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 53	93.57 1	97.27 1	78.23 22	95.55 2	93.00 1	93.98 18	96.01 40	87.53 1	97.69 1	96.81 1	97.33 1	95.34 4

LTVRE_ROB	86.82 1	91.55 3	94.43 3	88.19 11	83.19 116	86.35 68	93.60 38	78.79 19	95.48 4	91.79 2	93.08 27	97.21 22	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
PMVS	79.51 9	90.23 15	92.67 15	87.39 21	90.16 40	88.75 42	93.64 37	75.78 45	90.00 34	83.70 48	92.97 29	92.22 106	86.13 4	97.01 3	96.79 2	94.94 30	90.96 47
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
COLMAP_ROB	85.66 2	91.85 2	95.01 2	88.16 12	88.98 52	92.86 2	95.51 20	72.17 63	94.95 5	91.27 3	94.11 17	97.77 12	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
PS-CasMVS		89.07 33	93.23 8	84.21 52	92.44 9	88.23 48	90.54 64	82.95 3	90.50 26	75.31 104	95.80 7	98.37 7	71.16 102	96.30 5	93.32 22	92.88 64	90.11 53

CP-MVSNet		88.71 42	92.63 16	84.13 53	92.39 10	88.09 50	90.47 69	82.86 4	88.79 44	75.16 105	94.87 10	97.68 15	71.05 104	96.16 6	93.18 24	92.85 65	89.64 57

UA-Net		89.02 34	91.44 40	86.20 29	94.88 1	89.84 34	94.76 30	77.45 29	85.41 73	74.79 107	88.83 78	88.90 139	78.67 40	96.06 7	95.45 4	96.66 3	95.58 2

WR-MVS		89.79 24	93.66 5	85.27 38	91.32 24	88.27 46	93.49 39	79.86 10	92.75 10	75.37 103	96.86 1	98.38 6	75.10 73	95.93 8	94.07 15	96.46 5	89.39 59

WR-MVS_H		88.99 36	93.28 6	83.99 55	91.92 12	89.13 40	91.95 47	83.23 1	90.14 31	71.92 126	95.85 5	98.01 11	71.83 99	95.82 9	93.19 23	93.07 62	90.83 49

ACMM	80.67 7	90.67 7	92.46 20	88.57 8	91.35 23	89.93 32	96.34 12	77.36 31	90.17 30	86.88 30	87.32 93	96.63 25	83.32 13	95.79 10	94.49 10	96.19 9	92.91 26
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVS		91.09 5	92.33 26	89.65 2	92.16 11	90.41 27	96.46 10	80.38 8	88.26 47	89.17 11	87.00 98	96.34 32	83.95 10	95.77 11	94.72 8	95.81 17	93.78 10

SixPastTwentyTwo		89.14 30	92.19 32	85.58 33	84.62 94	82.56 94	90.53 65	71.93 66	91.95 13	85.89 36	94.22 15	97.25 21	85.42 5	95.73 12	91.71 41	95.08 28	91.89 38

PEN-MVS		88.86 40	92.92 10	84.11 54	92.92 5	88.05 51	90.83 56	82.67 5	91.04 19	74.83 106	95.97 4	98.47 4	70.38 109	95.70 13	92.43 31	93.05 63	88.78 65

DTE-MVSNet		88.99 36	92.77 13	84.59 44	93.31 2	88.10 49	90.96 53	83.09 2	91.38 15	76.21 96	96.03 3	98.04 9	70.78 108	95.65 14	92.32 33	93.18 59	87.84 73

ACMMP		90.63 8	92.40 21	88.56 9	91.24 29	91.60 6	96.49 9	77.53 27	87.89 50	86.87 31	87.24 95	96.46 27	82.87 16	95.59 15	94.50 9	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 22	88.43 10	90.88 33	91.15 11	95.35 22	77.65 26	86.26 66	87.23 24	90.45 55	97.35 19	83.20 14	95.44 16	93.41 21	96.28 8	92.63 27

ACMP	80.00 8	90.12 17	92.30 27	87.58 19	90.83 35	91.10 12	94.96 29	76.06 41	87.47 54	85.33 40	88.91 77	97.65 16	82.13 20	95.31 17	93.44 20	96.14 10	92.22 34
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH+	79.05 11	89.62 27	93.08 9	85.58 33	88.58 56	89.26 39	92.18 46	74.23 53	93.55 9	82.66 61	92.32 37	98.35 8	80.29 29	95.28 18	92.34 32	95.52 22	90.43 51

X-MVS		89.36 29	90.73 48	87.77 17	91.50 21	91.23 8	96.76 4	78.88 18	87.29 56	87.14 26	78.98 147	94.53 73	76.47 59	95.25 19	94.28 12	95.85 14	93.55 16

ACMMPR		91.30 4	92.88 12	89.46 4	91.92 12	91.61 5	96.60 5	79.46 14	90.08 32	88.53 14	89.54 66	95.57 49	84.25 7	95.24 20	94.27 13	95.97 11	93.85 8

RPSCF		88.05 47	92.61 18	82.73 68	84.24 101	88.40 44	90.04 75	66.29 110	91.46 14	82.29 63	88.93 76	96.01 40	79.38 32	95.15 21	94.90 6	94.15 42	93.40 20

SteuartSystems-ACMMP		90.00 18	91.73 36	87.97 13	91.21 30	90.29 28	96.51 7	78.00 24	86.33 64	85.32 41	88.23 83	94.67 71	82.08 21	95.13 22	93.88 17	94.72 36	93.59 13
Skip Steuart: Steuart Systems R&D Blog.
zzz-MVS		90.38 12	91.35 42	89.25 5	93.08 3	86.59 65	96.45 11	79.00 16	90.23 29	89.30 10	85.87 109	94.97 66	82.54 18	95.05 23	94.83 7	95.14 27	91.94 37

PGM-MVS		90.42 11	91.58 38	89.05 6	91.77 15	91.06 13	96.51 7	78.94 17	85.41 73	87.67 19	87.02 97	95.26 57	83.62 12	95.01 24	93.94 16	95.79 19	93.40 20

MP-MVS		90.84 6	91.95 35	89.55 3	92.92 5	90.90 19	96.56 6	79.60 11	86.83 61	88.75 13	89.00 74	94.38 79	84.01 9	94.94 25	94.34 11	95.45 24	93.24 23
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
anonymousdsp		85.62 62	90.53 49	79.88 95	64.64 209	76.35 145	96.28 13	53.53 194	85.63 70	81.59 72	92.81 31	97.71 14	86.88 2	94.56 26	92.83 25	96.35 6	93.84 9

Gipuma		86.47 58	89.25 57	83.23 58	83.88 108	78.78 126	85.35 118	68.42 95	92.69 11	89.03 12	91.94 38	96.32 34	81.80 22	94.45 27	86.86 86	90.91 91	83.69 103
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LS3D		89.02 34	91.69 37	85.91 31	89.72 44	90.81 20	92.56 45	71.69 69	90.83 22	87.24 23	89.71 64	92.07 109	78.37 41	94.43 28	92.59 28	95.86 13	91.35 43

ACMMP_NAP		89.86 20	91.96 34	87.42 20	91.00 31	90.08 30	96.00 16	76.61 37	89.28 36	87.73 18	90.04 58	91.80 114	78.71 38	94.36 29	93.82 18	94.48 40	94.32 6

