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.

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 4	91.18 1	81.17 2	89.55 2	87.93 8	91.01 8	96.21 1

DVP-MVS		88.67 3	91.62 2	85.22 4	90.47 18	92.36 2	90.69 10	76.15 4	93.08 2	82.75 5	92.19 6	90.71 3	80.45 6	89.27 6	87.91 9	90.82 12	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
SED-MVS		88.85 2	91.59 3	85.67 2	90.54 16	92.29 3	91.71 3	76.40 2	92.41 3	83.24 2	92.50 3	90.64 4	81.10 3	89.53 3	88.02 7	91.00 9	95.73 3

DPE-MVS		88.63 4	91.29 4	85.53 3	90.87 9	92.20 4	91.98 2	76.00 6	90.55 8	82.09 7	93.85 1	90.75 2	81.25 1	88.62 8	87.59 14	90.96 10	95.48 4
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CSCG		85.28 23	87.68 19	82.49 26	89.95 25	91.99 5	88.82 26	71.20 39	86.41 23	79.63 18	79.26 30	88.36 10	73.94 43	86.64 33	86.67 26	91.40 2	94.41 8

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 10	92.54 2	89.18 6	80.89 4	87.99 15	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
APDe-MVS		88.00 6	90.50 6	85.08 5	90.95 8	91.58 7	92.03 1	75.53 13	91.15 5	80.10 16	92.27 5	88.34 11	80.80 5	88.00 14	86.99 19	91.09 6	95.16 6

xxxxxxxxxxxxxcwj		85.35 20	85.76 31	84.86 8	91.26 6	91.10 8	90.90 6	75.65 8	89.21 9	81.25 8	91.12 8	61.35 120	78.82 10	87.42 20	86.23 31	91.28 3	93.90 13

SF-MVS		87.47 8	89.70 8	84.86 8	91.26 6	91.10 8	90.90 6	75.65 8	89.21 9	81.25 8	91.12 8	88.93 7	78.82 10	87.42 20	86.23 31	91.28 3	93.90 13

SMA-MVS		87.56 7	90.17 7	84.52 10	91.71 3	90.57 10	90.77 9	75.19 14	90.67 7	80.50 15	86.59 18	88.86 8	78.09 17	89.92 1	89.41 1	90.84 11	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
DeepPCF-MVS	79.04 1	85.30 22	88.93 12	81.06 33	88.77 37	90.48 11	85.46 47	73.08 30	90.97 6	73.77 38	84.81 23	85.95 21	77.43 24	88.22 11	87.73 11	87.85 84	94.34 9

DeepC-MVS	78.47 2	84.81 27	86.03 29	83.37 20	89.29 33	90.38 12	88.61 28	76.50 1	86.25 24	77.22 25	75.12 40	80.28 46	77.59 23	88.39 10	88.17 6	91.02 7	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
APD-MVS		86.84 12	88.91 14	84.41 11	90.66 12	90.10 13	90.78 8	75.64 10	87.38 18	78.72 20	90.68 11	86.82 17	80.15 7	87.13 26	86.45 29	90.51 22	93.83 15
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_NAP		86.52 13	89.01 11	83.62 18	90.28 20	90.09 14	90.32 14	74.05 21	88.32 15	79.74 17	87.04 16	85.59 24	76.97 30	89.35 4	88.44 4	90.35 31	94.27 11

CNVR-MVS		86.36 14	88.19 17	84.23 13	91.33 5	89.84 15	90.34 12	75.56 11	87.36 19	78.97 19	81.19 29	86.76 18	78.74 12	89.30 5	88.58 2	90.45 28	94.33 10

SteuartSystems-ACMMP		85.99 16	88.31 16	83.27 22	90.73 11	89.84 15	90.27 15	74.31 16	84.56 31	75.88 31	87.32 15	85.04 25	77.31 25	89.01 7	88.46 3	91.14 5	93.96 12
Skip Steuart: Steuart Systems R&D Blog.
MCST-MVS		85.13 24	86.62 24	83.39 19	90.55 15	89.82 17	89.29 23	73.89 24	84.38 32	76.03 30	79.01 32	85.90 22	78.47 13	87.81 16	86.11 35	92.11 1	93.29 23

3Dnovator+	75.73 4	82.40 36	82.76 41	81.97 30	88.02 39	89.67 18	86.60 38	71.48 38	81.28 44	78.18 22	64.78 86	77.96 53	77.13 28	87.32 24	86.83 22	90.41 29	91.48 37

PHI-MVS		82.36 37	85.89 30	78.24 50	86.40 49	89.52 19	85.52 45	69.52 50	82.38 41	65.67 70	81.35 28	82.36 35	73.07 49	87.31 25	86.76 24	89.24 53	91.56 36

MP-MVS		85.50 19	87.40 21	83.28 21	90.65 13	89.51 20	89.16 25	74.11 20	83.70 35	78.06 23	85.54 21	84.89 28	77.31 25	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.
ACMMPR		85.52 18	87.53 20	83.17 23	90.13 21	89.27 21	89.30 22	73.97 22	86.89 21	77.14 26	86.09 19	83.18 33	77.74 21	87.42 20	87.20 16	90.77 14	92.63 26

DeepC-MVS_fast	78.24 3	84.27 30	85.50 32	82.85 24	90.46 19	89.24 22	87.83 34	74.24 18	84.88 27	76.23 29	75.26 39	81.05 44	77.62 22	88.02 13	87.62 13	90.69 17	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
SD-MVS		86.96 10	89.45 9	84.05 16	90.13 21	89.23 23	89.77 19	74.59 15	89.17 11	80.70 12	89.93 12	89.67 5	78.47 13	87.57 19	86.79 23	90.67 18	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
HFP-MVS		86.15 15	87.95 18	84.06 15	90.80 10	89.20 24	89.62 21	74.26 17	87.52 16	80.63 13	86.82 17	84.19 30	78.22 15	87.58 18	87.19 17	90.81 13	93.13 25

MVS_0304		81.73 40	83.86 37	79.26 43	86.22 51	89.18 25	86.41 39	67.15 66	75.28 56	70.75 54	74.59 42	83.49 32	74.42 40	87.05 29	86.34 30	90.58 21	91.08 41

NCCC		85.34 21	86.59 25	83.88 17	91.48 4	88.88 26	89.79 18	75.54 12	86.67 22	77.94 24	76.55 36	84.99 26	78.07 18	88.04 12	87.68 12	90.46 27	93.31 22

abl_6				79.05 44	87.27 43	88.85 27	83.62 57	68.25 56	81.68 42	72.94 41	73.79 46	84.45 29	72.55 53			89.66 48	90.64 45

PGM-MVS		84.42 29	86.29 28	82.23 27	90.04 23	88.82 28	89.23 24	71.74 37	82.82 38	74.61 34	84.41 24	82.09 36	77.03 29	87.13 26	86.73 25	90.73 16	92.06 33

XVS					86.63 47	88.68 29	85.00 48			71.81 47		81.92 38				90.47 24

X-MVStestdata					86.63 47	88.68 29	85.00 48			71.81 47		81.92 38				90.47 24

X-MVS		83.23 34	85.20 34	80.92 35	89.71 28	88.68 29	88.21 33	73.60 25	82.57 39	71.81 47	77.07 34	81.92 38	71.72 61	86.98 30	86.86 21	90.47 24	92.36 30

