	94.42 179	91.20 157	78.74 171	86.23 194	59.39 219	68.98 194	76.54 137	91.83 142	93.48 151	94.00 126	98.31 208	98.03 177

FMVSNet1		85.85 177	84.91 194	86.96 144	82.70 201	91.39 217	91.54 152	77.45 185	85.29 201	79.56 142	60.70 220	72.68 180	92.37 136	94.12 135	93.73 129	98.12 212	96.44 207

FC-MVSNet-test		85.51 178	89.08 158	81.35 196	85.31 185	93.35 187	87.65 188	77.55 183	90.01 173	64.07 192	79.63 135	81.83 122	74.94 242	92.08 169	90.83 187	98.55 193	95.81 216

ACMH	85.22 13	85.40 179	85.73 190	85.02 162	91.76 90	94.46 178	84.97 222	81.54 148	85.18 203	65.22 185	76.92 154	64.22 214	88.58 172	90.17 188	90.25 194	98.03 215	98.90 136
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		85.35 180	86.00 189	84.59 167	84.97 190	95.57 165	88.98 182	77.29 190	81.44 214	71.36 164	71.48 185	75.00 155	87.03 183	91.92 174	92.21 165	97.92 218	94.40 231

ACMH+	85.62 12	85.27 181	84.96 193	85.64 158	90.84 108	94.78 172	87.46 190	81.30 151	86.94 188	67.35 176	74.56 172	64.09 215	88.70 170	88.14 206	89.00 205	98.22 210	97.19 196

USDC		85.11 182	85.35 192	84.83 163	89.45 158	94.93 171	92.98 129	77.30 188	90.53 168	61.80 207	76.69 158	59.62 225	88.90 168	92.78 162	90.79 189	98.53 196	92.12 239

IterMVS		85.02 183	88.98 161	80.41 204	87.03 177	90.34 231	89.78 172	69.45 235	89.77 174	54.04 239	73.71 176	82.05 121	83.44 208	95.11 120	93.64 132	98.75 176	98.22 170
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		84.91 184	88.90 166	80.25 207	87.04 176	90.27 232	89.23 180	69.25 237	89.17 176	54.04 239	73.65 177	82.22 119	83.23 213	95.11 120	93.63 136	98.73 177	98.23 168

PatchT		84.89 185	90.67 131	78.13 232	87.83 173	94.99 170	72.46 256	60.22 261	91.74 157	60.81 211	72.16 183	86.95 99	88.13 176	96.03 90	93.64 132	99.36 114	99.22 106

pmmvs4		84.88 186	84.67 195	85.13 161	82.80 200	92.37 201	87.29 191	79.08 167	90.51 169	74.94 155	70.37 189	62.49 218	88.17 175	92.01 171	88.51 210	98.49 199	96.44 207

usedtu_dtu_shiyan1		84.68 187	86.53 185	82.52 187	74.54 248	93.47 186	88.46 184	81.15 153	90.11 172	66.48 178	69.83 190	73.29 170	85.61 186	93.85 143	90.47 192	98.90 162	94.73 229

CVMVSNet		84.01 188	86.91 183	80.61 202	88.39 169	93.29 188	86.06 206	82.29 137	83.13 207	54.29 235	72.68 181	79.59 130	75.11 241	91.23 180	92.91 150	97.54 227	95.58 223

tpm		83.97 189	87.97 173	79.31 217	87.35 175	93.21 190	86.00 208	61.90 254	90.69 167	54.01 241	79.42 136	75.61 150	88.65 171	87.18 216	90.48 191	97.95 217	99.21 108

GA-MVS		83.83 190	86.63 184	80.58 203	85.40 184	94.73 174	87.27 192	78.76 170	86.49 190	49.57 252	74.21 173	67.67 202	83.38 209	95.28 119	90.92 184	99.08 150	97.09 200

UniMVSNet_NR-MVSNet		83.83 190	83.70 198	83.98 173	81.41 212	92.56 200	86.54 201	82.96 133	85.98 196	66.27 180	66.16 204	63.63 216	87.78 180	87.65 211	90.81 188	98.94 160	99.13 118

usedtu_blend_shiyan5		83.68 192	83.60 199	83.79 175	64.08 257	87.81 242	93.63 103	77.82 177	79.98 226	86.01 56	85.86 100	76.34 139	84.87 190	81.05 250	78.09 251	96.30 240	96.04 211

