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.

HPM-MVS++		87.09 10	88.92 14	84.95 6	92.61 1	87.91 41	90.23 16	76.06 5	88.85 13	81.20 9	87.33 14	87.93 13	79.47 10	88.59 9	88.23 5	90.15 35	93.60 21

DVP-MVS++		89.14 1	91.86 1	85.97 1	92.55 2	92.38 1	91.69 4	76.31 3	93.31 1	83.11 3	92.44 5	91.18 1	81.17 2	89.55 2	87.93 8	91.01 7	96.21 1

SMA-MVS		87.56 8	90.17 8	84.52 10	91.71 3	90.57 10	90.77 9	75.19 13	90.67 8	80.50 13	86.59 18	88.86 9	78.09 16	89.92 1	89.41 1	90.84 12	95.19 5
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		85.34 20	86.59 26	83.88 16	91.48 4	88.88 26	89.79 18	75.54 11	86.67 21	77.94 24	76.55 36	84.99 26	78.07 17	88.04 13	87.68 13	90.46 27	93.31 22

CNVR-MVS		86.36 15	88.19 18	84.23 12	91.33 5	89.84 16	90.34 12	75.56 10	87.36 18	78.97 18	81.19 30	86.76 19	78.74 12	89.30 5	88.58 2	90.45 28	94.33 11

SF-MVS		87.47 9	89.70 9	84.86 8	91.26 6	91.10 9	90.90 7	75.65 8	89.21 10	81.25 7	91.12 9	88.93 8	78.82 11	87.42 21	86.23 31	91.28 3	93.90 14

APDe-MVS		88.00 7	90.50 7	85.08 5	90.95 7	91.58 7	92.03 1	75.53 12	91.15 5	80.10 15	92.27 6	88.34 12	80.80 6	88.00 15	86.99 19	91.09 5	95.16 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DPE-MVS		88.63 4	91.29 4	85.53 3	90.87 8	92.20 4	91.98 2	76.00 6	90.55 9	82.09 6	93.85 2	90.75 2	81.25 1	88.62 8	87.59 15	90.96 9	95.48 4
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HFP-MVS		86.15 16	87.95 19	84.06 14	90.80 9	89.20 25	89.62 20	74.26 17	87.52 15	80.63 11	86.82 17	84.19 30	78.22 15	87.58 19	87.19 17	90.81 14	93.13 26

SteuartSystems-ACMMP		85.99 17	88.31 17	83.27 21	90.73 10	89.84 16	90.27 15	74.31 16	84.56 30	75.88 32	87.32 15	85.04 25	77.31 24	89.01 7	88.46 3	91.14 4	93.96 13
Skip Steuart: Steuart Systems R&D Blog.
APD-MVS		86.84 13	88.91 15	84.41 11	90.66 11	90.10 14	90.78 8	75.64 9	87.38 17	78.72 19	90.68 11	86.82 18	80.15 8	87.13 26	86.45 30	90.51 22	93.83 15
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MP-MVS		85.50 19	87.40 22	83.28 20	90.65 12	89.51 21	89.16 24	74.11 19	83.70 35	78.06 23	85.54 21	84.89 29	77.31 24	87.40 23	87.14 18	90.41 29	93.65 20
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
train_agg		84.86 25	87.21 24	82.11 27	90.59 13	85.47 57	89.81 17	73.55 26	83.95 32	73.30 40	89.84 13	87.23 16	75.61 34	86.47 34	85.46 39	89.78 41	92.06 33

MCST-MVS		85.13 23	86.62 25	83.39 18	90.55 14	89.82 18	89.29 22	73.89 23	84.38 31	76.03 31	79.01 33	85.90 22	78.47 13	87.81 18	86.11 34	92.11 1	93.29 23

SED-MVS		88.85 2	91.59 3	85.67 2	90.54 15	92.29 3	91.71 3	76.40 2	92.41 3	83.24 2	92.50 4	90.64 4	81.10 3	89.53 3	88.02 7	91.00 8	95.73 3

ME-MVS		88.06 6	90.84 5	84.81 9	90.52 16	91.48 8	91.13 6	75.02 14	90.82 7	80.35 14	94.25 1	90.29 5	80.86 5	87.82 17	86.80 23	90.95 10	94.45 8

DVP-MVS		88.67 3	91.62 2	85.22 4	90.47 17	92.36 2	90.69 10	76.15 4	93.08 2	82.75 4	92.19 7	90.71 3	80.45 7	89.27 6	87.91 9	90.82 13	95.84 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
DeepC-MVS_fast	78.24 3	84.27 30	85.50 32	82.85 23	90.46 18	89.24 23	87.83 34	74.24 18	84.88 26	76.23 30	75.26 41	81.05 44	77.62 21	88.02 14	87.62 14	90.69 18	92.41 29
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP_NAP		86.52 14	89.01 12	83.62 17	90.28 19	90.09 15	90.32 14	74.05 20	88.32 14	79.74 16	87.04 16	85.59 24	76.97 29	89.35 4	88.44 4	90.35 31	94.27 12

SD-MVS		86.96 11	89.45 10	84.05 15	90.13 20	89.23 24	89.77 19	74.59 15	89.17 11	80.70 10	89.93 12	89.67 6	78.47 13	87.57 20	86.79 24	90.67 19	93.76 17
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
ACMMPR		85.52 18	87.53 21	83.17 22	90.13 20	89.27 22	89.30 21	73.97 21	86.89 20	77.14 26	86.09 19	83.18 33	77.74 20	87.42 21	87.20 16	90.77 15	92.63 27

TPM-MVS					90.07 22	88.36 36	88.45 30			77.10 27	75.60 39	83.98 31	71.33 65			89.75 44	89.62 54
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
PGM-MVS		84.42 29	86.29 29	82.23 26	90.04 23	88.82 27	89.23 23	71.74 36	82.82 40	74.61 35	84.41 24	82.09 36	77.03 28	87.13 26	86.73 26	90.73 17	92.06 33

MSP-MVS		88.09 5	90.84 5	84.88 7	90.00 24	91.80 6	91.63 5	75.80 7	91.99 4	81.23 8	92.54 3	89.18 7	80.89 4	87.99 16	87.91 9	89.70 46	94.51 7
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
CSCG		85.28 22	87.68 20	82.49 25	89.95 25	91.99 5	88.82 25	71.20 38	86.41 22	79.63 17	79.26 31	88.36 11	73.94 42	86.64 32	86.67 27	91.40 2	94.41 9

mPP-MVS					89.90 26							81.29 43

TSAR-MVS + MP.		86.88 12	89.23 11	84.14 13	89.78 27	88.67 31	90.59 11	73.46 27	88.99 12	80.52 12	91.26 8	88.65 10	79.91 9	86.96 30	86.22 32	90.59 21	93.83 15
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
X-MVS		83.23 34	85.20 34	80.92 35	89.71 28	88.68 28	88.21 33	73.60 24	82.57 41	71.81 47	77.07 34	81.92 38	71.72 59	86.98 29	86.86 21	90.47 24	92.36 30

DPM-MVS		83.30 33	84.33 36	82.11 27	89.56 29	88.49 34	90.33 13	73.24 28	83.85 33	76.46 29	72.43 53	82.65 34	73.02 49	86.37 36	86.91 20	90.03 37	89.62 54

TSAR-MVS + ACMM		85.10 24	88.81 16	80.77 36	89.55 30	88.53 33	88.59 28	72.55 31	87.39 16	71.90 44	90.95 10	87.55 14	74.57 37	87.08 28	86.54 28	87.47 107	93.67 18

CP-MVS		84.74 27	86.43 28	82.77 24	89.48 31	88.13 40	88.64 26	73.93 22	84.92 25	76.77 28	81.94 28	83.50 32	77.29 26	86.92 31	86.49 29	90.49 23	93.14 25

CDPH-MVS		82.64 35	85.03 35	79.86 40	89.41 32	88.31 37	88.32 31	71.84 35	80.11 47	67.47 77	82.09 27	81.44 42	71.85 57	85.89 42	86.15 33	90.24 33	91.25 39

DeepC-MVS	78.47 2	84.81 26	86.03 30	83.37 19	89.29 33	90.38 13	88.61 27	76.50 1	86.25 23	77.22 25	75.12 42	80.28 46	77.59 22	88.39 10	88.17 6	91.02 6	93.66 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		79.74 47	78.62 65	81.05 34	89.23 34	86.06 53	84.95 50	71.96 34	79.39 50	75.51 33	63.16 112	68.84 114	76.51 30	83.55 62	82.85 60	88.13 78	86.46 84