TSAR-MVS + MP.		86.88 11	89.23 10	84.14 14	89.78 27	88.67 32	90.59 11	73.46 28	88.99 12	80.52 14	91.26 7	88.65 9	79.91 8	86.96 31	86.22 33	90.59 20	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
CANet		81.62 41	83.41 38	79.53 42	87.06 44	88.59 33	85.47 46	67.96 60	76.59 54	74.05 35	74.69 41	81.98 37	72.98 51	86.14 40	85.47 39	89.68 47	90.42 48

TSAR-MVS + ACMM		85.10 25	88.81 15	80.77 36	89.55 30	88.53 34	88.59 29	72.55 32	87.39 17	71.90 44	90.95 10	87.55 13	74.57 38	87.08 28	86.54 27	87.47 91	93.67 18

DPM-MVS		83.30 33	84.33 36	82.11 28	89.56 29	88.49 35	90.33 13	73.24 29	83.85 34	76.46 28	72.43 52	82.65 34	73.02 50	86.37 37	86.91 20	90.03 39	89.62 55

ACMMP		83.42 32	85.27 33	81.26 32	88.47 38	88.49 35	88.31 32	72.09 34	83.42 36	72.77 42	82.65 25	78.22 51	75.18 37	86.24 39	85.76 37	90.74 15	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
CDPH-MVS		82.64 35	85.03 35	79.86 40	89.41 32	88.31 37	88.32 31	71.84 36	80.11 46	67.47 64	82.09 26	81.44 42	71.85 59	85.89 43	86.15 34	90.24 34	91.25 39

PCF-MVS	73.28 6	79.42 51	80.41 55	78.26 49	84.88 62	88.17 38	86.08 40	69.85 45	75.23 58	68.43 59	68.03 74	78.38 49	71.76 60	81.26 87	80.65 88	88.56 67	91.18 40
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MAR-MVS		79.21 53	80.32 56	77.92 52	87.46 41	88.15 39	83.95 54	67.48 65	74.28 60	68.25 60	64.70 87	77.04 55	72.17 55	85.42 45	85.00 44	88.22 71	87.62 68
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
CP-MVS		84.74 28	86.43 27	82.77 25	89.48 31	88.13 40	88.64 27	73.93 23	84.92 26	76.77 27	81.94 27	83.50 31	77.29 27	86.92 32	86.49 28	90.49 23	93.14 24

HPM-MVS++		87.09 9	88.92 13	84.95 6	92.61 1	87.91 41	90.23 16	76.06 5	88.85 13	81.20 11	87.33 14	87.93 12	79.47 9	88.59 9	88.23 5	90.15 37	93.60 21

3Dnovator	73.76 5	79.75 47	80.52 54	78.84 46	84.94 61	87.35 42	84.43 53	65.54 77	78.29 50	73.97 36	63.00 94	75.62 61	74.07 42	85.00 50	85.34 41	90.11 38	89.04 57

DELS-MVS		79.15 55	81.07 50	76.91 57	83.54 63	87.31 43	84.45 52	64.92 82	69.98 70	69.34 56	71.62 56	76.26 57	69.84 70	86.57 34	85.90 36	89.39 51	89.88 52
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
MSLP-MVS++		82.09 38	82.66 42	81.42 31	87.03 45	87.22 44	85.82 43	70.04 44	80.30 45	78.66 21	68.67 71	81.04 45	77.81 20	85.19 48	84.88 45	89.19 56	91.31 38

zzz-MVS		85.71 17	86.88 23	84.34 12	90.54 16	87.11 45	89.77 19	74.17 19	88.54 14	83.08 4	78.60 33	86.10 20	78.11 16	87.80 17	87.46 15	90.35 31	92.56 27

TSAR-MVS + GP.		83.69 31	86.58 26	80.32 37	85.14 56	86.96 46	84.91 51	70.25 43	84.71 30	73.91 37	85.16 22	85.63 23	77.92 19	85.44 44	85.71 38	89.77 43	92.45 28

CLD-MVS		79.35 52	81.23 48	77.16 56	85.01 59	86.92 47	85.87 42	60.89 131	80.07 48	75.35 33	72.96 49	73.21 69	68.43 79	85.41 46	84.63 46	87.41 92	85.44 89
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OPM-MVS		79.68 49	79.28 61	80.15 39	87.99 40	86.77 48	88.52 30	72.72 31	64.55 98	67.65 63	67.87 75	74.33 65	74.31 41	86.37 37	85.25 42	89.73 45	89.81 53
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
QAPM		78.47 58	80.22 57	76.43 59	85.03 58	86.75 49	80.62 68	66.00 74	73.77 63	65.35 71	65.54 82	78.02 52	72.69 52	83.71 60	83.36 58	88.87 62	90.41 49

PVSNet_Blended_VisFu		76.57 67	77.90 66	75.02 67	80.56 85	86.58 50	79.24 80	66.18 71	64.81 95	68.18 61	65.61 80	71.45 74	67.05 82	84.16 56	81.80 67	88.90 60	90.92 42

CPTT-MVS		81.77 39	83.10 40	80.21 38	85.93 52	86.45 51	87.72 35	70.98 40	82.54 40	71.53 50	74.23 45	81.49 41	76.31 32	82.85 69	81.87 66	88.79 64	92.26 31

canonicalmvs		79.16 54	82.37 44	75.41 64	82.33 71	86.38 52	80.80 66	63.18 97	82.90 37	67.34 65	72.79 50	76.07 59	69.62 71	83.46 65	84.41 47	89.20 55	90.60 46

AdaColmap		79.74 48	78.62 63	81.05 34	89.23 34	86.06 53	84.95 50	71.96 35	79.39 49	75.51 32	63.16 92	68.84 96	76.51 31	83.55 62	82.85 60	88.13 75	86.46 78

ACMP	73.23 7	79.79 46	80.53 53	78.94 45	85.61 54	85.68 54	85.61 44	69.59 48	77.33 52	71.00 53	74.45 43	69.16 91	71.88 57	83.15 66	83.37 57	89.92 40	90.57 47
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		80.26 43	82.59 43	77.54 53	83.04 64	85.54 55	83.25 59	65.05 81	87.32 20	72.42 43	72.04 54	78.97 48	73.30 47	83.86 58	81.60 70	88.15 74	88.83 59

train_agg		84.86 26	87.21 22	82.11 28	90.59 14	85.47 56	89.81 17	73.55 27	83.95 33	73.30 39	89.84 13	87.23 15	75.61 36	86.47 35	85.46 40	89.78 42	92.06 33

HQP-MVS		81.19 42	83.27 39	78.76 47	87.40 42	85.45 57	86.95 36	70.47 42	81.31 43	66.91 67	79.24 31	76.63 56	71.67 62	84.43 55	83.78 52	89.19 56	92.05 35

UA-Net		74.47 77	77.80 67	70.59 93	85.33 55	85.40 58	73.54 142	65.98 75	60.65 130	56.00 110	72.11 53	79.15 47	54.63 167	83.13 67	82.25 63	88.04 78	81.92 126

LGP-MVS_train		79.83 45	81.22 49	78.22 51	86.28 50	85.36 59	86.76 37	69.59 48	77.34 51	65.14 72	75.68 38	70.79 79	71.37 65	84.60 52	84.01 48	90.18 36	90.74 44