UniMVSNet (Re)		83.28 193	83.16 201	83.42 178	81.93 206	93.12 192	86.27 204	80.83 154	85.88 197	68.23 175	64.56 212	60.58 220	84.25 196	89.13 202	89.44 200	99.04 154	99.40 91

thisisatest0515		83.17 194	86.49 186	79.30 218	82.04 204	93.12 192	78.70 243	77.92 175	86.43 191	63.05 197	74.91 168	73.01 175	75.56 240	92.10 168	88.05 223	98.50 198	97.76 182

FE-MVSNET3		83.03 195	83.56 200	82.42 188	64.08 257	87.81 242	85.44 215	77.82 177	79.99 224	86.01 56	85.86 100	76.34 139	84.87 190	81.05 250	78.09 251	96.30 240	95.79 219

TinyColmap		83.03 195	82.24 205	83.95 174	88.88 163	93.22 189	89.48 174	76.89 192	87.53 186	62.12 202	68.46 196	55.03 241	88.43 174	90.87 184	89.65 196	97.89 220	90.91 246

testgi		82.88 197	86.14 188	79.08 220	86.05 182	92.20 209	81.23 239	74.77 207	88.70 180	57.63 224	86.73 91	61.53 219	76.83 235	90.33 187	89.43 201	97.99 216	94.05 232

DU-MVS		82.87 198	82.16 206	83.70 177	80.77 224	92.24 205	86.54 201	81.91 140	86.41 192	66.27 180	63.95 213	55.66 239	87.78 180	86.83 223	90.86 186	98.94 160	99.13 118

MIMVSNet		82.87 198	86.17 187	79.02 221	77.23 245	92.88 195	84.88 223	60.62 259	86.72 189	64.16 191	73.58 178	71.48 184	88.51 173	94.14 134	93.50 142	98.72 179	90.87 248

NR-MVSNet		82.37 200	81.95 208	82.85 184	82.56 203	92.24 205	87.49 189	81.91 140	86.41 192	65.51 183	63.95 213	52.93 250	80.80 221	89.41 198	89.61 197	98.85 168	99.10 123

Baseline_NR-MVSNet		82.08 201	80.64 215	83.77 176	80.77 224	88.50 238	86.88 199	81.71 145	85.58 198	68.80 172	58.20 235	57.75 231	86.16 185	86.83 223	88.68 207	98.33 206	98.90 136

TranMVSNet+NR-MVSNet		82.07 202	81.36 211	82.90 183	80.43 230	91.39 217	87.16 195	82.75 134	84.28 205	62.98 198	62.28 219	56.01 238	85.30 189	86.06 229	90.69 190	98.80 169	98.80 140

pm-mvs1		81.68 203	81.70 209	81.65 194	82.61 202	92.26 204	85.54 214	78.95 168	76.29 244	63.81 194	58.43 234	66.33 205	80.63 222	92.30 165	89.93 195	98.37 205	96.39 209

TDRefinement		81.49 204	80.08 221	83.13 182	91.02 105	94.53 176	91.66 151	82.43 136	81.70 212	62.12 202	62.30 218	59.32 226	73.93 246	87.31 214	85.29 234	97.61 223	90.14 250

anonymousdsp		81.29 205	84.52 197	77.52 234	79.83 238	92.62 199	82.61 234	70.88 231	80.76 218	50.82 248	68.35 198	68.76 200	82.45 216	93.00 156	89.45 199	98.55 193	98.69 146

gg-mvs-nofinetune		81.27 206	84.65 196	77.32 235	87.96 172	98.48 84	95.64 65	56.36 265	59.35 265	32.80 271	47.96 258	92.11 76	91.49 148	98.12 27	97.00 51	99.65 26	99.56 72

tfpnnormal		81.11 207	79.33 229	83.19 181	84.23 193	92.29 203	86.76 200	82.27 138	72.67 250	62.02 204	56.10 245	53.86 247	85.35 188	92.06 170	89.23 203	98.49 199	99.11 122

UniMVSNet_ETH3D		80.95 208	77.71 242	84.74 164	84.45 192	93.11 194	86.45 203	79.97 161	75.21 246	70.22 169	51.24 255	50.26 256	89.55 165	84.47 240	91.12 181	97.81 221	98.53 154

