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.

TDRefinement		93.16 1	95.57 1	90.36 1	88.79 52	93.57 1	97.27 1	78.23 21	95.55 1	93.00 1	93.98 17	96.01 38	87.53 1	97.69 1	96.81 1	97.33 1	95.34 4

COLMAP_ROB	85.66 2	91.85 2	95.01 2	88.16 11	88.98 51	92.86 2	95.51 19	72.17 62	94.95 4	91.27 3	94.11 16	97.77 11	84.22 8	96.49 4	95.27 5	96.79 2	93.60 12
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LTVRE_ROB	86.82 1	91.55 3	94.43 3	88.19 10	83.19 112	86.35 65	93.60 37	78.79 18	95.48 3	91.79 2	93.08 26	97.21 20	86.34 3	97.06 2	96.27 3	95.46 23	95.56 3
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
ACMMPR		91.30 4	92.88 11	89.46 4	91.92 11	91.61 5	96.60 5	79.46 14	90.08 30	88.53 13	89.54 65	95.57 47	84.25 7	95.24 20	94.27 12	95.97 11	93.85 8

CP-MVS		91.09 5	92.33 25	89.65 2	92.16 10	90.41 27	96.46 10	80.38 8	88.26 44	89.17 10	87.00 97	96.34 30	83.95 10	95.77 11	94.72 7	95.81 17	93.78 10

MP-MVS		90.84 6	91.95 34	89.55 3	92.92 4	90.90 19	96.56 6	79.60 11	86.83 58	88.75 12	89.00 73	94.38 77	84.01 9	94.94 24	94.34 10	95.45 24	93.24 23
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMM	80.67 7	90.67 7	92.46 19	88.57 7	91.35 22	89.93 32	96.34 11	77.36 30	90.17 28	86.88 29	87.32 92	96.63 23	83.32 13	95.79 10	94.49 9	96.19 9	92.91 26
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMMP		90.63 8	92.40 20	88.56 8	91.24 28	91.60 6	96.49 9	77.53 26	87.89 47	86.87 30	87.24 94	96.46 25	82.87 16	95.59 15	94.50 8	96.35 6	93.51 18
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
LGP-MVS_train		90.56 9	92.38 21	88.43 9	90.88 32	91.15 11	95.35 21	77.65 25	86.26 63	87.23 23	90.45 55	97.35 17	83.20 14	95.44 16	93.41 20	96.28 8	92.63 27

DVP-MVS++		90.50 10	94.18 4	86.21 27	92.52 7	90.29 28	95.29 22	76.02 41	94.24 5	82.82 55	95.84 5	97.56 15	76.82 55	93.13 38	91.20 44	93.78 45	97.01 1

PGM-MVS		90.42 11	91.58 37	89.05 5	91.77 14	91.06 13	96.51 7	78.94 16	85.41 70	87.67 18	87.02 96	95.26 56	83.62 12	95.01 23	93.94 15	95.79 19	93.40 20

DeepC-MVS	83.59 4	90.37 12	92.56 18	87.82 14	91.26 27	92.33 3	94.72 30	80.04 9	90.01 31	84.61 42	93.33 22	94.22 78	80.59 27	92.90 43	92.52 28	95.69 21	92.57 28
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		90.32 13	92.37 22	87.94 13	91.46 21	90.91 18	95.69 17	79.49 12	89.94 33	83.50 50	89.06 72	94.44 75	81.68 22	94.17 30	94.19 13	95.81 17	93.87 7

PMVS	79.51 9	90.23 14	92.67 14	87.39 20	90.16 39	88.75 42	93.64 36	75.78 44	90.00 32	83.70 47	92.97 28	92.22 103	86.13 4	97.01 3	96.79 2	94.94 28	90.96 45
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
SMA-MVS		90.13 15	92.26 27	87.64 17	91.68 16	90.44 26	95.22 24	77.34 32	90.79 22	87.80 16	90.42 56	92.05 108	79.05 35	93.89 32	93.59 18	94.77 32	94.62 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
ACMP	80.00 8	90.12 16	92.30 26	87.58 18	90.83 34	91.10 12	94.96 28	76.06 40	87.47 51	85.33 39	88.91 76	97.65 14	82.13 19	95.31 17	93.44 19	96.14 10	92.22 33
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SteuartSystems-ACMMP		90.00 17	91.73 35	87.97 12	91.21 29	90.29 28	96.51 7	78.00 23	86.33 61	85.32 40	88.23 82	94.67 69	82.08 20	95.13 22	93.88 16	94.72 35	93.59 13
Skip Steuart: Steuart Systems R&D Blog.
SD-MVS		89.91 18	92.23 30	87.19 21	91.31 24	89.79 35	94.31 32	75.34 47	89.26 36	81.79 67	92.68 31	95.08 62	83.88 11	93.10 39	92.69 25	96.54 4	93.02 24
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
ACMMP_NAP		89.86 19	91.96 33	87.42 19	91.00 30	90.08 30	96.00 15	76.61 36	89.28 34	87.73 17	90.04 58	91.80 112	78.71 38	94.36 28	93.82 17	94.48 37	94.32 6

APDe-MVS		89.85 20	92.91 10	86.29 26	90.47 38	91.34 7	96.04 14	76.41 39	91.11 17	78.50 89	93.44 21	95.82 42	81.55 23	93.16 37	91.90 38	94.77 32	93.58 15

OPM-MVS		89.82 21	92.24 29	86.99 22	90.86 33	89.35 38	95.07 27	75.91 43	91.16 16	86.87 30	91.07 51	97.29 18	79.13 34	93.32 35	91.99 37	94.12 40	91.49 40
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DPE-MVS		89.81 22	92.34 24	86.86 23	89.69 44	91.00 16	95.53 18	76.91 33	88.18 45	83.43 53	93.48 20	95.19 57	81.07 26	92.75 45	92.07 36	94.55 36	93.74 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
WR-MVS		89.79 23	93.66 5	85.27 37	91.32 23	88.27 46	93.49 38	79.86 10	92.75 9	75.37 102	96.86 1	98.38 5	75.10 71	95.93 8	94.07 14	96.46 5	89.39 56

TSAR-MVS + MP.		89.67 24	92.25 28	86.65 25	91.53 18	90.98 17	96.15 13	73.30 56	87.88 48	81.83 66	92.92 29	95.15 60	82.23 18	93.58 34	92.25 33	94.87 29	93.01 25
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CPTT-MVS		89.63 25	90.52 47	88.59 6	90.95 31	90.74 21	95.71 16	79.13 15	87.70 49	85.68 38	80.05 141	95.74 45	84.77 6	94.28 29	92.68 26	95.28 26	92.45 31

ACMH+	79.05 11	89.62 26	93.08 8	85.58 32	88.58 55	89.26 39	92.18 45	74.23 52	93.55 8	82.66 58	92.32 36	98.35 7	80.29 29	95.28 18	92.34 31	95.52 22	90.43 48

DVP-MVS		89.40 27	92.69 13	85.56 34	89.01 50	89.85 33	93.72 35	75.42 45	92.28 11	80.49 72	94.36 13	94.87 65	81.46 24	92.49 49	91.42 41	93.27 53	93.54 17
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
X-MVS		89.36 28	90.73 45	87.77 16	91.50 20	91.23 8	96.76 4	78.88 17	87.29 53	87.14 25	78.98 146	94.53 71	76.47 57	95.25 19	94.28 11	95.85 14	93.55 16

