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		86.29 1	90.77 1	81.06 1	75.10 52	83.76 2	93.79 1	61.08 18	89.57 1	86.19 1	90.06 10	93.01 24	76.72 2	94.71 1	92.72 1	93.47 1	91.56 2

COLMAP_ROB	75.87 2	84.34 2	89.80 2	77.97 11	75.52 50	82.76 4	90.39 19	54.21 54	89.37 2	83.18 2	89.90 12	95.58 11	72.34 10	92.31 4	90.04 5	92.17 5	88.61 18
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CP-MVS		84.06 3	86.79 10	80.86 2	81.81 8	79.66 29	92.67 6	64.48 1	83.13 30	82.32 3	80.89 72	92.97 25	72.51 9	91.74 6	90.02 6	91.40 17	89.14 8

ACMMPR		83.94 4	87.20 4	80.14 4	81.04 13	81.92 8	92.57 8	63.14 5	84.35 19	79.45 11	83.37 46	92.04 35	72.82 8	90.66 12	88.96 11	91.80 6	89.13 9

MP-MVS		83.50 5	86.11 19	80.45 3	82.58 4	80.60 24	92.68 5	63.48 3	81.43 44	80.21 9	81.95 62	90.76 53	72.86 6	90.14 19	89.30 10	90.92 19	88.59 19
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMP		83.17 6	86.75 11	79.01 7	80.11 25	82.01 7	92.29 10	60.35 25	82.20 38	78.32 15	80.59 73	93.14 22	70.67 15	91.30 8	89.36 9	92.30 4	88.62 17
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
PGM-MVS		83.03 7	85.67 26	79.95 5	80.69 17	81.09 15	92.40 9	63.06 6	79.38 58	80.21 9	80.31 75	91.44 40	71.75 12	90.46 15	88.53 14	91.57 9	88.50 20

LGP-MVS_train		82.91 8	86.50 13	78.72 8	78.72 34	81.03 16	89.78 23	61.16 17	80.15 54	80.44 6	84.83 36	94.19 14	70.52 17	90.70 11	87.19 22	91.71 8	87.37 29

ACMM	71.24 7	82.85 9	86.59 12	78.50 9	80.10 26	78.59 32	91.77 11	60.76 22	84.43 17	76.49 24	81.58 67	93.50 17	70.45 18	91.38 7	89.42 8	91.42 16	87.22 31
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HFP-MVS		82.37 10	86.28 15	77.81 14	79.94 27	80.96 18	91.13 14	63.30 4	84.04 21	71.81 39	82.39 57	89.59 69	69.16 23	89.08 25	88.83 13	91.49 13	89.10 10

DeepC-MVS	73.80 3	82.34 11	86.87 8	77.06 18	78.62 35	84.34 1	90.30 21	63.54 2	83.10 31	71.30 44	86.91 23	90.54 59	67.12 29	87.81 34	87.05 23	91.46 15	88.37 21
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CPTT-MVS		82.32 12	85.00 34	79.19 6	80.73 16	80.86 21	91.68 12	62.59 10	82.55 35	75.53 28	73.88 117	92.28 31	73.74 5	90.07 20	87.65 18	90.87 20	87.74 25

ACMP	70.35 9	82.17 13	86.45 14	77.18 17	79.33 28	81.00 17	89.27 27	58.63 31	81.35 46	75.46 29	82.97 52	95.08 12	68.90 24	90.49 14	87.43 21	91.48 14	86.84 33
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SteuartSystems-ACMMP		82.16 14	85.55 28	78.21 10	80.48 19	79.28 30	92.65 7	61.03 19	80.55 52	77.00 22	81.80 65	90.71 54	68.73 25	90.25 17	87.94 17	89.36 27	88.30 22
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		82.15 15	85.93 21	77.74 15	80.13 24	80.25 26	91.01 15	60.61 23	85.54 11	78.61 14	83.21 49	86.96 98	65.95 34	88.10 31	87.59 19	90.11 21	89.83 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
SD-MVS		82.13 16	86.80 9	76.67 19	80.36 22	80.66 22	89.48 25	56.93 34	82.50 36	67.55 67	87.05 21	91.40 42	72.84 7	88.66 27	88.32 15	92.85 2	89.04 11
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
LTVRE_ROB	75.99 1	82.04 17	87.16 5	76.07 22	63.57 124	70.27 74	86.48 41	62.99 7	89.00 5	80.32 7	86.25 26	91.04 47	74.66 4	92.58 3	90.29 4	88.42 34	90.72 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
DVP-MVS++		82.03 18	88.03 3	75.05 27	82.08 6	78.96 31	88.98 31	56.44 39	89.29 3	72.39 37	93.25 1	93.86 16	63.42 49	85.46 45	81.36 56	86.96 46	94.00 1

PMVS	70.37 8	81.82 19	87.08 6	75.68 24	77.06 42	77.23 40	87.77 38	56.25 42	83.33 29	67.18 72	89.48 14	87.94 82	77.70 1	93.02 2	92.57 2	88.13 36	86.00 38
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMMP_NAP		81.79 20	85.72 24	77.21 16	79.15 32	79.68 28	91.62 13	59.66 27	83.55 26	77.74 18	83.72 44	87.34 91	65.36 35	88.61 28	87.56 20	89.73 26	89.58 6

X-MVS		81.61 21	84.73 36	77.97 11	80.31 23	81.29 12	93.53 2	62.50 11	81.41 45	77.45 19	72.04 129	90.19 62	62.50 56	90.57 13	88.87 12	91.54 10	88.73 15

OPM-MVS		81.44 22	85.68 25	76.49 20	79.27 29	78.21 35	89.84 22	58.67 30	85.25 12	76.26 25	85.28 33	92.88 26	66.03 33	87.20 37	85.40 27	88.86 31	85.58 42
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
TSAR-MVS + MP.		81.23 23	86.13 17	75.52 25	80.74 15	83.22 3	90.55 16	55.12 49	80.87 49	67.62 66	88.01 16	92.38 30	70.61 16	86.64 39	83.10 42	88.51 32	88.67 16
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
TSAR-MVS + ACMM		81.20 24	86.92 7	74.52 29	77.60 38	82.29 5	84.41 48	62.95 8	82.99 32	64.03 81	87.71 17	89.17 71	71.98 11	88.19 30	88.10 16	86.18 55	89.95 4

APDe-MVS		81.08 25	86.12 18	75.20 26	79.25 30	80.91 19	90.38 20	57.05 33	85.83 10	66.07 77	87.34 20	91.27 43	69.45 19	85.99 43	82.55 44	88.98 30	88.95 13

DPE-MVS		81.01 26	85.18 30	76.15 21	78.58 36	80.64 23	89.77 24	57.92 32	81.66 43	73.45 32	86.84 24	89.80 67	69.33 21	85.40 46	82.91 43	87.87 38	89.01 12
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APD-MVS		80.60 27	84.63 37	75.91 23	81.22 11	81.48 10	90.49 17	58.81 29	77.54 64	67.49 68	85.90 28	89.82 66	69.43 20	86.08 42	83.80 37	88.01 37	87.77 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HPM-MVS++		80.44 28	82.57 49	77.96 13	81.99 7	72.76 61	90.48 18	61.31 14	80.85 50	77.90 17	81.93 63	87.01 95	68.20 27	84.15 56	85.27 29	87.85 39	86.00 38

