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 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 51	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 31	82.32 3	80.89 88	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 52	92.04 38	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 45	80.21 9	81.95 74	90.76 61	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 39	78.32 15	80.59 89	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 60	80.21 9	80.31 92	91.44 43	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 55	80.44 6	84.83 38	94.19 14	70.52 17	90.70 11	87.19 22	91.71 8	87.37 30

ACMM	71.24 7	82.85 9	86.59 12	78.50 9	80.10 26	78.59 33	91.77 11	60.76 22	84.43 17	76.49 24	81.58 82	93.50 17	70.45 18	91.38 7	89.42 8	91.42 16	87.22 32
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 66	89.59 83	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 32	71.30 44	86.91 24	90.54 67	67.12 30	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 36	75.53 28	73.88 139	92.28 32	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 47	75.46 29	82.97 59	95.08 12	68.90 24	90.49 14	87.43 21	91.48 14	86.84 34
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 53	77.00 22	81.80 77	90.71 62	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 55	86.96 119	65.95 35	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 37	67.55 67	87.05 22	91.40 45	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	63.57 137	70.27 75	86.48 44	62.99 7	89.00 5	80.32 7	86.25 27	91.04 53	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 32	88.98 31	56.44 39	89.29 3	72.39 37	93.25 1	93.86 16	63.42 50	85.46 45	81.36 57	86.96 46	94.00 1

PMVS	70.37 8	81.82 19	87.08 6	75.68 24	77.06 43	77.23 41	87.77 40	56.25 42	83.33 30	67.18 74	89.48 15	87.94 103	77.70 1	93.02 2	92.57 2	88.13 36	86.00 39
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 27	77.74 18	83.72 49	87.34 111	65.36 36	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 46	77.45 19	72.04 151	90.19 76	62.50 57	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 36	89.84 22	58.67 30	85.25 12	76.26 25	85.28 35	92.88 26	66.03 34	87.20 37	85.40 27	88.86 31	85.58 43
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 50	67.62 66	88.01 17	92.38 31	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 39	82.29 5	84.41 50	62.95 8	82.99 33	64.03 90	87.71 18	89.17 89	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 79	87.34 21	91.27 47	69.45 19	85.99 43	82.55 44	88.98 30	88.95 13
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
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 44	73.45 32	86.84 25	89.80 81	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 69	67.49 69	85.90 29	89.82 80	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 50	77.96 13	81.99 7	72.76 62	90.48 18	61.31 14	80.85 51	77.90 17	81.93 75	87.01 116	68.20 27	84.15 57	85.27 29	87.85 39	86.00 39

DVP-MVS		80.31 29	85.60 27	74.15 33	76.23 47	78.39 34	86.62 42	55.79 45	86.47 9	71.32 43	90.96 7	89.02 91	69.28 22	84.62 55	81.64 54	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 60	76.41 44	83.59 54	54.82 52	87.35 6	70.86 48	86.98 23	96.27 4	66.50 31	89.17 24	83.39 39	89.26 28	83.56 49

OMC-MVS		79.95 31	85.28 29	73.74 36	72.95 63	80.10 27	87.87 37	48.13 86	84.62 16	79.42 12	80.27 93	92.49 29	64.14 44	87.25 36	85.11 30	89.92 24	87.10 33

SED-MVS		79.70 32	85.16 31	73.34 38	75.83 50	78.11 37	88.77 33	56.45 38	84.85 14	69.45 59	90.70 9	88.38 98	63.16 52	85.12 51	81.28 58	86.40 52	87.63 26

MSP-MVS		79.65 33	84.28 41	74.25 31	78.92 33	81.86 9	89.07 28	60.49 24	83.85 24	70.05 54	85.12 36	90.92 59	62.99 54	81.15 77	81.64 54	83.99 70	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 26	56.24 43	82.41 38	71.58 41	82.27 68	86.47 123	66.47 32	84.80 53	84.16 35	87.26 44	87.34 31

LS3D		79.33 35	84.03 43	73.84 34	75.37 52	78.09 38	83.30 55	52.94 63	84.42 18	76.01 26	84.16 44	87.44 110	65.34 37	86.30 40	82.08 51	90.09 22	85.70 41

ME-MVS		78.93 36	84.62 38	72.30 44	77.98 38	79.20 31	87.82 38	53.22 60	83.71 26	62.73 92	85.63 34	91.37 46	67.58 28	84.40 56	80.56 59	84.46 65	87.45 29

3Dnovator+	72.94 4	78.78 37	83.05 47	73.80 35	70.70 76	81.34 11	88.33 34	56.01 44	81.33 48	72.87 36	78.06 111	81.15 154	63.83 47	87.39 35	85.82 25	91.06 18	86.28 38

UA-Net		78.65 38	83.96 44	72.46 42	84.87 1	76.15 45	89.06 29	55.70 46	77.25 70	53.14 132	79.73 98	82.09 152	59.69 73	92.21 5	90.93 3	92.32 3	89.36 7

DeepC-MVS_fast	71.40 6	78.48 39	82.92 48	73.31 39	76.44 46	82.23 6	87.59 41	56.56 37	77.79 67	68.91 63	77.00 116	87.32 112	61.90 59	85.40 46	84.37 32	88.46 33	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 46	72.41 43	76.04 49	75.72 48	83.86 52	51.81 67	84.00 23	70.65 51	81.27 84	92.22 33	64.64 42	83.28 67	80.28 61	87.44 43	87.49 28

WR-MVS		78.32 41	86.09 20	69.25 61	76.22 48	72.33 69	85.71 47	59.02 28	86.66 7	51.41 138	92.91 2	96.76 1	53.09 109	90.21 18	85.30 28	90.05 23	78.46 76

ACMH	66.19 11	78.12 42	84.55 39	70.63 52	69.62 82	72.40 68	80.77 70	46.43 99	89.24 4	77.99 16	87.42 20	95.83 9	62.95 55	86.27 41	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 60	74.77 28	80.92 14	69.60 76	88.87 32	56.32 41	74.03 94	71.03 46	83.67 50	87.68 106	64.75 41	83.70 59	81.85 53	86.71 48	82.73 50