MVS_111021_HR		80.13 44	81.46 46	78.58 48	85.77 53	85.17 60	83.45 58	69.28 51	74.08 62	70.31 55	74.31 44	75.26 62	73.13 48	86.46 36	85.15 43	89.53 49	89.81 53

TSAR-MVS + COLMAP		78.34 59	81.64 45	74.48 75	80.13 92	85.01 61	81.73 60	65.93 76	84.75 29	61.68 85	85.79 20	66.27 105	71.39 64	82.91 68	80.78 79	86.01 132	85.98 80

OpenMVS	70.44 10	76.15 70	76.82 78	75.37 65	85.01 59	84.79 62	78.99 84	62.07 120	71.27 69	67.88 62	57.91 121	72.36 72	70.15 69	82.23 74	81.41 71	88.12 76	87.78 67

DROMVSNet		79.44 50	81.35 47	77.22 55	82.95 65	84.67 63	81.31 62	63.65 91	72.47 68	68.75 57	73.15 48	78.33 50	75.99 33	86.06 41	83.96 50	90.67 18	90.79 43

TAPA-MVS	71.42 9	77.69 63	80.05 58	74.94 68	80.68 83	84.52 64	81.36 61	63.14 98	84.77 28	64.82 75	68.72 69	75.91 60	71.86 58	81.62 76	79.55 104	87.80 86	85.24 92
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CNLPA		77.20 65	77.54 69	76.80 58	82.63 67	84.31 65	79.77 74	64.64 83	85.17 25	73.18 40	56.37 128	69.81 86	74.53 39	81.12 91	78.69 115	86.04 131	87.29 71

Vis-MVSNet		72.77 87	77.20 75	67.59 127	74.19 140	84.01 66	76.61 108	61.69 125	60.62 131	50.61 141	70.25 63	71.31 77	55.57 163	83.85 59	82.28 62	86.90 104	88.08 64
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		74.00 81	77.41 72	70.02 100	80.53 86	83.91 67	74.99 119	62.68 111	65.06 93	49.77 145	68.68 70	72.09 73	63.06 107	82.49 73	80.73 80	89.12 58	88.91 58

EIA-MVS		75.64 72	76.60 79	74.53 74	82.43 70	83.84 68	78.32 91	62.28 119	65.96 88	63.28 82	68.95 67	67.54 101	71.61 63	82.55 71	81.63 69	89.24 53	85.72 83

PVSNet_BlendedMVS		76.21 68	77.52 70	74.69 71	79.46 96	83.79 69	77.50 98	64.34 87	69.88 71	71.88 45	68.54 72	70.42 81	67.05 82	83.48 63	79.63 100	87.89 82	86.87 74

PVSNet_Blended		76.21 68	77.52 70	74.69 71	79.46 96	83.79 69	77.50 98	64.34 87	69.88 71	71.88 45	68.54 72	70.42 81	67.05 82	83.48 63	79.63 100	87.89 82	86.87 74

casdiffmvs		76.76 66	78.46 64	74.77 70	80.32 89	83.73 71	80.65 67	63.24 96	73.58 64	66.11 69	69.39 66	74.09 66	69.49 73	82.52 72	79.35 109	88.84 63	86.52 77

ETV-MVS		77.32 64	78.81 62	75.58 63	82.24 72	83.64 72	79.98 70	64.02 89	69.64 74	63.90 78	70.89 60	69.94 85	73.41 46	85.39 47	83.91 51	89.92 40	88.31 62

IS_MVSNet		73.33 83	77.34 74	68.65 115	81.29 76	83.47 73	74.45 124	63.58 94	65.75 90	48.49 150	67.11 79	70.61 80	54.63 167	84.51 54	83.58 54	89.48 50	86.34 79

ACMM	72.26 8	78.86 57	78.13 65	79.71 41	86.89 46	83.40 74	86.02 41	70.50 41	75.28 56	71.49 51	63.01 93	69.26 90	73.57 45	84.11 57	83.98 49	89.76 44	87.84 66
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ECVR-MVS		72.20 90	73.91 89	70.20 97	81.49 75	83.27 75	75.74 109	67.59 64	68.19 78	49.31 148	55.77 130	62.00 118	58.82 135	84.76 51	82.94 59	88.27 70	80.41 140

Effi-MVS+		75.28 74	76.20 80	74.20 76	81.15 78	83.24 76	81.11 64	63.13 99	66.37 84	60.27 89	64.30 90	68.88 95	70.93 68	81.56 78	81.69 68	88.61 65	87.35 69

UGNet		72.78 86	77.67 68	67.07 137	71.65 164	83.24 76	75.20 113	63.62 93	64.93 94	56.72 106	71.82 55	73.30 67	49.02 180	81.02 92	80.70 86	86.22 123	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
EPNet		79.08 56	80.62 52	77.28 54	88.90 36	83.17 78	83.65 56	72.41 33	74.41 59	67.15 66	76.78 35	74.37 64	64.43 99	83.70 61	83.69 53	87.15 95	88.19 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ECVR-MVS11		71.56 95	73.44 92	69.38 108	81.16 77	82.95 79	74.99 119	67.68 62	66.89 82	46.33 164	55.19 136	60.91 122	57.99 142	84.59 53	82.70 61	88.12 76	80.85 134

HyFIR lowres test		69.47 118	68.94 132	70.09 99	76.77 118	82.93 80	76.63 107	60.17 140	59.00 138	54.03 119	40.54 200	65.23 108	67.89 81	76.54 148	78.30 120	85.03 149	80.07 143

IB-MVS	66.94 12	71.21 100	71.66 108	70.68 90	79.18 98	82.83 81	72.61 148	61.77 124	59.66 135	63.44 81	53.26 152	59.65 129	59.16 134	76.78 145	82.11 64	87.90 81	87.33 70
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
LS3D		74.08 80	73.39 94	74.88 69	85.05 57	82.62 82	79.71 76	68.66 54	72.82 65	58.80 93	57.61 122	61.31 121	71.07 67	80.32 101	78.87 114	86.00 133	80.18 142

ACMH	65.37 14	70.71 103	70.00 118	71.54 85	82.51 69	82.47 83	77.78 95	68.13 57	56.19 157	46.06 167	54.30 140	51.20 183	68.68 77	80.66 96	80.72 81	86.07 127	84.45 105
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_part1		74.24 78	73.44 92	75.18 66	82.02 74	82.34 84	83.88 55	62.40 117	60.93 128	68.68 58	49.25 180	69.71 87	65.73 97	81.26 87	81.98 65	88.35 68	88.60 61

CS-MVS		77.88 62	80.66 51	74.64 73	79.87 93	82.07 85	78.63 86	59.93 145	72.57 67	63.22 83	73.66 47	77.72 54	75.97 35	85.12 49	83.58 54	90.23 35	89.93 51

CANet_DTU		73.29 84	76.96 77	69.00 112	77.04 116	82.06 86	79.49 78	56.30 168	67.85 79	53.29 126	71.12 59	70.37 83	61.81 121	81.59 77	80.96 77	86.09 126	84.73 100

Anonymous202405211			72.16 105		80.85 82	81.85 87	76.88 105	65.40 78	62.89 113		46.35 187	67.99 100	62.05 114	81.15 90	80.38 92	85.97 134	84.50 103

CS-MVS-test		78.10 61	79.79 60	76.13 60	80.59 84	81.68 88	81.31 62	63.65 91	68.34 76	64.91 74	72.52 51	76.25 58	75.99 33	86.06 41	83.55 56	90.31 33	90.16 50