DVP-MVS		80.31 29	85.60 27	74.15 33	76.23 46	78.39 33	86.62 40	55.79 45	86.47 9	71.32 43	90.96 7	89.02 73	69.28 22	84.62 55	81.64 53	85.66 60	88.09 23
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
ACMH+	67.97 10	80.15 30	86.16 16	73.14 40	73.82 59	76.41 43	83.59 52	54.82 52	87.35 6	70.86 48	86.98 22	96.27 4	66.50 30	89.17 24	83.39 39	89.26 28	83.56 48

OMC-MVS		79.95 31	85.28 29	73.74 36	72.95 62	80.10 27	87.87 37	48.13 84	84.62 16	79.42 12	80.27 76	92.49 28	64.14 43	87.25 36	85.11 30	89.92 24	87.10 32

SED-MVS		79.70 32	85.16 31	73.34 38	75.83 49	78.11 36	88.77 33	56.45 38	84.85 14	69.45 59	90.70 9	88.38 77	63.16 51	85.12 51	81.28 57	86.40 52	87.63 26

MSP-MVS		79.65 33	84.28 40	74.25 31	78.92 33	81.86 9	89.07 28	60.49 24	83.85 24	70.05 54	85.12 34	90.92 51	62.99 53	81.15 76	81.64 53	83.99 68	85.42 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
DeepPCF-MVS	71.57 5	79.49 34	84.05 41	74.17 32	74.14 56	80.88 20	89.33 26	56.24 43	82.41 37	71.58 41	82.27 58	86.47 101	66.47 31	84.80 53	84.16 35	87.26 44	87.34 30

LS3D		79.33 35	84.03 42	73.84 34	75.37 51	78.09 37	83.30 53	52.94 61	84.42 18	76.01 26	84.16 40	87.44 90	65.34 36	86.30 40	82.08 51	90.09 22	85.70 40

3Dnovator+	72.94 4	78.78 36	83.05 46	73.80 35	70.70 75	81.34 11	88.33 34	56.01 44	81.33 47	72.87 36	78.06 91	81.15 128	63.83 46	87.39 35	85.82 25	91.06 18	86.28 37

UA-Net		78.65 37	83.96 43	72.46 42	84.87 1	76.15 44	89.06 29	55.70 46	77.25 65	53.14 116	79.73 80	82.09 126	59.69 72	92.21 5	90.93 3	92.32 3	89.36 7

DeepC-MVS_fast	71.40 6	78.48 38	82.92 47	73.31 39	76.44 45	82.23 6	87.59 39	56.56 37	77.79 62	68.91 63	77.00 95	87.32 92	61.90 58	85.40 46	84.37 32	88.46 33	86.33 36
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SF-MVS		78.36 39	83.47 45	72.41 43	76.04 48	75.72 47	83.86 50	51.81 65	84.00 23	70.65 51	81.27 69	92.22 32	64.64 41	83.28 65	80.28 59	87.44 43	87.49 28

WR-MVS		78.32 40	86.09 20	69.25 60	76.22 47	72.33 68	85.71 44	59.02 28	86.66 7	51.41 122	92.91 2	96.76 1	53.09 106	90.21 18	85.30 28	90.05 23	78.46 75

ACMH	66.19 11	78.12 41	84.55 38	70.63 51	69.62 81	72.40 67	80.77 68	46.43 96	89.24 4	77.99 16	87.42 19	95.83 9	62.95 54	86.27 41	78.24 70	86.00 58	82.46 50
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
train_agg		77.83 42	80.47 59	74.77 28	80.92 14	69.60 75	88.87 32	56.32 41	74.03 81	71.03 46	83.67 45	87.68 85	64.75 40	83.70 58	81.85 52	86.71 48	82.73 49

NCCC		77.82 43	80.72 58	74.43 30	79.24 31	75.72 47	88.06 35	56.36 40	79.61 56	73.22 34	67.75 142	87.05 94	63.09 52	85.62 44	84.00 36	86.62 49	85.30 45

CNVR-MVS		77.79 44	81.57 53	73.38 37	78.37 37	75.91 45	87.97 36	55.11 50	79.41 57	70.98 47	74.70 113	86.43 102	61.77 59	85.10 52	83.73 38	86.10 57	85.68 41

WR-MVS_H		77.56 45	85.88 22	67.86 64	80.54 18	74.32 55	83.23 54	61.78 12	83.47 27	47.46 142	91.81 6	95.84 8	50.50 118	90.44 16	84.37 32	83.63 73	80.89 61

RPSCF		77.56 45	84.51 39	69.46 59	65.17 112	74.36 54	79.74 75	47.45 87	84.01 22	72.89 35	77.89 92	90.67 55	65.14 38	88.25 29	89.74 7	86.38 53	86.64 35

PS-CasMVS		77.46 47	85.80 23	67.73 66	81.24 10	72.88 60	80.63 69	61.28 15	84.14 20	50.53 128	92.13 4	96.76 1	50.12 121	91.02 9	84.46 31	82.60 85	79.19 68

DTE-MVSNet		77.28 48	84.87 35	68.42 62	82.94 3	72.70 63	81.60 63	61.78 12	85.03 13	51.40 123	92.11 5	96.00 6	49.42 124	89.73 22	82.52 46	83.39 78	75.98 85

SixPastTwentyTwo		77.24 49	83.65 44	69.78 55	65.14 113	64.85 97	77.44 85	47.74 86	82.76 34	68.52 64	87.65 18	93.31 19	71.68 13	89.49 23	82.41 47	88.14 35	85.05 46

CDPH-MVS		77.22 50	81.05 57	72.75 41	77.29 40	77.46 39	86.36 42	54.02 56	73.00 87	69.75 57	77.78 93	88.90 75	61.31 63	84.09 57	82.54 45	87.79 40	83.57 47

PEN-MVS		77.06 51	85.05 32	67.74 65	82.29 5	72.59 64	80.86 67	61.03 19	84.66 15	50.08 131	92.19 3	96.59 3	49.12 126	89.83 21	82.35 48	83.06 79	77.14 80

CP-MVSNet		77.01 52	85.04 33	67.65 67	81.16 12	72.72 62	80.54 70	61.18 16	82.09 39	50.41 129	90.81 8	95.89 7	50.03 122	90.86 10	84.30 34	82.56 87	78.65 74

CSCG		76.95 53	82.08 51	70.97 47	73.32 61	78.35 34	81.08 66	47.19 88	83.47 27	69.82 56	80.44 74	87.19 93	64.59 42	81.01 79	77.26 77	89.83 25	86.84 33

CNLPA		76.67 54	81.72 52	70.77 50	70.75 73	76.68 42	86.14 43	46.11 98	81.82 41	74.68 30	76.37 97	86.23 104	62.92 55	85.28 49	83.29 40	84.02 67	82.40 51

MSLP-MVS++		76.66 55	82.32 50	70.06 53	70.51 76	80.27 25	79.77 74	55.58 47	77.79 62	63.09 82	67.25 146	89.50 70	71.01 14	88.10 31	85.74 26	80.39 99	87.56 27

TSAR-MVS + COLMAP		75.85 56	81.06 55	69.77 56	71.15 69	76.90 41	82.93 56	52.43 63	79.25 59	70.13 52	82.78 53	87.00 96	60.02 68	80.30 83	79.61 63	81.95 91	81.61 57

