High-res multi-view results

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

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

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

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

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

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

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

SED-MVS		98.87 1	99.20 2	98.48 1	99.32 12	99.85 2	99.55 7	96.20 7	99.48 3	96.78 3	98.51 16	99.99 1	99.36 1	98.98 8	97.59 29	99.67 21	99.99 3

DVP-MVS++		98.75 2	99.11 7	98.33 3	99.41 5	99.85 2	99.61 3	96.22 6	99.32 10	95.80 6	98.27 19	99.97 4	99.22 4	98.95 9	97.48 33	99.71 19	99.98 5

MSP-MVS		98.75 2	99.27 1	98.15 9	99.21 18	99.82 7	99.58 5	96.09 14	99.32 10	95.16 10	98.79 6	99.55 9	99.05 6	99.54 1	97.88 21	99.84 3	99.99 3
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
CNVR-MVS		98.73 4	99.17 5	98.22 6	99.47 4	99.85 2	99.57 6	96.23 4	99.30 12	94.90 12	98.65 10	98.93 20	99.36 1	99.46 3	98.21 11	99.81 6	99.80 34

DPE-MVS		98.69 5	99.14 6	98.16 8	99.37 8	99.82 7	99.66 2	96.26 1	99.18 17	95.02 11	98.62 13	99.98 3	98.88 12	98.90 12	97.51 32	99.75 10	99.97 8
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		98.65 6	98.87 13	98.38 2	99.30 14	99.85 2	99.14 24	96.23 4	99.51 2	97.16 1	96.01 35	99.99 1	98.90 11	98.89 13	97.88 21	99.56 51	99.98 5
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
APDe-MVS		98.60 7	98.97 10	98.18 7	99.38 7	99.78 12	99.35 16	96.14 10	99.24 14	95.66 8	98.19 21	99.01 17	98.66 18	98.77 15	97.80 24	99.86 2	99.97 8

SF-MVS		98.55 8	98.75 15	98.32 4	99.48 1	99.68 21	99.51 9	96.24 2	99.08 21	95.94 4	98.64 11	99.30 13	99.02 8	97.94 29	96.86 51	99.75 10	99.76 37

SMA-MVS		98.47 9	99.06 8	97.77 13	99.48 1	99.78 12	99.37 13	96.14 10	99.29 13	93.03 21	97.59 29	99.97 4	99.03 7	98.94 10	98.30 9	99.60 34	99.58 66
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
NCCC		98.41 10	99.18 3	97.52 17	99.36 9	99.84 6	99.55 7	96.08 16	99.33 9	91.77 26	98.79 6	99.46 11	98.59 20	99.15 7	98.07 18	99.73 14	99.64 55

SD-MVS		98.33 11	99.01 9	97.54 16	97.17 52	99.77 14	99.14 24	96.09 14	99.34 8	94.06 17	97.91 26	99.89 6	99.18 5	97.99 28	98.21 11	99.63 28	99.95 13
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
APD-MVS		98.28 12	98.69 16	97.80 11	99.31 13	99.62 29	99.31 19	96.15 9	99.19 16	93.60 18	97.28 30	98.35 28	98.72 17	98.27 21	98.22 10	99.73 14	99.89 25
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MCST-MVS		98.20 13	99.18 3	97.06 23	99.27 16	99.87 1	99.37 13	96.11 12	99.37 6	89.29 35	98.76 8	99.50 10	98.37 26	99.23 5	97.64 27	99.95 1	99.87 29

HPM-MVS++		98.16 14	98.87 13	97.32 19	99.39 6	99.70 19	99.18 22	96.10 13	99.09 20	91.14 28	98.02 24	99.89 6	98.44 24	98.75 16	97.03 46	99.67 21	99.63 58

MSLP-MVS++		98.12 15	98.23 28	97.99 10	99.28 15	99.72 16	99.59 4	95.27 30	98.61 34	94.79 13	96.11 34	97.79 37	99.27 3	96.62 68	98.96 5	99.77 9	99.80 34

HFP-MVS		98.02 16	98.55 20	97.40 18	99.11 22	99.69 20	99.41 11	95.41 28	98.79 31	91.86 25	98.61 14	98.16 30	99.02 8	97.87 34	97.40 35	99.60 34	99.35 85

TSAR-MVS + MP.		97.98 17	98.62 19	97.23 21	97.08 53	99.55 35	99.17 23	95.69 23	99.40 5	93.04 20	96.68 32	98.96 19	98.58 21	98.82 14	96.95 48	99.81 6	99.96 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
zzz-MVS		97.93 18	98.05 32	97.80 11	99.20 19	99.64 25	99.40 12	95.76 21	98.01 54	94.31 16	96.54 33	98.49 26	98.58 21	98.22 24	96.23 67	99.54 61	99.23 91

SteuartSystems-ACMMP		97.86 19	98.91 11	96.64 27	98.89 28	99.79 9	99.34 17	95.20 32	98.48 36	89.91 33	98.58 15	98.69 22	96.84 50	98.92 11	98.16 15	99.66 23	99.74 40
Skip Steuart: Steuart Systems R&D Blog.
CP-MVS		97.81 20	98.26 27	97.28 20	99.00 25	99.65 24	99.10 26	95.32 29	98.38 42	92.21 24	98.33 18	97.74 38	98.50 23	97.66 43	96.55 61	99.57 46	99.48 75

ACMMPR		97.78 21	98.28 25	97.20 22	99.03 24	99.68 21	99.37 13	95.24 31	98.86 30	91.16 27	97.86 27	97.26 40	98.79 15	97.64 45	97.40 35	99.60 34	99.25 90

DeepC-MVS_fast	95.01 1	97.67 22	98.22 29	97.02 24	99.00 25	99.79 9	99.10 26	95.82 19	99.05 24	89.53 34	93.54 50	96.77 43	98.83 13	99.34 4	99.44 2	99.82 4	99.63 58
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		97.54 23	97.35 39	97.77 13	99.17 20	99.55 35	98.57 32	95.76 21	99.04 25	94.66 14	97.94 25	94.39 57	98.82 14	96.21 77	94.78 87	99.62 30	99.52 71

ACMMP_NAP		97.51 24	98.27 26	96.63 28	99.34 10	99.72 16	99.25 20	95.94 18	98.11 48	87.10 48	96.98 31	98.50 25	98.61 19	98.58 18	96.83 54	99.56 51	99.14 99

MP-MVS		97.46 25	98.30 24	96.48 29	98.93 27	99.43 45	99.20 21	95.42 27	98.43 38	87.60 45	98.19 21	98.01 36	98.09 28	98.05 27	96.67 58	99.64 26	99.35 85
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
train_agg		97.42 26	98.88 12	95.71 34	98.46 35	99.60 32	99.05 28	95.16 33	99.10 19	84.38 64	98.47 17	98.85 21	97.61 32	98.54 19	97.66 26	99.62 30	99.93 19

CPTT-MVS		97.32 27	97.60 38	96.99 25	98.29 38	99.31 56	99.04 29	94.67 37	97.99 55	93.12 19	98.03 23	98.26 29	98.77 16	96.08 80	94.26 95	98.07 181	99.27 89

X-MVS		97.20 28	98.42 23	95.77 32	99.04 23	99.64 25	98.95 31	95.10 35	98.16 46	83.97 70	98.27 19	98.08 33	97.95 29	97.89 31	97.46 34	99.58 42	99.47 76

