High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

SED-MVS		98.87 1	99.20 2	98.48 1	99.32 11	99.85 2	99.55 6	96.20 6	99.48 3	96.78 3	98.51 16	99.99 1	99.36 1	98.98 8	97.59 29	99.67 20	99.99 3

MSP-MVS		98.75 2	99.27 1	98.15 8	99.21 17	99.82 6	99.58 4	96.09 13	99.32 10	95.16 9	98.79 6	99.55 8	99.05 5	99.54 1	97.88 21	99.84 3	99.99 3
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
CNVR-MVS		98.73 3	99.17 5	98.22 5	99.47 4	99.85 2	99.57 5	96.23 4	99.30 11	94.90 11	98.65 10	98.93 19	99.36 1	99.46 3	98.21 11	99.81 6	99.80 33

DPE-MVS		98.69 4	99.14 6	98.16 7	99.37 7	99.82 6	99.66 2	96.26 1	99.18 16	95.02 10	98.62 13	99.98 3	98.88 11	98.90 11	97.51 32	99.75 10	99.97 7
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		98.65 5	98.87 12	98.38 2	99.30 13	99.85 2	99.14 23	96.23 4	99.51 2	97.16 1	96.01 34	99.99 1	98.90 10	98.89 12	97.88 21	99.56 50	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 6	98.97 9	98.18 6	99.38 6	99.78 11	99.35 15	96.14 9	99.24 13	95.66 7	98.19 20	99.01 16	98.66 17	98.77 14	97.80 24	99.86 2	99.97 7

SF-MVS		98.55 7	98.75 14	98.32 3	99.48 1	99.68 20	99.51 8	96.24 2	99.08 20	95.94 4	98.64 11	99.30 12	99.02 7	97.94 28	96.86 51	99.75 10	99.76 36

SMA-MVS		98.47 8	99.06 7	97.77 12	99.48 1	99.78 11	99.37 12	96.14 9	99.29 12	93.03 20	97.59 28	99.97 4	99.03 6	98.94 9	98.30 9	99.60 33	99.58 64
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
NCCC		98.41 9	99.18 3	97.52 16	99.36 8	99.84 5	99.55 6	96.08 15	99.33 9	91.77 25	98.79 6	99.46 10	98.59 19	99.15 7	98.07 18	99.73 14	99.64 53

SD-MVS		98.33 10	99.01 8	97.54 15	97.17 51	99.77 13	99.14 23	96.09 13	99.34 8	94.06 16	97.91 25	99.89 5	99.18 4	97.99 27	98.21 11	99.63 27	99.95 12
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 11	98.69 15	97.80 10	99.31 12	99.62 28	99.31 18	96.15 8	99.19 15	93.60 17	97.28 29	98.35 27	98.72 16	98.27 20	98.22 10	99.73 14	99.89 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MCST-MVS		98.20 12	99.18 3	97.06 22	99.27 15	99.87 1	99.37 12	96.11 11	99.37 6	89.29 33	98.76 8	99.50 9	98.37 25	99.23 5	97.64 27	99.95 1	99.87 28

HPM-MVS++		98.16 13	98.87 12	97.32 18	99.39 5	99.70 18	99.18 21	96.10 12	99.09 19	91.14 27	98.02 23	99.89 5	98.44 23	98.75 15	97.03 46	99.67 20	99.63 56

MSLP-MVS++		98.12 14	98.23 27	97.99 9	99.28 14	99.72 15	99.59 3	95.27 29	98.61 32	94.79 12	96.11 33	97.79 36	99.27 3	96.62 63	98.96 5	99.77 9	99.80 33

HFP-MVS		98.02 15	98.55 19	97.40 17	99.11 21	99.69 19	99.41 10	95.41 27	98.79 30	91.86 24	98.61 14	98.16 29	99.02 7	97.87 33	97.40 34	99.60 33	99.35 83

TSAR-MVS + MP.		97.98 16	98.62 18	97.23 20	97.08 52	99.55 34	99.17 22	95.69 22	99.40 5	93.04 19	96.68 31	98.96 18	98.58 20	98.82 13	96.95 48	99.81 6	99.96 9
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
zzz-MVS		97.93 17	98.05 31	97.80 10	99.20 18	99.64 24	99.40 11	95.76 20	98.01 52	94.31 15	96.54 32	98.49 25	98.58 20	98.22 23	96.23 61	99.54 59	99.23 89

SteuartSystems-ACMMP		97.86 18	98.91 10	96.64 26	98.89 27	99.79 8	99.34 16	95.20 31	98.48 35	89.91 31	98.58 15	98.69 21	96.84 47	98.92 10	98.16 15	99.66 22	99.74 39
Skip Steuart: Steuart Systems R&D Blog.
CP-MVS		97.81 19	98.26 26	97.28 19	99.00 24	99.65 23	99.10 25	95.32 28	98.38 41	92.21 23	98.33 18	97.74 37	98.50 22	97.66 42	96.55 59	99.57 45	99.48 73

ACMMPR		97.78 20	98.28 24	97.20 21	99.03 23	99.68 20	99.37 12	95.24 30	98.86 29	91.16 26	97.86 26	97.26 39	98.79 14	97.64 44	97.40 34	99.60 33	99.25 88

DeepC-MVS_fast	95.01 1	97.67 21	98.22 28	97.02 23	99.00 24	99.79 8	99.10 25	95.82 18	99.05 23	89.53 32	93.54 49	96.77 42	98.83 12	99.34 4	99.44 2	99.82 4	99.63 56
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		97.54 22	97.35 38	97.77 12	99.17 19	99.55 34	98.57 31	95.76 20	99.04 24	94.66 13	97.94 24	94.39 56	98.82 13	96.21 72	94.78 82	99.62 29	99.52 69

ACMMP_NAP		97.51 23	98.27 25	96.63 27	99.34 9	99.72 15	99.25 19	95.94 17	98.11 46	87.10 46	96.98 30	98.50 24	98.61 18	98.58 17	96.83 53	99.56 50	99.14 97

MP-MVS		97.46 24	98.30 23	96.48 28	98.93 26	99.43 44	99.20 20	95.42 26	98.43 37	87.60 43	98.19 20	98.01 35	98.09 27	98.05 26	96.67 56	99.64 25	99.35 83
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
train_agg		97.42 25	98.88 11	95.71 33	98.46 34	99.60 31	99.05 27	95.16 32	99.10 18	84.38 60	98.47 17	98.85 20	97.61 31	98.54 18	97.66 26	99.62 29	99.93 18

CPTT-MVS		97.32 26	97.60 37	96.99 24	98.29 37	99.31 55	99.04 28	94.67 36	97.99 53	93.12 18	98.03 22	98.26 28	98.77 15	96.08 75	94.26 91	98.07 177	99.27 87

X-MVS		97.20 27	98.42 22	95.77 31	99.04 22	99.64 24	98.95 30	95.10 34	98.16 44	83.97 66	98.27 19	98.08 32	97.95 28	97.89 30	97.46 33	99.58 41	99.47 74

PHI-MVS		97.09 28	98.69 15	95.22 38	97.99 43	99.59 33	97.56 44	92.16 40	98.41 39	87.11 45	98.70 9	99.42 11	96.95 43	96.88 59	98.16 15	99.56 50	99.70 45

