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 50	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 63
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 51

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 45	99.67 20	99.63 55

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 33	94.79 12	96.11 33	97.79 36	99.27 3	96.62 62	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 31	91.86 24	98.61 14	98.16 29	99.02 7	97.87 33	97.40 34	99.60 33	99.35 82

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 47	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 60	99.54 60	99.23 88

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 46	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 58	99.57 45	99.48 72

ACMMPR		97.78 20	98.28 24	97.20 21	99.03 23	99.68 20	99.37 12	95.24 30	98.86 30	91.16 26	97.86 26	97.26 39	98.79 14	97.64 44	97.40 34	99.60 33	99.25 87

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 55
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 57	98.82 13	96.21 71	94.78 81	99.62 29	99.52 68

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 52	99.56 50	99.14 96

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 55	99.64 25	99.35 82
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 74	94.26 89	98.07 175	99.27 86

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 73

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 42	96.88 58	98.16 15	99.56 50	99.70 44

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	97.44 152

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 42	99.58 41	99.06 104

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 26	96.80 2	93.21 53	95.64 47	97.36 34	96.37 66	94.79 80	99.41 82	98.12 137
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 67	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 38	96.78 60	96.74 53	99.63 27	99.94 15

xxxxxxxxxxxxxcwj		96.27 34	94.51 62	98.32 3	99.48 1	99.68 20	99.51 8	96.24 2	99.08 20	95.94 4	98.64 11	69.64 155	99.02 7	97.94 28	96.86 50	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 60	94.61 77	97.20 51	97.23 38	99.57 45	99.59 60
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNLPA		96.14 36	95.43 52	96.98 25	98.55 31	99.41 48	95.91 58	95.15 33	99.00 27	95.71 6	84.21 102	94.55 55	97.25 36	95.50 96	96.23 60	99.28 102	99.09 103

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 32	84.86 57	99.38 3	94.08 61	95.78 65	97.81 36	96.73 54	99.43 79	99.42 76

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 58	95.67 86	93.33 109	99.55 55	99.12 99
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 95	97.33 38	97.55 32	97.32 47	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	91.45 111	94.42 37	99.76 1	86.48 48	92.88 55	98.12 31	92.62 96	99.49 2	99.32 3	95.15 200	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	78.62 103	94.80 41	95.74 46	96.11 57	97.86 34	98.67 7	99.59 36	99.56 65

CSCG		95.77 42	95.35 54	96.26 29	99.13 20	99.60 31	98.14 37	91.89 43	96.57 75	92.61 21	89.65 63	91.74 75	96.96 40	93.69 120	96.58 57	98.86 129	99.63 55

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 29	90.45 29	88.31 69	94.64 52	96.35 53	96.02 77	95.99 69	99.34 91	97.65 148

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 33	82.29 80	98.85 5	94.66 51	94.63 75	97.80 37	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 94	94.62 53	97.66 29	97.10 54	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 97	95.54 49	95.87 62	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 96	95.60 48	97.21 38	97.10 54	98.19 14	99.57 45	99.65 49

MVSTER		94.75 48	96.50 44	92.70 61	90.91 98	94.51 139	97.37 49	83.37 92	98.40 40	89.04 35	93.23 52	97.04 41	95.91 61	97.73 38	95.59 77	99.61 31	99.01 105

TAPA-MVS	92.04 6	94.72 49	95.13 57	94.24 44	97.72 45	99.17 59	97.61 42	92.16 40	97.66 58	81.99 84	87.84 74	93.94 63	96.50 50	95.74 83	94.27 88	99.46 75	97.31 153
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CS-MVS		94.60 50	97.10 41	91.67 66	90.73 101	98.52 77	95.51 64	83.30 94	99.02 25	84.42 59	94.12 47	94.58 54	96.52 49	97.70 40	96.12 64	99.55 55	99.64 51

DeepC-MVS	92.23 5	94.53 51	94.26 71	94.86 41	96.73 54	99.50 36	97.85 39	95.45 24	96.22 82	82.73 75	80.68 111	88.02 87	96.92 43	97.49 46	98.20 13	99.47 69	99.69 46
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	74.91 156	98.43 37	85.32 55	94.78 42	99.19 14	94.95 71	97.02 56	96.18 63	99.35 87	99.36 81

ETV-MVS		94.49 53	97.23 40	91.29 73	90.43 108	98.55 73	93.41 91	84.53 85	99.16 17	83.13 71	94.72 43	94.08 61	96.61 48	97.72 39	96.60 56	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	83.50 104	93.19 67	94.71 73	98.24 22	98.07 18	99.68 19	99.83 30

MAR-MVS		94.18 55	95.12 58	93.09 56	98.40 36	99.17 59	94.20 80	81.92 102	98.47 36	86.52 47	90.92 58	84.21 106	98.12 26	95.88 80	97.59 29	99.40 83	99.58 63
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 74	94.10 47	96.07 59	99.25 57	96.82 54	95.51 23	92.00 124	81.51 89	82.97 107	93.88 65	95.63 66	94.24 108	94.71 83	99.09 112	99.70 44
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DELS-MVS		93.82 57	93.82 74	93.81 50	96.34 56	99.47 39	97.26 50	88.53 55	92.13 122	87.80 41	79.67 114	88.01 88	93.14 88	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 77	93.34 52	98.46 34	99.39 49	97.00 53	87.66 64	95.37 89	81.21 91	75.96 129	91.58 76	96.21 56	96.37 66	97.10 41	99.52 61	99.54 67

EIA-MVS		93.32 59	95.32 55	90.99 76	90.45 107	98.53 76	93.46 90	84.68 84	97.56 62	81.38 90	91.04 57	87.37 91	96.39 52	97.27 48	95.73 74	99.59 36	99.76 36

PVSNet_BlendedMVS		93.30 60	93.46 81	93.10 54	95.60 61	99.38 50	93.59 88	88.70 51	98.09 47	88.10 39	86.96 82	75.02 129	93.08 89	97.89 30	96.90 48	99.56 50	100.00 1