TSAR-MVS + ACMM		89.14 29	92.11 32	85.67 31	89.27 47	90.61 24	90.98 52	79.48 13	88.86 39	79.80 79	93.01 27	93.53 87	83.17 15	92.75 45	92.45 29	91.32 82	93.59 13

SixPastTwentyTwo		89.14 29	92.19 31	85.58 32	84.62 89	82.56 91	90.53 63	71.93 64	91.95 12	85.89 35	94.22 14	97.25 19	85.42 5	95.73 12	91.71 40	95.08 27	91.89 36

APD-MVS		89.14 29	91.25 42	86.67 24	91.73 15	91.02 15	95.50 20	77.74 24	84.04 82	79.47 82	91.48 45	94.85 66	81.14 25	92.94 41	92.20 35	94.47 38	92.24 32
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PS-CasMVS		89.07 32	93.23 7	84.21 50	92.44 8	88.23 48	90.54 62	82.95 3	90.50 25	75.31 103	95.80 6	98.37 6	71.16 100	96.30 5	93.32 21	92.88 61	90.11 50

UA-Net		89.02 33	91.44 39	86.20 28	94.88 1	89.84 34	94.76 29	77.45 28	85.41 70	74.79 106	88.83 77	88.90 136	78.67 40	96.06 7	95.45 4	96.66 3	95.58 2

LS3D		89.02 33	91.69 36	85.91 30	89.72 43	90.81 20	92.56 44	71.69 66	90.83 21	87.24 22	89.71 63	92.07 106	78.37 42	94.43 27	92.59 27	95.86 13	91.35 41

DTE-MVSNet		88.99 35	92.77 12	84.59 43	93.31 2	88.10 49	90.96 53	83.09 2	91.38 14	76.21 96	96.03 2	98.04 8	70.78 106	95.65 14	92.32 32	93.18 56	87.84 71

WR-MVS_H		88.99 35	93.28 6	83.99 53	91.92 11	89.13 40	91.95 46	83.23 1	90.14 29	71.92 125	95.85 4	98.01 10	71.83 97	95.82 9	93.19 22	93.07 59	90.83 47

SED-MVS		88.96 37	92.37 22	84.99 40	88.64 54	89.65 37	95.11 25	75.98 42	90.73 23	80.15 77	94.21 15	94.51 74	76.59 56	92.94 41	91.17 45	93.46 50	93.37 22

ACMH	78.40 12	88.94 38	92.62 16	84.65 42	86.45 74	87.16 59	91.47 49	68.79 87	95.49 2	89.74 6	93.55 19	98.50 2	77.96 46	94.14 31	89.57 61	93.49 47	89.94 52
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PEN-MVS		88.86 39	92.92 9	84.11 52	92.92 4	88.05 51	90.83 55	82.67 5	91.04 18	74.83 105	95.97 3	98.47 3	70.38 107	95.70 13	92.43 30	93.05 60	88.78 62

HPM-MVS++		88.74 40	89.54 52	87.80 15	92.58 6	85.69 69	95.10 26	78.01 22	87.08 55	87.66 19	87.89 85	92.07 106	80.28 30	90.97 69	91.41 43	93.17 57	91.69 37

CP-MVSNet		88.71 41	92.63 15	84.13 51	92.39 9	88.09 50	90.47 66	82.86 4	88.79 41	75.16 104	94.87 9	97.68 13	71.05 102	96.16 6	93.18 23	92.85 62	89.64 54

MSP-MVS		88.51 42	91.36 40	85.19 39	90.63 36	92.01 4	95.29 22	77.52 27	90.48 26	80.21 76	90.21 57	96.08 34	76.38 59	88.30 96	91.42 41	91.12 87	91.01 44
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
OMC-MVS		88.16 43	91.34 41	84.46 46	86.85 70	90.63 23	93.01 41	67.00 103	90.35 27	87.40 21	86.86 99	96.35 29	77.66 49	92.63 47	90.84 46	94.84 30	91.68 38

3Dnovator+	83.71 3	88.13 44	90.00 50	85.94 29	86.82 71	91.06 13	94.26 33	75.39 46	88.85 40	85.76 37	85.74 109	86.92 145	78.02 45	93.03 40	92.21 34	95.39 25	92.21 34

CSCG		88.12 45	91.45 38	84.23 48	88.12 61	90.59 25	90.57 60	68.60 89	91.37 15	83.45 52	89.94 59	95.14 61	78.71 38	91.45 58	88.21 72	95.96 12	93.44 19

RPSCF		88.05 46	92.61 17	82.73 65	84.24 96	88.40 44	90.04 72	66.29 107	91.46 13	82.29 60	88.93 75	96.01 38	79.38 32	95.15 21	94.90 6	94.15 39	93.40 20

DeepPCF-MVS	81.61 6	87.95 47	90.29 49	85.22 38	87.48 65	90.01 31	93.79 34	73.54 54	88.93 38	83.89 45	89.40 67	90.84 121	80.26 31	90.62 72	90.19 53	92.36 70	92.03 35

SF-MVS		87.85 48	90.95 44	84.22 49	88.17 60	87.90 53	90.80 56	71.80 65	89.28 34	82.70 57	89.90 60	95.37 54	77.91 47	91.69 54	90.04 54	93.95 44	92.47 29

DeepC-MVS_fast	81.78 5	87.38 49	89.64 51	84.75 41	89.89 42	90.70 22	92.74 43	74.45 50	86.02 64	82.16 64	86.05 106	91.99 110	75.84 65	91.16 63	90.44 49	93.41 51	91.09 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v7n		87.11 50	90.46 48	83.19 56	85.22 85	83.69 80	90.03 73	68.20 95	91.01 19	86.71 33	94.80 10	98.46 4	77.69 48	91.10 65	85.98 92	91.30 83	88.19 66

CNVR-MVS		86.93 51	88.98 56	84.54 44	90.11 40	87.41 57	93.23 40	73.47 55	86.31 62	82.25 61	82.96 129	92.15 104	76.04 62	91.69 54	90.69 47	92.17 73	91.64 39

NCCC		86.74 52	87.97 68	85.31 36	90.64 35	87.25 58	93.27 39	74.59 49	86.50 59	83.72 46	75.92 172	92.39 100	77.08 53	91.72 53	90.68 48	92.57 67	91.30 42

train_agg		86.67 53	87.73 69	85.43 35	91.51 19	82.72 88	94.47 31	74.22 53	81.71 100	81.54 70	89.20 71	92.87 94	78.33 43	90.12 79	88.47 68	92.51 69	89.04 59

CDPH-MVS		86.66 54	88.52 59	84.48 45	89.61 45	88.27 46	92.86 42	72.69 61	80.55 118	82.71 56	86.92 98	93.32 89	75.55 67	91.00 68	89.85 56	93.47 49	89.71 53

Gipuma		86.47 55	89.25 54	83.23 55	83.88 103	78.78 124	85.35 116	68.42 91	92.69 10	89.03 11	91.94 38	96.32 32	81.80 21	94.45 26	86.86 82	90.91 88	83.69 100
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PHI-MVS		86.37 56	88.14 65	84.30 47	86.65 73	87.56 55	90.76 57	70.16 73	82.55 92	89.65 7	84.89 117	92.40 99	75.97 63	90.88 70	89.70 58	92.58 65	89.03 60