DI_MVS_plusplus_trai		75.13 75	76.12 81	73.96 77	78.18 104	81.55 89	80.97 65	62.54 113	68.59 75	65.13 73	61.43 96	74.81 63	69.32 74	81.01 93	79.59 102	87.64 89	85.89 81

Anonymous20231211		71.90 92	72.48 102	71.21 86	80.14 91	81.53 90	76.92 103	62.89 102	64.46 100	58.94 91	43.80 191	70.98 78	62.22 111	80.70 95	80.19 95	86.18 124	85.73 82

MVS_111021_LR		78.13 60	79.85 59	76.13 60	81.12 79	81.50 91	80.28 69	65.25 79	76.09 55	71.32 52	76.49 37	72.87 71	72.21 54	82.79 70	81.29 72	86.59 117	87.91 65

GeoE		74.23 79	74.84 85	73.52 78	80.42 88	81.46 92	79.77 74	61.06 129	67.23 81	63.67 79	59.56 108	68.74 97	67.90 80	80.25 105	79.37 108	88.31 69	87.26 72

EG-PatchMatch MVS		67.24 146	66.94 153	67.60 126	78.73 101	81.35 93	73.28 146	59.49 148	46.89 199	51.42 137	43.65 192	53.49 164	55.50 164	81.38 82	80.66 87	87.15 95	81.17 132

MVS_Test		75.37 73	77.13 76	73.31 80	79.07 99	81.32 94	79.98 70	60.12 142	69.72 73	64.11 77	70.53 61	73.22 68	68.90 75	80.14 107	79.48 106	87.67 88	85.50 87

PLC	68.99 11	75.68 71	75.31 83	76.12 62	82.94 66	81.26 95	79.94 72	66.10 72	77.15 53	66.86 68	59.13 111	68.53 98	73.73 44	80.38 100	79.04 110	87.13 99	81.68 128
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ET-MVSNet_ETH3D		72.46 89	74.19 87	70.44 94	62.50 199	81.17 96	79.90 73	62.46 116	64.52 99	57.52 102	71.49 58	59.15 131	72.08 56	78.61 125	81.11 74	88.16 73	83.29 114

ACMH+	66.54 13	71.36 99	70.09 117	72.85 81	82.59 68	81.13 97	78.56 87	68.04 58	61.55 122	52.52 132	51.50 169	54.14 156	68.56 78	78.85 122	79.50 105	86.82 107	83.94 108

v1144		69.93 113	69.36 127	70.61 92	74.89 133	80.93 98	79.11 82	60.64 133	55.97 159	55.31 113	53.85 147	54.14 156	66.54 91	78.10 130	77.44 133	87.14 98	85.09 94

UniMVSNet (Re)		69.53 116	71.90 106	66.76 142	76.42 119	80.93 98	72.59 149	68.03 59	61.75 121	41.68 181	58.34 119	57.23 140	53.27 172	79.53 114	80.62 89	88.57 66	84.90 98

v1192		69.50 117	68.83 133	70.29 96	74.49 137	80.92 100	78.55 88	60.54 135	55.04 165	54.21 116	52.79 161	52.33 176	66.92 86	77.88 132	77.35 136	87.04 102	85.51 86

Fast-Effi-MVS+		73.11 85	73.66 90	72.48 82	77.72 110	80.88 101	78.55 88	58.83 158	65.19 92	60.36 88	59.98 105	62.42 117	71.22 66	81.66 75	80.61 90	88.20 72	84.88 99

UniMVSNet_NR-MVSNet		70.59 104	72.19 103	68.72 113	77.72 110	80.72 102	73.81 139	69.65 47	61.99 118	43.23 176	60.54 101	57.50 138	58.57 136	79.56 113	81.07 75	89.34 52	83.97 106

v144192		69.34 119	68.68 137	70.12 98	74.06 141	80.54 103	78.08 94	60.54 135	54.99 167	54.13 118	52.92 159	52.80 174	66.73 89	77.13 140	76.72 143	87.15 95	85.63 84

Effi-MVS+-dtu		71.82 93	71.86 107	71.78 84	78.77 100	80.47 104	78.55 88	61.67 127	60.68 129	55.49 111	58.48 115	65.48 107	68.85 76	76.92 142	75.55 154	87.35 93	85.46 88

v1921920		69.03 122	68.32 141	69.86 101	74.03 142	80.37 105	77.55 96	60.25 139	54.62 169	53.59 124	52.36 165	51.50 182	66.75 88	77.17 139	76.69 145	86.96 103	85.56 85

diffmvs		74.86 76	77.37 73	71.93 83	75.62 126	80.35 106	79.42 79	60.15 141	72.81 66	64.63 76	71.51 57	73.11 70	66.53 92	79.02 120	77.98 123	85.25 146	86.83 76

v2v482		70.05 112	69.46 126	70.74 88	74.62 136	80.32 107	79.00 83	60.62 134	57.41 148	56.89 105	55.43 135	55.14 151	66.39 93	77.25 138	77.14 138	86.90 104	83.57 113

v10		70.22 109	69.76 122	70.74 88	74.79 134	80.30 108	79.22 81	59.81 146	57.71 146	56.58 108	54.22 145	55.31 149	66.95 85	78.28 128	77.47 132	87.12 101	85.07 95

v1240		68.64 127	67.89 146	69.51 106	73.89 144	80.26 109	76.73 106	59.97 144	53.43 177	53.08 127	51.82 168	50.84 185	66.62 90	76.79 144	76.77 142	86.78 109	85.34 90

v8		70.23 108	69.86 120	70.67 91	74.69 135	79.82 110	78.79 85	59.18 151	58.80 139	58.20 99	55.00 137	57.33 139	66.31 94	77.51 135	76.71 144	86.82 107	83.88 109

DU-MVS		69.63 115	70.91 111	68.13 119	75.99 121	79.54 111	73.81 139	69.20 52	61.20 126	43.23 176	58.52 113	53.50 163	58.57 136	79.22 117	80.45 91	87.97 79	83.97 106

NR-MVSNet		68.79 125	70.56 113	66.71 144	77.48 113	79.54 111	73.52 143	69.20 52	61.20 126	39.76 183	58.52 113	50.11 189	51.37 176	80.26 104	80.71 85	88.97 59	83.59 112

TranMVSNet+NR-MVSNet		69.25 120	70.81 112	67.43 128	77.23 115	79.46 113	73.48 144	69.66 46	60.43 132	39.56 184	58.82 112	53.48 165	55.74 161	79.59 111	81.21 73	88.89 61	82.70 116

MSDG		71.52 96	69.87 119	73.44 79	82.21 73	79.35 114	79.52 77	64.59 84	66.15 86	61.87 84	53.21 154	56.09 146	65.85 96	78.94 121	78.50 117	86.60 116	76.85 164

DCV-MVSNet		73.65 82	75.78 82	71.16 87	80.19 90	79.27 115	77.45 100	61.68 126	66.73 83	58.72 94	65.31 83	69.96 84	62.19 112	81.29 86	80.97 76	86.74 110	86.91 73

v7n		67.05 148	66.94 153	67.17 134	72.35 157	78.97 116	73.26 147	58.88 157	51.16 188	50.90 139	48.21 183	50.11 189	60.96 126	77.70 133	77.38 134	86.68 114	85.05 96

tfpn200view9		68.11 130	68.72 136	67.40 129	77.83 108	78.93 117	74.28 129	62.81 103	56.64 152	46.82 160	52.65 162	53.47 166	56.59 153	80.41 97	78.43 118	86.11 125	80.52 138