DPM-MVS		97.07 29	97.99 32	96.00 30	97.25 50	99.16 61	99.67 1	95.99 16	99.08 20	85.97 50	93.00 54	98.44 26	97.47 33	99.22 6	99.62 1	99.66 22	97.44 154

PGM-MVS		97.03 30	98.14 30	95.73 32	99.34 9	99.61 30	99.34 16	89.99 46	97.70 56	87.67 42	99.44 2	96.45 45	98.44 23	97.65 43	97.09 43	99.58 41	99.06 105

PLC	94.37 2	97.03 30	96.54 43	97.60 14	98.84 28	98.64 70	98.17 36	94.99 35	99.01 25	96.80 2	93.21 53	95.64 47	97.36 34	96.37 67	94.79 81	99.41 82	98.12 139
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TSAR-MVS + ACMM		96.90 32	98.64 17	94.88 40	98.12 41	99.47 39	99.01 29	95.43 25	99.23 14	81.98 85	95.95 35	99.16 15	95.13 68	98.61 16	98.11 17	99.58 41	99.93 18

TSAR-MVS + GP.		96.47 33	98.45 21	94.17 45	92.12 83	99.29 56	97.76 40	88.05 57	99.36 7	90.26 30	97.82 27	99.21 13	97.21 39	96.78 61	96.74 54	99.63 27	99.94 15

xxxxxxxxxxxxxcwj		96.27 34	94.51 63	98.32 3	99.48 1	99.68 20	99.51 8	96.24 2	99.08 20	95.94 4	98.64 11	69.64 156	99.02 7	97.94 28	96.86 51	99.75 10	99.76 36

EPNet		96.23 35	97.89 34	94.29 43	97.62 46	99.44 43	97.14 52	88.63 53	98.16 44	88.14 38	99.46 1	94.15 59	94.61 79	97.20 52	97.23 38	99.57 45	99.59 61
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNLPA		96.14 36	95.43 53	96.98 25	98.55 31	99.41 48	95.91 58	95.15 33	99.00 26	95.71 6	84.21 103	94.55 54	97.25 37	95.50 97	96.23 61	99.28 103	99.09 104

MVS_111021_LR		96.07 37	97.94 33	93.88 48	97.86 44	99.43 44	95.70 61	89.65 49	98.73 31	84.86 58	99.38 3	94.08 60	95.78 66	97.81 37	96.73 55	99.43 79	99.42 77

ACMMP		96.05 38	96.70 42	95.29 37	98.01 42	99.43 44	97.60 43	94.33 38	97.62 59	86.17 49	98.92 4	92.81 68	96.10 59	95.67 87	93.33 111	99.55 55	99.12 100
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
3Dnovator+	90.72 7	95.99 39	96.42 45	95.50 35	98.18 39	99.33 54	97.44 46	87.73 62	97.93 54	92.36 22	84.67 96	97.33 38	97.55 32	97.32 48	98.47 8	99.72 18	99.88 25

DeepPCF-MVS	94.02 3	95.92 40	98.47 20	92.95 57	97.57 47	99.79 8	91.45 112	94.42 37	99.76 1	86.48 48	92.88 55	98.12 31	92.62 98	99.49 2	99.32 3	95.15 202	99.95 12

CDPH-MVS		95.90 41	97.77 36	93.72 51	98.28 38	99.43 44	98.40 32	91.30 44	98.34 42	78.62 104	94.80 41	95.74 46	96.11 58	97.86 34	98.67 7	99.59 36	99.56 66

CSCG		95.77 42	95.35 55	96.26 29	99.13 20	99.60 31	98.14 37	91.89 43	96.57 76	92.61 21	89.65 64	91.74 75	96.96 41	93.69 122	96.58 58	98.86 131	99.63 56

OMC-MVS		95.75 43	95.84 49	95.64 34	98.52 33	99.34 53	97.15 51	92.02 42	98.94 28	90.45 29	88.31 70	94.64 52	96.35 54	96.02 78	95.99 69	99.34 91	97.65 150

MVS_111021_HR		95.70 44	98.16 29	92.83 58	97.57 47	99.77 13	94.78 73	88.05 57	98.61 32	82.29 80	98.85 5	94.66 51	94.63 77	97.80 38	97.63 28	99.64 25	99.79 35

3Dnovator	90.31 8	95.67 45	96.16 47	95.11 39	98.59 30	99.37 52	97.50 45	87.98 59	98.02 51	89.09 34	85.36 95	94.62 53	97.66 29	97.10 55	98.90 6	99.82 4	99.73 41

CANet		95.40 46	96.27 46	94.40 42	96.25 57	99.62 28	98.37 33	88.59 54	98.09 47	87.58 44	84.57 98	95.54 49	95.87 63	98.12 24	98.03 20	99.73 14	99.90 23

QAPM		95.17 47	96.05 48	94.14 46	98.55 31	99.49 37	97.41 47	87.88 60	97.72 55	84.21 63	84.59 97	95.60 48	97.21 39	97.10 55	98.19 14	99.57 45	99.65 51

MVSTER		94.75 48	96.50 44	92.70 61	90.91 98	94.51 141	97.37 49	83.37 92	98.40 40	89.04 35	93.23 52	97.04 41	95.91 62	97.73 39	95.59 78	99.61 31	99.01 106

TAPA-MVS	92.04 6	94.72 49	95.13 58	94.24 44	97.72 45	99.17 59	97.61 42	92.16 40	97.66 58	81.99 84	87.84 75	93.94 62	96.50 51	95.74 84	94.27 90	99.46 74	97.31 155
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CS-MVS		94.64 50	97.07 41	91.80 65	89.92 119	97.79 88	95.28 69	83.29 94	98.54 34	85.49 55	94.51 46	93.94 62	97.35 35	97.83 35	97.14 39	99.45 78	99.73 41

DeepC-MVS	92.23 5	94.53 51	94.26 72	94.86 41	96.73 54	99.50 36	97.85 39	95.45 24	96.22 83	82.73 75	80.68 112	88.02 88	96.92 44	97.49 46	98.20 13	99.47 68	99.69 48
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CHOSEN 280x420		94.51 52	97.78 35	90.70 79	95.54 63	99.49 37	94.14 81	74.91 157	98.43 37	85.32 56	94.78 42	99.19 14	94.95 72	97.02 57	96.18 64	99.35 87	99.36 82

ETV-MVS		94.49 53	97.23 40	91.29 73	90.43 107	98.55 73	93.41 92	84.53 85	99.16 17	83.13 71	94.72 43	94.08 60	96.61 50	97.72 40	96.60 57	99.61 31	99.81 32

MVS_0304		94.35 54	95.66 51	92.83 58	94.82 65	99.46 41	98.19 35	87.75 61	97.32 65	81.83 88	83.50 105	93.19 67	94.71 75	98.24 22	98.07 18	99.68 19	99.83 30

