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	73.88 114	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	63.57 120	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	72.04 124	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 81	87.71 17	89.17 73	71.98 11	88.19 31	88.10 17	86.18 55	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 77	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 60	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 53	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 77	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 92	81.15 129	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	53.14 114	79.73 81	82.09 127	59.69 70	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 96	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	51.41 120	92.91 2	96.76 1	53.09 106	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 58	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 49	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	67.75 137	87.05 95	63.09 50	85.62 45	84.00 37	86.62 50	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	74.70 111	86.43 103	61.77 57	85.10 52	83.73 39	86.10 57	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	47.46 140	91.81 6	95.84 8	50.50 117	90.44 16	84.37 33	83.63 73	80.89 61

RPSCF		77.56 46	84.51 39	69.46 60	65.17 109	74.36 54	79.74 75	47.45 84	84.01 23	72.89 36	77.89 93	90.67 57	65.14 38	88.25 30	89.74 7	86.38 54	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	50.53 126	92.13 4	96.76 1	50.12 120	91.02 9	84.46 32	82.60 85	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	51.40 121	92.11 5	96.00 6	49.42 124	89.73 23	82.52 47	83.39 77	75.98 85

SixPastTwentyTwo		77.24 50	83.65 46	69.78 56	65.14 110	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 94	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	50.08 129	92.19 3	96.59 3	49.12 125	89.83 22	82.35 49	83.06 78	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	50.41 127	90.81 8	95.89 7	50.03 121	90.86 10	84.30 35	82.56 87	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 80	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 98	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 82	67.25 141	89.50 72	71.01 14	88.10 32	85.74 27	80.39 99	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 66	80.30 84	79.61 64	81.95 91	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	69.48 129	88.07 81	57.76 78	79.42 89	78.44 68	86.48 51	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	71.32 126	84.82 115	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 98	93.19 1	95.21 12	49.99 122	82.20 72	77.98 73	86.80 48	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	65.61 152	88.04 82	61.58 58	83.21 66	80.12 61	87.17 46	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	73.65 116	87.50 90	61.98 55	81.10 78	78.48 67	83.60 74	81.99 53

anonymousdsp		74.76 63	82.59 50	65.63 82	45.61 200	61.13 121	89.06 30	32.58 187	74.11 82	59.55 89	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 76	66.06 150	78.16 144	61.32 60	85.37 48	82.20 51	85.95 59	79.27 67

v7n		74.47 65	81.06 57	66.77 73	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 82	73.49 98	82.21 89	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	67.60 138	85.36 112	58.30 76	78.61 92	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 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	71.97 125	86.20 106	59.50 71	78.51 93	74.06 95	85.42 61	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	62.26 141	67.27 71	75.87 101	87.57 88	57.49 81	81.20 76	79.50 65	85.10 62	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 92	66.20 149	78.77 141	65.29 37	83.65 59	83.14 42	83.54 75	81.47 58

Gipuma		73.40 70	79.27 64	66.55 77	63.64 119	59.35 129	70.28 123	45.92 98	83.79 27	71.78 40	84.04 41	93.07 24	68.69 27	87.90 34	76.76 81	78.98 110	69.96 118
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 86	64.30 106	82.93 57	48.98 75	74.25 80	65.39 78	73.59 117	84.11 120	59.48 72	82.61 69	78.38 69	82.66 84	75.59 87

TinyColmap		71.85 72	76.11 83	66.87 72	66.07 100	65.34 93	74.35 103	49.30 74	79.93 57	75.93 28	75.66 103	87.74 85	54.72 97	80.66 83	70.42 117	80.85 97	73.02 102

UniMVSNet_ETH3D		71.84 73	81.36 56	60.74 108	76.46 44	66.01 87	66.49 141	60.24 27	86.58 8	41.87 163	90.04 11	96.02 5	43.72 151	85.14 50	77.30 78	75.64 130	68.40 132

TranMVSNet+NR-MVSNet		71.66 74	79.23 66	62.83 101	72.54 65	65.64 89	74.77 101	55.27 48	75.91 72	45.50 152	89.55 13	94.25 15	45.96 142	82.74 68	77.03 80	82.96 80	69.48 125

MVS_111021_LR		71.60 75	75.21 86	67.38 69	67.42 91	62.44 113	81.73 64	46.24 96	70.89 99	66.80 75	73.19 119	84.98 113	60.09 65	81.94 73	77.77 76	82.00 90	75.29 88

EG-PatchMatch MVS		71.50 76	76.82 77	65.30 85	70.74 74	66.50 84	74.23 105	43.25 120	72.02 91	59.11 90	79.85 80	86.88 100	63.95 45	80.29 85	75.25 89	80.51 98	76.98 82

DPM-MVS		71.35 77	73.50 103	68.84 62	74.93 54	73.35 60	84.07 49	50.56 68	71.91 92	67.06 74	61.21 171	77.02 149	52.64 109	74.15 112	75.14 90	83.79 71	81.74 56

UniMVSNet (Re)		71.29 78	78.14 69	63.30 96	70.29 78	66.57 83	75.98 92	54.74 53	70.20 105	46.20 150	85.08 35	93.21 21	48.19 130	82.50 70	78.33 70	84.40 65	71.08 113