HQP-MVS		75.81 57	78.91 65	72.18 44	77.41 39	75.38 50	84.75 45	53.35 58	76.12 69	73.32 33	69.48 134	88.07 80	57.76 80	79.42 89	78.44 67	86.48 50	85.50 43

PLC	64.88 15	75.76 58	80.22 60	70.57 52	70.46 77	77.75 38	82.01 61	48.84 79	80.74 51	70.85 49	71.32 131	84.82 114	63.69 47	84.73 54	82.35 48	87.54 41	79.80 65
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAPA-MVS	66.11 12	75.37 59	79.24 63	70.86 48	67.63 88	74.09 56	83.17 55	44.75 113	81.82 41	80.83 5	65.61 157	88.04 81	61.58 60	83.21 66	80.12 60	87.17 45	81.82 54
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PHI-MVS		75.17 60	78.37 66	71.43 45	71.13 70	72.46 66	82.28 60	50.55 71	73.39 84	79.05 13	73.65 119	87.50 89	61.98 57	81.10 77	78.48 66	83.60 74	81.99 52

anonymousdsp		74.76 61	82.59 48	65.63 84	45.61 206	61.13 124	89.06 29	32.58 192	74.11 80	59.55 91	84.06 41	94.12 15	75.24 3	88.94 26	86.95 24	91.74 7	88.81 14

AdaColmap		74.73 62	77.57 71	71.40 46	76.90 43	75.76 46	84.54 47	53.08 60	76.20 68	66.64 75	66.06 155	78.16 148	61.32 62	85.37 48	82.20 50	85.95 59	79.27 67

v7n		74.47 63	81.06 55	66.77 73	66.98 94	67.10 78	76.76 89	45.88 100	81.98 40	67.43 69	88.38 15	95.67 10	61.38 61	80.76 81	73.49 98	82.21 89	80.06 63

PCF-MVS	65.25 14	73.99 64	76.74 76	70.79 49	71.61 68	75.33 51	83.76 51	50.40 72	74.88 72	74.50 31	67.60 143	85.36 111	58.30 78	78.61 95	74.25 93	86.15 56	81.13 60
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MCST-MVS		73.84 65	77.44 72	69.63 58	73.75 60	74.73 53	81.38 65	48.58 80	74.77 73	69.16 61	71.97 130	86.20 105	59.50 73	78.51 96	74.06 95	85.42 62	81.85 53

MVS_0304		73.74 66	77.16 74	69.74 57	74.24 55	73.47 57	84.70 46	49.62 74	62.26 143	67.27 70	75.87 101	87.57 87	57.49 83	81.20 75	79.50 64	85.10 63	80.27 62

TSAR-MVS + GP.		73.42 67	76.31 77	70.05 54	77.15 41	71.13 71	81.59 64	54.11 55	69.84 114	58.65 94	66.20 154	78.77 144	65.29 37	83.65 59	83.14 41	83.54 75	81.47 58

Gipuma		73.40 68	79.27 62	66.55 78	63.64 123	59.35 133	70.28 124	45.92 99	83.79 25	71.78 40	84.04 42	93.07 23	68.69 26	87.90 33	76.76 79	78.98 112	69.96 120
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVS_111021_HR		72.37 69	76.12 80	68.00 63	68.55 85	64.30 105	82.93 56	48.98 78	74.25 78	65.39 78	73.59 120	84.11 119	59.48 74	82.61 69	78.38 68	82.66 84	75.59 87

TinyColmap		71.85 70	76.11 81	66.87 72	66.07 103	65.34 92	74.35 101	49.30 77	79.93 55	75.93 27	75.66 103	87.74 84	54.72 98	80.66 82	70.42 120	80.85 97	73.02 103

UniMVSNet_ETH3D		71.84 71	81.36 54	60.74 108	76.46 44	66.01 86	66.49 144	60.24 26	86.58 8	41.87 165	90.04 11	96.02 5	43.72 153	85.14 50	77.30 76	75.64 133	68.40 134

TranMVSNet+NR-MVSNet		71.66 72	79.23 64	62.83 100	72.54 65	65.64 88	74.77 99	55.27 48	75.91 70	45.50 154	89.55 13	94.25 13	45.96 144	82.74 68	77.03 78	82.96 81	69.48 128

MVS_111021_LR		71.60 73	75.21 84	67.38 68	67.42 89	62.44 114	81.73 62	46.24 97	70.89 99	66.80 74	73.19 123	84.98 112	60.09 67	81.94 72	77.77 74	82.00 90	75.29 88

EG-PatchMatch MVS		71.50 74	76.82 75	65.30 85	70.74 74	66.50 82	74.23 103	43.25 121	72.02 91	59.11 92	79.85 79	86.88 99	63.95 45	80.29 84	75.25 89	80.51 98	76.98 81

DPM-MVS		71.35 75	73.50 102	68.84 61	74.93 53	73.35 58	84.07 49	50.56 70	71.91 92	67.06 73	61.21 177	77.02 153	52.64 109	74.15 115	75.14 90	83.79 71	81.74 55

UniMVSNet (Re)		71.29 76	78.14 67	63.30 95	70.29 78	66.57 81	75.98 91	54.74 53	70.20 107	46.20 152	85.08 35	93.21 20	48.19 132	82.50 70	78.33 69	84.40 65	71.08 114

CLD-MVS		71.24 77	78.12 68	63.20 97	74.03 57	71.60 69	82.82 58	32.91 189	74.23 79	69.32 60	79.65 81	91.54 38	47.02 140	81.22 74	79.01 65	73.09 150	69.63 124
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CANet		71.07 78	75.09 86	66.39 79	72.57 64	71.53 70	82.38 59	47.10 89	59.81 149	59.81 90	74.97 108	84.37 118	54.25 101	79.89 87	77.64 75	82.25 88	77.40 78

v1192		71.06 79	74.87 88	66.61 75	66.38 98	65.80 87	78.27 79	45.28 105	70.19 108	70.79 50	83.37 46	91.79 36	58.76 77	70.86 150	69.02 127	80.16 102	73.08 101

DU-MVS		71.03 80	77.92 69	62.98 99	70.81 71	65.48 90	73.93 106	56.76 35	69.95 112	46.77 148	85.70 31	93.49 18	46.91 141	83.47 60	77.82 73	82.72 83	69.54 125

v1240		70.94 81	74.52 92	66.76 74	66.54 97	64.40 101	77.76 82	45.29 104	70.05 110	71.45 42	83.36 48	90.96 49	60.37 65	70.50 153	68.68 128	79.14 110	73.68 97

v1921920		70.82 82	74.46 94	66.58 77	66.33 99	64.35 104	77.72 83	45.07 107	70.39 104	71.18 45	83.15 50	90.62 57	59.97 69	70.90 148	68.43 135	79.19 109	73.39 98

UniMVSNet_NR-MVSNet		70.82 82	77.44 72	63.11 98	71.75 67	66.02 85	73.93 106	55.00 51	70.90 98	46.77 148	86.68 25	91.54 38	46.91 141	81.07 78	76.32 83	84.28 66	69.54 125

PVSNet_Blended_VisFu		70.70 84	73.62 101	67.28 70	63.53 125	72.96 59	77.97 80	52.10 64	63.65 137	62.66 83	71.14 132	73.46 164	63.55 48	79.35 93	75.34 88	83.90 69	79.43 66

v144192		70.68 85	74.40 96	66.34 80	65.94 105	64.38 102	77.63 84	45.18 106	69.97 111	70.11 53	82.70 55	90.77 52	59.84 71	71.43 146	68.46 131	79.31 108	73.08 101