MAR-MVS		94.18 55	95.12 59	93.09 56	98.40 36	99.17 59	94.20 80	81.92 102	98.47 36	86.52 47	90.92 58	84.21 107	98.12 26	95.88 81	97.59 29	99.40 83	99.58 64
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PCF-MVS	92.56 4	93.95 56	93.82 75	94.10 47	96.07 59	99.25 57	96.82 54	95.51 23	92.00 126	81.51 89	82.97 108	93.88 65	95.63 67	94.24 110	94.71 84	99.09 114	99.70 45
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DELS-MVS		93.82 57	93.82 75	93.81 50	96.34 56	99.47 39	97.26 50	88.53 55	92.13 124	87.80 41	79.67 115	88.01 89	93.14 90	98.28 19	99.22 4	99.80 8	99.98 5
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
OpenMVS	88.43 11	93.49 58	93.62 78	93.34 52	98.46 34	99.39 49	97.00 53	87.66 64	95.37 90	81.21 91	75.96 130	91.58 76	96.21 57	96.37 67	97.10 42	99.52 60	99.54 68

EIA-MVS		93.32 59	95.32 56	90.99 76	90.45 106	98.53 76	93.46 91	84.68 84	97.56 62	81.38 90	91.04 57	87.37 92	96.39 53	97.27 49	95.73 74	99.59 36	99.76 36

PVSNet_BlendedMVS		93.30 60	93.46 82	93.10 54	95.60 61	99.38 50	93.59 89	88.70 51	98.09 47	88.10 39	86.96 83	75.02 130	93.08 91	97.89 30	96.90 49	99.56 50	100.00 1

PVSNet_Blended		93.30 60	93.46 82	93.10 54	95.60 61	99.38 50	93.59 89	88.70 51	98.09 47	88.10 39	86.96 83	75.02 130	93.08 91	97.89 30	96.90 49	99.56 50	100.00 1

PMMVS		93.05 62	95.40 54	90.31 83	91.41 91	97.54 96	92.62 104	83.25 95	98.08 50	79.44 102	95.18 39	88.52 87	96.43 52	95.70 85	93.88 94	98.68 147	98.91 109

CS-MVS-test		92.94 63	95.57 52	89.88 89	89.51 121	97.24 98	93.68 88	79.93 121	96.78 75	80.14 98	90.65 60	91.54 77	96.67 49	97.42 47	95.67 76	99.33 96	99.70 45

LS3D		92.70 64	92.23 92	93.26 53	96.24 58	98.72 65	97.93 38	96.17 7	96.41 77	72.46 119	81.39 111	80.76 120	97.66 29	95.69 86	95.62 77	99.07 116	97.02 162

baseline1		92.67 65	93.62 78	91.55 68	91.16 94	97.15 100	93.92 86	85.97 74	94.76 97	84.07 65	87.17 79	86.89 95	94.62 78	96.72 62	95.90 72	99.57 45	96.79 166

IS_MVSNet		92.67 65	94.99 61	89.96 88	91.17 93	98.54 74	92.77 99	84.00 86	92.72 120	81.90 87	85.67 93	92.47 70	90.39 118	97.82 36	97.81 23	99.51 61	99.91 22

TSAR-MVS + COLMAP		92.56 67	92.44 90	92.71 60	94.61 67	97.69 92	97.69 41	91.09 45	98.96 27	76.71 109	94.68 44	69.41 157	96.91 45	95.80 83	94.18 92	99.26 104	96.33 170

baseline		92.56 67	94.38 68	90.43 82	90.71 102	98.23 82	95.07 70	80.73 116	97.52 63	82.45 79	87.34 78	85.91 99	94.07 85	96.29 71	95.94 71	99.58 41	99.47 74

canonicalmvs		92.54 69	93.28 84	91.68 66	91.44 90	98.24 81	95.45 66	81.84 106	95.98 87	84.85 59	90.69 59	78.53 125	96.96 41	92.97 128	97.06 44	99.57 45	99.47 74

PatchMatch-RL		92.54 69	92.82 89	92.21 62	96.57 55	98.74 64	91.85 109	86.30 69	96.23 82	85.18 57	95.21 38	73.58 136	94.22 84	95.40 100	93.08 115	99.14 111	97.49 153

MVS_Test		92.42 71	94.43 64	90.08 87	90.69 103	98.26 80	94.78 73	80.81 115	97.27 66	78.76 103	87.06 81	84.25 106	95.84 64	97.67 41	97.56 31	99.59 36	98.93 108

thisisatest0530		92.31 72	95.14 57	89.02 98	90.02 114	98.45 78	91.30 113	83.58 89	96.90 71	77.90 106	90.45 62	94.33 57	91.98 104	95.57 91	91.43 137	99.31 98	98.81 112

tttt0517		92.29 73	95.12 59	88.99 99	90.02 114	98.44 79	91.19 117	83.58 89	96.88 72	77.86 107	90.45 62	94.32 58	91.98 104	95.54 93	91.43 137	99.31 98	98.78 114

EPP-MVSNet		92.29 73	94.35 70	89.88 89	90.36 109	97.69 92	90.89 120	83.31 93	93.39 112	83.47 70	85.56 94	93.92 64	91.93 106	95.49 98	94.77 83	99.34 91	99.62 59

HQP-MVS		91.94 75	93.03 86	90.66 81	93.69 69	96.48 114	95.92 57	89.73 47	97.33 64	72.65 117	95.37 36	73.56 137	92.75 97	94.85 107	94.12 93	99.23 108	99.51 70

MSDG		91.93 76	90.28 119	93.85 49	97.36 49	97.12 101	95.88 59	94.07 39	94.52 101	84.13 64	76.74 124	80.89 119	92.54 99	93.97 118	93.61 105	99.14 111	95.10 179

UGNet		91.71 77	94.43 64	88.53 101	92.72 79	98.00 85	90.22 127	84.81 83	94.45 102	83.05 72	87.65 77	92.74 69	81.04 171	94.51 109	94.45 87	99.32 97	99.21 93
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 78	91.52 98	91.88 64	91.61 85	98.89 62	95.49 64	86.96 66	93.24 113	80.82 93	87.90 72	71.15 146	96.88 46	96.00 79	93.51 107	99.51 61	99.95 12

CLD-MVS		91.67 79	91.30 103	92.10 63	91.25 92	96.59 111	95.93 56	87.25 65	96.86 73	85.55 54	87.08 80	73.01 138	93.26 89	93.07 126	92.84 121	99.34 91	99.68 49
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ET-MVSNet_ETH3D		91.59 80	94.96 62	87.65 104	72.75 209	97.24 98	95.29 67	82.73 98	96.81 74	78.49 105	95.30 37	90.48 83	97.23 38	91.60 143	94.31 88	99.43 79	99.01 106

tfpn200view9		91.47 81	91.31 101	91.65 67	91.61 85	98.69 67	95.03 71	86.17 70	93.24 113	80.82 93	87.90 72	71.15 146	96.80 48	95.53 94	92.82 123	99.47 68	99.88 25

CANet_DTU		91.36 82	95.75 50	86.23 115	92.31 82	98.71 66	95.60 63	78.41 132	98.20 43	56.48 178	94.38 47	87.96 90	95.11 69	96.89 58	96.07 65	99.48 66	98.01 143

thres200		91.36 82	91.19 105	91.55 68	91.60 87	98.69 67	94.98 72	86.17 70	92.16 123	80.76 95	87.66 76	71.15 146	96.35 54	95.53 94	93.23 113	99.47 68	99.92 21