CLD-MVS		71.24 79	78.12 70	63.20 98	74.03 58	71.60 71	82.82 59	32.91 184	74.23 81	69.32 61	79.65 82	91.54 39	47.02 138	81.22 75	79.01 66	73.09 145	69.63 121
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 80	75.09 88	66.39 78	72.57 64	71.53 72	82.38 61	47.10 88	59.81 147	59.81 88	74.97 107	84.37 119	54.25 100	79.89 88	77.64 77	82.25 88	77.40 79

v1192		71.06 81	74.87 90	66.61 75	66.38 97	65.80 88	78.27 79	45.28 104	70.19 106	70.79 50	83.37 45	91.79 37	58.76 75	70.86 145	69.02 124	80.16 101	73.08 100

DU-MVS		71.03 82	77.92 71	62.98 100	70.81 71	65.48 91	73.93 108	56.76 36	69.95 110	46.77 146	85.70 31	93.49 19	46.91 139	83.47 60	77.82 75	82.72 83	69.54 122

v1240		70.94 83	74.52 94	66.76 74	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 63	70.50 148	68.68 125	79.14 108	73.68 96

v1921920		70.82 84	74.46 96	66.58 76	66.33 98	64.35 105	77.72 84	45.07 106	70.39 102	71.18 45	83.15 49	90.62 59	59.97 67	70.90 143	68.43 132	79.19 107	73.39 97

UniMVSNet_NR-MVSNet		70.82 84	77.44 74	63.11 99	71.75 66	66.02 86	73.93 108	55.00 51	70.90 98	46.77 146	86.68 25	91.54 39	46.91 139	81.07 79	76.32 85	84.28 66	69.54 122

PVSNet_Blended_VisFu		70.70 86	73.62 102	67.28 71	63.53 121	72.96 61	77.97 81	52.10 64	63.65 135	62.66 84	71.14 127	73.46 159	63.55 48	79.35 91	75.34 88	83.90 69	79.43 66

v144192		70.68 87	74.40 98	66.34 79	65.94 102	64.38 103	77.63 85	45.18 105	69.97 109	70.11 54	82.70 54	90.77 53	59.84 69	71.43 141	68.46 128	79.31 106	73.08 100

FPMVS		70.46 88	74.89 89	65.28 86	69.09 84	61.42 119	77.07 88	46.92 91	76.73 69	53.53 110	67.33 139	75.07 155	67.23 29	83.41 62	81.54 56	77.86 117	78.73 72

v1144		70.45 89	74.50 95	65.73 81	65.74 104	64.88 97	77.33 87	44.16 113	70.59 101	69.63 59	83.15 49	91.42 42	57.79 77	71.29 142	68.53 127	79.72 104	71.63 112

v10		70.25 90	74.59 93	65.19 87	65.32 107	66.46 85	76.60 91	44.84 109	67.38 121	67.21 72	82.75 53	90.56 60	57.70 79	71.69 138	68.63 126	79.44 105	74.67 91

Effi-MVS+-dtu		70.10 91	73.76 101	65.82 80	70.23 79	74.92 52	79.47 76	44.49 112	56.98 162	54.34 104	64.26 156	84.78 116	59.97 67	80.96 81	80.38 58	86.44 52	74.05 94

MAR-MVS		70.00 92	72.28 114	67.34 70	69.89 80	72.57 67	80.09 73	49.49 73	60.28 146	69.03 63	59.29 181	80.79 131	54.68 98	78.39 95	76.00 86	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 72	60.91 107	60.67 135	66.71 81	77.94 82	48.58 77	69.10 115	45.78 151	80.21 77	83.58 123	53.41 105	82.92 67	80.11 62	79.08 109	81.21 59
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		69.51 94	76.17 80	61.74 105	68.38 89	66.60 82	71.77 114	46.98 89	73.60 85	41.79 164	82.06 60	69.65 170	52.51 110	83.41 62	79.94 63	89.02 30	77.94 77

3Dnovator	65.69 13	69.43 95	75.74 85	62.06 104	60.78 134	70.50 75	75.85 95	39.57 154	74.44 77	57.41 95	75.91 99	77.73 146	55.34 93	76.86 99	75.61 87	83.44 76	79.14 69

Effi-MVS+		69.04 96	73.01 107	64.40 91	67.20 93	64.83 99	74.87 100	43.97 115	63.33 137	60.90 86	73.06 120	85.79 109	55.61 90	73.58 118	76.41 84	83.84 70	74.09 93

v2v482		69.01 97	73.39 105	63.89 93	63.86 114	62.99 110	75.26 98	42.05 129	70.22 104	68.46 66	82.64 55	91.61 38	55.38 91	70.89 144	66.93 146	78.30 113	68.48 131

MSDG		68.98 98	73.31 106	63.92 92	67.08 94	68.27 78	75.41 97	40.77 145	67.61 119	64.89 79	75.75 102	78.96 138	53.70 102	76.72 101	73.95 96	81.71 93	71.93 110

v8		68.77 99	73.50 103	63.26 97	63.74 117	64.47 101	74.22 106	42.07 128	67.30 122	64.89 79	82.08 59	90.23 63	56.50 88	71.85 137	66.57 147	78.14 114	72.02 108