EC-MVSNet		70.50 86	73.32 105	67.20 71	72.07 66	66.21 84	70.86 121	50.10 73	57.66 162	60.49 89	74.97 108	79.42 139	63.32 50	79.65 88	75.46 87	86.35 54	79.87 64

FPMVS		70.46 87	74.89 87	65.28 86	69.09 83	61.42 120	77.07 87	46.92 92	76.73 67	53.53 112	67.33 144	75.07 159	67.23 28	83.41 62	81.54 55	77.86 119	78.73 72

v1144		70.45 88	74.50 93	65.73 83	65.74 107	64.88 96	77.33 86	44.16 115	70.59 103	69.63 58	83.15 50	91.42 41	57.79 79	71.29 147	68.53 130	79.72 106	71.63 113

v10		70.25 89	74.59 91	65.19 87	65.32 110	66.46 83	76.60 90	44.84 110	67.38 123	67.21 71	82.75 54	90.56 58	57.70 81	71.69 143	68.63 129	79.44 107	74.67 91

Effi-MVS+-dtu		70.10 90	73.76 100	65.82 82	70.23 79	74.92 52	79.47 76	44.49 114	56.98 167	54.34 106	64.26 161	84.78 115	59.97 69	80.96 80	80.38 58	86.44 51	74.05 95

CS-MVS-test		70.00 91	72.92 108	66.59 76	66.71 96	64.06 107	80.28 72	44.82 111	58.41 154	58.08 97	73.57 121	80.94 130	63.98 44	83.29 64	75.93 85	85.65 61	74.23 92

MAR-MVS		70.00 91	72.28 116	67.34 69	69.89 80	72.57 65	80.09 73	49.49 76	60.28 148	69.03 62	59.29 186	80.79 132	54.68 99	78.39 98	76.00 84	80.87 96	78.67 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
Vis-MVSNet		69.95 93	77.69 70	60.91 106	60.67 140	66.71 79	77.94 81	48.58 80	69.10 117	45.78 153	80.21 77	83.58 122	53.41 105	82.92 67	80.11 61	79.08 111	81.21 59
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CS-MVS		69.56 94	72.49 113	66.14 81	66.85 95	64.10 106	72.55 111	42.91 123	58.90 151	60.78 87	72.93 126	83.21 124	66.32 32	78.77 94	72.84 105	87.45 42	76.72 82

EPP-MVSNet		69.51 95	76.17 78	61.74 104	68.38 87	66.60 80	71.77 113	46.98 90	73.60 83	41.79 166	82.06 61	69.65 175	52.51 110	83.41 62	79.94 62	89.02 29	77.94 76

3Dnovator	65.69 13	69.43 96	75.74 83	62.06 103	60.78 139	70.50 73	75.85 93	39.57 157	74.44 75	57.41 99	75.91 99	77.73 150	55.34 94	76.86 102	75.61 86	83.44 76	79.14 69

Effi-MVS+		69.04 97	73.01 107	64.40 90	67.20 92	64.83 98	74.87 98	43.97 117	63.33 139	60.90 86	73.06 124	85.79 108	55.61 91	73.58 121	76.41 82	83.84 70	74.09 94

v2v482		69.01 98	73.39 104	63.89 92	63.86 117	62.99 111	75.26 96	42.05 132	70.22 106	68.46 65	82.64 56	91.61 37	55.38 92	70.89 149	66.93 151	78.30 115	68.48 133

MSDG		68.98 99	73.31 106	63.92 91	67.08 93	68.27 76	75.41 95	40.77 148	67.61 121	64.89 79	75.75 102	78.96 141	53.70 103	76.72 104	73.95 96	81.71 93	71.93 111

v8		68.77 100	73.50 102	63.26 96	63.74 121	64.47 100	74.22 104	42.07 131	67.30 124	64.89 79	82.08 60	90.23 61	56.50 89	71.85 142	66.57 152	78.14 116	72.02 109

NR-MVSNet		68.66 101	76.15 79	59.93 113	65.49 108	65.48 90	74.42 100	56.76 35	69.95 112	45.38 155	85.70 31	91.13 44	34.68 185	74.52 114	76.75 80	82.83 82	69.49 127

USDC		68.53 102	71.82 122	64.68 88	63.53 125	61.87 119	70.12 126	46.98 90	77.89 61	76.58 23	68.55 139	86.88 99	50.50 118	73.73 118	65.62 154	80.39 99	68.21 136

IS_MVSNet		68.20 103	74.41 95	60.96 105	68.55 85	64.36 103	71.47 116	48.33 82	70.11 109	43.30 161	80.90 71	74.54 161	47.19 139	81.25 73	77.97 72	86.94 47	71.76 112

Baseline_NR-MVSNet		68.15 104	75.12 85	60.02 112	70.81 71	55.67 156	75.88 92	53.40 57	71.25 95	43.96 159	85.88 29	92.68 27	45.76 145	83.47 60	68.34 136	70.34 167	68.58 132

GeoE		68.11 105	72.10 120	63.47 94	67.32 90	62.42 115	78.32 78	43.22 122	64.06 136	55.72 104	73.97 116	84.58 116	55.35 93	76.09 108	70.41 121	80.89 95	73.14 100

casdiffmvs_mvg		68.01 106	74.22 98	60.78 107	63.75 120	62.08 118	70.21 125	42.58 125	72.11 90	58.53 95	84.80 38	88.98 74	49.37 125	73.25 125	67.54 147	80.24 101	70.75 116
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Fast-Effi-MVS+		67.71 107	72.54 112	62.07 102	63.83 118	63.68 108	75.74 94	39.94 154	60.89 147	54.29 107	73.00 125	86.19 106	56.85 86	78.46 97	73.23 101	81.74 92	72.36 107

thisisatest0515		66.95 108	72.29 115	60.72 109	56.37 164	56.05 154	71.08 117	38.81 162	67.59 122	53.26 115	78.21 89	79.79 138	60.11 66	75.69 110	73.02 103	84.69 64	75.66 86

EPNet		66.87 109	68.89 136	64.53 89	73.97 58	61.13 124	78.46 77	61.03 19	56.78 169	53.41 113	66.91 149	70.91 170	43.49 154	76.08 109	76.68 81	76.81 123	73.73 96
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
canonicalmvs		66.37 110	74.37 97	57.04 130	65.89 106	65.06 93	62.58 163	42.55 126	76.82 66	46.87 147	67.33 144	86.38 103	45.49 147	76.77 103	71.85 110	78.87 113	76.35 83

QAPM		66.36 111	72.76 111	58.90 118	59.57 147	65.01 94	64.05 157	41.17 143	73.09 86	56.82 101	69.42 135	77.78 149	55.07 96	73.00 128	72.07 108	76.71 124	78.96 70

casdiffmvs		66.19 112	72.34 114	59.02 117	62.75 129	60.61 130	69.06 131	41.38 140	69.49 115	54.11 108	84.00 43	89.74 68	49.12 126	70.74 152	62.70 169	77.70 121	69.14 130
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
V42		65.79 113	72.11 119	58.42 122	51.89 186	58.69 135	73.80 108	34.50 181	65.40 131	57.10 100	79.54 83	89.09 72	57.51 82	71.98 140	67.83 144	75.70 131	72.26 108

