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 53	83.76 2	93.79 1	61.08 19	89.57 2	86.19 1	90.06 10	93.01 25	76.72 2	94.71 1	92.72 1	93.47 1	91.56 1

COLMAP_ROB	75.87 2	84.34 2	89.80 2	77.97 12	75.52 51	82.76 4	90.39 20	54.21 54	89.37 3	83.18 2	89.90 12	95.58 11	72.34 10	92.31 4	90.04 5	92.17 5	88.61 17
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 9	80.86 2	81.81 8	79.66 29	92.67 6	64.48 1	83.13 32	82.32 3	80.89 72	92.97 26	72.51 9	91.74 6	90.02 6	91.40 17	89.14 7

ACMMPR		83.94 4	87.20 3	80.14 4	81.04 13	81.92 8	92.57 8	63.14 5	84.35 20	79.45 12	83.37 45	92.04 36	72.82 8	90.66 12	88.96 12	91.80 6	89.13 8

MP-MVS		83.50 5	86.11 18	80.45 3	82.58 5	80.60 24	92.68 5	63.48 3	81.43 46	80.21 9	81.95 61	90.76 54	72.86 6	90.14 19	89.30 11	90.92 19	88.59 18
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMP		83.17 6	86.75 10	79.01 8	80.11 25	82.01 7	92.29 11	60.35 26	82.20 40	78.32 16	80.59 73	93.14 23	70.67 15	91.30 8	89.36 10	92.30 4	88.62 16
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 25	79.95 5	80.69 17	81.09 15	92.40 10	63.06 6	79.38 60	80.21 9	80.31 75	91.44 41	71.75 12	90.46 15	88.53 15	91.57 9	88.50 19

LGP-MVS_train		82.91 8	86.50 12	78.72 9	78.72 34	81.03 16	89.78 24	61.16 18	80.15 56	80.44 6	84.83 36	94.19 16	70.52 18	90.70 11	87.19 23	91.71 8	87.37 29

ACMM	71.24 7	82.85 9	86.59 11	78.50 10	80.10 26	78.59 31	91.77 12	60.76 23	84.43 18	76.49 25	81.58 66	93.50 18	70.45 19	91.38 7	89.42 9	91.42 16	87.22 31
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
zzz-MVS		82.61 10	85.04 32	79.79 6	82.59 4	73.90 58	92.42 9	62.39 12	84.54 17	80.21 9	79.86 79	90.74 55	70.63 16	90.01 21	89.71 8	90.48 21	86.49 36

HFP-MVS		82.37 11	86.28 14	77.81 15	79.94 27	80.96 18	91.13 15	63.30 4	84.04 22	71.81 39	82.39 56	89.59 71	69.16 24	89.08 26	88.83 14	91.49 13	89.10 9

DeepC-MVS	73.80 3	82.34 12	86.87 7	77.06 19	78.62 35	84.34 1	90.30 22	63.54 2	83.10 33	71.30 44	86.91 23	90.54 61	67.12 30	87.81 35	87.05 24	91.46 15	88.37 20
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 13	85.00 34	79.19 7	80.73 16	80.86 21	91.68 13	62.59 10	82.55 37	75.53 29	73.88 115	92.28 32	73.74 5	90.07 20	87.65 19	90.87 20	87.74 24

ACMP	70.35 9	82.17 14	86.45 13	77.18 18	79.33 28	81.00 17	89.27 28	58.63 32	81.35 48	75.46 30	82.97 51	95.08 13	68.90 25	90.49 14	87.43 22	91.48 14	86.84 33
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SteuartSystems-ACMMP		82.16 15	85.55 27	78.21 11	80.48 19	79.28 30	92.65 7	61.03 20	80.55 54	77.00 23	81.80 64	90.71 56	68.73 26	90.25 17	87.94 18	89.36 28	88.30 21
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		82.15 16	85.93 20	77.74 16	80.13 24	80.25 26	91.01 16	60.61 24	85.54 11	78.61 15	83.21 48	86.96 99	65.95 34	88.10 32	87.59 20	90.11 22	89.83 4
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 17	86.80 8	76.67 20	80.36 22	80.66 22	89.48 26	56.93 35	82.50 38	67.55 68	87.05 21	91.40 43	72.84 7	88.66 28	88.32 16	92.85 2	89.04 10
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 18	87.16 4	76.07 23	63.57 122	70.27 76	86.48 41	62.99 7	89.00 5	80.32 7	86.25 26	91.04 48	74.66 4	92.58 3	90.29 4	88.42 35	90.72 2
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	70.37 8	81.82 19	87.08 5	75.68 25	77.06 42	77.23 39	87.77 38	56.25 42	83.33 31	67.18 73	89.48 14	87.94 83	77.70 1	93.02 2	92.57 2	88.13 37	86.00 39
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMMP_NAP		81.79 20	85.72 23	77.21 17	79.15 32	79.68 28	91.62 14	59.66 28	83.55 28	77.74 19	83.72 43	87.34 92	65.36 35	88.61 29	87.56 21	89.73 27	89.58 5

X-MVS		81.61 21	84.73 36	77.97 12	80.31 23	81.29 12	93.53 2	62.50 11	81.41 47	77.45 20	72.04 126	90.19 64	62.50 54	90.57 13	88.87 13	91.54 10	88.73 14

OPM-MVS		81.44 22	85.68 24	76.49 21	79.27 29	78.21 34	89.84 23	58.67 31	85.25 12	76.26 26	85.28 33	92.88 27	66.03 33	87.20 38	85.40 28	88.86 32	85.58 43

TSAR-MVS + MP.		81.23 23	86.13 16	75.52 26	80.74 15	83.22 3	90.55 17	55.12 49	80.87 51	67.62 67	88.01 16	92.38 31	70.61 17	86.64 40	83.10 43	88.51 33	88.67 15
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 6	74.52 29	77.60 38	82.29 5	84.41 48	62.95 8	82.99 34	64.03 82	87.71 17	89.17 73	71.98 11	88.19 31	88.10 17	86.18 56	89.95 3

APDe-MVS		81.08 25	86.12 17	75.20 27	79.25 30	80.91 19	90.38 21	57.05 34	85.83 10	66.07 78	87.34 20	91.27 44	69.45 20	85.99 44	82.55 45	88.98 31	88.95 12

DPE-MVS		81.01 26	85.18 29	76.15 22	78.58 36	80.64 23	89.77 25	57.92 33	81.66 45	73.45 33	86.84 24	89.80 69	69.33 22	85.40 46	82.91 44	87.87 39	89.01 11
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 24	81.22 11	81.48 10	90.49 18	58.81 30	77.54 66	67.49 69	85.90 28	89.82 68	69.43 21	86.08 43	83.80 38	88.01 38	87.77 23
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HPM-MVS++		80.44 28	82.57 51	77.96 14	81.99 7	72.76 63	90.48 19	61.31 15	80.85 52	77.90 18	81.93 62	87.01 96	68.20 28	84.15 56	85.27 30	87.85 40	86.00 39