PHI-MVS		97.09 29	98.69 16	95.22 39	97.99 44	99.59 34	97.56 45	92.16 41	98.41 40	87.11 47	98.70 9	99.42 12	96.95 46	96.88 61	98.16 15	99.56 51	99.70 47

DPM-MVS		97.07 30	97.99 33	96.00 31	97.25 51	99.16 65	99.67 1	95.99 17	99.08 21	85.97 53	93.00 55	98.44 27	97.47 35	99.22 6	99.62 1	99.66 23	97.44 155

PGM-MVS		97.03 31	98.14 31	95.73 33	99.34 10	99.61 31	99.34 17	89.99 47	97.70 59	87.67 44	99.44 2	96.45 46	98.44 24	97.65 44	97.09 43	99.58 42	99.06 107

PLC	94.37 2	97.03 31	96.54 44	97.60 15	98.84 29	98.64 74	98.17 37	94.99 36	99.01 26	96.80 2	93.21 54	95.64 48	97.36 36	96.37 72	94.79 86	99.41 87	98.12 140
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TSAR-MVS + ACMM		96.90 33	98.64 18	94.88 41	98.12 42	99.47 40	99.01 30	95.43 26	99.23 15	81.98 90	95.95 36	99.16 16	95.13 70	98.61 17	98.11 17	99.58 42	99.93 19

TSAR-MVS + GP.		96.47 34	98.45 22	94.17 46	92.12 87	99.29 57	97.76 41	88.05 58	99.36 7	90.26 32	97.82 28	99.21 14	97.21 40	96.78 66	96.74 56	99.63 28	99.94 16

xxxxxxxxxxxxxcwj		96.27 35	94.51 65	98.32 4	99.48 1	99.68 21	99.51 9	96.24 2	99.08 21	95.94 4	98.64 11	69.64 161	99.02 8	97.94 29	96.86 51	99.75 10	99.76 37

EPNet		96.23 36	97.89 35	94.29 44	97.62 47	99.44 44	97.14 53	88.63 54	98.16 46	88.14 40	99.46 1	94.15 60	94.61 80	97.20 54	97.23 39	99.57 46	99.59 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNLPA		96.14 37	95.43 55	96.98 26	98.55 32	99.41 49	95.91 59	95.15 34	99.00 27	95.71 7	84.21 106	94.55 55	97.25 38	95.50 102	96.23 67	99.28 108	99.09 106

MVS_111021_LR		96.07 38	97.94 34	93.88 49	97.86 45	99.43 45	95.70 62	89.65 50	98.73 32	84.86 62	99.38 3	94.08 61	95.78 68	97.81 38	96.73 57	99.43 83	99.42 79

ACMMP		96.05 39	96.70 43	95.29 38	98.01 43	99.43 45	97.60 44	94.33 39	97.62 62	86.17 52	98.92 4	92.81 70	96.10 61	95.67 92	93.33 115	99.55 56	99.12 102
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 40	96.42 46	95.50 36	98.18 40	99.33 55	97.44 47	87.73 63	97.93 56	92.36 23	84.67 99	97.33 39	97.55 34	97.32 50	98.47 8	99.72 18	99.88 26

DeepPCF-MVS	94.02 3	95.92 41	98.47 21	92.95 58	97.57 48	99.79 9	91.45 117	94.42 38	99.76 1	86.48 51	92.88 56	98.12 32	92.62 99	99.49 2	99.32 3	95.15 206	99.95 13

CDPH-MVS		95.90 42	97.77 37	93.72 52	98.28 39	99.43 45	98.40 33	91.30 45	98.34 43	78.62 109	94.80 42	95.74 47	96.11 60	97.86 35	98.67 7	99.59 37	99.56 68

CSCG		95.77 43	95.35 57	96.26 30	99.13 21	99.60 32	98.14 38	91.89 44	96.57 78	92.61 22	89.65 65	91.74 77	96.96 44	93.69 126	96.58 60	98.86 135	99.63 58

OMC-MVS		95.75 44	95.84 52	95.64 35	98.52 34	99.34 54	97.15 52	92.02 43	98.94 29	90.45 31	88.31 72	94.64 53	96.35 56	96.02 83	95.99 75	99.34 97	97.65 151

MVS_111021_HR		95.70 45	98.16 30	92.83 59	97.57 48	99.77 14	94.78 75	88.05 58	98.61 34	82.29 85	98.85 5	94.66 52	94.63 78	97.80 39	97.63 28	99.64 26	99.79 36

3Dnovator	90.31 8	95.67 46	96.16 48	95.11 40	98.59 31	99.37 53	97.50 46	87.98 60	98.02 53	89.09 36	85.36 98	94.62 54	97.66 30	97.10 57	98.90 6	99.82 4	99.73 43

CANet		95.40 47	96.27 47	94.40 43	96.25 58	99.62 29	98.37 34	88.59 55	98.09 49	87.58 46	84.57 101	95.54 50	95.87 65	98.12 25	98.03 20	99.73 14	99.90 24

QAPM		95.17 48	96.05 50	94.14 47	98.55 32	99.49 38	97.41 48	87.88 61	97.72 58	84.21 67	84.59 100	95.60 49	97.21 40	97.10 57	98.19 14	99.57 46	99.65 53

MVSTER		94.75 49	96.50 45	92.70 62	90.91 102	94.51 145	97.37 50	83.37 96	98.40 41	89.04 37	93.23 53	97.04 42	95.91 64	97.73 40	95.59 83	99.61 32	99.01 108

TAPA-MVS	92.04 6	94.72 50	95.13 60	94.24 45	97.72 46	99.17 63	97.61 43	92.16 41	97.66 61	81.99 89	87.84 77	93.94 63	96.50 53	95.74 89	94.27 94	99.46 77	97.31 158
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepC-MVS	92.23 5	94.53 51	94.26 74	94.86 42	96.73 55	99.50 37	97.85 40	95.45 25	96.22 85	82.73 80	80.68 115	88.02 90	96.92 47	97.49 49	98.20 13	99.47 71	99.69 50
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 36	90.70 84	95.54 64	99.49 38	94.14 86	74.91 162	98.43 38	85.32 59	94.78 43	99.19 15	94.95 74	97.02 59	96.18 70	99.35 93	99.36 84

ETV-MVS		94.49 53	97.23 41	91.29 77	90.43 111	98.55 77	93.41 97	84.53 89	99.16 18	83.13 75	94.72 44	94.08 61	96.61 52	97.72 41	96.60 59	99.61 32	99.81 33

MVS_0304		94.35 54	95.66 54	92.83 59	94.82 66	99.46 42	98.19 36	87.75 62	97.32 68	81.83 93	83.50 108	93.19 69	94.71 76	98.24 23	98.07 18	99.68 20	99.83 31

DROMVSNet		94.33 55	96.88 42	91.36 75	90.12 118	97.70 96	95.20 70	80.27 121	98.63 33	85.97 53	93.92 49	93.85 66	97.09 42	97.54 47	96.81 55	99.49 67	99.70 47