IterMVS-LS		65.76 114	70.85 129	59.81 115	65.33 109	57.78 140	64.63 154	48.02 85	65.65 130	51.05 125	81.31 68	77.47 151	54.94 97	69.46 161	69.36 126	74.90 139	74.95 89
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PM-MVS		65.66 115	71.25 128	59.14 116	58.92 153	54.88 165	73.66 110	38.55 165	66.12 127	49.91 133	69.87 133	86.97 97	60.61 64	76.30 106	74.75 91	73.19 148	69.83 121

UGNet		65.61 116	74.79 89	54.91 142	54.54 180	68.20 77	70.97 120	48.21 83	67.14 126	41.67 167	74.15 115	80.65 134	36.10 180	79.39 90	77.99 71	77.95 118	76.01 84
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
DELS-MVS		65.54 117	71.79 123	58.24 125	59.68 146	65.55 89	70.99 118	38.69 164	62.29 142	49.27 136	75.03 107	81.42 127	50.93 114	73.71 120	71.35 112	79.90 104	73.20 99
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
pmmvs-eth3d		65.36 118	70.09 132	59.85 114	63.05 128	53.61 168	74.29 102	46.45 95	68.14 120	51.45 121	78.83 87	85.78 109	49.87 123	70.44 154	70.45 119	74.00 143	63.38 156

FA-MVS(training)		65.22 119	69.19 135	60.58 110	60.91 137	57.60 144	71.57 115	38.82 161	65.84 129	61.18 85	75.95 98	74.52 162	51.18 113	73.32 123	67.41 148	79.80 105	69.70 123

v148		64.92 120	70.58 131	58.32 123	59.89 144	57.09 147	66.04 146	35.27 180	69.11 116	60.66 88	79.57 82	90.93 50	53.91 102	69.81 160	62.22 170	74.14 141	65.31 148

FC-MVSNet-train		64.87 121	74.76 90	53.33 146	65.24 111	58.05 137	69.69 128	41.92 135	70.99 97	32.62 191	85.75 30	88.23 78	32.10 195	77.61 101	74.41 92	78.43 114	68.25 135

pmmvs6		64.78 122	75.82 82	51.89 152	62.41 130	57.13 146	60.24 170	45.59 102	82.90 33	34.69 182	84.83 36	93.18 21	36.22 179	76.43 105	71.13 115	72.21 154	65.12 149

OpenMVS	60.79 16	64.42 123	69.72 133	58.23 126	61.63 134	62.17 116	64.11 156	37.54 174	67.17 125	55.71 105	65.89 156	74.89 160	52.67 108	72.20 138	68.29 138	77.73 120	77.39 79

test1111		64.34 124	71.57 124	55.90 137	67.25 91	60.24 132	66.66 142	51.63 68	73.36 85	34.69 182	75.63 104	80.67 133	39.43 165	78.17 99	71.69 111	75.71 130	61.23 162

DCV-MVSNet		64.34 124	72.84 110	54.42 144	63.79 119	62.09 117	62.50 164	42.72 124	74.32 77	41.34 168	66.96 147	88.57 76	39.18 166	75.20 112	70.35 122	77.01 122	72.37 106

ETV-MVS		64.30 126	64.76 151	63.77 93	68.59 84	62.49 113	77.02 88	45.31 103	49.27 195	50.88 126	56.23 198	59.91 198	57.12 85	80.19 86	74.23 94	83.68 72	71.03 115

ECVR-MVS		63.93 127	71.52 125	55.08 140	66.19 100	61.34 121	63.84 158	51.79 66	70.75 101	34.77 180	74.70 113	81.10 129	38.92 167	79.39 90	73.43 99	75.00 137	59.92 169

Anonymous20231211		63.69 128	72.86 109	53.00 149	63.72 122	60.25 131	60.33 169	40.96 144	72.49 88	38.91 171	81.77 66	88.17 79	37.60 174	73.30 124	68.01 141	76.47 128	66.06 145

TransMVSNet (Re)		63.49 129	73.86 99	51.39 158	64.26 116	56.07 153	61.17 167	42.23 129	78.81 60	34.80 179	85.94 27	90.63 56	34.35 189	72.73 133	67.98 142	71.50 157	64.84 150

DI_MVS_plusplus_trai		63.43 130	67.54 140	58.63 119	62.34 131	58.06 136	65.75 150	42.15 130	63.05 140	53.28 114	75.88 100	75.92 156	50.18 120	68.04 164	64.20 160	78.07 117	67.65 137

EIA-MVS		63.24 131	64.16 155	62.16 101	69.30 82	63.20 110	72.40 112	40.82 147	48.31 201	51.50 120	59.63 184	62.23 190	57.33 84	78.00 100	71.94 109	81.59 94	65.82 146

Fast-Effi-MVS+-dtu		63.22 132	65.55 145	60.49 111	61.24 136	64.70 99	74.15 105	53.24 59	51.46 180	49.67 134	58.03 193	78.42 145	48.05 134	72.03 139	71.14 114	76.60 127	63.09 157

IterMVS-SCA-FT		62.67 133	68.00 138	56.45 136	56.92 162	64.92 95	57.51 183	38.12 166	59.44 150	53.62 111	74.74 112	71.60 167	64.84 39	70.24 156	65.27 156	67.70 175	69.83 121

diffmvs		62.64 134	69.66 134	54.46 143	56.19 167	55.06 162	67.36 140	36.74 178	64.18 134	50.58 127	79.54 83	87.55 88	45.13 149	68.04 164	63.20 165	70.78 162	70.02 119
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		62.58 135	67.46 141	56.89 132	59.52 148	55.90 155	64.94 152	38.83 160	57.08 166	56.55 102	76.53 96	84.49 117	47.45 136	66.95 167	62.01 171	74.04 142	69.27 129

MDA-MVSNet-bldmvs		62.46 136	72.13 118	51.19 160	34.32 216	56.10 152	68.65 133	38.85 159	69.05 118	49.50 135	78.17 90	85.43 110	51.32 111	86.67 38	67.40 149	64.46 180	62.08 159

pm-mvs1		61.97 137	72.01 121	50.25 166	60.64 141	55.23 160	58.67 178	42.44 127	74.40 76	33.63 188	81.03 70	89.86 65	34.87 184	72.93 131	67.95 143	71.28 158	62.65 158

FMVSNet1		61.92 138	71.36 126	50.90 162	57.67 161	59.29 134	59.48 176	44.14 116	70.24 105	34.72 181	75.45 106	84.94 113	36.75 176	72.33 135	68.45 132	72.66 151	68.83 131

PVSNet_BlendedMVS		61.75 139	65.07 149	57.87 128	56.27 165	60.99 127	65.81 148	43.75 118	51.27 184	54.08 109	62.12 172	78.84 142	50.67 115	71.49 144	63.91 162	76.64 125	66.86 139

PVSNet_Blended		61.75 139	65.07 149	57.87 128	56.27 165	60.99 127	65.81 148	43.75 118	51.27 184	54.08 109	62.12 172	78.84 142	50.67 115	71.49 144	63.91 162	76.64 125	66.86 139

tttt0517		61.44 141	63.85 157	58.62 120	55.20 176	55.61 157	68.80 132	38.02 169	55.70 171	50.01 132	66.93 148	48.90 209	56.69 87	73.84 117	71.10 116	82.99 80	74.89 90

tfpnnormal		61.41 142	71.33 127	49.83 167	61.73 133	54.90 164	58.52 179	41.24 141	75.20 71	32.00 196	82.13 59	87.87 83	35.63 183	72.75 132	66.30 153	69.87 168	60.14 166