SR-MVS					88.99 35			73.57 25				87.54 15

EPNet		79.08 57	80.62 54	77.28 52	88.90 36	83.17 85	83.65 55	72.41 32	74.41 59	67.15 81	76.78 35	74.37 67	64.43 118	83.70 61	83.69 54	87.15 111	88.19 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	79.04 1	85.30 21	88.93 13	81.06 33	88.77 37	90.48 12	85.46 47	73.08 29	90.97 6	73.77 39	84.81 23	85.95 21	77.43 23	88.22 11	87.73 11	87.85 97	94.34 10

MGCNet		84.63 28	87.25 23	81.59 30	88.58 38	90.50 11	87.82 35	69.16 53	83.82 34	78.46 21	82.32 26	84.97 27	74.56 38	88.16 12	87.72 12	90.94 11	93.24 24

ACMMP		83.42 32	85.27 33	81.26 32	88.47 39	88.49 34	88.31 32	72.09 33	83.42 36	72.77 42	82.65 25	78.22 51	75.18 35	86.24 39	85.76 36	90.74 16	92.13 32
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+	75.73 4	82.40 36	82.76 40	81.97 29	88.02 40	89.67 19	86.60 39	71.48 37	81.28 45	78.18 22	64.78 106	77.96 53	77.13 27	87.32 24	86.83 22	90.41 29	91.48 37

OPM-MVS		79.68 48	79.28 63	80.15 39	87.99 41	86.77 47	88.52 29	72.72 30	64.55 119	67.65 76	67.87 86	74.33 68	74.31 40	86.37 36	85.25 41	89.73 45	89.81 52
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MAR-MVS		79.21 53	80.32 58	77.92 50	87.46 42	88.15 39	83.95 54	67.48 65	74.28 60	68.25 70	64.70 107	77.04 54	72.17 53	85.42 44	85.00 43	88.22 74	87.62 69
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
HQP-MVS		81.19 41	83.27 38	78.76 45	87.40 43	85.45 58	86.95 37	70.47 41	81.31 44	66.91 82	79.24 32	76.63 55	71.67 61	84.43 55	83.78 53	89.19 57	92.05 35

CANet		81.62 40	83.41 37	79.53 42	87.06 44	88.59 32	85.47 46	67.96 59	76.59 55	74.05 36	74.69 43	81.98 37	72.98 50	86.14 40	85.47 38	89.68 47	90.42 47

MSLP-MVS++		82.09 38	82.66 41	81.42 31	87.03 45	87.22 44	85.82 43	70.04 43	80.30 46	78.66 20	68.67 80	81.04 45	77.81 19	85.19 47	84.88 44	89.19 57	91.31 38

ACMM	72.26 8	78.86 58	78.13 68	79.71 41	86.89 46	83.40 80	86.02 41	70.50 40	75.28 57	71.49 51	63.01 113	69.26 108	73.57 44	84.11 57	83.98 49	89.76 43	87.84 66
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVS					86.63 47	88.68 28	85.00 48			71.81 47		81.92 38				90.47 24

X-MVStestdata					86.63 47	88.68 28	85.00 48			71.81 47		81.92 38				90.47 24

PHI-MVS		82.36 37	85.89 31	78.24 48	86.40 49	89.52 20	85.52 45	69.52 49	82.38 43	65.67 85	81.35 29	82.36 35	73.07 48	87.31 25	86.76 25	89.24 53	91.56 36

LGP-MVS_train		79.83 44	81.22 50	78.22 49	86.28 50	85.36 60	86.76 38	69.59 47	77.34 52	65.14 89	75.68 38	70.79 98	71.37 64	84.60 51	84.01 48	90.18 34	90.74 43

CPTT-MVS		81.77 39	83.10 39	80.21 38	85.93 51	86.45 50	87.72 36	70.98 39	82.54 42	71.53 50	74.23 46	81.49 41	76.31 32	82.85 72	81.87 68	88.79 66	92.26 31

MVS_111021_HR		80.13 43	81.46 47	78.58 46	85.77 52	85.17 61	83.45 56	69.28 50	74.08 63	70.31 59	74.31 45	75.26 64	73.13 47	86.46 35	85.15 42	89.53 48	89.81 52

ACMP	73.23 7	79.79 45	80.53 55	78.94 43	85.61 53	85.68 55	85.61 44	69.59 47	77.33 53	71.00 54	74.45 44	69.16 109	71.88 55	83.15 68	83.37 56	89.92 38	90.57 46
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UA-Net		74.47 94	77.80 71	70.59 111	85.33 54	85.40 59	73.54 164	65.98 74	60.65 151	56.00 129	72.11 54	79.15 47	54.63 194	83.13 69	82.25 65	88.04 85	81.92 146

TSAR-MVS + GP.		83.69 31	86.58 27	80.32 37	85.14 55	86.96 45	84.91 51	70.25 42	84.71 29	73.91 38	85.16 22	85.63 23	77.92 18	85.44 43	85.71 37	89.77 42	92.45 28

LS3D		74.08 96	73.39 114	74.88 80	85.05 56	82.62 100	79.71 90	68.66 54	72.82 68	58.80 112	57.61 144	61.31 141	71.07 67	80.32 117	78.87 129	86.00 151	80.18 163

QAPM		78.47 60	80.22 59	76.43 59	85.03 57	86.75 48	80.62 80	66.00 73	73.77 65	65.35 88	65.54 102	78.02 52	72.69 51	83.71 60	83.36 57	88.87 63	90.41 48

OpenMVS	70.44 10	76.15 82	76.82 89	75.37 76	85.01 58	84.79 63	78.99 100	62.07 134	71.27 75	67.88 74	57.91 143	72.36 77	70.15 70	82.23 78	81.41 74	88.12 79	87.78 67

CLD-MVS		79.35 51	81.23 49	77.16 54	85.01 58	86.92 46	85.87 42	60.89 148	80.07 49	75.35 34	72.96 49	73.21 73	68.43 93	85.41 45	84.63 45	87.41 108	85.44 106
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
3Dnovator	73.76 5	79.75 46	80.52 56	78.84 44	84.94 60	87.35 42	84.43 53	65.54 76	78.29 51	73.97 37	63.00 114	75.62 63	74.07 41	85.00 48	85.34 40	90.11 36	89.04 57

PCF-MVS	73.28 6	79.42 50	80.41 57	78.26 47	84.88 61	88.17 38	86.08 40	69.85 44	75.23 58	68.43 69	68.03 85	78.38 49	71.76 58	81.26 94	80.65 91	88.56 69	91.18 40
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DELS-MVS		79.15 56	81.07 52	76.91 56	83.54 62	87.31 43	84.45 52	64.92 82	69.98 80	69.34 66	71.62 57	76.26 56	69.84 71	86.57 33	85.90 35	89.39 50	89.88 51
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
OMC-MVS		80.26 42	82.59 42	77.54 51	83.04 63	85.54 56	83.25 57	65.05 81	87.32 19	72.42 43	72.04 55	78.97 48	73.30 46	83.86 58	81.60 73	88.15 77	88.83 59

EC-MVSNet		79.44 49	81.35 48	77.22 53	82.95 64	84.67 65	81.31 74	63.65 94	72.47 70	68.75 67	73.15 48	78.33 50	75.99 33	86.06 41	83.96 50	90.67 19	90.79 42

PLC	68.99 11	75.68 86	75.31 100	76.12 62	82.94 65	81.26 111	79.94 86	66.10 71	77.15 54	66.86 83	59.13 133	68.53 116	73.73 43	80.38 116	79.04 124	87.13 115	81.68 148
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		77.20 67	77.54 73	76.80 57	82.63 66	84.31 68	79.77 88	64.64 83	85.17 24	73.18 41	56.37 150	69.81 105	74.53 39	81.12 97	78.69 131	86.04 149	87.29 72

ACMH+	66.54 13	71.36 120	70.09 138	72.85 93	82.59 67	81.13 113	78.56 104	68.04 57	61.55 144	52.52 154	51.50 194	54.14 183	68.56 92	78.85 141	79.50 114	86.82 123	83.94 128