NCCC		77.82 44	80.72 59	74.43 30	79.24 31	75.72 48	88.06 35	56.36 40	79.61 57	73.22 34	67.75 165	87.05 115	63.09 53	85.62 44	84.00 36	86.62 49	85.30 46

CNVR-MVS		77.79 45	81.57 54	73.38 37	78.37 37	75.91 46	87.97 36	55.11 50	79.41 59	70.98 47	74.70 134	86.43 124	61.77 60	85.10 52	83.73 38	86.10 57	85.68 42

WR-MVS_H		77.56 46	85.88 22	67.86 65	80.54 18	74.32 57	83.23 56	61.78 12	83.47 28	47.46 161	91.81 6	95.84 8	50.50 133	90.44 16	84.37 32	83.63 75	80.89 63

RPSCF		77.56 46	84.51 40	69.46 60	65.17 116	74.36 56	79.74 77	47.45 89	84.01 22	72.89 35	77.89 112	90.67 63	65.14 39	88.25 29	89.74 7	86.38 53	86.64 36

PS-CasMVS		77.46 48	85.80 23	67.73 67	81.24 10	72.88 61	80.63 71	61.28 15	84.14 20	50.53 146	92.13 4	96.76 1	50.12 136	91.02 9	84.46 31	82.60 87	79.19 69

DTE-MVSNet		77.28 49	84.87 35	68.42 63	82.94 3	72.70 64	81.60 65	61.78 12	85.03 13	51.40 139	92.11 5	96.00 6	49.42 140	89.73 22	82.52 46	83.39 80	75.98 88

SixPastTwentyTwo		77.24 50	83.65 45	69.78 57	65.14 117	64.85 100	77.44 87	47.74 88	82.76 35	68.52 64	87.65 19	93.31 19	71.68 13	89.49 23	82.41 47	88.14 35	85.05 47

CDPH-MVS		77.22 51	81.05 58	72.75 41	77.29 41	77.46 40	86.36 45	54.02 56	73.00 103	69.75 57	77.78 113	88.90 93	61.31 64	84.09 58	82.54 45	87.79 40	83.57 48

PEN-MVS		77.06 52	85.05 32	67.74 66	82.29 5	72.59 65	80.86 69	61.03 19	84.66 15	50.08 149	92.19 3	96.59 3	49.12 142	89.83 21	82.35 48	83.06 81	77.14 81

CP-MVSNet		77.01 53	85.04 33	67.65 68	81.16 12	72.72 63	80.54 72	61.18 16	82.09 40	50.41 147	90.81 8	95.89 7	50.03 137	90.86 10	84.30 34	82.56 89	78.65 75

CSCG		76.95 54	82.08 52	70.97 48	73.32 62	78.35 35	81.08 68	47.19 90	83.47 28	69.82 56	80.44 90	87.19 113	64.59 43	81.01 80	77.26 78	89.83 25	86.84 34

CNLPA		76.67 55	81.72 53	70.77 51	70.75 74	76.68 43	86.14 46	46.11 101	81.82 42	74.68 30	76.37 118	86.23 127	62.92 56	85.28 49	83.29 40	84.02 69	82.40 52

MSLP-MVS++		76.66 56	82.32 51	70.06 54	70.51 77	80.27 25	79.77 76	55.58 47	77.79 67	63.09 91	67.25 171	89.50 84	71.01 14	88.10 31	85.74 26	80.39 101	87.56 27

TSAR-MVS + COLMAP		75.85 57	81.06 56	69.77 58	71.15 70	76.90 42	82.93 58	52.43 65	79.25 61	70.13 52	82.78 60	87.00 117	60.02 69	80.30 84	79.61 65	81.95 93	81.61 59

HQP-MVS		75.81 58	78.91 67	72.18 45	77.41 40	75.38 51	84.75 48	53.35 58	76.12 80	73.32 33	69.48 156	88.07 101	57.76 81	79.42 90	78.44 68	86.48 50	85.50 44

PLC	64.88 15	75.76 59	80.22 61	70.57 53	70.46 78	77.75 39	82.01 63	48.84 80	80.74 52	70.85 49	71.32 153	84.82 139	63.69 48	84.73 54	82.35 48	87.54 41	79.80 66
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 60	79.24 65	70.86 49	67.63 90	74.09 58	83.17 57	44.75 117	81.82 42	80.83 5	65.61 188	88.04 102	61.58 61	83.21 68	80.12 62	87.17 45	81.82 55
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PHI-MVS		75.17 61	78.37 68	71.43 46	71.13 71	72.46 67	82.28 62	50.55 73	73.39 100	79.05 13	73.65 141	87.50 109	61.98 58	81.10 78	78.48 67	83.60 76	81.99 53

MGCNet		74.91 62	79.25 64	69.85 56	75.01 54	74.95 53	86.61 43	48.67 81	68.87 141	67.51 68	80.13 95	83.78 146	55.77 91	83.37 65	81.89 52	85.55 62	81.75 56

anonymousdsp		74.76 63	82.59 49	65.63 85	45.61 243	61.13 130	89.06 29	32.58 227	74.11 93	59.55 105	84.06 45	94.12 15	75.24 3	88.94 26	86.95 24	91.74 7	88.81 14

AdaColmap		74.73 64	77.57 73	71.40 47	76.90 44	75.76 47	84.54 49	53.08 62	76.20 78	66.64 77	66.06 184	78.16 177	61.32 63	85.37 48	82.20 50	85.95 59	79.27 68

v7n		74.47 65	81.06 56	66.77 74	66.98 96	67.10 79	76.76 91	45.88 103	81.98 41	67.43 70	88.38 16	95.67 10	61.38 62	80.76 82	73.49 100	82.21 91	80.06 64

PCF-MVS	65.25 14	73.99 66	76.74 77	70.79 50	71.61 69	75.33 52	83.76 53	50.40 74	74.88 83	74.50 31	67.60 166	85.36 136	58.30 79	78.61 96	74.25 95	86.15 56	81.13 62
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 55	81.38 67	48.58 82	74.77 84	69.16 61	71.97 152	86.20 128	59.50 74	78.51 97	74.06 97	85.42 63	81.85 54

