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		95.53 1	95.79 1	95.23 1	97.60 10	98.92 1	95.99 5	92.05 8	97.14 1	94.19 1	94.71 7	93.25 1	95.08 1	94.32 11	92.59 15	96.49 18	99.58 3

DPE-MVS		95.10 2	95.53 2	94.60 6	97.77 8	98.64 4	96.60 4	92.45 6	96.34 6	91.41 6	96.70 2	92.26 5	93.56 5	93.68 18	91.73 30	95.79 37	99.37 7
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		95.06 3	95.37 4	94.70 3	97.59 11	98.89 2	95.37 12	92.04 9	96.85 3	94.00 2	92.81 15	93.02 2	92.93 6	94.22 14	92.15 21	96.30 25	99.61 2
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
DVP-MVS++		95.03 4	95.03 5	95.03 2	97.91 6	98.84 3	95.80 6	91.88 11	96.65 5	93.15 3	93.79 9	90.11 12	95.03 2	94.20 16	92.39 16	96.44 22	99.22 10

MSP-MVS		95.00 5	95.47 3	94.45 7	96.78 19	98.11 10	95.72 8	90.91 15	96.68 4	91.57 5	96.98 1	89.47 14	94.76 3	95.24 3	92.15 21	96.98 7	99.64 1
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		94.53 6	94.85 7	94.15 9	98.03 4	98.59 5	95.56 9	92.91 3	94.86 14	88.46 16	91.32 22	90.83 10	94.03 4	95.20 4	94.16 5	95.89 33	99.01 16

SF-MVS		94.40 7	94.15 12	94.70 3	98.25 2	98.24 7	96.86 2	93.46 1	94.87 12	90.26 10	95.96 3	88.42 17	92.76 9	92.29 30	90.84 41	96.62 13	98.44 27

APDe-MVS		94.31 8	94.30 10	94.33 8	97.57 12	98.06 12	95.79 7	91.98 10	95.50 9	92.19 4	95.25 5	87.97 20	92.93 6	93.01 24	91.02 39	95.52 39	99.29 8

MCST-MVS		94.10 9	94.77 8	93.31 11	98.31 1	98.34 6	95.43 10	92.54 5	94.41 18	83.05 32	91.38 20	90.97 9	92.24 14	95.05 6	94.02 6	98.31 1	99.20 11

HPM-MVS++		94.04 10	94.96 6	92.96 13	97.93 5	97.71 18	94.65 15	91.01 14	95.91 7	87.43 18	93.52 12	92.63 4	92.29 13	94.22 14	92.34 18	94.47 63	98.37 30

NCCC		93.59 11	94.00 14	93.10 12	97.90 7	97.93 14	95.40 11	92.39 7	94.47 17	84.94 23	91.21 23	89.32 15	92.53 11	93.90 17	92.98 12	95.44 41	98.22 33

SMA-MVS		93.47 12	94.29 11	92.52 15	97.72 9	97.77 17	94.46 18	90.19 18	94.96 11	87.15 19	90.15 26	90.99 8	91.49 17	94.31 12	93.33 10	94.10 69	98.53 25
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
APD-MVS		93.47 12	93.44 17	93.50 10	97.06 15	97.09 28	95.27 13	91.47 12	95.71 8	89.57 13	93.66 10	86.28 26	92.81 8	92.06 34	90.70 43	94.83 59	98.60 22
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SD-MVS		93.36 14	94.33 9	92.22 17	94.68 45	97.89 16	94.56 16	90.89 16	94.80 15	90.04 12	93.53 11	90.14 11	89.78 24	92.74 26	92.17 19	93.35 108	99.07 14
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
TSAR-MVS + MP.		93.07 15	93.53 16	92.53 14	94.23 48	97.54 22	94.75 14	89.87 19	95.26 10	89.20 15	93.16 13	88.19 19	92.15 15	91.79 40	89.65 58	94.99 55	99.16 12
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPM-MVS		92.86 16	93.19 19	92.47 16	95.78 36	97.40 23	97.39 1	92.56 4	92.88 27	81.84 40	81.31 41	92.95 3	91.21 18	96.54 1	97.33 1	96.01 31	93.94 111

SteuartSystems-ACMMP		92.31 17	93.31 18	91.15 24	96.88 17	97.36 24	93.95 22	89.44 21	92.62 28	83.20 29	94.34 8	85.55 28	88.95 32	93.07 23	91.90 26	94.51 61	98.30 31
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_NAP		92.16 18	92.91 22	91.28 23	96.95 16	97.36 24	93.66 23	89.23 23	93.33 22	83.71 27	90.53 24	86.84 23	90.39 20	93.30 22	91.56 32	93.74 80	97.43 48

HFP-MVS		92.02 19	92.13 24	91.89 21	97.16 14	96.46 41	93.57 24	87.60 27	93.79 20	88.17 17	93.15 14	83.94 39	91.19 19	90.81 51	89.83 53	93.66 84	96.94 64

train_agg		91.99 20	93.71 15	89.98 29	96.42 28	97.03 30	94.31 20	89.05 24	93.33 22	77.75 47	95.06 6	88.27 18	88.38 40	92.02 37	91.41 34	94.00 72	98.84 19

xxxxxxxxxxxxxcwj		91.86 21	89.43 38	94.70 3	98.25 2	98.24 7	96.86 2	93.46 1	94.87 12	90.26 10	95.96 3	55.37 150	92.76 9	92.29 30	90.84 41	96.62 13	98.44 27

DeepC-MVS_fast	86.59 2	91.69 22	91.39 27	92.05 20	97.43 13	96.92 33	94.05 21	90.23 17	93.31 25	83.19 30	77.91 47	84.23 35	92.42 12	94.62 9	94.83 3	95.00 54	97.88 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
zzz-MVS		91.59 23	91.12 28	92.13 18	96.76 20	96.68 36	93.39 25	88.00 26	93.63 21	90.76 9	83.97 37	85.33 30	89.89 23	91.60 43	89.65 58	94.00 72	96.97 62

TSAR-MVS + GP.		91.29 24	93.11 21	89.18 35	87.81 89	96.21 47	92.51 34	83.83 47	94.24 19	83.77 26	91.87 19	89.62 13	90.07 21	90.40 55	90.31 47	97.09 6	99.10 13

ACMMPR		91.15 25	91.44 26	90.81 25	96.61 22	96.25 45	93.09 26	87.08 30	93.32 24	84.78 24	92.08 18	82.10 45	89.71 25	90.24 56	89.82 54	93.61 89	96.30 76

DeepPCF-MVS	86.71 1	91.00 26	94.05 13	87.43 46	95.58 39	98.17 9	86.22 75	88.59 25	97.01 2	76.77 53	85.11 35	88.90 16	87.29 46	95.02 7	94.69 4	90.15 179	99.48 6

TSAR-MVS + ACMM		90.98 27	93.18 20	88.42 40	95.69 37	96.73 35	94.52 17	86.97 33	92.99 26	76.32 54	92.31 17	86.64 24	84.40 71	92.97 25	92.02 23	92.62 131	98.59 23

MP-MVS		90.81 28	91.45 25	90.06 28	96.59 23	96.33 44	92.46 35	87.19 29	90.27 41	82.54 36	91.38 20	84.88 32	88.27 41	90.58 53	89.30 64	93.30 110	97.44 46
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		90.57 29	90.68 30	90.44 26	96.13 30	95.90 52	92.77 31	86.86 34	92.12 32	84.19 25	89.18 29	82.37 43	89.43 30	89.65 68	88.43 75	93.27 111	97.13 56