NR-MVSNet		68.66 100	76.15 81	59.93 112	65.49 105	65.48 91	74.42 102	56.76 36	69.95 110	45.38 153	85.70 31	91.13 45	34.68 180	74.52 111	76.75 82	82.83 82	69.49 124

USDC		68.53 101	71.82 120	64.68 89	63.53 121	61.87 117	70.12 124	46.98 89	77.89 63	76.58 24	68.55 134	86.88 100	50.50 117	73.73 115	65.62 149	80.39 99	68.21 134

IS_MVSNet		68.20 102	74.41 97	60.96 106	68.55 86	64.36 104	71.47 116	48.33 79	70.11 107	43.30 159	80.90 71	74.54 157	47.19 137	81.25 74	77.97 74	86.94 47	71.76 111

Baseline_NR-MVSNet		68.15 103	75.12 87	60.02 111	70.81 71	55.67 151	75.88 94	53.40 57	71.25 95	43.96 157	85.88 29	92.68 28	45.76 143	83.47 60	68.34 133	70.34 162	68.58 130

GeoE		68.11 104	72.10 118	63.47 95	67.32 92	62.42 114	78.32 78	43.22 121	64.06 134	55.72 102	73.97 113	84.58 117	55.35 92	76.09 105	70.41 118	80.89 95	73.14 99

Fast-Effi-MVS+		67.71 105	72.54 111	62.07 103	63.83 115	63.68 107	75.74 96	39.94 151	60.89 145	54.29 105	73.00 121	86.19 107	56.85 84	78.46 94	73.23 99	81.74 92	72.36 106

thisisatest0515		66.95 106	72.29 113	60.72 109	56.37 158	56.05 149	71.08 117	38.81 158	67.59 120	53.26 113	78.21 90	79.79 136	60.11 64	75.69 107	73.02 101	84.69 63	75.66 86

EPNet		66.87 107	68.89 131	64.53 90	73.97 59	61.13 121	78.46 77	61.03 20	56.78 164	53.41 111	66.91 144	70.91 165	43.49 152	76.08 106	76.68 83	76.81 121	73.73 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
canonicalmvs		66.37 108	74.37 99	57.04 129	65.89 103	65.06 94	62.58 158	42.55 123	76.82 68	46.87 145	67.33 139	86.38 104	45.49 145	76.77 100	71.85 107	78.87 111	76.35 83

QAPM		66.36 109	72.76 110	58.90 117	59.57 142	65.01 95	64.05 154	41.17 140	73.09 87	56.82 97	69.42 130	77.78 145	55.07 95	73.00 123	72.07 105	76.71 122	78.96 70

casdiffmvs		66.19 110	72.34 112	59.02 116	62.75 125	60.61 127	69.06 129	41.38 137	69.49 113	54.11 106	84.00 42	89.74 70	49.12 125	70.74 147	62.70 164	77.70 119	69.14 128

V42		65.79 111	72.11 117	58.42 121	51.89 180	58.69 131	73.80 110	34.50 176	65.40 129	57.10 96	79.54 84	89.09 74	57.51 80	71.98 135	67.83 141	75.70 128	72.26 107

IterMVS-LS		65.76 112	70.85 125	59.81 114	65.33 106	57.78 136	64.63 151	48.02 82	65.65 128	51.05 123	81.31 67	77.47 147	54.94 96	69.46 156	69.36 123	74.90 134	74.95 89
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PM-MVS		65.66 113	71.25 124	59.14 115	58.92 148	54.88 160	73.66 112	38.55 161	66.12 126	49.91 131	69.87 128	86.97 98	60.61 62	76.30 103	74.75 91	73.19 143	69.83 119

UGNet		65.61 114	74.79 91	54.91 138	54.54 174	68.20 79	70.97 120	48.21 80	67.14 124	41.67 165	74.15 112	80.65 132	36.10 175	79.39 90	77.99 72	77.95 116	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		65.54 115	71.79 121	58.24 124	59.68 141	65.55 90	70.99 118	38.69 160	62.29 140	49.27 134	75.03 106	81.42 128	50.93 113	73.71 117	71.35 108	79.90 103	73.20 98
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		65.36 116	70.09 128	59.85 113	63.05 124	53.61 163	74.29 104	46.45 94	68.14 118	51.45 119	78.83 88	85.78 110	49.87 123	70.44 149	70.45 116	74.00 138	63.38 154

v148		64.92 117	70.58 127	58.32 122	59.89 139	57.09 142	66.04 143	35.27 175	69.11 114	60.66 87	79.57 83	90.93 51	53.91 101	69.81 155	62.22 165	74.14 136	65.31 146

CS-MVS		64.89 118	64.54 148	65.31 84	68.39 88	61.63 118	75.90 93	43.28 119	54.33 169	63.06 83	55.59 194	62.41 184	56.76 85	77.69 97	70.80 115	84.46 64	69.33 126

FC-MVSNet-train		64.87 119	74.76 92	53.33 142	65.24 108	58.05 133	69.69 126	41.92 132	70.99 97	32.62 186	85.75 30	88.23 79	32.10 190	77.61 98	74.41 92	78.43 112	68.25 133

pmmvs6		64.78 120	75.82 84	51.89 148	62.41 126	57.13 141	60.24 165	45.59 101	82.90 35	34.69 179	84.83 36	93.18 22	36.22 174	76.43 102	71.13 111	72.21 149	65.12 147