PVSNet_Blended		93.30 60	93.46 81	93.10 54	95.60 61	99.38 50	93.59 88	88.70 51	98.09 47	88.10 39	86.96 82	75.02 129	93.08 89	97.89 30	96.90 48	99.56 50	100.00 1

PMMVS		93.05 62	95.40 53	90.31 83	91.41 91	97.54 96	92.62 103	83.25 95	98.08 50	79.44 101	95.18 39	88.52 86	96.43 51	95.70 84	93.88 92	98.68 145	98.91 108

LS3D		92.70 63	92.23 91	93.26 53	96.24 58	98.72 65	97.93 38	96.17 7	96.41 76	72.46 118	81.39 110	80.76 119	97.66 29	95.69 85	95.62 76	99.07 114	97.02 160

baseline1		92.67 64	93.62 77	91.55 68	91.16 94	97.15 99	93.92 86	85.97 74	94.76 96	84.07 65	87.17 78	86.89 94	94.62 76	96.72 61	95.90 72	99.57 45	96.79 164

IS_MVSNet		92.67 64	94.99 60	89.96 88	91.17 93	98.54 74	92.77 98	84.00 86	92.72 118	81.90 87	85.67 92	92.47 70	90.39 116	97.82 35	97.81 23	99.51 62	99.91 22

TSAR-MVS + COLMAP		92.56 66	92.44 89	92.71 60	94.61 67	97.69 92	97.69 41	91.09 45	98.96 28	76.71 108	94.68 44	69.41 156	96.91 44	95.80 82	94.18 90	99.26 103	96.33 168

baseline		92.56 66	94.38 67	90.43 82	90.71 103	98.23 83	95.07 70	80.73 116	97.52 63	82.45 79	87.34 77	85.91 98	94.07 83	96.29 70	95.94 71	99.58 41	99.47 73

canonicalmvs		92.54 68	93.28 83	91.68 65	91.44 90	98.24 82	95.45 67	81.84 106	95.98 86	84.85 58	90.69 59	78.53 124	96.96 40	92.97 126	97.06 43	99.57 45	99.47 73

PatchMatch-RL		92.54 68	92.82 88	92.21 62	96.57 55	98.74 64	91.85 108	86.30 69	96.23 81	85.18 56	95.21 38	73.58 135	94.22 82	95.40 99	93.08 113	99.14 109	97.49 151

MVS_Test		92.42 70	94.43 63	90.08 87	90.69 104	98.26 81	94.78 73	80.81 115	97.27 66	78.76 102	87.06 80	84.25 105	95.84 63	97.67 41	97.56 31	99.59 36	98.93 107

thisisatest0530		92.31 71	95.14 56	89.02 97	90.02 115	98.45 79	91.30 112	83.58 89	96.90 71	77.90 105	90.45 61	94.33 58	91.98 102	95.57 90	91.43 135	99.31 97	98.81 111

tttt0517		92.29 72	95.12 58	88.99 98	90.02 115	98.44 80	91.19 115	83.58 89	96.88 72	77.86 106	90.45 61	94.32 59	91.98 102	95.54 92	91.43 135	99.31 97	98.78 113

EPP-MVSNet		92.29 72	94.35 69	89.88 89	90.36 110	97.69 92	90.89 118	83.31 93	93.39 111	83.47 70	85.56 93	93.92 64	91.93 104	95.49 97	94.77 82	99.34 91	99.62 58

HQP-MVS		91.94 74	93.03 85	90.66 81	93.69 69	96.48 113	95.92 57	89.73 47	97.33 64	72.65 116	95.37 36	73.56 136	92.75 95	94.85 105	94.12 91	99.23 106	99.51 69

MSDG		91.93 75	90.28 118	93.85 49	97.36 49	97.12 100	95.88 59	94.07 39	94.52 100	84.13 64	76.74 123	80.89 118	92.54 97	93.97 116	93.61 103	99.14 109	95.10 177

UGNet		91.71 76	94.43 63	88.53 100	92.72 79	98.00 86	90.22 125	84.81 83	94.45 101	83.05 72	87.65 76	92.74 69	81.04 169	94.51 107	94.45 85	99.32 96	99.21 92
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 77	91.52 97	91.88 64	91.61 85	98.89 62	95.49 65	86.96 66	93.24 112	80.82 93	87.90 71	71.15 145	96.88 45	96.00 78	93.51 105	99.51 62	99.95 12

CLD-MVS		91.67 78	91.30 102	92.10 63	91.25 92	96.59 110	95.93 56	87.25 65	96.86 73	85.55 54	87.08 79	73.01 137	93.26 87	93.07 124	92.84 119	99.34 91	99.68 47
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 79	94.96 61	87.65 103	72.75 207	97.24 98	95.29 68	82.73 98	96.81 74	78.49 104	95.30 37	90.48 82	97.23 37	91.60 141	94.31 86	99.43 79	99.01 105

tfpn200view9		91.47 80	91.31 100	91.65 67	91.61 85	98.69 67	95.03 71	86.17 70	93.24 112	80.82 93	87.90 71	71.15 145	96.80 47	95.53 93	92.82 121	99.47 69	99.88 25

CANet_DTU		91.36 81	95.75 50	86.23 114	92.31 82	98.71 66	95.60 63	78.41 131	98.20 43	56.48 176	94.38 46	87.96 89	95.11 68	96.89 57	96.07 65	99.48 67	98.01 141

thres200		91.36 81	91.19 104	91.55 68	91.60 87	98.69 67	94.98 72	86.17 70	92.16 121	80.76 95	87.66 75	71.15 145	96.35 53	95.53 93	93.23 111	99.47 69	99.92 21