MAR-MVS		94.18 56	95.12 61	93.09 57	98.40 37	99.17 63	94.20 85	81.92 106	98.47 37	86.52 50	90.92 60	84.21 110	98.12 27	95.88 86	97.59 29	99.40 88	99.58 66
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PCF-MVS	92.56 4	93.95 57	93.82 77	94.10 48	96.07 60	99.25 58	96.82 55	95.51 24	92.00 130	81.51 94	82.97 111	93.88 65	95.63 69	94.24 114	94.71 89	99.09 118	99.70 47
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DELS-MVS		93.82 58	93.82 77	93.81 51	96.34 57	99.47 40	97.26 51	88.53 56	92.13 127	87.80 43	79.67 118	88.01 91	93.14 91	98.28 20	99.22 4	99.80 8	99.98 5
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
CS-MVS-test		93.60 59	96.02 51	90.78 83	89.70 126	96.59 115	95.20 70	80.27 121	97.92 57	83.05 76	92.48 57	91.67 78	97.09 42	97.54 47	96.24 66	99.36 91	99.72 44

CS-MVS		93.54 60	96.14 49	90.51 87	89.94 123	98.14 87	93.50 95	82.81 100	98.32 44	79.69 104	88.76 68	93.30 68	97.56 33	97.83 36	96.85 53	99.55 56	99.74 40

OpenMVS	88.43 11	93.49 61	93.62 80	93.34 53	98.46 35	99.39 50	97.00 54	87.66 65	95.37 92	81.21 96	75.96 133	91.58 79	96.21 59	96.37 72	97.10 42	99.52 62	99.54 70

EIA-MVS		93.32 62	95.32 58	90.99 80	90.45 110	98.53 80	93.46 96	84.68 88	97.56 65	81.38 95	91.04 59	87.37 94	96.39 55	97.27 51	95.73 80	99.59 37	99.76 37

PVSNet_BlendedMVS		93.30 63	93.46 84	93.10 55	95.60 62	99.38 51	93.59 93	88.70 52	98.09 49	88.10 41	86.96 85	75.02 133	93.08 92	97.89 31	96.90 49	99.56 51	100.00 1

PVSNet_Blended		93.30 63	93.46 84	93.10 55	95.60 62	99.38 51	93.59 93	88.70 52	98.09 49	88.10 41	86.96 85	75.02 133	93.08 92	97.89 31	96.90 49	99.56 51	100.00 1

test2506		93.08 65	93.40 86	92.70 62	92.76 80	99.20 60	94.67 78	86.82 70	92.58 123	90.81 29	86.28 90	85.24 105	91.69 108	96.85 62	96.33 63	99.45 81	97.34 157

PMMVS		93.05 66	95.40 56	90.31 89	91.41 95	97.54 101	92.62 109	83.25 98	98.08 52	79.44 107	95.18 40	88.52 89	96.43 54	95.70 90	93.88 98	98.68 151	98.91 111

LS3D		92.70 67	92.23 95	93.26 54	96.24 59	98.72 69	97.93 39	96.17 8	96.41 79	72.46 124	81.39 114	80.76 123	97.66 30	95.69 91	95.62 82	99.07 120	97.02 166

baseline1		92.67 68	93.62 80	91.55 71	91.16 98	97.15 104	93.92 91	85.97 78	94.76 99	84.07 69	87.17 81	86.89 97	94.62 79	96.72 67	95.90 78	99.57 46	96.79 170

IS_MVSNet		92.67 68	94.99 63	89.96 94	91.17 97	98.54 78	92.77 104	84.00 90	92.72 121	81.90 92	85.67 96	92.47 72	90.39 122	97.82 37	97.81 23	99.51 63	99.91 23

TSAR-MVS + COLMAP		92.56 70	92.44 93	92.71 61	94.61 68	97.69 97	97.69 42	91.09 46	98.96 28	76.71 114	94.68 45	69.41 162	96.91 48	95.80 88	94.18 96	99.26 109	96.33 174

baseline		92.56 70	94.38 70	90.43 88	90.71 106	98.23 86	95.07 72	80.73 120	97.52 66	82.45 84	87.34 80	85.91 101	94.07 86	96.29 76	95.94 77	99.58 42	99.47 76

canonicalmvs		92.54 72	93.28 87	91.68 68	91.44 94	98.24 85	95.45 67	81.84 110	95.98 89	84.85 63	90.69 61	78.53 128	96.96 44	92.97 132	97.06 44	99.57 46	99.47 76

PatchMatch-RL		92.54 72	92.82 92	92.21 64	96.57 56	98.74 68	91.85 114	86.30 73	96.23 84	85.18 60	95.21 39	73.58 139	94.22 85	95.40 105	93.08 119	99.14 115	97.49 154

MVS_Test		92.42 74	94.43 66	90.08 93	90.69 107	98.26 84	94.78 75	80.81 119	97.27 69	78.76 108	87.06 83	84.25 109	95.84 66	97.67 42	97.56 31	99.59 37	98.93 110

thisisatest0530		92.31 75	95.14 59	89.02 103	90.02 119	98.45 82	91.30 118	83.58 93	96.90 74	77.90 111	90.45 63	94.33 58	91.98 105	95.57 96	91.43 141	99.31 103	98.81 114

tttt0517		92.29 76	95.12 61	88.99 104	90.02 119	98.44 83	91.19 121	83.58 93	96.88 75	77.86 112	90.45 63	94.32 59	91.98 105	95.54 98	91.43 141	99.31 103	98.78 116

EPP-MVSNet		92.29 76	94.35 72	89.88 95	90.36 113	97.69 97	90.89 124	83.31 97	93.39 114	83.47 74	85.56 97	93.92 64	91.93 107	95.49 103	94.77 88	99.34 97	99.62 61

HQP-MVS		91.94 78	93.03 89	90.66 86	93.69 70	96.48 119	95.92 58	89.73 48	97.33 67	72.65 122	95.37 37	73.56 140	92.75 98	94.85 111	94.12 97	99.23 112	99.51 72

MSDG		91.93 79	90.28 122	93.85 50	97.36 50	97.12 105	95.88 60	94.07 40	94.52 103	84.13 68	76.74 127	80.89 122	92.54 100	93.97 122	93.61 109	99.14 115	95.10 183

UGNet		91.71 80	94.43 66	88.53 106	92.72 82	98.00 90	90.22 131	84.81 87	94.45 104	83.05 76	87.65 79	92.74 71	81.04 175	94.51 113	94.45 91	99.32 102	99.21 95
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
thres100view900		91.69 81	91.52 101	91.88 67	91.61 89	98.89 66	95.49 65	86.96 67	93.24 115	80.82 98	87.90 74	71.15 151	96.88 49	96.00 84	93.51 111	99.51 63	99.95 13

CLD-MVS		91.67 82	91.30 106	92.10 65	91.25 96	96.59 115	95.93 57	87.25 66	96.86 76	85.55 58	87.08 82	73.01 141	93.26 90	93.07 130	92.84 125	99.34 97	99.68 51
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ET-MVSNet_ETH3D		91.59 83	94.96 64	87.65 109	72.75 213	97.24 103	95.29 68	82.73 102	96.81 77	78.49 110	95.30 38	90.48 85	97.23 39	91.60 147	94.31 92	99.43 83	99.01 108

tfpn200view9		91.47 84	91.31 104	91.65 69	91.61 89	98.69 71	95.03 73	86.17 74	93.24 115	80.82 98	87.90 74	71.15 151	96.80 51	95.53 99	92.82 127	99.47 71	99.88 26