ACMH	65.37 14	70.71 124	70.00 139	71.54 101	82.51 68	82.47 101	77.78 112	68.13 56	56.19 179	46.06 193	54.30 162	51.20 210	68.68 91	80.66 109	80.72 84	86.07 145	84.45 125
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EIA-MVS		75.64 87	76.60 93	74.53 85	82.43 69	83.84 73	78.32 108	62.28 132	65.96 107	63.28 100	68.95 74	67.54 121	71.61 62	82.55 75	81.63 72	89.24 53	85.72 96

sasdasda		79.16 54	82.37 43	75.41 74	82.33 70	86.38 51	80.80 77	63.18 106	82.90 38	67.34 78	72.79 50	76.07 58	69.62 74	83.46 65	84.41 46	89.20 55	90.60 44

canonicalmvs		79.16 54	82.37 43	75.41 74	82.33 70	86.38 51	80.80 77	63.18 106	82.90 38	67.34 78	72.79 50	76.07 58	69.62 74	83.46 65	84.41 46	89.20 55	90.60 44

ETV-MVS		77.32 66	78.81 64	75.58 69	82.24 72	83.64 78	79.98 84	64.02 90	69.64 87	63.90 96	70.89 61	69.94 104	73.41 45	85.39 46	83.91 52	89.92 38	88.31 62

MSDG		71.52 117	69.87 140	73.44 91	82.21 73	79.35 133	79.52 92	64.59 84	66.15 105	61.87 101	53.21 177	56.09 170	65.85 116	78.94 140	78.50 133	86.60 133	76.85 191

MGCFI-Net		76.55 72	81.71 45	70.52 112	81.71 74	84.62 66	75.02 139	62.17 133	82.91 37	53.58 146	72.78 52	75.87 62	61.75 141	82.96 70	82.61 63	88.86 64	90.26 49

casdiffmvs_mvg		77.79 63	79.55 62	75.73 63	81.56 75	84.70 64	82.12 59	64.26 89	74.27 61	67.93 73	70.83 62	74.66 66	69.19 88	83.33 67	81.94 67	89.29 52	87.14 75
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test2506		71.72 114	72.95 118	70.29 115	81.49 76	83.27 81	75.74 128	67.59 63	68.19 95	49.81 167	61.15 118	49.73 218	58.82 155	84.76 49	82.94 58	88.27 72	80.63 157

ECVR-MVS		72.20 110	73.91 110	70.20 117	81.49 76	83.27 81	75.74 128	67.59 63	68.19 95	49.31 171	55.77 152	62.00 139	58.82 155	84.76 49	82.94 58	88.27 72	80.41 161

CS-MVS		79.22 52	81.11 51	77.01 55	81.36 78	84.03 69	80.35 81	63.25 100	73.43 67	70.37 58	74.10 47	76.03 60	76.40 31	86.32 38	83.95 51	90.34 32	89.93 50

IS_MVSNet		73.33 101	77.34 81	68.65 135	81.29 79	83.47 79	74.45 146	63.58 96	65.75 109	48.49 173	67.11 96	70.61 99	54.63 194	84.51 53	83.58 55	89.48 49	86.34 85

test1111		71.56 116	73.44 113	69.38 128	81.16 80	82.95 95	74.99 140	67.68 61	66.89 101	46.33 190	55.19 158	60.91 142	57.99 163	84.59 52	82.70 62	88.12 79	80.85 154

Effi-MVS+		75.28 89	76.20 96	74.20 88	81.15 81	83.24 83	81.11 75	63.13 110	66.37 103	60.27 108	64.30 110	68.88 113	70.93 68	81.56 82	81.69 71	88.61 67	87.35 70

MVS_111021_LR		78.13 62	79.85 61	76.13 61	81.12 82	81.50 106	80.28 83	65.25 79	76.09 56	71.32 52	76.49 37	72.87 75	72.21 52	82.79 73	81.29 75	86.59 134	87.91 65

SPE-MVS-test		78.79 59	80.72 53	76.53 58	81.11 83	83.88 72	79.69 91	63.72 93	73.80 64	69.95 63	75.40 40	76.17 57	74.85 36	84.50 54	82.78 61	89.87 40	88.54 61

FC-MVSNet-train		72.60 106	75.07 102	69.71 123	81.10 84	78.79 140	73.74 163	65.23 80	66.10 106	53.34 147	70.36 65	63.40 135	56.92 173	81.44 87	80.96 80	87.93 92	84.46 124

MS-PatchMatch		70.17 131	70.49 135	69.79 122	80.98 85	77.97 154	77.51 114	58.95 174	62.33 137	55.22 133	53.14 178	65.90 127	62.03 134	79.08 138	77.11 159	84.08 185	77.91 182

Anonymous202405211			72.16 126		80.85 86	81.85 103	76.88 124	65.40 77	62.89 134		46.35 215	67.99 120	62.05 133	81.15 96	80.38 95	85.97 152	84.50 123

E2		76.70 69	77.54 73	75.73 63	80.76 87	83.07 88	81.91 66	63.15 108	72.42 71	71.09 53	70.03 67	72.22 78	69.53 80	80.57 110	78.80 130	87.91 93	85.64 99

viewcassd2359sk11		76.64 70	77.43 78	75.72 65	80.75 88	83.07 88	81.95 65	63.20 105	72.02 74	70.88 55	69.50 70	72.02 80	69.58 79	80.68 108	78.98 126	87.97 90	85.74 94

E3new		76.51 73	77.22 83	75.69 66	80.74 89	83.07 88	81.99 62	63.23 103	71.18 76	70.52 57	68.77 76	71.75 82	69.61 76	80.73 103	79.18 120	88.03 88	85.85 91

E3		76.51 73	77.21 84	75.69 66	80.74 89	83.06 91	81.98 63	63.22 104	71.17 77	70.55 56	68.77 76	71.76 81	69.61 76	80.73 103	79.18 120	88.03 88	85.84 93

E6new		76.06 83	76.54 94	75.51 72	80.71 91	83.10 86	81.74 68	63.03 111	68.89 90	69.71 64	66.73 97	70.84 95	69.76 72	80.88 101	79.61 109	88.11 81	85.72 96

E6		76.06 83	76.54 94	75.51 72	80.71 91	83.10 86	81.74 68	63.03 111	68.89 90	69.71 64	66.73 97	70.84 95	69.76 72	80.88 101	79.61 109	88.11 81	85.72 96

E5new		76.23 78	76.79 90	75.58 69	80.69 93	83.05 92	82.00 60	63.37 97	69.73 83	70.01 61	67.77 88	71.43 87	69.37 85	80.50 111	79.13 122	88.04 85	85.92 88

E5		76.23 78	76.79 90	75.58 69	80.69 93	83.05 92	82.00 60	63.37 97	69.73 83	70.01 61	67.77 88	71.43 87	69.37 85	80.50 111	79.13 122	88.04 85	85.92 88

E4		76.24 77	76.77 92	75.61 68	80.69 93	83.05 92	81.98 63	63.25 100	69.47 88	70.06 60	67.40 91	71.46 84	69.59 78	80.73 103	79.37 117	88.10 83	85.95 87

TAPA-MVS	71.42 9	77.69 64	80.05 60	74.94 79	80.68 96	84.52 67	81.36 73	63.14 109	84.77 27	64.82 91	68.72 78	75.91 61	71.86 56	81.62 80	79.55 113	87.80 99	85.24 111
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_Blended_VisFu		76.57 71	77.90 69	75.02 78	80.56 97	86.58 49	79.24 96	66.18 70	64.81 116	68.18 71	65.61 100	71.45 85	67.05 98	84.16 56	81.80 70	88.90 61	90.92 41

EPP-MVSNet		74.00 97	77.41 79	70.02 120	80.53 98	83.91 71	74.99 140	62.68 125	65.06 114	49.77 168	68.68 79	72.09 79	63.06 126	82.49 77	80.73 83	89.12 59	88.91 58

COLMAP_ROB	62.73 15	67.66 160	66.76 178	68.70 134	80.49 99	77.98 152	75.29 132	62.95 114	63.62 128	49.96 165	47.32 214	50.72 213	58.57 157	76.87 166	75.50 179	84.94 177	75.33 203
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
GeoE		74.23 95	74.84 104	73.52 90	80.42 100	81.46 107	79.77 88	61.06 144	67.23 100	63.67 97	59.56 130	68.74 115	67.90 94	80.25 121	79.37 117	88.31 71	87.26 73

casdiffmvs		76.76 68	78.46 66	74.77 81	80.32 101	83.73 77	80.65 79	63.24 102	73.58 66	66.11 84	69.39 72	74.09 69	69.49 83	82.52 76	79.35 119	88.84 65	86.52 83
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DCV-MVSNet		73.65 99	75.78 99	71.16 103	80.19 102	79.27 134	77.45 117	61.68 140	66.73 102	58.72 113	65.31 103	69.96 103	62.19 131	81.29 93	80.97 79	86.74 127	86.91 76

viewdifsd2359ckpt13		76.26 76	77.31 82	75.03 77	80.14 103	83.77 76	81.58 72	62.80 117	70.34 79	67.83 75	68.06 84	70.93 94	70.20 69	81.46 85	79.88 102	87.63 104	86.71 81