DVP-MVS		80.31 29	85.60 26	74.15 33	76.23 46	78.39 32	86.62 40	55.79 45	86.47 9	71.32 43	90.96 7	89.02 75	69.28 23	84.62 55	81.64 54	85.66 61	88.09 22
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 15	73.14 40	73.82 60	76.41 42	83.59 53	54.82 52	87.35 6	70.86 48	86.98 22	96.27 4	66.50 31	89.17 25	83.39 40	89.26 29	83.56 49

OMC-MVS		79.95 31	85.28 28	73.74 36	72.95 63	80.10 27	87.87 37	48.13 81	84.62 16	79.42 13	80.27 76	92.49 29	64.14 44	87.25 37	85.11 31	89.92 25	87.10 32

SED-MVS		79.70 32	85.16 30	73.34 38	75.83 50	78.11 35	88.77 33	56.45 39	84.85 14	69.45 60	90.70 9	88.38 78	63.16 49	85.12 51	81.28 57	86.40 54	87.63 25

MSP-MVS		79.65 33	84.28 41	74.25 31	78.92 33	81.86 9	89.07 29	60.49 25	83.85 26	70.05 55	85.12 34	90.92 52	62.99 51	81.15 78	81.64 54	83.99 68	85.42 45
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 42	74.17 32	74.14 57	80.88 20	89.33 27	56.24 43	82.41 39	71.58 41	82.27 57	86.47 102	66.47 32	84.80 53	84.16 36	87.26 45	87.34 30

LS3D		79.33 35	84.03 43	73.84 34	75.37 52	78.09 36	83.30 54	52.94 61	84.42 19	76.01 27	84.16 39	87.44 91	65.34 36	86.30 41	82.08 52	90.09 23	85.70 41

3Dnovator+	72.94 4	78.78 36	83.05 48	73.80 35	70.70 75	81.34 11	88.33 34	56.01 44	81.33 49	72.87 37	78.06 93	81.15 131	63.83 46	87.39 36	85.82 26	91.06 18	86.28 38

UA-Net		78.65 37	83.96 44	72.46 42	84.87 1	76.15 43	89.06 30	55.70 46	77.25 67	53.14 116	79.73 81	82.09 129	59.69 71	92.21 5	90.93 3	92.32 3	89.36 6

xxxxxxxxxxxxxcwj		78.58 38	83.87 45	72.41 43	76.04 48	75.72 46	83.86 50	51.81 65	84.00 24	70.65 51	81.27 68	95.06 14	64.64 41	83.28 64	80.28 59	87.44 43	87.49 27

DeepC-MVS_fast	71.40 6	78.48 39	82.92 49	73.31 39	76.44 45	82.23 6	87.59 39	56.56 38	77.79 64	68.91 64	77.00 97	87.32 93	61.90 56	85.40 46	84.37 33	88.46 34	86.33 37
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 40	83.47 47	72.41 43	76.04 48	75.72 46	83.86 50	51.81 65	84.00 24	70.65 51	81.27 68	92.22 33	64.64 41	83.28 64	80.28 59	87.44 43	87.49 27

WR-MVS		78.32 41	86.09 19	69.25 61	76.22 47	72.33 70	85.71 44	59.02 29	86.66 7	51.41 122	92.91 2	96.76 1	53.09 108	90.21 18	85.30 29	90.05 24	78.46 75

ACMH	66.19 11	78.12 42	84.55 38	70.63 52	69.62 81	72.40 69	80.77 70	46.43 95	89.24 4	77.99 17	87.42 19	95.83 9	62.95 52	86.27 42	78.24 71	86.00 59	82.46 51
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
train_agg		77.83 43	80.47 61	74.77 28	80.92 14	69.60 77	88.87 32	56.32 41	74.03 83	71.03 46	83.67 44	87.68 86	64.75 40	83.70 58	81.85 53	86.71 50	82.73 50

NCCC		77.82 44	80.72 60	74.43 30	79.24 31	75.72 46	88.06 35	56.36 40	79.61 58	73.22 35	67.75 139	87.05 95	63.09 50	85.62 45	84.00 37	86.62 51	85.30 46

CNVR-MVS		77.79 45	81.57 55	73.38 37	78.37 37	75.91 44	87.97 36	55.11 50	79.41 59	70.98 47	74.70 112	86.43 103	61.77 57	85.10 52	83.73 39	86.10 58	85.68 42

WR-MVS_H		77.56 46	85.88 21	67.86 65	80.54 18	74.32 55	83.23 55	61.78 13	83.47 29	47.46 142	91.81 6	95.84 8	50.50 119	90.44 16	84.37 33	83.63 74	80.89 61

RPSCF		77.56 46	84.51 39	69.46 60	65.17 110	74.36 54	79.74 75	47.45 84	84.01 23	72.89 36	77.89 94	90.67 57	65.14 38	88.25 30	89.74 7	86.38 55	86.64 35

PS-CasMVS		77.46 48	85.80 22	67.73 67	81.24 10	72.88 62	80.63 71	61.28 16	84.14 21	50.53 128	92.13 4	96.76 1	50.12 122	91.02 9	84.46 32	82.60 86	79.19 68

DTE-MVSNet		77.28 49	84.87 35	68.42 63	82.94 3	72.70 65	81.60 65	61.78 13	85.03 13	51.40 123	92.11 5	96.00 6	49.42 126	89.73 23	82.52 47	83.39 78	75.98 86

SixPastTwentyTwo		77.24 50	83.65 46	69.78 56	65.14 111	64.85 98	77.44 86	47.74 83	82.76 36	68.52 65	87.65 18	93.31 20	71.68 13	89.49 24	82.41 48	88.14 36	85.05 47

CDPH-MVS		77.22 51	81.05 59	72.75 41	77.29 40	77.46 38	86.36 42	54.02 56	73.00 88	69.75 58	77.78 95	88.90 76	61.31 61	84.09 57	82.54 46	87.79 41	83.57 48

PEN-MVS		77.06 52	85.05 31	67.74 66	82.29 6	72.59 66	80.86 69	61.03 20	84.66 15	50.08 131	92.19 3	96.59 3	49.12 127	89.83 22	82.35 49	83.06 79	77.14 81

CP-MVSNet		77.01 53	85.04 32	67.65 68	81.16 12	72.72 64	80.54 72	61.18 17	82.09 41	50.41 129	90.81 8	95.89 7	50.03 123	90.86 10	84.30 35	82.56 88	78.65 74

CSCG		76.95 54	82.08 53	70.97 48	73.32 62	78.35 33	81.08 68	47.19 86	83.47 29	69.82 57	80.44 74	87.19 94	64.59 43	81.01 81	77.26 79	89.83 26	86.84 33

CNLPA		76.67 55	81.72 54	70.77 51	70.75 73	76.68 41	86.14 43	46.11 97	81.82 43	74.68 31	76.37 99	86.23 105	62.92 53	85.28 49	83.29 41	84.02 67	82.40 52

MSLP-MVS++		76.66 56	82.32 52	70.06 54	70.51 76	80.27 25	79.77 74	55.58 47	77.79 64	63.09 83	67.25 143	89.50 72	71.01 14	88.10 32	85.74 27	80.39 101	87.56 26