TSAR-MVS + GP.		73.42 68	76.31 78	70.05 55	77.15 42	71.13 72	81.59 66	54.11 55	69.84 133	58.65 108	66.20 182	78.77 172	65.29 38	83.65 60	83.14 41	83.54 77	81.47 60

Gipuma		73.40 69	79.27 63	66.55 79	63.64 136	59.35 149	70.28 141	45.92 102	83.79 25	71.78 40	84.04 46	93.07 23	68.69 26	87.90 33	76.76 80	78.98 116	69.96 132
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVS_111021_HR		72.37 70	76.12 81	68.00 64	68.55 86	64.30 108	82.93 58	48.98 79	74.25 91	65.39 83	73.59 142	84.11 144	59.48 75	82.61 71	78.38 69	82.66 86	75.59 90

TinyColmap		71.85 71	76.11 82	66.87 73	66.07 105	65.34 94	74.35 106	49.30 78	79.93 56	75.93 27	75.66 124	87.74 105	54.72 100	80.66 83	70.42 123	80.85 99	73.02 107

UniMVSNet_ETH3D		71.84 72	81.36 55	60.74 120	76.46 45	66.01 88	66.49 164	60.24 26	86.58 8	41.87 189	90.04 11	96.02 5	43.72 174	85.14 50	77.30 77	75.64 148	68.40 153

TranMVSNet+NR-MVSNet		71.66 73	79.23 66	62.83 105	72.54 66	65.64 90	74.77 104	55.27 48	75.91 81	45.50 174	89.55 13	94.25 13	45.96 163	82.74 70	77.03 79	82.96 83	69.48 140

MVS_111021_LR		71.60 74	75.21 87	67.38 69	67.42 91	62.44 119	81.73 64	46.24 100	70.89 117	66.80 76	73.19 145	84.98 137	60.09 68	81.94 74	77.77 75	82.00 92	75.29 91

EG-PatchMatch MVS		71.50 75	76.82 76	65.30 86	70.74 75	66.50 84	74.23 108	43.25 126	72.02 108	59.11 106	79.85 97	86.88 121	63.95 46	80.29 85	75.25 91	80.51 100	76.98 82

DPM-MVS		71.35 76	73.50 111	68.84 62	74.93 55	73.35 59	84.07 51	50.56 72	71.91 109	67.06 75	61.21 209	77.02 184	52.64 114	74.15 121	75.14 92	83.79 73	81.74 57

UniMVSNet (Re)		71.29 77	78.14 69	63.30 97	70.29 79	66.57 82	75.98 94	54.74 53	70.20 125	46.20 172	85.08 37	93.21 20	48.19 150	82.50 72	78.33 70	84.40 67	71.08 121

CLD-MVS		71.24 78	78.12 70	63.20 99	74.03 58	71.60 70	82.82 60	32.91 223	74.23 92	69.32 60	79.65 99	91.54 41	47.02 158	81.22 76	79.01 66	73.09 171	69.63 136
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 79	75.09 89	66.39 80	72.57 65	71.53 71	82.38 61	47.10 91	59.81 182	59.81 104	74.97 129	84.37 143	54.25 104	79.89 88	77.64 76	82.25 90	77.40 79

v1192		71.06 80	74.87 91	66.61 76	66.38 100	65.80 89	78.27 81	45.28 108	70.19 126	70.79 50	83.37 52	91.79 39	58.76 78	70.86 166	69.02 130	80.16 105	73.08 105

DU-MVS		71.03 81	77.92 71	62.98 102	70.81 72	65.48 92	73.93 112	56.76 35	69.95 131	46.77 168	85.70 32	93.49 18	46.91 159	83.47 61	77.82 74	82.72 85	69.54 137

v1240		70.94 82	74.52 95	66.76 75	66.54 99	64.40 104	77.76 84	45.29 107	70.05 129	71.45 42	83.36 54	90.96 55	60.37 66	70.50 171	68.68 132	79.14 113	73.68 101

v1921920		70.82 83	74.46 99	66.58 78	66.33 101	64.35 107	77.72 85	45.07 110	70.39 122	71.18 45	83.15 56	90.62 65	59.97 70	70.90 164	68.43 139	79.19 112	73.39 102

UniMVSNet_NR-MVSNet		70.82 83	77.44 74	63.11 101	71.75 68	66.02 87	73.93 112	55.00 51	70.90 116	46.77 168	86.68 26	91.54 41	46.91 159	81.07 79	76.32 85	84.28 68	69.54 137

PVSNet_Blended_VisFu		70.70 85	73.62 109	67.28 71	63.53 138	72.96 60	77.97 82	52.10 66	63.65 163	62.66 93	71.14 154	73.46 196	63.55 49	79.35 94	75.34 90	83.90 71	79.43 67

v144192		70.68 86	74.40 101	66.34 81	65.94 107	64.38 105	77.63 86	45.18 109	69.97 130	70.11 53	82.70 62	90.77 60	59.84 72	71.43 159	68.46 135	79.31 111	73.08 105

EC-MVSNet		70.50 87	73.32 116	67.20 72	72.07 67	66.21 86	70.86 137	50.10 75	57.66 196	60.49 103	74.97 129	79.42 166	63.32 51	79.65 89	75.46 89	86.35 54	79.87 65

FPMVS		70.46 88	74.89 90	65.28 87	69.09 84	61.42 125	77.07 89	46.92 94	76.73 75	53.53 128	67.33 168	75.07 191	67.23 29	83.41 63	81.54 56	77.86 125	78.73 73

v1144		70.45 89	74.50 98	65.73 84	65.74 110	64.88 99	77.33 88	44.16 119	70.59 121	69.63 58	83.15 56	91.42 44	57.79 80	71.29 161	68.53 134	79.72 109	71.63 118

v10		70.25 90	74.59 94	65.19 88	65.32 114	66.46 85	76.60 93	44.84 114	67.38 148	67.21 73	82.75 61	90.56 66	57.70 82	71.69 154	68.63 133	79.44 110	74.67 94