Anonymous20231211		71.90 112	72.48 123	71.21 102	80.14 103	81.53 105	76.92 120	62.89 115	64.46 121	58.94 110	43.80 219	70.98 93	62.22 130	80.70 107	80.19 99	86.18 141	85.73 95

TSAR-MVS + COLMAP		78.34 61	81.64 46	74.48 87	80.13 105	85.01 62	81.73 70	65.93 75	84.75 28	61.68 102	85.79 20	66.27 126	71.39 63	82.91 71	80.78 82	86.01 150	85.98 86

viewdifsd2359ckpt09		77.36 65	78.39 67	76.16 60	79.98 106	85.78 54	82.78 58	65.29 78	70.87 78	68.68 68	68.99 73	70.81 97	71.70 60	82.68 74	81.86 69	88.56 69	87.71 68

viewmanbaseed2359cas		76.36 75	77.87 70	74.60 84	79.81 107	82.88 97	81.69 71	61.02 146	72.14 73	67.97 72	69.61 69	72.45 76	69.53 80	81.53 83	79.83 104	87.57 105	86.65 82

baseline1		70.10 132	72.17 125	67.69 144	79.74 108	76.80 164	73.91 157	64.38 86	62.74 135	48.30 175	64.94 104	64.08 132	54.17 196	81.46 85	78.92 127	85.66 157	76.22 194

viewmacassd2359aftdt		75.85 85	77.01 87	74.49 86	79.69 109	82.87 98	81.77 67	61.06 144	69.37 89	67.26 80	66.73 97	71.63 83	69.48 84	81.51 84	80.20 97	87.69 101	86.77 80

EPNet_dtu		68.08 152	71.00 131	64.67 174	79.64 110	68.62 213	75.05 138	63.30 99	66.36 104	45.27 197	67.40 91	66.84 125	43.64 218	75.37 175	74.98 182	81.15 198	77.44 186
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewdifsd2359ckpt07		74.55 93	76.09 98	72.75 94	79.51 111	81.32 109	80.29 82	58.44 179	68.61 92	65.63 86	68.17 83	71.24 91	67.64 96	80.13 124	77.62 147	84.96 176	85.56 101

PVSNet_BlendedMVS		76.21 80	77.52 75	74.69 82	79.46 112	83.79 74	77.50 115	64.34 87	69.88 81	71.88 45	68.54 81	70.42 100	67.05 98	83.48 63	79.63 107	87.89 95	86.87 77

PVSNet_Blended		76.21 80	77.52 75	74.69 82	79.46 112	83.79 74	77.50 115	64.34 87	69.88 81	71.88 45	68.54 81	70.42 100	67.05 98	83.48 63	79.63 107	87.89 95	86.87 77

IB-MVS	66.94 12	71.21 121	71.66 129	70.68 106	79.18 114	82.83 99	72.61 170	61.77 138	59.66 156	63.44 99	53.26 175	59.65 149	59.16 154	76.78 168	82.11 66	87.90 94	87.33 71
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
MVS_Test		75.37 88	77.13 86	73.31 92	79.07 115	81.32 109	79.98 84	60.12 160	69.72 85	64.11 95	70.53 64	73.22 72	68.90 89	80.14 123	79.48 115	87.67 102	85.50 104

Effi-MVS+-dtu		71.82 113	71.86 128	71.78 100	78.77 116	80.47 121	78.55 105	61.67 141	60.68 150	55.49 130	58.48 137	65.48 128	68.85 90	76.92 165	75.55 178	87.35 109	85.46 105

EG-PatchMatch MVS		67.24 167	66.94 176	67.60 146	78.73 117	81.35 108	73.28 168	59.49 166	46.89 227	51.42 159	43.65 220	53.49 191	55.50 187	81.38 89	80.66 90	87.15 111	81.17 152

gg-mvs-nofinetune		62.55 197	65.05 191	59.62 205	78.72 118	77.61 158	70.83 179	53.63 201	39.71 240	22.04 242	36.36 233	64.32 131	47.53 210	81.16 95	79.03 125	85.00 175	77.17 188

FA-MVS(training)		73.66 98	74.95 103	72.15 96	78.63 119	80.46 122	78.92 102	54.79 200	69.71 86	65.37 87	62.04 115	66.89 124	67.10 97	80.72 106	79.87 103	88.10 83	84.97 116

Vis-MVSNet (Re-imp)		67.83 157	73.52 112	61.19 196	78.37 120	76.72 166	66.80 202	62.96 113	65.50 112	34.17 223	67.19 95	69.68 106	39.20 227	79.39 135	79.44 116	85.68 156	76.73 193

DI_MVS_pp		75.13 90	76.12 97	73.96 89	78.18 121	81.55 104	80.97 76	62.54 127	68.59 93	65.13 90	61.43 117	74.81 65	69.32 87	81.01 99	79.59 111	87.64 103	85.89 90

thres600view7		67.68 159	68.43 161	66.80 161	77.90 122	78.86 138	73.84 159	62.75 118	56.07 180	44.70 202	52.85 183	52.81 200	55.58 185	80.41 113	77.77 144	86.05 147	80.28 162

thres400		67.95 154	68.62 159	67.17 154	77.90 122	78.59 143	74.27 152	62.72 120	56.34 178	45.77 195	53.00 180	53.35 196	56.46 175	80.21 122	78.43 134	85.91 154	80.43 160

thres200		67.98 153	68.55 160	67.30 152	77.89 124	78.86 138	74.18 155	62.75 118	56.35 177	46.48 189	52.98 181	53.54 189	56.46 175	80.41 113	77.97 141	86.05 147	79.78 167

thres100view900		67.60 163	68.02 165	67.12 156	77.83 125	77.75 156	73.90 158	62.52 128	56.64 174	46.82 186	52.65 187	53.47 193	55.92 181	78.77 142	77.62 147	85.72 155	79.23 171

tfpn200view9		68.11 151	68.72 157	67.40 149	77.83 125	78.93 136	74.28 151	62.81 116	56.64 174	46.82 186	52.65 187	53.47 193	56.59 174	80.41 113	78.43 134	86.11 142	80.52 159

Fast-Effi-MVS+		73.11 103	73.66 111	72.48 95	77.72 127	80.88 117	78.55 105	58.83 177	65.19 113	60.36 107	59.98 127	62.42 138	71.22 66	81.66 79	80.61 93	88.20 75	84.88 119

UniMVSNet_NR-MVSNet		70.59 125	72.19 124	68.72 133	77.72 127	80.72 118	73.81 161	69.65 46	61.99 139	43.23 204	60.54 123	57.50 160	58.57 157	79.56 131	81.07 78	89.34 51	83.97 126

IterMVS-LS		71.69 115	72.82 121	70.37 114	77.54 129	76.34 170	75.13 137	60.46 154	61.53 145	57.57 120	64.89 105	67.33 122	66.04 115	77.09 164	77.37 155	85.48 162	85.18 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NR-MVSNet		68.79 146	70.56 134	66.71 164	77.48 130	79.54 130	73.52 165	69.20 51	61.20 148	39.76 211	58.52 135	50.11 216	51.37 204	80.26 120	80.71 88	88.97 60	83.59 132

TransMVSNet (Re)		64.74 182	65.66 184	63.66 184	77.40 131	75.33 179	69.86 182	62.67 126	47.63 224	41.21 210	50.01 200	52.33 203	45.31 214	79.57 130	77.69 146	85.49 161	77.07 190

TranMVSNet+NR-MVSNet		69.25 141	70.81 133	67.43 148	77.23 132	79.46 132	73.48 166	69.66 45	60.43 153	39.56 212	58.82 134	53.48 192	55.74 184	79.59 129	81.21 76	88.89 62	82.70 136