CPTT-MVS		89.63 26	90.52 50	88.59 7	90.95 32	90.74 21	95.71 17	79.13 15	87.70 52	85.68 39	80.05 142	95.74 47	84.77 6	94.28 30	92.68 27	95.28 26	92.45 32

HFP-MVS		90.32 14	92.37 23	87.94 14	91.46 22	90.91 18	95.69 18	79.49 12	89.94 35	83.50 51	89.06 73	94.44 77	81.68 23	94.17 31	94.19 14	95.81 17	93.87 7

ACMH	78.40 12	88.94 39	92.62 17	84.65 43	86.45 77	87.16 60	91.47 49	68.79 91	95.49 3	89.74 6	93.55 20	98.50 3	77.96 46	94.14 32	89.57 64	93.49 51	89.94 55
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SMA-MVS		90.13 16	92.26 28	87.64 18	91.68 17	90.44 26	95.22 25	77.34 33	90.79 23	87.80 17	90.42 56	92.05 111	79.05 35	93.89 33	93.59 19	94.77 34	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
UniMVSNet_ETH3D		85.39 65	91.12 46	78.71 103	90.48 38	83.72 83	81.76 141	82.41 6	93.84 7	64.43 160	95.41 8	98.76 1	63.72 143	93.63 34	89.74 60	89.47 108	82.74 115

TSAR-MVS + MP.		89.67 25	92.25 29	86.65 26	91.53 19	90.98 17	96.15 14	73.30 57	87.88 51	81.83 69	92.92 30	95.15 61	82.23 19	93.58 35	92.25 34	94.87 31	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
OPM-MVS		89.82 22	92.24 30	86.99 23	90.86 34	89.35 38	95.07 28	75.91 44	91.16 17	86.87 31	91.07 51	97.29 20	79.13 34	93.32 36	91.99 38	94.12 43	91.49 42
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MSLP-MVS++		86.29 60	89.10 58	83.01 61	85.71 85	89.79 35	87.04 106	74.39 52	85.17 75	78.92 87	77.59 156	93.57 87	82.60 17	93.23 37	91.88 40	89.42 109	92.46 31

APDe-MVS		89.85 21	92.91 11	86.29 27	90.47 39	91.34 7	96.04 15	76.41 40	91.11 18	78.50 89	93.44 22	95.82 44	81.55 24	93.16 38	91.90 39	94.77 34	93.58 15

DVP-MVS++		90.50 10	94.18 4	86.21 28	92.52 8	90.29 28	95.29 23	76.02 42	94.24 6	82.82 57	95.84 6	97.56 17	76.82 57	93.13 39	91.20 45	93.78 49	97.01 1

SD-MVS		89.91 19	92.23 31	87.19 22	91.31 25	89.79 35	94.31 33	75.34 48	89.26 39	81.79 70	92.68 32	95.08 63	83.88 11	93.10 40	92.69 26	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
3Dnovator+	83.71 3	88.13 45	90.00 53	85.94 30	86.82 74	91.06 13	94.26 34	75.39 47	88.85 43	85.76 38	85.74 111	86.92 148	78.02 45	93.03 41	92.21 35	95.39 25	92.21 35

SED-MVS		88.96 38	92.37 23	84.99 41	88.64 55	89.65 37	95.11 26	75.98 43	90.73 24	80.15 80	94.21 16	94.51 76	76.59 58	92.94 42	91.17 46	93.46 53	93.37 22

APD-MVS		89.14 30	91.25 45	86.67 25	91.73 16	91.02 15	95.50 21	77.74 25	84.04 85	79.47 84	91.48 45	94.85 68	81.14 26	92.94 42	92.20 36	94.47 41	92.24 33
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test_part1		87.86 50	93.26 7	81.56 77	87.23 72	86.76 63	90.91 54	70.06 77	96.50 1	76.74 94	96.63 2	98.62 2	69.45 116	92.93 44	90.92 47	94.98 29	90.46 50

DeepC-MVS	83.59 4	90.37 13	92.56 19	87.82 15	91.26 28	92.33 3	94.72 31	80.04 9	90.01 33	84.61 43	93.33 23	94.22 80	80.59 28	92.90 45	92.52 29	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
MDA-MVSNet-bldmvs		76.51 140	82.87 130	69.09 165	50.71 220	74.72 161	84.05 127	60.27 167	81.62 105	71.16 131	88.21 84	91.58 115	69.62 113	92.78 46	77.48 162	78.75 182	73.69 168

DPE-MVS		89.81 23	92.34 25	86.86 24	89.69 45	91.00 16	95.53 19	76.91 34	88.18 48	83.43 54	93.48 21	95.19 58	81.07 27	92.75 47	92.07 37	94.55 38	93.74 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TSAR-MVS + ACMM		89.14 30	92.11 33	85.67 32	89.27 48	90.61 24	90.98 52	79.48 13	88.86 42	79.80 81	93.01 28	93.53 89	83.17 15	92.75 47	92.45 30	91.32 85	93.59 13

OMC-MVS		88.16 44	91.34 43	84.46 47	86.85 73	90.63 23	93.01 42	67.00 106	90.35 28	87.40 22	86.86 100	96.35 31	77.66 50	92.63 49	90.84 48	94.84 32	91.68 40

EPP-MVSNet		82.76 95	86.47 81	78.45 106	86.00 83	84.47 78	85.39 117	68.42 95	84.17 82	62.97 164	89.26 71	76.84 184	72.13 96	92.56 50	90.40 53	95.76 20	87.56 76

DVP-MVS		89.40 28	92.69 14	85.56 35	89.01 51	89.85 33	93.72 36	75.42 46	92.28 12	80.49 75	94.36 14	94.87 67	81.46 25	92.49 51	91.42 42	93.27 56	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
CS-MVS		83.23 87	85.14 97	81.00 81	85.59 86	79.28 121	89.80 77	63.29 145	73.02 151	75.70 101	85.28 114	92.81 96	77.09 54	91.92 52	87.93 77	94.53 39	85.76 87

DU-MVS		84.88 71	88.27 67	80.92 82	88.30 58	83.59 85	87.06 104	78.35 20	80.64 118	70.49 134	92.67 33	96.91 23	68.13 121	91.79 53	89.29 67	93.20 58	83.02 109

Baseline_NR-MVSNet		82.79 94	86.51 79	78.44 107	88.30 58	75.62 153	87.81 94	74.97 49	81.53 106	66.84 155	94.71 13	96.46 27	66.90 129	91.79 53	83.37 125	85.83 154	82.09 120

NCCC		86.74 55	87.97 71	85.31 37	90.64 36	87.25 59	93.27 40	74.59 50	86.50 62	83.72 47	75.92 173	92.39 103	77.08 55	91.72 55	90.68 50	92.57 70	91.30 44

xxxxxxxxxxxxxcwj		88.03 48	91.29 44	84.22 50	88.17 61	87.90 53	90.80 57	71.80 67	89.28 36	82.70 59	89.90 60	97.72 13	77.91 47	91.69 56	90.04 56	93.95 47	92.47 29

SF-MVS		87.85 51	90.95 47	84.22 50	88.17 61	87.90 53	90.80 57	71.80 67	89.28 36	82.70 59	89.90 60	95.37 55	77.91 47	91.69 56	90.04 56	93.95 47	92.47 29