FMVSNet3		91.25 83	92.13 93	90.21 84	85.64 147	93.14 148	95.29 68	80.09 117	96.40 77	85.74 51	77.13 118	86.81 95	94.98 70	97.19 52	97.11 40	99.55 55	97.13 157

thres400		91.24 84	91.01 110	91.50 71	91.56 88	98.77 63	94.66 76	86.41 68	91.87 126	80.56 96	87.05 81	71.01 148	96.35 53	95.67 86	92.82 121	99.48 67	99.88 25

PVSNet_Blended_VisFu		91.20 85	92.89 87	89.23 95	93.41 72	98.61 72	89.80 127	85.39 78	92.84 116	82.80 74	74.21 133	91.38 78	84.64 148	97.22 50	96.04 68	99.34 91	99.93 18

DCV-MVSNet		91.15 86	92.00 94	90.17 86	90.78 100	92.23 165	93.70 87	81.17 113	95.16 92	82.98 73	89.46 65	83.31 108	93.98 84	91.79 140	92.87 116	98.41 163	99.18 94

DI_MVS_plusplus_trai		91.11 87	91.47 98	90.68 80	90.01 117	97.77 90	95.87 60	83.56 91	94.72 97	82.12 83	68.46 151	87.46 90	93.07 91	96.46 65	95.73 74	99.47 69	99.71 43

diffmvs		91.05 88	91.15 105	90.93 77	90.15 113	97.79 89	94.05 82	85.45 76	95.63 87	81.95 86	80.45 113	73.01 137	94.47 78	95.56 91	95.89 73	99.49 66	99.72 42

Vis-MVSNet (Re-imp)		91.05 88	94.43 63	87.11 105	91.05 96	97.99 87	92.53 104	83.82 88	92.71 119	76.28 109	84.50 98	92.43 71	79.52 174	97.24 49	97.68 25	99.43 79	98.45 124

thres600view7		90.97 90	90.70 112	91.30 72	91.53 89	98.69 67	94.33 77	86.17 70	91.75 128	80.19 97	86.06 90	70.90 149	96.10 58	95.53 93	92.08 128	99.47 69	99.86 29

baseline2		90.91 91	94.40 66	86.84 108	87.54 138	96.83 106	89.95 126	79.22 126	96.00 85	77.04 107	88.68 66	89.73 83	88.01 137	96.35 68	93.51 105	99.29 99	99.68 47

ACMP	89.80 9	90.72 92	91.15 105	90.21 84	92.55 80	96.52 112	92.63 102	85.71 75	94.65 98	81.06 92	93.32 50	70.56 152	90.52 115	92.68 130	91.05 140	98.76 137	99.31 85
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
casdiffmvs		90.69 93	90.56 115	90.85 78	90.14 114	97.81 88	92.94 96	85.30 79	93.47 110	82.50 78	76.34 127	74.12 133	94.67 74	96.51 64	96.26 59	99.55 55	99.42 76

ACMM	89.40 10	90.58 94	90.02 121	91.23 74	93.30 74	94.75 135	90.69 121	88.22 56	95.20 90	82.70 76	88.54 67	71.40 144	93.48 86	93.64 121	90.94 141	98.99 120	95.72 173
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		90.49 95	91.12 108	89.75 91	84.99 150	92.73 153	93.94 83	80.09 117	96.40 77	85.74 51	77.13 118	86.81 95	94.42 79	94.12 110	93.73 94	99.35 87	96.90 161

test1		90.49 95	91.12 108	89.75 91	84.99 150	92.73 153	93.94 83	80.09 117	96.40 77	85.74 51	77.13 118	86.81 95	94.42 79	94.12 110	93.73 94	99.35 87	96.90 161

LGP-MVS_train		90.34 97	91.63 96	88.83 99	93.31 73	96.14 118	95.49 65	85.24 81	93.91 105	68.71 130	93.96 48	71.63 142	91.12 112	93.82 118	92.79 123	99.07 114	99.16 95

EPNet_dtu		89.82 98	94.18 72	84.74 124	96.87 53	95.54 128	92.65 101	86.91 67	96.99 68	54.17 187	92.41 56	88.54 85	78.35 177	96.15 73	96.05 67	99.47 69	93.60 185
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		89.81 99	89.75 122	89.88 89	93.22 76	93.99 142	94.78 73	85.23 82	94.01 104	82.52 77	95.00 40	87.23 92	92.01 101	85.16 194	83.48 199	91.54 205	89.38 199

MDTV_nov1_ep13		89.63 100	94.38 67	84.09 131	88.76 130	97.53 97	89.37 135	68.46 188	96.95 69	70.27 125	87.88 73	93.67 66	91.04 113	93.12 122	93.83 93	96.62 193	97.68 147

UA-Net		89.56 101	93.03 85	85.52 120	92.46 81	97.55 95	91.92 107	81.91 103	85.24 159	71.39 120	83.57 103	96.56 44	76.01 188	96.81 59	97.04 44	99.46 75	94.41 180

FMVSNet2		89.51 102	89.63 123	89.38 93	84.99 150	92.73 153	93.94 83	79.28 124	93.73 107	84.28 62	69.36 150	82.32 111	94.42 79	96.16 72	96.22 62	99.35 87	96.90 161

CostFormer		89.42 103	91.67 95	86.80 110	89.99 118	96.33 115	90.75 119	64.79 191	95.17 91	83.62 69	86.20 88	82.15 113	92.96 92	89.22 163	92.94 114	98.68 145	99.65 49

FC-MVSNet-train		89.37 104	89.62 124	89.08 96	90.48 106	94.16 141	89.45 131	83.99 87	91.09 131	80.09 98	82.84 108	74.52 132	91.44 109	93.79 119	91.57 134	99.01 118	99.35 82

OPM-MVS		89.33 105	87.45 139	91.53 70	94.49 68	96.20 117	96.47 55	89.72 48	82.77 166	75.43 110	80.53 112	70.86 150	93.80 85	94.00 114	91.85 132	99.29 99	95.91 171

test-LLR		89.31 106	93.60 79	84.30 128	88.08 134	96.98 102	88.10 140	78.00 132	94.83 94	62.43 150	84.29 100	90.96 79	89.70 121	95.63 88	92.86 117	99.51 62	99.64 51