TSAR-MVS + COLMAP		75.85 57	81.06 57	69.77 57	71.15 69	76.90 40	82.93 57	52.43 63	79.25 61	70.13 53	82.78 52	87.00 97	60.02 67	80.30 85	79.61 64	81.95 92	81.61 57

HQP-MVS		75.81 58	78.91 67	72.18 45	77.41 39	75.38 50	84.75 45	53.35 58	76.12 71	73.32 34	69.48 131	88.07 81	57.76 79	79.42 90	78.44 68	86.48 52	85.50 44

PLC	64.88 15	75.76 59	80.22 62	70.57 53	70.46 77	77.75 37	82.01 63	48.84 76	80.74 53	70.85 49	71.32 128	84.82 116	63.69 47	84.73 54	82.35 49	87.54 42	79.80 65
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_part1		75.44 60	84.35 40	65.04 88	71.41 68	70.75 74	78.24 80	47.16 87	90.52 1	56.65 99	93.19 1	95.21 12	49.99 124	82.20 73	77.98 73	86.80 49	80.63 62

TAPA-MVS	66.11 12	75.37 61	79.24 65	70.86 49	67.63 90	74.09 56	83.17 56	44.75 111	81.82 43	80.83 5	65.61 155	88.04 82	61.58 58	83.21 66	80.12 61	87.17 47	81.82 55
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PHI-MVS		75.17 62	78.37 68	71.43 46	71.13 70	72.46 68	82.28 62	50.55 69	73.39 86	79.05 14	73.65 117	87.50 90	61.98 55	81.10 79	78.48 67	83.60 75	81.99 53

anonymousdsp		74.76 63	82.59 50	65.63 83	45.61 202	61.13 122	89.06 30	32.58 189	74.11 82	59.55 90	84.06 40	94.12 17	75.24 3	88.94 27	86.95 25	91.74 7	88.81 13

AdaColmap		74.73 64	77.57 73	71.40 47	76.90 43	75.76 45	84.54 47	53.08 60	76.20 70	66.64 77	66.06 153	78.16 147	61.32 60	85.37 48	82.20 51	85.95 60	79.27 67

v7n		74.47 65	81.06 57	66.77 74	66.98 95	67.10 80	76.76 90	45.88 99	81.98 42	67.43 70	88.38 15	95.67 10	61.38 59	80.76 83	73.49 99	82.21 90	80.06 64

PCF-MVS	65.25 14	73.99 66	76.74 78	70.79 50	71.61 67	75.33 51	83.76 52	50.40 70	74.88 74	74.50 32	67.60 140	85.36 113	58.30 77	78.61 93	74.25 94	86.15 57	81.13 60
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MCST-MVS		73.84 67	77.44 74	69.63 59	73.75 61	74.73 53	81.38 67	48.58 77	74.77 75	69.16 62	71.97 127	86.20 106	59.50 72	78.51 94	74.06 96	85.42 62	81.85 54

MVS_0304		73.74 68	77.16 76	69.74 58	74.24 56	73.47 59	84.70 46	49.62 71	62.26 142	67.27 71	75.87 102	87.57 88	57.49 82	81.20 77	79.50 65	85.10 63	80.27 63

TSAR-MVS + GP.		73.42 69	76.31 79	70.05 55	77.15 41	71.13 73	81.59 66	54.11 55	69.84 112	58.65 93	66.20 152	78.77 144	65.29 37	83.65 59	83.14 42	83.54 76	81.47 58

Gipuma		73.40 70	79.27 64	66.55 78	63.64 121	59.35 131	70.28 125	45.92 98	83.79 27	71.78 40	84.04 41	93.07 24	68.69 27	87.90 34	76.76 81	78.98 112	69.96 121
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVS_111021_HR		72.37 71	76.12 82	68.00 64	68.55 87	64.30 106	82.93 57	48.98 75	74.25 80	65.39 79	73.59 118	84.11 121	59.48 73	82.61 70	78.38 69	82.66 85	75.59 88

TinyColmap		71.85 72	76.11 83	66.87 72	66.07 101	65.34 93	74.35 104	49.30 74	79.93 57	75.93 28	75.66 104	87.74 85	54.72 99	80.66 84	70.42 119	80.85 99	73.02 103

UniMVSNet_ETH3D		71.84 73	81.36 56	60.74 110	76.46 44	66.01 87	66.49 143	60.24 27	86.58 8	41.87 165	90.04 11	96.02 5	43.72 153	85.14 50	77.30 78	75.64 132	68.40 134

TranMVSNet+NR-MVSNet		71.66 74	79.23 66	62.83 102	72.54 65	65.64 89	74.77 102	55.27 48	75.91 72	45.50 154	89.55 13	94.25 15	45.96 144	82.74 69	77.03 80	82.96 81	69.48 128

MVS_111021_LR		71.60 75	75.21 87	67.38 69	67.42 91	62.44 115	81.73 64	46.24 96	70.89 99	66.80 76	73.19 120	84.98 114	60.09 66	81.94 74	77.77 76	82.00 91	75.29 89

CS-MVS-test		71.51 76	75.56 86	66.78 73	68.66 85	63.48 110	75.59 97	40.99 140	67.22 123	67.00 75	79.39 87	83.26 126	61.23 62	82.79 68	76.23 86	87.21 46	76.56 83

EG-PatchMatch MVS		71.50 77	76.82 77	65.30 85	70.74 74	66.50 84	74.23 106	43.25 119	72.02 91	59.11 91	79.85 80	86.88 100	63.95 45	80.29 86	75.25 90	80.51 100	76.98 82

DPM-MVS		71.35 78	73.50 104	68.84 62	74.93 54	73.35 60	84.07 49	50.56 68	71.91 92	67.06 74	61.21 174	77.02 152	52.64 111	74.15 114	75.14 91	83.79 71	81.74 56

UniMVSNet (Re)		71.29 79	78.14 69	63.30 96	70.29 78	66.57 83	75.98 92	54.74 53	70.20 105	46.20 152	85.08 35	93.21 21	48.19 132	82.50 71	78.33 70	84.40 65	71.08 115

CLD-MVS		71.24 80	78.12 70	63.20 98	74.03 58	71.60 71	82.82 59	32.91 186	74.23 81	69.32 61	79.65 82	91.54 39	47.02 140	81.22 76	79.01 66	73.09 147	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 81	75.09 89	66.39 79	72.57 64	71.53 72	82.38 61	47.10 88	59.81 149	59.81 89	74.97 108	84.37 120	54.25 102	79.89 89	77.64 77	82.25 89	77.40 79

v1192		71.06 82	74.87 91	66.61 76	66.38 98	65.80 88	78.27 79	45.28 104	70.19 106	70.79 50	83.37 45	91.79 37	58.76 76	70.86 147	69.02 126	80.16 103	73.08 101

DU-MVS		71.03 83	77.92 71	62.98 100	70.81 71	65.48 91	73.93 109	56.76 36	69.95 110	46.77 148	85.70 31	93.49 19	46.91 141	83.47 60	77.82 75	82.72 84	69.54 125

v1240		70.94 84	74.52 95	66.76 75	66.54 96	64.40 102	77.76 83	45.29 103	70.05 108	71.45 42	83.36 47	90.96 50	60.37 64	70.50 150	68.68 127	79.14 110	73.68 97

v1921920		70.82 85	74.46 97	66.58 77	66.33 99	64.35 105	77.72 84	45.07 106	70.39 102	71.18 45	83.15 49	90.62 59	59.97 68	70.90 145	68.43 134	79.19 109	73.39 98