CNVR-MVS		86.93 54	88.98 59	84.54 45	90.11 41	87.41 58	93.23 41	73.47 56	86.31 65	82.25 64	82.96 130	92.15 107	76.04 64	91.69 56	90.69 49	92.17 76	91.64 41

TranMVSNet+NR-MVSNet		85.23 68	89.38 56	80.39 93	88.78 54	83.77 82	87.40 99	76.75 35	85.47 71	68.99 142	95.18 9	97.55 18	67.13 128	91.61 59	89.13 68	93.26 57	82.95 112

AdaColmap		84.15 76	85.14 97	83.00 62	89.08 50	87.14 61	90.56 63	70.90 72	82.40 96	80.41 76	73.82 184	84.69 159	75.19 72	91.58 60	89.90 58	91.87 79	86.48 80

CSCG		88.12 46	91.45 39	84.23 49	88.12 63	90.59 25	90.57 62	68.60 93	91.37 16	83.45 53	89.94 59	95.14 62	78.71 38	91.45 61	88.21 76	95.96 12	93.44 19

Vis-MVSNet		83.32 86	88.12 69	77.71 110	77.91 159	83.44 87	90.58 61	69.49 82	81.11 114	67.10 154	89.85 62	91.48 118	71.71 100	91.34 62	89.37 65	89.48 107	90.26 52
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CNLPA		85.50 64	88.58 60	81.91 72	84.55 96	87.52 57	90.89 55	63.56 141	88.18 48	84.06 45	83.85 127	91.34 120	76.46 60	91.27 63	89.00 69	91.96 77	88.88 64

FPMVS		81.56 105	84.04 119	78.66 104	82.92 118	75.96 149	86.48 110	65.66 120	84.67 79	71.47 129	77.78 154	83.22 163	77.57 51	91.24 64	90.21 54	87.84 130	85.21 91

UniMVSNet (Re)		84.95 70	88.53 61	80.78 84	87.82 65	84.21 79	88.03 92	76.50 38	81.18 113	69.29 140	92.63 35	96.83 24	69.07 117	91.23 65	89.60 63	93.97 46	84.00 101

DeepC-MVS_fast	81.78 5	87.38 52	89.64 54	84.75 42	89.89 43	90.70 22	92.74 44	74.45 51	86.02 67	82.16 67	86.05 107	91.99 113	75.84 67	91.16 66	90.44 51	93.41 54	91.09 45
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		85.32 67	87.41 76	82.89 65	90.07 42	85.69 73	89.07 85	72.99 61	82.45 95	74.52 110	85.09 117	87.67 145	79.24 33	91.11 67	90.41 52	91.45 82	89.45 58

v7n		87.11 53	90.46 51	83.19 59	85.22 89	83.69 84	90.03 76	68.20 99	91.01 20	86.71 34	94.80 11	98.46 5	77.69 49	91.10 68	85.98 95	91.30 86	88.19 68

FC-MVSNet-test		75.91 147	83.59 125	66.95 177	76.63 172	69.07 179	85.33 119	64.97 125	84.87 78	41.95 210	93.17 25	87.04 147	47.78 199	91.09 69	85.56 101	85.06 161	74.34 163

IS_MVSNet		81.72 104	85.01 99	77.90 109	86.19 80	82.64 93	85.56 113	70.02 78	80.11 123	63.52 162	87.28 94	81.18 169	67.26 126	91.08 70	89.33 66	94.82 33	83.42 106

CDPH-MVS		86.66 57	88.52 62	84.48 46	89.61 46	88.27 46	92.86 43	72.69 62	80.55 120	82.71 58	86.92 99	93.32 91	75.55 69	91.00 71	89.85 59	93.47 52	89.71 56

HPM-MVS++		88.74 41	89.54 55	87.80 16	92.58 7	85.69 73	95.10 27	78.01 23	87.08 58	87.66 20	87.89 86	92.07 109	80.28 30	90.97 72	91.41 44	93.17 60	91.69 39

PHI-MVS		86.37 59	88.14 68	84.30 48	86.65 76	87.56 56	90.76 59	70.16 76	82.55 94	89.65 7	84.89 120	92.40 102	75.97 65	90.88 73	89.70 61	92.58 68	89.03 63

PLC	76.06 15	85.38 66	87.46 74	82.95 64	85.79 84	88.84 41	88.86 87	68.70 92	87.06 59	83.60 49	79.02 145	90.05 129	77.37 53	90.88 73	89.66 62	93.37 55	86.74 79
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DeepPCF-MVS	81.61 6	87.95 49	90.29 52	85.22 39	87.48 67	90.01 31	93.79 35	73.54 55	88.93 41	83.89 46	89.40 68	90.84 123	80.26 31	90.62 75	90.19 55	92.36 73	92.03 36

UniMVSNet_NR-MVSNet		84.62 74	88.00 70	80.68 88	88.18 60	83.83 81	87.06 104	76.47 39	81.46 109	70.49 134	93.24 24	95.56 50	68.13 121	90.43 76	88.47 72	93.78 49	83.02 109

CS-MVS-test		83.73 80	84.09 118	83.31 57	86.38 78	80.24 112	85.50 114	72.00 64	65.58 185	83.11 55	84.64 122	92.52 100	78.14 43	90.40 77	88.92 70	94.71 37	86.34 83

DROMVSNet		83.70 81	84.77 105	82.46 69	87.47 68	82.79 90	85.50 114	72.00 64	69.81 166	77.66 92	85.02 119	89.63 130	78.14 43	90.40 77	87.56 80	94.00 44	88.16 69

MVS_111021_HR		83.95 78	86.10 86	81.44 78	84.62 94	80.29 111	90.51 66	68.05 100	84.07 84	80.38 77	84.74 121	91.37 119	74.23 79	90.37 79	87.25 82	90.86 92	84.59 94

test2506		75.32 151	76.87 159	73.50 138	84.55 96	80.37 109	79.63 159	73.23 58	82.64 92	55.41 183	76.87 163	45.42 226	59.61 157	90.35 80	86.46 90	88.58 122	75.98 156

ECVR-MVS		79.31 126	84.20 115	73.60 136	84.55 96	80.37 109	79.63 159	73.23 58	82.64 92	55.98 180	87.50 89	86.85 149	59.61 157	90.35 80	86.46 90	88.58 122	75.26 162

ETV-MVS		79.01 129	77.98 149	80.22 94	86.69 75	79.73 117	88.80 88	68.27 98	63.22 197	71.56 128	70.25 203	73.63 194	73.66 86	90.30 82	86.77 88	92.33 74	81.95 122

train_agg		86.67 56	87.73 72	85.43 36	91.51 20	82.72 91	94.47 32	74.22 54	81.71 102	81.54 73	89.20 72	92.87 95	78.33 42	90.12 83	88.47 72	92.51 72	89.04 62

TAPA-MVS	78.00 13	85.88 61	88.37 64	82.96 63	84.69 92	88.62 43	90.62 60	64.22 131	89.15 40	88.05 15	78.83 149	93.71 84	76.20 63	90.11 84	88.22 75	94.00 44	89.97 54
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EG-PatchMatch MVS		84.35 75	87.55 73	80.62 89	86.38 78	82.24 96	86.75 107	64.02 136	84.24 81	78.17 91	89.38 69	95.03 65	78.78 37	89.95 85	86.33 92	89.59 105	85.65 89

test1111		79.67 119	84.40 109	74.16 134	85.29 88	79.56 119	81.16 145	73.13 60	84.65 80	56.08 179	88.38 82	86.14 152	60.49 153	89.78 86	85.59 100	88.79 116	76.68 153