OpenMVS	60.79 16	64.42 121	69.72 129	58.23 125	61.63 130	62.17 115	64.11 153	37.54 169	67.17 123	55.71 103	65.89 151	74.89 156	52.67 108	72.20 133	68.29 135	77.73 118	77.39 80

DCV-MVSNet		64.34 122	72.84 109	54.42 140	63.79 116	62.09 116	62.50 159	42.72 122	74.32 79	41.34 166	66.96 142	88.57 77	39.18 163	75.20 109	70.35 119	77.01 120	72.37 105

ETV-MVS		64.30 123	64.76 146	63.77 94	68.59 85	62.49 112	77.02 89	45.31 102	49.27 191	50.88 124	56.23 192	59.91 193	57.12 83	80.19 87	74.23 94	83.68 72	71.03 114

Anonymous20231211		63.69 124	72.86 108	53.00 145	63.72 118	60.25 128	60.33 164	40.96 141	72.49 89	38.91 169	81.77 65	88.17 80	37.60 169	73.30 120	68.01 138	76.47 126	66.06 143

TransMVSNet (Re)		63.49 125	73.86 100	51.39 154	64.26 113	56.07 148	61.17 162	42.23 126	78.81 62	34.80 177	85.94 27	90.63 58	34.35 184	72.73 128	67.98 139	71.50 152	64.84 148

DI_MVS_plusplus_trai		63.43 126	67.54 135	58.63 118	62.34 127	58.06 132	65.75 147	42.15 127	63.05 138	53.28 112	75.88 100	75.92 152	50.18 119	68.04 159	64.20 155	78.07 115	67.65 135

EIA-MVS		63.24 127	64.16 151	62.16 102	69.30 83	63.20 109	72.40 113	40.82 144	48.31 197	51.50 118	59.63 179	62.23 185	57.33 82	78.00 96	71.94 106	81.59 94	65.82 144

Fast-Effi-MVS+-dtu		63.22 128	65.55 140	60.49 110	61.24 132	64.70 100	74.15 107	53.24 59	51.46 176	49.67 132	58.03 187	78.42 142	48.05 132	72.03 134	71.14 110	76.60 125	63.09 155

IterMVS-SCA-FT		62.67 129	68.00 133	56.45 135	56.92 156	64.92 96	57.51 177	38.12 162	59.44 148	53.62 109	74.74 110	71.60 162	64.84 39	70.24 151	65.27 151	67.70 170	69.83 119

diffmvs		62.64 130	69.66 130	54.46 139	56.19 161	55.06 157	67.36 138	36.74 173	64.18 132	50.58 125	79.54 84	87.55 89	45.13 147	68.04 159	63.20 160	70.78 157	70.02 117

MVS_Test		62.58 131	67.46 136	56.89 131	59.52 143	55.90 150	64.94 149	38.83 157	57.08 161	56.55 100	76.53 97	84.49 118	47.45 134	66.95 162	62.01 166	74.04 137	69.27 127

MDA-MVSNet-bldmvs		62.46 132	72.13 116	51.19 156	34.32 210	56.10 147	68.65 131	38.85 156	69.05 116	49.50 133	78.17 91	85.43 111	51.32 111	86.67 39	67.40 144	64.46 175	62.08 157

pm-mvs1		61.97 133	72.01 119	50.25 162	60.64 136	55.23 155	58.67 172	42.44 124	74.40 78	33.63 183	81.03 70	89.86 67	34.87 179	72.93 126	67.95 140	71.28 153	62.65 156

FMVSNet1		61.92 134	71.36 122	50.90 158	57.67 155	59.29 130	59.48 170	44.14 114	70.24 103	34.72 178	75.45 105	84.94 114	36.75 171	72.33 130	68.45 129	72.66 146	68.83 129

PVSNet_BlendedMVS		61.75 135	65.07 144	57.87 127	56.27 159	60.99 124	65.81 145	43.75 116	51.27 180	54.08 107	62.12 166	78.84 139	50.67 114	71.49 139	63.91 157	76.64 123	66.86 137

PVSNet_Blended		61.75 135	65.07 144	57.87 127	56.27 159	60.99 124	65.81 145	43.75 116	51.27 180	54.08 107	62.12 166	78.84 139	50.67 114	71.49 139	63.91 157	76.64 123	66.86 137

tttt0517		61.44 137	63.85 152	58.62 119	55.20 170	55.61 152	68.80 130	38.02 165	55.70 166	50.01 130	66.93 143	48.90 204	56.69 86	73.84 114	71.10 112	82.99 79	74.89 90

tfpnnormal		61.41 138	71.33 123	49.83 163	61.73 129	54.90 159	58.52 173	41.24 138	75.20 73	32.00 191	82.13 58	87.87 84	35.63 178	72.75 127	66.30 148	69.87 163	60.14 162