EPMVS		89.31 106	93.70 76	84.18 130	91.10 95	98.10 84	89.17 137	62.71 195	96.24 80	70.21 127	86.46 86	92.37 72	92.79 93	91.95 138	93.59 104	99.10 111	97.19 154

Anonymous20231211		89.22 108	87.56 137	91.16 75	90.23 112	96.62 109	93.22 93	85.44 77	92.89 115	84.37 61	60.13 170	81.25 116	96.02 60	90.61 148	92.01 129	97.70 183	99.41 78

Effi-MVS+		88.96 109	91.13 107	86.43 112	89.12 126	97.62 94	93.15 94	75.52 150	93.90 106	66.40 134	86.23 87	70.51 153	95.03 69	95.89 79	94.28 87	99.37 84	99.51 69

SCA		88.76 110	94.29 70	82.30 147	89.33 123	96.81 107	87.68 142	61.52 200	96.95 69	64.68 140	88.35 68	94.80 50	91.58 106	92.23 132	93.21 112	98.99 120	97.70 146

test0.0.03 1		88.71 111	92.22 92	84.63 126	88.08 134	94.71 137	85.91 165	78.00 132	95.54 88	72.96 114	86.10 89	85.88 100	83.59 156	92.95 128	93.24 110	99.25 105	97.09 158

PatchmatchNet		88.67 112	94.10 73	82.34 146	89.38 122	97.72 91	87.24 148	62.18 198	97.00 67	64.79 139	87.97 70	94.43 56	91.55 107	91.21 146	92.77 124	98.90 125	97.60 150
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dps		88.66 113	90.19 119	86.88 107	89.94 119	96.48 113	89.56 129	64.08 193	94.12 103	89.00 36	83.39 105	82.56 110	90.16 119	86.81 186	89.26 160	98.53 158	98.71 115

TESTMET0.1,1		88.63 114	93.60 79	82.84 143	84.07 157	96.98 102	88.10 140	73.22 170	94.83 94	62.43 150	84.29 100	90.96 79	89.70 121	95.63 88	92.86 117	99.51 62	99.64 51

CHOSEN 1792x2688		88.63 114	89.01 128	88.19 101	94.83 64	99.21 58	92.66 100	79.85 121	92.40 120	72.18 119	56.38 191	80.22 121	90.24 117	97.64 44	97.28 37	99.37 84	99.94 15

CDS-MVSNet		88.59 116	90.13 120	86.79 111	86.98 143	95.43 129	92.03 106	81.33 111	85.54 156	74.51 113	77.07 121	85.14 102	87.03 142	93.90 117	95.18 78	98.88 127	98.67 117
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IB-MVS	84.67 14	88.34 117	90.61 114	85.70 117	92.99 78	98.62 71	78.85 191	86.07 73	94.35 102	88.64 37	85.99 91	75.69 127	68.09 201	88.21 166	91.43 135	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		88.25 118	93.27 84	82.38 145	83.89 158	96.86 105	87.10 152	72.80 172	94.58 99	61.85 155	83.21 106	90.65 81	89.18 125	95.43 98	92.58 126	99.46 75	99.61 59

COLMAP_ROB	84.42 15	88.24 119	87.32 140	89.32 94	95.83 60	95.82 122	92.81 97	87.68 63	92.09 123	72.64 117	72.34 141	79.96 122	88.79 128	89.54 158	89.46 156	98.16 172	92.00 191
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
IterMVS-LS		87.95 120	89.40 126	86.26 113	88.79 129	90.93 181	91.23 114	76.05 147	90.87 132	71.07 122	75.51 130	81.18 117	91.21 111	94.11 113	95.01 79	99.20 108	98.23 132
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		87.86 121	88.25 133	87.40 104	94.67 66	98.54 74	90.33 124	76.51 146	89.60 140	70.89 123	51.43 202	85.69 101	92.79 93	96.59 63	95.96 70	99.22 107	99.94 15

Vis-MVSNet		87.60 122	91.31 100	83.27 138	89.14 125	98.04 85	90.35 123	79.42 122	87.23 145	66.92 133	79.10 117	84.63 104	74.34 195	95.81 81	96.06 66	99.46 75	98.32 128
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GeoE		87.55 123	88.17 134	86.82 109	88.74 131	96.32 116	92.75 99	74.93 155	90.13 137	72.73 115	69.47 149	74.03 134	92.51 98	93.99 115	93.62 102	99.29 99	99.59 60

RPMNet		87.35 124	92.41 90	81.45 151	88.85 128	96.06 119	89.42 134	59.59 207	93.57 108	61.81 156	76.48 126	91.48 77	90.18 118	96.32 69	93.37 108	98.87 128	99.59 60

tpm cat1		87.34 125	88.52 132	85.95 115	89.83 120	95.80 123	90.73 120	64.91 190	92.99 114	82.21 82	71.19 147	82.68 109	90.13 120	86.38 187	90.87 143	97.90 180	99.74 39

MS-PatchMatch		87.19 126	88.59 131	85.55 119	93.15 77	96.58 111	92.35 105	74.19 163	91.97 125	70.33 124	71.42 145	85.89 99	84.28 150	93.12 122	89.16 162	99.00 119	91.99 192

Effi-MVS+-dtu		87.18 127	90.48 116	83.32 137	86.51 144	95.76 125	91.16 117	74.28 162	90.44 136	61.31 159	86.72 85	72.68 140	91.25 110	95.01 103	93.64 97	95.45 199	99.12 99

FMVSNet5		87.06 128	89.52 125	84.20 129	79.92 195	86.57 201	87.11 151	72.37 174	96.06 83	75.41 111	84.33 99	91.76 74	91.60 105	91.51 142	91.22 138	98.77 134	85.16 204