Effi-MVS+-dtu		70.10 91	73.76 108	65.82 83	70.23 80	74.92 54	79.47 78	44.49 118	56.98 201	54.34 122	64.26 193	84.78 140	59.97 70	80.96 81	80.38 60	86.44 51	74.05 99

viewdifsd2359ckpt09		70.05 92	75.98 83	63.14 100	63.10 141	66.55 83	76.63 92	46.51 97	69.53 136	56.89 116	77.65 114	89.44 87	55.48 93	77.98 102	76.58 83	80.24 103	74.13 97

SPE-MVS-test		70.00 93	72.92 119	66.59 77	66.71 98	64.06 110	80.28 74	44.82 115	58.41 188	58.08 112	73.57 143	80.94 156	63.98 45	83.29 66	75.93 87	85.65 61	74.23 95

MAR-MVS		70.00 93	72.28 132	67.34 70	69.89 81	72.57 66	80.09 75	49.49 77	60.28 176	69.03 62	59.29 218	80.79 158	54.68 101	78.39 99	76.00 86	80.87 98	78.67 74
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
casdiffseed414692147		69.96 95	75.50 86	63.50 95	67.73 89	63.35 114	72.92 123	44.85 113	73.46 98	62.21 94	82.98 58	92.85 27	54.29 102	74.81 117	68.84 131	79.00 115	72.21 113

Vis-MVSNet		69.95 96	77.69 72	60.91 118	60.67 158	66.71 80	77.94 83	48.58 82	69.10 139	45.78 173	80.21 94	83.58 148	53.41 108	82.92 69	80.11 63	79.08 114	81.21 61
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CS-MVS		69.56 97	72.49 128	66.14 82	66.85 97	64.10 109	72.55 124	42.91 128	58.90 185	60.78 99	72.93 148	83.21 150	66.32 33	78.77 95	72.84 107	87.45 42	76.72 83

EPP-MVSNet		69.51 98	76.17 79	61.74 114	68.38 88	66.60 81	71.77 128	46.98 92	73.60 97	41.79 190	82.06 71	69.65 207	52.51 115	83.41 63	79.94 64	89.02 29	77.94 77

3Dnovator	65.69 13	69.43 99	75.74 85	62.06 111	60.78 157	70.50 74	75.85 96	39.57 176	74.44 86	57.41 114	75.91 120	77.73 181	55.34 96	76.86 104	75.61 88	83.44 78	79.14 70

E6new		69.19 100	74.52 95	62.96 103	64.65 120	60.53 137	74.96 100	41.17 150	78.08 64	67.31 71	84.59 42	92.19 34	53.04 111	72.20 147	65.00 168	76.67 131	69.09 146

E6		69.19 100	74.52 95	62.96 103	64.65 120	60.53 137	74.96 100	41.17 150	78.08 64	67.31 71	84.59 42	92.19 34	53.04 111	72.20 147	65.00 168	76.67 131	69.09 146

Effi-MVS+		69.04 102	73.01 118	64.40 91	67.20 94	64.83 101	74.87 103	43.97 121	63.33 165	60.90 98	73.06 146	85.79 133	55.61 92	73.58 129	76.41 84	83.84 72	74.09 98

v2v482		69.01 103	73.39 115	63.89 93	63.86 130	62.99 116	75.26 99	42.05 139	70.22 124	68.46 65	82.64 63	91.61 40	55.38 94	70.89 165	66.93 156	78.30 121	68.48 152

MSDG		68.98 104	73.31 117	63.92 92	67.08 95	68.27 77	75.41 98	40.77 158	67.61 146	64.89 86	75.75 123	78.96 168	53.70 106	76.72 107	73.95 98	81.71 95	71.93 116

v8		68.77 105	73.50 111	63.26 98	63.74 134	64.47 103	74.22 109	42.07 138	67.30 149	64.89 86	82.08 70	90.23 74	56.50 89	71.85 153	66.57 158	78.14 122	72.02 114

NR-MVSNet		68.66 106	76.15 80	59.93 126	65.49 111	65.48 92	74.42 105	56.76 35	69.95 131	45.38 175	85.70 32	91.13 50	34.68 221	74.52 120	76.75 81	82.83 84	69.49 139

E4		68.56 107	73.87 106	62.38 106	64.56 122	60.30 139	74.15 110	40.68 160	77.12 72	65.86 80	83.44 51	91.14 49	52.51 115	71.34 160	64.56 171	76.49 136	69.19 144

USDC		68.53 108	71.82 141	64.68 89	63.53 138	61.87 124	70.12 143	46.98 92	77.89 66	76.58 23	68.55 161	86.88 121	50.50 133	73.73 126	65.62 160	80.39 101	68.21 156

IS_MVSNet		68.20 109	74.41 100	60.96 117	68.55 86	64.36 106	71.47 132	48.33 84	70.11 128	43.30 181	80.90 87	74.54 193	47.19 157	81.25 75	77.97 73	86.94 47	71.76 117

E5new		68.19 110	73.41 113	62.09 108	64.54 123	60.26 140	73.68 118	40.53 163	76.37 76	65.41 81	82.61 64	90.32 71	52.09 118	70.81 167	64.16 175	76.33 138	69.36 141

E5		68.19 110	73.41 113	62.09 108	64.54 123	60.26 140	73.68 118	40.53 163	76.37 76	65.41 81	82.61 64	90.32 71	52.09 118	70.81 167	64.16 175	76.33 138	69.36 141

Baseline_NR-MVSNet		68.15 112	75.12 88	60.02 125	70.81 72	55.67 174	75.88 95	53.40 57	71.25 113	43.96 179	85.88 30	92.68 28	45.76 164	83.47 61	68.34 140	70.34 200	68.58 151

GeoE		68.11 113	72.10 137	63.47 96	67.32 92	62.42 120	78.32 80	43.22 127	64.06 162	55.72 120	73.97 138	84.58 141	55.35 95	76.09 111	70.41 124	80.89 97	73.14 104

casdiffmvs_mvg		68.01 114	74.22 104	60.78 119	63.75 133	62.08 123	70.21 142	42.58 130	72.11 107	58.53 109	84.80 40	88.98 92	49.37 141	73.25 133	67.54 152	80.24 103	70.75 124
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
E3		67.77 115	72.78 122	61.92 112	64.26 125	60.09 145	73.72 117	40.40 168	74.39 88	65.10 84	81.79 78	90.27 73	51.99 120	70.42 174	63.76 182	76.04 140	68.75 149