FMVSNet3		91.25 84	92.13 94	90.21 84	85.64 149	93.14 150	95.29 67	80.09 117	96.40 78	85.74 51	77.13 119	86.81 96	94.98 71	97.19 53	97.11 41	99.55 55	97.13 159

thres400		91.24 85	91.01 111	91.50 71	91.56 88	98.77 63	94.66 76	86.41 68	91.87 128	80.56 96	87.05 82	71.01 149	96.35 54	95.67 87	92.82 123	99.48 66	99.88 25

PVSNet_Blended_VisFu		91.20 86	92.89 88	89.23 96	93.41 72	98.61 72	89.80 129	85.39 78	92.84 117	82.80 74	74.21 134	91.38 79	84.64 150	97.22 51	96.04 68	99.34 91	99.93 18

DCV-MVSNet		91.15 87	92.00 95	90.17 86	90.78 100	92.23 167	93.70 87	81.17 113	95.16 93	82.98 73	89.46 66	83.31 109	93.98 86	91.79 142	92.87 118	98.41 165	99.18 95

DI_MVS_plusplus_trai		91.11 88	91.47 99	90.68 80	90.01 116	97.77 90	95.87 60	83.56 91	94.72 98	82.12 83	68.46 153	87.46 91	93.07 93	96.46 66	95.73 74	99.47 68	99.71 44

diffmvs		91.05 89	91.15 106	90.93 77	90.15 112	97.79 88	94.05 82	85.45 76	95.63 88	81.95 86	80.45 114	73.01 138	94.47 80	95.56 92	95.89 73	99.49 65	99.72 43

Vis-MVSNet (Re-imp)		91.05 89	94.43 64	87.11 106	91.05 96	97.99 86	92.53 105	83.82 88	92.71 121	76.28 110	84.50 99	92.43 71	79.52 176	97.24 50	97.68 25	99.43 79	98.45 126

thres600view7		90.97 91	90.70 113	91.30 72	91.53 89	98.69 67	94.33 77	86.17 70	91.75 130	80.19 97	86.06 91	70.90 150	96.10 59	95.53 94	92.08 130	99.47 68	99.86 29

baseline2		90.91 92	94.40 67	86.84 109	87.54 139	96.83 107	89.95 128	79.22 127	96.00 86	77.04 108	88.68 67	89.73 84	88.01 139	96.35 69	93.51 107	99.29 100	99.68 49

ACMP	89.80 9	90.72 93	91.15 106	90.21 84	92.55 80	96.52 113	92.63 103	85.71 75	94.65 99	81.06 92	93.32 50	70.56 153	90.52 117	92.68 132	91.05 142	98.76 139	99.31 86
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
casdiffmvs		90.69 94	90.56 116	90.85 78	90.14 113	97.81 87	92.94 97	85.30 79	93.47 111	82.50 78	76.34 128	74.12 134	94.67 76	96.51 65	96.26 60	99.55 55	99.42 77

ACMM	89.40 10	90.58 95	90.02 122	91.23 74	93.30 74	94.75 137	90.69 123	88.22 56	95.20 91	82.70 76	88.54 68	71.40 145	93.48 88	93.64 123	90.94 143	98.99 122	95.72 175
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		90.49 96	91.12 109	89.75 92	84.99 152	92.73 155	93.94 83	80.09 117	96.40 78	85.74 51	77.13 119	86.81 96	94.42 81	94.12 112	93.73 96	99.35 87	96.90 163

test1		90.49 96	91.12 109	89.75 92	84.99 152	92.73 155	93.94 83	80.09 117	96.40 78	85.74 51	77.13 119	86.81 96	94.42 81	94.12 112	93.73 96	99.35 87	96.90 163

LGP-MVS_train		90.34 98	91.63 97	88.83 100	93.31 73	96.14 119	95.49 64	85.24 81	93.91 106	68.71 132	93.96 48	71.63 143	91.12 114	93.82 120	92.79 125	99.07 116	99.16 96

EPNet_dtu		89.82 99	94.18 73	84.74 126	96.87 53	95.54 130	92.65 102	86.91 67	96.99 68	54.17 189	92.41 56	88.54 86	78.35 179	96.15 74	96.05 67	99.47 68	93.60 187
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		89.81 100	89.75 123	89.88 89	93.22 76	93.99 144	94.78 73	85.23 82	94.01 105	82.52 77	95.00 40	87.23 93	92.01 103	85.16 196	83.48 201	91.54 207	89.38 201

MDTV_nov1_ep13		89.63 101	94.38 68	84.09 133	88.76 131	97.53 97	89.37 137	68.46 190	96.95 69	70.27 126	87.88 74	93.67 66	91.04 115	93.12 124	93.83 95	96.62 195	97.68 149

UA-Net		89.56 102	93.03 86	85.52 122	92.46 81	97.55 95	91.92 108	81.91 103	85.24 161	71.39 121	83.57 104	96.56 44	76.01 190	96.81 60	97.04 45	99.46 74	94.41 182

FMVSNet2		89.51 103	89.63 124	89.38 94	84.99 152	92.73 155	93.94 83	79.28 125	93.73 108	84.28 62	69.36 152	82.32 112	94.42 81	96.16 73	96.22 63	99.35 87	96.90 163

CostFormer		89.42 104	91.67 96	86.80 111	89.99 117	96.33 116	90.75 121	64.79 193	95.17 92	83.62 69	86.20 89	82.15 114	92.96 94	89.22 165	92.94 116	98.68 147	99.65 51

FC-MVSNet-train		89.37 105	89.62 125	89.08 97	90.48 105	94.16 143	89.45 133	83.99 87	91.09 133	80.09 99	82.84 109	74.52 133	91.44 111	93.79 121	91.57 136	99.01 120	99.35 83

OPM-MVS		89.33 106	87.45 141	91.53 70	94.49 68	96.20 118	96.47 55	89.72 48	82.77 168	75.43 111	80.53 113	70.86 151	93.80 87	94.00 116	91.85 134	99.29 100	95.91 173

test-LLR		89.31 107	93.60 80	84.30 130	88.08 135	96.98 103	88.10 142	78.00 133	94.83 95	62.43 152	84.29 101	90.96 80	89.70 123	95.63 89	92.86 119	99.51 61	99.64 53

EPMVS		89.31 107	93.70 77	84.18 132	91.10 95	98.10 83	89.17 139	62.71 197	96.24 81	70.21 128	86.46 87	92.37 72	92.79 95	91.95 140	93.59 106	99.10 113	97.19 156

Anonymous20231211		89.22 109	87.56 139	91.16 75	90.23 111	96.62 110	93.22 94	85.44 77	92.89 116	84.37 61	60.13 172	81.25 117	96.02 61	90.61 150	92.01 131	97.70 185	99.41 79

Effi-MVS+		88.96 110	91.13 108	86.43 113	89.12 127	97.62 94	93.15 95	75.52 151	93.90 107	66.40 136	86.23 88	70.51 154	95.03 70	95.89 80	94.28 89	99.37 84	99.51 70