MSLP-MVS++		86.29 57	89.10 55	83.01 58	85.71 82	89.79 35	87.04 104	74.39 51	85.17 72	78.92 86	77.59 155	93.57 85	82.60 17	93.23 36	91.88 39	89.42 107	92.46 30

TAPA-MVS	78.00 13	85.88 58	88.37 61	82.96 60	84.69 87	88.62 43	90.62 58	64.22 129	89.15 37	88.05 14	78.83 148	93.71 82	76.20 61	90.11 80	88.22 71	94.00 41	89.97 51
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
anonymousdsp		85.62 59	90.53 46	79.88 92	64.64 208	76.35 143	96.28 12	53.53 192	85.63 67	81.59 69	92.81 30	97.71 12	86.88 2	94.56 25	92.83 24	96.35 6	93.84 9

TSAR-MVS + COLMAP		85.51 60	88.36 62	82.19 67	86.05 79	87.69 54	90.50 65	70.60 72	86.40 60	82.33 59	89.69 64	92.52 98	74.01 81	87.53 100	86.84 83	89.63 102	87.80 72

CNLPA		85.50 61	88.58 57	81.91 71	84.55 91	87.52 56	90.89 54	63.56 139	88.18 45	84.06 44	83.85 126	91.34 118	76.46 58	91.27 60	89.00 66	91.96 74	88.88 61

UniMVSNet_ETH3D		85.39 62	91.12 43	78.71 99	90.48 37	83.72 79	81.76 140	82.41 6	93.84 6	64.43 159	95.41 7	98.76 1	63.72 141	93.63 33	89.74 57	89.47 106	82.74 112

PLC	76.06 15	85.38 63	87.46 71	82.95 61	85.79 81	88.84 41	88.86 83	68.70 88	87.06 56	83.60 48	79.02 144	90.05 127	77.37 52	90.88 70	89.66 59	93.37 52	86.74 77
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TSAR-MVS + GP.		85.32 64	87.41 73	82.89 62	90.07 41	85.69 69	89.07 81	72.99 60	82.45 93	74.52 109	85.09 114	87.67 142	79.24 33	91.11 64	90.41 50	91.45 79	89.45 55

TranMVSNet+NR-MVSNet		85.23 65	89.38 53	80.39 90	88.78 53	83.77 78	87.40 96	76.75 34	85.47 68	68.99 141	95.18 8	97.55 16	67.13 124	91.61 56	89.13 65	93.26 54	82.95 109

HQP-MVS		85.02 66	86.41 79	83.40 54	89.19 48	86.59 63	91.28 50	71.60 67	82.79 89	83.48 51	78.65 150	93.54 86	72.55 89	86.49 111	85.89 95	92.28 72	90.95 46

UniMVSNet (Re)		84.95 67	88.53 58	80.78 81	87.82 63	84.21 75	88.03 88	76.50 37	81.18 111	69.29 139	92.63 34	96.83 22	69.07 114	91.23 62	89.60 60	93.97 43	84.00 98

DU-MVS		84.88 68	88.27 64	80.92 79	88.30 57	83.59 81	87.06 102	78.35 19	80.64 116	70.49 133	92.67 32	96.91 21	68.13 117	91.79 51	89.29 64	93.20 55	83.02 106

MCST-MVS		84.79 69	86.48 77	82.83 63	87.30 67	87.03 61	90.46 67	69.33 81	83.14 86	82.21 63	81.69 137	92.14 105	75.09 72	87.27 103	84.78 106	92.58 65	89.30 57

MVS_0304		84.73 70	86.19 81	83.02 57	88.32 56	86.71 62	91.55 48	70.87 70	73.79 147	82.88 54	85.13 113	93.35 88	72.55 89	88.62 91	87.69 74	91.93 75	88.05 70

UniMVSNet_NR-MVSNet		84.62 71	88.00 67	80.68 85	88.18 59	83.83 77	87.06 102	76.47 38	81.46 107	70.49 133	93.24 23	95.56 48	68.13 117	90.43 73	88.47 68	93.78 45	83.02 106

EG-PatchMatch MVS		84.35 72	87.55 70	80.62 86	86.38 75	82.24 93	86.75 105	64.02 134	84.24 78	78.17 92	89.38 68	95.03 64	78.78 37	89.95 81	86.33 89	89.59 103	85.65 85

AdaColmap		84.15 73	85.14 95	83.00 59	89.08 49	87.14 60	90.56 61	70.90 69	82.40 94	80.41 73	73.82 183	84.69 156	75.19 70	91.58 57	89.90 55	91.87 76	86.48 78

PCF-MVS	76.59 14	84.11 74	85.27 92	82.76 64	86.12 78	88.30 45	91.24 51	69.10 82	82.36 95	84.45 43	77.56 156	90.40 126	72.91 88	85.88 116	83.88 113	92.72 64	88.53 63
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_111021_HR		83.95 75	86.10 83	81.44 76	84.62 89	80.29 111	90.51 64	68.05 96	84.07 81	80.38 74	84.74 120	91.37 117	74.23 77	90.37 75	87.25 78	90.86 89	84.59 90

TinyColmap		83.79 76	86.12 82	81.07 78	83.42 109	81.44 99	85.42 114	68.55 90	88.71 42	89.46 8	87.60 87	92.72 95	70.34 108	89.29 86	81.94 131	89.20 108	81.12 125

EC-MVSNet		83.70 77	84.77 104	82.46 66	87.47 66	82.79 87	85.50 112	72.00 63	69.81 165	77.66 93	85.02 116	89.63 128	78.14 44	90.40 74	87.56 75	94.00 41	88.16 67

v1192		83.61 78	85.23 93	81.72 73	84.05 98	82.15 94	89.54 76	66.20 108	81.38 109	86.76 32	91.79 42	96.03 36	74.88 74	81.81 151	80.92 139	88.91 114	82.50 114

CS-MVS-test		83.59 79	84.86 101	82.10 69	83.04 114	81.05 105	91.58 47	67.48 102	72.52 154	78.42 90	84.75 119	91.82 111	78.62 41	91.98 50	87.54 76	93.48 48	84.35 93

CS-MVS		83.57 80	84.79 103	82.14 68	83.83 104	81.48 98	87.29 97	66.54 105	72.73 153	80.05 78	84.04 124	93.12 93	80.35 28	89.50 83	86.34 88	94.76 34	86.32 81

v1240		83.57 80	84.94 99	81.97 70	84.05 98	81.27 101	89.46 78	66.06 110	81.31 110	87.50 20	91.88 41	95.46 51	76.25 60	81.16 156	80.51 143	88.52 124	82.98 108

v1921920		83.49 82	84.94 99	81.80 72	83.78 105	81.20 103	89.50 77	65.91 113	81.64 102	87.18 24	91.70 43	95.39 53	75.85 64	81.56 154	80.27 145	88.60 119	82.80 110

v144192		83.43 83	84.97 98	81.63 75	83.43 108	81.23 102	89.42 79	66.04 112	81.45 108	86.40 34	91.46 46	95.70 46	75.76 66	82.14 147	80.23 146	88.74 116	82.57 113

Vis-MVSNet		83.32 84	88.12 66	77.71 106	77.91 158	83.44 83	90.58 59	69.49 78	81.11 112	67.10 153	89.85 61	91.48 116	71.71 98	91.34 59	89.37 62	89.48 105	90.26 49
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1144		83.22 85	85.01 96	81.14 77	83.76 106	81.60 97	88.95 82	65.58 118	81.89 99	85.80 36	91.68 44	95.84 41	74.04 80	82.12 148	80.56 142	88.70 118	81.41 123