IB-MVS	57.02 17	61.37 143	65.39 146	56.69 133	56.65 163	60.85 129	70.70 122	37.90 171	49.37 194	45.37 156	48.75 210	79.14 140	53.55 104	76.26 107	70.85 118	75.97 129	72.50 105
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		61.22 144	67.07 142	54.40 145	59.89 144	63.62 109	70.98 119	36.77 177	50.49 187	47.15 143	62.45 170	80.81 131	37.90 173	71.87 141	70.09 124	73.69 144	70.19 118

pmmvs4		61.12 145	64.61 152	57.04 130	60.88 138	52.15 173	70.59 123	44.82 111	61.35 146	46.91 146	72.08 128	73.27 165	46.79 143	65.06 171	67.76 145	72.28 152	60.58 164

thisisatest0530		61.02 146	63.44 162	58.19 127	54.75 178	55.09 161	68.03 137	38.02 169	55.45 172	49.06 137	66.58 152	48.69 210	56.69 87	73.07 126	71.10 116	82.60 85	74.14 93

Vis-MVSNet (Re-imp)		60.99 147	67.78 139	53.06 148	64.66 115	53.49 169	67.40 138	49.52 75	68.55 119	28.00 205	79.53 85	71.41 169	33.08 193	75.30 111	71.28 113	75.69 132	54.91 181

PatchMatch-RL		60.96 148	63.00 165	58.57 121	59.16 152	52.18 172	67.38 139	41.99 133	57.85 160	48.16 138	53.55 206	69.77 174	59.47 75	73.73 118	72.49 107	75.27 136	61.44 161

GA-MVS		60.73 149	64.24 154	56.64 134	59.38 151	57.45 145	65.07 151	36.65 179	57.39 164	58.17 96	73.43 122	69.10 178	47.38 137	64.47 176	63.63 164	73.19 148	64.22 153

CVMVSNet		60.45 150	63.72 159	56.63 135	54.82 177	53.75 166	68.41 134	41.95 134	55.07 173	48.03 139	58.08 192	68.67 179	55.09 95	69.14 162	68.34 136	71.51 156	72.97 104

ET-MVSNet_ETH3D		60.33 151	62.10 168	58.27 124	58.61 156	58.05 137	68.06 135	41.20 142	51.40 181	51.10 124	64.06 162	49.42 208	50.61 117	74.72 113	70.29 123	80.05 103	66.74 141

FC-MVSNet-test		60.28 152	70.83 130	47.96 178	54.69 179	47.12 187	68.06 135	41.68 139	71.42 93	23.73 213	84.70 39	77.41 152	28.92 200	82.33 71	73.08 102	70.68 163	59.77 170

test2506		59.86 153	64.01 156	55.02 141	66.19 100	61.34 121	63.84 158	51.79 66	70.75 101	34.39 186	62.65 169	39.92 224	38.92 167	79.39 90	73.43 99	75.00 137	60.56 165

EU-MVSNet		59.77 154	66.07 143	52.42 150	47.81 195	51.86 175	62.98 162	32.28 194	62.08 144	47.10 144	59.94 182	83.42 123	53.08 107	70.06 159	63.19 166	71.26 160	71.96 110

IterMVS		59.24 155	64.45 153	53.16 147	50.98 188	61.29 123	66.51 143	32.85 190	58.17 156	46.31 151	72.58 127	70.23 172	54.26 100	64.81 174	60.24 175	68.04 174	63.81 155
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		59.15 156	62.28 167	55.49 139	52.42 184	62.59 112	71.76 114	39.74 155	50.25 189	41.92 164	62.88 167	69.16 177	55.85 90	62.77 181	67.18 150	71.27 159	61.11 163

thres600view7		58.87 157	65.91 144	50.66 164	61.27 135	56.32 150	59.88 174	40.63 150	64.88 132	32.10 195	64.82 158	69.83 173	36.72 177	72.99 129	72.55 106	73.34 146	59.97 168

CMPMVS	45.32 18	58.10 158	65.24 148	49.76 168	47.88 194	46.86 190	48.16 208	32.82 191	58.06 157	61.35 84	59.64 183	80.00 135	47.27 138	70.15 157	64.10 161	61.08 184	77.85 77
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view		58.09 159	63.54 161	51.74 154	50.13 190	46.56 191	66.95 141	33.41 187	63.52 138	58.77 93	74.84 110	84.10 120	43.12 155	65.95 170	54.69 190	58.04 190	55.13 180

CDS-MVSNet		57.90 160	63.57 160	51.28 159	62.30 132	53.17 170	64.70 153	51.61 69	57.41 163	32.75 190	63.73 163	70.53 171	27.12 202	72.49 134	73.02 103	69.22 171	54.68 182
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
FMVSNet2		57.80 161	65.39 146	48.94 174	55.88 168	57.61 141	57.26 184	42.37 128	58.21 155	33.19 189	68.36 140	75.55 158	34.58 186	66.91 168	64.55 158	70.38 164	66.56 142

thres400		57.25 162	63.73 158	49.70 169	60.19 143	54.95 163	58.16 180	39.60 156	62.42 141	31.98 198	62.33 171	69.20 176	35.96 181	70.07 158	68.03 140	72.28 152	59.12 172

gm-plane-assit		56.76 163	57.64 182	55.73 138	66.01 104	55.45 159	74.96 97	30.54 199	73.71 82	56.04 103	81.81 64	30.91 227	43.83 151	58.77 191	54.71 189	63.02 182	48.13 197

MIMVSNet1		56.72 164	68.69 137	42.76 192	46.70 201	42.81 198	69.13 129	30.52 200	81.01 48	32.00 196	74.82 111	91.10 46	26.83 204	73.98 116	64.72 157	51.40 202	52.38 186

EPNet_dtu		56.63 165	60.77 174	51.80 153	55.47 173	44.63 192	69.83 127	38.74 163	50.27 188	47.64 140	58.01 194	72.27 166	33.71 191	68.60 163	67.72 146	65.39 178	63.86 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		56.54 166	63.26 163	48.70 175	55.88 168	57.61 141	57.26 184	41.75 136	49.06 196	32.37 192	61.81 174	67.02 181	34.58 186	72.33 135	68.45 132	70.38 164	66.56 142

test1		56.54 166	63.26 163	48.70 175	55.88 168	57.61 141	57.26 184	41.75 136	49.06 196	32.37 192	61.81 174	67.02 181	34.58 186	72.33 135	68.45 132	70.38 164	66.56 142

gg-mvs-nofinetune		56.45 168	61.04 172	51.10 161	63.42 127	49.40 183	53.71 195	52.52 62	74.77 73	46.93 145	77.31 94	53.88 202	26.42 206	62.51 182	57.81 182	63.60 181	51.57 189

thres200		56.35 169	62.36 166	49.34 171	58.87 154	56.32 150	55.91 188	40.63 150	58.51 153	31.34 199	58.81 190	67.31 180	35.96 181	72.99 129	65.51 155	73.34 146	57.07 175

MS-PatchMatch		56.31 170	60.22 177	51.73 155	60.53 142	55.53 158	63.41 160	37.18 175	51.34 183	37.44 173	60.53 180	62.19 191	45.52 146	64.25 177	63.17 167	66.33 176	64.56 151

tfpn200view9		56.07 171	61.85 169	49.34 171	58.57 157	56.48 149	58.01 182	40.72 149	53.23 174	31.01 200	56.41 196	66.40 186	34.18 190	73.02 127	68.06 139	73.53 145	59.35 171