CANet_DTU		91.36 85	95.75 53	86.23 120	92.31 86	98.71 70	95.60 64	78.41 137	98.20 45	56.48 182	94.38 47	87.96 92	95.11 71	96.89 60	96.07 71	99.48 69	98.01 144

thres200		91.36 85	91.19 108	91.55 71	91.60 91	98.69 71	94.98 74	86.17 74	92.16 126	80.76 100	87.66 78	71.15 151	96.35 56	95.53 99	93.23 117	99.47 71	99.92 22

FMVSNet3		91.25 87	92.13 97	90.21 90	85.64 153	93.14 154	95.29 68	80.09 123	96.40 80	85.74 55	77.13 122	86.81 98	94.98 73	97.19 55	97.11 41	99.55 56	97.13 163

thres400		91.24 88	91.01 114	91.50 74	91.56 92	98.77 67	94.66 80	86.41 72	91.87 132	80.56 101	87.05 84	71.01 154	96.35 56	95.67 92	92.82 127	99.48 69	99.88 26

PVSNet_Blended_VisFu		91.20 89	92.89 91	89.23 101	93.41 73	98.61 76	89.80 133	85.39 82	92.84 119	82.80 79	74.21 137	91.38 81	84.64 154	97.22 53	96.04 74	99.34 97	99.93 19

DCV-MVSNet		91.15 90	92.00 98	90.17 92	90.78 104	92.23 171	93.70 92	81.17 117	95.16 95	82.98 78	89.46 67	83.31 112	93.98 87	91.79 146	92.87 122	98.41 169	99.18 97

DI_MVS_plusplus_trai		91.11 91	91.47 102	90.68 85	90.01 121	97.77 94	95.87 61	83.56 95	94.72 100	82.12 88	68.46 156	87.46 93	93.07 94	96.46 71	95.73 80	99.47 71	99.71 46

diffmvs		91.05 92	91.15 109	90.93 81	90.15 116	97.79 93	94.05 87	85.45 80	95.63 90	81.95 91	80.45 117	73.01 141	94.47 81	95.56 97	95.89 79	99.49 67	99.72 44

Vis-MVSNet (Re-imp)		91.05 92	94.43 66	87.11 111	91.05 100	97.99 91	92.53 110	83.82 92	92.71 122	76.28 115	84.50 102	92.43 73	79.52 180	97.24 52	97.68 25	99.43 83	98.45 127

thres600view7		90.97 94	90.70 116	91.30 76	91.53 93	98.69 71	94.33 81	86.17 74	91.75 134	80.19 102	86.06 94	70.90 155	96.10 61	95.53 99	92.08 134	99.47 71	99.86 30

baseline2		90.91 95	94.40 69	86.84 114	87.54 144	96.83 111	89.95 132	79.22 132	96.00 88	77.04 113	88.68 69	89.73 86	88.01 143	96.35 74	93.51 111	99.29 105	99.68 51

ACMP	89.80 9	90.72 96	91.15 109	90.21 90	92.55 84	96.52 118	92.63 108	85.71 79	94.65 101	81.06 97	93.32 51	70.56 158	90.52 121	92.68 136	91.05 146	98.76 143	99.31 88
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
casdiffmvs		90.69 97	90.56 119	90.85 82	90.14 117	97.81 92	92.94 102	85.30 83	93.47 113	82.50 83	76.34 131	74.12 137	94.67 77	96.51 70	96.26 65	99.55 56	99.42 79

ACMM	89.40 10	90.58 98	90.02 125	91.23 78	93.30 75	94.75 141	90.69 127	88.22 57	95.20 93	82.70 81	88.54 70	71.40 149	93.48 89	93.64 127	90.94 147	98.99 126	95.72 179
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		90.49 99	91.12 112	89.75 97	84.99 156	92.73 159	93.94 88	80.09 123	96.40 80	85.74 55	77.13 122	86.81 98	94.42 82	94.12 116	93.73 100	99.35 93	96.90 167

test1		90.49 99	91.12 112	89.75 97	84.99 156	92.73 159	93.94 88	80.09 123	96.40 80	85.74 55	77.13 122	86.81 98	94.42 82	94.12 116	93.73 100	99.35 93	96.90 167

ECVR-MVS		90.37 101	88.96 134	92.01 66	92.76 80	99.20 60	94.67 78	86.82 70	92.58 123	86.71 49	68.95 155	71.46 148	91.69 108	96.85 62	96.33 63	99.45 81	97.38 156

LGP-MVS_train		90.34 102	91.63 100	88.83 105	93.31 74	96.14 124	95.49 65	85.24 85	93.91 108	68.71 136	93.96 48	71.63 146	91.12 117	93.82 124	92.79 129	99.07 120	99.16 98

test1111		90.01 103	88.67 136	91.57 70	92.68 83	99.20 60	94.25 84	86.90 69	92.03 129	85.04 61	67.79 160	71.21 150	91.12 117	96.83 64	96.34 62	99.42 86	97.28 159

EPNet_dtu		89.82 104	94.18 75	84.74 130	96.87 54	95.54 134	92.65 107	86.91 68	96.99 71	54.17 193	92.41 58	88.54 88	78.35 183	96.15 79	96.05 73	99.47 71	93.60 191
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		89.81 105	89.75 126	89.88 95	93.22 77	93.99 148	94.78 75	85.23 86	94.01 107	82.52 82	95.00 41	87.23 95	92.01 104	85.16 200	83.48 205	91.54 211	89.38 205

MDTV_nov1_ep13		89.63 106	94.38 70	84.09 137	88.76 136	97.53 102	89.37 141	68.46 194	96.95 72	70.27 131	87.88 76	93.67 67	91.04 119	93.12 128	93.83 99	96.62 199	97.68 150

UA-Net		89.56 107	93.03 89	85.52 126	92.46 85	97.55 100	91.92 113	81.91 107	85.24 165	71.39 126	83.57 107	96.56 45	76.01 194	96.81 65	97.04 45	99.46 77	94.41 186

FMVSNet2		89.51 108	89.63 127	89.38 99	84.99 156	92.73 159	93.94 88	79.28 130	93.73 110	84.28 66	69.36 154	82.32 115	94.42 82	96.16 78	96.22 69	99.35 93	96.90 167

CostFormer		89.42 109	91.67 99	86.80 116	89.99 122	96.33 121	90.75 125	64.79 197	95.17 94	83.62 73	86.20 92	82.15 117	92.96 95	89.22 169	92.94 120	98.68 151	99.65 53

FC-MVSNet-train		89.37 110	89.62 128	89.08 102	90.48 109	94.16 147	89.45 137	83.99 91	91.09 137	80.09 103	82.84 112	74.52 136	91.44 114	93.79 125	91.57 140	99.01 124	99.35 85

OPM-MVS		89.33 111	87.45 145	91.53 73	94.49 69	96.20 123	96.47 56	89.72 49	82.77 172	75.43 116	80.53 116	70.86 156	93.80 88	94.00 120	91.85 138	99.29 105	95.91 177
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test-LLR		89.31 112	93.60 82	84.30 134	88.08 140	96.98 107	88.10 146	78.00 138	94.83 97	62.43 156	84.29 104	90.96 82	89.70 127	95.63 94	92.86 123	99.51 63	99.64 55

EPMVS		89.31 112	93.70 79	84.18 136	91.10 99	98.10 88	89.17 143	62.71 201	96.24 83	70.21 133	86.46 89	92.37 74	92.79 96	91.95 144	93.59 110	99.10 117	97.19 160