MSLP-MVS++		90.33 30	88.82 41	92.10 19	96.52 26	95.93 48	94.35 19	86.26 35	88.37 55	89.24 14	75.94 52	82.60 42	89.71 25	89.45 71	92.17 19	96.51 17	97.24 53

CANet		89.98 31	90.42 34	89.47 34	94.13 49	98.05 13	91.76 40	83.27 50	90.87 38	81.90 39	72.32 61	84.82 33	88.42 38	94.52 10	93.78 8	97.34 4	98.58 24

PGM-MVS		89.97 32	90.64 32	89.18 35	96.53 25	95.90 52	93.06 27	82.48 58	90.04 43	80.37 42	92.75 16	80.96 50	88.93 33	89.88 64	89.08 67	93.69 83	95.86 80

PHI-MVS		89.88 33	92.75 23	86.52 56	94.97 42	97.57 21	89.99 51	84.56 43	92.52 30	69.72 87	90.35 25	87.11 22	84.89 63	91.82 39	92.37 17	95.02 53	97.51 44

CSCG		89.81 34	89.69 35	89.96 30	96.55 24	97.90 15	92.89 29	87.06 31	88.74 53	86.17 20	78.24 46	86.53 25	84.75 66	87.82 91	90.59 44	92.32 136	98.01 36

X-MVS		89.73 35	90.65 31	88.66 38	96.44 27	95.93 48	92.26 37	86.98 32	90.73 39	76.32 54	89.56 28	82.05 46	86.51 52	89.98 62	89.60 60	93.43 103	96.72 71

EPNet		89.30 36	90.89 29	87.44 45	95.67 38	96.81 34	91.13 43	83.12 52	91.14 35	76.31 58	87.60 31	80.40 54	84.45 69	92.13 33	91.12 38	93.96 74	97.01 60
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepC-MVS	84.14 3	88.80 37	88.03 46	89.71 32	94.83 43	96.56 37	92.57 33	89.38 22	89.25 49	79.59 44	70.02 70	77.05 66	88.24 42	92.44 28	92.79 13	93.65 87	98.10 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CDPH-MVS		88.76 38	90.43 33	86.81 52	96.04 32	96.53 40	92.95 28	85.95 37	90.36 40	67.93 95	85.80 34	80.69 51	83.82 74	90.81 51	91.85 29	94.18 66	96.99 61

3Dnovator+	81.14 5	88.59 39	87.49 49	89.88 31	95.83 35	96.45 43	91.94 39	82.41 59	87.09 61	85.94 22	62.80 99	85.37 29	89.46 28	91.51 44	91.89 28	93.72 81	97.30 51

ACMMP		88.48 40	88.71 42	88.22 42	94.61 46	95.53 57	90.64 47	85.60 39	90.97 36	78.62 46	89.88 27	74.20 81	86.29 53	88.16 88	86.37 94	93.57 90	95.86 80
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
AdaColmap		88.46 41	85.75 64	91.62 22	96.25 29	95.35 61	90.71 45	91.08 13	90.22 42	86.17 20	74.33 55	73.67 84	92.00 16	86.31 107	85.82 102	93.52 93	94.53 98

MVS_0304		88.43 42	89.46 37	87.21 47	91.85 61	97.60 19	92.62 32	81.10 65	87.16 60	73.80 65	72.19 63	83.36 41	87.03 47	94.64 8	93.67 9	96.88 9	97.64 43

3Dnovator	80.58 8	88.20 43	86.53 56	90.15 27	96.86 18	96.46 41	91.97 38	83.06 53	85.16 67	83.66 28	62.28 102	82.15 44	88.98 31	90.99 49	92.65 14	96.38 24	96.03 77

CPTT-MVS		88.17 44	87.84 47	88.55 39	93.33 51	93.75 81	92.33 36	84.75 42	89.87 45	81.72 41	83.93 38	81.12 49	88.45 37	85.42 116	84.07 121	90.72 171	96.72 71

MVS_111021_HR		87.82 45	88.84 40	86.62 54	94.42 47	97.36 24	88.21 60	83.26 51	83.42 70	72.52 75	82.63 39	76.93 67	84.95 62	91.93 38	91.15 37	96.39 23	98.49 26

DELS-MVS		87.75 46	86.92 53	88.71 37	94.69 44	97.34 27	92.78 30	84.50 44	77.87 93	81.94 38	67.17 78	75.49 75	82.84 80	95.38 2	95.93 2	95.55 38	99.27 9
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
MVSTER		87.68 47	89.12 39	86.01 58	88.11 87	90.05 119	89.28 54	77.05 88	91.37 33	79.97 43	76.70 50	85.25 31	84.89 63	93.53 19	91.41 34	96.73 11	95.55 87

MVS_111021_LR		87.58 48	88.67 43	86.31 57	92.58 55	95.89 54	86.20 76	82.49 57	89.08 51	77.47 50	86.20 33	74.22 80	85.49 58	90.03 61	88.52 73	93.66 84	96.74 70

QAPM		87.06 49	86.46 57	87.75 43	96.63 21	97.09 28	91.71 41	82.62 56	80.58 83	71.28 80	66.04 85	84.24 34	87.01 48	89.93 63	89.91 52	97.26 5	97.44 46

PVSNet_BlendedMVS		86.98 50	87.05 51	86.90 49	93.03 52	96.98 31	86.57 72	81.82 61	89.78 46	82.78 34	71.54 64	66.07 113	80.73 91	93.46 20	91.97 24	96.45 20	99.53 4

PVSNet_Blended		86.98 50	87.05 51	86.90 49	93.03 52	96.98 31	86.57 72	81.82 61	89.78 46	82.78 34	71.54 64	66.07 113	80.73 91	93.46 20	91.97 24	96.45 20	99.53 4

ETV-MVS		86.94 52	89.49 36	83.95 69	87.28 94	95.61 56	83.58 104	76.37 93	92.59 29	73.20 67	80.35 42	76.42 71	87.38 45	92.20 32	90.45 46	95.90 32	98.83 20

DROMVSNet		86.42 53	88.31 44	84.20 67	86.61 101	94.08 75	86.20 76	72.18 125	89.06 52	76.02 59	74.48 54	80.47 53	88.90 34	92.03 35	90.07 50	95.30 42	98.00 37

OMC-MVS		86.38 54	86.21 61	86.57 55	92.30 57	94.35 73	87.60 64	83.51 49	92.32 31	77.37 51	72.27 62	77.83 59	86.59 51	87.62 93	85.95 99	92.08 140	93.11 124

HQP-MVS		86.17 55	87.35 50	84.80 64	91.41 64	92.37 98	91.05 44	84.35 46	88.52 54	64.21 102	87.05 32	68.91 102	84.80 65	89.12 74	88.16 79	92.96 122	97.31 50

canonicalmvs		85.93 56	86.26 60	85.54 59	88.94 77	95.44 58	89.56 52	76.01 95	87.83 57	77.70 48	76.43 51	68.66 104	87.80 44	87.02 96	91.51 33	93.25 112	96.95 63