CHOSEN 1792x2688		69.20 121	69.26 128	69.13 109	76.86 117	78.93 117	77.27 101	60.12 142	61.86 120	54.42 115	42.54 195	61.61 119	66.91 87	78.55 126	78.14 122	79.23 176	83.23 115

thres600view7		67.68 138	68.43 140	66.80 141	77.90 105	78.86 119	73.84 137	62.75 104	56.07 158	44.70 174	52.85 160	52.81 173	55.58 162	80.41 97	77.77 126	86.05 129	80.28 141

thres200		67.98 132	68.55 139	67.30 132	77.89 107	78.86 119	74.18 133	62.75 104	56.35 155	46.48 163	52.98 158	53.54 162	56.46 154	80.41 97	77.97 124	86.05 129	79.78 146

FC-MVSNet-train		72.60 88	75.07 84	69.71 103	81.10 80	78.79 121	73.74 141	65.23 80	66.10 87	53.34 125	70.36 62	63.40 114	56.92 152	81.44 80	80.96 77	87.93 80	84.46 104

tttt0517		71.41 98	72.95 98	69.60 105	73.70 147	78.70 122	74.42 127	59.12 152	63.89 105	58.35 98	64.56 89	58.39 135	64.27 100	80.29 102	80.17 96	87.74 87	84.69 101

thisisatest0530		71.48 97	73.01 97	69.70 104	73.83 145	78.62 123	74.53 123	59.12 152	64.13 101	58.63 95	64.60 88	58.63 133	64.27 100	80.28 103	80.17 96	87.82 85	84.64 102

thres400		67.95 133	68.62 138	67.17 134	77.90 105	78.59 124	74.27 130	62.72 106	56.34 156	45.77 169	53.00 157	53.35 169	56.46 154	80.21 106	78.43 118	85.91 136	80.43 139

GA-MVS		68.14 129	69.17 130	66.93 140	73.77 146	78.50 125	74.45 124	58.28 160	55.11 164	48.44 151	60.08 103	53.99 159	61.50 123	78.43 127	77.57 130	85.13 147	80.54 137

UniMVSNet_ETH3D		67.18 147	67.03 152	67.36 130	74.44 138	78.12 126	74.07 134	66.38 69	52.22 182	46.87 159	48.64 181	51.84 180	56.96 150	77.29 137	78.53 116	85.42 143	82.59 117

V42		68.76 126	69.63 123	67.74 122	64.93 195	78.01 127	78.30 92	56.48 167	58.65 140	56.30 109	54.26 143	57.03 142	64.85 98	77.47 136	77.01 140	85.60 140	84.96 97

GBi-Net		70.78 101	73.37 95	67.76 120	72.95 152	78.00 128	75.15 114	62.72 106	64.13 101	51.44 134	58.37 116	69.02 92	57.59 144	81.33 83	80.72 81	86.70 111	82.02 120

test1		70.78 101	73.37 95	67.76 120	72.95 152	78.00 128	75.15 114	62.72 106	64.13 101	51.44 134	58.37 116	69.02 92	57.59 144	81.33 83	80.72 81	86.70 111	82.02 120

FMVSNet2		70.39 107	72.67 101	67.72 123	72.95 152	78.00 128	75.15 114	62.69 110	63.29 109	51.25 138	55.64 131	68.49 99	57.59 144	80.91 94	80.35 93	86.70 111	82.02 120

FMVSNet3		70.49 105	72.90 99	67.67 125	72.88 155	77.98 131	74.96 121	62.72 106	64.13 101	51.44 134	58.37 116	69.02 92	57.43 147	79.43 115	79.57 103	86.59 117	81.81 127

COLMAP_ROB	62.73 15	67.66 139	66.76 155	68.70 114	80.49 87	77.98 131	75.29 112	62.95 101	63.62 107	49.96 143	47.32 186	50.72 186	58.57 136	76.87 143	75.50 155	84.94 151	75.33 175
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Fast-Effi-MVS+-dtu		68.34 128	69.47 125	67.01 138	75.15 129	77.97 133	77.12 102	55.40 170	57.87 141	46.68 162	56.17 129	60.39 123	62.36 110	76.32 149	76.25 150	85.35 145	81.34 130

MS-PatchMatch		70.17 110	70.49 114	69.79 102	80.98 81	77.97 133	77.51 97	58.95 155	62.33 116	55.22 114	53.14 155	65.90 106	62.03 115	79.08 119	77.11 139	84.08 156	77.91 156

thres100view900		67.60 142	68.02 143	67.12 136	77.83 108	77.75 135	73.90 136	62.52 114	56.64 152	46.82 160	52.65 162	53.47 166	55.92 158	78.77 123	77.62 129	85.72 137	79.23 149

FMVSNet1		68.84 124	70.47 115	66.94 139	71.35 169	77.68 136	74.71 122	62.35 118	56.93 150	49.94 144	50.01 175	64.59 109	57.07 149	81.33 83	80.72 81	86.25 122	82.00 123

gg-mvs-nofinetune		62.55 169	65.05 167	59.62 178	78.72 102	77.61 137	70.83 156	53.63 171	39.71 211	22.04 212	36.36 204	64.32 110	47.53 182	81.16 89	79.03 111	85.00 150	77.17 161

WR-MVS		63.03 165	67.40 150	57.92 184	75.14 130	77.60 138	60.56 197	66.10 72	54.11 174	23.88 206	53.94 146	53.58 161	34.50 202	73.93 161	77.71 127	87.35 93	80.94 133

v148		67.85 135	67.53 147	68.23 117	73.25 150	77.57 139	74.26 131	57.36 165	55.70 160	57.45 103	53.53 148	55.42 148	61.96 117	75.23 153	73.92 162	85.08 148	81.32 131

LTVRE_ROB	59.44 16	61.82 181	62.64 183	60.87 171	72.83 156	77.19 140	64.37 186	58.97 154	33.56 216	28.00 202	52.59 164	42.21 207	63.93 103	74.52 157	76.28 148	77.15 183	82.13 119
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
baseline2		69.69 114	70.27 116	69.01 111	75.72 125	77.13 141	73.82 138	58.94 156	61.35 124	57.09 104	61.68 95	57.17 141	61.99 116	78.10 130	76.58 146	86.48 120	79.85 144

WR-MVS_H		61.83 180	65.87 159	57.12 187	71.72 162	76.87 142	61.45 195	66.19 70	51.97 185	22.92 210	53.13 156	52.30 178	33.80 203	71.03 180	75.00 157	86.65 115	80.78 135

baseline1		70.10 111	72.17 104	67.69 124	79.74 94	76.80 143	73.91 135	64.38 86	62.74 114	48.30 152	64.94 84	64.08 111	54.17 169	81.46 79	78.92 112	85.66 139	76.22 166

TDRefinement		66.09 151	65.03 168	67.31 131	69.73 179	76.75 144	75.33 110	64.55 85	60.28 133	49.72 146	45.63 189	42.83 206	60.46 131	75.75 150	75.95 151	84.08 156	78.04 155