MVS_111021_LR		83.20 89	85.33 93	80.73 87	82.88 120	78.23 130	89.61 79	65.23 123	82.08 99	81.19 74	85.31 113	92.04 112	75.22 71	89.50 87	85.90 97	90.24 95	84.23 97

CLD-MVS		82.75 96	87.22 77	77.54 113	88.01 64	85.76 72	90.23 72	54.52 188	82.28 98	82.11 68	88.48 81	95.27 56	63.95 141	89.41 88	88.29 74	86.45 145	81.01 128
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TinyColmap		83.79 79	86.12 85	81.07 80	83.42 113	81.44 101	85.42 116	68.55 94	88.71 45	89.46 8	87.60 88	92.72 97	70.34 110	89.29 89	81.94 134	89.20 110	81.12 127

pmmvs6		80.46 112	88.34 66	71.26 148	81.96 127	77.51 134	77.54 168	68.83 90	93.72 8	55.92 181	93.94 19	98.03 10	55.94 171	89.21 90	85.61 99	87.36 136	80.38 132

ambc			88.38 63		91.62 18	87.97 52	84.48 125		88.64 46	87.93 16	87.38 92	94.82 70	74.53 78	89.14 91	83.86 118	85.94 152	86.84 78

MAR-MVS		81.98 102	82.92 129	80.88 83	85.18 90	85.85 70	89.13 84	69.52 80	71.21 162	82.25 64	71.28 195	88.89 140	69.69 111	88.71 92	86.96 83	89.52 106	87.57 75
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
DCV-MVSNet		80.04 115	85.67 92	73.48 139	82.91 119	81.11 106	80.44 150	66.06 113	85.01 76	62.53 167	78.84 148	94.43 78	58.51 162	88.66 93	85.91 96	90.41 94	85.73 88

MVS_0304		84.73 73	86.19 84	83.02 60	88.32 57	86.71 64	91.55 48	70.87 73	73.79 149	82.88 56	85.13 116	93.35 90	72.55 91	88.62 94	87.69 79	91.93 78	88.05 72

canonicalmvs		81.22 110	86.04 88	75.60 121	83.17 117	83.18 88	80.29 151	65.82 119	85.97 68	67.98 150	77.74 155	91.51 117	65.17 137	88.62 94	86.15 94	91.17 89	89.09 61

EIA-MVS		78.57 130	77.90 150	79.35 100	87.24 71	80.71 107	86.16 111	64.03 135	62.63 202	73.49 117	73.60 185	76.12 188	73.83 84	88.49 96	84.93 107	91.36 84	78.78 145

Effi-MVS+-dtu		82.04 101	83.39 127	80.48 92	85.48 87	86.57 67	88.40 90	68.28 97	69.04 173	73.13 120	76.26 168	91.11 122	74.74 77	88.40 97	87.76 78	92.84 66	84.57 95

UGNet		79.62 121	85.91 89	72.28 145	73.52 180	83.91 80	86.64 108	69.51 81	79.85 125	62.57 166	85.82 110	89.63 130	53.18 185	88.39 98	87.35 81	88.28 127	86.43 81
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
MSP-MVS		88.51 43	91.36 41	85.19 40	90.63 37	92.01 4	95.29 23	77.52 28	90.48 27	80.21 79	90.21 57	96.08 36	76.38 61	88.30 99	91.42 42	91.12 90	91.01 46
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
FC-MVSNet-train		79.20 127	86.29 83	70.94 152	84.06 102	77.67 133	85.68 112	64.11 133	82.90 90	52.22 196	92.57 36	93.69 85	49.52 196	88.30 99	86.93 84	90.03 98	81.95 122

Fast-Effi-MVS+		81.42 106	83.82 122	78.62 105	82.24 125	80.62 108	87.72 95	63.51 142	73.01 152	74.75 108	83.80 128	92.70 98	73.44 88	88.15 101	85.26 103	90.05 97	83.17 107

CANet		82.84 93	84.60 107	80.78 84	87.30 69	85.20 76	90.23 72	69.00 87	72.16 158	78.73 88	84.49 124	90.70 126	69.54 114	87.65 102	86.17 93	89.87 101	85.84 86

TSAR-MVS + COLMAP		85.51 63	88.36 65	82.19 70	86.05 82	87.69 55	90.50 67	70.60 75	86.40 63	82.33 62	89.69 65	92.52 100	74.01 83	87.53 103	86.84 87	89.63 104	87.80 74

Anonymous20231211		79.37 123	85.78 90	71.89 146	82.87 121	79.66 118	78.77 165	63.93 139	83.36 86	59.39 171	90.54 53	94.66 72	56.46 169	87.38 104	84.12 114	89.92 100	80.74 129

PVSNet_Blended_VisFu		83.00 91	84.16 116	81.65 75	82.17 126	86.01 69	88.03 92	71.23 71	76.05 142	79.54 83	83.88 126	83.44 160	77.49 52	87.38 104	84.93 107	91.41 83	87.40 77

MCST-MVS		84.79 72	86.48 80	82.83 66	87.30 69	87.03 62	90.46 70	69.33 85	83.14 88	82.21 66	81.69 138	92.14 108	75.09 74	87.27 106	84.78 109	92.58 68	89.30 60

3Dnovator	79.41 10	82.21 98	86.07 87	77.71 110	79.31 144	84.61 77	87.18 101	61.02 162	85.65 69	76.11 97	85.07 118	85.38 157	70.96 106	87.22 107	86.47 89	91.66 80	88.12 71

MSDG		81.39 108	84.23 114	78.09 108	82.40 124	82.47 95	85.31 120	60.91 163	79.73 126	80.26 78	86.30 103	88.27 143	69.67 112	87.20 108	84.98 106	89.97 99	80.67 130

NR-MVSNet		82.89 92	87.43 75	77.59 112	83.91 107	83.59 85	87.10 103	78.35 20	80.64 118	68.85 143	92.67 33	96.50 26	54.19 181	87.19 109	88.68 71	93.16 61	82.75 114

Vis-MVSNet (Re-imp)		76.15 144	80.84 138	70.68 153	83.66 111	74.80 160	81.66 143	69.59 79	80.48 121	46.94 205	87.44 91	80.63 171	53.14 186	86.87 110	84.56 112	89.12 111	71.12 173

GeoE		81.92 103	83.87 120	79.66 97	84.64 93	79.87 114	89.75 78	65.90 117	76.12 141	75.87 99	84.62 123	92.23 105	71.96 98	86.83 111	83.60 119	89.83 102	83.81 102

Anonymous202405211			84.68 106		83.92 106	79.45 120	79.03 163	67.79 102	82.01 100		88.77 80	92.58 99	55.93 172	86.68 112	84.26 113	88.92 114	78.98 143

PM-MVS		80.42 114	83.63 124	76.67 116	78.04 156	72.37 169	87.14 102	60.18 168	80.13 122	71.75 127	86.12 106	93.92 83	77.08 55	86.56 113	85.12 105	85.83 154	81.18 126