MAR-MVS		85.65 57	86.30 59	84.88 63	95.51 41	95.89 54	86.50 74	76.71 89	89.23 50	68.59 92	70.93 68	74.49 78	88.55 36	89.40 72	90.30 48	93.42 104	93.88 115
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	82.38 4	85.52 58	84.41 69	86.81 52	91.51 63	96.23 46	90.27 48	89.81 20	77.87 93	70.67 83	69.20 72	77.86 57	85.55 57	85.92 112	86.38 93	93.03 119	97.43 48
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		85.43 59	84.24 72	86.83 51	87.69 91	93.16 89	90.01 50	82.72 55	87.17 59	79.28 45	71.43 67	65.81 115	86.02 54	87.33 95	86.96 87	95.25 48	97.83 40
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CS-MVS		85.28 60	87.63 48	82.55 78	86.43 106	94.13 74	81.68 114	73.90 109	88.34 56	69.17 90	74.12 58	76.60 69	89.62 27	91.71 41	90.10 49	94.48 62	98.42 29

OpenMVS	77.91 11	85.09 61	83.42 76	87.03 48	96.12 31	96.55 39	89.36 53	81.59 63	79.19 89	75.20 61	55.84 128	79.04 56	84.45 69	88.47 82	89.35 63	95.48 40	95.48 88

TSAR-MVS + COLMAP		84.93 62	85.79 63	83.92 70	90.90 66	93.57 85	89.25 55	82.00 60	91.29 34	61.66 111	88.25 30	59.46 136	86.71 50	89.79 65	87.09 85	93.01 120	91.09 146

TAPA-MVS	80.99 7	84.83 63	84.42 68	85.31 60	91.89 60	93.73 83	88.53 59	82.80 54	89.99 44	69.78 86	71.53 66	75.03 77	85.47 59	86.26 108	84.54 116	93.39 106	89.90 156
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CS-MVS-test		84.81 64	86.58 55	82.74 76	85.56 112	91.16 107	86.20 76	72.18 125	86.26 64	69.45 89	74.32 57	75.21 76	88.90 34	92.03 35	88.89 71	94.17 67	98.21 34

PLC	81.02 6	84.81 64	81.81 93	88.31 41	93.77 50	90.35 114	88.80 57	84.47 45	86.76 62	82.17 37	66.56 81	71.01 95	88.41 39	85.48 114	84.28 119	92.26 138	88.21 169
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EIA-MVS		84.75 66	86.43 58	82.79 75	86.88 97	95.36 60	82.84 110	76.39 92	87.61 58	71.03 81	74.33 55	71.12 94	85.16 60	89.69 67	88.70 72	94.40 64	98.23 32

CNLPA		84.72 67	82.14 88	87.73 44	92.85 54	93.83 80	84.70 93	85.07 40	90.90 37	83.16 31	56.28 124	71.53 91	88.14 43	84.19 121	84.00 125	92.48 133	94.26 105

MVS_Test		84.60 68	85.13 67	83.99 68	88.17 85	95.27 65	88.21 60	73.15 116	84.31 69	70.55 84	68.67 76	68.78 103	86.99 49	91.71 41	91.90 26	96.84 10	95.27 92

casdiffmvs		83.84 69	82.65 84	85.22 61	87.25 95	94.62 71	86.01 81	79.62 68	79.48 86	77.59 49	61.92 105	64.34 119	85.57 56	90.55 54	90.51 45	95.26 46	97.14 55

baseline		83.83 70	84.38 70	83.18 74	86.65 99	94.59 72	85.79 84	73.78 113	85.83 65	72.94 68	69.28 71	70.80 97	83.45 77	86.80 99	87.59 81	96.47 19	95.77 84

diffmvs		83.69 71	83.17 80	84.31 65	85.45 115	93.92 76	86.89 67	78.62 71	82.71 76	75.95 60	66.78 80	63.90 122	83.84 73	87.90 90	89.16 65	95.10 52	97.82 41

CANet_DTU		83.33 72	86.59 54	79.53 100	88.88 78	94.87 68	86.63 71	68.85 150	85.45 66	50.54 156	77.86 48	69.94 100	85.62 55	92.63 27	90.88 40	96.63 12	94.46 99

DI_MVS_plusplus_trai		83.32 73	82.53 86	84.25 66	86.26 108	93.66 84	90.23 49	77.16 87	77.05 100	74.06 64	53.74 137	74.33 79	83.61 76	91.40 46	89.82 54	94.17 67	97.73 42

baseline1		82.63 74	82.02 89	83.34 73	88.30 84	91.89 102	88.03 63	80.86 66	75.05 107	65.96 97	64.27 92	72.20 89	80.01 95	91.32 47	89.56 61	96.90 8	89.85 157

PVSNet_Blended_VisFu		82.55 75	83.70 75	81.21 88	89.66 70	95.15 67	82.41 111	77.36 86	72.53 125	73.64 66	61.15 108	77.19 65	70.35 153	91.31 48	89.72 57	93.84 76	98.85 18

ET-MVSNet_ETH3D		82.37 76	85.68 65	78.51 110	62.90 212	94.66 69	87.06 66	73.57 114	83.13 72	61.52 113	78.37 45	76.01 73	89.99 22	84.14 122	89.03 68	96.03 30	94.42 100

PMMVS		82.26 77	85.48 66	78.51 110	85.92 111	91.92 101	78.30 142	70.77 135	86.30 63	61.11 115	82.46 40	70.88 96	84.70 67	88.05 89	84.78 112	90.24 178	93.98 109

ACMP	79.58 9	82.23 78	81.82 92	82.71 77	88.15 86	90.95 111	85.23 89	78.52 73	81.70 78	72.52 75	78.41 44	60.63 130	80.48 93	82.88 132	83.44 129	91.37 157	94.70 96
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CHOSEN 280x420		82.15 79	85.87 62	77.80 115	86.54 103	93.42 87	81.74 113	59.96 191	78.99 91	63.99 103	74.50 53	83.95 38	80.99 86	89.53 70	85.01 107	93.56 92	95.71 86

LGP-MVS_train		82.12 80	82.57 85	81.59 84	89.26 74	90.23 117	88.76 58	78.05 74	81.26 80	61.64 112	79.52 43	62.11 125	79.59 97	85.20 117	84.68 114	92.27 137	95.02 94

FMVSNet3		81.93 81	81.98 90	81.88 83	79.49 152	87.02 134	88.15 62	72.57 119	83.02 73	72.63 72	56.55 120	73.48 85	82.34 83	91.49 45	91.20 36	96.07 26	91.13 145

test2506		81.91 82	81.78 95	82.06 82	89.09 75	95.32 62	84.61 95	77.54 82	74.61 111	68.77 91	63.80 96	67.53 107	77.09 106	90.19 58	89.01 69	95.27 43	92.00 138

thisisatest0530		81.67 83	84.27 71	78.63 106	85.53 113	93.88 79	81.77 112	73.84 110	81.35 79	63.85 105	68.79 74	77.64 61	73.02 134	88.73 80	85.73 103	93.76 79	93.80 119

tttt0517		81.51 84	84.12 74	78.47 112	85.33 117	93.74 82	81.42 117	73.84 110	81.21 81	63.59 106	68.73 75	77.46 64	73.02 134	88.47 82	85.73 103	93.63 88	93.49 123