UniMVSNet_NR-MVSNet		70.82 85	77.44 74	63.11 99	71.75 66	66.02 86	73.93 109	55.00 51	70.90 98	46.77 148	86.68 25	91.54 39	46.91 141	81.07 80	76.32 85	84.28 66	69.54 125

PVSNet_Blended_VisFu		70.70 87	73.62 103	67.28 71	63.53 123	72.96 61	77.97 81	52.10 64	63.65 136	62.66 84	71.14 129	73.46 162	63.55 48	79.35 92	75.34 89	83.90 69	79.43 66

v144192		70.68 88	74.40 99	66.34 80	65.94 103	64.38 103	77.63 85	45.18 105	69.97 109	70.11 54	82.70 54	90.77 53	59.84 70	71.43 143	68.46 130	79.31 108	73.08 101

FPMVS		70.46 89	74.89 90	65.28 86	69.09 84	61.42 120	77.07 88	46.92 91	76.73 69	53.53 112	67.33 141	75.07 158	67.23 29	83.41 62	81.54 56	77.86 119	78.73 72

v1144		70.45 90	74.50 96	65.73 82	65.74 105	64.88 97	77.33 87	44.16 113	70.59 101	69.63 59	83.15 49	91.42 42	57.79 78	71.29 144	68.53 129	79.72 106	71.63 114

v10		70.25 91	74.59 94	65.19 87	65.32 108	66.46 85	76.60 91	44.84 109	67.38 121	67.21 72	82.75 53	90.56 60	57.70 80	71.69 140	68.63 128	79.44 107	74.67 92

Effi-MVS+-dtu		70.10 92	73.76 102	65.82 81	70.23 79	74.92 52	79.47 76	44.49 112	56.98 164	54.34 105	64.26 159	84.78 117	59.97 68	80.96 82	80.38 58	86.44 53	74.05 95

MAR-MVS		70.00 93	72.28 116	67.34 70	69.89 80	72.57 67	80.09 73	49.49 73	60.28 148	69.03 63	59.29 184	80.79 133	54.68 100	78.39 97	76.00 87	80.87 98	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 94	77.69 72	60.91 109	60.67 137	66.71 81	77.94 82	48.58 77	69.10 115	45.78 153	80.21 77	83.58 124	53.41 107	82.92 67	80.11 62	79.08 111	81.21 59
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		69.51 95	76.17 80	61.74 107	68.38 89	66.60 82	71.77 116	46.98 89	73.60 85	41.79 166	82.06 60	69.65 173	52.51 112	83.41 62	79.94 63	89.02 30	77.94 77

3Dnovator	65.69 13	69.43 96	75.74 85	62.06 106	60.78 136	70.50 75	75.85 94	39.57 155	74.44 77	57.41 96	75.91 100	77.73 149	55.34 95	76.86 101	75.61 88	83.44 77	79.14 69

Effi-MVS+		69.04 97	73.01 108	64.40 91	67.20 93	64.83 99	74.87 101	43.97 115	63.33 138	60.90 86	73.06 121	85.79 110	55.61 92	73.58 120	76.41 84	83.84 70	74.09 94

v2v482		69.01 98	73.39 106	63.89 93	63.86 115	62.99 112	75.26 99	42.05 128	70.22 104	68.46 66	82.64 55	91.61 38	55.38 93	70.89 146	66.93 148	78.30 115	68.48 133

MSDG		68.98 99	73.31 107	63.92 92	67.08 94	68.27 78	75.41 98	40.77 145	67.61 119	64.89 80	75.75 103	78.96 141	53.70 104	76.72 103	73.95 97	81.71 95	71.93 112

v8		68.77 100	73.50 104	63.26 97	63.74 119	64.47 101	74.22 107	42.07 127	67.30 122	64.89 80	82.08 59	90.23 63	56.50 90	71.85 139	66.57 149	78.14 116	72.02 110

NR-MVSNet		68.66 101	76.15 81	59.93 114	65.49 106	65.48 91	74.42 103	56.76 36	69.95 110	45.38 155	85.70 31	91.13 45	34.68 182	74.52 113	76.75 82	82.83 83	69.49 127

USDC		68.53 102	71.82 122	64.68 89	63.53 123	61.87 119	70.12 126	46.98 89	77.89 63	76.58 24	68.55 136	86.88 100	50.50 119	73.73 117	65.62 151	80.39 101	68.21 136

IS_MVSNet		68.20 103	74.41 98	60.96 108	68.55 87	64.36 104	71.47 118	48.33 79	70.11 107	43.30 161	80.90 71	74.54 160	47.19 139	81.25 75	77.97 74	86.94 48	71.76 113

Baseline_NR-MVSNet		68.15 104	75.12 88	60.02 113	70.81 71	55.67 153	75.88 93	53.40 57	71.25 95	43.96 159	85.88 29	92.68 28	45.76 145	83.47 60	68.34 135	70.34 164	68.58 132

GeoE		68.11 105	72.10 120	63.47 95	67.32 92	62.42 116	78.32 78	43.22 120	64.06 135	55.72 103	73.97 114	84.58 118	55.35 94	76.09 107	70.41 120	80.89 97	73.14 100

Fast-Effi-MVS+		67.71 106	72.54 112	62.07 104	63.83 116	63.68 107	75.74 95	39.94 151	60.89 146	54.29 106	73.00 122	86.19 107	56.85 86	78.46 95	73.23 100	81.74 93	72.36 107

DROMVSNet		67.71 106	72.54 112	62.07 104	63.83 116	63.68 107	75.74 95	39.94 151	60.89 146	54.29 106	73.00 122	86.19 107	56.85 86	78.46 95	73.23 100	81.74 93	72.36 107

thisisatest0515		66.95 108	72.29 115	60.72 111	56.37 160	56.05 151	71.08 119	38.81 159	67.59 120	53.26 115	78.21 91	79.79 138	60.11 65	75.69 109	73.02 103	84.69 64	75.66 87

EPNet		66.87 109	68.89 133	64.53 90	73.97 59	61.13 122	78.46 77	61.03 20	56.78 166	53.41 113	66.91 147	70.91 168	43.49 154	76.08 108	76.68 83	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	74.37 100	57.04 131	65.89 104	65.06 94	62.58 160	42.55 122	76.82 68	46.87 147	67.33 141	86.38 104	45.49 147	76.77 102	71.85 109	78.87 113	76.35 84

QAPM		66.36 111	72.76 111	58.90 119	59.57 144	65.01 95	64.05 156	41.17 139	73.09 87	56.82 98	69.42 132	77.78 148	55.07 97	73.00 125	72.07 107	76.71 124	78.96 70

casdiffmvs		66.19 112	72.34 114	59.02 118	62.75 127	60.61 128	69.06 131	41.38 136	69.49 113	54.11 108	84.00 42	89.74 70	49.12 127	70.74 149	62.70 166	77.70 121	69.14 130