Fast-Effi-MVS+-dtu		86.94 129	91.27 103	81.89 148	86.27 145	95.06 130	90.68 122	68.93 185	91.76 127	57.18 174	89.56 64	75.85 126	89.19 124	94.56 106	92.84 119	99.07 114	99.23 88

Fast-Effi-MVS+		86.94 129	87.88 136	85.84 116	86.99 142	95.80 123	91.24 113	73.48 169	92.75 117	69.22 128	72.70 139	65.71 162	94.84 72	94.98 104	94.71 83	99.26 103	98.48 123

tpmrst		86.78 131	90.29 117	82.69 144	90.55 105	96.95 104	88.49 139	62.58 196	95.09 93	63.52 146	76.67 125	84.00 107	92.05 100	87.93 169	91.89 131	98.98 122	99.50 71

CR-MVSNet		86.73 132	91.47 98	81.20 154	88.56 132	96.06 119	89.43 132	61.37 201	93.57 108	60.81 161	72.89 138	88.85 84	88.13 135	96.03 75	93.64 97	98.89 126	99.22 90

ADS-MVSNet		86.68 133	90.79 111	81.88 149	90.38 109	96.81 107	86.90 153	60.50 205	96.01 84	63.93 143	81.67 109	84.72 103	90.78 114	87.03 180	91.67 133	98.77 134	97.63 149

FMVSNet1		85.85 134	84.91 150	86.96 106	82.70 163	91.39 175	91.54 110	77.45 138	85.29 158	79.56 100	60.70 167	72.68 140	92.37 99	94.12 110	93.73 94	98.12 173	96.44 165

FC-MVSNet-test		85.51 135	89.08 127	81.35 152	85.31 149	93.35 144	87.65 143	77.55 137	90.01 138	64.07 142	79.63 115	81.83 115	74.94 192	92.08 135	90.83 145	98.55 155	95.81 172

ACMH	85.22 13	85.40 136	85.73 147	85.02 122	91.76 84	94.46 140	84.97 171	81.54 109	85.18 160	65.22 138	76.92 122	64.22 163	88.58 131	90.17 150	90.25 151	98.03 176	98.90 109
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		85.35 137	86.00 146	84.59 127	84.97 153	95.57 127	88.98 138	77.29 141	81.44 171	71.36 121	71.48 144	75.00 131	87.03 142	91.92 139	92.21 127	97.92 179	94.40 181

ACMH+	85.62 12	85.27 138	84.96 149	85.64 118	90.84 99	94.78 134	87.46 145	81.30 112	86.94 146	67.35 132	74.56 132	64.09 164	88.70 129	88.14 167	89.00 163	98.22 171	97.19 154

USDC		85.11 139	85.35 148	84.83 123	89.45 121	94.93 133	92.98 95	77.30 140	90.53 134	61.80 157	76.69 124	59.62 174	88.90 127	92.78 129	90.79 147	98.53 158	92.12 189

IterMVS		85.02 140	88.98 129	80.41 160	87.03 141	90.34 189	89.78 128	69.45 182	89.77 139	54.04 188	73.71 135	82.05 114	83.44 159	95.11 101	93.64 97	98.75 138	98.22 134
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		84.91 141	88.90 130	80.25 163	87.04 140	90.27 190	89.23 136	69.25 184	89.17 141	54.04 188	73.65 136	82.22 112	83.23 164	95.11 101	93.63 101	98.73 139	98.23 132

PatchT		84.89 142	90.67 113	78.13 183	87.83 137	94.99 132	72.46 203	60.22 206	91.74 129	60.81 161	72.16 142	86.95 93	88.13 135	96.03 75	93.64 97	99.36 86	99.22 90

pmmvs4		84.88 143	84.67 151	85.13 121	82.80 162	92.37 158	87.29 146	79.08 127	90.51 135	74.94 112	70.37 148	62.49 167	88.17 134	92.01 137	88.51 168	98.49 161	96.44 165

test_part1		84.71 144	82.08 161	87.78 102	89.19 124	91.40 174	91.19 115	79.25 125	79.62 183	82.23 81	57.07 187	70.79 151	88.95 126	87.46 174	89.91 153	95.89 198	98.31 130

CVMVSNet		84.01 145	86.91 141	80.61 158	88.39 133	93.29 145	86.06 161	82.29 100	83.13 164	54.29 184	72.68 140	79.59 123	75.11 191	91.23 145	92.91 115	97.54 187	95.58 174

tpm		83.97 146	87.97 135	79.31 173	87.35 139	93.21 147	86.00 163	61.90 199	90.69 133	54.01 190	79.42 116	75.61 128	88.65 130	87.18 178	90.48 149	97.95 178	99.21 92

GA-MVS		83.83 147	86.63 142	80.58 159	85.40 148	94.73 136	87.27 147	78.76 130	86.49 148	49.57 198	74.21 133	67.67 159	83.38 160	95.28 100	90.92 142	99.08 113	97.09 158

UniMVSNet_NR-MVSNet		83.83 147	83.70 154	83.98 132	81.41 173	92.56 157	86.54 156	82.96 96	85.98 153	66.27 135	66.16 158	63.63 165	87.78 139	87.65 172	90.81 146	98.94 123	99.13 97

UniMVSNet (Re)		83.28 149	83.16 155	83.42 136	81.93 168	93.12 149	86.27 159	80.83 114	85.88 154	68.23 131	64.56 161	60.58 169	84.25 151	89.13 164	89.44 158	99.04 117	99.40 79

thisisatest0515		83.17 150	86.49 143	79.30 174	82.04 166	93.12 149	78.70 192	77.92 134	86.43 149	63.05 147	74.91 131	73.01 137	75.56 190	92.10 134	88.05 181	98.50 160	97.76 145