OPM-MVS		81.34 85	78.18 111	85.02 62	91.27 65	91.78 103	90.66 46	83.62 48	62.39 154	65.91 98	63.35 97	64.33 120	85.03 61	87.77 92	85.88 101	93.66 84	91.75 142
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
baseline2		81.21 86	83.36 79	78.70 104	83.22 131	92.71 91	80.32 123	74.25 108	80.39 84	63.94 104	68.89 73	68.44 105	74.67 120	89.61 69	86.68 91	95.83 36	96.81 69

IS_MVSNet		80.92 87	84.14 73	77.16 118	87.43 92	93.90 78	80.44 119	74.64 102	75.05 107	61.10 116	65.59 87	76.89 68	67.39 161	90.88 50	90.05 51	91.95 144	96.62 74

ACMM	78.09 10	80.91 88	78.39 108	83.86 71	89.61 73	87.71 131	85.16 90	80.67 67	79.04 90	74.18 63	63.82 95	60.84 129	82.59 81	84.33 119	83.59 128	90.96 165	89.39 162
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPP-MVSNet		80.82 89	82.79 82	78.52 108	86.31 107	92.37 98	79.83 126	74.51 103	73.79 118	64.46 101	67.01 79	80.63 52	74.33 123	85.63 113	84.35 118	91.68 150	95.79 83

CostFormer		80.72 90	81.81 93	79.44 102	86.50 104	91.65 104	84.31 97	59.84 192	80.86 82	72.69 70	62.46 101	73.74 82	79.93 96	82.58 136	84.50 117	93.37 107	96.90 67

GBi-Net		80.72 90	80.49 97	81.00 91	78.18 156	86.19 148	86.73 68	72.57 119	83.02 73	72.63 72	56.55 120	73.48 85	80.99 86	86.57 101	86.83 88	94.89 56	90.77 149

test1		80.72 90	80.49 97	81.00 91	78.18 156	86.19 148	86.73 68	72.57 119	83.02 73	72.63 72	56.55 120	73.48 85	80.99 86	86.57 101	86.83 88	94.89 56	90.77 149

UGNet		80.71 93	83.09 81	77.93 114	87.02 96	92.71 91	80.28 124	76.53 90	73.83 117	71.35 79	70.07 69	73.71 83	58.93 181	87.39 94	86.97 86	93.48 100	96.94 64
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
CHOSEN 1792x2688		80.23 94	79.16 105	81.48 86	91.97 58	96.56 37	86.18 79	75.40 99	76.17 103	61.32 114	37.43 199	61.08 128	76.52 112	92.35 29	91.64 31	97.46 3	98.86 17

thres100view900		79.83 95	77.79 115	82.21 79	88.42 81	93.54 86	87.07 65	81.11 64	70.15 132	61.01 117	56.65 118	51.22 154	81.78 84	89.77 66	85.95 99	93.84 76	97.26 52

Effi-MVS+		79.80 96	80.04 99	79.52 101	85.53 113	93.31 88	85.28 87	70.68 137	74.15 113	58.79 126	62.03 104	60.51 131	83.37 78	88.41 84	86.09 98	93.49 99	95.80 82

ECVR-MVS		79.76 97	78.27 109	81.50 85	89.09 75	95.32 62	84.61 95	77.54 82	74.61 111	65.38 99	50.22 149	56.31 147	77.09 106	90.19 58	89.01 69	95.27 43	92.25 133

DCV-MVSNet		79.76 97	79.17 104	80.44 96	84.65 121	84.51 172	84.20 99	72.36 124	75.17 106	70.81 82	66.21 84	66.56 110	80.99 86	82.89 131	84.56 115	89.65 184	94.30 104

FC-MVSNet-train		79.54 99	78.20 110	81.09 90	86.55 102	88.63 127	79.96 125	78.53 72	70.90 130	68.24 93	65.87 86	56.45 146	80.29 94	86.20 110	84.08 120	92.97 121	95.31 91

test-LLR		79.52 100	83.42 76	74.97 127	81.79 136	91.26 105	76.17 163	70.57 138	77.71 95	52.14 143	66.26 82	77.47 62	73.10 130	87.02 96	87.16 83	96.05 28	97.02 58

FMVSNet2		79.24 101	78.14 112	80.53 95	78.18 156	86.19 148	86.73 68	71.91 127	72.97 120	70.48 85	50.63 147	66.56 110	80.99 86	90.10 60	89.77 56	94.89 56	90.77 149

TESTMET0.1,1		79.15 102	83.42 76	74.18 133	79.81 150	91.26 105	76.17 163	67.83 163	77.71 95	52.14 143	66.26 82	77.47 62	73.10 130	87.02 96	87.16 83	96.05 28	97.02 58

tfpn200view9		79.05 103	77.21 119	81.18 89	88.42 81	92.55 96	85.12 91	77.94 76	70.15 132	61.01 117	56.65 118	51.22 154	81.11 85	88.23 85	84.80 111	93.50 98	96.90 67

test1111		78.99 104	77.77 116	80.42 97	88.64 79	95.31 64	83.39 105	77.67 80	72.76 123	61.91 109	49.58 152	55.59 149	75.67 117	90.23 57	89.09 66	95.23 49	91.83 141

PatchMatch-RL		78.75 105	76.47 126	81.41 87	88.53 80	91.10 108	78.09 143	77.51 85	77.33 97	71.98 77	64.38 91	48.10 166	82.55 82	84.06 123	82.35 138	89.78 181	87.97 171

LS3D		78.72 106	75.79 130	82.15 80	91.91 59	89.39 124	83.66 102	85.88 38	76.81 101	59.22 125	57.67 115	58.53 140	83.72 75	82.07 141	81.63 149	88.50 192	84.39 182

thres200		78.69 107	76.71 122	80.99 93	88.35 83	92.56 94	86.03 80	77.94 76	66.27 139	60.66 119	56.08 125	51.11 156	79.45 98	88.23 85	85.54 106	93.52 93	97.20 54

Anonymous20231211		78.61 108	75.57 133	82.15 80	84.43 125	90.26 115	84.08 100	77.68 79	71.09 128	72.90 69	39.24 192	66.21 112	84.23 72	82.15 139	84.04 122	89.61 185	96.03 77

IB-MVS	74.10 12	78.52 109	78.51 107	78.52 108	90.15 68	95.39 59	71.95 183	77.53 84	74.95 109	77.25 52	58.93 112	55.92 148	58.37 183	79.01 166	87.89 80	95.88 34	97.47 45
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
EPNet_dtu		78.49 110	81.96 91	74.45 132	92.57 56	88.74 126	82.98 106	78.83 70	83.28 71	44.64 187	77.40 49	67.73 106	53.98 192	85.44 115	84.91 108	93.71 82	86.22 177
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres400		78.39 111	76.39 127	80.73 94	88.02 88	92.94 90	84.77 92	78.88 69	65.20 147	59.70 123	55.20 131	50.85 157	79.45 98	88.81 77	84.81 110	93.57 90	96.91 66

UA-Net		78.30 112	80.92 96	75.25 126	87.42 93	92.48 97	79.54 129	75.49 98	60.47 158	60.52 120	68.44 77	84.08 37	57.54 185	88.54 81	88.45 74	90.96 165	83.97 184

Vis-MVSNet (Re-imp)		78.28 113	82.68 83	73.16 144	86.64 100	92.68 93	78.07 144	74.48 104	74.05 114	53.47 136	64.22 93	76.52 70	54.28 188	88.96 76	88.29 77	92.03 142	94.00 108