E3new		67.76 116	72.77 123	61.92 112	64.26 125	60.10 144	73.73 116	40.41 167	74.39 88	65.08 85	81.78 79	90.23 74	51.98 121	70.43 173	63.75 183	76.04 140	68.75 149

Fast-Effi-MVS+		67.71 117	72.54 127	62.07 110	63.83 131	63.68 112	75.74 97	39.94 171	60.89 175	54.29 123	73.00 147	86.19 129	56.85 86	78.46 98	73.23 103	81.74 94	72.36 111

viewmacassd2359aftdt		67.58 118	74.22 104	59.84 128	61.60 151	59.46 148	72.40 125	35.74 204	76.19 79	62.14 95	83.74 48	90.96 55	51.94 122	73.07 134	65.37 165	75.17 153	70.72 125

viewcassd2359sk11		67.13 119	71.88 140	61.59 115	64.04 128	59.95 146	73.40 121	40.24 169	72.16 106	64.55 88	80.41 91	89.49 85	51.60 123	69.66 182	63.11 188	75.72 144	68.40 153

thisisatest0515		66.95 120	72.29 131	60.72 121	56.37 188	56.05 172	71.08 133	38.81 181	67.59 147	53.26 131	78.21 108	79.79 165	60.11 67	75.69 114	73.02 105	84.69 64	75.66 89

EPNet		66.87 121	68.89 159	64.53 90	73.97 59	61.13 130	78.46 79	61.03 19	56.78 203	53.41 129	66.91 176	70.91 202	43.49 175	76.08 112	76.68 82	76.81 129	73.73 100
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewdifsd2359ckpt13		66.73 122	72.00 139	60.58 122	62.91 143	61.21 129	72.39 127	39.64 174	68.65 142	60.54 102	78.08 110	88.54 97	52.11 117	71.52 156	65.24 167	75.88 143	70.78 123

E2		66.57 123	71.07 148	61.32 116	63.87 129	59.84 147	73.12 122	40.09 170	70.14 127	64.12 89	79.09 105	88.79 94	51.25 125	68.97 186	62.58 191	75.46 149	68.09 157

sasdasda		66.37 124	74.37 102	57.04 145	65.89 108	65.06 95	62.58 184	42.55 131	76.82 73	46.87 166	67.33 168	86.38 125	45.49 166	76.77 105	71.85 112	78.87 118	76.35 84

canonicalmvs		66.37 124	74.37 102	57.04 145	65.89 108	65.06 95	62.58 184	42.55 131	76.82 73	46.87 166	67.33 168	86.38 125	45.49 166	76.77 105	71.85 112	78.87 118	76.35 84

QAPM		66.36 126	72.76 124	58.90 133	59.57 165	65.01 97	64.05 178	41.17 150	73.09 102	56.82 117	69.42 157	77.78 180	55.07 98	73.00 137	72.07 110	76.71 130	78.96 71

viewmanbaseed2359cas		66.24 127	72.42 129	59.03 131	61.13 154	59.13 151	71.64 130	35.37 205	71.67 110	60.68 100	80.93 86	89.48 86	50.83 129	71.60 155	64.04 179	74.50 159	70.09 130

casdiffmvs		66.19 128	72.34 130	59.02 132	62.75 144	60.61 136	69.06 148	41.38 147	69.49 137	54.11 124	84.00 47	89.74 82	49.12 142	70.74 170	62.70 190	77.70 127	69.14 145
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		65.79 129	72.11 135	58.42 137	51.89 218	58.69 152	73.80 114	34.50 213	65.40 157	57.10 115	79.54 101	89.09 90	57.51 83	71.98 151	67.83 148	75.70 146	72.26 112

IterMVS-LS		65.76 130	70.85 151	59.81 129	65.33 113	57.78 157	64.63 175	48.02 87	65.65 156	51.05 142	81.31 83	77.47 182	54.94 99	69.46 183	69.36 129	74.90 157	74.95 92
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PM-MVS		65.66 131	71.25 147	59.14 130	58.92 174	54.88 189	73.66 120	38.55 184	66.12 153	49.91 151	69.87 155	86.97 118	60.61 65	76.30 109	74.75 93	73.19 169	69.83 133

UGNet		65.61 132	74.79 92	54.91 160	54.54 212	68.20 78	70.97 136	48.21 85	67.14 151	41.67 191	74.15 137	80.65 160	36.10 212	79.39 91	77.99 72	77.95 124	76.01 87
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		65.54 133	71.79 142	58.24 140	59.68 164	65.55 91	70.99 134	38.69 183	62.29 168	49.27 155	75.03 128	81.42 153	50.93 128	73.71 128	71.35 115	79.90 107	73.20 103
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		65.36 134	70.09 155	59.85 127	63.05 142	53.61 194	74.29 107	46.45 98	68.14 144	51.45 137	78.83 106	85.78 134	49.87 139	70.44 172	70.45 122	74.00 162	63.38 179

FA-MVS(training)		65.22 135	69.19 158	60.58 122	60.91 155	57.60 161	71.57 131	38.82 180	65.84 155	61.18 97	75.95 119	74.52 194	51.18 127	73.32 131	67.41 153	79.80 108	69.70 135

v148		64.92 136	70.58 154	58.32 138	59.89 162	57.09 164	66.04 167	35.27 206	69.11 138	60.66 101	79.57 100	90.93 58	53.91 105	69.81 181	62.22 193	74.14 160	65.31 170

FC-MVSNet-train		64.87 137	74.76 93	53.33 168	65.24 115	58.05 154	69.69 145	41.92 142	70.99 115	32.62 228	85.75 31	88.23 99	32.10 232	77.61 103	74.41 94	78.43 120	68.25 155

pmmvs6		64.78 138	75.82 84	51.89 175	62.41 146	57.13 163	60.24 194	45.59 105	82.90 34	34.69 213	84.83 38	93.18 21	36.22 211	76.43 108	71.13 118	72.21 177	65.12 171

viewdifsd2359ckpt07		64.71 139	72.11 135	56.08 153	62.59 145	56.76 166	70.41 140	32.26 230	73.93 95	51.19 140	82.32 67	90.96 55	49.92 138	69.24 184	61.27 197	70.10 201	70.27 127