MVS_111021_LR		83.20 86	85.33 91	80.73 84	82.88 117	78.23 128	89.61 75	65.23 120	82.08 97	81.19 71	85.31 111	92.04 109	75.22 69	89.50 83	85.90 94	90.24 92	84.23 94

v10		83.17 87	85.22 94	80.78 81	83.26 111	82.99 86	88.66 85	66.49 106	79.24 127	83.60 48	91.46 46	95.47 50	74.12 78	82.60 146	80.66 140	88.53 123	84.11 97

PVSNet_Blended_VisFu		83.00 88	84.16 115	81.65 74	82.17 124	86.01 66	88.03 88	71.23 68	76.05 140	79.54 81	83.88 125	83.44 158	77.49 51	87.38 101	84.93 104	91.41 80	87.40 75

NR-MVSNet		82.89 89	87.43 72	77.59 108	83.91 102	83.59 81	87.10 101	78.35 19	80.64 116	68.85 142	92.67 32	96.50 24	54.19 180	87.19 106	88.68 67	93.16 58	82.75 111

CANet		82.84 90	84.60 106	80.78 81	87.30 67	85.20 72	90.23 69	69.00 83	72.16 157	78.73 88	84.49 122	90.70 124	69.54 112	87.65 99	86.17 90	89.87 99	85.84 83

Baseline_NR-MVSNet		82.79 91	86.51 76	78.44 103	88.30 57	75.62 151	87.81 90	74.97 48	81.53 104	66.84 154	94.71 12	96.46 25	66.90 125	91.79 51	83.37 122	85.83 153	82.09 117

EPP-MVSNet		82.76 92	86.47 78	78.45 102	86.00 80	84.47 74	85.39 115	68.42 91	84.17 79	62.97 163	89.26 70	76.84 183	72.13 94	92.56 48	90.40 51	95.76 20	87.56 74

CLD-MVS		82.75 93	87.22 74	77.54 109	88.01 62	85.76 68	90.23 69	54.52 186	82.28 96	82.11 65	88.48 80	95.27 55	63.95 139	89.41 85	88.29 70	86.45 144	81.01 126
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Effi-MVS+		82.33 94	83.87 118	80.52 88	84.51 94	81.32 100	87.53 94	68.05 96	74.94 145	79.67 80	82.37 134	92.31 101	72.21 91	85.06 123	86.91 81	91.18 85	84.20 95

3Dnovator	79.41 10	82.21 95	86.07 84	77.71 106	79.31 143	84.61 73	87.18 99	61.02 160	85.65 66	76.11 97	85.07 115	85.38 154	70.96 104	87.22 104	86.47 85	91.66 77	88.12 69

v8		82.20 96	84.56 107	79.45 95	82.42 121	81.65 96	87.26 98	64.27 128	79.36 126	81.70 68	91.04 52	95.75 44	73.30 87	82.82 142	79.18 153	87.74 131	82.09 117

v2v482		82.20 96	84.26 111	79.81 93	82.67 120	80.18 112	87.67 92	63.96 136	81.69 101	84.73 41	91.27 49	96.33 31	72.05 95	81.94 150	79.56 150	87.79 130	78.84 143

Effi-MVS+-dtu		82.04 98	83.39 125	80.48 89	85.48 83	86.57 64	88.40 86	68.28 93	69.04 172	73.13 118	76.26 167	91.11 120	74.74 75	88.40 94	87.76 73	92.84 63	84.57 91

MAR-MVS		81.98 99	82.92 127	80.88 80	85.18 86	85.85 67	89.13 80	69.52 76	71.21 161	82.25 61	71.28 193	88.89 137	69.69 109	88.71 89	86.96 79	89.52 104	87.57 73
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
GeoE		81.92 100	83.87 118	79.66 94	84.64 88	79.87 113	89.75 74	65.90 114	76.12 139	75.87 99	84.62 121	92.23 102	71.96 96	86.83 108	83.60 116	89.83 100	83.81 99

IS_MVSNet		81.72 101	85.01 96	77.90 105	86.19 76	82.64 90	85.56 111	70.02 74	80.11 121	63.52 161	87.28 93	81.18 168	67.26 122	91.08 67	89.33 63	94.82 31	83.42 103

FPMVS		81.56 102	84.04 117	78.66 100	82.92 115	75.96 147	86.48 108	65.66 117	84.67 76	71.47 128	77.78 153	83.22 161	77.57 50	91.24 61	90.21 52	87.84 129	85.21 87

casdiffmvs_mvg		81.50 103	85.70 88	76.60 115	82.68 119	80.54 108	83.50 127	64.49 127	83.40 83	72.53 119	92.15 37	95.40 52	65.84 132	84.69 130	81.89 132	90.59 90	81.86 121
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DPM-MVS		81.42 104	82.11 131	80.62 86	87.54 64	85.30 71	90.18 71	68.96 84	81.00 114	79.15 84	70.45 199	83.29 160	67.67 121	82.81 143	83.46 117	90.19 93	88.48 64

Fast-Effi-MVS+		81.42 104	83.82 120	78.62 101	82.24 123	80.62 107	87.72 91	63.51 140	73.01 149	74.75 107	83.80 127	92.70 96	73.44 86	88.15 98	85.26 100	90.05 94	83.17 104

USDC		81.39 106	83.07 126	79.43 96	81.48 128	78.95 123	82.62 135	66.17 109	87.45 52	90.73 4	82.40 133	93.65 84	66.57 127	83.63 138	77.97 156	89.00 112	77.45 151

MSDG		81.39 106	84.23 113	78.09 104	82.40 122	82.47 92	85.31 118	60.91 161	79.73 124	80.26 75	86.30 102	88.27 140	69.67 110	87.20 105	84.98 103	89.97 96	80.67 128

canonicalmvs		81.22 108	86.04 85	75.60 119	83.17 113	83.18 85	80.29 150	65.82 116	85.97 65	67.98 149	77.74 154	91.51 115	65.17 135	88.62 91	86.15 91	91.17 86	89.09 58

thisisatest0515		81.18 109	84.32 110	77.52 110	76.73 169	74.84 158	85.06 119	61.37 157	81.05 113	73.95 111	88.79 78	89.25 133	75.49 68	85.98 115	84.78 106	92.53 68	85.56 86

pmmvs6		80.46 110	88.34 63	71.26 146	81.96 125	77.51 132	77.54 167	68.83 86	93.72 7	55.92 180	93.94 18	98.03 9	55.94 170	89.21 87	85.61 96	87.36 135	80.38 130

QAPM		80.43 111	84.34 109	75.86 117	79.40 142	82.06 95	79.86 155	61.94 154	83.28 85	74.73 108	81.74 136	85.44 153	70.97 103	84.99 128	84.71 108	88.29 125	88.14 68

PM-MVS		80.42 112	83.63 122	76.67 113	78.04 155	72.37 168	87.14 100	60.18 166	80.13 120	71.75 126	86.12 105	93.92 81	77.08 53	86.56 110	85.12 102	85.83 153	81.18 124