V42		80.88 209	80.74 213	81.05 199	81.21 215	92.01 211	85.96 209	77.75 182	81.62 213	59.73 218	59.93 226	58.35 230	82.98 215	86.90 222	88.06 222	98.69 182	98.32 164

v2v482		80.86 210	80.52 219	81.25 197	80.79 223	91.85 212	85.68 212	78.78 169	81.05 215	58.09 222	60.46 221	56.08 236	85.45 187	87.27 215	88.53 209	98.73 177	98.38 161

v8		80.61 211	80.61 217	80.62 201	81.51 209	91.00 222	86.06 206	74.07 215	81.78 211	59.93 217	60.10 225	58.42 229	83.35 210	86.99 221	88.11 220	98.79 170	97.83 180

pmmvs5		80.48 212	81.43 210	79.36 216	81.50 210	92.24 205	82.07 237	74.08 214	78.10 237	55.86 230	67.72 201	54.35 244	83.91 204	92.97 157	88.65 208	98.77 172	96.01 213

v10		80.38 213	80.73 214	79.96 209	81.22 214	90.40 230	86.11 205	71.63 227	82.42 210	57.65 223	58.74 232	57.47 232	84.44 194	89.75 192	88.28 213	98.71 180	98.06 176

v1144		80.36 214	80.63 216	80.05 208	80.86 222	91.56 215	85.78 211	75.22 201	80.73 219	55.83 231	58.51 233	56.99 234	83.93 203	89.79 191	88.25 214	98.68 183	98.56 152

SixPastTwentyTwo		80.28 215	82.06 207	78.21 231	81.89 208	92.35 202	77.72 244	74.48 208	83.04 208	54.22 236	76.06 162	56.40 235	83.55 206	86.83 223	84.83 237	97.38 228	94.93 227

CP-MVSNet		79.90 216	79.49 226	80.38 205	80.72 226	90.83 224	82.98 231	75.17 202	79.70 230	61.39 208	59.74 227	51.98 253	83.31 211	87.37 213	88.38 211	98.71 180	98.45 158

v1192		79.84 217	80.05 223	79.61 212	80.49 229	91.04 221	85.56 213	74.37 210	80.73 219	54.35 234	57.07 240	54.54 243	84.23 197	89.94 189	88.38 211	98.63 187	98.61 150

WR-MVS_H		79.76 218	80.07 222	79.40 215	81.25 213	91.73 214	82.77 232	74.82 206	79.02 235	62.55 199	59.41 229	57.32 233	76.27 237	87.61 212	87.30 228	98.78 171	98.09 174

WR-MVS		79.67 219	80.25 220	79.00 222	80.65 227	91.16 219	83.31 229	76.57 194	80.97 216	60.50 216	59.20 230	58.66 228	74.38 244	85.85 234	87.76 225	98.61 188	98.14 171

v148		79.66 220	79.13 232	80.27 206	81.02 220	91.76 213	81.90 238	79.32 164	79.24 233	63.79 195	58.07 237	54.34 245	77.17 233	84.42 241	87.52 227	98.40 202	98.59 151

LTVRE_ROB	79.45 16	79.66 220	80.55 218	78.61 229	83.01 199	92.19 210	87.18 194	73.69 218	71.70 253	43.22 265	71.22 187	50.85 254	87.82 179	89.47 197	90.43 193	96.75 234	98.00 179
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		79.61 222	79.77 224	79.41 214	80.28 231	91.06 220	84.87 224	73.86 216	79.65 231	55.38 232	57.76 238	55.20 240	83.46 207	88.42 204	87.89 224	98.61 188	98.42 160

v1921920		79.55 223	79.77 224	79.30 218	80.24 232	90.77 226	85.37 219	73.75 217	80.38 221	53.78 242	56.89 242	54.18 246	84.05 201	89.55 195	88.13 219	98.59 190	98.52 155

TransMVSNet (Re)		79.51 224	78.36 238	80.84 200	83.17 197	89.72 234	84.22 227	81.45 149	73.98 249	60.79 214	57.20 239	56.05 237	77.11 234	89.88 190	88.86 206	98.30 209	92.83 237

MVS-HIRNet		79.34 225	82.56 202	75.57 240	84.11 194	95.02 169	75.03 252	57.28 264	85.50 200	55.88 229	53.00 251	70.51 197	83.05 214	92.12 167	91.96 169	98.09 213	89.83 251