OpenMVS	60.79 16	64.42 140	69.72 156	58.23 141	61.63 150	62.17 121	64.11 177	37.54 195	67.17 150	55.71 121	65.89 185	74.89 192	52.67 113	72.20 147	68.29 142	77.73 126	77.39 80

MGCFI-Net		64.40 141	73.52 110	53.76 167	65.41 112	63.86 111	58.32 205	42.38 134	77.23 71	37.76 201	68.03 163	86.11 131	39.76 192	75.70 113	67.69 151	78.96 117	76.03 86

test1111		64.34 142	71.57 143	55.90 154	67.25 93	60.24 143	66.66 162	51.63 70	73.36 101	34.69 213	75.63 125	80.67 159	39.43 195	78.17 100	71.69 114	75.71 145	61.23 189

DCV-MVSNet		64.34 142	72.84 121	54.42 163	63.79 132	62.09 122	62.50 186	42.72 129	74.32 90	41.34 196	66.96 173	88.57 96	39.18 196	75.20 116	70.35 125	77.01 128	72.37 110

ETV-MVS		64.30 144	64.76 176	63.77 94	68.59 85	62.49 118	77.02 90	45.31 106	49.27 229	50.88 143	56.23 230	59.91 230	57.12 85	80.19 87	74.23 96	83.68 74	71.03 122

viewdifsd2359ckpt11		64.18 145	72.66 125	54.28 166	59.33 170	55.48 180	68.20 152	34.30 215	69.68 134	42.09 187	82.03 72	90.43 70	48.52 145	73.95 123	65.57 161	73.27 167	71.49 119

viewmsd2359difaftdt		64.18 145	72.66 125	54.29 165	59.33 170	55.49 179	68.20 152	34.31 214	69.68 134	42.10 186	82.03 72	90.45 69	48.51 146	73.94 124	65.57 161	73.27 167	71.48 120

ECVR-MVS		63.93 147	71.52 144	55.08 158	66.19 102	61.34 126	63.84 179	51.79 68	70.75 119	34.77 211	74.70 134	81.10 155	38.92 197	79.39 91	73.43 101	75.00 155	59.92 201

Anonymous20231211		63.69 148	72.86 120	53.00 171	63.72 135	60.25 142	60.33 193	40.96 154	72.49 104	38.91 199	81.77 80	88.17 100	37.60 205	73.30 132	68.01 145	76.47 137	66.06 167

diffmvs_AUTHOR		63.60 149	71.07 148	54.90 161	56.75 186	55.35 182	67.91 157	37.08 198	66.87 152	50.84 144	81.64 81	88.73 95	45.49 166	70.02 180	64.15 177	71.31 189	70.70 126

TransMVSNet (Re)		63.49 150	73.86 107	51.39 181	64.26 125	56.07 171	61.17 189	42.23 136	78.81 63	34.80 210	85.94 28	90.63 64	34.35 225	72.73 142	67.98 146	71.50 186	64.84 172

DI_MVS_pp		63.43 151	67.54 164	58.63 134	62.34 147	58.06 153	65.75 171	42.15 137	63.05 166	53.28 130	75.88 122	75.92 188	50.18 135	68.04 188	64.20 174	78.07 123	67.65 159

EIA-MVS		63.24 152	64.16 180	62.16 107	69.30 83	63.20 115	72.40 125	40.82 157	48.31 235	51.50 136	59.63 216	62.23 222	57.33 84	78.00 101	71.94 111	81.59 96	65.82 168

Fast-Effi-MVS+-dtu		63.22 153	65.55 170	60.49 124	61.24 153	64.70 102	74.15 110	53.24 59	51.46 214	49.67 153	58.03 225	78.42 174	48.05 152	72.03 150	71.14 117	76.60 135	63.09 180

IterMVS-SCA-FT		62.67 154	68.00 161	56.45 152	56.92 185	64.92 98	57.51 211	38.12 187	59.44 184	53.62 127	74.74 133	71.60 199	64.84 40	70.24 176	65.27 166	67.70 209	69.83 133

diffmvs		62.64 155	69.66 157	54.46 162	56.19 191	55.06 185	67.36 160	36.74 201	64.18 160	50.58 145	79.54 101	87.55 108	45.13 170	68.04 188	63.20 185	70.78 195	70.02 131
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		62.58 156	67.46 165	56.89 148	59.52 166	55.90 173	64.94 173	38.83 179	57.08 200	56.55 118	76.53 117	84.49 142	47.45 154	66.95 192	62.01 194	74.04 161	69.27 143

MDA-MVSNet-bldmvs		62.46 157	72.13 134	51.19 183	34.32 254	56.10 170	68.65 150	38.85 178	69.05 140	49.50 154	78.17 109	85.43 135	51.32 124	86.67 38	67.40 154	64.46 217	62.08 183

FE-MVSNET2		62.31 158	70.75 153	52.46 172	57.40 184	55.60 176	60.43 192	40.97 153	73.41 99	42.12 185	74.36 136	91.26 48	38.76 199	71.29 161	66.83 157	75.11 154	62.50 182

pm-mvs1		61.97 159	72.01 138	50.25 192	60.64 159	55.23 183	58.67 203	42.44 133	74.40 87	33.63 225	81.03 85	89.86 79	34.87 220	72.93 140	67.95 147	71.28 190	62.65 181

FMVSNet1		61.92 160	71.36 145	50.90 186	57.67 183	59.29 150	59.48 200	44.14 120	70.24 123	34.72 212	75.45 127	84.94 138	36.75 208	72.33 144	68.45 136	72.66 172	68.83 148

viewmambaseed2359dif		61.91 161	67.70 163	55.15 157	58.68 177	54.97 186	66.48 165	38.16 186	61.22 174	49.79 152	75.90 121	85.95 132	46.19 162	67.70 190	60.19 201	71.60 183	67.93 158