CANet_DTU		73.29 102	76.96 88	69.00 132	77.04 133	82.06 102	79.49 93	56.30 196	67.85 98	53.29 148	71.12 60	70.37 102	61.81 140	81.59 81	80.96 80	86.09 144	84.73 120

CHOSEN 1792x2688		69.20 142	69.26 149	69.13 129	76.86 134	78.93 136	77.27 118	60.12 160	61.86 141	54.42 134	42.54 223	61.61 140	66.91 103	78.55 145	78.14 139	79.23 206	83.23 135

HyFIR lowres test		69.47 139	68.94 153	70.09 119	76.77 135	82.93 96	76.63 126	60.17 158	59.00 159	54.03 138	40.54 229	65.23 129	67.89 95	76.54 171	78.30 137	85.03 174	80.07 164

UniMVSNet (Re)		69.53 137	71.90 127	66.76 162	76.42 136	80.93 114	72.59 171	68.03 58	61.75 143	41.68 209	58.34 141	57.23 162	53.27 200	79.53 132	80.62 92	88.57 68	84.90 118

gm-plane-assit		57.00 222	57.62 229	56.28 218	76.10 137	62.43 237	47.62 245	46.57 236	33.84 244	23.24 238	37.52 230	40.19 240	59.61 153	79.81 127	77.55 150	84.55 182	72.03 214

DU-MVS		69.63 136	70.91 132	68.13 139	75.99 138	79.54 130	73.81 161	69.20 51	61.20 148	43.23 204	58.52 135	53.50 190	58.57 157	79.22 136	80.45 94	87.97 90	83.97 126

Baseline_NR-MVSNet		67.53 164	68.77 156	66.09 167	75.99 138	74.75 184	72.43 172	68.41 55	61.33 147	38.33 216	51.31 195	54.13 185	56.03 180	79.22 136	78.19 138	85.37 165	82.45 138

CostFormer		68.92 144	69.58 145	68.15 138	75.98 140	76.17 172	78.22 110	51.86 215	65.80 108	61.56 103	63.57 111	62.83 136	61.85 138	70.40 214	68.67 210	79.42 204	79.62 169

dmvs_re		67.22 168	67.92 167	66.40 165	75.94 141	70.55 206	74.97 142	63.87 91	57.07 171	44.75 200	54.29 163	56.72 166	54.65 193	79.53 132	77.51 151	84.20 184	79.78 167

viewdifsd2359ckpt11		72.49 107	74.10 107	70.61 108	75.87 142	78.53 144	76.92 120	58.16 181	65.69 110	61.34 105	67.21 93	68.35 118	66.51 111	77.91 151	75.60 175	84.86 179	85.43 107

viewmsd2359difaftdt		72.49 107	74.10 107	70.61 108	75.87 142	78.53 144	76.92 120	58.16 181	65.69 110	61.33 106	67.21 93	68.34 119	66.51 111	77.91 151	75.60 175	84.86 179	85.42 108

viewmambaseed2359dif		73.61 100	75.14 101	71.84 99	75.87 142	79.69 129	78.99 100	60.42 155	68.19 95	64.15 94	67.85 87	71.20 92	66.55 107	77.41 159	75.78 173	85.04 173	85.85 91

tfpnnormal		64.27 185	63.64 204	65.02 171	75.84 145	75.61 176	71.24 178	62.52 128	47.79 223	42.97 206	42.65 222	44.49 232	52.66 202	78.77 142	76.86 161	84.88 178	79.29 170

baseline2		69.69 135	70.27 137	69.01 131	75.72 146	77.13 162	73.82 160	58.94 175	61.35 146	57.09 123	61.68 116	57.17 163	61.99 135	78.10 149	76.58 166	86.48 137	79.85 165

diffmvs		74.86 92	77.37 80	71.93 97	75.62 147	80.35 124	79.42 95	60.15 159	72.81 69	64.63 92	71.51 58	73.11 74	66.53 110	79.02 139	77.98 140	85.25 170	86.83 79
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tpm cat1		65.41 176	63.81 201	67.28 153	75.61 148	72.88 195	75.32 131	52.85 209	62.97 132	63.66 98	53.24 176	53.29 198	61.83 139	65.54 229	64.14 231	74.43 227	74.60 205

diffmvs_AUTHOR		74.91 91	77.47 77	71.92 98	75.60 149	80.50 120	79.48 94	60.02 162	72.41 72	64.39 93	70.63 63	73.27 71	66.55 107	79.97 125	78.34 136	85.46 163	87.17 74

CDS-MVSNet		67.65 161	69.83 142	65.09 170	75.39 150	76.55 167	74.42 149	63.75 92	53.55 198	49.37 170	59.41 131	62.45 137	44.44 216	79.71 128	79.82 105	83.17 191	77.36 187
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+-dtu		68.34 149	69.47 146	67.01 158	75.15 151	77.97 154	77.12 119	55.40 198	57.87 162	46.68 188	56.17 151	60.39 143	62.36 129	76.32 172	76.25 171	85.35 166	81.34 150

WR-MVS		63.03 190	67.40 173	57.92 211	75.14 152	77.60 159	60.56 226	66.10 71	54.11 197	23.88 236	53.94 169	53.58 188	34.50 232	73.93 185	77.71 145	87.35 109	80.94 153

test-LLR		64.42 183	64.36 197	64.49 175	75.02 153	63.93 228	66.61 204	61.96 135	54.41 193	47.77 181	57.46 145	60.25 144	55.20 188	70.80 206	69.33 205	80.40 202	74.38 207

test0.0.03 1		58.80 217	61.58 218	55.56 220	75.02 153	68.45 214	59.58 230	61.96 135	52.74 201	29.57 228	49.75 204	54.56 181	31.46 236	71.19 201	69.77 203	75.75 220	64.57 231

v1144		69.93 134	69.36 148	70.61 108	74.89 155	80.93 114	79.11 98	60.64 150	55.97 181	55.31 132	53.85 170	54.14 183	66.54 109	78.10 149	77.44 153	87.14 114	85.09 113

v10		70.22 130	69.76 143	70.74 104	74.79 156	80.30 126	79.22 97	59.81 164	57.71 167	56.58 127	54.22 168	55.31 173	66.95 101	78.28 147	77.47 152	87.12 117	85.07 114

v8		70.23 129	69.86 141	70.67 107	74.69 157	79.82 128	78.79 103	59.18 170	58.80 160	58.20 118	55.00 159	57.33 161	66.31 114	77.51 157	76.71 164	86.82 123	83.88 129

v2v482		70.05 133	69.46 147	70.74 104	74.62 158	80.32 125	79.00 99	60.62 151	57.41 169	56.89 124	55.43 157	55.14 175	66.39 113	77.25 161	77.14 158	86.90 120	83.57 133

v1192		69.50 138	68.83 154	70.29 115	74.49 159	80.92 116	78.55 105	60.54 152	55.04 188	54.21 135	52.79 184	52.33 203	66.92 102	77.88 153	77.35 156	87.04 118	85.51 103

UniMVSNet_ETH3D		67.18 169	67.03 175	67.36 150	74.44 160	78.12 147	74.07 156	66.38 68	52.22 205	46.87 185	48.64 206	51.84 207	56.96 171	77.29 160	78.53 132	85.42 164	82.59 137

DTE-MVSNet		61.85 206	64.96 193	58.22 210	74.32 161	74.39 186	61.01 225	67.85 60	51.76 210	21.91 243	53.28 174	48.17 221	37.74 229	72.22 194	76.44 168	86.52 136	78.49 175

Vis-MVSNet		72.77 105	77.20 85	67.59 147	74.19 162	84.01 70	76.61 127	61.69 139	60.62 152	50.61 163	70.25 66	71.31 90	55.57 186	83.85 59	82.28 64	86.90 120	88.08 64
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v144192		69.34 140	68.68 158	70.12 118	74.06 163	80.54 119	78.08 111	60.54 152	54.99 190	54.13 137	52.92 182	52.80 201	66.73 105	77.13 163	76.72 163	87.15 111	85.63 100

v1921920		69.03 143	68.32 162	69.86 121	74.03 164	80.37 123	77.55 113	60.25 157	54.62 192	53.59 145	52.36 190	51.50 209	66.75 104	77.17 162	76.69 165	86.96 119	85.56 101