MSDG		78.11 114	73.17 146	83.86 71	91.78 62	86.83 136	85.25 88	86.02 36	72.84 122	69.69 88	51.43 144	54.00 152	77.61 102	81.95 144	82.27 140	92.83 127	82.91 189

HyFIR lowres test		78.08 115	76.81 120	79.56 99	90.77 67	94.64 70	82.97 107	69.85 143	69.81 134	59.53 124	33.52 204	64.66 116	78.97 100	88.77 79	88.38 76	95.27 43	97.86 39

GeoE		78.04 116	77.52 118	78.65 105	84.51 123	90.84 112	80.94 118	69.24 148	72.86 121	66.06 96	53.45 138	60.46 132	77.37 103	84.20 120	84.85 109	93.78 78	96.00 79

test-mter		77.90 117	82.44 87	72.60 149	78.52 154	90.24 116	73.85 176	65.31 177	76.37 102	51.29 147	65.58 88	75.94 74	71.36 144	85.98 111	86.26 95	95.26 46	96.71 73

thres600view7		77.66 118	75.67 131	79.98 98	87.71 90	92.56 94	83.79 101	77.94 76	64.41 149	58.69 127	54.32 136	50.54 158	78.23 101	88.23 85	83.06 132	93.52 93	96.55 75

MS-PatchMatch		77.47 119	76.48 125	78.63 106	89.89 69	90.42 113	85.42 86	69.53 145	70.79 131	60.43 121	50.05 150	70.62 99	70.66 150	86.71 100	82.54 135	95.86 35	84.23 183

Fast-Effi-MVS+		77.37 120	76.68 123	78.17 113	82.84 133	89.94 120	81.47 116	68.01 159	72.99 119	60.26 122	55.07 132	53.20 153	82.99 79	86.47 106	86.12 97	93.46 101	92.98 127

Vis-MVSNet		77.24 121	79.99 102	74.02 134	84.62 122	93.92 76	80.33 122	72.55 122	62.58 153	55.25 134	64.45 90	69.49 101	57.00 186	88.78 78	88.21 78	94.36 65	92.54 130
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MDTV_nov1_ep13		77.20 122	80.04 99	73.90 136	82.22 134	90.14 118	79.25 133	61.52 187	78.63 92	56.98 128	65.52 89	72.80 88	73.05 132	80.93 152	83.20 130	90.36 175	89.05 165

EPMVS		77.16 123	79.08 106	74.92 128	86.73 98	91.98 100	78.62 138	55.44 200	79.43 87	56.59 130	61.24 107	70.73 98	76.97 109	80.59 155	81.43 156	95.15 51	88.17 170

tpm cat1		76.93 124	76.19 129	77.79 116	85.08 120	88.58 128	82.96 108	59.33 193	75.72 105	72.64 71	51.25 145	64.41 118	75.74 116	77.90 175	80.10 172	90.97 164	95.35 89

PatchmatchNet		76.85 125	80.03 101	73.15 145	84.08 127	91.04 110	77.76 148	55.85 199	79.43 87	52.74 141	62.08 103	76.02 72	74.56 121	79.92 160	81.41 157	93.92 75	90.29 154
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IterMVS-LS		76.80 126	76.33 128	77.35 117	84.07 128	84.11 174	81.54 115	68.52 152	66.17 140	61.74 110	57.84 114	64.31 121	74.88 119	83.48 128	86.21 96	93.34 109	92.16 135
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		76.57 127	76.78 121	76.32 121	80.94 143	89.75 121	82.94 109	72.64 118	59.01 164	62.95 108	58.60 113	62.67 124	66.91 163	86.26 108	87.20 82	91.57 152	93.97 110
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SCA		76.41 128	79.90 103	72.35 153	84.26 126	85.24 163	75.57 170	54.56 202	79.95 85	52.72 142	64.22 93	77.84 58	73.73 127	80.48 156	81.37 158	93.25 112	90.20 155

tpmrst		76.27 129	77.65 117	74.66 130	86.13 110	89.53 123	79.31 132	54.91 201	77.19 99	56.27 131	55.87 127	64.58 117	77.25 104	80.85 153	80.21 169	94.07 70	95.32 90

dps		75.76 130	75.02 135	76.63 120	84.51 123	88.12 129	77.51 149	58.33 195	75.91 104	71.98 77	57.37 116	57.85 141	76.81 111	77.89 176	78.40 181	90.63 172	89.63 159

CR-MVSNet		74.84 131	77.91 113	71.26 166	81.77 138	85.52 159	78.32 140	54.14 204	74.05 114	51.09 150	50.00 151	71.38 93	70.77 148	86.48 104	84.03 123	91.46 156	93.92 112

Effi-MVS+-dtu		74.57 132	74.60 139	74.53 131	81.38 140	86.74 138	80.39 121	67.70 164	67.36 138	53.06 137	59.86 110	57.50 142	75.84 115	80.19 158	78.62 179	88.79 191	91.95 140

test_part1		74.38 133	70.16 159	79.31 103	83.30 130	84.45 173	84.31 97	71.43 132	55.24 177	74.88 62	38.77 194	59.61 135	75.29 118	78.96 167	81.53 151	86.63 200	92.55 129

RPSCF		74.27 134	73.24 145	75.48 125	81.01 142	80.18 196	76.24 162	72.37 123	74.84 110	68.24 93	72.47 60	67.39 108	73.89 124	71.05 200	69.38 207	81.14 212	77.37 201

FMVSNet1		74.26 135	71.95 151	76.95 119	74.28 188	83.94 176	83.61 103	69.99 141	57.08 170	65.08 100	42.39 181	57.41 143	76.98 108	86.57 101	86.83 88	91.77 149	89.42 160

GA-MVS		73.62 136	74.52 140	72.58 150	79.93 148	89.29 125	78.02 145	71.67 130	60.79 157	42.68 191	54.41 135	49.07 162	70.07 154	89.39 73	86.55 92	93.13 117	92.12 136

Fast-Effi-MVS+-dtu		73.56 137	75.32 134	71.50 162	80.35 145	86.83 136	79.72 127	58.07 196	67.64 137	44.83 184	60.28 109	54.07 151	73.59 129	81.90 146	82.30 139	92.46 134	94.18 106

tpm		73.50 138	74.85 136	71.93 156	83.19 132	86.84 135	78.61 139	55.91 198	65.64 142	48.90 163	56.30 123	61.09 127	72.31 136	79.10 165	80.61 168	92.68 129	94.35 103

RPMNet		73.46 139	77.85 114	68.34 176	81.71 139	85.52 159	73.83 177	50.54 211	74.05 114	46.10 178	53.03 141	71.91 90	66.31 165	83.55 126	82.18 142	91.55 154	94.71 95

USDC		73.43 140	72.31 149	74.73 129	80.86 144	86.21 146	80.42 120	71.83 129	71.69 127	46.94 171	59.60 111	42.58 187	76.47 113	82.66 135	81.22 161	91.88 146	82.24 195

pmmvs4		73.38 141	71.53 154	75.55 124	75.95 174	85.24 163	77.25 153	71.59 131	71.03 129	63.10 107	49.09 157	44.22 177	73.73 127	82.04 142	80.18 170	91.68 150	88.89 167