PS-CasMVS		79.06 226	78.58 237	79.63 211	80.59 228	90.55 228	82.54 235	75.04 203	77.76 238	58.84 220	58.16 236	50.11 258	82.09 218	87.05 219	88.18 217	98.66 186	98.27 167

gbinet_0.2-2-1-0.02		79.05 227	79.33 229	78.73 226	64.88 255	87.74 248	85.16 221	77.52 184	79.51 232	66.15 182	64.75 211	66.08 206	82.42 217	81.26 248	78.24 249	96.25 246	97.75 183

v1240		78.97 228	79.27 231	78.63 228	80.04 233	90.61 227	84.25 226	72.95 221	79.22 234	52.70 244	56.22 244	52.88 252	83.28 212	89.60 194	88.20 216	98.56 192	98.14 171

pmnet_mix02		78.91 229	81.17 212	76.28 239	81.91 207	90.82 225	74.25 253	77.87 176	86.17 195	49.04 253	67.97 199	62.93 217	77.40 231	82.75 246	82.11 245	97.18 232	95.42 224

wanda-best-256-512		78.88 230	78.96 233	78.78 223	64.08 257	87.81 242	85.44 215	77.82 177	79.99 224	64.86 186	65.31 205	64.67 210	84.16 198	81.05 250	78.09 251	96.30 240	95.81 216

FE-blended-shiyan7		78.88 230	78.96 233	78.78 223	64.08 257	87.81 242	85.44 215	77.82 177	79.98 226	64.86 186	65.31 205	64.66 211	84.16 198	81.05 250	78.09 251	96.30 240	95.81 216

MDTV_nov1_ep13_2view		78.83 232	82.35 203	74.73 243	78.65 240	91.51 216	79.18 241	62.52 251	84.51 204	52.51 245	67.49 202	67.29 203	78.90 225	85.52 236	86.34 231	96.62 236	93.76 233

PEN-MVS		78.80 233	78.13 240	79.58 213	80.03 234	89.67 235	83.61 228	75.83 197	77.71 240	58.41 221	60.11 224	50.00 259	81.02 220	84.08 242	88.14 218	98.59 190	97.18 198

blended_shiyan8		78.78 234	78.79 236	78.77 225	64.07 261	87.81 242	85.39 218	77.38 187	79.94 228	65.37 184	64.85 209	64.30 213	84.14 200	80.95 255	77.97 256	96.26 245	95.72 221

blended_shiyan6		78.78 234	78.90 235	78.64 227	64.04 262	87.78 247	85.34 220	77.30 188	79.93 229	64.84 188	65.18 208	64.66 211	84.03 202	80.99 254	78.00 255	96.27 244	95.79 219

EG-PatchMatch MVS		78.32 236	79.42 228	77.03 237	83.03 198	93.77 185	84.47 225	69.26 236	75.85 245	53.69 243	55.68 246	60.23 223	73.20 248	89.69 193	88.22 215	98.55 193	92.54 238

DTE-MVSNet		77.92 237	77.42 243	78.51 230	79.34 239	89.00 237	83.05 230	75.60 198	76.89 242	56.58 226	59.63 228	50.31 255	78.09 230	82.57 247	87.56 226	98.38 203	95.95 214

v7n		77.71 238	78.25 239	77.09 236	78.49 241	90.55 228	82.15 236	71.11 230	76.79 243	54.18 237	55.63 247	50.20 257	78.28 228	89.36 200	87.15 229	98.33 206	98.07 175

gm-plane-assit		77.20 239	82.26 204	71.30 248	81.10 219	82.00 261	54.33 269	64.41 246	63.80 264	40.93 268	59.04 231	76.57 136	87.30 182	98.26 25	97.36 40	99.74 15	98.76 143

N_pmnet		76.83 240	77.97 241	75.50 241	80.96 221	88.23 240	72.81 254	76.83 193	80.87 217	50.55 249	56.94 241	60.09 224	75.70 239	83.28 244	84.23 239	96.14 248	92.12 239

pmmvs6		76.79 241	75.69 249	78.09 233	79.95 236	89.57 236	80.92 240	74.46 209	64.79 262	60.74 215	45.71 260	60.55 221	78.37 226	88.04 207	86.00 232	94.07 256	95.15 225