SCA		88.76 111	94.29 71	82.30 149	89.33 124	96.81 108	87.68 144	61.52 202	96.95 69	64.68 142	88.35 69	94.80 50	91.58 108	92.23 134	93.21 114	98.99 122	97.70 148

test0.0.03 1		88.71 112	92.22 93	84.63 128	88.08 135	94.71 139	85.91 167	78.00 133	95.54 89	72.96 115	86.10 90	85.88 101	83.59 158	92.95 130	93.24 112	99.25 107	97.09 160

PatchmatchNet		88.67 113	94.10 74	82.34 148	89.38 123	97.72 91	87.24 150	62.18 200	97.00 67	64.79 141	87.97 71	94.43 55	91.55 109	91.21 148	92.77 126	98.90 127	97.60 152
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dps		88.66 114	90.19 120	86.88 108	89.94 118	96.48 114	89.56 131	64.08 195	94.12 104	89.00 36	83.39 106	82.56 111	90.16 121	86.81 188	89.26 162	98.53 160	98.71 116

TESTMET0.1,1		88.63 115	93.60 80	82.84 145	84.07 159	96.98 103	88.10 142	73.22 172	94.83 95	62.43 152	84.29 101	90.96 80	89.70 123	95.63 89	92.86 119	99.51 61	99.64 53

CHOSEN 1792x2688		88.63 115	89.01 129	88.19 102	94.83 64	99.21 58	92.66 101	79.85 122	92.40 122	72.18 120	56.38 193	80.22 122	90.24 119	97.64 44	97.28 37	99.37 84	99.94 15

CDS-MVSNet		88.59 117	90.13 121	86.79 112	86.98 145	95.43 131	92.03 107	81.33 111	85.54 158	74.51 114	77.07 122	85.14 103	87.03 144	93.90 119	95.18 79	98.88 129	98.67 118
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IB-MVS	84.67 14	88.34 118	90.61 115	85.70 119	92.99 78	98.62 71	78.85 193	86.07 73	94.35 103	88.64 37	85.99 92	75.69 128	68.09 203	88.21 168	91.43 137	99.55 55	99.96 9
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		88.25 119	93.27 85	82.38 147	83.89 160	96.86 106	87.10 154	72.80 174	94.58 100	61.85 157	83.21 107	90.65 82	89.18 127	95.43 99	92.58 128	99.46 74	99.61 60

COLMAP_ROB	84.42 15	88.24 120	87.32 142	89.32 95	95.83 60	95.82 123	92.81 98	87.68 63	92.09 125	72.64 118	72.34 143	79.96 123	88.79 130	89.54 160	89.46 158	98.16 174	92.00 193
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
IterMVS-LS		87.95 121	89.40 127	86.26 114	88.79 130	90.93 183	91.23 116	76.05 148	90.87 134	71.07 123	75.51 131	81.18 118	91.21 113	94.11 115	95.01 80	99.20 110	98.23 134
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		87.86 122	88.25 134	87.40 105	94.67 66	98.54 74	90.33 126	76.51 147	89.60 142	70.89 124	51.43 204	85.69 102	92.79 95	96.59 64	95.96 70	99.22 109	99.94 15

Vis-MVSNet		87.60 123	91.31 101	83.27 140	89.14 126	98.04 84	90.35 125	79.42 123	87.23 147	66.92 135	79.10 118	84.63 105	74.34 197	95.81 82	96.06 66	99.46 74	98.32 130
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GeoE		87.55 124	88.17 135	86.82 110	88.74 132	96.32 117	92.75 100	74.93 156	90.13 139	72.73 116	69.47 151	74.03 135	92.51 100	93.99 117	93.62 104	99.29 100	99.59 61

RPMNet		87.35 125	92.41 91	81.45 153	88.85 129	96.06 120	89.42 136	59.59 209	93.57 109	61.81 158	76.48 127	91.48 78	90.18 120	96.32 70	93.37 110	98.87 130	99.59 61

tpm cat1		87.34 126	88.52 133	85.95 116	89.83 120	95.80 124	90.73 122	64.91 192	92.99 115	82.21 82	71.19 149	82.68 110	90.13 122	86.38 189	90.87 145	97.90 182	99.74 39

MS-PatchMatch		87.19 127	88.59 132	85.55 121	93.15 77	96.58 112	92.35 106	74.19 164	91.97 127	70.33 125	71.42 147	85.89 100	84.28 152	93.12 124	89.16 164	99.00 121	91.99 194

Effi-MVS+-dtu		87.18 128	90.48 117	83.32 139	86.51 146	95.76 127	91.16 119	74.28 163	90.44 138	61.31 161	86.72 86	72.68 141	91.25 112	95.01 104	93.64 99	95.45 201	99.12 100

FMVSNet5		87.06 129	89.52 126	84.20 131	79.92 197	86.57 203	87.11 153	72.37 176	96.06 84	75.41 112	84.33 100	91.76 74	91.60 107	91.51 144	91.22 140	98.77 136	85.16 206

Fast-Effi-MVS+-dtu		86.94 130	91.27 104	81.89 150	86.27 147	95.06 132	90.68 124	68.93 187	91.76 129	57.18 176	89.56 65	75.85 127	89.19 126	94.56 108	92.84 121	99.07 116	99.23 89

Fast-Effi-MVS+		86.94 130	87.88 137	85.84 117	86.99 143	95.80 124	91.24 114	73.48 170	92.75 118	69.22 129	72.70 140	65.71 163	94.84 73	94.98 105	94.71 84	99.26 104	98.48 124

DROMVSNet		86.94 130	87.88 137	85.84 117	86.99 143	95.80 124	91.24 114	73.48 170	92.75 118	69.22 129	72.70 140	65.71 163	94.84 73	94.98 105	94.71 84	99.26 104	98.48 124

tpmrst		86.78 133	90.29 118	82.69 146	90.55 104	96.95 105	88.49 141	62.58 198	95.09 94	63.52 148	76.67 126	84.00 108	92.05 102	87.93 171	91.89 133	98.98 124	99.50 72

CR-MVSNet		86.73 134	91.47 99	81.20 156	88.56 133	96.06 120	89.43 134	61.37 203	93.57 109	60.81 163	72.89 139	88.85 85	88.13 137	96.03 76	93.64 99	98.89 128	99.22 91

ADS-MVSNet		86.68 135	90.79 112	81.88 151	90.38 108	96.81 108	86.90 155	60.50 207	96.01 85	63.93 145	81.67 110	84.72 104	90.78 116	87.03 182	91.67 135	98.77 136	97.63 151

FMVSNet1		85.85 136	84.91 152	86.96 107	82.70 165	91.39 177	91.54 111	77.45 139	85.29 160	79.56 101	60.70 169	72.68 141	92.37 101	94.12 112	93.73 96	98.12 175	96.44 167

FC-MVSNet-test		85.51 137	89.08 128	81.35 154	85.31 151	93.35 146	87.65 145	77.55 138	90.01 140	64.07 144	79.63 116	81.83 116	74.94 194	92.08 137	90.83 147	98.55 157	95.81 174

ACMH	85.22 13	85.40 138	85.73 149	85.02 124	91.76 84	94.46 142	84.97 173	81.54 109	85.18 162	65.22 140	76.92 123	64.22 165	88.58 133	90.17 152	90.25 153	98.03 178	98.90 110
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		85.35 139	86.00 148	84.59 129	84.97 155	95.57 129	88.98 140	77.29 142	81.44 173	71.36 122	71.48 146	75.00 132	87.03 144	91.92 141	92.21 129	97.92 181	94.40 183