Vis-MVSNet (Re-imp)		67.83 136	73.52 91	61.19 169	78.37 103	76.72 145	66.80 174	62.96 100	65.50 91	34.17 195	67.19 78	69.68 88	39.20 198	79.39 116	79.44 107	85.68 138	76.73 165

CDS-MVSNet		67.65 140	69.83 121	65.09 149	75.39 128	76.55 146	74.42 127	63.75 90	53.55 175	49.37 147	59.41 109	62.45 116	44.44 187	79.71 110	79.82 98	83.17 162	77.36 160
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
pm-mvs1		65.62 152	67.42 149	63.53 161	73.66 148	76.39 147	69.66 158	60.87 132	49.73 192	43.97 175	51.24 171	57.00 143	48.16 181	79.89 108	77.84 125	84.85 153	79.82 145

IterMVS-LS		71.69 94	72.82 100	70.37 95	77.54 112	76.34 148	75.13 117	60.46 137	61.53 123	57.57 101	64.89 85	67.33 102	66.04 95	77.09 141	77.37 135	85.48 142	85.18 93
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GG-mvs-BLEND		46.86 208	67.51 148	22.75 214	0.05 225	76.21 149	64.69 184	0.04 222	61.90 119	0.09 226	55.57 132	71.32 76	0.08 221	70.54 184	67.19 193	71.58 203	69.86 190

CostFormer		68.92 123	69.58 124	68.15 118	75.98 123	76.17 150	78.22 93	51.86 184	65.80 89	61.56 86	63.57 91	62.83 115	61.85 119	70.40 188	68.67 185	79.42 174	79.62 147

MVSTER		72.06 91	74.24 86	69.51 106	70.39 175	75.97 151	76.91 104	57.36 165	64.64 97	61.39 87	68.86 68	63.76 112	63.46 104	81.44 80	79.70 99	87.56 90	85.31 91

baseline		70.45 106	74.09 88	66.20 145	70.95 172	75.67 152	74.26 131	53.57 172	68.33 77	58.42 96	69.87 64	71.45 74	61.55 122	74.84 156	74.76 159	78.42 178	83.72 111

IterMVS-SCA-FT		66.89 149	69.22 129	64.17 155	71.30 170	75.64 153	71.33 153	53.17 176	57.63 147	49.08 149	60.72 99	60.05 127	63.09 106	74.99 155	73.92 162	77.07 184	81.57 129

tfpnnormal		64.27 161	63.64 177	65.02 150	75.84 124	75.61 154	71.24 155	62.52 114	47.79 196	42.97 178	42.65 194	44.49 204	52.66 174	78.77 123	76.86 141	84.88 152	79.29 148

PEN-MVS		62.96 166	65.77 160	59.70 177	73.98 143	75.45 155	63.39 190	67.61 63	52.49 180	25.49 205	53.39 149	49.12 192	40.85 195	71.94 173	77.26 137	86.86 106	80.72 136

CP-MVSNet		62.68 168	65.49 163	59.40 180	71.84 160	75.34 156	62.87 192	67.04 67	52.64 179	27.19 203	53.38 150	48.15 194	41.40 193	71.26 176	75.68 152	86.07 127	82.00 123

TransMVSNet (Re)		64.74 158	65.66 161	63.66 160	77.40 114	75.33 157	69.86 157	62.67 112	47.63 197	41.21 182	50.01 175	52.33 176	45.31 186	79.57 112	77.69 128	85.49 141	77.07 163

thisisatest0515		67.40 144	68.78 134	65.80 147	70.02 177	75.24 158	69.36 161	57.37 164	54.94 168	53.67 123	55.53 134	54.85 152	58.00 141	78.19 129	78.91 113	86.39 121	83.78 110

USDC		67.36 145	67.90 145	66.74 143	71.72 162	75.23 159	71.58 152	60.28 138	67.45 80	50.54 142	60.93 97	45.20 203	62.08 113	76.56 147	74.50 160	84.25 155	75.38 174

PS-CasMVS		62.38 174	65.06 166	59.25 181	71.73 161	75.21 160	62.77 193	66.99 68	51.94 186	26.96 204	52.00 167	47.52 197	41.06 194	71.16 179	75.60 153	85.97 134	81.97 125

pmmvs6		62.41 172	62.88 180	61.87 166	71.38 168	75.18 161	67.76 167	59.45 150	41.64 207	42.52 180	37.33 202	52.91 172	46.87 183	77.67 134	76.26 149	83.23 161	79.18 150

Baseline_NR-MVSNet		67.53 143	68.77 135	66.09 146	75.99 121	74.75 162	72.43 150	68.41 55	61.33 125	38.33 188	51.31 170	54.13 158	56.03 157	79.22 117	78.19 121	85.37 144	82.45 118

IterMVS		66.36 150	68.30 142	64.10 156	69.48 182	74.61 163	73.41 145	50.79 190	57.30 149	48.28 153	60.64 100	59.92 128	60.85 130	74.14 160	72.66 169	81.80 165	78.82 152
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DTE-MVSNet		61.85 178	64.96 169	58.22 183	74.32 139	74.39 164	61.01 196	67.85 61	51.76 187	21.91 213	53.28 151	48.17 193	37.74 199	72.22 170	76.44 147	86.52 119	78.49 153

anonymousdsp		65.28 155	67.98 144	62.13 165	58.73 207	73.98 165	67.10 171	50.69 191	48.41 195	47.66 158	54.27 141	52.75 175	61.45 125	76.71 146	80.20 94	87.13 99	89.53 56

pmmvs4		67.89 134	67.39 151	68.48 116	71.60 166	73.57 166	74.45 124	60.98 130	64.65 96	57.97 100	54.95 138	51.73 181	61.88 118	73.78 162	75.11 156	83.99 158	77.91 156

SCA		65.40 154	66.58 157	64.02 157	70.65 173	73.37 167	67.35 168	53.46 174	63.66 106	54.14 117	60.84 98	60.20 126	61.50 123	69.96 189	68.14 190	77.01 185	69.91 189

tpm cat1		65.41 153	63.81 176	67.28 133	75.61 127	72.88 168	75.32 111	52.85 178	62.97 111	63.66 80	53.24 153	53.29 171	61.83 120	65.54 199	64.14 201	74.43 196	74.60 177

PatchMatch-RL		67.78 137	66.65 156	69.10 110	73.01 151	72.69 169	68.49 164	61.85 123	62.93 112	60.20 90	56.83 127	50.42 187	69.52 72	75.62 151	74.46 161	81.51 166	73.62 183

SixPastTwentyTwo		61.84 179	62.45 185	61.12 170	69.20 183	72.20 170	62.03 194	57.40 163	46.54 200	38.03 190	57.14 126	41.72 208	58.12 140	69.67 190	71.58 173	81.94 164	78.30 154

dps		64.00 163	62.99 179	65.18 148	73.29 149	72.07 171	68.98 163	53.07 177	57.74 145	58.41 97	55.55 133	47.74 196	60.89 129	69.53 191	67.14 194	76.44 188	71.19 187