DCV-MVSNet		80.04 113	85.67 90	73.48 137	82.91 116	81.11 104	80.44 149	66.06 110	85.01 73	62.53 166	78.84 147	94.43 76	58.51 161	88.66 90	85.91 93	90.41 91	85.73 84

casdiffmvs		79.93 114	84.11 116	75.05 124	81.41 130	78.99 122	82.95 132	62.90 147	81.53 104	68.60 146	91.94 38	96.03 36	65.84 132	82.89 141	77.07 164	88.59 120	80.34 134
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
IterMVS-LS		79.79 115	82.56 129	76.56 116	81.83 126	77.85 130	79.90 154	69.42 80	78.93 129	71.21 129	90.47 54	85.20 155	70.86 105	80.54 161	80.57 141	86.15 146	84.36 92
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DELS-MVS		79.71 116	83.74 121	75.01 126	79.31 143	82.68 89	84.79 121	60.06 167	75.43 143	69.09 140	86.13 104	89.38 131	67.16 123	85.12 122	83.87 114	89.65 101	83.57 101
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
test1111		79.67 117	84.40 108	74.16 132	85.29 84	79.56 118	81.16 144	73.13 59	84.65 77	56.08 178	88.38 81	86.14 149	60.49 152	89.78 82	85.59 97	88.79 115	76.68 152

pmmvs-eth3d		79.64 118	82.06 132	76.83 112	80.05 136	72.64 166	87.47 95	66.59 104	80.83 115	73.50 114	89.32 69	93.20 90	67.78 119	80.78 159	81.64 135	85.58 156	76.01 154

UGNet		79.62 119	85.91 86	72.28 143	73.52 179	83.91 76	86.64 106	69.51 77	79.85 123	62.57 165	85.82 108	89.63 128	53.18 184	88.39 95	87.35 77	88.28 126	86.43 79
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
V42		79.59 120	83.59 123	74.93 129	69.61 192	77.05 139	86.59 107	55.84 181	78.42 131	77.29 94	89.84 62	95.08 62	74.12 78	83.05 139	80.11 148	86.12 147	81.59 122

Anonymous20231211		79.37 121	85.78 87	71.89 144	82.87 118	79.66 117	78.77 164	63.93 137	83.36 84	59.39 170	90.54 53	94.66 70	56.46 168	87.38 101	84.12 111	89.92 97	80.74 127

EPNet		79.36 122	79.44 141	79.27 98	89.51 46	77.20 137	88.35 87	77.35 31	68.27 174	74.29 110	76.31 165	79.22 173	59.63 155	85.02 127	85.45 99	86.49 143	84.61 89
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v148		79.33 123	82.32 130	75.84 118	80.14 135	75.74 148	81.98 139	57.06 178	81.51 106	79.36 83	89.42 66	96.42 27	71.32 99	81.54 155	75.29 173	85.20 158	76.32 153

ECVR-MVS		79.31 124	84.20 114	73.60 134	84.55 91	80.37 109	79.63 158	73.23 57	82.64 90	55.98 179	87.50 88	86.85 146	59.61 156	90.35 76	86.46 86	88.58 121	75.26 161

FC-MVSNet-train		79.20 125	86.29 80	70.94 150	84.06 97	77.67 131	85.68 110	64.11 131	82.90 88	52.22 195	92.57 35	93.69 83	49.52 195	88.30 96	86.93 80	90.03 95	81.95 119

TransMVSNet (Re)		79.05 126	86.66 75	70.18 156	83.32 110	75.99 146	77.54 167	63.98 135	90.68 24	55.84 181	94.80 10	96.06 35	53.73 183	86.27 113	83.22 123	86.65 139	79.61 140

ETV-MVS		79.01 127	77.98 148	80.22 91	86.69 72	79.73 116	88.80 84	68.27 94	63.22 195	71.56 127	70.25 201	73.63 193	73.66 84	90.30 78	86.77 84	92.33 71	81.95 119

FA-MVS(training)		78.93 128	80.63 137	76.93 111	79.79 139	75.57 152	85.44 113	61.95 153	77.19 135	78.97 85	84.82 118	82.47 163	66.43 130	84.09 135	80.13 147	89.02 111	80.15 137

EIA-MVS		78.57 129	77.90 149	79.35 97	87.24 69	80.71 106	86.16 109	64.03 133	62.63 200	73.49 115	73.60 184	76.12 187	73.83 82	88.49 93	84.93 104	91.36 81	78.78 144

OpenMVS	75.38 16	78.44 130	81.39 135	74.99 127	80.46 133	79.85 114	79.99 152	58.31 175	77.34 134	73.85 112	77.19 159	82.33 166	68.60 116	84.67 131	81.95 130	88.72 117	86.40 80

pm-mvs1		78.21 131	85.68 89	69.50 161	80.38 134	75.73 149	76.25 175	65.04 121	87.59 50	54.47 186	93.16 25	95.99 40	54.20 179	86.37 112	82.98 126	86.64 140	77.96 149

FMVSNet1		78.20 132	84.83 102	70.46 154	78.62 150	79.03 121	77.90 166	67.53 101	83.02 87	55.10 184	87.19 95	93.18 91	55.65 173	85.57 117	83.39 119	87.98 128	82.40 115

DI_MVS_plusplus_trai		77.64 133	79.64 140	75.31 122	79.87 138	76.89 140	81.55 143	63.64 138	76.21 138	72.03 124	85.59 110	82.97 162	66.63 126	79.27 167	77.78 158	88.14 127	78.76 145

IterMVS-SCA-FT		77.23 134	79.18 143	74.96 128	76.67 170	79.85 114	75.58 184	61.34 158	73.10 148	73.79 113	86.23 103	79.61 172	79.00 36	80.28 163	75.50 172	83.41 170	79.70 139

tfpnnormal		77.16 135	84.26 111	68.88 164	81.02 131	75.02 155	76.52 174	63.30 142	87.29 53	52.40 193	91.24 50	93.97 79	54.85 177	85.46 120	81.08 137	85.18 159	75.76 158

Fast-Effi-MVS+-dtu		76.92 136	77.18 154	76.62 114	79.55 140	79.17 120	84.80 120	77.40 29	64.46 190	68.75 144	70.81 197	86.57 147	63.36 146	81.74 152	81.76 133	85.86 152	75.78 157

diffmvs		76.74 137	81.61 134	71.06 148	75.64 174	74.45 161	80.68 148	57.57 177	77.48 132	67.62 152	88.95 74	93.94 80	61.98 149	79.74 164	76.18 168	82.85 171	80.50 129
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_Test		76.72 138	79.40 142	73.60 134	78.85 149	74.99 156	79.91 153	61.56 156	69.67 166	72.44 120	85.98 107	90.78 122	63.50 144	78.30 169	75.74 171	85.33 157	80.31 135

MDA-MVSNet-bldmvs		76.51 139	82.87 128	69.09 163	50.71 219	74.72 160	84.05 125	60.27 165	81.62 103	71.16 130	88.21 83	91.58 113	69.62 111	92.78 44	77.48 161	78.75 181	73.69 167

EU-MVSNet		76.48 140	80.53 138	71.75 145	67.62 198	70.30 173	81.74 141	54.06 189	75.47 142	71.01 131	80.10 139	93.17 92	73.67 83	83.73 137	77.85 157	82.40 172	83.07 105

PVSNet_BlendedMVS		76.45 141	78.12 146	74.49 130	76.76 163	78.46 125	79.65 156	63.26 143	65.42 186	73.15 116	75.05 177	88.96 134	66.51 128	82.73 144	77.66 159	87.61 132	78.60 146