ACMH+	85.62 12	85.27 140	84.96 151	85.64 120	90.84 99	94.78 136	87.46 147	81.30 112	86.94 148	67.35 134	74.56 133	64.09 166	88.70 131	88.14 169	89.00 165	98.22 173	97.19 156

USDC		85.11 141	85.35 150	84.83 125	89.45 122	94.93 135	92.98 96	77.30 141	90.53 136	61.80 159	76.69 125	59.62 176	88.90 129	92.78 131	90.79 149	98.53 160	92.12 191

IterMVS		85.02 142	88.98 130	80.41 162	87.03 142	90.34 191	89.78 130	69.45 184	89.77 141	54.04 190	73.71 136	82.05 115	83.44 161	95.11 102	93.64 99	98.75 140	98.22 136
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		84.91 143	88.90 131	80.25 165	87.04 141	90.27 192	89.23 138	69.25 186	89.17 143	54.04 190	73.65 137	82.22 113	83.23 166	95.11 102	93.63 103	98.73 141	98.23 134

PatchT		84.89 144	90.67 114	78.13 185	87.83 138	94.99 134	72.46 205	60.22 208	91.74 131	60.81 163	72.16 144	86.95 94	88.13 137	96.03 76	93.64 99	99.36 86	99.22 91

pmmvs4		84.88 145	84.67 153	85.13 123	82.80 164	92.37 160	87.29 148	79.08 128	90.51 137	74.94 113	70.37 150	62.49 169	88.17 136	92.01 139	88.51 170	98.49 163	96.44 167

test_part1		84.71 146	82.08 163	87.78 103	89.19 125	91.40 176	91.19 117	79.25 126	79.62 185	82.23 81	57.07 189	70.79 152	88.95 128	87.46 176	89.91 155	95.89 200	98.31 132

CVMVSNet		84.01 147	86.91 143	80.61 160	88.39 134	93.29 147	86.06 163	82.29 100	83.13 166	54.29 186	72.68 142	79.59 124	75.11 193	91.23 147	92.91 117	97.54 189	95.58 176

tpm		83.97 148	87.97 136	79.31 175	87.35 140	93.21 149	86.00 165	61.90 201	90.69 135	54.01 192	79.42 117	75.61 129	88.65 132	87.18 180	90.48 151	97.95 180	99.21 93

GA-MVS		83.83 149	86.63 144	80.58 161	85.40 150	94.73 138	87.27 149	78.76 131	86.49 150	49.57 200	74.21 134	67.67 160	83.38 162	95.28 101	90.92 144	99.08 115	97.09 160

UniMVSNet_NR-MVSNet		83.83 149	83.70 156	83.98 134	81.41 175	92.56 159	86.54 158	82.96 96	85.98 155	66.27 137	66.16 160	63.63 167	87.78 141	87.65 174	90.81 148	98.94 125	99.13 98

UniMVSNet (Re)		83.28 151	83.16 157	83.42 138	81.93 170	93.12 151	86.27 161	80.83 114	85.88 156	68.23 133	64.56 163	60.58 171	84.25 153	89.13 166	89.44 160	99.04 119	99.40 80

thisisatest0515		83.17 152	86.49 145	79.30 176	82.04 168	93.12 151	78.70 194	77.92 135	86.43 151	63.05 149	74.91 132	73.01 138	75.56 192	92.10 136	88.05 183	98.50 162	97.76 147

TinyColmap		83.03 153	82.24 161	83.95 135	88.88 128	93.22 148	89.48 132	76.89 144	87.53 146	62.12 154	68.46 153	55.03 192	88.43 135	90.87 149	89.65 156	97.89 183	90.91 197

testgi		82.88 154	86.14 147	79.08 178	86.05 148	92.20 168	81.23 190	74.77 159	88.70 144	57.63 175	86.73 85	61.53 170	76.83 187	90.33 151	89.43 161	97.99 179	94.05 184

DU-MVS		82.87 155	82.16 162	83.70 137	80.77 184	92.24 164	86.54 158	81.91 103	86.41 152	66.27 137	63.95 164	55.66 190	87.78 141	86.83 185	90.86 146	98.94 125	99.13 98

MIMVSNet		82.87 155	86.17 146	79.02 179	77.23 205	92.88 154	84.88 174	60.62 206	86.72 149	64.16 143	73.58 138	71.48 144	88.51 134	94.14 111	93.50 109	98.72 143	90.87 198

NR-MVSNet		82.37 157	81.95 165	82.85 144	82.56 167	92.24 164	87.49 146	81.91 103	86.41 152	65.51 139	63.95 164	52.93 201	80.80 173	89.41 162	89.61 157	98.85 132	99.10 103

Baseline_NR-MVSNet		82.08 158	80.64 172	83.77 136	80.77 184	88.50 198	86.88 156	81.71 107	85.58 157	68.80 131	58.20 184	57.75 182	86.16 146	86.83 185	88.68 167	98.33 170	98.90 110

TranMVSNet+NR-MVSNet		82.07 159	81.36 168	82.90 143	80.43 190	91.39 177	87.16 152	82.75 97	84.28 164	62.98 150	62.28 168	56.01 189	85.30 149	86.06 191	90.69 150	98.80 133	98.80 113

pm-mvs1		81.68 160	81.70 166	81.65 152	82.61 166	92.26 163	85.54 171	78.95 129	76.29 196	63.81 146	58.43 183	66.33 162	80.63 174	92.30 133	89.93 154	98.37 169	96.39 169

TDRefinement		81.49 161	80.08 178	83.13 142	91.02 97	94.53 140	91.66 110	82.43 99	81.70 171	62.12 154	62.30 167	59.32 177	73.93 198	87.31 178	85.29 194	97.61 186	90.14 199

anonymousdsp		81.29 162	84.52 155	77.52 187	79.83 198	92.62 158	82.61 185	70.88 181	80.76 177	50.82 197	68.35 155	68.76 158	82.45 169	93.00 127	89.45 159	98.55 157	98.69 117

gg-mvs-nofinetune		81.27 163	84.65 154	77.32 188	87.96 137	98.48 77	95.64 62	56.36 212	59.35 214	32.80 218	47.96 208	92.11 73	91.49 110	98.12 24	97.00 47	99.65 24	99.56 66

tfpnnormal		81.11 164	79.33 186	83.19 141	84.23 157	92.29 162	86.76 157	82.27 101	72.67 202	62.02 156	56.10 195	53.86 198	85.35 148	92.06 138	89.23 163	98.49 163	99.11 102

UniMVSNet_ETH3D		80.95 165	77.71 194	84.74 126	84.45 156	93.11 153	86.45 160	79.97 120	75.21 198	70.22 127	51.24 205	50.26 207	89.55 125	84.47 198	91.12 141	97.81 184	98.53 122