Anonymous20231211		89.22 114	87.56 143	91.16 79	90.23 115	96.62 114	93.22 99	85.44 81	92.89 118	84.37 65	60.13 176	81.25 120	96.02 63	90.61 154	92.01 135	97.70 189	99.41 81

Effi-MVS+		88.96 115	91.13 111	86.43 118	89.12 132	97.62 99	93.15 100	75.52 156	93.90 109	66.40 140	86.23 91	70.51 159	95.03 72	95.89 85	94.28 93	99.37 89	99.51 72

SCA		88.76 116	94.29 73	82.30 153	89.33 129	96.81 112	87.68 148	61.52 206	96.95 72	64.68 146	88.35 71	94.80 51	91.58 111	92.23 138	93.21 118	98.99 126	97.70 149

test0.0.03 1		88.71 117	92.22 96	84.63 132	88.08 140	94.71 143	85.91 171	78.00 138	95.54 91	72.96 120	86.10 93	85.88 103	83.59 162	92.95 134	93.24 116	99.25 111	97.09 164

PatchmatchNet		88.67 118	94.10 76	82.34 152	89.38 128	97.72 95	87.24 154	62.18 204	97.00 70	64.79 145	87.97 73	94.43 56	91.55 112	91.21 152	92.77 130	98.90 131	97.60 153
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dps		88.66 119	90.19 123	86.88 113	89.94 123	96.48 119	89.56 135	64.08 199	94.12 106	89.00 38	83.39 109	82.56 114	90.16 125	86.81 192	89.26 166	98.53 164	98.71 118

TESTMET0.1,1		88.63 120	93.60 82	82.84 149	84.07 163	96.98 107	88.10 146	73.22 176	94.83 97	62.43 156	84.29 104	90.96 82	89.70 127	95.63 94	92.86 123	99.51 63	99.64 55

CHOSEN 1792x2688		88.63 120	89.01 132	88.19 107	94.83 65	99.21 59	92.66 106	79.85 127	92.40 125	72.18 125	56.38 197	80.22 125	90.24 123	97.64 45	97.28 38	99.37 89	99.94 16

CDS-MVSNet		88.59 122	90.13 124	86.79 117	86.98 149	95.43 135	92.03 112	81.33 115	85.54 162	74.51 119	77.07 125	85.14 106	87.03 148	93.90 123	95.18 84	98.88 133	98.67 120
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IB-MVS	84.67 14	88.34 123	90.61 118	85.70 123	92.99 79	98.62 75	78.85 197	86.07 77	94.35 105	88.64 39	85.99 95	75.69 131	68.09 207	88.21 172	91.43 141	99.55 56	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		88.25 124	93.27 88	82.38 151	83.89 164	96.86 110	87.10 158	72.80 178	94.58 102	61.85 161	83.21 110	90.65 84	89.18 131	95.43 104	92.58 132	99.46 77	99.61 62

COLMAP_ROB	84.42 15	88.24 125	87.32 146	89.32 100	95.83 61	95.82 128	92.81 103	87.68 64	92.09 128	72.64 123	72.34 145	79.96 126	88.79 134	89.54 164	89.46 162	98.16 178	92.00 197
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
IterMVS-LS		87.95 126	89.40 130	86.26 119	88.79 135	90.93 187	91.23 120	76.05 153	90.87 138	71.07 128	75.51 134	81.18 121	91.21 116	94.11 119	95.01 85	99.20 114	98.23 135
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		87.86 127	88.25 139	87.40 110	94.67 67	98.54 78	90.33 130	76.51 152	89.60 146	70.89 129	51.43 208	85.69 104	92.79 96	96.59 69	95.96 76	99.22 113	99.94 16

Vis-MVSNet		87.60 128	91.31 104	83.27 144	89.14 131	98.04 89	90.35 129	79.42 128	87.23 151	66.92 139	79.10 121	84.63 108	74.34 201	95.81 87	96.06 72	99.46 77	98.32 131
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GeoE		87.55 129	88.17 140	86.82 115	88.74 137	96.32 122	92.75 105	74.93 161	90.13 143	72.73 121	69.47 153	74.03 138	92.51 101	93.99 121	93.62 108	99.29 105	99.59 63

RPMNet		87.35 130	92.41 94	81.45 157	88.85 134	96.06 125	89.42 140	59.59 213	93.57 111	61.81 162	76.48 130	91.48 80	90.18 124	96.32 75	93.37 114	98.87 134	99.59 63

tpm cat1		87.34 131	88.52 138	85.95 121	89.83 125	95.80 129	90.73 126	64.91 196	92.99 117	82.21 87	71.19 151	82.68 113	90.13 126	86.38 193	90.87 149	97.90 186	99.74 40

MS-PatchMatch		87.19 132	88.59 137	85.55 125	93.15 78	96.58 117	92.35 111	74.19 169	91.97 131	70.33 130	71.42 149	85.89 102	84.28 156	93.12 128	89.16 168	99.00 125	91.99 198

Effi-MVS+-dtu		87.18 133	90.48 120	83.32 143	86.51 150	95.76 131	91.16 123	74.28 168	90.44 142	61.31 165	86.72 88	72.68 144	91.25 115	95.01 109	93.64 103	95.45 205	99.12 102

FMVSNet5		87.06 134	89.52 129	84.20 135	79.92 201	86.57 207	87.11 157	72.37 180	96.06 86	75.41 117	84.33 103	91.76 76	91.60 110	91.51 148	91.22 144	98.77 140	85.16 210

Fast-Effi-MVS+-dtu		86.94 135	91.27 107	81.89 154	86.27 151	95.06 136	90.68 128	68.93 191	91.76 133	57.18 180	89.56 66	75.85 130	89.19 130	94.56 112	92.84 125	99.07 120	99.23 91

Fast-Effi-MVS+		86.94 135	87.88 142	85.84 122	86.99 148	95.80 129	91.24 119	73.48 175	92.75 120	69.22 134	72.70 143	65.71 168	94.84 75	94.98 110	94.71 89	99.26 109	98.48 126

tpmrst		86.78 137	90.29 121	82.69 150	90.55 108	96.95 109	88.49 145	62.58 202	95.09 96	63.52 152	76.67 129	84.00 111	92.05 103	87.93 175	91.89 137	98.98 128	99.50 74

CR-MVSNet		86.73 138	91.47 102	81.20 160	88.56 138	96.06 125	89.43 138	61.37 207	93.57 111	60.81 167	72.89 142	88.85 87	88.13 141	96.03 81	93.64 103	98.89 132	99.22 93

ADS-MVSNet		86.68 139	90.79 115	81.88 155	90.38 112	96.81 112	86.90 159	60.50 211	96.01 87	63.93 149	81.67 113	84.72 107	90.78 120	87.03 186	91.67 139	98.77 140	97.63 152

FMVSNet1		85.85 140	84.91 156	86.96 112	82.70 169	91.39 181	91.54 116	77.45 144	85.29 164	79.56 106	60.70 173	72.68 144	92.37 102	94.12 116	93.73 100	98.12 179	96.44 171

FC-MVSNet-test		85.51 141	89.08 131	81.35 158	85.31 155	93.35 150	87.65 149	77.55 143	90.01 144	64.07 148	79.63 119	81.83 119	74.94 198	92.08 141	90.83 151	98.55 161	95.81 178