IB-MVS	57.02 17	61.37 139	65.39 141	56.69 132	56.65 157	60.85 126	70.70 121	37.90 167	49.37 190	45.37 154	48.75 205	79.14 137	53.55 104	76.26 104	70.85 114	75.97 127	72.50 104
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		61.22 140	67.07 137	54.40 141	59.89 139	63.62 108	70.98 119	36.77 172	50.49 183	47.15 141	62.45 164	80.81 130	37.90 168	71.87 136	70.09 121	73.69 139	70.19 116

pmmvs4		61.12 141	64.61 147	57.04 129	60.88 133	52.15 168	70.59 122	44.82 110	61.35 144	46.91 144	72.08 123	73.27 160	46.79 141	65.06 165	67.76 142	72.28 147	60.58 161

thisisatest0530		61.02 142	63.44 157	58.19 126	54.75 172	55.09 156	68.03 135	38.02 165	55.45 167	49.06 135	66.58 147	48.69 205	56.69 86	73.07 121	71.10 112	82.60 85	74.14 92

Vis-MVSNet (Re-imp)		60.99 143	67.78 134	53.06 144	64.66 112	53.49 164	67.40 136	49.52 72	68.55 117	28.00 199	79.53 86	71.41 164	33.08 188	75.30 108	71.28 109	75.69 129	54.91 175

PatchMatch-RL		60.96 144	63.00 160	58.57 120	59.16 147	52.18 167	67.38 137	41.99 130	57.85 156	48.16 136	53.55 201	69.77 169	59.47 73	73.73 115	72.49 104	75.27 133	61.44 159

GA-MVS		60.73 145	64.24 150	56.64 133	59.38 146	57.45 140	65.07 148	36.65 174	57.39 159	58.17 93	73.43 118	69.10 173	47.38 135	64.47 170	63.63 159	73.19 143	64.22 151

CVMVSNet		60.45 146	63.72 154	56.63 134	54.82 171	53.75 161	68.41 132	41.95 131	55.07 168	48.03 137	58.08 186	68.67 174	55.09 94	69.14 157	68.34 133	71.51 151	72.97 103

ET-MVSNet_ETH3D		60.33 147	62.10 163	58.27 123	58.61 151	58.05 133	68.06 133	41.20 139	51.40 177	51.10 122	64.06 157	49.42 203	50.61 116	74.72 110	70.29 120	80.05 102	66.74 139

FC-MVSNet-test		60.28 148	70.83 126	47.96 174	54.69 173	47.12 182	68.06 133	41.68 136	71.42 93	23.73 207	84.70 38	77.41 148	28.92 194	82.33 71	73.08 100	70.68 158	59.77 164

EU-MVSNet		59.77 149	66.07 138	52.42 146	47.81 189	51.86 170	62.98 157	32.28 189	62.08 142	47.10 142	59.94 177	83.42 124	53.08 107	70.06 154	63.19 161	71.26 155	71.96 109

IterMVS		59.24 150	64.45 149	53.16 143	50.98 182	61.29 120	66.51 140	32.85 185	58.17 152	46.31 149	72.58 122	70.23 167	54.26 99	64.81 168	60.24 170	68.04 169	63.81 153
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		59.15 151	62.28 162	55.49 137	52.42 178	62.59 111	71.76 115	39.74 152	50.25 185	41.92 162	62.88 162	69.16 172	55.85 89	62.77 175	67.18 145	71.27 154	61.11 160

thres600view7		58.87 152	65.91 139	50.66 160	61.27 131	56.32 145	59.88 168	40.63 147	64.88 130	32.10 190	64.82 153	69.83 168	36.72 172	72.99 124	72.55 103	73.34 141	59.97 163

CMPMVS	45.32 18	58.10 153	65.24 143	49.76 164	47.88 188	46.86 185	48.16 202	32.82 186	58.06 153	61.35 85	59.64 178	80.00 133	47.27 136	70.15 152	64.10 156	61.08 178	77.85 78
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view		58.09 154	63.54 156	51.74 150	50.13 184	46.56 186	66.95 139	33.41 182	63.52 136	58.77 91	74.84 108	84.10 121	43.12 153	65.95 164	54.69 184	58.04 184	55.13 174

CDS-MVSNet		57.90 155	63.57 155	51.28 155	62.30 128	53.17 165	64.70 150	51.61 67	57.41 158	32.75 185	63.73 158	70.53 166	27.12 196	72.49 129	73.02 101	69.22 166	54.68 176
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
FMVSNet2		57.80 156	65.39 141	48.94 170	55.88 162	57.61 137	57.26 178	42.37 125	58.21 151	33.19 184	68.36 135	75.55 154	34.58 181	66.91 163	64.55 153	70.38 159	66.56 140

thres400		57.25 157	63.73 153	49.70 165	60.19 138	54.95 158	58.16 174	39.60 153	62.42 139	31.98 193	62.33 165	69.20 171	35.96 176	70.07 153	68.03 137	72.28 147	59.12 166

gm-plane-assit		56.76 158	57.64 177	55.73 136	66.01 101	55.45 154	74.96 99	30.54 194	73.71 84	56.04 101	81.81 63	30.91 221	43.83 149	58.77 185	54.71 183	63.02 177	48.13 191

MIMVSNet1		56.72 159	68.69 132	42.76 187	46.70 195	42.81 192	69.13 127	30.52 195	81.01 50	32.00 191	74.82 109	91.10 47	26.83 198	73.98 113	64.72 152	51.40 196	52.38 180