V42		80.88 166	80.74 170	81.05 157	81.21 178	92.01 170	85.96 166	77.75 137	81.62 172	59.73 170	59.93 175	58.35 181	82.98 168	86.90 184	88.06 182	98.69 146	98.32 130

v2v482		80.86 167	80.52 176	81.25 155	80.79 183	91.85 171	85.68 169	78.78 130	81.05 174	58.09 173	60.46 170	56.08 187	85.45 147	87.27 179	88.53 169	98.73 141	98.38 129

v8		80.61 168	80.61 174	80.62 159	81.51 173	91.00 182	86.06 163	74.07 166	81.78 170	59.93 169	60.10 174	58.42 180	83.35 163	86.99 183	88.11 180	98.79 134	97.83 145

pmmvs5		80.48 169	81.43 167	79.36 174	81.50 174	92.24 164	82.07 188	74.08 165	78.10 189	55.86 181	67.72 157	54.35 195	83.91 157	92.97 128	88.65 168	98.77 136	96.01 171

v10		80.38 170	80.73 171	79.96 167	81.22 177	90.40 190	86.11 162	71.63 178	82.42 169	57.65 174	58.74 181	57.47 183	84.44 151	89.75 156	88.28 173	98.71 144	98.06 142

v1144		80.36 171	80.63 173	80.05 166	80.86 182	91.56 174	85.78 168	75.22 153	80.73 178	55.83 182	58.51 182	56.99 185	83.93 156	89.79 155	88.25 174	98.68 147	98.56 121

SixPastTwentyTwo		80.28 172	82.06 164	78.21 184	81.89 172	92.35 161	77.72 195	74.48 160	83.04 167	54.22 187	76.06 129	56.40 186	83.55 159	86.83 185	84.83 196	97.38 190	94.93 180

CP-MVSNet		79.90 173	79.49 183	80.38 163	80.72 186	90.83 184	82.98 182	75.17 154	79.70 183	61.39 160	59.74 176	51.98 204	83.31 164	87.37 177	88.38 171	98.71 144	98.45 126

v1192		79.84 174	80.05 180	79.61 170	80.49 189	91.04 181	85.56 170	74.37 162	80.73 178	54.35 185	57.07 189	54.54 194	84.23 154	89.94 153	88.38 171	98.63 151	98.61 119

WR-MVS_H		79.76 175	80.07 179	79.40 173	81.25 176	91.73 173	82.77 183	74.82 158	79.02 188	62.55 151	59.41 178	57.32 184	76.27 189	87.61 175	87.30 188	98.78 135	98.09 140

WR-MVS		79.67 176	80.25 177	79.00 180	80.65 187	91.16 179	83.31 180	76.57 146	80.97 175	60.50 168	59.20 179	58.66 179	74.38 196	85.85 193	87.76 185	98.61 152	98.14 137

v148		79.66 177	79.13 188	80.27 164	81.02 180	91.76 172	81.90 189	79.32 124	79.24 186	63.79 147	58.07 186	54.34 196	77.17 185	84.42 199	87.52 187	98.40 166	98.59 120

LTVRE_ROB	79.45 16	79.66 177	80.55 175	78.61 182	83.01 163	92.19 169	87.18 151	73.69 169	71.70 205	43.22 213	71.22 148	50.85 205	87.82 140	89.47 161	90.43 152	96.75 193	98.00 144
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		79.61 179	79.77 181	79.41 172	80.28 191	91.06 180	84.87 175	73.86 167	79.65 184	55.38 183	57.76 187	55.20 191	83.46 160	88.42 167	87.89 184	98.61 152	98.42 128

v1921920		79.55 180	79.77 181	79.30 176	80.24 192	90.77 186	85.37 172	73.75 168	80.38 180	53.78 193	56.89 192	54.18 197	84.05 155	89.55 159	88.13 179	98.59 154	98.52 123

TransMVSNet (Re)		79.51 181	78.36 190	80.84 158	83.17 161	89.72 194	84.22 178	81.45 110	73.98 201	60.79 166	57.20 188	56.05 188	77.11 186	89.88 154	88.86 166	98.30 172	92.83 189

MVS-HIRNet		79.34 182	82.56 158	75.57 193	84.11 158	95.02 133	75.03 202	57.28 211	85.50 159	55.88 180	53.00 201	70.51 154	83.05 167	92.12 135	91.96 132	98.09 176	89.83 200

PS-CasMVS		79.06 183	78.58 189	79.63 169	80.59 188	90.55 188	82.54 186	75.04 155	77.76 190	58.84 171	58.16 185	50.11 209	82.09 170	87.05 181	88.18 177	98.66 150	98.27 133

v1240		78.97 184	79.27 187	78.63 181	80.04 193	90.61 187	84.25 177	72.95 173	79.22 187	52.70 195	56.22 194	52.88 203	83.28 165	89.60 158	88.20 176	98.56 156	98.14 137

pmnet_mix02		78.91 185	81.17 169	76.28 192	81.91 171	90.82 185	74.25 203	77.87 136	86.17 154	49.04 201	67.97 156	62.93 168	77.40 183	82.75 204	82.11 203	97.18 191	95.42 177

MDTV_nov1_ep13_2view		78.83 186	82.35 159	74.73 196	78.65 200	91.51 175	79.18 192	62.52 199	84.51 163	52.51 196	67.49 158	67.29 161	78.90 177	85.52 195	86.34 191	96.62 195	93.76 185

PEN-MVS		78.80 187	78.13 192	79.58 171	80.03 194	89.67 195	83.61 179	75.83 149	77.71 192	58.41 172	60.11 173	50.00 210	81.02 172	84.08 200	88.14 178	98.59 154	97.18 158

EG-PatchMatch MVS		78.32 188	79.42 185	77.03 190	83.03 162	93.77 145	84.47 176	69.26 185	75.85 197	53.69 194	55.68 196	60.23 174	73.20 199	89.69 157	88.22 175	98.55 157	92.54 190

DTE-MVSNet		77.92 189	77.42 195	78.51 183	79.34 199	89.00 197	83.05 181	75.60 150	76.89 194	56.58 177	59.63 177	50.31 206	78.09 182	82.57 205	87.56 186	98.38 167	95.95 172

v7n		77.71 190	78.25 191	77.09 189	78.49 201	90.55 188	82.15 187	71.11 180	76.79 195	54.18 188	55.63 197	50.20 208	78.28 180	89.36 164	87.15 189	98.33 170	98.07 141

gm-plane-assit		77.20 191	82.26 160	71.30 199	81.10 179	82.00 211	54.33 216	64.41 194	63.80 213	40.93 215	59.04 180	76.57 126	87.30 143	98.26 21	97.36 36	99.74 13	98.76 115

N_pmnet		76.83 192	77.97 193	75.50 194	80.96 181	88.23 200	72.81 204	76.83 145	80.87 176	50.55 198	56.94 191	60.09 175	75.70 191	83.28 202	84.23 198	96.14 199	92.12 191

pmmvs6		76.79 193	75.69 200	78.09 186	79.95 196	89.57 196	80.92 191	74.46 161	64.79 211	60.74 167	45.71 210	60.55 172	78.37 178	88.04 170	86.00 192	94.07 204	95.15 178