HQP-MVS		85.02 69	86.41 82	83.40 56	89.19 49	86.59 65	91.28 50	71.60 70	82.79 91	83.48 52	78.65 151	93.54 88	72.55 91	86.49 114	85.89 98	92.28 75	90.95 48

pm-mvs1		78.21 132	85.68 91	69.50 163	80.38 136	75.73 151	76.25 176	65.04 124	87.59 53	54.47 187	93.16 26	95.99 42	54.20 180	86.37 115	82.98 129	86.64 141	77.96 150

TransMVSNet (Re)		79.05 128	86.66 78	70.18 158	83.32 114	75.99 148	77.54 168	63.98 137	90.68 25	55.84 182	94.80 11	96.06 37	53.73 184	86.27 116	83.22 126	86.65 140	79.61 141

IB-MVS	71.28 17	75.21 152	77.00 157	73.12 143	76.76 164	77.45 135	83.05 131	58.92 174	63.01 198	64.31 161	59.99 216	87.57 146	68.64 119	86.26 117	82.34 132	87.05 139	82.36 119
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
thisisatest0515		81.18 111	84.32 111	77.52 114	76.73 170	74.84 159	85.06 121	61.37 159	81.05 115	73.95 113	88.79 79	89.25 136	75.49 70	85.98 118	84.78 109	92.53 71	85.56 90

PCF-MVS	76.59 14	84.11 77	85.27 94	82.76 67	86.12 81	88.30 45	91.24 51	69.10 86	82.36 97	84.45 44	77.56 157	90.40 128	72.91 90	85.88 119	83.88 116	92.72 67	88.53 66
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GBi-Net		73.17 161	77.64 151	67.95 172	76.76 164	77.36 136	75.77 180	64.57 127	62.99 199	51.83 197	76.05 169	77.76 180	52.73 189	85.57 120	83.39 122	86.04 149	80.37 133

test1		73.17 161	77.64 151	67.95 172	76.76 164	77.36 136	75.77 180	64.57 127	62.99 199	51.83 197	76.05 169	77.76 180	52.73 189	85.57 120	83.39 122	86.04 149	80.37 133

FMVSNet1		78.20 133	84.83 104	70.46 156	78.62 151	79.03 123	77.90 167	67.53 105	83.02 89	55.10 185	87.19 96	93.18 93	55.65 174	85.57 120	83.39 122	87.98 129	82.40 118

tfpnnormal		77.16 136	84.26 112	68.88 166	81.02 133	75.02 156	76.52 175	63.30 144	87.29 56	52.40 194	91.24 50	93.97 81	54.85 178	85.46 123	81.08 139	85.18 160	75.76 159

MIMVSNet1		73.40 159	81.85 135	63.55 189	72.90 183	64.37 193	84.58 124	53.60 193	90.84 21	53.92 188	87.75 87	96.10 35	45.31 202	85.37 124	79.32 153	70.98 197	69.18 182

DELS-MVS		79.71 118	83.74 123	75.01 128	79.31 144	82.68 92	84.79 123	60.06 169	75.43 145	69.09 141	86.13 105	89.38 133	67.16 127	85.12 125	83.87 117	89.65 103	83.57 104
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
tfpn200view9		72.01 168	75.40 170	68.06 171	77.97 157	76.44 144	77.04 172	62.67 151	66.81 178	50.82 201	67.30 207	75.67 190	52.46 192	85.06 126	82.64 130	87.41 135	73.86 166

Effi-MVS+		82.33 97	83.87 120	80.52 91	84.51 99	81.32 102	87.53 97	68.05 100	74.94 147	79.67 82	82.37 135	92.31 104	72.21 93	85.06 126	86.91 85	91.18 88	84.20 98

thres600view7		74.34 157	78.43 146	69.56 162	80.47 134	76.28 146	78.65 166	62.56 152	77.39 135	52.53 192	74.03 182	76.78 185	55.90 173	85.06 126	85.19 104	87.25 137	74.29 164

thres200		72.41 167	76.00 167	68.21 170	78.28 153	76.28 146	74.94 186	62.56 152	72.14 159	51.35 200	69.59 205	76.51 186	54.89 176	85.06 126	80.51 145	87.25 137	71.92 172

EPNet		79.36 124	79.44 142	79.27 102	89.51 47	77.20 139	88.35 91	77.35 32	68.27 175	74.29 111	76.31 166	79.22 174	59.63 156	85.02 130	85.45 102	86.49 144	84.61 93
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
QAPM		80.43 113	84.34 110	75.86 119	79.40 143	82.06 98	79.86 156	61.94 156	83.28 87	74.73 109	81.74 137	85.44 156	70.97 105	84.99 131	84.71 111	88.29 126	88.14 70

PatchMatch-RL		76.05 145	76.64 160	75.36 123	77.84 160	69.87 177	81.09 147	63.43 143	71.66 160	68.34 149	71.70 191	81.76 168	74.98 75	84.83 132	83.44 121	86.45 145	73.22 170

OpenMVS	75.38 16	78.44 131	81.39 137	74.99 129	80.46 135	79.85 115	79.99 153	58.31 177	77.34 136	73.85 114	77.19 160	82.33 167	68.60 120	84.67 133	81.95 133	88.72 118	86.40 82

tttt0517		75.86 148	76.23 164	75.42 122	75.55 176	74.06 163	82.73 134	60.31 165	69.24 169	70.24 136	79.18 144	58.79 212	72.17 94	84.49 134	83.08 127	91.54 81	84.80 92

ET-MVSNet_ETH3D		74.71 155	74.19 175	75.31 124	79.22 146	75.29 154	82.70 135	64.05 134	65.45 187	70.96 133	77.15 161	57.70 214	65.89 134	84.40 135	81.65 136	89.03 112	77.67 151

CDS-MVSNet		73.07 164	77.02 156	68.46 168	81.62 129	72.89 166	79.56 161	70.78 74	69.56 168	52.52 193	77.37 159	81.12 170	42.60 204	84.20 136	83.93 115	83.65 167	70.07 178
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thisisatest0530		75.54 150	75.95 168	75.05 126	75.08 177	73.56 164	82.15 139	60.31 165	69.17 170	69.32 139	79.02 145	58.78 213	72.17 94	83.88 137	83.08 127	91.30 86	84.20 98

EU-MVSNet		76.48 141	80.53 139	71.75 147	67.62 199	70.30 174	81.74 142	54.06 191	75.47 144	71.01 132	80.10 140	93.17 94	73.67 85	83.73 138	77.85 158	82.40 173	83.07 108

USDC		81.39 108	83.07 128	79.43 99	81.48 130	78.95 125	82.62 136	66.17 112	87.45 55	90.73 4	82.40 134	93.65 86	66.57 131	83.63 139	77.97 157	89.00 113	77.45 152

V42		79.59 122	83.59 125	74.93 131	69.61 193	77.05 141	86.59 109	55.84 183	78.42 133	77.29 93	89.84 63	95.08 63	74.12 80	83.05 140	80.11 149	86.12 148	81.59 124

thres400		73.13 163	76.99 158	68.62 167	79.46 142	74.93 158	77.23 170	61.23 161	75.54 143	52.31 195	72.20 190	77.10 183	54.89 176	82.92 141	82.62 131	86.57 143	73.66 169

casdiffmvs		79.93 116	84.11 117	75.05 126	81.41 132	78.99 124	82.95 133	62.90 150	81.53 106	68.60 147	91.94 38	96.03 38	65.84 135	82.89 142	77.07 165	88.59 121	80.34 136

v8		82.20 99	84.56 108	79.45 98	82.42 123	81.65 99	87.26 100	64.27 130	79.36 128	81.70 71	91.04 52	95.75 46	73.30 89	82.82 143	79.18 154	87.74 132	82.09 120