EPNet_dtu		56.63 160	60.77 169	51.80 149	55.47 167	44.63 187	69.83 125	38.74 159	50.27 184	47.64 138	58.01 188	72.27 161	33.71 186	68.60 158	67.72 143	65.39 173	63.86 152
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		56.54 161	63.26 158	48.70 171	55.88 162	57.61 137	57.26 178	41.75 133	49.06 192	32.37 187	61.81 168	67.02 176	34.58 181	72.33 130	68.45 129	70.38 159	66.56 140

test1		56.54 161	63.26 158	48.70 171	55.88 162	57.61 137	57.26 178	41.75 133	49.06 192	32.37 187	61.81 168	67.02 176	34.58 181	72.33 130	68.45 129	70.38 159	66.56 140

gg-mvs-nofinetune		56.45 163	61.04 167	51.10 157	63.42 123	49.40 178	53.71 189	52.52 62	74.77 75	46.93 143	77.31 95	53.88 197	26.42 200	62.51 176	57.81 176	63.60 176	51.57 183

thres200		56.35 164	62.36 161	49.34 167	58.87 149	56.32 145	55.91 182	40.63 147	58.51 150	31.34 194	58.81 185	67.31 175	35.96 176	72.99 124	65.51 150	73.34 141	57.07 169

MS-PatchMatch		56.31 165	60.22 172	51.73 151	60.53 137	55.53 153	63.41 155	37.18 170	51.34 179	37.44 171	60.53 175	62.19 186	45.52 144	64.25 171	63.17 162	66.33 171	64.56 149

tfpn200view9		56.07 166	61.85 164	49.34 167	58.57 152	56.48 144	58.01 176	40.72 146	53.23 170	31.01 195	56.41 190	66.40 180	34.18 185	73.02 122	68.06 136	73.53 140	59.35 165

FMVSNet3		54.77 167	60.73 170	47.81 175	54.29 175	56.88 143	55.89 183	41.75 133	49.06 192	32.37 187	61.81 168	67.02 176	33.67 187	62.88 174	61.96 167	68.88 167	65.53 145

thres100view900		53.88 168	59.19 173	47.68 176	58.57 152	52.74 166	54.45 186	38.07 164	53.23 170	31.01 195	56.41 190	66.40 180	32.80 189	65.03 167	64.43 154	71.18 156	56.10 172

CR-MVSNet		53.82 169	55.40 181	51.98 147	51.57 181	50.23 173	45.00 204	44.97 107	46.90 199	52.60 115	67.91 136	46.99 212	48.37 128	59.15 183	59.53 173	69.38 165	57.07 169

baseline2		53.55 170	55.19 182	51.62 152	55.27 169	51.95 169	60.89 163	34.23 177	46.69 201	42.47 160	53.56 200	50.01 200	45.33 146	64.63 169	61.22 168	71.56 150	58.28 168

test20.03		53.49 171	60.95 168	44.78 183	64.73 111	47.25 181	61.58 161	43.30 118	65.86 127	22.85 208	66.87 146	79.85 134	22.99 202	62.38 177	56.95 178	53.25 193	47.46 193

baseline		53.46 172	61.55 165	44.01 184	45.83 198	48.77 179	57.26 178	28.75 198	49.99 186	38.85 170	68.78 132	75.65 153	38.30 165	60.80 178	59.78 172	55.10 191	67.07 136

MVSTER		53.08 173	56.39 179	49.21 169	47.19 191	51.08 171	60.14 167	31.74 191	40.63 209	38.97 168	55.78 193	46.74 213	42.47 156	67.29 161	62.99 163	74.73 135	70.23 115

CHOSEN 1792x2688		52.99 174	56.91 178	48.42 173	47.32 190	50.10 175	64.18 152	33.85 179	45.46 204	36.95 173	55.20 197	66.49 179	51.20 112	59.28 181	59.81 171	57.01 186	61.99 158

baseline1		52.90 175	58.38 174	46.51 182	58.87 149	50.01 176	54.17 187	40.45 150	56.81 163	29.25 198	62.72 163	58.99 194	30.25 192	65.05 166	60.57 169	66.07 172	54.54 177

CostFormer		52.59 176	55.14 183	49.61 166	52.72 176	50.40 172	66.28 142	33.78 180	52.85 172	43.43 158	66.30 148	51.37 199	41.78 159	54.92 195	51.18 191	59.68 180	58.98 167

SCA		52.47 177	53.97 185	50.71 159	46.95 194	57.79 135	60.18 166	46.89 92	51.92 175	46.71 148	60.73 172	49.97 201	47.69 133	56.39 192	52.98 188	55.82 188	48.03 192

testgi		51.94 178	61.37 166	40.94 190	58.38 154	47.03 183	65.88 144	30.49 196	70.87 100	22.64 209	57.53 189	87.59 87	18.30 209	63.01 173	54.32 185	49.93 199	49.27 186

tpm cat1		50.98 179	51.28 191	50.62 161	55.74 165	49.92 177	63.13 156	38.12 162	52.38 174	47.61 139	60.11 176	44.51 216	44.86 148	51.31 203	47.49 198	54.25 192	53.24 179