CS-MVS		65.81 113	68.30 135	62.90 101	66.53 97	60.12 130	72.70 114	37.18 171	52.37 176	60.30 88	67.07 144	79.31 139	56.97 85	77.49 100	71.01 116	83.74 72	70.73 117

V42		65.79 114	72.11 119	58.42 123	51.89 182	58.69 133	73.80 111	34.50 178	65.40 130	57.10 97	79.54 84	89.09 74	57.51 81	71.98 137	67.83 143	75.70 130	72.26 109

IterMVS-LS		65.76 115	70.85 127	59.81 116	65.33 107	57.78 138	64.63 153	48.02 82	65.65 129	51.05 125	81.31 67	77.47 150	54.94 98	69.46 158	69.36 125	74.90 136	74.95 90
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PM-MVS		65.66 116	71.25 126	59.14 117	58.92 150	54.88 162	73.66 113	38.55 162	66.12 127	49.91 133	69.87 130	86.97 98	60.61 63	76.30 105	74.75 92	73.19 145	69.83 122

UGNet		65.61 117	74.79 92	54.91 140	54.54 176	68.20 79	70.97 122	48.21 80	67.14 125	41.67 167	74.15 113	80.65 134	36.10 177	79.39 91	77.99 72	77.95 118	76.01 85
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		65.54 118	71.79 123	58.24 126	59.68 143	65.55 90	70.99 120	38.69 161	62.29 141	49.27 136	75.03 107	81.42 130	50.93 115	73.71 119	71.35 110	79.90 105	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		65.36 119	70.09 130	59.85 115	63.05 126	53.61 165	74.29 105	46.45 94	68.14 118	51.45 121	78.83 89	85.78 111	49.87 125	70.44 151	70.45 118	74.00 140	63.38 156

v148		64.92 120	70.58 129	58.32 124	59.89 141	57.09 144	66.04 145	35.27 177	69.11 114	60.66 87	79.57 83	90.93 51	53.91 103	69.81 157	62.22 167	74.14 138	65.31 148

FC-MVSNet-train		64.87 121	74.76 93	53.33 144	65.24 109	58.05 135	69.69 128	41.92 131	70.99 97	32.62 188	85.75 30	88.23 79	32.10 192	77.61 99	74.41 93	78.43 114	68.25 135

pmmvs6		64.78 122	75.82 84	51.89 150	62.41 128	57.13 143	60.24 167	45.59 101	82.90 35	34.69 181	84.83 36	93.18 22	36.22 176	76.43 104	71.13 113	72.21 151	65.12 149

OpenMVS	60.79 16	64.42 123	69.72 131	58.23 127	61.63 132	62.17 117	64.11 155	37.54 170	67.17 124	55.71 104	65.89 154	74.89 159	52.67 110	72.20 135	68.29 137	77.73 120	77.39 80

DCV-MVSNet		64.34 124	72.84 110	54.42 142	63.79 118	62.09 118	62.50 161	42.72 121	74.32 79	41.34 168	66.96 145	88.57 77	39.18 165	75.20 111	70.35 121	77.01 122	72.37 106

ETV-MVS		64.30 125	64.76 149	63.77 94	68.59 86	62.49 114	77.02 89	45.31 102	49.27 193	50.88 126	56.23 195	59.91 195	57.12 84	80.19 88	74.23 95	83.68 73	71.03 116

Anonymous20231211		63.69 126	72.86 109	53.00 147	63.72 120	60.25 129	60.33 166	40.96 141	72.49 89	38.91 171	81.77 65	88.17 80	37.60 171	73.30 122	68.01 140	76.47 128	66.06 145

TransMVSNet (Re)		63.49 127	73.86 101	51.39 156	64.26 114	56.07 150	61.17 164	42.23 125	78.81 62	34.80 179	85.94 27	90.63 58	34.35 186	72.73 130	67.98 141	71.50 154	64.84 150

DI_MVS_plusplus_trai		63.43 128	67.54 138	58.63 120	62.34 129	58.06 134	65.75 149	42.15 126	63.05 139	53.28 114	75.88 101	75.92 155	50.18 121	68.04 161	64.20 157	78.07 117	67.65 137

EIA-MVS		63.24 129	64.16 153	62.16 103	69.30 83	63.20 111	72.40 115	40.82 144	48.31 199	51.50 120	59.63 182	62.23 187	57.33 83	78.00 98	71.94 108	81.59 96	65.82 146

Fast-Effi-MVS+-dtu		63.22 130	65.55 143	60.49 112	61.24 134	64.70 100	74.15 108	53.24 59	51.46 178	49.67 134	58.03 190	78.42 145	48.05 134	72.03 136	71.14 112	76.60 127	63.09 157

IterMVS-SCA-FT		62.67 131	68.00 136	56.45 137	56.92 158	64.92 96	57.51 179	38.12 163	59.44 150	53.62 111	74.74 111	71.60 165	64.84 39	70.24 153	65.27 153	67.70 172	69.83 122

diffmvs		62.64 132	69.66 132	54.46 141	56.19 163	55.06 159	67.36 140	36.74 175	64.18 133	50.58 127	79.54 84	87.55 89	45.13 149	68.04 161	63.20 162	70.78 159	70.02 120

MVS_Test		62.58 133	67.46 139	56.89 133	59.52 145	55.90 152	64.94 151	38.83 158	57.08 163	56.55 101	76.53 98	84.49 119	47.45 136	66.95 164	62.01 168	74.04 139	69.27 129

MDA-MVSNet-bldmvs		62.46 134	72.13 118	51.19 158	34.32 212	56.10 149	68.65 133	38.85 157	69.05 116	49.50 135	78.17 92	85.43 112	51.32 113	86.67 39	67.40 146	64.46 177	62.08 159

pm-mvs1		61.97 135	72.01 121	50.25 164	60.64 138	55.23 157	58.67 174	42.44 123	74.40 78	33.63 185	81.03 70	89.86 67	34.87 181	72.93 128	67.95 142	71.28 155	62.65 158

FMVSNet1		61.92 136	71.36 124	50.90 160	57.67 157	59.29 132	59.48 172	44.14 114	70.24 103	34.72 180	75.45 106	84.94 115	36.75 173	72.33 132	68.45 131	72.66 148	68.83 131

PVSNet_BlendedMVS		61.75 137	65.07 147	57.87 129	56.27 161	60.99 125	65.81 147	43.75 116	51.27 182	54.08 109	62.12 169	78.84 142	50.67 116	71.49 141	63.91 159	76.64 125	66.86 139

PVSNet_Blended		61.75 137	65.07 147	57.87 129	56.27 161	60.99 125	65.81 147	43.75 116	51.27 182	54.08 109	62.12 169	78.84 142	50.67 116	71.49 141	63.91 159	76.64 125	66.86 139

tttt0517		61.44 139	63.85 154	58.62 121	55.20 172	55.61 154	68.80 132	38.02 166	55.70 168	50.01 132	66.93 146	48.90 206	56.69 88	73.84 116	71.10 114	82.99 80	74.89 91

tfpnnormal		61.41 140	71.33 125	49.83 165	61.73 131	54.90 161	58.52 175	41.24 137	75.20 73	32.00 193	82.13 58	87.87 84	35.63 180	72.75 129	66.30 150	69.87 165	60.14 164