DPM-MVS		81.42 106	82.11 133	80.62 89	87.54 66	85.30 75	90.18 74	68.96 88	81.00 116	79.15 86	70.45 201	83.29 162	67.67 125	82.81 144	83.46 120	90.19 96	88.48 67

PVSNet_BlendedMVS		76.45 142	78.12 147	74.49 132	76.76 164	78.46 127	79.65 157	63.26 146	65.42 188	73.15 118	75.05 178	88.96 137	66.51 132	82.73 145	77.66 160	87.61 133	78.60 147

PVSNet_Blended		76.45 142	78.12 147	74.49 132	76.76 164	78.46 127	79.65 157	63.26 146	65.42 188	73.15 118	75.05 178	88.96 137	66.51 132	82.73 145	77.66 160	87.61 133	78.60 147

v10		83.17 90	85.22 96	80.78 84	83.26 115	82.99 89	88.66 89	66.49 109	79.24 129	83.60 49	91.46 46	95.47 52	74.12 80	82.60 147	80.66 142	88.53 124	84.11 100

v144192		83.43 85	84.97 101	81.63 76	83.43 112	81.23 104	89.42 83	66.04 115	81.45 110	86.40 35	91.46 46	95.70 48	75.76 68	82.14 148	80.23 148	88.74 117	82.57 116

v1144		83.22 88	85.01 99	81.14 79	83.76 110	81.60 100	88.95 86	65.58 121	81.89 101	85.80 37	91.68 44	95.84 43	74.04 82	82.12 149	80.56 144	88.70 119	81.41 125

CANet_DTU		75.04 153	78.45 145	71.07 149	77.27 161	77.96 131	83.88 128	58.00 178	64.11 193	68.67 146	75.65 175	88.37 142	53.92 183	82.05 150	81.11 138	84.67 162	79.88 139

v2v482		82.20 99	84.26 112	79.81 96	82.67 122	80.18 113	87.67 96	63.96 138	81.69 103	84.73 42	91.27 49	96.33 33	72.05 97	81.94 151	79.56 151	87.79 131	78.84 144

v1192		83.61 82	85.23 95	81.72 74	84.05 103	82.15 97	89.54 80	66.20 111	81.38 111	86.76 33	91.79 42	96.03 38	74.88 76	81.81 152	80.92 141	88.91 115	82.50 117

Fast-Effi-MVS+-dtu		76.92 137	77.18 155	76.62 117	79.55 141	79.17 122	84.80 122	77.40 30	64.46 192	68.75 145	70.81 199	86.57 150	63.36 148	81.74 153	81.76 135	85.86 153	75.78 158

CMPMVS	55.74 18	71.56 170	76.26 163	66.08 182	68.11 197	63.91 195	63.17 211	50.52 203	68.79 174	75.49 102	70.78 200	85.67 154	63.54 145	81.58 154	77.20 164	75.63 184	85.86 85
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v1921920		83.49 84	84.94 102	81.80 73	83.78 109	81.20 105	89.50 81	65.91 116	81.64 104	87.18 25	91.70 43	95.39 54	75.85 66	81.56 155	80.27 147	88.60 120	82.80 113

v148		79.33 125	82.32 132	75.84 120	80.14 137	75.74 150	81.98 140	57.06 180	81.51 108	79.36 85	89.42 67	96.42 29	71.32 101	81.54 156	75.29 174	85.20 159	76.32 154

v1240		83.57 83	84.94 102	81.97 71	84.05 103	81.27 103	89.46 82	66.06 113	81.31 112	87.50 21	91.88 41	95.46 53	76.25 62	81.16 157	80.51 145	88.52 125	82.98 111

CVMVSNet		75.65 149	77.62 153	73.35 142	71.95 186	69.89 176	83.04 132	60.84 164	69.12 171	68.76 144	79.92 143	78.93 176	73.64 87	81.02 158	81.01 140	81.86 176	83.43 105

E-PMN		59.07 205	62.79 205	54.72 204	67.01 203	47.81 217	60.44 215	43.40 208	72.95 153	44.63 207	70.42 202	73.17 195	58.73 161	80.97 159	51.98 216	54.14 216	42.26 218

pmmvs-eth3d		79.64 120	82.06 134	76.83 115	80.05 138	72.64 167	87.47 98	66.59 108	80.83 117	73.50 116	89.32 70	93.20 92	67.78 123	80.78 160	81.64 137	85.58 157	76.01 155

FMVSNet2		74.43 156	79.70 140	68.27 169	76.76 164	77.36 136	75.77 180	65.36 122	72.28 156	52.97 191	81.92 136	85.61 155	52.73 189	80.66 161	79.73 150	86.04 149	80.37 133

IterMVS-LS		79.79 117	82.56 131	76.56 118	81.83 128	77.85 132	79.90 155	69.42 84	78.93 131	71.21 130	90.47 54	85.20 158	70.86 107	80.54 162	80.57 143	86.15 147	84.36 96
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EMVS		58.97 206	62.63 207	54.70 205	66.26 208	48.71 215	61.74 213	42.71 209	72.80 155	46.00 206	73.01 189	71.66 196	57.91 165	80.41 163	50.68 218	53.55 217	41.11 219

IterMVS-SCA-FT		77.23 135	79.18 144	74.96 130	76.67 171	79.85 115	75.58 185	61.34 160	73.10 150	73.79 115	86.23 104	79.61 173	79.00 36	80.28 164	75.50 173	83.41 171	79.70 140

diffmvs		76.74 138	81.61 136	71.06 150	75.64 175	74.45 162	80.68 149	57.57 179	77.48 134	67.62 153	88.95 75	93.94 82	61.98 150	79.74 165	76.18 169	82.85 172	80.50 131

MVSTER		68.08 184	69.73 186	66.16 180	66.33 207	70.06 175	75.71 183	52.36 197	55.18 216	58.64 173	70.23 204	56.72 217	57.34 166	79.68 166	76.03 170	86.61 142	80.20 138

baseline1		69.62 176	73.55 179	65.02 188	78.95 149	70.39 173	71.38 196	62.03 155	70.97 163	47.95 204	78.47 152	68.19 200	47.77 200	79.65 167	76.94 167	82.05 174	70.27 176

DI_MVS_plusplus_trai		77.64 134	79.64 141	75.31 124	79.87 140	76.89 142	81.55 144	63.64 140	76.21 140	72.03 125	85.59 112	82.97 164	66.63 130	79.27 168	77.78 159	88.14 128	78.76 146

EPNet_dtu		71.90 169	73.03 181	70.59 154	78.28 153	61.64 198	82.44 137	64.12 132	63.26 196	69.74 137	71.47 193	82.41 165	51.89 193	78.83 169	78.01 156	77.07 183	75.60 160
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_Test		76.72 139	79.40 143	73.60 136	78.85 150	74.99 157	79.91 154	61.56 158	69.67 167	72.44 121	85.98 108	90.78 124	63.50 146	78.30 170	75.74 172	85.33 158	80.31 137

thres100view900		69.86 175	72.97 182	66.24 179	77.97 157	72.49 168	73.29 190	59.12 172	66.81 178	50.82 201	67.30 207	75.67 190	50.54 195	78.24 171	79.40 152	85.71 156	70.88 174

gg-mvs-nofinetune		72.68 166	75.21 172	69.73 160	81.48 130	69.04 180	70.48 197	76.67 36	86.92 60	67.80 152	88.06 85	64.67 202	42.12 206	77.60 172	73.65 177	79.81 178	66.57 185

test20.03		69.91 174	76.20 165	62.58 190	84.01 105	67.34 185	75.67 184	65.88 118	79.98 124	40.28 214	82.65 131	89.31 135	39.63 209	77.41 173	73.28 178	69.98 198	63.40 193