PEN-MVS		62.96 193	65.77 183	59.70 204	73.98 165	75.45 177	63.39 218	67.61 62	52.49 203	25.49 235	53.39 172	49.12 220	40.85 224	71.94 197	77.26 157	86.86 122	80.72 156

v1240		68.64 148	67.89 169	69.51 126	73.89 166	80.26 127	76.73 125	59.97 163	53.43 200	53.08 149	51.82 193	50.84 212	66.62 106	76.79 167	76.77 162	86.78 126	85.34 109

thisisatest0530		71.48 118	73.01 117	69.70 124	73.83 167	78.62 142	74.53 145	59.12 171	64.13 122	58.63 114	64.60 108	58.63 153	64.27 119	80.28 119	80.17 100	87.82 98	84.64 122

GA-MVS		68.14 150	69.17 151	66.93 160	73.77 168	78.50 146	74.45 146	58.28 180	55.11 187	48.44 174	60.08 125	53.99 186	61.50 143	78.43 146	77.57 149	85.13 171	80.54 158

tttt0517		71.41 119	72.95 118	69.60 125	73.70 169	78.70 141	74.42 149	59.12 171	63.89 126	58.35 117	64.56 109	58.39 157	64.27 119	80.29 118	80.17 100	87.74 100	84.69 121

pm-mvs1		65.62 175	67.42 172	63.53 185	73.66 170	76.39 169	69.66 183	60.87 149	49.73 219	43.97 203	51.24 196	57.00 165	48.16 209	79.89 126	77.84 143	84.85 181	79.82 166

dps		64.00 188	62.99 207	65.18 169	73.29 171	72.07 199	68.98 190	53.07 208	57.74 166	58.41 116	55.55 155	47.74 224	60.89 149	69.53 220	67.14 224	76.44 218	71.19 216

v148		67.85 156	67.53 170	68.23 137	73.25 172	77.57 160	74.26 153	57.36 188	55.70 182	57.45 122	53.53 171	55.42 172	61.96 136	75.23 177	73.92 186	85.08 172	81.32 151

PatchMatch-RL		67.78 158	66.65 179	69.10 130	73.01 173	72.69 196	68.49 192	61.85 137	62.93 133	60.20 109	56.83 149	50.42 214	69.52 82	75.62 174	74.46 185	81.51 195	73.62 212

GBi-Net		70.78 122	73.37 115	67.76 140	72.95 174	78.00 149	75.15 134	62.72 120	64.13 122	51.44 156	58.37 138	69.02 110	57.59 165	81.33 90	80.72 84	86.70 128	82.02 140

test1		70.78 122	73.37 115	67.76 140	72.95 174	78.00 149	75.15 134	62.72 120	64.13 122	51.44 156	58.37 138	69.02 110	57.59 165	81.33 90	80.72 84	86.70 128	82.02 140

FMVSNet2		70.39 128	72.67 122	67.72 143	72.95 174	78.00 149	75.15 134	62.69 124	63.29 130	51.25 160	55.64 153	68.49 117	57.59 165	80.91 100	80.35 96	86.70 128	82.02 140

FMVSNet3		70.49 126	72.90 120	67.67 145	72.88 177	77.98 152	74.96 143	62.72 120	64.13 122	51.44 156	58.37 138	69.02 110	57.43 168	79.43 134	79.57 112	86.59 134	81.81 147

LTVRE_ROB	59.44 16	61.82 209	62.64 211	60.87 198	72.83 178	77.19 161	64.37 214	58.97 173	33.56 245	28.00 232	52.59 189	42.21 236	63.93 122	74.52 181	76.28 169	77.15 213	82.13 139
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
v7n		67.05 170	66.94 176	67.17 154	72.35 179	78.97 135	73.26 169	58.88 176	51.16 213	50.90 161	48.21 208	50.11 216	60.96 146	77.70 154	77.38 154	86.68 131	85.05 115

tpm		62.41 200	63.15 206	61.55 195	72.24 180	63.79 230	71.31 177	46.12 238	57.82 163	55.33 131	59.90 128	54.74 180	53.63 198	67.24 228	64.29 230	70.65 237	74.25 210

test20.03		53.93 230	56.28 231	51.19 229	72.19 181	65.83 221	53.20 239	61.08 143	42.74 233	22.08 241	37.07 232	45.76 230	24.29 244	70.44 210	69.04 207	74.31 228	63.05 235

CP-MVSNet		62.68 196	65.49 186	59.40 207	71.84 182	75.34 178	62.87 220	67.04 66	52.64 202	27.19 233	53.38 173	48.15 222	41.40 222	71.26 200	75.68 174	86.07 145	82.00 143

PS-CasMVS		62.38 202	65.06 190	59.25 208	71.73 183	75.21 182	62.77 221	66.99 67	51.94 209	26.96 234	52.00 192	47.52 225	41.06 223	71.16 203	75.60 175	85.97 152	81.97 145

WR-MVS_H		61.83 208	65.87 182	57.12 214	71.72 184	76.87 163	61.45 224	66.19 69	51.97 208	22.92 240	53.13 179	52.30 205	33.80 234	71.03 204	75.00 181	86.65 132	80.78 155

USDC		67.36 166	67.90 168	66.74 163	71.72 184	75.23 181	71.58 175	60.28 156	67.45 99	50.54 164	60.93 119	45.20 231	62.08 132	76.56 170	74.50 184	84.25 183	75.38 202

UGNet		72.78 104	77.67 72	67.07 157	71.65 186	83.24 83	75.20 133	63.62 95	64.93 115	56.72 125	71.82 56	73.30 70	49.02 208	81.02 98	80.70 89	86.22 140	88.67 60
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
tpmrst		62.00 204	62.35 215	61.58 194	71.62 187	64.14 226	69.07 187	48.22 234	62.21 138	53.93 139	58.26 142	55.30 174	55.81 183	63.22 235	62.62 234	70.85 236	70.70 217

pmmvs4		67.89 155	67.39 174	68.48 136	71.60 188	73.57 193	74.45 146	60.98 147	64.65 117	57.97 119	54.95 160	51.73 208	61.88 137	73.78 186	75.11 180	83.99 187	77.91 182

testgi		54.39 229	57.86 227	50.35 230	71.59 189	67.24 217	54.95 236	53.25 205	43.36 232	23.78 237	44.64 218	47.87 223	24.96 241	70.45 209	68.66 211	73.60 230	62.78 236

pmmvs6		62.41 200	62.88 208	61.87 193	71.38 190	75.18 183	67.76 195	59.45 168	41.64 235	42.52 208	37.33 231	52.91 199	46.87 211	77.67 155	76.26 170	83.23 190	79.18 172

FMVSNet1		68.84 145	70.47 136	66.94 159	71.35 191	77.68 157	74.71 144	62.35 131	56.93 172	49.94 166	50.01 200	64.59 130	57.07 170	81.33 90	80.72 84	86.25 139	82.00 143

IterMVS-SCA-FT		66.89 171	69.22 150	64.17 177	71.30 192	75.64 175	71.33 176	53.17 206	57.63 168	49.08 172	60.72 121	60.05 147	63.09 125	74.99 179	73.92 186	77.07 214	81.57 149

PatchmatchNet		64.21 187	64.65 195	63.69 183	71.29 193	68.66 212	69.63 184	51.70 217	63.04 131	53.77 143	59.83 129	58.34 158	60.23 152	68.54 225	66.06 227	75.56 222	68.08 225
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
baseline		70.45 127	74.09 109	66.20 166	70.95 194	75.67 174	74.26 153	53.57 202	68.33 94	58.42 115	69.87 68	71.45 85	61.55 142	74.84 180	74.76 183	78.42 208	83.72 131

SCA		65.40 177	66.58 180	64.02 180	70.65 195	73.37 194	67.35 196	53.46 204	63.66 127	54.14 136	60.84 120	60.20 146	61.50 143	69.96 217	68.14 217	77.01 215	69.91 218

CR-MVSNet		64.83 180	65.54 185	64.01 181	70.64 196	69.41 208	65.97 207	52.74 210	57.81 164	52.65 151	54.27 164	56.31 169	60.92 147	72.20 195	73.09 191	81.12 199	75.69 199

MVSTER		72.06 111	74.24 105	69.51 126	70.39 197	75.97 173	76.91 123	57.36 188	64.64 118	61.39 104	68.86 75	63.76 133	63.46 123	81.44 87	79.70 106	87.56 106	85.31 110