IB-MVS	57.02 17	61.37 141	65.39 144	56.69 134	56.65 159	60.85 127	70.70 123	37.90 168	49.37 192	45.37 156	48.75 207	79.14 140	53.55 106	76.26 106	70.85 117	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		61.22 142	67.07 140	54.40 143	59.89 141	63.62 109	70.98 121	36.77 174	50.49 185	47.15 143	62.45 167	80.81 132	37.90 170	71.87 138	70.09 123	73.69 141	70.19 119

pmmvs4		61.12 143	64.61 150	57.04 131	60.88 135	52.15 170	70.59 124	44.82 110	61.35 145	46.91 146	72.08 125	73.27 163	46.79 143	65.06 167	67.76 144	72.28 149	60.58 163

thisisatest0530		61.02 144	63.44 159	58.19 128	54.75 174	55.09 158	68.03 137	38.02 166	55.45 169	49.06 137	66.58 150	48.69 207	56.69 88	73.07 123	71.10 114	82.60 86	74.14 93

Vis-MVSNet (Re-imp)		60.99 145	67.78 137	53.06 146	64.66 113	53.49 166	67.40 138	49.52 72	68.55 117	28.00 201	79.53 86	71.41 167	33.08 190	75.30 110	71.28 111	75.69 131	54.91 177

PatchMatch-RL		60.96 146	63.00 162	58.57 122	59.16 149	52.18 169	67.38 139	41.99 129	57.85 158	48.16 138	53.55 203	69.77 172	59.47 74	73.73 117	72.49 106	75.27 135	61.44 161

GA-MVS		60.73 147	64.24 152	56.64 135	59.38 148	57.45 142	65.07 150	36.65 176	57.39 161	58.17 94	73.43 119	69.10 176	47.38 137	64.47 172	63.63 161	73.19 145	64.22 153

CVMVSNet		60.45 148	63.72 156	56.63 136	54.82 173	53.75 163	68.41 134	41.95 130	55.07 170	48.03 139	58.08 189	68.67 177	55.09 96	69.14 159	68.34 135	71.51 153	72.97 104

ET-MVSNet_ETH3D		60.33 149	62.10 165	58.27 125	58.61 153	58.05 135	68.06 135	41.20 138	51.40 179	51.10 124	64.06 160	49.42 205	50.61 118	74.72 112	70.29 122	80.05 104	66.74 141

FC-MVSNet-test		60.28 150	70.83 128	47.96 176	54.69 175	47.12 184	68.06 135	41.68 135	71.42 93	23.73 209	84.70 38	77.41 151	28.92 196	82.33 72	73.08 102	70.68 160	59.77 166

EU-MVSNet		59.77 151	66.07 141	52.42 148	47.81 191	51.86 172	62.98 159	32.28 191	62.08 143	47.10 144	59.94 180	83.42 125	53.08 109	70.06 156	63.19 163	71.26 157	71.96 111

IterMVS		59.24 152	64.45 151	53.16 145	50.98 184	61.29 121	66.51 142	32.85 187	58.17 154	46.31 151	72.58 124	70.23 170	54.26 101	64.81 170	60.24 172	68.04 171	63.81 155
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		59.15 153	62.28 164	55.49 139	52.42 180	62.59 113	71.76 117	39.74 153	50.25 187	41.92 164	62.88 165	69.16 175	55.85 91	62.77 177	67.18 147	71.27 156	61.11 162

thres600view7		58.87 154	65.91 142	50.66 162	61.27 133	56.32 147	59.88 170	40.63 147	64.88 131	32.10 192	64.82 156	69.83 171	36.72 174	72.99 126	72.55 105	73.34 143	59.97 165

CMPMVS	45.32 18	58.10 155	65.24 146	49.76 166	47.88 190	46.86 187	48.16 204	32.82 188	58.06 155	61.35 85	59.64 181	80.00 135	47.27 138	70.15 154	64.10 158	61.08 180	77.85 78
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view		58.09 156	63.54 158	51.74 152	50.13 186	46.56 188	66.95 141	33.41 184	63.52 137	58.77 92	74.84 109	84.10 122	43.12 155	65.95 166	54.69 186	58.04 186	55.13 176

CDS-MVSNet		57.90 157	63.57 157	51.28 157	62.30 130	53.17 167	64.70 152	51.61 67	57.41 160	32.75 187	63.73 161	70.53 169	27.12 198	72.49 131	73.02 103	69.22 168	54.68 178
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
FMVSNet2		57.80 158	65.39 144	48.94 172	55.88 164	57.61 139	57.26 180	42.37 124	58.21 153	33.19 186	68.36 137	75.55 157	34.58 183	66.91 165	64.55 155	70.38 161	66.56 142

thres400		57.25 159	63.73 155	49.70 167	60.19 140	54.95 160	58.16 176	39.60 154	62.42 140	31.98 195	62.33 168	69.20 174	35.96 178	70.07 155	68.03 139	72.28 149	59.12 168

gm-plane-assit		56.76 160	57.64 179	55.73 138	66.01 102	55.45 156	74.96 100	30.54 196	73.71 84	56.04 102	81.81 63	30.91 223	43.83 151	58.77 187	54.71 185	63.02 179	48.13 193

MIMVSNet1		56.72 161	68.69 134	42.76 189	46.70 197	42.81 194	69.13 129	30.52 197	81.01 50	32.00 193	74.82 110	91.10 47	26.83 200	73.98 115	64.72 154	51.40 198	52.38 182

EPNet_dtu		56.63 162	60.77 171	51.80 151	55.47 169	44.63 189	69.83 127	38.74 160	50.27 186	47.64 140	58.01 191	72.27 164	33.71 188	68.60 160	67.72 145	65.39 175	63.86 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		56.54 163	63.26 160	48.70 173	55.88 164	57.61 139	57.26 180	41.75 132	49.06 194	32.37 189	61.81 171	67.02 179	34.58 183	72.33 132	68.45 131	70.38 161	66.56 142

test1		56.54 163	63.26 160	48.70 173	55.88 164	57.61 139	57.26 180	41.75 132	49.06 194	32.37 189	61.81 171	67.02 179	34.58 183	72.33 132	68.45 131	70.38 161	66.56 142

gg-mvs-nofinetune		56.45 165	61.04 169	51.10 159	63.42 125	49.40 180	53.71 191	52.52 62	74.77 75	46.93 145	77.31 96	53.88 199	26.42 202	62.51 178	57.81 178	63.60 178	51.57 185

thres200		56.35 166	62.36 163	49.34 169	58.87 151	56.32 147	55.91 184	40.63 147	58.51 152	31.34 196	58.81 188	67.31 178	35.96 178	72.99 126	65.51 152	73.34 143	57.07 171