UniMVSNet_NR-MVSNet		73.11 142	72.59 147	73.71 139	76.90 165	86.58 142	77.01 154	75.82 96	65.59 143	48.82 164	50.97 146	48.42 164	71.61 140	79.19 164	83.03 133	92.11 139	94.37 101

FMVSNet5		72.83 143	73.89 143	71.59 160	67.42 206	76.28 204	75.88 167	63.74 181	77.27 98	54.59 135	53.32 139	71.48 92	73.85 125	81.95 144	81.69 147	94.06 71	75.20 205

PatchT		72.66 144	76.58 124	68.09 178	79.02 153	86.09 152	59.81 205	51.78 209	72.00 126	51.09 150	46.84 161	66.70 109	70.77 148	86.48 104	84.03 123	96.07 26	93.92 112

ACMH	71.22 14	72.65 145	70.13 160	75.59 123	86.19 109	86.14 151	75.76 168	77.63 81	54.79 179	46.16 177	53.28 140	47.28 168	77.24 105	78.91 168	81.18 162	90.57 173	89.33 163
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS		72.43 146	74.05 141	70.55 170	80.34 146	81.17 190	77.44 150	61.00 190	63.57 152	46.82 173	55.88 126	59.09 139	65.03 167	83.15 129	83.83 126	92.67 130	91.65 143
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH+	72.14 13	72.38 147	69.34 167	75.93 122	85.21 118	84.89 167	76.96 157	76.04 94	59.76 159	51.63 146	50.37 148	48.69 163	76.90 110	76.06 184	78.69 177	88.85 190	86.90 175

DU-MVS		72.19 148	71.35 155	73.17 143	75.95 174	86.02 153	77.01 154	74.42 105	65.39 145	48.82 164	49.10 155	42.81 185	71.61 140	78.67 169	83.10 131	91.22 160	94.37 101

IterMVS-SCA-FT		72.18 149	73.96 142	70.11 172	80.15 147	81.11 191	77.42 151	61.09 189	63.67 151	46.73 174	55.77 129	59.15 138	63.95 170	82.83 133	83.70 127	91.31 158	91.49 144

UniMVSNet (Re)		72.12 150	72.28 150	71.93 156	76.77 166	87.38 133	75.73 169	73.51 115	65.76 141	50.24 158	48.65 158	46.49 169	63.85 171	80.10 159	82.47 136	91.49 155	95.13 93

ADS-MVSNet		72.11 151	73.72 144	70.24 171	81.24 141	86.59 141	74.75 173	50.56 210	72.58 124	49.17 161	55.40 130	61.46 126	73.80 126	76.01 185	78.14 182	91.93 145	85.86 178

gg-mvs-nofinetune		72.10 152	74.79 137	68.97 175	83.31 129	95.22 66	85.66 85	48.77 212	35.68 215	22.17 221	30.49 207	77.73 60	76.37 114	94.30 13	93.03 11	97.55 2	97.05 57

TAMVS		72.06 153	71.76 153	72.41 152	76.68 167	88.12 129	74.82 172	68.09 157	53.52 184	56.91 129	52.94 142	56.93 145	66.91 163	81.37 149	82.44 137	91.07 162	86.99 174

v2v482		71.73 154	69.80 162	73.99 135	75.88 178	86.66 140	79.58 128	71.90 128	57.58 168	50.41 157	45.35 165	43.24 183	73.05 132	79.69 161	82.18 142	93.08 118	93.87 116

test0.0.03 1		71.70 155	74.68 138	68.23 177	81.79 136	83.81 177	68.64 187	70.57 138	68.81 136	43.47 188	62.77 100	60.09 134	51.77 199	82.48 137	81.67 148	93.16 115	83.13 187

V42		71.58 156	70.11 161	73.30 142	75.66 181	86.68 139	79.17 135	69.92 142	59.29 163	52.80 140	44.36 169	45.66 171	68.83 155	79.48 163	81.49 153	93.44 102	93.82 118

NR-MVSNet		71.47 157	71.11 156	71.90 158	77.73 161	86.02 153	76.88 158	74.42 105	65.39 145	46.09 179	49.10 155	39.87 200	64.27 169	81.40 148	82.24 141	91.99 143	93.75 120

v8		71.42 158	69.69 163	73.43 141	76.45 170	85.12 166	79.53 130	67.47 167	59.34 162	52.90 139	44.60 167	45.82 170	71.05 146	79.56 162	81.45 155	93.17 114	91.96 139

TranMVSNet+NR-MVSNet		71.12 159	70.24 158	72.15 154	76.01 173	84.80 169	76.55 160	75.65 97	61.99 155	45.29 182	48.42 159	43.07 184	67.55 159	78.28 172	82.83 134	91.85 147	92.29 131

v10		70.97 160	69.44 164	72.75 146	75.90 177	84.58 171	79.43 131	66.45 172	58.07 166	49.93 159	43.87 175	43.68 178	71.91 138	82.04 142	81.70 146	92.89 125	92.11 137

v1144		70.93 161	69.42 166	72.70 147	75.48 182	86.26 144	79.22 134	69.39 147	55.61 175	48.05 169	43.47 176	42.55 188	71.51 142	82.11 140	81.74 145	92.56 132	94.17 107

thisisatest0515		70.62 162	71.94 152	69.07 174	76.48 169	85.59 158	68.03 188	68.02 158	59.70 160	52.94 138	52.19 143	50.36 159	58.10 184	83.15 129	81.63 149	90.87 168	90.99 147

Baseline_NR-MVSNet		70.61 163	68.87 170	72.65 148	75.95 174	80.49 194	75.92 166	74.75 101	65.10 148	48.78 166	41.28 187	44.28 176	68.45 156	78.67 169	79.64 173	92.04 141	92.62 128

v148		70.34 164	68.46 173	72.54 151	76.04 172	86.38 143	74.83 171	72.73 117	55.88 174	55.26 133	43.32 178	43.49 179	64.52 168	76.93 182	80.11 171	91.85 147	93.11 124

v1192		70.32 165	68.77 171	72.12 155	74.76 184	85.62 157	78.73 136	68.53 151	55.08 178	46.34 176	42.39 181	40.67 195	71.90 139	82.27 138	81.53 151	92.43 135	93.86 117

v144192		70.10 166	68.55 172	71.90 158	74.55 185	85.67 156	77.81 146	68.22 156	54.65 180	46.91 172	42.76 179	41.27 192	70.95 147	80.48 156	81.11 166	92.96 122	93.90 114

pmmvs5		70.01 167	69.31 168	70.82 169	75.80 180	86.26 144	72.94 178	67.91 160	53.84 183	47.22 170	47.31 160	41.47 191	67.61 158	83.93 125	81.93 144	93.42 104	90.42 153

COLMAP_ROB	66.31 15	69.91 168	66.61 178	73.76 137	86.44 105	82.76 181	76.59 159	76.46 91	63.82 150	50.92 154	45.60 164	49.13 161	65.87 166	74.96 190	74.45 197	86.30 202	75.57 204
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v1921920		69.85 169	68.38 174	71.58 161	74.35 186	85.39 161	77.78 147	67.88 162	54.64 181	45.39 181	42.11 184	39.97 199	71.10 145	81.68 147	81.17 164	92.96 122	93.69 122

pm-mvs1		69.62 170	68.07 176	71.44 163	77.21 163	85.32 162	76.11 165	71.05 133	46.55 204	51.17 149	41.83 185	48.20 165	61.81 177	84.00 124	81.14 165	91.28 159	89.42 160