GA-MVS		75.01 154	76.39 162	73.39 140	78.37 152	75.66 152	80.03 152	58.40 176	70.51 164	75.85 100	83.24 129	76.14 187	63.75 142	77.28 174	76.62 168	83.97 166	75.30 161

new-patchmatchnet		62.59 198	73.79 178	49.53 212	76.98 163	53.57 208	53.46 220	54.64 187	85.43 72	28.81 219	91.94 38	96.41 30	25.28 217	76.80 175	53.66 215	57.99 213	58.69 206

MDTV_nov1_ep13_2view		72.96 165	75.59 169	69.88 159	71.15 190	64.86 192	82.31 138	54.45 189	76.30 139	78.32 90	86.52 101	91.58 115	61.35 151	76.80 175	66.83 195	71.70 190	66.26 186

MS-PatchMatch		71.18 173	73.99 177	67.89 174	77.16 162	71.76 170	77.18 171	56.38 182	67.35 176	55.04 186	74.63 180	75.70 189	62.38 149	76.62 177	75.97 171	79.22 180	75.90 157

FMVSNet3		71.40 172	75.20 173	66.97 176	75.00 178	76.59 143	74.29 187	64.57 127	62.99 199	51.83 197	76.05 169	77.76 180	51.49 194	76.58 178	77.03 166	84.62 163	79.43 142

testgi		68.20 183	76.05 166	59.04 196	79.99 139	67.32 186	81.16 145	51.78 199	84.91 77	39.36 215	73.42 186	95.19 58	32.79 215	76.54 179	70.40 187	69.14 201	64.55 189

baseline2		68.71 181	68.34 190	69.14 164	75.69 174	69.70 178	76.60 174	55.53 185	60.13 207	62.07 169	66.76 209	60.35 207	60.77 152	76.53 180	74.03 176	84.19 165	70.88 174

IterMVS		73.62 158	76.53 161	70.23 157	71.83 187	77.18 140	80.69 148	53.22 195	72.23 157	66.62 156	85.21 115	78.96 175	69.54 114	76.28 181	71.63 184	79.45 179	74.25 165
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs4		75.92 146	77.48 154	74.10 135	78.21 155	70.94 171	84.06 126	64.78 126	75.13 146	68.47 148	84.12 125	83.32 161	64.74 140	75.93 182	79.14 155	84.31 164	73.77 167

Anonymous20231206		67.28 185	73.41 180	60.12 195	76.45 173	63.61 196	74.21 188	56.52 181	76.35 138	42.23 209	75.81 174	90.47 127	41.51 207	74.52 183	69.97 189	69.83 199	63.17 194

TAMVS		63.02 193	69.30 187	55.70 203	70.12 191	56.89 204	69.63 201	45.13 207	70.23 165	38.00 216	77.79 153	75.15 192	42.60 204	74.48 184	72.81 182	68.70 202	57.75 209

baseline		69.33 178	75.37 171	62.28 192	66.54 205	66.67 188	73.95 189	48.07 204	66.10 181	59.26 172	82.45 132	86.30 151	54.44 179	74.42 185	73.25 179	71.42 193	78.43 149

HyFIR lowres test		73.29 160	74.14 176	72.30 144	73.08 182	78.33 129	83.12 130	62.41 154	63.81 194	62.13 168	76.67 165	78.50 177	71.09 103	74.13 186	77.47 163	81.98 175	70.10 177

FMVSNet5		56.37 210	60.14 212	51.98 211	60.83 211	59.58 200	66.85 208	42.37 210	52.68 218	41.33 212	47.09 219	54.68 220	35.28 212	73.88 187	70.77 186	65.24 207	62.26 197

PMMVS2		48.13 215	64.06 200	29.55 216	44.06 222	36.69 222	51.95 221	29.97 216	74.75 148	8.90 226	76.02 172	91.24 121	7.53 220	73.78 188	55.91 210	34.87 221	40.01 220

gm-plane-assit		71.56 170	69.99 185	73.39 140	84.43 100	73.21 165	90.42 71	51.36 201	84.08 83	76.00 98	91.30 48	37.09 227	59.01 160	73.65 189	70.24 188	79.09 181	60.37 202

CHOSEN 280x420		56.32 211	58.85 217	53.36 207	51.63 217	39.91 221	69.12 205	38.61 213	56.29 213	36.79 217	48.84 218	62.59 204	63.39 147	73.61 190	67.66 193	60.61 208	63.07 195

pmmvs3		62.72 196	68.71 189	55.74 202	50.74 219	57.10 203	70.05 199	28.82 217	61.57 206	57.39 175	71.19 197	85.73 153	53.96 182	73.36 191	69.43 191	73.47 188	62.55 196

pmmvs5		68.91 179	74.35 174	62.56 191	67.45 201	66.78 187	71.70 193	51.47 200	67.17 177	56.25 178	82.41 133	88.59 141	47.21 201	73.21 192	74.23 175	81.30 177	68.03 184

CHOSEN 1792x2688		68.80 180	71.09 183	66.13 181	69.11 195	68.89 181	78.98 164	54.68 186	61.63 204	56.69 176	71.56 192	78.39 178	67.69 124	72.13 193	72.01 183	69.63 200	73.02 171

CR-MVSNet		69.56 177	68.34 190	70.99 151	72.78 185	67.63 183	64.47 209	67.74 103	59.93 208	72.30 122	80.10 140	56.77 216	65.04 138	71.64 194	72.91 180	83.61 169	69.40 180

PatchT		66.25 188	66.76 194	65.67 185	55.87 215	60.75 199	70.17 198	59.00 173	59.80 210	72.30 122	78.68 150	54.12 221	65.04 138	71.64 194	72.91 180	71.63 192	69.40 180

MIMVSNet		63.02 193	69.02 188	56.01 201	68.20 196	59.26 201	70.01 200	53.79 192	71.56 161	41.26 213	71.38 194	82.38 166	36.38 211	71.43 196	67.32 194	66.45 206	59.83 204

test0.0.03 1		61.79 201	65.33 197	57.65 199	79.07 147	64.09 194	68.51 206	62.93 148	61.59 205	33.71 218	61.58 215	71.58 198	33.43 214	70.95 197	68.68 192	68.26 203	58.82 205

pmnet_mix02		62.60 197	70.81 184	53.02 208	66.56 204	50.44 214	62.81 212	46.84 206	79.13 130	43.76 208	87.45 90	90.75 125	39.85 208	70.48 198	57.09 209	58.27 212	60.32 203

RPMNet		67.02 186	63.99 201	70.56 155	71.55 188	67.63 183	75.81 178	69.44 83	59.93 208	63.24 163	64.32 211	47.51 225	59.68 155	70.37 199	69.64 190	83.64 168	68.49 183