MS-PatchMatch		56.31 167	60.22 174	51.73 153	60.53 139	55.53 155	63.41 157	37.18 171	51.34 181	37.44 173	60.53 178	62.19 188	45.52 146	64.25 173	63.17 164	66.33 173	64.56 151

tfpn200view9		56.07 168	61.85 166	49.34 169	58.57 154	56.48 146	58.01 178	40.72 146	53.23 171	31.01 197	56.41 193	66.40 183	34.18 187	73.02 124	68.06 138	73.53 142	59.35 167

FMVSNet3		54.77 169	60.73 172	47.81 177	54.29 177	56.88 145	55.89 185	41.75 132	49.06 194	32.37 189	61.81 171	67.02 179	33.67 189	62.88 176	61.96 169	68.88 169	65.53 147

thres100view900		53.88 170	59.19 175	47.68 178	58.57 154	52.74 168	54.45 188	38.07 165	53.23 171	31.01 197	56.41 193	66.40 183	32.80 191	65.03 169	64.43 156	71.18 158	56.10 174

CR-MVSNet		53.82 171	55.40 183	51.98 149	51.57 183	50.23 175	45.00 206	44.97 107	46.90 201	52.60 117	67.91 138	46.99 214	48.37 130	59.15 185	59.53 175	69.38 167	57.07 171

baseline2		53.55 172	55.19 184	51.62 154	55.27 171	51.95 171	60.89 165	34.23 179	46.69 203	42.47 162	53.56 202	50.01 202	45.33 148	64.63 171	61.22 170	71.56 152	58.28 170

test20.03		53.49 173	60.95 170	44.78 185	64.73 112	47.25 183	61.58 163	43.30 118	65.86 128	22.85 210	66.87 149	79.85 136	22.99 204	62.38 179	56.95 180	53.25 195	47.46 195

baseline		53.46 174	61.55 167	44.01 186	45.83 200	48.77 181	57.26 180	28.75 200	49.99 188	38.85 172	68.78 134	75.65 156	38.30 167	60.80 180	59.78 174	55.10 193	67.07 138

MVSTER		53.08 175	56.39 181	49.21 171	47.19 193	51.08 173	60.14 169	31.74 193	40.63 211	38.97 170	55.78 196	46.74 215	42.47 158	67.29 163	62.99 165	74.73 137	70.23 118

CHOSEN 1792x2688		52.99 176	56.91 180	48.42 175	47.32 192	50.10 177	64.18 154	33.85 181	45.46 206	36.95 175	55.20 199	66.49 182	51.20 114	59.28 183	59.81 173	57.01 188	61.99 160

baseline1		52.90 177	58.38 176	46.51 184	58.87 151	50.01 178	54.17 189	40.45 150	56.81 165	29.25 200	62.72 166	58.99 196	30.25 194	65.05 168	60.57 171	66.07 174	54.54 179

CostFormer		52.59 178	55.14 185	49.61 168	52.72 178	50.40 174	66.28 144	33.78 182	52.85 173	43.43 160	66.30 151	51.37 201	41.78 161	54.92 197	51.18 193	59.68 182	58.98 169

SCA		52.47 179	53.97 187	50.71 161	46.95 196	57.79 137	60.18 168	46.89 92	51.92 177	46.71 150	60.73 175	49.97 203	47.69 135	56.39 194	52.98 190	55.82 190	48.03 194

testgi		51.94 180	61.37 168	40.94 192	58.38 156	47.03 185	65.88 146	30.49 198	70.87 100	22.64 211	57.53 192	87.59 87	18.30 211	63.01 175	54.32 187	49.93 201	49.27 188

tpm cat1		50.98 181	51.28 193	50.62 163	55.74 167	49.92 179	63.13 158	38.12 163	52.38 175	47.61 141	60.11 179	44.51 218	44.86 150	51.31 205	47.49 200	54.25 194	53.24 181

RPMNet		50.92 182	50.32 195	51.62 154	50.25 185	50.23 175	59.16 173	46.70 93	46.90 201	42.39 163	48.97 206	37.23 221	41.78 161	57.30 192	56.18 182	69.44 166	55.43 175

pmmvs5		50.64 183	58.01 177	42.05 190	47.01 195	43.67 192	49.27 202	29.43 199	50.77 184	33.83 184	68.69 135	76.16 154	27.82 197	57.53 191	57.07 179	64.95 176	52.18 183

PatchT		50.55 184	53.55 189	47.05 182	37.59 211	42.26 195	50.55 199	37.56 169	46.37 204	52.60 117	66.91 147	43.54 220	48.37 130	59.15 185	59.53 175	55.62 191	57.07 171

Anonymous20231206		50.28 185	57.94 178	41.35 191	55.45 170	43.65 193	58.06 177	34.12 180	62.02 144	24.25 208	59.33 183	79.80 137	24.49 203	59.55 181	54.28 188	51.74 197	46.94 197

dps		49.71 186	51.97 191	47.07 181	52.37 181	47.00 186	53.02 194	40.52 149	44.91 207	41.23 169	64.55 157	44.27 219	40.12 164	57.71 190	51.97 192	55.14 192	53.41 180

MDTV_nov1_ep13		49.60 187	51.57 192	47.31 179	46.28 198	44.61 190	59.82 171	30.96 194	48.80 198	50.20 130	59.26 185	52.38 200	38.56 166	56.20 195	49.70 196	58.04 186	50.01 186

PatchmatchNet		48.67 188	50.10 196	46.99 183	48.29 189	41.00 196	55.54 186	38.94 156	51.38 180	45.15 157	63.22 163	48.45 209	42.83 156	53.80 202	48.50 199	51.19 200	44.37 199
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
new-patchmatchnet		47.33 189	60.49 173	31.99 208	55.69 168	33.86 208	36.84 216	33.31 185	72.36 90	14.33 216	80.09 78	92.14 34	13.27 213	63.54 174	40.09 208	38.51 210	41.32 207

tpm		46.67 190	49.20 201	43.72 187	49.60 187	36.60 205	53.93 190	26.84 202	52.70 174	58.05 95	69.04 133	47.96 210	30.06 195	48.33 208	42.76 203	43.88 204	47.01 196

pmmvs3		46.64 191	54.13 186	37.90 199	31.23 216	40.68 197	49.83 201	15.34 213	46.31 205	36.34 177	53.15 204	74.40 161	36.36 175	58.43 188	56.64 181	58.32 185	49.29 187

TAMVS		46.64 191	53.62 188	38.49 197	49.56 188	36.87 202	53.16 193	25.76 204	56.33 167	22.55 212	60.72 176	61.80 190	27.12 198	59.50 182	58.33 177	52.79 196	41.82 206

test-LLR		46.01 193	45.06 210	47.11 180	59.39 146	36.72 203	51.28 196	40.95 142	36.41 215	34.45 182	46.14 209	47.02 212	38.00 168	51.78 203	48.53 197	58.60 183	48.84 190