UniMVSNet_ETH3D		69.49 171	65.86 180	73.72 138	76.51 168	85.88 155	78.65 137	70.52 140	48.08 201	55.71 132	37.64 196	40.56 196	71.38 143	75.05 189	81.49 153	89.57 187	92.29 131

tfpnnormal		69.29 172	65.58 181	73.62 140	79.87 149	84.82 168	76.97 156	75.12 100	45.29 205	49.03 162	35.57 202	37.20 208	68.02 157	82.70 134	81.24 160	92.69 128	92.20 134

v1240		69.28 173	67.82 177	71.00 168	74.09 190	85.13 165	76.54 161	67.28 169	53.17 185	44.70 185	41.55 186	39.38 201	70.51 152	81.29 150	81.18 162	92.88 126	93.02 126

CVMVSNet		68.95 174	70.79 157	66.79 184	79.69 151	83.75 178	72.05 182	70.90 134	56.20 172	36.30 203	54.94 134	59.22 137	54.03 191	78.33 171	78.65 178	87.77 197	84.44 181

MIMVSNet		68.66 175	69.43 165	67.76 179	64.92 209	84.68 170	74.16 174	54.10 206	60.85 156	51.27 148	39.47 191	49.48 160	67.48 160	84.86 118	85.57 105	94.63 60	81.10 196

TDRefinement		67.82 176	64.91 187	71.22 167	82.08 135	81.45 186	77.42 151	73.79 112	59.62 161	48.35 168	42.35 183	42.40 189	60.87 179	74.69 191	74.64 196	84.83 206	79.20 199

anonymousdsp		67.61 177	68.94 169	66.04 185	71.44 202	83.97 175	66.45 192	63.53 183	50.54 194	42.42 192	49.39 153	45.63 172	62.84 174	77.99 174	81.34 159	89.59 186	93.75 120

TinyColmap		67.16 178	63.51 194	71.42 164	77.94 159	79.54 200	72.80 179	69.78 144	56.58 171	45.52 180	44.53 168	33.53 213	74.45 122	76.91 183	77.06 188	88.03 196	76.41 202

FC-MVSNet-test		67.04 179	72.47 148	60.70 202	76.92 164	81.41 187	61.52 202	69.45 146	65.58 144	26.74 217	61.79 106	60.40 133	41.17 208	77.60 178	77.78 184	88.41 193	82.70 191

TransMVSNet (Re)		66.87 180	64.30 189	69.88 173	78.32 155	81.35 189	73.88 175	74.34 107	43.19 209	45.20 183	40.12 189	42.37 190	55.97 187	80.85 153	79.15 174	91.56 153	83.06 188

CMPMVS	50.59 17	66.74 181	62.72 198	71.42 164	85.40 116	89.72 122	72.69 180	70.72 136	51.24 190	51.75 145	38.91 193	44.40 174	63.74 172	70.84 201	71.52 201	84.19 207	72.45 209
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v7n		66.43 182	65.51 182	67.51 180	71.63 201	83.10 179	70.89 186	65.02 178	50.13 197	44.68 186	39.59 190	38.77 202	62.57 175	77.59 179	78.91 175	90.29 177	90.44 152

EG-PatchMatch MVS		66.23 183	65.20 184	67.43 181	77.74 160	86.20 147	72.51 181	63.68 182	43.95 207	43.44 189	36.22 201	45.43 173	54.04 190	81.00 151	80.95 167	93.15 116	82.67 192

WR-MVS		64.98 184	66.59 179	63.09 195	74.34 187	82.68 182	64.98 198	69.17 149	54.42 182	36.18 204	44.32 170	44.35 175	44.65 202	73.60 192	77.83 183	89.21 189	88.96 166

gm-plane-assit		64.86 185	68.15 175	61.02 201	76.44 171	68.29 213	41.60 218	53.37 207	34.68 217	26.19 219	33.22 205	57.09 144	71.97 137	95.12 5	93.97 7	96.54 16	94.66 97

CP-MVSNet		64.84 186	64.97 185	64.69 190	72.09 197	81.04 192	66.66 191	67.53 166	52.45 187	37.40 199	44.00 174	38.37 204	53.54 194	72.26 196	76.93 189	90.94 167	89.75 158

MDTV_nov1_ep13_2view		64.72 187	64.94 186	64.46 191	71.14 203	81.94 185	67.53 189	54.54 203	55.92 173	43.29 190	44.02 173	43.27 182	59.87 180	71.85 198	74.77 195	90.36 175	82.82 190

MVS-HIRNet		64.63 188	64.03 193	65.33 187	75.01 183	82.84 180	58.54 209	52.10 208	55.42 176	49.29 160	29.83 210	43.48 180	66.97 162	78.28 172	78.81 176	90.07 180	79.52 198

pmnet_mix02		64.58 189	64.11 192	65.12 188	74.16 189	80.17 197	63.24 200	67.91 160	57.87 167	41.69 193	45.86 163	40.99 194	53.97 193	69.92 204	71.67 200	89.77 182	82.29 194

LTVRE_ROB	63.07 16	64.49 190	63.16 197	66.04 185	77.47 162	82.64 183	70.98 185	65.02 178	34.01 218	29.61 213	49.12 154	35.58 212	70.57 151	75.10 188	78.45 180	82.60 210	87.24 173
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
PEN-MVS		64.35 191	64.29 190	64.42 192	72.67 193	79.83 198	66.97 190	68.24 155	51.21 191	35.29 206	44.09 171	38.51 203	52.36 197	71.06 199	77.65 185	90.99 163	87.68 172

pmmvs6		64.24 192	61.77 202	67.12 182	72.39 196	81.39 188	71.33 184	65.95 176	36.05 214	48.48 167	30.55 206	43.45 181	58.75 182	77.88 177	76.36 192	85.83 203	86.70 176

pmmvs-eth3d		64.24 192	61.96 200	66.90 183	66.35 207	76.04 206	66.09 194	66.31 173	52.59 186	50.94 153	37.61 197	32.79 215	62.43 176	75.78 186	75.48 194	89.27 188	83.39 186

PS-CasMVS		64.22 194	64.19 191	64.25 193	71.86 199	80.67 193	66.42 193	67.43 168	50.64 193	36.48 201	42.60 180	37.46 207	52.56 196	71.98 197	76.69 191	90.76 169	89.29 164

WR-MVS_H		64.14 195	65.36 183	62.71 197	72.47 195	82.33 184	65.13 195	66.99 170	51.81 189	36.47 202	43.33 177	42.77 186	43.99 204	72.41 195	75.99 193	91.20 161	88.86 168

SixPastTwentyTwo		63.75 196	63.42 195	64.13 194	72.91 192	80.34 195	61.29 203	63.90 180	49.58 198	40.42 195	54.99 133	37.13 209	60.90 178	68.46 205	70.80 202	85.37 205	82.65 193

PM-MVS		63.52 197	62.51 199	64.70 189	64.79 211	76.08 205	65.07 196	62.08 185	58.13 165	46.56 175	44.98 166	31.31 216	62.89 173	72.58 194	69.93 206	86.81 199	84.55 180