ACMH	85.22 13	85.40 142	85.73 153	85.02 128	91.76 88	94.46 146	84.97 177	81.54 113	85.18 166	65.22 144	76.92 126	64.22 169	88.58 137	90.17 156	90.25 157	98.03 182	98.90 112
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		85.35 143	86.00 152	84.59 133	84.97 159	95.57 133	88.98 144	77.29 147	81.44 177	71.36 127	71.48 148	75.00 135	87.03 148	91.92 145	92.21 133	97.92 185	94.40 187

ACMH+	85.62 12	85.27 144	84.96 155	85.64 124	90.84 103	94.78 140	87.46 151	81.30 116	86.94 152	67.35 138	74.56 136	64.09 170	88.70 135	88.14 173	89.00 169	98.22 177	97.19 160

USDC		85.11 145	85.35 154	84.83 129	89.45 127	94.93 139	92.98 101	77.30 146	90.53 140	61.80 163	76.69 128	59.62 180	88.90 133	92.78 135	90.79 153	98.53 164	92.12 195

IterMVS		85.02 146	88.98 133	80.41 166	87.03 147	90.34 195	89.78 134	69.45 188	89.77 145	54.04 194	73.71 139	82.05 118	83.44 165	95.11 107	93.64 103	98.75 144	98.22 137
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		84.91 147	88.90 135	80.25 169	87.04 146	90.27 196	89.23 142	69.25 190	89.17 147	54.04 194	73.65 140	82.22 116	83.23 170	95.11 107	93.63 107	98.73 145	98.23 135

PatchT		84.89 148	90.67 117	78.13 189	87.83 143	94.99 138	72.46 209	60.22 212	91.74 135	60.81 167	72.16 146	86.95 96	88.13 141	96.03 81	93.64 103	99.36 91	99.22 93

pmmvs4		84.88 149	84.67 157	85.13 127	82.80 168	92.37 164	87.29 152	79.08 133	90.51 141	74.94 118	70.37 152	62.49 173	88.17 140	92.01 143	88.51 174	98.49 167	96.44 171

test_part1		84.71 150	82.08 167	87.78 108	89.19 130	91.40 180	91.19 121	79.25 131	79.62 189	82.23 86	57.07 193	70.79 157	88.95 132	87.46 180	89.91 159	95.89 204	98.31 133

CVMVSNet		84.01 151	86.91 147	80.61 164	88.39 139	93.29 151	86.06 167	82.29 104	83.13 170	54.29 190	72.68 144	79.59 127	75.11 197	91.23 151	92.91 121	97.54 193	95.58 180

tpm		83.97 152	87.97 141	79.31 179	87.35 145	93.21 153	86.00 169	61.90 205	90.69 139	54.01 196	79.42 120	75.61 132	88.65 136	87.18 184	90.48 155	97.95 184	99.21 95

GA-MVS		83.83 153	86.63 148	80.58 165	85.40 154	94.73 142	87.27 153	78.76 136	86.49 154	49.57 204	74.21 137	67.67 165	83.38 166	95.28 106	90.92 148	99.08 119	97.09 164

UniMVSNet_NR-MVSNet		83.83 153	83.70 160	83.98 138	81.41 179	92.56 163	86.54 162	82.96 99	85.98 159	66.27 141	66.16 164	63.63 171	87.78 145	87.65 178	90.81 152	98.94 129	99.13 100

UniMVSNet (Re)		83.28 155	83.16 161	83.42 142	81.93 174	93.12 155	86.27 165	80.83 118	85.88 160	68.23 137	64.56 167	60.58 175	84.25 157	89.13 170	89.44 164	99.04 123	99.40 82

thisisatest0515		83.17 156	86.49 149	79.30 180	82.04 172	93.12 155	78.70 198	77.92 140	86.43 155	63.05 153	74.91 135	73.01 141	75.56 196	92.10 140	88.05 187	98.50 166	97.76 148

TinyColmap		83.03 157	82.24 165	83.95 139	88.88 133	93.22 152	89.48 136	76.89 149	87.53 150	62.12 158	68.46 156	55.03 196	88.43 139	90.87 153	89.65 160	97.89 187	90.91 201

testgi		82.88 158	86.14 151	79.08 182	86.05 152	92.20 172	81.23 194	74.77 164	88.70 148	57.63 179	86.73 87	61.53 174	76.83 191	90.33 155	89.43 165	97.99 183	94.05 188

DU-MVS		82.87 159	82.16 166	83.70 141	80.77 188	92.24 168	86.54 162	81.91 107	86.41 156	66.27 141	63.95 168	55.66 194	87.78 145	86.83 189	90.86 150	98.94 129	99.13 100

MIMVSNet		82.87 159	86.17 150	79.02 183	77.23 209	92.88 158	84.88 178	60.62 210	86.72 153	64.16 147	73.58 141	71.48 147	88.51 138	94.14 115	93.50 113	98.72 147	90.87 202

NR-MVSNet		82.37 161	81.95 169	82.85 148	82.56 171	92.24 168	87.49 150	81.91 107	86.41 156	65.51 143	63.95 168	52.93 205	80.80 177	89.41 166	89.61 161	98.85 136	99.10 105

Baseline_NR-MVSNet		82.08 162	80.64 176	83.77 140	80.77 188	88.50 202	86.88 160	81.71 111	85.58 161	68.80 135	58.20 188	57.75 186	86.16 150	86.83 189	88.68 171	98.33 174	98.90 112

TranMVSNet+NR-MVSNet		82.07 163	81.36 172	82.90 147	80.43 194	91.39 181	87.16 156	82.75 101	84.28 168	62.98 154	62.28 172	56.01 193	85.30 153	86.06 195	90.69 154	98.80 137	98.80 115

pm-mvs1		81.68 164	81.70 170	81.65 156	82.61 170	92.26 167	85.54 175	78.95 134	76.29 200	63.81 150	58.43 187	66.33 167	80.63 178	92.30 137	89.93 158	98.37 173	96.39 173

TDRefinement		81.49 165	80.08 182	83.13 146	91.02 101	94.53 144	91.66 115	82.43 103	81.70 175	62.12 158	62.30 171	59.32 181	73.93 202	87.31 182	85.29 198	97.61 190	90.14 203

anonymousdsp		81.29 166	84.52 159	77.52 191	79.83 202	92.62 162	82.61 189	70.88 185	80.76 181	50.82 201	68.35 158	68.76 163	82.45 173	93.00 131	89.45 163	98.55 161	98.69 119

gg-mvs-nofinetune		81.27 167	84.65 158	77.32 192	87.96 142	98.48 81	95.64 63	56.36 216	59.35 218	32.80 222	47.96 212	92.11 75	91.49 113	98.12 25	97.00 47	99.65 25	99.56 68

tfpnnormal		81.11 168	79.33 190	83.19 145	84.23 161	92.29 166	86.76 161	82.27 105	72.67 206	62.02 160	56.10 199	53.86 202	85.35 152	92.06 142	89.23 167	98.49 167	99.11 104

UniMVSNet_ETH3D		80.95 169	77.71 198	84.74 130	84.45 160	93.11 157	86.45 164	79.97 126	75.21 202	70.22 132	51.24 209	50.26 211	89.55 129	84.47 202	91.12 145	97.81 188	98.53 124