PVSNet_BlendedMVS		61.75 162	65.07 174	57.87 143	56.27 189	60.99 133	65.81 169	43.75 123	51.27 218	54.08 125	62.12 204	78.84 170	50.67 130	71.49 157	63.91 180	76.64 133	66.86 161

PVSNet_Blended		61.75 162	65.07 174	57.87 143	56.27 189	60.99 133	65.81 169	43.75 123	51.27 218	54.08 125	62.12 204	78.84 170	50.67 130	71.49 157	63.91 180	76.64 133	66.86 161

tttt0517		61.44 164	63.85 182	58.62 135	55.20 204	55.61 175	68.80 149	38.02 190	55.70 205	50.01 150	66.93 175	48.90 241	56.69 87	73.84 125	71.10 119	82.99 82	74.89 93

tfpnnormal		61.41 165	71.33 146	49.83 195	61.73 149	54.90 188	58.52 204	41.24 148	75.20 82	32.00 233	82.13 69	87.87 104	35.63 216	72.75 141	66.30 159	69.87 202	60.14 197

IB-MVS	57.02 17	61.37 166	65.39 171	56.69 149	56.65 187	60.85 135	70.70 138	37.90 192	49.37 228	45.37 176	48.75 242	79.14 167	53.55 107	76.26 110	70.85 121	75.97 142	72.50 109
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		61.22 167	67.07 167	54.40 164	59.89 162	63.62 113	70.98 135	36.77 200	50.49 221	47.15 162	62.45 202	80.81 157	37.90 204	71.87 152	70.09 127	73.69 163	70.19 129

pmmvs4		61.12 168	64.61 177	57.04 145	60.88 156	52.15 206	70.59 139	44.82 115	61.35 173	46.91 165	72.08 150	73.27 197	46.79 161	65.06 196	67.76 149	72.28 175	60.58 193

thisisatest0530		61.02 169	63.44 189	58.19 142	54.75 210	55.09 184	68.03 156	38.02 190	55.45 206	49.06 156	66.58 179	48.69 242	56.69 87	73.07 134	71.10 119	82.60 87	74.14 96

Vis-MVSNet (Re-imp)		60.99 170	67.78 162	53.06 170	64.66 119	53.49 195	67.40 158	49.52 76	68.55 143	28.00 243	79.53 103	71.41 201	33.08 229	75.30 115	71.28 116	75.69 147	54.91 214

PatchMatch-RL		60.96 171	63.00 195	58.57 136	59.16 172	52.18 205	67.38 159	41.99 140	57.85 194	48.16 157	53.55 238	69.77 206	59.47 76	73.73 126	72.49 109	75.27 152	61.44 185

GA-MVS		60.73 172	64.24 179	56.64 150	59.38 169	57.45 162	65.07 172	36.65 202	57.39 198	58.17 111	73.43 144	69.10 210	47.38 155	64.47 202	63.63 184	73.19 169	64.22 176

CVMVSNet		60.45 173	63.72 185	56.63 151	54.82 209	53.75 190	68.41 151	41.95 141	55.07 207	48.03 158	58.08 224	68.67 211	55.09 97	69.14 185	68.34 140	71.51 185	72.97 108

ET-MVSNet_ETH3D		60.33 174	62.10 200	58.27 139	58.61 178	58.05 154	68.06 154	41.20 149	51.40 215	51.10 141	64.06 194	49.42 240	50.61 132	74.72 118	70.29 126	80.05 106	66.74 163

FC-MVSNet-test		60.28 175	70.83 152	47.96 208	54.69 211	47.12 225	68.06 154	41.68 146	71.42 111	23.73 251	84.70 41	77.41 183	28.92 237	82.33 73	73.08 104	70.68 196	59.77 202

test2506		59.86 176	64.01 181	55.02 159	66.19 102	61.34 126	63.84 179	51.79 68	70.75 119	34.39 218	62.65 201	39.92 262	38.92 197	79.39 91	73.43 101	75.00 155	60.56 194

EU-MVSNet		59.77 177	66.07 168	52.42 173	47.81 229	51.86 208	62.98 183	32.28 229	62.08 169	47.10 163	59.94 214	83.42 149	53.08 110	70.06 179	63.19 186	71.26 192	71.96 115

IterMVS		59.24 178	64.45 178	53.16 169	50.98 220	61.29 128	66.51 163	32.85 224	58.17 190	46.31 171	72.58 149	70.23 204	54.26 103	64.81 199	60.24 200	68.04 208	63.81 178
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		59.15 179	62.28 197	55.49 156	52.42 216	62.59 117	71.76 129	39.74 172	50.25 223	41.92 188	62.88 199	69.16 209	55.85 90	62.77 209	67.18 155	71.27 191	61.11 190

thres600view7		58.87 180	65.91 169	50.66 188	61.27 152	56.32 168	59.88 198	40.63 161	64.88 158	32.10 232	64.82 190	69.83 205	36.72 209	72.99 138	72.55 108	73.34 165	59.97 200

FE-MVSNET		58.15 181	67.14 166	47.65 212	49.53 225	50.47 210	57.09 219	37.15 197	67.85 145	35.64 209	67.57 167	87.16 114	35.36 217	71.23 163	65.57 161	71.17 194	60.12 198

CMPMVS	45.32 18	58.10 182	65.24 173	49.76 196	47.88 228	46.86 228	48.16 246	32.82 225	58.06 191	61.35 96	59.64 215	80.00 162	47.27 156	70.15 177	64.10 178	61.08 221	77.85 78
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view		58.09 183	63.54 188	51.74 177	50.13 222	46.56 229	66.95 161	33.41 221	63.52 164	58.77 107	74.84 131	84.10 145	43.12 176	65.95 195	54.69 225	58.04 228	55.13 213

CDS-MVSNet		57.90 184	63.57 187	51.28 182	62.30 148	53.17 200	64.70 174	51.61 71	57.41 197	32.75 227	63.73 195	70.53 203	27.12 240	72.49 143	73.02 105	69.22 205	54.68 215
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
gbinet_0.2-2-1-0.02		57.89 185	63.71 186	51.09 185	55.89 192	55.60 176	59.23 201	36.08 203	59.64 183	42.40 183	66.53 180	76.89 185	40.76 185	64.76 200	57.84 208	72.19 178	64.74 173