PVSNet_Blended		76.45 141	78.12 146	74.49 130	76.76 163	78.46 125	79.65 156	63.26 143	65.42 186	73.15 116	75.05 177	88.96 134	66.51 128	82.73 144	77.66 159	87.61 132	78.60 146

Vis-MVSNet (Re-imp)		76.15 143	80.84 136	70.68 151	83.66 107	74.80 159	81.66 142	69.59 75	80.48 119	46.94 204	87.44 90	80.63 170	53.14 185	86.87 107	84.56 109	89.12 109	71.12 172

PatchMatch-RL		76.05 144	76.64 159	75.36 121	77.84 159	69.87 176	81.09 146	63.43 141	71.66 159	68.34 148	71.70 189	81.76 167	74.98 73	84.83 129	83.44 118	86.45 144	73.22 169

pmmvs4		75.92 145	77.48 153	74.10 133	78.21 154	70.94 170	84.06 124	64.78 123	75.13 144	68.47 147	84.12 123	83.32 159	64.74 138	75.93 181	79.14 154	84.31 163	73.77 166

FC-MVSNet-test		75.91 146	83.59 123	66.95 175	76.63 171	69.07 178	85.33 117	64.97 122	84.87 75	41.95 209	93.17 24	87.04 144	47.78 198	91.09 66	85.56 98	85.06 160	74.34 162

tttt0517		75.86 147	76.23 163	75.42 120	75.55 175	74.06 162	82.73 133	60.31 163	69.24 168	70.24 135	79.18 143	58.79 211	72.17 92	84.49 132	83.08 124	91.54 78	84.80 88

CVMVSNet		75.65 148	77.62 152	73.35 140	71.95 185	69.89 175	83.04 131	60.84 162	69.12 170	68.76 143	79.92 142	78.93 175	73.64 85	81.02 157	81.01 138	81.86 175	83.43 102

thisisatest0530		75.54 149	75.95 167	75.05 124	75.08 176	73.56 163	82.15 138	60.31 163	69.17 169	69.32 138	79.02 144	58.78 212	72.17 92	83.88 136	83.08 124	91.30 83	84.20 95

test2506		75.32 150	76.87 158	73.50 136	84.55 91	80.37 109	79.63 158	73.23 57	82.64 90	55.41 182	76.87 162	45.42 225	59.61 156	90.35 76	86.46 86	88.58 121	75.98 155

IB-MVS	71.28 17	75.21 151	77.00 156	73.12 141	76.76 163	77.45 133	83.05 130	58.92 172	63.01 196	64.31 160	59.99 215	87.57 143	68.64 115	86.26 114	82.34 129	87.05 138	82.36 116
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
CANet_DTU		75.04 152	78.45 144	71.07 147	77.27 160	77.96 129	83.88 126	58.00 176	64.11 191	68.67 145	75.65 174	88.37 139	53.92 182	82.05 149	81.11 136	84.67 161	79.88 138

GA-MVS		75.01 153	76.39 161	73.39 138	78.37 151	75.66 150	80.03 151	58.40 174	70.51 163	75.85 100	83.24 128	76.14 186	63.75 140	77.28 173	76.62 167	83.97 165	75.30 160

ET-MVSNet_ETH3D		74.71 154	74.19 174	75.31 122	79.22 145	75.29 153	82.70 134	64.05 132	65.45 185	70.96 132	77.15 160	57.70 213	65.89 131	84.40 133	81.65 134	89.03 110	77.67 150

FMVSNet2		74.43 155	79.70 139	68.27 167	76.76 163	77.36 134	75.77 179	65.36 119	72.28 155	52.97 190	81.92 135	85.61 152	52.73 188	80.66 160	79.73 149	86.04 148	80.37 131

thres600view7		74.34 156	78.43 145	69.56 160	80.47 132	76.28 144	78.65 165	62.56 149	77.39 133	52.53 191	74.03 181	76.78 184	55.90 172	85.06 123	85.19 101	87.25 136	74.29 163

IterMVS		73.62 157	76.53 160	70.23 155	71.83 186	77.18 138	80.69 147	53.22 193	72.23 156	66.62 155	85.21 112	78.96 174	69.54 112	76.28 180	71.63 183	79.45 178	74.25 164
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MIMVSNet1		73.40 158	81.85 133	63.55 187	72.90 182	64.37 192	84.58 122	53.60 191	90.84 20	53.92 187	87.75 86	96.10 33	45.31 201	85.37 121	79.32 152	70.98 196	69.18 181

HyFIR lowres test		73.29 159	74.14 175	72.30 142	73.08 181	78.33 127	83.12 129	62.41 151	63.81 192	62.13 167	76.67 164	78.50 176	71.09 101	74.13 185	77.47 162	81.98 174	70.10 176

GBi-Net		73.17 160	77.64 150	67.95 170	76.76 163	77.36 134	75.77 179	64.57 124	62.99 197	51.83 196	76.05 168	77.76 179	52.73 188	85.57 117	83.39 119	86.04 148	80.37 131

test1		73.17 160	77.64 150	67.95 170	76.76 163	77.36 134	75.77 179	64.57 124	62.99 197	51.83 196	76.05 168	77.76 179	52.73 188	85.57 117	83.39 119	86.04 148	80.37 131

thres400		73.13 162	76.99 157	68.62 165	79.46 141	74.93 157	77.23 169	61.23 159	75.54 141	52.31 194	72.20 188	77.10 182	54.89 175	82.92 140	82.62 128	86.57 142	73.66 168

CDS-MVSNet		73.07 163	77.02 155	68.46 166	81.62 127	72.89 165	79.56 160	70.78 71	69.56 167	52.52 192	77.37 158	81.12 169	42.60 203	84.20 134	83.93 112	83.65 166	70.07 177
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MDTV_nov1_ep13_2view		72.96 164	75.59 168	69.88 157	71.15 189	64.86 191	82.31 137	54.45 187	76.30 137	78.32 91	86.52 100	91.58 113	61.35 150	76.80 174	66.83 194	71.70 189	66.26 185

gg-mvs-nofinetune		72.68 165	75.21 171	69.73 158	81.48 128	69.04 179	70.48 196	76.67 35	86.92 57	67.80 151	88.06 84	64.67 201	42.12 205	77.60 171	73.65 176	79.81 177	66.57 184

thres200		72.41 166	76.00 166	68.21 168	78.28 152	76.28 144	74.94 185	62.56 149	72.14 158	51.35 199	69.59 204	76.51 185	54.89 175	85.06 123	80.51 143	87.25 136	71.92 171

tfpn200view9		72.01 167	75.40 169	68.06 169	77.97 156	76.44 142	77.04 171	62.67 148	66.81 177	50.82 200	67.30 206	75.67 189	52.46 191	85.06 123	82.64 127	87.41 134	73.86 165

EPNet_dtu		71.90 168	73.03 180	70.59 152	78.28 152	61.64 197	82.44 136	64.12 130	63.26 194	69.74 136	71.47 191	82.41 164	51.89 192	78.83 168	78.01 155	77.07 182	75.60 159
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
gm-plane-assit		71.56 169	69.99 184	73.39 138	84.43 95	73.21 164	90.42 68	51.36 199	84.08 80	76.00 98	91.30 48	37.09 226	59.01 159	73.65 188	70.24 187	79.09 180	60.37 201