FMVSNet3		54.77 172	60.73 175	47.81 179	54.29 181	56.88 148	55.89 189	41.75 136	49.06 196	32.37 192	61.81 174	67.02 181	33.67 192	62.88 180	61.96 172	68.88 172	65.53 147

thres100view900		53.88 173	59.19 178	47.68 180	58.57 157	52.74 171	54.45 192	38.07 168	53.23 174	31.01 200	56.41 196	66.40 186	32.80 194	65.03 173	64.43 159	71.18 161	56.10 178

CR-MVSNet		53.82 174	55.40 186	51.98 151	51.57 187	50.23 178	45.00 210	44.97 108	46.90 203	52.60 117	67.91 141	46.99 217	48.37 129	59.15 189	59.53 178	69.38 170	57.07 175

baseline2		53.55 175	55.19 187	51.62 156	55.27 175	51.95 174	60.89 168	34.23 182	46.69 205	42.47 162	53.56 205	50.01 205	45.33 148	64.63 175	61.22 173	71.56 155	58.28 174

test20.03		53.49 176	60.95 173	44.78 188	64.73 114	47.25 186	61.58 166	43.30 120	65.86 128	22.85 214	66.87 151	79.85 136	22.99 208	62.38 183	56.95 184	53.25 199	47.46 199

baseline		53.46 177	61.55 170	44.01 189	45.83 204	48.77 184	57.26 184	28.75 203	49.99 190	38.85 172	68.78 137	75.65 157	38.30 170	60.80 184	59.78 177	55.10 197	67.07 138

MVSTER		53.08 178	56.39 184	49.21 173	47.19 197	51.08 176	60.14 172	31.74 196	40.63 214	38.97 170	55.78 199	46.74 218	42.47 158	67.29 166	62.99 168	74.73 140	70.23 117

CHOSEN 1792x2688		52.99 179	56.91 183	48.42 177	47.32 196	50.10 180	64.18 155	33.85 184	45.46 208	36.95 175	55.20 202	66.49 185	51.20 112	59.28 187	59.81 176	57.01 192	61.99 160

baseline1		52.90 180	58.38 179	46.51 186	58.87 154	50.01 181	54.17 193	40.45 153	56.81 168	29.25 203	62.72 168	58.99 199	30.25 198	65.05 172	60.57 174	66.07 177	54.54 183

CostFormer		52.59 181	55.14 188	49.61 170	52.72 182	50.40 177	66.28 145	33.78 185	52.85 176	43.43 160	66.30 153	51.37 204	41.78 161	54.92 201	51.18 197	59.68 186	58.98 173

SCA		52.47 182	53.97 191	50.71 163	46.95 200	57.79 139	60.18 171	46.89 93	51.92 179	46.71 150	60.73 178	49.97 206	47.69 135	56.39 198	52.98 194	55.82 194	48.03 198

testgi		51.94 183	61.37 171	40.94 195	58.38 159	47.03 188	65.88 147	30.49 201	70.87 100	22.64 215	57.53 195	87.59 86	18.30 215	63.01 179	54.32 191	49.93 205	49.27 192

dmvs_re		51.01 184	54.88 189	46.49 187	58.06 160	44.35 195	60.08 173	37.67 172	42.11 212	28.68 204	45.12 215	66.70 184	31.90 197	66.62 169	59.18 180	62.59 183	60.11 167

tpm cat1		50.98 185	51.28 197	50.62 165	55.74 171	49.92 182	63.13 161	38.12 166	52.38 178	47.61 141	60.11 181	44.51 221	44.86 150	51.31 209	47.49 204	54.25 198	53.24 185

RPMNet		50.92 186	50.32 199	51.62 156	50.25 189	50.23 178	59.16 177	46.70 94	46.90 203	42.39 163	48.97 209	37.23 225	41.78 161	57.30 196	56.18 186	69.44 169	55.43 179

pmmvs5		50.64 187	58.01 180	42.05 193	47.01 199	43.67 196	49.27 206	29.43 202	50.77 186	33.83 187	68.69 138	76.16 155	27.82 201	57.53 195	57.07 183	64.95 179	52.18 187

PatchT		50.55 188	53.55 193	47.05 184	37.59 215	42.26 199	50.55 203	37.56 173	46.37 206	52.60 117	66.91 149	43.54 223	48.37 129	59.15 189	59.53 178	55.62 195	57.07 175

Anonymous20231206		50.28 189	57.94 181	41.35 194	55.45 174	43.65 197	58.06 181	34.12 183	62.02 145	24.25 212	59.33 185	79.80 137	24.49 207	59.55 185	54.28 192	51.74 201	46.94 201

dps		49.71 190	51.97 195	47.07 183	52.37 185	47.00 189	53.02 198	40.52 152	44.91 209	41.23 169	64.55 159	44.27 222	40.12 164	57.71 194	51.97 196	55.14 196	53.41 184

MDTV_nov1_ep13		49.60 191	51.57 196	47.31 181	46.28 202	44.61 193	59.82 175	30.96 197	48.80 200	50.20 130	59.26 187	52.38 203	38.56 169	56.20 199	49.70 200	58.04 190	50.01 190

PatchmatchNet		48.67 192	50.10 200	46.99 185	48.29 193	41.00 200	55.54 190	38.94 158	51.38 182	45.15 157	63.22 165	48.45 212	42.83 156	53.80 206	48.50 203	51.19 204	44.37 203
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
new-patchmatchnet		47.33 193	60.49 176	31.99 211	55.69 172	33.86 212	36.84 220	33.31 188	72.36 89	14.33 220	80.09 78	92.14 33	13.27 217	63.54 178	40.09 212	38.51 214	41.32 211

tpm		46.67 194	49.20 205	43.72 190	49.60 191	36.60 209	53.93 194	26.84 205	52.70 177	58.05 98	69.04 136	47.96 213	30.06 199	48.33 212	42.76 207	43.88 208	47.01 200

pmmvs3		46.64 195	54.13 190	37.90 202	31.23 220	40.68 201	49.83 205	15.34 216	46.31 207	36.34 177	53.15 207	74.40 163	36.36 178	58.43 192	56.64 185	58.32 189	49.29 191

TAMVS		46.64 195	53.62 192	38.49 200	49.56 192	36.87 206	53.16 197	25.76 207	56.33 170	22.55 216	60.72 179	61.80 193	27.12 202	59.50 186	58.33 181	52.79 200	41.82 210

test-LLR		46.01 197	45.06 214	47.11 182	59.39 149	36.72 207	51.28 200	40.95 145	36.41 218	34.45 184	46.14 212	47.02 215	38.00 171	51.78 207	48.53 201	58.60 187	48.84 194

MIMVSNet		45.83 198	53.46 194	36.94 203	45.38 208	39.50 203	52.20 199	30.68 198	57.09 165	24.53 211	55.22 201	71.54 168	21.74 211	55.81 200	51.08 198	47.11 206	43.96 204

pmnet_mix02		45.67 199	55.99 185	33.63 210	45.77 205	31.22 216	42.04 215	27.60 204	64.14 135	24.89 208	75.50 105	82.30 125	21.88 210	54.53 204	41.22 209	39.62 212	43.05 206

test0.0.03 1		45.40 200	49.55 203	40.57 197	59.39 149	44.36 194	53.37 196	40.95 145	47.14 202	19.23 217	45.49 214	60.24 196	19.24 213	54.82 202	51.98 195	51.21 203	42.82 207