Anonymous20231206		56.36 224	57.80 228	54.67 223	70.08 198	66.39 220	60.46 227	57.54 185	49.50 221	29.30 230	33.86 237	46.64 226	35.18 231	70.44 210	68.88 209	75.47 223	68.88 224

thisisatest0515		67.40 165	68.78 155	65.80 168	70.02 199	75.24 180	69.36 186	57.37 187	54.94 191	53.67 144	55.53 156	54.85 179	58.00 162	78.19 148	78.91 128	86.39 138	83.78 130

CMPMVS	47.78 17	62.49 199	62.52 212	62.46 188	70.01 200	70.66 205	62.97 219	51.84 216	51.98 207	56.71 126	42.87 221	53.62 187	57.80 164	72.23 193	70.37 202	75.45 224	75.91 196
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TDRefinement		66.09 174	65.03 192	67.31 151	69.73 201	76.75 165	75.33 130	64.55 85	60.28 154	49.72 169	45.63 217	42.83 235	60.46 151	75.75 173	75.95 172	84.08 185	78.04 181

TinyColmap		62.84 194	61.03 220	64.96 172	69.61 202	71.69 200	68.48 193	59.76 165	55.41 183	47.69 183	47.33 213	34.20 246	62.76 128	74.52 181	72.59 195	81.44 196	71.47 215

RPMNet		61.71 210	62.88 208	60.34 200	69.51 203	69.41 208	63.48 217	49.23 226	57.81 164	45.64 196	50.51 198	50.12 215	53.13 201	68.17 227	68.49 215	81.07 200	75.62 201

IterMVS		66.36 172	68.30 163	64.10 178	69.48 204	74.61 185	73.41 167	50.79 221	57.30 170	48.28 176	60.64 122	59.92 148	60.85 150	74.14 184	72.66 194	81.80 194	78.82 174
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SixPastTwentyTwo		61.84 207	62.45 213	61.12 197	69.20 205	72.20 198	62.03 223	57.40 186	46.54 228	38.03 218	57.14 148	41.72 237	58.12 161	69.67 219	71.58 198	81.94 193	78.30 176

MDTV_nov1_ep13		64.37 184	65.24 187	63.37 187	68.94 206	70.81 203	72.40 173	50.29 224	60.10 155	53.91 140	60.07 126	59.15 151	57.21 169	69.43 222	67.30 222	77.47 211	69.78 220

EPMVS		60.00 215	61.97 216	57.71 212	68.46 207	63.17 234	64.54 213	48.23 233	63.30 129	44.72 201	60.19 124	56.05 171	50.85 205	65.27 232	62.02 235	69.44 239	63.81 233

our_test_3					67.93 208	70.99 202	66.89 200

FE-MVSNET3		66.26 173	68.15 164	64.06 179	67.01 209	76.52 168	70.61 180	61.10 142	61.86 141	44.86 198	49.77 203	56.69 167	53.97 197	77.58 156	77.88 142	86.80 125	76.78 192

FC-MVSNet-test		56.90 223	65.20 189	47.21 235	66.98 210	63.20 233	49.11 244	58.60 178	59.38 158	11.50 251	65.60 101	56.68 168	24.66 243	71.17 202	71.36 200	72.38 233	69.02 223

CVMVSNet		62.55 197	65.89 181	58.64 209	66.95 211	69.15 210	66.49 206	56.29 197	52.46 204	32.70 224	59.27 132	58.21 159	50.09 206	71.77 198	71.39 199	79.31 205	78.99 173

FPMVS		51.87 233	50.00 239	54.07 224	66.83 212	57.25 241	60.25 228	50.91 219	50.25 217	34.36 222	36.04 234	32.02 248	41.49 221	58.98 241	56.07 241	70.56 238	59.36 241

pmmvs-eth3d		63.52 189	62.44 214	64.77 173	66.82 213	70.12 207	69.41 185	59.48 167	54.34 196	52.71 150	46.24 216	44.35 233	56.93 172	72.37 190	73.77 188	83.30 189	75.91 196

TAMVS		59.58 216	62.81 210	55.81 219	66.03 214	65.64 224	63.86 216	48.74 229	49.95 218	37.07 220	54.77 161	58.54 156	44.44 216	72.29 192	71.79 196	74.70 226	66.66 227

MDTV_nov1_ep13_2view		60.16 214	60.51 223	59.75 203	65.39 215	69.05 211	68.00 194	48.29 232	51.99 206	45.95 194	48.01 212	49.64 219	53.39 199	68.83 224	66.52 226	77.47 211	69.55 221

blend_shiyan4		64.82 181	65.21 188	64.37 176	65.04 216	74.06 189	70.30 181	55.30 199	55.39 184	53.88 141	52.71 185	58.58 154	56.43 177	69.45 221	68.13 220	85.30 167	78.14 179

pmmvs5		62.37 203	64.04 199	60.42 199	65.03 217	71.67 201	67.17 198	52.70 212	50.30 216	44.80 199	54.23 167	51.19 211	49.37 207	72.88 189	73.48 190	83.45 188	74.55 206

ambc			53.42 233		64.99 218	63.36 232	49.96 242		47.07 226	37.12 219	28.97 242	16.36 255	41.82 220	75.10 178	67.34 221	71.55 235	75.72 198

V42		68.76 147	69.63 144	67.74 142	64.93 219	78.01 148	78.30 109	56.48 193	58.65 161	56.30 128	54.26 166	57.03 164	64.85 117	77.47 158	77.01 160	85.60 158	84.96 117

pmnet_mix02		55.30 226	57.01 230	53.30 228	64.14 220	59.09 239	58.39 233	50.24 225	53.47 199	38.68 215	49.75 204	45.86 229	40.14 226	65.38 231	60.22 237	68.19 241	65.33 230

PMVS	39.38 18	46.06 240	43.30 243	49.28 233	62.93 221	38.75 249	41.88 247	53.50 203	33.33 246	35.46 221	28.90 243	31.01 249	33.04 235	58.61 243	54.63 244	68.86 240	57.88 242
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new-patchmatchnet		46.97 238	49.47 240	44.05 239	62.82 222	56.55 242	45.35 246	52.01 214	42.47 234	17.04 248	35.73 235	35.21 245	21.84 247	61.27 238	54.83 243	65.26 243	60.26 238

ET-MVSNet_ETH3D		72.46 109	74.19 106	70.44 113	62.50 223	81.17 112	79.90 87	62.46 130	64.52 120	57.52 121	71.49 59	59.15 151	72.08 54	78.61 144	81.11 77	88.16 76	83.29 134

ADS-MVSNet		55.94 225	58.01 226	53.54 227	62.48 224	58.48 240	59.12 231	46.20 237	59.65 157	42.88 207	52.34 191	53.31 197	46.31 212	62.00 237	60.02 238	64.23 244	60.24 240

RPSCF		67.64 162	71.25 130	63.43 186	61.86 225	70.73 204	67.26 197	50.86 220	74.20 62	58.91 111	67.49 90	69.33 107	64.10 121	71.41 199	68.45 216	77.61 210	77.17 188

MIMVSNet		58.52 220	61.34 219	55.22 221	60.76 226	67.01 218	66.81 201	49.02 228	56.43 176	38.90 214	40.59 228	54.54 182	40.57 225	73.16 188	71.65 197	75.30 225	66.00 228

PatchT		61.97 205	64.04 199	59.55 206	60.49 227	67.40 216	56.54 234	48.65 230	56.69 173	52.65 151	51.10 197	52.14 206	60.92 147	72.20 195	73.09 191	78.03 209	75.69 199

N_pmnet		47.35 237	50.13 238	44.11 238	59.98 228	51.64 246	51.86 240	44.80 239	49.58 220	20.76 244	40.65 227	40.05 242	29.64 237	59.84 239	55.15 242	57.63 245	54.00 243

blended_shiyan8		62.98 191	63.65 203	62.21 189	59.20 229	74.17 187	69.03 189	56.52 191	51.08 215	47.96 179	48.07 211	55.02 176	55.00 191	70.43 212	68.60 212	85.52 159	78.15 178

blended_shiyan6		62.98 191	63.66 202	62.19 190	59.20 229	74.17 187	69.04 188	56.52 191	51.09 214	47.91 180	48.11 210	55.02 176	54.98 192	70.43 212	68.59 213	85.51 160	78.20 177

FE-blended-shiyan7		62.84 194	63.46 205	62.12 192	59.06 231	74.03 190	68.92 191	56.37 194	51.17 211	48.02 178	48.12 209	54.93 178	55.08 190	70.13 215	68.14 217	85.26 168	77.73 184

usedtu_blend_shiyan5		64.27 185	64.70 194	63.77 182	59.06 231	74.03 190	71.65 174	56.37 194	51.17 211	53.88 141	52.71 185	58.58 154	56.43 177	70.13 215	68.14 217	85.26 168	78.14 179