DTE-MVSNet		63.26 198	63.41 196	63.08 196	72.59 194	78.56 201	65.03 197	68.28 154	50.53 195	32.38 210	44.03 172	37.79 206	49.48 200	70.83 202	76.73 190	90.73 170	85.42 179

testgi		63.11 199	64.88 188	61.05 200	75.83 179	78.51 202	60.42 204	66.20 174	48.77 199	34.56 207	56.96 117	40.35 197	40.95 209	77.46 180	77.22 187	88.37 195	74.86 207

GG-mvs-BLEND		62.08 200	88.31 44	31.46 215	0.16 226	98.10 11	91.57 42	0.09 223	85.07 68	0.21 227	73.90 59	83.74 40	0.19 224	88.98 75	89.39 62	96.58 15	99.02 15

Anonymous20231206		62.05 201	61.83 201	62.30 199	72.09 197	77.84 203	63.10 201	67.62 165	50.20 196	36.68 200	29.59 211	37.05 210	43.90 205	77.33 181	77.31 186	90.41 174	83.49 185

N_pmnet		60.52 202	58.83 205	62.50 198	68.97 205	75.61 207	59.72 207	66.47 171	51.90 188	41.26 194	35.42 203	35.63 211	52.25 198	67.07 208	70.08 205	86.35 201	76.10 203

EU-MVSNet		58.73 203	60.92 203	56.17 205	66.17 208	72.39 210	58.85 208	61.24 188	48.47 200	27.91 215	46.70 162	40.06 198	39.07 210	68.27 206	70.34 204	83.77 208	80.23 197

test20.03		57.93 204	59.22 204	56.44 204	71.84 200	73.78 209	53.55 212	65.96 175	43.02 210	28.46 214	37.50 198	38.17 205	30.41 214	75.25 187	74.42 198	88.41 193	72.37 210

MDA-MVSNet-bldmvs		54.99 205	52.66 209	57.71 203	52.74 217	74.87 208	55.61 210	68.41 153	43.65 208	32.54 208	37.93 195	22.11 222	54.11 189	48.85 215	67.34 208	82.85 209	73.88 208

new-patchmatchnet		53.91 206	52.69 208	55.33 207	64.83 210	70.90 211	52.24 213	61.75 186	41.09 211	30.82 211	29.90 209	28.22 218	36.69 211	61.52 210	65.08 209	85.64 204	72.14 211

MIMVSNet1		52.76 207	53.95 207	51.38 209	41.96 220	70.79 212	53.56 211	63.03 184	39.36 212	27.83 216	22.73 216	33.07 214	34.47 213	70.49 203	72.69 199	87.41 198	68.51 212

pmmvs3		52.59 208	52.43 210	52.78 208	54.53 216	64.49 215	50.07 214	46.89 215	35.31 216	30.19 212	27.27 213	26.96 220	53.02 195	67.28 207	70.54 203	81.96 211	75.20 205

new_pmnet		50.32 209	51.36 211	49.11 210	49.19 218	64.89 214	48.66 216	47.99 214	47.55 202	26.27 218	29.51 212	28.66 217	44.89 201	61.12 211	62.74 211	77.66 213	65.03 213

FPMVS		50.25 210	45.67 213	55.58 206	70.48 204	60.12 216	59.78 206	59.33 193	46.66 203	37.94 197	30.22 208	27.51 219	35.94 212	50.98 214	47.90 214	70.02 215	56.31 214

test_method		47.92 211	55.39 206	39.21 213	19.90 224	49.24 218	39.29 219	34.65 220	57.37 169	32.54 208	25.11 214	41.02 193	44.31 203	66.58 209	57.57 213	64.59 218	90.82 148

PMVS	36.83 18	40.62 212	36.39 214	45.56 211	58.40 213	33.20 221	32.62 221	56.02 197	28.25 219	37.92 198	22.29 217	26.15 221	25.29 216	48.49 216	43.82 217	63.13 219	52.53 217
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		35.20 213	33.96 215	36.65 214	43.30 219	32.51 222	26.96 223	48.31 213	38.87 213	20.08 222	8.08 219	7.41 226	26.44 215	53.60 212	58.43 212	54.81 220	38.79 219
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		32.52 214	33.92 216	30.88 216	34.15 223	44.70 220	27.79 222	39.69 219	22.21 220	4.31 226	15.73 218	14.13 224	12.45 221	40.11 217	47.00 215	66.88 216	53.54 215

E-PMN		21.42 215	17.56 218	25.94 217	36.25 222	19.02 225	11.56 224	43.72 217	15.25 222	6.99 224	8.04 220	4.53 228	21.77 218	16.13 220	26.16 219	35.34 222	33.77 220

MVE	25.07 19	21.25 216	23.51 217	18.62 219	15.07 225	29.77 224	10.67 226	34.60 221	12.51 223	9.46 223	7.84 221	3.82 229	14.38 220	27.45 219	42.42 218	27.56 224	40.74 218
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		20.61 217	16.32 219	25.62 218	36.41 221	18.93 226	11.51 225	43.75 216	15.65 221	6.53 225	7.56 222	4.68 227	22.03 217	14.56 221	23.10 220	33.51 223	29.77 221

testmvs		0.76 218	1.23 220	0.21 220	0.05 227	0.21 227	0.38 228	0.09 223	0.94 224	0.05 228	2.13 224	0.08 230	0.60 223	0.82 222	0.77 221	0.11 225	3.62 223

test123		0.67 219	1.11 221	0.16 221	0.01 228	0.14 228	0.20 229	0.04 225	0.77 225	0.02 229	2.15 223	0.02 231	0.61 222	0.23 223	0.72 222	0.07 226	3.76 222

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										39.41 196

9.14												91.16 7

SR-MVS					96.04 32			87.51 28				87.60 21

Anonymous202405211			75.59 132		85.13 119	91.06 109	84.62 94	77.96 75	69.47 135		40.79 188	63.84 123	84.57 68	83.55 126	84.69 113	89.69 183	95.75 85

our_test_3					73.80 191	79.57 199	64.47 199

ambc			50.35 212		55.61 215	59.93 217	48.73 215		44.08 206	35.81 205	24.01 215	10.64 225	41.57 207	72.83 193	63.35 210	74.99 214	77.61 200

MTAPA										91.14 7		85.84 27

MTMP										90.95 8		84.13 36

Patchmatch-RL test							8.17 227

tmp_tt				39.78 212	56.31 214	31.71 223	35.84 220	15.08 222	82.57 77	50.83 155	63.07 98	47.51 167	15.28 219	52.23 213	44.24 216	65.35 217

XVS					89.65 71	95.93 48	85.97 82			76.32 54		82.05 46				93.51 96

X-MVStestdata					89.65 71	95.93 48	85.97 82			76.32 54		82.05 46				93.51 96

abl_6				89.54 33	95.55 40	97.59 20	89.01 56	85.00 41	94.67 16	83.04 33	84.70 36	91.47 6	89.46 28			95.20 50	98.63 21

mPP-MVS					95.90 34							80.22 55

NP-MVS									89.55 48

Patchmtry						87.41 132	78.32 140	54.14 204		51.09 150

DeepMVS_CX						48.96 219	43.77 217	40.58 218	50.93 192	24.67 220	36.95 200	20.18 223	41.60 206	38.92 218		52.37 221	53.31 216

High-resolution multi-view benchmark