CMPMVS	58.73 17	76.78 242	74.27 250	79.70 210	93.26 78	95.58 164	82.74 233	77.44 186	71.46 256	56.29 228	53.58 250	59.13 227	77.33 232	79.20 256	79.71 248	91.14 261	81.24 261
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		76.76 243	79.47 227	73.60 244	79.99 235	87.47 250	77.39 245	75.43 200	77.62 241	47.83 256	64.78 210	60.44 222	64.80 254	86.28 228	86.53 230	96.17 247	93.19 236

PM-MVS		75.81 244	76.11 248	75.46 242	73.81 249	85.48 255	76.42 247	70.57 232	80.05 223	54.75 233	62.33 217	39.56 269	80.59 223	87.71 210	82.81 244	96.61 238	94.81 228

pmmvs-eth3d		75.17 245	74.09 251	76.43 238	72.92 250	84.49 257	76.61 246	72.42 223	74.33 247	61.28 210	54.71 249	39.42 270	78.20 229	87.77 209	84.25 238	97.17 233	93.63 234

dtuonlycased		75.07 246	76.40 246	73.51 245	75.65 246	87.67 249	72.62 255	71.52 228	78.23 236	52.32 246	63.34 215	65.19 209	73.66 247	73.36 258	75.24 258	95.31 253	91.75 243

Anonymous20231206		74.59 247	77.00 244	71.78 246	77.89 244	87.45 251	75.14 251	72.29 225	77.76 238	46.65 258	52.14 252	52.93 250	61.10 258	89.37 199	88.09 221	97.59 224	91.30 245

test20.03		72.81 248	76.24 247	68.80 251	78.31 242	85.40 256	71.04 257	71.20 229	71.85 252	43.40 264	65.31 205	54.71 242	51.27 262	85.92 232	84.18 240	97.58 225	86.35 257

test_method		71.90 249	76.72 245	66.28 256	60.87 265	78.37 264	69.75 262	49.81 270	83.44 206	49.63 251	47.13 259	53.23 249	76.38 236	91.32 179	85.76 233	91.22 260	97.77 181

new_pmnet		71.86 250	73.67 252	69.75 250	72.56 253	84.20 258	70.95 259	66.81 242	80.34 222	43.62 263	51.60 253	53.81 248	71.24 251	82.91 245	80.93 246	93.35 258	81.92 260

FE-MVSNET2		71.63 251	71.59 253	71.68 247	60.60 266	86.30 253	75.64 248	72.07 226	69.87 257	51.83 247	38.70 263	42.10 267	72.39 250	88.69 203	85.13 235	97.55 226	90.33 249

MDA-MVSNet-bldmvs		69.61 252	70.36 255	68.74 252	62.88 263	88.50 238	65.40 266	77.01 191	71.60 255	43.93 260	66.71 203	35.33 272	72.47 249	61.01 265	80.63 247	90.73 262	88.75 254

pmmvs3		69.04 253	70.75 254	67.04 254	66.83 254	78.54 263	64.99 267	60.92 258	64.67 263	40.61 269	55.08 248	40.29 268	74.89 243	83.76 243	84.01 241	93.98 257	88.88 253

MIMVSNet1		68.63 254	70.24 256	66.76 255	56.86 268	83.26 259	67.93 264	70.26 234	68.05 259	46.80 257	40.44 262	48.15 260	62.01 256	84.96 239	84.86 236	96.69 235	81.93 259

FE-MVSNET		68.01 255	70.02 257	65.66 257	53.56 269	81.28 262	68.74 263	70.37 233	67.27 260	42.26 267	42.17 261	42.41 266	62.95 255	85.18 237	83.97 242	96.09 249	87.90 256

GG-mvs-BLEND		67.99 256	97.35 41	33.72 266	1.22 276	99.72 19	98.30 38	0.57 274	97.61 65	1.18 278	93.26 54	96.63 46	1.74 273	97.15 57	97.14 43	99.34 120	99.96 10

new-patchmatchnet		67.66 257	68.07 258	67.18 253	72.85 251	82.86 260	63.09 268	68.61 240	66.60 261	42.64 266	49.28 256	38.68 271	61.21 257	75.84 257	75.22 259	94.67 255	88.00 255

FPMVS		63.27 258	61.31 262	65.57 258	78.25 243	74.42 268	75.23 250	68.92 239	72.33 251	43.87 261	49.01 257	43.94 263	48.64 264	61.15 264	58.81 266	78.51 269	69.49 266

usedtu_dtu_shiyan2		63.25 259	63.29 260	63.21 259	48.45 272	77.92 265	69.85 260	62.49 252	52.94 266	50.43 250	32.38 267	43.14 264	59.67 259	73.05 259	72.69 261	88.34 263	90.90 247