RPMNet		50.92 180	50.32 193	51.62 152	50.25 183	50.23 173	59.16 171	46.70 93	46.90 199	42.39 161	48.97 204	37.23 219	41.78 159	57.30 190	56.18 180	69.44 164	55.43 173

pmmvs5		50.64 181	58.01 175	42.05 188	47.01 193	43.67 190	49.27 200	29.43 197	50.77 182	33.83 182	68.69 133	76.16 151	27.82 195	57.53 189	57.07 177	64.95 174	52.18 181

PatchT		50.55 182	53.55 187	47.05 180	37.59 209	42.26 193	50.55 197	37.56 168	46.37 202	52.60 115	66.91 144	43.54 218	48.37 128	59.15 183	59.53 173	55.62 189	57.07 169

Anonymous20231206		50.28 183	57.94 176	41.35 189	55.45 168	43.65 191	58.06 175	34.12 178	62.02 143	24.25 206	59.33 180	79.80 135	24.49 201	59.55 179	54.28 186	51.74 195	46.94 195

dps		49.71 184	51.97 189	47.07 179	52.37 179	47.00 184	53.02 192	40.52 149	44.91 205	41.23 167	64.55 154	44.27 217	40.12 162	57.71 188	51.97 190	55.14 190	53.41 178

MDTV_nov1_ep13		49.60 185	51.57 190	47.31 177	46.28 196	44.61 188	59.82 169	30.96 192	48.80 196	50.20 128	59.26 182	52.38 198	38.56 164	56.20 193	49.70 194	58.04 184	50.01 184

PatchmatchNet		48.67 186	50.10 194	46.99 181	48.29 187	41.00 194	55.54 184	38.94 155	51.38 178	45.15 155	63.22 160	48.45 207	42.83 154	53.80 200	48.50 197	51.19 198	44.37 197
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
new-patchmatchnet		47.33 187	60.49 171	31.99 206	55.69 166	33.86 206	36.84 214	33.31 183	72.36 90	14.33 214	80.09 78	92.14 34	13.27 211	63.54 172	40.09 206	38.51 208	41.32 205

tpm		46.67 188	49.20 199	43.72 185	49.60 185	36.60 203	53.93 188	26.84 200	52.70 173	58.05 94	69.04 131	47.96 208	30.06 193	48.33 206	42.76 201	43.88 202	47.01 194

pmmvs3		46.64 189	54.13 184	37.90 197	31.23 214	40.68 195	49.83 199	15.34 211	46.31 203	36.34 175	53.15 202	74.40 158	36.36 173	58.43 186	56.64 179	58.32 183	49.29 185

TAMVS		46.64 189	53.62 186	38.49 195	49.56 186	36.87 200	53.16 191	25.76 202	56.33 165	22.55 210	60.72 173	61.80 188	27.12 196	59.50 180	58.33 175	52.79 194	41.82 204

test-LLR		46.01 191	45.06 208	47.11 178	59.39 144	36.72 201	51.28 194	40.95 142	36.41 213	34.45 180	46.14 207	47.02 210	38.00 166	51.78 201	48.53 195	58.60 181	48.84 188

MIMVSNet		45.83 192	53.46 188	36.94 198	45.38 202	39.50 197	52.20 193	30.68 193	57.09 160	24.53 205	55.22 196	71.54 163	21.74 205	55.81 194	51.08 192	47.11 200	43.96 198

pmnet_mix02		45.67 193	55.99 180	33.63 205	45.77 199	31.22 210	42.04 209	27.60 199	64.14 133	24.89 202	75.50 104	82.30 126	21.88 204	54.53 198	41.22 203	39.62 206	43.05 200

test0.0.03 1		45.40 194	49.55 197	40.57 192	59.39 144	44.36 189	53.37 190	40.95 142	47.14 198	19.23 211	45.49 209	60.24 191	19.24 207	54.82 196	51.98 189	51.21 197	42.82 201

PMMVS		45.37 195	49.29 198	40.79 191	27.75 215	35.07 205	50.88 196	19.88 208	39.27 211	35.78 176	50.11 203	61.29 189	42.04 157	54.13 199	55.95 181	68.43 168	49.19 187

MVS-HIRNet		44.56 196	45.52 206	43.44 186	40.98 205	31.03 211	39.52 213	36.96 171	42.80 207	44.37 156	53.80 199	60.04 192	41.85 158	47.97 208	41.08 204	56.99 187	41.95 203

test-mter		44.18 197	47.60 201	40.18 193	33.20 211	39.03 198	55.28 185	13.91 213	39.07 212	36.63 174	48.09 206	49.52 202	41.12 161	54.55 197	50.91 193	60.97 179	52.03 182

EMVS		43.85 198	49.91 195	36.77 200	45.46 201	32.70 207	44.09 206	25.33 203	57.88 155	26.62 200	58.99 184	61.14 190	42.77 155	70.26 150	38.52 210	36.38 210	29.87 211

E-PMN		43.83 199	49.81 196	36.84 199	46.09 197	31.86 209	42.77 208	25.85 201	57.76 157	25.53 201	55.50 195	62.47 183	43.77 150	70.78 146	39.51 207	37.04 209	30.79 210

tpmrst		43.31 200	46.14 204	40.02 194	47.05 192	36.48 204	48.01 203	32.17 190	49.50 189	37.26 172	63.66 159	47.04 209	31.98 191	42.00 212	40.55 205	43.64 203	43.75 199