TinyColmap		83.03 151	82.24 159	83.95 133	88.88 127	93.22 146	89.48 130	76.89 143	87.53 144	62.12 152	68.46 151	55.03 190	88.43 133	90.87 147	89.65 154	97.89 181	90.91 195

testgi		82.88 152	86.14 145	79.08 176	86.05 146	92.20 166	81.23 188	74.77 158	88.70 142	57.63 173	86.73 84	61.53 168	76.83 185	90.33 149	89.43 159	97.99 177	94.05 182

DU-MVS		82.87 153	82.16 160	83.70 135	80.77 182	92.24 162	86.54 156	81.91 103	86.41 150	66.27 135	63.95 162	55.66 188	87.78 139	86.83 183	90.86 144	98.94 123	99.13 97

MIMVSNet		82.87 153	86.17 144	79.02 177	77.23 203	92.88 152	84.88 172	60.62 204	86.72 147	64.16 141	73.58 137	71.48 143	88.51 132	94.14 109	93.50 107	98.72 141	90.87 196

NR-MVSNet		82.37 155	81.95 163	82.85 142	82.56 165	92.24 162	87.49 144	81.91 103	86.41 150	65.51 137	63.95 162	52.93 199	80.80 171	89.41 160	89.61 155	98.85 130	99.10 102

Baseline_NR-MVSNet		82.08 156	80.64 170	83.77 134	80.77 182	88.50 196	86.88 154	81.71 107	85.58 155	68.80 129	58.20 182	57.75 180	86.16 144	86.83 183	88.68 165	98.33 168	98.90 109

TranMVSNet+NR-MVSNet		82.07 157	81.36 166	82.90 141	80.43 188	91.39 175	87.16 150	82.75 97	84.28 162	62.98 148	62.28 166	56.01 187	85.30 147	86.06 189	90.69 148	98.80 131	98.80 112

pm-mvs1		81.68 158	81.70 164	81.65 150	82.61 164	92.26 161	85.54 169	78.95 128	76.29 194	63.81 144	58.43 181	66.33 161	80.63 172	92.30 131	89.93 152	98.37 167	96.39 167

TDRefinement		81.49 159	80.08 176	83.13 140	91.02 97	94.53 138	91.66 109	82.43 99	81.70 169	62.12 152	62.30 165	59.32 175	73.93 196	87.31 176	85.29 192	97.61 184	90.14 197

anonymousdsp		81.29 160	84.52 153	77.52 185	79.83 196	92.62 156	82.61 183	70.88 179	80.76 175	50.82 195	68.35 153	68.76 157	82.45 167	93.00 125	89.45 157	98.55 155	98.69 116

gg-mvs-nofinetune		81.27 161	84.65 152	77.32 186	87.96 136	98.48 78	95.64 62	56.36 210	59.35 212	32.80 216	47.96 206	92.11 73	91.49 108	98.12 24	97.00 46	99.65 24	99.56 65

tfpnnormal		81.11 162	79.33 184	83.19 139	84.23 155	92.29 160	86.76 155	82.27 101	72.67 200	62.02 154	56.10 193	53.86 196	85.35 146	92.06 136	89.23 161	98.49 161	99.11 101

UniMVSNet_ETH3D		80.95 163	77.71 192	84.74 124	84.45 154	93.11 151	86.45 158	79.97 120	75.21 196	70.22 126	51.24 203	50.26 205	89.55 123	84.47 196	91.12 139	97.81 182	98.53 121

V42		80.88 164	80.74 168	81.05 155	81.21 176	92.01 168	85.96 164	77.75 136	81.62 170	59.73 168	59.93 173	58.35 179	82.98 166	86.90 182	88.06 180	98.69 144	98.32 128

v2v482		80.86 165	80.52 174	81.25 153	80.79 181	91.85 169	85.68 167	78.78 129	81.05 172	58.09 171	60.46 168	56.08 185	85.45 145	87.27 177	88.53 167	98.73 139	98.38 127

v8		80.61 166	80.61 172	80.62 157	81.51 171	91.00 180	86.06 161	74.07 165	81.78 168	59.93 167	60.10 172	58.42 178	83.35 161	86.99 181	88.11 178	98.79 132	97.83 143

pmmvs5		80.48 167	81.43 165	79.36 172	81.50 172	92.24 162	82.07 186	74.08 164	78.10 187	55.86 179	67.72 155	54.35 193	83.91 155	92.97 126	88.65 166	98.77 134	96.01 169

v10		80.38 168	80.73 169	79.96 165	81.22 175	90.40 188	86.11 160	71.63 176	82.42 167	57.65 172	58.74 179	57.47 181	84.44 149	89.75 154	88.28 171	98.71 142	98.06 140

v1144		80.36 169	80.63 171	80.05 164	80.86 180	91.56 172	85.78 166	75.22 152	80.73 176	55.83 180	58.51 180	56.99 183	83.93 154	89.79 153	88.25 172	98.68 145	98.56 120

SixPastTwentyTwo		80.28 170	82.06 162	78.21 182	81.89 170	92.35 159	77.72 193	74.48 159	83.04 165	54.22 185	76.06 128	56.40 184	83.55 157	86.83 183	84.83 194	97.38 188	94.93 178

CP-MVSNet		79.90 171	79.49 181	80.38 161	80.72 184	90.83 182	82.98 180	75.17 153	79.70 181	61.39 158	59.74 174	51.98 202	83.31 162	87.37 175	88.38 169	98.71 142	98.45 124

v1192		79.84 172	80.05 178	79.61 168	80.49 187	91.04 179	85.56 168	74.37 161	80.73 176	54.35 183	57.07 187	54.54 192	84.23 152	89.94 151	88.38 169	98.63 149	98.61 118

WR-MVS_H		79.76 173	80.07 177	79.40 171	81.25 174	91.73 171	82.77 181	74.82 157	79.02 186	62.55 149	59.41 176	57.32 182	76.27 187	87.61 173	87.30 186	98.78 133	98.09 138