V42		80.88 170	80.74 174	81.05 161	81.21 182	92.01 174	85.96 170	77.75 142	81.62 176	59.73 174	59.93 179	58.35 185	82.98 172	86.90 188	88.06 186	98.69 150	98.32 131

v2v482		80.86 171	80.52 180	81.25 159	80.79 187	91.85 175	85.68 173	78.78 135	81.05 178	58.09 177	60.46 174	56.08 191	85.45 151	87.27 183	88.53 173	98.73 145	98.38 130

v8		80.61 172	80.61 178	80.62 163	81.51 177	91.00 186	86.06 167	74.07 171	81.78 174	59.93 173	60.10 178	58.42 184	83.35 167	86.99 187	88.11 184	98.79 138	97.83 146

pmmvs5		80.48 173	81.43 171	79.36 178	81.50 178	92.24 168	82.07 192	74.08 170	78.10 193	55.86 185	67.72 161	54.35 199	83.91 161	92.97 132	88.65 172	98.77 140	96.01 175

v10		80.38 174	80.73 175	79.96 171	81.22 181	90.40 194	86.11 166	71.63 182	82.42 173	57.65 178	58.74 185	57.47 187	84.44 155	89.75 160	88.28 177	98.71 148	98.06 143

v1144		80.36 175	80.63 177	80.05 170	80.86 186	91.56 178	85.78 172	75.22 158	80.73 182	55.83 186	58.51 186	56.99 189	83.93 160	89.79 159	88.25 178	98.68 151	98.56 123

SixPastTwentyTwo		80.28 176	82.06 168	78.21 188	81.89 176	92.35 165	77.72 199	74.48 165	83.04 171	54.22 191	76.06 132	56.40 190	83.55 163	86.83 189	84.83 200	97.38 194	94.93 184

CP-MVSNet		79.90 177	79.49 187	80.38 167	80.72 190	90.83 188	82.98 186	75.17 159	79.70 187	61.39 164	59.74 180	51.98 208	83.31 168	87.37 181	88.38 175	98.71 148	98.45 127

v1192		79.84 178	80.05 184	79.61 174	80.49 193	91.04 185	85.56 174	74.37 167	80.73 182	54.35 189	57.07 193	54.54 198	84.23 158	89.94 157	88.38 175	98.63 155	98.61 121

WR-MVS_H		79.76 179	80.07 183	79.40 177	81.25 180	91.73 177	82.77 187	74.82 163	79.02 192	62.55 155	59.41 182	57.32 188	76.27 193	87.61 179	87.30 192	98.78 139	98.09 141

WR-MVS		79.67 180	80.25 181	79.00 184	80.65 191	91.16 183	83.31 184	76.57 151	80.97 179	60.50 172	59.20 183	58.66 183	74.38 200	85.85 197	87.76 189	98.61 156	98.14 138

v148		79.66 181	79.13 192	80.27 168	81.02 184	91.76 176	81.90 193	79.32 129	79.24 190	63.79 151	58.07 190	54.34 200	77.17 189	84.42 203	87.52 191	98.40 170	98.59 122

LTVRE_ROB	79.45 16	79.66 181	80.55 179	78.61 186	83.01 167	92.19 173	87.18 155	73.69 174	71.70 209	43.22 217	71.22 150	50.85 209	87.82 144	89.47 165	90.43 156	96.75 197	98.00 145
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		79.61 183	79.77 185	79.41 176	80.28 195	91.06 184	84.87 179	73.86 172	79.65 188	55.38 187	57.76 191	55.20 195	83.46 164	88.42 171	87.89 188	98.61 156	98.42 129

v1921920		79.55 184	79.77 185	79.30 180	80.24 196	90.77 190	85.37 176	73.75 173	80.38 184	53.78 197	56.89 196	54.18 201	84.05 159	89.55 163	88.13 183	98.59 158	98.52 125

TransMVSNet (Re)		79.51 185	78.36 194	80.84 162	83.17 165	89.72 198	84.22 182	81.45 114	73.98 205	60.79 170	57.20 192	56.05 192	77.11 190	89.88 158	88.86 170	98.30 176	92.83 193

MVS-HIRNet		79.34 186	82.56 162	75.57 197	84.11 162	95.02 137	75.03 206	57.28 215	85.50 163	55.88 184	53.00 205	70.51 159	83.05 171	92.12 139	91.96 136	98.09 180	89.83 204

PS-CasMVS		79.06 187	78.58 193	79.63 173	80.59 192	90.55 192	82.54 190	75.04 160	77.76 194	58.84 175	58.16 189	50.11 213	82.09 174	87.05 185	88.18 181	98.66 154	98.27 134

v1240		78.97 188	79.27 191	78.63 185	80.04 197	90.61 191	84.25 181	72.95 177	79.22 191	52.70 199	56.22 198	52.88 207	83.28 169	89.60 162	88.20 180	98.56 160	98.14 138

pmnet_mix02		78.91 189	81.17 173	76.28 196	81.91 175	90.82 189	74.25 207	77.87 141	86.17 158	49.04 205	67.97 159	62.93 172	77.40 187	82.75 208	82.11 207	97.18 195	95.42 181

MDTV_nov1_ep13_2view		78.83 190	82.35 163	74.73 200	78.65 204	91.51 179	79.18 196	62.52 203	84.51 167	52.51 200	67.49 162	67.29 166	78.90 181	85.52 199	86.34 195	96.62 199	93.76 189

PEN-MVS		78.80 191	78.13 196	79.58 175	80.03 198	89.67 199	83.61 183	75.83 154	77.71 196	58.41 176	60.11 177	50.00 214	81.02 176	84.08 204	88.14 182	98.59 158	97.18 162

EG-PatchMatch MVS		78.32 192	79.42 189	77.03 194	83.03 166	93.77 149	84.47 180	69.26 189	75.85 201	53.69 198	55.68 200	60.23 178	73.20 203	89.69 161	88.22 179	98.55 161	92.54 194

DTE-MVSNet		77.92 193	77.42 199	78.51 187	79.34 203	89.00 201	83.05 185	75.60 155	76.89 198	56.58 181	59.63 181	50.31 210	78.09 186	82.57 209	87.56 190	98.38 171	95.95 176

v7n		77.71 194	78.25 195	77.09 193	78.49 205	90.55 192	82.15 191	71.11 184	76.79 199	54.18 192	55.63 201	50.20 212	78.28 184	89.36 168	87.15 193	98.33 174	98.07 142

gm-plane-assit		77.20 195	82.26 164	71.30 203	81.10 183	82.00 215	54.33 220	64.41 198	63.80 217	40.93 219	59.04 184	76.57 129	87.30 147	98.26 22	97.36 37	99.74 13	98.76 117

N_pmnet		76.83 196	77.97 197	75.50 198	80.96 185	88.23 204	72.81 208	76.83 150	80.87 180	50.55 202	56.94 195	60.09 179	75.70 195	83.28 206	84.23 202	96.14 203	92.12 195

pmmvs6		76.79 197	75.69 204	78.09 190	79.95 200	89.57 200	80.92 195	74.46 166	64.79 215	60.74 171	45.71 214	60.55 176	78.37 182	88.04 174	86.00 196	94.07 208	95.15 182

CMPMVS	58.73 17	76.78 198	74.27 205	79.70 172	93.26 76	95.58 132	82.74 188	77.44 145	71.46 212	56.29 183	53.58 204	59.13 182	77.33 188	79.20 210	79.71 210	91.14 213	81.24 213
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		76.76 199	79.47 188	73.60 201	79.99 199	87.47 205	77.39 200	75.43 157	77.62 197	47.83 208	64.78 166	60.44 177	64.80 208	86.28 194	86.53 194	96.17 202	93.19 192