TESTMET0.1,1		41.79 201	45.06 208	37.97 196	31.32 213	36.72 201	51.28 194	14.17 212	36.41 213	34.45 180	46.14 207	47.02 210	38.00 166	51.78 201	48.53 195	58.60 181	48.84 188

ADS-MVSNet		40.61 202	46.31 202	33.96 203	40.70 206	30.42 212	40.42 211	33.44 181	58.01 154	30.87 197	63.05 161	54.48 196	22.67 203	44.35 211	39.23 209	35.64 211	34.64 208

CHOSEN 280x420		40.24 203	44.14 211	35.69 201	32.36 212	23.58 215	50.30 198	21.21 207	40.94 208	18.84 212	32.75 213	48.65 206	48.13 131	59.16 182	55.31 182	43.28 204	48.62 190

EPMVS		40.11 204	44.96 210	34.44 202	41.55 204	32.65 208	41.74 210	32.39 188	49.89 188	24.83 203	64.44 155	46.38 214	26.57 199	44.75 210	39.47 208	39.59 207	37.16 207

FMVSNet5		39.83 205	45.08 207	33.71 204	39.24 207	39.56 196	48.77 201	23.55 205	39.45 210	24.55 204	33.73 212	44.57 215	20.97 206	58.27 187	54.23 187	45.16 201	45.77 196

N_pmnet		39.50 206	51.01 192	26.09 208	44.48 203	25.59 214	40.20 212	21.49 206	64.20 131	7.98 218	73.86 115	76.67 150	13.66 210	50.17 204	36.69 212	28.71 213	29.86 212

MVE	28.01 19	35.86 207	43.56 212	26.88 207	22.33 217	19.75 217	30.85 217	23.88 204	49.90 187	10.48 216	43.64 210	61.87 187	48.99 127	47.26 209	42.15 202	24.76 214	40.37 206
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet		35.76 208	45.64 205	24.22 209	38.59 208	25.83 213	31.87 216	19.24 209	49.06 192	9.01 217	54.34 198	64.73 182	12.46 212	49.21 205	44.91 199	34.17 212	31.41 209

PMMVS2		34.11 209	48.55 200	17.26 210	25.45 216	20.72 216	35.08 215	16.26 210	58.71 149	4.16 220	59.22 183	78.40 143	3.65 214	57.24 191	38.31 211	18.94 216	27.28 213

GG-mvs-BLEND		31.54 210	46.27 203	14.37 211	0.07 222	48.65 180	42.97 207	0.08 219	44.04 206	1.21 222	39.77 211	57.94 195	0.15 218	48.19 207	42.82 200	41.70 205	42.46 202

test_method		13.28 211	15.83 213	10.30 212	1.05 219	2.18 220	15.40 218	2.23 215	22.43 215	13.84 215	22.00 215	33.14 220	9.78 213	17.80 214	9.93 214	19.50 215	3.31 215

test123		0.53 212	0.60 215	0.46 214	0.22 220	0.25 221	0.33 223	0.13 218	0.66 218	1.37 221	1.10 217	0.00 225	0.43 216	0.68 216	0.61 215	0.26 219	0.88 216

testmvs		0.47 213	0.69 214	0.21 215	0.17 221	0.17 222	0.35 222	0.16 217	0.66 218	0.18 223	1.05 218	0.99 224	0.27 217	0.62 217	0.54 216	0.15 220	0.77 217

uanet_test		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 225	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

sosnet-low-res		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 225	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

sosnet		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 225	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

RE-MVS-def										70.04 56

9.14												83.64 122

SR-MVS					81.31 9			62.63 9				91.11 46

Anonymous202405211			72.22 115		66.19 99	61.09 123	62.23 160	45.87 100	71.25 95		79.33 87	86.16 108	37.36 170	73.54 119	69.84 122	75.45 131	64.32 150

our_test_3					52.72 176	53.66 162	69.11 128

ambc			79.96 63		74.57 55	75.48 49	73.75 111		80.32 55	72.34 38	78.46 89	92.41 30	59.05 74	80.24 86	73.95 96	75.41 132	78.85 71

MTAPA										80.26 8		90.53 62

MTMP										82.07 4		91.00 49

Patchmatch-RL test							2.05 221

tmp_tt				7.47 213	8.89 218	3.32 219	4.35 220	1.14 216	15.58 217	15.76 213	8.50 216	5.90 223	2.00 215	20.02 213	21.51 213	12.70 217

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 83	69.37 82	74.02 57	82.50 60	47.39 85	66.39 125	56.63 99	60.61 174	82.76 125	53.68 103			82.92 81	78.39 76

mPP-MVS					82.97 2							92.12 35

NP-MVS									71.39 94

Patchmtry						37.73 199	45.00 204	44.97 107		52.60 115

DeepMVS_CX						8.52 218	9.75 219	3.19 214	16.70 216	5.02 219	23.06 214	19.33 222	18.69 208	13.75 215		11.34 218	25.07 214

High-resolution multi-view benchmark