TinyColmap		62.84 167	61.03 192	64.96 151	69.61 180	71.69 172	68.48 165	59.76 147	55.41 161	47.69 157	47.33 185	34.20 215	62.76 109	74.52 157	72.59 170	81.44 167	71.47 186

pmmvs5		62.37 175	64.04 174	60.42 172	65.03 193	71.67 173	67.17 170	52.70 181	50.30 189	44.80 172	54.23 144	51.19 184	49.37 179	72.88 165	73.48 166	83.45 159	74.55 178

our_test_3					67.93 186	70.99 174	66.89 172

MDTV_nov1_ep13		64.37 160	65.24 164	63.37 163	68.94 184	70.81 175	72.40 151	50.29 193	60.10 134	53.91 121	60.07 104	59.15 131	57.21 148	69.43 192	67.30 192	77.47 181	69.78 191

RPSCF		67.64 141	71.25 109	63.43 162	61.86 201	70.73 176	67.26 169	50.86 189	74.20 61	58.91 92	67.49 76	69.33 89	64.10 102	71.41 175	68.45 189	77.61 180	77.17 161

CMPMVS	47.78 17	62.49 171	62.52 184	62.46 164	70.01 178	70.66 177	62.97 191	51.84 185	51.98 184	56.71 107	42.87 193	53.62 160	57.80 143	72.23 169	70.37 177	75.45 193	75.91 168
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs-eth3d		63.52 164	62.44 186	64.77 152	66.82 190	70.12 178	69.41 160	59.48 149	54.34 173	52.71 128	46.24 188	44.35 205	56.93 151	72.37 166	73.77 164	83.30 160	75.91 168

CR-MVSNet		64.83 157	65.54 162	64.01 158	70.64 174	69.41 179	65.97 179	52.74 179	57.81 143	52.65 129	54.27 141	56.31 145	60.92 127	72.20 171	73.09 167	81.12 169	75.69 171

RPMNet		61.71 182	62.88 180	60.34 173	69.51 181	69.41 179	63.48 189	49.23 195	57.81 143	45.64 170	50.51 173	50.12 188	53.13 173	68.17 197	68.49 188	81.07 170	75.62 173

CVMVSNet		62.55 169	65.89 158	58.64 182	66.95 188	69.15 181	66.49 178	56.29 169	52.46 181	32.70 196	59.27 110	58.21 137	50.09 178	71.77 174	71.39 174	79.31 175	78.99 151

MDTV_nov1_ep13_2view		60.16 186	60.51 194	59.75 176	65.39 192	69.05 182	68.00 166	48.29 201	51.99 183	45.95 168	48.01 184	49.64 191	53.39 171	68.83 194	66.52 196	77.47 181	69.55 192

PatchmatchNet		64.21 162	64.65 170	63.69 159	71.29 171	68.66 183	69.63 159	51.70 186	63.04 110	53.77 122	59.83 107	58.34 136	60.23 132	68.54 195	66.06 197	75.56 191	68.08 195
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPNet_dtu		68.08 131	71.00 110	64.67 153	79.64 95	68.62 184	75.05 118	63.30 95	66.36 85	45.27 171	67.40 77	66.84 104	43.64 189	75.37 152	74.98 158	81.15 168	77.44 159
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test0.0.03 1		58.80 189	61.58 190	55.56 192	75.02 131	68.45 185	59.58 201	61.96 121	52.74 178	29.57 199	49.75 178	54.56 154	31.46 205	71.19 177	69.77 178	75.75 189	64.57 200

MDA-MVSNet-bldmvs		53.37 202	53.01 205	53.79 198	43.67 217	67.95 186	59.69 200	57.92 161	43.69 203	32.41 197	41.47 196	27.89 220	52.38 175	56.97 212	65.99 198	76.68 186	67.13 196

PatchT		61.97 177	64.04 174	59.55 179	60.49 203	67.40 187	56.54 204	48.65 199	56.69 151	52.65 129	51.10 172	52.14 179	60.92 127	72.20 171	73.09 167	78.03 179	75.69 171

testgi		54.39 200	57.86 198	50.35 202	71.59 167	67.24 188	54.95 206	53.25 175	43.36 204	23.78 207	44.64 190	47.87 195	24.96 210	70.45 185	68.66 186	73.60 199	62.78 205

MIMVSNet		58.52 191	61.34 191	55.22 193	60.76 202	67.01 189	66.81 173	49.02 197	56.43 154	38.90 186	40.59 199	54.54 155	40.57 196	73.16 164	71.65 172	75.30 194	66.00 198

PM-MVS		60.48 185	60.94 193	59.94 175	58.85 206	66.83 190	64.27 187	51.39 187	55.03 166	48.03 154	50.00 177	40.79 210	58.26 139	69.20 193	67.13 195	78.84 177	77.60 158

Anonymous20231206		56.36 195	57.80 199	54.67 195	70.08 176	66.39 191	60.46 198	57.54 162	49.50 194	29.30 200	33.86 207	46.64 198	35.18 201	70.44 186	68.88 184	75.47 192	68.88 194

test20.03		53.93 201	56.28 202	51.19 201	72.19 159	65.83 192	53.20 208	61.08 128	42.74 205	22.08 211	37.07 203	45.76 202	24.29 213	70.44 186	69.04 182	74.31 197	63.05 204

Patchmtry						65.80 193	65.97 179	52.74 179		52.65 129

TAMVS		59.58 188	62.81 182	55.81 191	66.03 191	65.64 194	63.86 188	48.74 198	49.95 191	37.07 192	54.77 139	58.54 134	44.44 187	72.29 168	71.79 171	74.70 195	66.66 197

test-mter		60.84 184	64.62 171	56.42 189	55.99 211	64.18 195	65.39 181	34.23 214	54.39 172	46.21 166	57.40 125	59.49 130	55.86 159	71.02 181	69.65 179	80.87 171	76.20 167

tpmrst		62.00 176	62.35 187	61.58 167	71.62 165	64.14 196	69.07 162	48.22 203	62.21 117	53.93 120	58.26 120	55.30 150	55.81 160	63.22 204	62.62 203	70.85 205	70.70 188

test-LLR		64.42 159	64.36 172	64.49 154	75.02 131	63.93 197	66.61 176	61.96 121	54.41 170	47.77 155	57.46 123	60.25 124	55.20 165	70.80 182	69.33 180	80.40 172	74.38 179

TESTMET0.1,1		61.10 183	64.36 172	57.29 186	57.53 208	63.93 197	66.61 176	36.22 213	54.41 170	47.77 155	57.46 123	60.25 124	55.20 165	70.80 182	69.33 180	80.40 172	74.38 179

tpm		62.41 172	63.15 178	61.55 168	72.24 158	63.79 199	71.31 154	46.12 207	57.82 142	55.33 112	59.90 106	54.74 153	53.63 170	67.24 198	64.29 200	70.65 206	74.25 181

PMMVS		65.06 156	69.17 130	60.26 174	55.25 213	63.43 200	66.71 175	43.01 209	62.41 115	50.64 140	69.44 65	67.04 103	63.29 105	74.36 159	73.54 165	82.68 163	73.99 182

ambc			53.42 203		64.99 194	63.36 201	49.96 211		47.07 198	37.12 191	28.97 211	16.36 223	41.82 191	75.10 154	67.34 191	71.55 204	75.72 170

FC-MVSNet-test		56.90 194	65.20 165	47.21 205	66.98 187	63.20 202	49.11 213	58.60 159	59.38 137	11.50 220	65.60 81	56.68 144	24.66 212	71.17 178	71.36 175	72.38 202	69.02 193