CMPMVS	58.73 17	76.78 194	74.27 201	79.70 168	93.26 75	95.58 128	82.74 184	77.44 140	71.46 208	56.29 179	53.58 200	59.13 178	77.33 184	79.20 206	79.71 206	91.14 209	81.24 209
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		76.76 195	79.47 184	73.60 197	79.99 195	87.47 201	77.39 196	75.43 152	77.62 193	47.83 204	64.78 162	60.44 173	64.80 204	86.28 190	86.53 190	96.17 198	93.19 188

PM-MVS		75.81 196	76.11 199	75.46 195	73.81 206	85.48 205	76.42 198	70.57 182	80.05 182	54.75 184	62.33 166	39.56 216	80.59 175	87.71 173	82.81 202	96.61 197	94.81 181

pmmvs-eth3d		75.17 197	74.09 202	76.43 191	72.92 207	84.49 207	76.61 197	72.42 175	74.33 199	61.28 162	54.71 199	39.42 217	78.20 181	87.77 172	84.25 197	97.17 192	93.63 186

Anonymous20231206		74.59 198	77.00 196	71.78 198	77.89 204	87.45 202	75.14 201	72.29 177	77.76 190	46.65 206	52.14 202	52.93 201	61.10 207	89.37 163	88.09 181	97.59 187	91.30 196

test20.03		72.81 199	76.24 198	68.80 202	78.31 202	85.40 206	71.04 206	71.20 179	71.85 204	43.40 212	65.31 161	54.71 193	51.27 210	85.92 192	84.18 199	97.58 188	86.35 205

test_method		71.90 200	76.72 197	66.28 207	60.87 215	78.37 213	69.75 210	49.81 217	83.44 165	49.63 199	47.13 209	53.23 200	76.38 188	91.32 146	85.76 193	91.22 208	97.77 146

new_pmnet		71.86 201	73.67 203	69.75 201	72.56 210	84.20 208	70.95 208	66.81 191	80.34 181	43.62 211	51.60 203	53.81 199	71.24 201	82.91 203	80.93 204	93.35 206	81.92 208

MDA-MVSNet-bldmvs		69.61 202	70.36 205	68.74 203	62.88 213	88.50 198	65.40 213	77.01 143	71.60 207	43.93 208	66.71 159	35.33 219	72.47 200	61.01 212	80.63 205	90.73 210	88.75 203

pmmvs3		69.04 203	70.75 204	67.04 205	66.83 211	78.54 212	64.99 214	60.92 205	64.67 212	40.61 216	55.08 198	40.29 215	74.89 195	83.76 201	84.01 200	93.98 205	88.88 202

MIMVSNet1		68.63 204	70.24 206	66.76 206	56.86 217	83.26 209	67.93 211	70.26 183	68.05 209	46.80 205	40.44 211	48.15 211	62.01 205	84.96 197	84.86 195	96.69 194	81.93 207

GG-mvs-BLEND		67.99 205	97.35 38	33.72 214	1.22 223	99.72 15	98.30 34	0.57 221	97.61 61	1.18 224	93.26 51	96.63 43	1.74 220	97.15 54	97.14 39	99.34 91	99.96 9

new-patchmatchnet		67.66 206	68.07 207	67.18 204	72.85 208	82.86 210	63.09 215	68.61 189	66.60 210	42.64 214	49.28 206	38.68 218	61.21 206	75.84 207	75.22 208	94.67 203	88.00 204

FPMVS		63.27 207	61.31 209	65.57 208	78.25 203	74.42 216	75.23 200	68.92 188	72.33 203	43.87 209	49.01 207	43.94 213	48.64 212	61.15 211	58.81 213	78.51 216	69.49 214

Gipuma		54.59 208	53.98 210	55.30 209	59.03 216	52.63 218	47.17 218	56.08 213	71.68 206	37.54 217	20.90 217	19.00 221	52.33 209	71.69 209	75.20 209	79.64 215	66.79 215
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	49.05 18	51.88 209	50.56 212	53.42 210	64.21 212	43.30 220	42.64 219	62.93 196	50.56 215	43.72 210	37.44 212	42.95 214	35.05 215	58.76 214	54.58 214	71.95 217	66.33 216
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		50.69 210	52.33 211	48.78 211	51.24 218	64.81 217	47.91 217	53.79 216	44.95 216	21.75 219	29.98 215	25.90 220	31.98 217	59.95 213	65.37 211	86.00 213	75.36 212

E-PMN		37.15 211	34.82 214	39.86 212	47.53 220	35.42 222	23.79 221	55.26 214	35.18 219	14.12 221	17.38 220	14.13 223	39.73 214	32.24 216	46.98 215	58.76 218	62.39 218

EMVS		36.45 212	33.63 215	39.74 213	48.47 219	35.73 221	23.59 222	55.11 215	35.61 218	12.88 222	17.49 218	14.62 222	41.04 213	29.33 217	43.00 216	57.32 219	59.62 219

MVE	42.40 19	36.00 213	38.65 213	32.92 215	29.16 221	46.17 219	22.61 223	44.21 218	26.44 221	18.88 220	17.41 219	9.36 225	32.29 216	45.75 215	61.38 212	50.35 220	64.03 217
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		21.55 214	30.91 216	10.62 216	2.78 222	11.66 223	18.51 224	4.82 219	38.21 217	4.06 223	36.35 213	4.47 226	26.81 218	23.27 218	27.11 217	6.75 221	75.30 213

test123		16.81 215	24.80 217	7.48 217	0.82 224	8.38 224	11.92 225	2.60 220	28.96 220	1.12 225	28.39 216	1.26 227	24.51 219	8.93 219	22.19 218	3.90 222	75.49 211

uanet_test		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

sosnet-low-res		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

sosnet		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

RE-MVS-def										46.54 207

9.14												99.73 7

SR-MVS					99.27 15			95.82 18				99.00 17

Anonymous202405211			87.54 140		90.72 101	97.10 102	93.40 93	85.30 79	91.41 132		60.23 171	80.69 121	95.80 65	91.33 145	92.60 127	98.38 167	99.40 80

our_test_3					81.94 169	90.26 193	75.39 199

ambc			64.61 208		61.80 214	75.31 215	71.00 207		74.16 200	48.83 202	36.02 214	13.22 224	58.66 208	85.80 194	76.26 207	88.01 211	91.53 195

MTAPA										94.58 14		98.56 23

MTMP										95.24 8		98.13 30

Patchmatch-RL test							37.05 220

tmp_tt				71.24 200	90.29 110	76.39 214	65.81 212	59.43 210	97.62 59	79.65 100	90.60 61	68.71 159	49.71 211	72.71 208	65.70 210	82.54 214

XVS					93.63 70	99.64 24	94.32 78			83.97 66		98.08 32				99.59 36

X-MVStestdata					93.63 70	99.64 24	94.32 78			83.97 66		98.08 32				99.59 36

abl_6				95.40 36	98.18 39	99.45 42	97.39 48	89.27 50	99.48 3	90.52 28	94.52 45	98.63 22	97.32 36			99.73 14	99.82 31

mPP-MVS					98.66 29							97.11 40

NP-MVS									97.69 57

Patchmtry						95.86 122	89.43 134	61.37 203		60.81 163

DeepMVS_CX						85.88 204	69.83 209	81.56 108	87.99 145	48.22 203	71.85 145	45.52 212	68.67 202	63.21 210		86.64 212	80.03 210

High-resolution multi-view benchmark