MVS-HIRNet		54.41 228	52.10 236	57.11 215	58.99 233	56.10 243	49.68 243	49.10 227	46.18 229	52.15 155	33.18 238	46.11 228	56.10 179	63.19 236	59.70 239	76.64 217	60.25 239

PM-MVS		60.48 213	60.94 221	59.94 202	58.85 234	66.83 219	64.27 215	51.39 218	55.03 189	48.03 177	50.00 202	40.79 239	58.26 160	69.20 223	67.13 225	78.84 207	77.60 185

WB-MVS		40.01 241	45.06 242	34.13 241	58.84 235	53.28 245	28.60 250	58.10 183	32.93 247	4.65 256	40.92 225	28.33 251	7.26 250	58.86 242	56.09 240	47.36 248	44.98 245

anonymousdsp		65.28 178	67.98 166	62.13 191	58.73 236	73.98 192	67.10 199	50.69 222	48.41 222	47.66 184	54.27 164	52.75 202	61.45 145	76.71 169	80.20 97	87.13 115	89.53 56

TESTMET0.1,1		61.10 211	64.36 197	57.29 213	57.53 237	63.93 228	66.61 204	36.22 244	54.41 193	47.77 181	57.46 145	60.25 144	55.20 188	70.80 206	69.33 205	80.40 202	74.38 207

FE-MVSNET2		58.78 218	60.53 222	56.73 216	57.08 238	72.23 197	62.74 222	59.35 169	47.17 225	30.52 226	34.62 236	43.62 234	44.57 215	75.24 176	76.57 167	86.11 142	74.30 209

EU-MVSNet		54.63 227	58.69 225	49.90 231	56.99 239	62.70 236	56.41 235	50.64 223	45.95 230	23.14 239	50.42 199	46.51 227	36.63 230	65.51 230	64.85 229	75.57 221	74.91 204

FMVSNet5		57.24 221	60.02 224	53.99 225	56.45 240	62.74 235	65.27 210	47.03 235	55.14 186	39.55 213	40.88 226	53.42 195	41.83 219	72.35 191	71.10 201	73.79 229	64.50 232

test-mter		60.84 212	64.62 196	56.42 217	55.99 241	64.18 225	65.39 209	34.23 245	54.39 195	46.21 192	57.40 147	59.49 150	55.86 182	71.02 205	69.65 204	80.87 201	76.20 195

CHOSEN 280x420		58.70 219	61.88 217	54.98 222	55.45 242	50.55 247	64.92 211	40.36 241	55.21 185	38.13 217	48.31 207	63.76 133	63.03 127	73.73 187	68.58 214	68.00 242	73.04 213

PMMVS		65.06 179	69.17 151	60.26 201	55.25 243	63.43 231	66.71 203	43.01 240	62.41 136	50.64 162	69.44 71	67.04 123	63.29 124	74.36 183	73.54 189	82.68 192	73.99 211

FE-MVSNET		52.98 232	55.99 232	49.47 232	49.71 244	65.83 221	54.09 237	56.91 190	40.70 237	16.86 249	32.90 239	40.15 241	37.83 228	69.80 218	73.04 193	81.41 197	69.49 222

Gipuma		36.38 243	35.80 245	37.07 240	45.76 245	33.90 250	29.81 249	48.47 231	39.91 239	18.02 247	8.00 253	8.14 257	25.14 240	59.29 240	61.02 236	55.19 247	40.31 246
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs3		47.65 236	49.08 241	45.99 236	44.61 246	54.79 244	50.04 241	31.95 248	33.91 243	29.90 227	30.37 240	33.53 247	46.31 212	63.50 233	63.67 232	73.14 232	63.77 234

MIMVSNet1		49.27 234	53.25 234	44.62 237	44.61 246	61.52 238	53.61 238	52.18 213	41.62 236	18.68 246	28.14 244	41.58 238	25.50 239	68.46 226	69.04 207	73.15 231	62.37 237

MDA-MVSNet-bldmvs		53.37 231	53.01 235	53.79 226	43.67 248	67.95 215	59.69 229	57.92 184	43.69 231	32.41 225	41.47 224	27.89 252	52.38 203	56.97 244	65.99 228	76.68 216	67.13 226

E-PMN		21.77 246	18.24 249	25.89 243	40.22 249	19.58 253	12.46 255	39.87 242	18.68 252	6.71 253	9.57 250	4.31 260	22.36 246	19.89 251	27.28 249	33.73 251	28.34 250

EMVS		20.98 247	17.15 250	25.44 244	39.51 250	19.37 254	12.66 254	39.59 243	19.10 251	6.62 254	9.27 251	4.40 259	22.43 245	17.99 252	24.40 250	31.81 252	25.53 251

new_pmnet		38.40 242	42.64 244	33.44 242	37.54 251	45.00 248	36.60 248	32.72 247	40.27 238	12.72 250	29.89 241	28.90 250	24.78 242	53.17 245	52.90 245	56.31 246	48.34 244

usedtu_dtu_shiyan2		49.27 234	50.47 237	47.86 234	35.37 252	64.10 227	58.53 232	53.10 207	31.42 248	29.57 228	27.09 245	38.06 244	34.31 233	63.35 234	63.36 233	76.27 219	65.93 229

PMMVS2		25.60 244	29.75 246	20.76 246	28.00 253	30.93 251	23.10 252	29.18 249	23.14 250	1.46 257	18.23 249	16.54 254	5.08 251	40.22 246	41.40 247	37.76 249	37.79 248

tmp_tt				14.50 249	14.68 254	7.17 256	10.46 257	2.21 252	37.73 241	28.71 231	25.26 246	16.98 253	4.37 252	31.49 248	29.77 248	26.56 253

MVE	19.12 19	20.47 248	23.27 248	17.20 248	12.66 255	25.41 252	10.52 256	34.14 246	14.79 253	6.53 255	8.79 252	4.68 258	16.64 249	29.49 249	41.63 246	22.73 254	38.11 247
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		22.26 245	25.94 247	17.95 247	3.24 256	7.17 256	23.83 251	7.27 251	37.35 242	20.44 245	21.87 248	39.16 243	18.67 248	34.56 247	20.84 251	34.28 250	20.64 252

GG-mvs-BLEND		46.86 239	67.51 171	22.75 245	0.05 257	76.21 171	64.69 212	0.04 253	61.90 140	0.09 258	55.57 154	71.32 89	0.08 253	70.54 208	67.19 223	71.58 234	69.86 219

testmvs		0.09 249	0.15 251	0.02 250	0.01 258	0.02 258	0.05 259	0.01 254	0.11 254	0.01 259	0.26 255	0.01 261	0.06 255	0.10 253	0.10 252	0.01 256	0.43 254

uanet_test		0.00 251	0.00 253	0.00 252	0.00 259	0.00 260	0.00 261	0.00 256	0.00 256	0.00 260	0.00 256	0.00 262	0.00 256	0.00 255	0.00 254	0.00 258	0.00 255

sosnet-low-res		0.00 251	0.00 253	0.00 252	0.00 259	0.00 260	0.00 261	0.00 256	0.00 256	0.00 260	0.00 256	0.00 262	0.00 256	0.00 255	0.00 254	0.00 258	0.00 255

sosnet		0.00 251	0.00 253	0.00 252	0.00 259	0.00 260	0.00 261	0.00 256	0.00 256	0.00 260	0.00 256	0.00 262	0.00 256	0.00 255	0.00 254	0.00 258	0.00 255

test123		0.09 249	0.14 252	0.02 250	0.00 259	0.02 258	0.02 260	0.01 254	0.09 255	0.00 260	0.30 254	0.00 262	0.08 253	0.03 254	0.09 253	0.01 256	0.45 253

RE-MVS-def										46.24 191

9.14												86.88 17

MTAPA										83.48 1		86.45 20

MTMP										82.66 5		84.91 28

Patchmatch-RL test							2.85 258

NP-MVS									80.10 48

Patchmtry						65.80 223	65.97 207	52.74 210		52.65 151

DeepMVS_CX						18.74 255	18.55 253	8.02 250	26.96 249	7.33 252	23.81 247	13.05 256	25.99 238	25.17 250		22.45 255	36.25 249

High-resolution multi-view benchmark