PMMVS		45.37 201	49.29 204	40.79 196	27.75 221	35.07 211	50.88 202	19.88 213	39.27 216	35.78 178	50.11 208	61.29 194	42.04 159	54.13 205	55.95 187	68.43 173	49.19 193

MVS-HIRNet		44.56 202	45.52 212	43.44 191	40.98 211	31.03 217	39.52 219	36.96 176	42.80 211	44.37 158	53.80 204	60.04 197	41.85 160	47.97 214	41.08 210	56.99 193	41.95 209

test-mter		44.18 203	47.60 207	40.18 198	33.20 217	39.03 204	55.28 191	13.91 218	39.07 217	36.63 176	48.09 211	49.52 207	41.12 163	54.55 203	50.91 199	60.97 185	52.03 188

EMVS		43.85 204	49.91 201	36.77 205	45.46 207	32.70 213	44.09 212	25.33 208	57.88 159	26.62 206	58.99 189	61.14 195	42.77 157	70.26 155	38.52 216	36.38 216	29.87 217

E-PMN		43.83 205	49.81 202	36.84 204	46.09 203	31.86 215	42.77 214	25.85 206	57.76 161	25.53 207	55.50 200	62.47 189	43.77 152	70.78 151	39.51 213	37.04 215	30.79 216

tpmrst		43.31 206	46.14 210	40.02 199	47.05 198	36.48 210	48.01 209	32.17 195	49.50 193	37.26 174	63.66 164	47.04 214	31.98 196	42.00 218	40.55 211	43.64 209	43.75 205

TESTMET0.1,1		41.79 207	45.06 214	37.97 201	31.32 219	36.72 207	51.28 200	14.17 217	36.41 218	34.45 184	46.14 212	47.02 215	38.00 171	51.78 207	48.53 201	58.60 187	48.84 194

ADS-MVSNet		40.61 208	46.31 208	33.96 208	40.70 212	30.42 218	40.42 217	33.44 186	58.01 158	30.87 202	63.05 166	54.48 201	22.67 209	44.35 217	39.23 215	35.64 217	34.64 214

CHOSEN 280x420		40.24 209	44.14 217	35.69 206	32.36 218	23.58 221	50.30 204	21.21 212	40.94 213	18.84 218	32.75 219	48.65 211	48.13 133	59.16 188	55.31 188	43.28 210	48.62 196

EPMVS		40.11 210	44.96 216	34.44 207	41.55 210	32.65 214	41.74 216	32.39 193	49.89 192	24.83 209	64.44 160	46.38 219	26.57 205	44.75 216	39.47 214	39.59 213	37.16 213

FMVSNet5		39.83 211	45.08 213	33.71 209	39.24 213	39.56 202	48.77 207	23.55 210	39.45 215	24.55 210	33.73 218	44.57 220	20.97 212	58.27 193	54.23 193	45.16 207	45.77 202

N_pmnet		39.50 212	51.01 198	26.09 213	44.48 209	25.59 220	40.20 218	21.49 211	64.20 133	7.98 224	73.86 118	76.67 154	13.66 216	50.17 210	36.69 218	28.71 219	29.86 218

MVE	28.01 19	35.86 213	43.56 218	26.88 212	22.33 223	19.75 223	30.85 223	23.88 209	49.90 191	10.48 222	43.64 216	61.87 192	48.99 128	47.26 215	42.15 208	24.76 220	40.37 212
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet		35.76 214	45.64 211	24.22 214	38.59 214	25.83 219	31.87 222	19.24 214	49.06 196	9.01 223	54.34 203	64.73 188	12.46 218	49.21 211	44.91 205	34.17 218	31.41 215

PMMVS2		34.11 215	48.55 206	17.26 215	25.45 222	20.72 222	35.08 221	16.26 215	58.71 152	4.16 226	59.22 188	78.40 146	3.65 220	57.24 197	38.31 217	18.94 222	27.28 219

GG-mvs-BLEND		31.54 216	46.27 209	14.37 216	0.07 228	48.65 185	42.97 213	0.08 224	44.04 210	1.21 228	39.77 217	57.94 200	0.15 224	48.19 213	42.82 206	41.70 211	42.46 208

test_method		13.28 217	15.83 219	10.30 217	1.05 225	2.18 226	15.40 224	2.23 220	22.43 220	13.84 221	22.00 221	33.14 226	9.78 219	17.80 220	9.93 220	19.50 221	3.31 221

test123		0.53 218	0.60 221	0.46 219	0.22 226	0.25 227	0.33 229	0.13 223	0.66 223	1.37 227	1.10 223	0.00 231	0.43 222	0.68 222	0.61 221	0.26 225	0.88 222

testmvs		0.47 219	0.69 220	0.21 220	0.17 227	0.17 228	0.35 228	0.16 222	0.66 223	0.18 229	1.05 224	0.99 230	0.27 223	0.62 223	0.54 222	0.15 226	0.77 223

uanet_test		0.00 220	0.00 222	0.00 221	0.00 229	0.00 229	0.00 230	0.00 225	0.00 225	0.00 230	0.00 225	0.00 231	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet-low-res		0.00 220	0.00 222	0.00 221	0.00 229	0.00 229	0.00 230	0.00 225	0.00 225	0.00 230	0.00 225	0.00 231	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet		0.00 220	0.00 222	0.00 221	0.00 229	0.00 229	0.00 230	0.00 225	0.00 225	0.00 230	0.00 225	0.00 231	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

TPM-MVS					72.72 63	70.92 72	80.38 71			66.22 76	58.35 191	78.23 147	48.26 131			83.40 77	81.74 55

RE-MVS-def										70.04 55

9.14												83.64 121

SR-MVS					81.31 9			62.63 9				91.11 45

Anonymous202405211			72.22 117		66.19 100	61.09 126	62.23 165	45.87 101	71.25 95		79.33 86	86.16 107	37.36 175	73.54 122	69.84 125	75.45 134	64.32 152

our_test_3					52.72 182	53.66 167	69.11 130

ambc			79.96 61		74.57 54	75.48 49	73.75 109		80.32 53	72.34 38	78.46 88	92.41 29	59.05 76	80.24 85	73.95 96	75.41 135	78.85 71

MTAPA										80.26 8		90.53 60

MTMP										82.07 4		91.00 48

Patchmatch-RL test							2.05 227

tmp_tt				7.47 218	8.89 224	3.32 225	4.35 226	1.14 221	15.58 222	15.76 219	8.50 222	5.90 229	2.00 221	20.02 219	21.51 219	12.70 223

XVS					80.47 20	81.29 12	93.33 3			77.45 19		90.19 62				91.52 11

X-MVStestdata					80.47 20	81.29 12	93.33 3			77.45 19		90.19 62				91.52 11

mPP-MVS					82.97 2							92.12 34

NP-MVS									71.39 94

Patchmtry						37.73 205	45.00 210	44.97 108		52.60 117

DeepMVS_CX						8.52 224	9.75 225	3.19 219	16.70 221	5.02 225	23.06 220	19.33 228	18.69 214	13.75 221		11.34 224	25.07 220

High-resolution multi-view benchmark