CMPMVS	55.74 18	71.56 169	76.26 162	66.08 180	68.11 196	63.91 194	63.17 210	50.52 201	68.79 173	75.49 101	70.78 198	85.67 151	63.54 143	81.58 153	77.20 163	75.63 183	85.86 82
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet3		71.40 171	75.20 172	66.97 174	75.00 177	76.59 141	74.29 186	64.57 124	62.99 197	51.83 196	76.05 168	77.76 179	51.49 193	76.58 177	77.03 165	84.62 162	79.43 141

MS-PatchMatch		71.18 172	73.99 176	67.89 172	77.16 161	71.76 169	77.18 170	56.38 180	67.35 175	55.04 185	74.63 179	75.70 188	62.38 147	76.62 176	75.97 170	79.22 179	75.90 156

test20.03		69.91 173	76.20 164	62.58 188	84.01 100	67.34 184	75.67 183	65.88 115	79.98 122	40.28 213	82.65 130	89.31 132	39.63 208	77.41 172	73.28 177	69.98 197	63.40 192

thres100view900		69.86 174	72.97 181	66.24 177	77.97 156	72.49 167	73.29 189	59.12 170	66.81 177	50.82 200	67.30 206	75.67 189	50.54 194	78.24 170	79.40 151	85.71 155	70.88 173

baseline1		69.62 175	73.55 178	65.02 186	78.95 148	70.39 172	71.38 195	62.03 152	70.97 162	47.95 203	78.47 151	68.19 199	47.77 199	79.65 166	76.94 166	82.05 173	70.27 175

CR-MVSNet		69.56 176	68.34 189	70.99 149	72.78 184	67.63 182	64.47 208	67.74 99	59.93 206	72.30 121	80.10 139	56.77 215	65.04 136	71.64 193	72.91 179	83.61 168	69.40 179

baseline		69.33 177	75.37 170	62.28 190	66.54 204	66.67 187	73.95 188	48.07 202	66.10 180	59.26 171	82.45 131	86.30 148	54.44 178	74.42 184	73.25 178	71.42 192	78.43 148

pmmvs5		68.91 178	74.35 173	62.56 189	67.45 200	66.78 186	71.70 192	51.47 198	67.17 176	56.25 177	82.41 132	88.59 138	47.21 200	73.21 191	74.23 174	81.30 176	68.03 183

CHOSEN 1792x2688		68.80 179	71.09 182	66.13 179	69.11 194	68.89 180	78.98 163	54.68 184	61.63 202	56.69 175	71.56 190	78.39 177	67.69 120	72.13 192	72.01 182	69.63 199	73.02 170

baseline2		68.71 180	68.34 189	69.14 162	75.69 173	69.70 177	76.60 173	55.53 183	60.13 205	62.07 168	66.76 208	60.35 206	60.77 151	76.53 179	74.03 175	84.19 164	70.88 173

SCA		68.54 181	67.52 191	69.73 158	67.79 197	75.04 154	76.96 172	68.94 85	66.41 179	67.86 150	74.03 181	60.96 204	65.55 134	68.99 201	65.67 195	71.30 194	61.54 200

testgi		68.20 182	76.05 165	59.04 194	79.99 137	67.32 185	81.16 144	51.78 197	84.91 74	39.36 214	73.42 185	95.19 57	32.79 214	76.54 178	70.40 186	69.14 200	64.55 188

MVSTER		68.08 183	69.73 185	66.16 178	66.33 206	70.06 174	75.71 182	52.36 195	55.18 214	58.64 172	70.23 202	56.72 216	57.34 165	79.68 165	76.03 169	86.61 141	80.20 136

Anonymous20231206		67.28 184	73.41 179	60.12 193	76.45 172	63.61 195	74.21 187	56.52 179	76.35 136	42.23 208	75.81 173	90.47 125	41.51 206	74.52 182	69.97 188	69.83 198	63.17 193

RPMNet		67.02 185	63.99 200	70.56 153	71.55 187	67.63 182	75.81 177	69.44 79	59.93 206	63.24 162	64.32 210	47.51 224	59.68 154	70.37 198	69.64 189	83.64 167	68.49 182

CostFormer		66.81 186	66.94 192	66.67 176	72.79 183	68.25 181	79.55 161	55.57 182	65.52 184	62.77 164	76.98 161	60.09 207	56.73 167	65.69 209	62.35 198	72.59 188	69.71 178

PatchT		66.25 187	66.76 193	65.67 183	55.87 214	60.75 198	70.17 197	59.00 171	59.80 208	72.30 121	78.68 149	54.12 220	65.04 136	71.64 193	72.91 179	71.63 191	69.40 179

dps		65.14 188	64.50 198	65.89 182	71.41 188	65.81 190	71.44 194	61.59 155	58.56 209	61.43 169	75.45 175	52.70 222	58.06 163	69.57 200	64.65 196	71.39 193	64.77 187

MDTV_nov1_ep13		64.96 189	64.77 197	65.18 185	67.08 201	62.46 196	75.80 178	51.10 200	62.27 201	69.74 136	74.12 180	62.65 202	55.64 174	68.19 203	62.16 202	71.70 189	61.57 199

PatchmatchNet		64.81 190	63.74 201	66.06 181	69.21 193	58.62 201	73.16 190	60.01 168	65.92 181	66.19 157	76.27 166	59.09 208	60.45 153	66.58 206	61.47 204	67.33 203	58.24 206
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat1		64.79 191	62.74 205	67.17 173	74.61 178	65.91 189	76.18 176	59.32 169	64.88 189	66.41 156	71.21 194	53.56 221	59.17 158	61.53 213	58.16 207	67.33 203	63.95 189

MIMVSNet		63.02 192	69.02 187	56.01 199	68.20 195	59.26 200	70.01 199	53.79 190	71.56 160	41.26 212	71.38 192	82.38 165	36.38 210	71.43 195	67.32 193	66.45 205	59.83 203

TAMVS		63.02 192	69.30 186	55.70 201	70.12 190	56.89 203	69.63 200	45.13 205	70.23 164	38.00 215	77.79 152	75.15 191	42.60 203	74.48 183	72.81 181	68.70 201	57.75 208

tpm		62.79 194	63.25 202	62.26 191	70.09 191	53.78 206	71.65 193	47.31 203	65.72 183	76.70 95	80.62 138	56.40 218	48.11 197	64.20 211	58.54 205	59.70 209	63.47 191

pmmvs3		62.72 195	68.71 188	55.74 200	50.74 218	57.10 202	70.05 198	28.82 215	61.57 204	57.39 174	71.19 195	85.73 150	53.96 181	73.36 190	69.43 190	73.47 187	62.55 195

pmnet_mix02		62.60 196	70.81 183	53.02 206	66.56 203	50.44 213	62.81 211	46.84 204	79.13 128	43.76 207	87.45 89	90.75 123	39.85 207	70.48 197	57.09 208	58.27 211	60.32 202

new-patchmatchnet		62.59 197	73.79 177	49.53 210	76.98 162	53.57 207	53.46 219	54.64 185	85.43 69	28.81 218	91.94 38	96.41 28	25.28 216	76.80 174	53.66 214	57.99 212	58.69 205

test-LLR		62.15 198	59.46 214	65.29 184	79.07 146	52.66 209	69.46 202	62.93 145	50.76 217	53.81 188	63.11 212	58.91 209	52.87 186	66.54 207	62.34 199	73.59 185	61.87 197