WR-MVS		79.67 174	80.25 175	79.00 178	80.65 185	91.16 177	83.31 178	76.57 145	80.97 173	60.50 166	59.20 177	58.66 177	74.38 194	85.85 191	87.76 183	98.61 150	98.14 135

v148		79.66 175	79.13 186	80.27 162	81.02 178	91.76 170	81.90 187	79.32 123	79.24 184	63.79 145	58.07 184	54.34 194	77.17 183	84.42 197	87.52 185	98.40 164	98.59 119

LTVRE_ROB	79.45 16	79.66 175	80.55 173	78.61 180	83.01 161	92.19 167	87.18 149	73.69 168	71.70 203	43.22 211	71.22 146	50.85 203	87.82 138	89.47 159	90.43 150	96.75 191	98.00 142
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		79.61 177	79.77 179	79.41 170	80.28 189	91.06 178	84.87 173	73.86 166	79.65 182	55.38 181	57.76 185	55.20 189	83.46 158	88.42 165	87.89 182	98.61 150	98.42 126

v1921920		79.55 178	79.77 179	79.30 174	80.24 190	90.77 184	85.37 170	73.75 167	80.38 178	53.78 191	56.89 190	54.18 195	84.05 153	89.55 157	88.13 177	98.59 152	98.52 122

TransMVSNet (Re)		79.51 179	78.36 188	80.84 156	83.17 159	89.72 192	84.22 176	81.45 110	73.98 199	60.79 164	57.20 186	56.05 186	77.11 184	89.88 152	88.86 164	98.30 170	92.83 187

MVS-HIRNet		79.34 180	82.56 156	75.57 191	84.11 156	95.02 131	75.03 200	57.28 209	85.50 157	55.88 178	53.00 199	70.51 153	83.05 165	92.12 133	91.96 130	98.09 174	89.83 198

PS-CasMVS		79.06 181	78.58 187	79.63 167	80.59 186	90.55 186	82.54 184	75.04 154	77.76 188	58.84 169	58.16 183	50.11 207	82.09 168	87.05 179	88.18 175	98.66 148	98.27 131

v1240		78.97 182	79.27 185	78.63 179	80.04 191	90.61 185	84.25 175	72.95 171	79.22 185	52.70 193	56.22 192	52.88 201	83.28 163	89.60 156	88.20 174	98.56 154	98.14 135

pmnet_mix02		78.91 183	81.17 167	76.28 190	81.91 169	90.82 183	74.25 201	77.87 135	86.17 152	49.04 199	67.97 154	62.93 166	77.40 181	82.75 202	82.11 201	97.18 189	95.42 175

MDTV_nov1_ep13_2view		78.83 184	82.35 157	74.73 194	78.65 198	91.51 173	79.18 190	62.52 197	84.51 161	52.51 194	67.49 156	67.29 160	78.90 175	85.52 193	86.34 189	96.62 193	93.76 183

PEN-MVS		78.80 185	78.13 190	79.58 169	80.03 192	89.67 193	83.61 177	75.83 148	77.71 190	58.41 170	60.11 171	50.00 208	81.02 170	84.08 198	88.14 176	98.59 152	97.18 156

EG-PatchMatch MVS		78.32 186	79.42 183	77.03 188	83.03 160	93.77 143	84.47 174	69.26 183	75.85 195	53.69 192	55.68 194	60.23 172	73.20 197	89.69 155	88.22 173	98.55 155	92.54 188

DTE-MVSNet		77.92 187	77.42 193	78.51 181	79.34 197	89.00 195	83.05 179	75.60 149	76.89 192	56.58 175	59.63 175	50.31 204	78.09 180	82.57 203	87.56 184	98.38 165	95.95 170

v7n		77.71 188	78.25 189	77.09 187	78.49 199	90.55 186	82.15 185	71.11 178	76.79 193	54.18 186	55.63 195	50.20 206	78.28 178	89.36 162	87.15 187	98.33 168	98.07 139

gm-plane-assit		77.20 189	82.26 158	71.30 197	81.10 177	82.00 209	54.33 214	64.41 192	63.80 211	40.93 213	59.04 178	76.57 125	87.30 141	98.26 21	97.36 36	99.74 13	98.76 114

N_pmnet		76.83 190	77.97 191	75.50 192	80.96 179	88.23 198	72.81 202	76.83 144	80.87 174	50.55 196	56.94 189	60.09 173	75.70 189	83.28 200	84.23 196	96.14 197	92.12 189

pmmvs6		76.79 191	75.69 198	78.09 184	79.95 194	89.57 194	80.92 189	74.46 160	64.79 209	60.74 165	45.71 208	60.55 170	78.37 176	88.04 168	86.00 190	94.07 202	95.15 176

CMPMVS	58.73 17	76.78 192	74.27 199	79.70 166	93.26 75	95.58 126	82.74 182	77.44 139	71.46 206	56.29 177	53.58 198	59.13 176	77.33 182	79.20 204	79.71 204	91.14 207	81.24 207
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		76.76 193	79.47 182	73.60 195	79.99 193	87.47 199	77.39 194	75.43 151	77.62 191	47.83 202	64.78 160	60.44 171	64.80 202	86.28 188	86.53 188	96.17 196	93.19 186

PM-MVS		75.81 194	76.11 197	75.46 193	73.81 204	85.48 203	76.42 196	70.57 180	80.05 180	54.75 182	62.33 164	39.56 214	80.59 173	87.71 171	82.81 200	96.61 195	94.81 179

pmmvs-eth3d		75.17 195	74.09 200	76.43 189	72.92 205	84.49 205	76.61 195	72.42 173	74.33 197	61.28 160	54.71 197	39.42 215	78.20 179	87.77 170	84.25 195	97.17 190	93.63 184