MIMVSNet		45.83 194	53.46 190	36.94 200	45.38 204	39.50 199	52.20 195	30.68 195	57.09 162	24.53 207	55.22 198	71.54 166	21.74 207	55.81 196	51.08 194	47.11 202	43.96 200

pmnet_mix02		45.67 195	55.99 182	33.63 207	45.77 201	31.22 212	42.04 211	27.60 201	64.14 134	24.89 204	75.50 105	82.30 128	21.88 206	54.53 200	41.22 205	39.62 208	43.05 202

test0.0.03 1		45.40 196	49.55 199	40.57 194	59.39 146	44.36 191	53.37 192	40.95 142	47.14 200	19.23 213	45.49 211	60.24 193	19.24 209	54.82 198	51.98 191	51.21 199	42.82 203

PMMVS		45.37 197	49.29 200	40.79 193	27.75 217	35.07 207	50.88 198	19.88 210	39.27 213	35.78 178	50.11 205	61.29 191	42.04 159	54.13 201	55.95 183	68.43 170	49.19 189

MVS-HIRNet		44.56 198	45.52 208	43.44 188	40.98 207	31.03 213	39.52 215	36.96 173	42.80 209	44.37 158	53.80 201	60.04 194	41.85 160	47.97 210	41.08 206	56.99 189	41.95 205

test-mter		44.18 199	47.60 203	40.18 195	33.20 213	39.03 200	55.28 187	13.91 215	39.07 214	36.63 176	48.09 208	49.52 204	41.12 163	54.55 199	50.91 195	60.97 181	52.03 184

EMVS		43.85 200	49.91 197	36.77 202	45.46 203	32.70 209	44.09 208	25.33 205	57.88 157	26.62 202	58.99 187	61.14 192	42.77 157	70.26 152	38.52 212	36.38 212	29.87 213

E-PMN		43.83 201	49.81 198	36.84 201	46.09 199	31.86 211	42.77 210	25.85 203	57.76 159	25.53 203	55.50 197	62.47 186	43.77 152	70.78 148	39.51 209	37.04 211	30.79 212

tpmrst		43.31 202	46.14 206	40.02 196	47.05 194	36.48 206	48.01 205	32.17 192	49.50 191	37.26 174	63.66 162	47.04 211	31.98 193	42.00 214	40.55 207	43.64 205	43.75 201

TESTMET0.1,1		41.79 203	45.06 210	37.97 198	31.32 215	36.72 203	51.28 196	14.17 214	36.41 215	34.45 182	46.14 209	47.02 212	38.00 168	51.78 203	48.53 197	58.60 183	48.84 190

ADS-MVSNet		40.61 204	46.31 204	33.96 205	40.70 208	30.42 214	40.42 213	33.44 183	58.01 156	30.87 199	63.05 164	54.48 198	22.67 205	44.35 213	39.23 211	35.64 213	34.64 210

CHOSEN 280x420		40.24 205	44.14 213	35.69 203	32.36 214	23.58 217	50.30 200	21.21 209	40.94 210	18.84 214	32.75 215	48.65 208	48.13 133	59.16 184	55.31 184	43.28 206	48.62 192

EPMVS		40.11 206	44.96 212	34.44 204	41.55 206	32.65 210	41.74 212	32.39 190	49.89 190	24.83 205	64.44 158	46.38 216	26.57 201	44.75 212	39.47 210	39.59 209	37.16 209

FMVSNet5		39.83 207	45.08 209	33.71 206	39.24 209	39.56 198	48.77 203	23.55 207	39.45 212	24.55 206	33.73 214	44.57 217	20.97 208	58.27 189	54.23 189	45.16 203	45.77 198

N_pmnet		39.50 208	51.01 194	26.09 210	44.48 205	25.59 216	40.20 214	21.49 208	64.20 132	7.98 220	73.86 116	76.67 153	13.66 212	50.17 206	36.69 214	28.71 215	29.86 214

MVE	28.01 19	35.86 209	43.56 214	26.88 209	22.33 219	19.75 219	30.85 219	23.88 206	49.90 189	10.48 218	43.64 212	61.87 189	48.99 129	47.26 211	42.15 204	24.76 216	40.37 208
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet		35.76 210	45.64 207	24.22 211	38.59 210	25.83 215	31.87 218	19.24 211	49.06 194	9.01 219	54.34 200	64.73 185	12.46 214	49.21 207	44.91 201	34.17 214	31.41 211

PMMVS2		34.11 211	48.55 202	17.26 212	25.45 218	20.72 218	35.08 217	16.26 212	58.71 151	4.16 222	59.22 186	78.40 146	3.65 216	57.24 193	38.31 213	18.94 218	27.28 215

GG-mvs-BLEND		31.54 212	46.27 205	14.37 213	0.07 224	48.65 182	42.97 209	0.08 221	44.04 208	1.21 224	39.77 213	57.94 197	0.15 220	48.19 209	42.82 202	41.70 207	42.46 204

test_method		13.28 213	15.83 215	10.30 214	1.05 221	2.18 222	15.40 220	2.23 217	22.43 217	13.84 217	22.00 217	33.14 222	9.78 215	17.80 216	9.93 216	19.50 217	3.31 217

test123		0.53 214	0.60 217	0.46 216	0.22 222	0.25 223	0.33 225	0.13 220	0.66 220	1.37 223	1.10 219	0.00 227	0.43 218	0.68 218	0.61 217	0.26 221	0.88 218

testmvs		0.47 215	0.69 216	0.21 217	0.17 223	0.17 224	0.35 224	0.16 219	0.66 220	0.18 225	1.05 220	0.99 226	0.27 219	0.62 219	0.54 218	0.15 222	0.77 219

uanet_test		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 227	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

sosnet-low-res		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 227	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

sosnet		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 227	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

RE-MVS-def										70.04 56

9.14												83.64 123

SR-MVS					81.31 9			62.63 9				91.11 46

Anonymous202405211			72.22 117		66.19 100	61.09 124	62.23 162	45.87 100	71.25 95		79.33 88	86.16 109	37.36 172	73.54 121	69.84 124	75.45 133	64.32 152

our_test_3					52.72 178	53.66 164	69.11 130

ambc			79.96 63		74.57 55	75.48 49	73.75 112		80.32 55	72.34 38	78.46 90	92.41 30	59.05 75	80.24 87	73.95 97	75.41 134	78.85 71

MTAPA										80.26 8		90.53 62

MTMP										82.07 4		91.00 49

Patchmatch-RL test							2.05 223

tmp_tt				7.47 215	8.89 220	3.32 221	4.35 222	1.14 218	15.58 219	15.76 215	8.50 218	5.90 225	2.00 217	20.02 215	21.51 215	12.70 219

XVS					80.47 20	81.29 12	93.33 3			77.45 20		90.19 64				91.52 11

X-MVStestdata					80.47 20	81.29 12	93.33 3			77.45 20		90.19 64				91.52 11

abl_6				65.41 84	69.37 82	74.02 57	82.50 60	47.39 85	66.39 126	56.63 100	60.61 177	82.76 127	53.68 105			82.92 82	78.39 76

mPP-MVS					82.97 2							92.12 35

NP-MVS									71.39 94

Patchmtry						37.73 201	45.00 206	44.97 107		52.60 117

DeepMVS_CX						8.52 220	9.75 221	3.19 216	16.70 218	5.02 221	23.06 216	19.33 224	18.69 210	13.75 217		11.34 220	25.07 216

High-resolution multi-view benchmark