PMMVS		61.98 200	65.61 196	57.74 198	45.03 221	51.76 212	69.54 202	35.05 214	55.49 215	55.32 184	68.23 206	78.39 178	58.09 163	70.21 200	71.56 185	83.42 170	63.66 191

dps		65.14 189	64.50 199	65.89 184	71.41 189	65.81 191	71.44 195	61.59 157	58.56 211	61.43 170	75.45 176	52.70 223	58.06 164	69.57 201	64.65 197	71.39 194	64.77 188

SCA		68.54 182	67.52 192	69.73 160	67.79 198	75.04 155	76.96 173	68.94 89	66.41 180	67.86 151	74.03 182	60.96 205	65.55 136	68.99 202	65.67 196	71.30 195	61.54 201

test-mter		59.39 204	61.59 208	56.82 200	53.21 216	54.82 206	73.12 192	26.57 219	53.19 217	56.31 177	64.71 210	60.47 206	56.36 170	68.69 203	64.27 198	75.38 185	65.00 187

MDTV_nov1_ep13		64.96 190	64.77 198	65.18 187	67.08 202	62.46 197	75.80 179	51.10 202	62.27 203	69.74 137	74.12 181	62.65 203	55.64 175	68.19 204	62.16 203	71.70 190	61.57 200

MVE	41.12 19	51.80 214	60.92 210	41.16 214	35.21 223	34.14 223	48.45 223	41.39 211	69.11 172	19.53 222	63.33 212	73.80 193	63.56 144	67.19 205	61.51 204	38.85 220	57.38 210
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet		52.29 213	63.16 204	39.61 215	58.89 213	44.70 219	48.78 222	34.73 215	65.88 183	17.85 223	73.42 186	80.00 172	23.06 218	67.00 206	62.28 202	54.36 215	48.81 215

PatchmatchNet		64.81 191	63.74 202	66.06 183	69.21 194	58.62 202	73.16 191	60.01 170	65.92 182	66.19 158	76.27 167	59.09 209	60.45 154	66.58 207	61.47 205	67.33 204	58.24 207
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test-LLR		62.15 199	59.46 215	65.29 186	79.07 147	52.66 210	69.46 203	62.93 148	50.76 219	53.81 189	63.11 213	58.91 210	52.87 187	66.54 208	62.34 200	73.59 186	61.87 198

TESTMET0.1,1		57.21 207	59.46 215	54.60 206	50.95 218	52.66 210	69.46 203	26.91 218	50.76 219	53.81 189	63.11 213	58.91 210	52.87 187	66.54 208	62.34 200	73.59 186	61.87 198

CostFormer		66.81 187	66.94 193	66.67 178	72.79 184	68.25 182	79.55 162	55.57 184	65.52 186	62.77 165	76.98 162	60.09 208	56.73 168	65.69 210	62.35 199	72.59 189	69.71 179

N_pmnet		54.95 212	65.90 195	42.18 213	66.37 206	43.86 220	57.92 217	39.79 212	79.54 127	17.24 224	86.31 102	87.91 144	25.44 216	64.68 211	51.76 217	46.33 219	47.23 216

tpm		62.79 195	63.25 203	62.26 193	70.09 192	53.78 207	71.65 194	47.31 205	65.72 184	76.70 95	80.62 139	56.40 219	48.11 198	64.20 212	58.54 206	59.70 210	63.47 192

GG-mvs-BLEND		41.63 216	60.36 211	19.78 217	0.14 228	66.04 189	55.66 219	0.17 225	57.64 212	2.42 227	51.82 217	69.42 199	0.28 224	64.11 213	58.29 207	60.02 209	55.18 211

tpm cat1		64.79 192	62.74 206	67.17 175	74.61 179	65.91 190	76.18 177	59.32 171	64.88 191	66.41 157	71.21 196	53.56 222	59.17 159	61.53 214	58.16 208	67.33 204	63.95 190

MVS-HIRNet		59.74 202	58.74 218	60.92 194	57.74 214	45.81 218	56.02 218	58.69 175	55.69 214	65.17 159	70.86 198	71.66 196	56.75 167	61.11 215	53.74 214	71.17 196	52.28 213

EPMVS		56.62 209	59.77 214	52.94 209	62.41 210	50.55 213	60.66 214	52.83 196	65.15 190	41.80 211	77.46 158	57.28 215	42.68 203	59.81 216	54.82 212	57.23 214	53.35 212

ADS-MVSNet		56.89 208	61.09 209	52.00 210	59.48 212	48.10 216	58.02 216	54.37 190	72.82 154	49.19 203	75.32 177	65.97 201	37.96 210	59.34 217	54.66 213	52.99 218	51.42 214

tpmrst		59.42 203	60.02 213	58.71 197	67.56 200	53.10 209	66.99 207	51.88 198	63.80 195	57.68 174	76.73 164	56.49 218	48.73 197	56.47 218	55.55 211	59.43 211	58.02 208

tmp_tt				13.54 219	16.73 224	6.42 225	8.49 226	2.36 222	28.69 223	27.44 220	18.40 222	13.51 229	3.70 221	33.23 219	36.26 219	22.54 224

test_method		22.69 217	26.99 219	17.67 218	2.13 225	4.31 226	27.50 224	4.53 221	37.94 221	24.52 221	36.20 221	51.40 224	15.26 219	29.86 220	17.09 220	32.07 222	12.16 221

DeepMVS_CX						17.78 224	20.40 225	6.69 220	31.41 222	9.80 225	38.61 220	34.88 228	33.78 213	28.41 221		23.59 223	45.77 217

test123		1.06 218	1.41 220	0.64 220	0.39 226	0.48 227	0.52 229	0.25 224	1.11 225	1.37 228	2.01 224	1.98 230	0.87 222	1.43 222	1.27 221	0.46 226	1.62 223

testmvs		0.93 219	1.37 221	0.41 221	0.36 227	0.36 228	0.62 228	0.39 223	1.48 224	0.18 229	2.41 223	1.31 231	0.41 223	1.25 223	1.08 222	0.48 225	1.68 222

uanet_test		0.00 220	0.00 222	0.00 222	0.00 229	0.00 229	0.00 230	0.00 226	0.00 226	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet-low-res		0.00 220	0.00 222	0.00 222	0.00 229	0.00 229	0.00 230	0.00 226	0.00 226	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet		0.00 220	0.00 222	0.00 222	0.00 229	0.00 229	0.00 230	0.00 226	0.00 226	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

RE-MVS-def										87.10 29

9.14												89.43 132

SR-MVS					91.82 14			80.80 7				95.53 51

our_test_3					73.27 181	70.91 172	83.26 129

MTAPA										89.37 9		94.85 68

MTMP										90.54 5		95.16 60

Patchmatch-RL test							4.13 227

XVS					91.28 26	91.23 8	96.89 2			87.14 26		94.53 73				95.84 15

X-MVStestdata					91.28 26	91.23 8	96.89 2			87.14 26		94.53 73				95.84 15

abl_6				79.30 101	84.98 91	85.78 71	90.50 67	66.88 107	77.08 137	74.02 112	73.29 188	89.34 134	68.94 118			90.49 93	85.98 84

mPP-MVS					93.05 4							95.77 45

NP-MVS									78.65 132

Patchmtry						56.88 205	64.47 209	67.74 103		72.30 122

High-resolution multi-view benchmark