PMMVS		61.98 199	65.61 195	57.74 196	45.03 220	51.76 211	69.54 201	35.05 212	55.49 213	55.32 183	68.23 205	78.39 177	58.09 162	70.21 199	71.56 184	83.42 169	63.66 190

test0.0.03 1		61.79 200	65.33 196	57.65 197	79.07 146	64.09 193	68.51 205	62.93 145	61.59 203	33.71 217	61.58 214	71.58 197	33.43 213	70.95 196	68.68 191	68.26 202	58.82 204

MVS-HIRNet		59.74 201	58.74 217	60.92 192	57.74 213	45.81 217	56.02 217	58.69 173	55.69 212	65.17 158	70.86 196	71.66 195	56.75 166	61.11 214	53.74 213	71.17 195	52.28 212

tpmrst		59.42 202	60.02 212	58.71 195	67.56 199	53.10 208	66.99 206	51.88 196	63.80 193	57.68 173	76.73 163	56.49 217	48.73 196	56.47 217	55.55 210	59.43 210	58.02 207

test-mter		59.39 203	61.59 207	56.82 198	53.21 215	54.82 205	73.12 191	26.57 217	53.19 215	56.31 176	64.71 209	60.47 205	56.36 169	68.69 202	64.27 197	75.38 184	65.00 186

E-PMN		59.07 204	62.79 204	54.72 202	67.01 202	47.81 216	60.44 214	43.40 206	72.95 150	44.63 206	70.42 200	73.17 194	58.73 160	80.97 158	51.98 215	54.14 215	42.26 217

EMVS		58.97 205	62.63 206	54.70 203	66.26 207	48.71 214	61.74 212	42.71 207	72.80 152	46.00 205	73.01 187	71.66 195	57.91 164	80.41 162	50.68 217	53.55 216	41.11 218

TESTMET0.1,1		57.21 206	59.46 214	54.60 204	50.95 217	52.66 209	69.46 202	26.91 216	50.76 217	53.81 188	63.11 212	58.91 209	52.87 186	66.54 207	62.34 199	73.59 185	61.87 197

ADS-MVSNet		56.89 207	61.09 208	52.00 208	59.48 211	48.10 215	58.02 215	54.37 188	72.82 151	49.19 202	75.32 176	65.97 200	37.96 209	59.34 216	54.66 212	52.99 217	51.42 213

EPMVS		56.62 208	59.77 213	52.94 207	62.41 209	50.55 212	60.66 213	52.83 194	65.15 188	41.80 210	77.46 157	57.28 214	42.68 202	59.81 215	54.82 211	57.23 213	53.35 211

FMVSNet5		56.37 209	60.14 211	51.98 209	60.83 210	59.58 199	66.85 207	42.37 208	52.68 216	41.33 211	47.09 218	54.68 219	35.28 211	73.88 186	70.77 185	65.24 206	62.26 196

CHOSEN 280x420		56.32 210	58.85 216	53.36 205	51.63 216	39.91 220	69.12 204	38.61 211	56.29 211	36.79 216	48.84 217	62.59 203	63.39 145	73.61 189	67.66 192	60.61 207	63.07 194

N_pmnet		54.95 211	65.90 194	42.18 211	66.37 205	43.86 219	57.92 216	39.79 210	79.54 125	17.24 223	86.31 101	87.91 141	25.44 215	64.68 210	51.76 216	46.33 218	47.23 215

new_pmnet		52.29 212	63.16 203	39.61 213	58.89 212	44.70 218	48.78 221	34.73 213	65.88 182	17.85 222	73.42 185	80.00 171	23.06 217	67.00 205	62.28 201	54.36 214	48.81 214

MVE	41.12 19	51.80 213	60.92 209	41.16 212	35.21 222	34.14 222	48.45 222	41.39 209	69.11 171	19.53 221	63.33 211	73.80 192	63.56 142	67.19 204	61.51 203	38.85 219	57.38 209
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		48.13 214	64.06 199	29.55 214	44.06 221	36.69 221	51.95 220	29.97 214	74.75 146	8.90 225	76.02 171	91.24 119	7.53 219	73.78 187	55.91 209	34.87 220	40.01 219

GG-mvs-BLEND		41.63 215	60.36 210	19.78 215	0.14 227	66.04 188	55.66 218	0.17 223	57.64 210	2.42 226	51.82 216	69.42 198	0.28 223	64.11 212	58.29 206	60.02 208	55.18 210

test_method		22.69 216	26.99 218	17.67 216	2.13 224	4.31 225	27.50 223	4.53 219	37.94 219	24.52 220	36.20 220	51.40 223	15.26 218	29.86 219	17.09 219	32.07 221	12.16 220

test123		1.06 217	1.41 219	0.64 218	0.39 225	0.48 226	0.52 228	0.25 222	1.11 223	1.37 227	2.01 223	1.98 229	0.87 221	1.43 221	1.27 220	0.46 225	1.62 222

testmvs		0.93 218	1.37 220	0.41 219	0.36 226	0.36 227	0.62 227	0.39 221	1.48 222	0.18 228	2.41 222	1.31 230	0.41 222	1.25 222	1.08 221	0.48 224	1.68 221

uanet_test		0.00 219	0.00 221	0.00 220	0.00 228	0.00 228	0.00 229	0.00 224	0.00 224	0.00 229	0.00 224	0.00 231	0.00 224	0.00 223	0.00 222	0.00 226	0.00 223

sosnet-low-res		0.00 219	0.00 221	0.00 220	0.00 228	0.00 228	0.00 229	0.00 224	0.00 224	0.00 229	0.00 224	0.00 231	0.00 224	0.00 223	0.00 222	0.00 226	0.00 223

sosnet		0.00 219	0.00 221	0.00 220	0.00 228	0.00 228	0.00 229	0.00 224	0.00 224	0.00 229	0.00 224	0.00 231	0.00 224	0.00 223	0.00 222	0.00 226	0.00 223

TPM-MVS					86.18 77	83.43 84	87.57 93			78.77 87	69.75 203	84.63 157	62.24 148			89.88 98	88.48 64

RE-MVS-def										87.10 28

9.14												89.43 130

SR-MVS					91.82 13			80.80 7				95.53 49

Anonymous202405211			84.68 105		83.92 101	79.45 119	79.03 162	67.79 98	82.01 98		88.77 79	92.58 97	55.93 171	86.68 109	84.26 110	88.92 113	78.98 142

our_test_3					73.27 180	70.91 171	83.26 128

ambc			88.38 60		91.62 17	87.97 52	84.48 123		88.64 43	87.93 15	87.38 91	94.82 68	74.53 76	89.14 88	83.86 115	85.94 151	86.84 76

MTAPA										89.37 9		94.85 66

MTMP										90.54 5		95.16 59

Patchmatch-RL test							4.13 226

tmp_tt				13.54 217	16.73 223	6.42 224	8.49 225	2.36 220	28.69 221	27.44 219	18.40 221	13.51 228	3.70 220	33.23 218	36.26 218	22.54 223

XVS					91.28 25	91.23 8	96.89 2			87.14 25		94.53 71				95.84 15

X-MVStestdata					91.28 25	91.23 8	96.89 2			87.14 25		94.53 71				95.84 15

mPP-MVS					93.05 3							95.77 43

NP-MVS									78.65 130

Patchmtry						56.88 204	64.47 208	67.74 99		72.30 121

DeepMVS_CX						17.78 223	20.40 224	6.69 218	31.41 220	9.80 224	38.61 219	34.88 227	33.78 212	28.41 220		23.59 222	45.77 216

High-resolution multi-view benchmark