WB-MVS		56.28 260	63.25 261	48.16 263	75.24 247	65.97 269	39.91 273	74.13 213	69.25 258	10.01 276	62.67 216	44.05 262	20.71 272	70.43 262	69.57 262	68.94 271	60.78 271

Gipuma		54.59 261	53.98 263	55.30 260	59.03 267	52.63 271	47.17 271	56.08 266	71.68 254	37.54 270	20.90 270	19.00 274	52.33 261	71.69 261	75.20 260	79.64 268	66.79 267
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	49.05 18	51.88 262	50.56 265	53.42 261	64.21 256	43.30 273	42.64 272	62.93 248	50.56 267	43.72 262	37.44 264	42.95 265	35.05 267	58.76 267	54.58 267	71.95 270	66.33 268
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		50.69 263	52.33 264	48.78 262	51.24 270	64.81 270	47.91 270	53.79 269	44.95 268	21.75 272	29.98 268	25.90 273	31.98 269	59.95 266	65.37 264	86.00 266	75.36 264

E-PMN		37.15 264	34.82 267	39.86 264	47.53 273	35.42 275	23.79 275	55.26 267	35.18 271	14.12 274	17.38 273	14.13 276	39.73 266	32.24 269	46.98 268	58.76 272	62.39 270

EMVS		36.45 265	33.63 268	39.74 265	48.47 271	35.73 274	23.59 276	55.11 268	35.61 270	12.88 275	17.49 271	14.62 275	41.04 265	29.33 270	43.00 269	57.32 273	59.62 272

MVE	42.40 19	36.00 266	38.65 266	32.92 267	29.16 274	46.17 272	22.61 277	44.21 271	26.44 273	18.88 273	17.41 272	9.36 278	32.29 268	45.75 268	61.38 265	50.35 274	64.03 269
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		21.55 267	30.91 269	10.62 268	2.78 275	11.66 276	18.51 278	4.82 272	38.21 269	4.06 277	36.35 265	4.47 279	26.81 270	23.27 271	27.11 270	6.75 275	75.30 265

test123		16.81 268	24.80 270	7.48 269	0.82 277	8.38 277	11.92 279	2.60 273	28.96 272	1.12 279	28.39 269	1.26 280	24.51 271	8.93 272	22.19 271	3.90 276	75.49 263

uanet_test		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 281	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

sosnet-low-res		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 281	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

sosnet		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 281	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

TestfortrainingZip							99.36 16	96.46 1		99.32 1						99.83 4

TPM-MVS					99.50 1	99.78 14	99.69 1			88.49 41	97.88 29	98.84 25	99.42 1			99.76 12	97.44 190
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										46.54 259

9.14												99.73 10

SR-MVS					99.27 18			95.82 21				99.00 20

Anonymous202405211			87.54 178		90.72 111	97.10 136	93.40 113	85.30 99	91.41 159		60.23 222	80.69 127	95.80 71	91.33 178	92.60 160	98.38 203	99.40 91

our_test_3					81.94 205	90.26 233	75.39 249

ambc			64.61 259		61.80 264	75.31 267	71.00 258		74.16 248	48.83 254	36.02 266	13.22 277	58.66 260	85.80 235	76.26 257	88.01 264	91.53 244

MTAPA										94.58 17		98.56 27

MTMP										95.24 11		98.13 33

Patchmatch-RL test							37.05 274

tmp_tt				71.24 249	90.29 124	76.39 266	65.81 265	59.43 263	97.62 63	79.65 140	90.60 66	68.71 201	49.71 263	72.71 260	65.70 263	82.54 267

XVS					93.63 73	99.64 28	94.32 88			83.97 87		98.08 35				99.59 41

X-MVStestdata					93.63 73	99.64 28	94.32 88			83.97 87		98.08 35				99.59 41

mPP-MVS					98.66 31							97.11 43

NP-MVS									97.69 61

Patchmtry						95.86 157	89.43 176	61.37 256		60.81 211

DeepMVS_CX						85.88 254	69.83 261	81.56 147	87.99 184	48.22 255	71.85 184	45.52 261	68.67 252	63.21 263		86.64 265	80.03 262

High-resolution multi-view benchmark