Anonymous20231206		74.59 196	77.00 194	71.78 196	77.89 202	87.45 200	75.14 199	72.29 175	77.76 188	46.65 204	52.14 200	52.93 199	61.10 205	89.37 161	88.09 179	97.59 185	91.30 194

test20.03		72.81 197	76.24 196	68.80 200	78.31 200	85.40 204	71.04 204	71.20 177	71.85 202	43.40 210	65.31 159	54.71 191	51.27 208	85.92 190	84.18 197	97.58 186	86.35 203

test_method		71.90 198	76.72 195	66.28 205	60.87 213	78.37 211	69.75 208	49.81 215	83.44 163	49.63 197	47.13 207	53.23 198	76.38 186	91.32 144	85.76 191	91.22 206	97.77 144

new_pmnet		71.86 199	73.67 201	69.75 199	72.56 208	84.20 206	70.95 206	66.81 189	80.34 179	43.62 209	51.60 201	53.81 197	71.24 199	82.91 201	80.93 202	93.35 204	81.92 206

MDA-MVSNet-bldmvs		69.61 200	70.36 203	68.74 201	62.88 211	88.50 196	65.40 211	77.01 142	71.60 205	43.93 206	66.71 157	35.33 217	72.47 198	61.01 210	80.63 203	90.73 208	88.75 201

pmmvs3		69.04 201	70.75 202	67.04 203	66.83 209	78.54 210	64.99 212	60.92 203	64.67 210	40.61 214	55.08 196	40.29 213	74.89 193	83.76 199	84.01 198	93.98 203	88.88 200

MIMVSNet1		68.63 202	70.24 204	66.76 204	56.86 215	83.26 207	67.93 209	70.26 181	68.05 207	46.80 203	40.44 209	48.15 209	62.01 203	84.96 195	84.86 193	96.69 192	81.93 205

GG-mvs-BLEND		67.99 203	97.35 38	33.72 212	1.22 221	99.72 15	98.30 34	0.57 219	97.61 61	1.18 222	93.26 51	96.63 43	1.74 218	97.15 53	97.14 39	99.34 91	99.96 9

new-patchmatchnet		67.66 204	68.07 205	67.18 202	72.85 206	82.86 208	63.09 213	68.61 187	66.60 208	42.64 212	49.28 204	38.68 216	61.21 204	75.84 205	75.22 206	94.67 201	88.00 202

FPMVS		63.27 205	61.31 207	65.57 206	78.25 201	74.42 214	75.23 198	68.92 186	72.33 201	43.87 207	49.01 205	43.94 211	48.64 210	61.15 209	58.81 211	78.51 214	69.49 212

Gipuma		54.59 206	53.98 208	55.30 207	59.03 214	52.63 216	47.17 216	56.08 211	71.68 204	37.54 215	20.90 215	19.00 219	52.33 207	71.69 207	75.20 207	79.64 213	66.79 213
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	49.05 18	51.88 207	50.56 210	53.42 208	64.21 210	43.30 218	42.64 217	62.93 194	50.56 213	43.72 208	37.44 210	42.95 212	35.05 213	58.76 212	54.58 212	71.95 215	66.33 214
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		50.69 208	52.33 209	48.78 209	51.24 216	64.81 215	47.91 215	53.79 214	44.95 214	21.75 217	29.98 213	25.90 218	31.98 215	59.95 211	65.37 209	86.00 211	75.36 210

E-PMN		37.15 209	34.82 212	39.86 210	47.53 218	35.42 220	23.79 219	55.26 212	35.18 217	14.12 219	17.38 218	14.13 221	39.73 212	32.24 214	46.98 213	58.76 216	62.39 216

EMVS		36.45 210	33.63 213	39.74 211	48.47 217	35.73 219	23.59 220	55.11 213	35.61 216	12.88 220	17.49 216	14.62 220	41.04 211	29.33 215	43.00 214	57.32 217	59.62 217

MVE	42.40 19	36.00 211	38.65 211	32.92 213	29.16 219	46.17 217	22.61 221	44.21 216	26.44 219	18.88 218	17.41 217	9.36 223	32.29 214	45.75 213	61.38 210	50.35 218	64.03 215
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		21.55 212	30.91 214	10.62 214	2.78 220	11.66 221	18.51 222	4.82 217	38.21 215	4.06 221	36.35 211	4.47 224	26.81 216	23.27 216	27.11 215	6.75 219	75.30 211

test123		16.81 213	24.80 215	7.48 215	0.82 222	8.38 222	11.92 223	2.60 218	28.96 218	1.12 223	28.39 214	1.26 225	24.51 217	8.93 217	22.19 216	3.90 220	75.49 209

uanet_test		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

sosnet-low-res		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

sosnet		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

RE-MVS-def										46.54 205

9.14												99.73 7

SR-MVS					99.27 15			95.82 18				99.00 17

Anonymous202405211			87.54 138		90.72 102	97.10 101	93.40 92	85.30 79	91.41 130		60.23 169	80.69 120	95.80 64	91.33 143	92.60 125	98.38 165	99.40 79

our_test_3					81.94 167	90.26 191	75.39 197

ambc			64.61 206		61.80 212	75.31 213	71.00 205		74.16 198	48.83 200	36.02 212	13.22 222	58.66 206	85.80 192	76.26 205	88.01 209	91.53 193

MTAPA										94.58 14		98.56 23

MTMP										95.24 8		98.13 30

Patchmatch-RL test							37.05 218

tmp_tt				71.24 198	90.29 111	76.39 212	65.81 210	59.43 208	97.62 59	79.65 99	90.60 60	68.71 158	49.71 209	72.71 206	65.70 208	82.54 212

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 35			99.73 14	99.82 31

mPP-MVS					98.66 29							97.11 40

NP-MVS									97.69 57

Patchmtry						95.86 121	89.43 132	61.37 201		60.81 161

DeepMVS_CX						85.88 202	69.83 207	81.56 108	87.99 143	48.22 201	71.85 143	45.52 210	68.67 200	63.21 208		86.64 210	80.03 208

High-resolution multi-view benchmark