PM-MVS		75.81 200	76.11 203	75.46 199	73.81 210	85.48 209	76.42 202	70.57 186	80.05 186	54.75 188	62.33 170	39.56 220	80.59 179	87.71 177	82.81 206	96.61 201	94.81 185

pmmvs-eth3d		75.17 201	74.09 206	76.43 195	72.92 211	84.49 211	76.61 201	72.42 179	74.33 203	61.28 166	54.71 203	39.42 221	78.20 185	87.77 176	84.25 201	97.17 196	93.63 190

Anonymous20231206		74.59 202	77.00 200	71.78 202	77.89 208	87.45 206	75.14 205	72.29 181	77.76 194	46.65 210	52.14 206	52.93 205	61.10 211	89.37 167	88.09 185	97.59 191	91.30 200

test20.03		72.81 203	76.24 202	68.80 206	78.31 206	85.40 210	71.04 210	71.20 183	71.85 208	43.40 216	65.31 165	54.71 197	51.27 214	85.92 196	84.18 203	97.58 192	86.35 209

test_method		71.90 204	76.72 201	66.28 211	60.87 219	78.37 217	69.75 214	49.81 221	83.44 169	49.63 203	47.13 213	53.23 204	76.38 192	91.32 150	85.76 197	91.22 212	97.77 147

new_pmnet		71.86 205	73.67 207	69.75 205	72.56 214	84.20 212	70.95 212	66.81 195	80.34 185	43.62 215	51.60 207	53.81 203	71.24 205	82.91 207	80.93 208	93.35 210	81.92 212

MDA-MVSNet-bldmvs		69.61 206	70.36 209	68.74 207	62.88 217	88.50 202	65.40 217	77.01 148	71.60 211	43.93 212	66.71 163	35.33 223	72.47 204	61.01 216	80.63 209	90.73 214	88.75 207

pmmvs3		69.04 207	70.75 208	67.04 209	66.83 215	78.54 216	64.99 218	60.92 209	64.67 216	40.61 220	55.08 202	40.29 219	74.89 199	83.76 205	84.01 204	93.98 209	88.88 206

MIMVSNet1		68.63 208	70.24 210	66.76 210	56.86 221	83.26 213	67.93 215	70.26 187	68.05 213	46.80 209	40.44 215	48.15 215	62.01 209	84.96 201	84.86 199	96.69 198	81.93 211

GG-mvs-BLEND		67.99 209	97.35 39	33.72 218	1.22 227	99.72 16	98.30 35	0.57 225	97.61 64	1.18 228	93.26 52	96.63 44	1.74 224	97.15 56	97.14 40	99.34 97	99.96 10

new-patchmatchnet		67.66 210	68.07 211	67.18 208	72.85 212	82.86 214	63.09 219	68.61 193	66.60 214	42.64 218	49.28 210	38.68 222	61.21 210	75.84 211	75.22 212	94.67 207	88.00 208

FPMVS		63.27 211	61.31 213	65.57 212	78.25 207	74.42 220	75.23 204	68.92 192	72.33 207	43.87 213	49.01 211	43.94 217	48.64 216	61.15 215	58.81 217	78.51 220	69.49 218

Gipuma		54.59 212	53.98 214	55.30 213	59.03 220	52.63 222	47.17 222	56.08 217	71.68 210	37.54 221	20.90 221	19.00 225	52.33 213	71.69 213	75.20 213	79.64 219	66.79 219
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	49.05 18	51.88 213	50.56 216	53.42 214	64.21 216	43.30 224	42.64 223	62.93 200	50.56 219	43.72 214	37.44 216	42.95 218	35.05 219	58.76 218	54.58 218	71.95 221	66.33 220
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		50.69 214	52.33 215	48.78 215	51.24 222	64.81 221	47.91 221	53.79 220	44.95 220	21.75 223	29.98 219	25.90 224	31.98 221	59.95 217	65.37 215	86.00 217	75.36 216

E-PMN		37.15 215	34.82 218	39.86 216	47.53 224	35.42 226	23.79 225	55.26 218	35.18 223	14.12 225	17.38 224	14.13 227	39.73 218	32.24 220	46.98 219	58.76 222	62.39 222

EMVS		36.45 216	33.63 219	39.74 217	48.47 223	35.73 225	23.59 226	55.11 219	35.61 222	12.88 226	17.49 222	14.62 226	41.04 217	29.33 221	43.00 220	57.32 223	59.62 223

MVE	42.40 19	36.00 217	38.65 217	32.92 219	29.16 225	46.17 223	22.61 227	44.21 222	26.44 225	18.88 224	17.41 223	9.36 229	32.29 220	45.75 219	61.38 216	50.35 224	64.03 221
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		21.55 218	30.91 220	10.62 220	2.78 226	11.66 227	18.51 228	4.82 223	38.21 221	4.06 227	36.35 217	4.47 230	26.81 222	23.27 222	27.11 221	6.75 225	75.30 217

test123		16.81 219	24.80 221	7.48 221	0.82 228	8.38 228	11.92 229	2.60 224	28.96 224	1.12 229	28.39 220	1.26 231	24.51 223	8.93 223	22.19 222	3.90 226	75.49 215

uanet_test		0.00 220	0.00 222	0.00 222	0.00 229	0.00 229	0.00 230	0.00 226	0.00 226	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet-low-res		0.00 220	0.00 222	0.00 222	0.00 229	0.00 229	0.00 230	0.00 226	0.00 226	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet		0.00 220	0.00 222	0.00 222	0.00 229	0.00 229	0.00 230	0.00 226	0.00 226	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

RE-MVS-def										46.54 211

9.14												99.73 8

SR-MVS					99.27 16			95.82 19				99.00 18

Anonymous202405211			87.54 144		90.72 105	97.10 106	93.40 98	85.30 83	91.41 136		60.23 175	80.69 124	95.80 67	91.33 149	92.60 131	98.38 171	99.40 82

our_test_3					81.94 173	90.26 197	75.39 203

ambc			64.61 212		61.80 218	75.31 219	71.00 211		74.16 204	48.83 206	36.02 218	13.22 228	58.66 212	85.80 198	76.26 211	88.01 215	91.53 199

MTAPA										94.58 15		98.56 24

MTMP										95.24 9		98.13 31

Patchmatch-RL test							37.05 224

tmp_tt				71.24 204	90.29 114	76.39 218	65.81 216	59.43 214	97.62 62	79.65 105	90.60 62	68.71 164	49.71 215	72.71 212	65.70 214	82.54 218

XVS					93.63 71	99.64 25	94.32 82			83.97 70		98.08 33				99.59 37

X-MVStestdata					93.63 71	99.64 25	94.32 82			83.97 70		98.08 33				99.59 37

abl_6				95.40 37	98.18 40	99.45 43	97.39 49	89.27 51	99.48 3	90.52 30	94.52 46	98.63 23	97.32 37			99.73 14	99.82 32

mPP-MVS					98.66 30							97.11 41

NP-MVS									97.69 60

Patchmtry						95.86 127	89.43 138	61.37 207		60.81 167

DeepMVS_CX						85.88 208	69.83 213	81.56 112	87.99 149	48.22 207	71.85 147	45.52 216	68.67 206	63.21 214		86.64 216	80.03 214

High-resolution multi-view benchmark