EPMVS		60.00 187	61.97 188	57.71 185	68.46 185	63.17 203	64.54 185	48.23 202	63.30 108	44.72 173	60.19 102	56.05 147	50.85 177	65.27 202	62.02 204	69.44 208	63.81 202

FMVSNet5		57.24 192	60.02 195	53.99 197	56.45 210	62.74 204	65.27 182	47.03 204	55.14 163	39.55 185	40.88 197	53.42 168	41.83 190	72.35 167	71.10 176	73.79 198	64.50 201

EU-MVSNet		54.63 198	58.69 196	49.90 203	56.99 209	62.70 205	56.41 205	50.64 192	45.95 202	23.14 209	50.42 174	46.51 199	36.63 200	65.51 200	64.85 199	75.57 190	74.91 176

gm-plane-assit		57.00 193	57.62 200	56.28 190	76.10 120	62.43 206	47.62 214	46.57 205	33.84 215	23.24 208	37.52 201	40.19 211	59.61 133	79.81 109	77.55 131	84.55 154	72.03 185

MIMVSNet1		49.27 204	53.25 204	44.62 207	44.61 215	61.52 207	53.61 207	52.18 182	41.62 208	18.68 216	28.14 213	41.58 209	25.50 208	68.46 196	69.04 182	73.15 200	62.37 206

pmnet_mix02		55.30 197	57.01 201	53.30 200	64.14 196	59.09 208	58.39 203	50.24 194	53.47 176	38.68 187	49.75 178	45.86 201	40.14 197	65.38 201	60.22 206	68.19 210	65.33 199

ADS-MVSNet		55.94 196	58.01 197	53.54 199	62.48 200	58.48 209	59.12 202	46.20 206	59.65 136	42.88 179	52.34 166	53.31 170	46.31 184	62.00 206	60.02 207	64.23 213	60.24 209

FPMVS		51.87 203	50.00 208	54.07 196	66.83 189	57.25 210	60.25 199	50.91 188	50.25 190	34.36 194	36.04 205	32.02 217	41.49 192	58.98 210	56.07 209	70.56 207	59.36 210

new-patchmatchnet		46.97 207	49.47 209	44.05 209	62.82 198	56.55 211	45.35 215	52.01 183	42.47 206	17.04 218	35.73 206	35.21 214	21.84 216	61.27 207	54.83 211	65.26 212	60.26 207

MVS-HIRNet		54.41 199	52.10 206	57.11 188	58.99 205	56.10 212	49.68 212	49.10 196	46.18 201	52.15 133	33.18 208	46.11 200	56.10 156	63.19 205	59.70 208	76.64 187	60.25 208

pmmvs3		47.65 205	49.08 210	45.99 206	44.61 215	54.79 213	50.04 210	31.95 217	33.91 214	29.90 198	30.37 209	33.53 216	46.31 184	63.50 203	63.67 202	73.14 201	63.77 203

N_pmnet		47.35 206	50.13 207	44.11 208	59.98 204	51.64 214	51.86 209	44.80 208	49.58 193	20.76 214	40.65 198	40.05 212	29.64 206	59.84 208	55.15 210	57.63 214	54.00 212

CHOSEN 280x420		58.70 190	61.88 189	54.98 194	55.45 212	50.55 215	64.92 183	40.36 210	55.21 162	38.13 189	48.31 182	63.76 112	63.03 108	73.73 163	68.58 187	68.00 211	73.04 184

new_pmnet		38.40 210	42.64 212	33.44 211	37.54 220	45.00 216	36.60 217	32.72 216	40.27 209	12.72 219	29.89 210	28.90 219	24.78 211	53.17 213	52.90 213	56.31 215	48.34 213

PMVS	39.38 18	46.06 209	43.30 211	49.28 204	62.93 197	38.75 217	41.88 216	53.50 173	33.33 217	35.46 193	28.90 212	31.01 218	33.04 204	58.61 211	54.63 212	68.86 209	57.88 211
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		36.38 211	35.80 213	37.07 210	45.76 214	33.90 218	29.81 218	48.47 200	39.91 210	18.02 217	8.00 221	8.14 225	25.14 209	59.29 209	61.02 205	55.19 216	40.31 214
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		25.60 212	29.75 214	20.76 215	28.00 221	30.93 219	23.10 220	29.18 218	23.14 219	1.46 225	18.23 217	16.54 222	5.08 219	40.22 214	41.40 215	37.76 217	37.79 216

MVE	19.12 19	20.47 216	23.27 216	17.20 217	12.66 223	25.41 220	10.52 224	34.14 215	14.79 222	6.53 224	8.79 220	4.68 226	16.64 218	29.49 217	41.63 214	22.73 222	38.11 215
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		21.77 214	18.24 217	25.89 212	40.22 218	19.58 221	12.46 223	39.87 211	18.68 221	6.71 222	9.57 218	4.31 228	22.36 215	19.89 219	27.28 217	33.73 219	28.34 218

EMVS		20.98 215	17.15 218	25.44 213	39.51 219	19.37 222	12.66 222	39.59 212	19.10 220	6.62 223	9.27 219	4.40 227	22.43 214	17.99 220	24.40 218	31.81 220	25.53 219

DeepMVS_CX						18.74 223	18.55 221	8.02 219	26.96 218	7.33 221	23.81 215	13.05 224	25.99 207	25.17 218		22.45 223	36.25 217

test_method		22.26 213	25.94 215	17.95 216	3.24 224	7.17 224	23.83 219	7.27 220	37.35 213	20.44 215	21.87 216	39.16 213	18.67 217	34.56 215	20.84 219	34.28 218	20.64 220

tmp_tt				14.50 218	14.68 222	7.17 224	10.46 225	2.21 221	37.73 212	28.71 201	25.26 214	16.98 221	4.37 220	31.49 216	29.77 216	26.56 221

testmvs		0.09 217	0.15 219	0.02 219	0.01 226	0.02 226	0.05 227	0.01 223	0.11 223	0.01 227	0.26 223	0.01 229	0.06 223	0.10 221	0.10 220	0.01 224	0.43 222

test123		0.09 217	0.14 220	0.02 219	0.00 227	0.02 226	0.02 228	0.01 223	0.09 224	0.00 228	0.30 222	0.00 230	0.08 221	0.03 222	0.09 221	0.01 224	0.45 221

uanet_test		0.00 219	0.00 221	0.00 221	0.00 227	0.00 228	0.00 229	0.00 225	0.00 225	0.00 228	0.00 224	0.00 230	0.00 224	0.00 223	0.00 222	0.00 226	0.00 223

sosnet-low-res		0.00 219	0.00 221	0.00 221	0.00 227	0.00 228	0.00 229	0.00 225	0.00 225	0.00 228	0.00 224	0.00 230	0.00 224	0.00 223	0.00 222	0.00 226	0.00 223

sosnet		0.00 219	0.00 221	0.00 221	0.00 227	0.00 228	0.00 229	0.00 225	0.00 225	0.00 228	0.00 224	0.00 230	0.00 224	0.00 223	0.00 222	0.00 226	0.00 223

RE-MVS-def										46.24 165

9.14												86.88 16

SR-MVS					88.99 35			73.57 26				87.54 14

MTAPA										83.48 1		86.45 19

MTMP										82.66 6		84.91 27

Patchmatch-RL test							2.85 226

mPP-MVS					89.90 26							81.29 43

NP-MVS									80.10 47

High-resolution multi-view benchmark