FMVSNet2		57.80 186	65.39 171	48.94 203	55.88 193	57.61 158	57.26 212	42.37 135	58.21 189	33.19 226	68.36 162	75.55 190	34.58 222	66.91 193	64.55 172	70.38 197	66.56 164

blended_shiyan6		57.28 187	63.20 193	50.37 190	55.61 198	53.68 192	57.83 209	34.81 207	61.78 172	41.66 192	66.96 173	78.89 169	39.92 191	62.76 210	56.95 213	72.40 174	61.36 186

blended_shiyan8		57.27 188	63.19 194	50.37 190	55.60 199	53.69 191	57.78 210	34.81 207	61.83 171	41.63 193	67.00 172	78.75 173	39.97 190	62.78 208	56.97 212	72.41 173	61.29 188

thres400		57.25 189	63.73 184	49.70 197	60.19 161	54.95 187	58.16 206	39.60 175	62.42 167	31.98 235	62.33 203	69.20 208	35.96 213	70.07 178	68.03 144	72.28 175	59.12 204

WB-MVS		57.14 190	71.03 150	40.93 233	47.97 227	53.10 201	52.35 233	39.66 173	79.59 58	23.31 252	89.49 14	89.23 88	32.94 230	74.68 119	61.41 196	49.32 244	49.74 227

usedtu_dtu_shiyan1		56.93 191	63.39 190	49.39 199	55.48 200	53.03 202	56.77 220	38.34 185	60.26 177	37.42 203	64.85 189	80.06 161	36.94 207	63.97 204	62.55 192	71.36 188	58.99 205

gm-plane-assit		56.76 192	57.64 214	55.73 155	66.01 106	55.45 181	74.96 100	30.54 235	73.71 96	56.04 119	81.81 76	30.91 265	43.83 172	58.77 226	54.71 224	63.02 219	48.13 234

MIMVSNet1		56.72 193	68.69 160	42.76 229	46.70 236	42.81 236	69.13 146	30.52 236	81.01 49	32.00 233	74.82 132	91.10 52	26.83 242	73.98 122	64.72 170	51.40 240	52.38 222

EPNet_dtu		56.63 194	60.77 206	51.80 176	55.47 201	44.63 230	69.83 144	38.74 182	50.27 222	47.64 159	58.01 226	72.27 198	33.71 227	68.60 187	67.72 150	65.39 213	63.86 177
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wanda-best-256-512		56.57 195	62.27 198	49.92 193	55.08 205	53.36 196	57.15 216	34.52 209	60.20 178	41.40 194	65.86 186	78.11 178	39.54 193	61.94 214	55.90 218	71.82 179	60.53 195

FE-blended-shiyan7		56.57 195	62.27 198	49.92 193	55.08 205	53.36 196	57.15 216	34.52 209	60.20 178	41.40 194	65.86 186	78.11 178	39.54 193	61.94 214	55.90 218	71.82 179	60.53 195

GBi-Net		56.54 197	63.26 191	48.70 205	55.88 193	57.61 158	57.26 212	41.75 143	49.06 230	32.37 229	61.81 206	67.02 213	34.58 222	72.33 144	68.45 136	70.38 197	66.56 164

test1		56.54 197	63.26 191	48.70 205	55.88 193	57.61 158	57.26 212	41.75 143	49.06 230	32.37 229	61.81 206	67.02 213	34.58 222	72.33 144	68.45 136	70.38 197	66.56 164

gg-mvs-nofinetune		56.45 199	61.04 204	51.10 184	63.42 140	49.40 218	53.71 228	52.52 64	74.77 84	46.93 164	77.31 115	53.88 234	26.42 244	62.51 212	57.81 209	63.60 218	51.57 225

thres200		56.35 200	62.36 196	49.34 200	58.87 175	56.32 168	55.91 221	40.63 161	58.51 187	31.34 236	58.81 222	67.31 212	35.96 213	72.99 138	65.51 164	73.34 165	57.07 208

MS-PatchMatch		56.31 201	60.22 209	51.73 178	60.53 160	55.53 178	63.41 181	37.18 196	51.34 217	37.44 202	60.53 212	62.19 223	45.52 165	64.25 203	63.17 187	66.33 210	64.56 174

tfpn200view9		56.07 202	61.85 201	49.34 200	58.57 179	56.48 167	58.01 208	40.72 159	53.23 208	31.01 237	56.41 228	66.40 218	34.18 226	73.02 136	68.06 143	73.53 164	59.35 203

usedtu_dtu_shiyan2		55.91 203	63.79 183	46.72 218	59.06 173	49.07 219	60.88 191	43.83 122	79.17 62	29.73 240	66.13 183	86.96 119	27.91 238	62.70 211	57.21 210	58.91 224	45.28 240

FMVSNet3		54.77 204	60.73 207	47.81 209	54.29 213	56.88 165	55.89 222	41.75 143	49.06 230	32.37 229	61.81 206	67.02 213	33.67 228	62.88 207	61.96 195	68.88 206	65.53 169

thres100view900		53.88 205	59.19 210	47.68 211	58.57 179	52.74 204	54.45 225	38.07 189	53.23 208	31.01 237	56.41 228	66.40 218	32.80 231	65.03 198	64.43 173	71.18 193	56.10 211

CR-MVSNet		53.82 206	55.40 218	51.98 174	51.57 219	50.23 212	45.00 249	44.97 111	46.90 237	52.60 133	67.91 164	46.99 255	48.37 147	59.15 224	59.53 204	69.38 204	57.07 208

baseline2		53.55 207	55.19 219	51.62 179	55.27 203	51.95 207	60.89 190	34.23 216	46.69 239	42.47 182	53.56 237	50.01 237	45.33 169	64.63 201	61.22 198	71.56 184	58.28 207

test20.03		53.49 208	60.95 205	44.78 222	64.73 118	47.25 224	61.58 188	43.30 125	65.86 154	22.85 253	66.87 178	79.85 163	22.99 246	62.38 213	56.95 213	53.25 237	47.46 236

baseline		53.46 209	61.55 202	44.01 224	45.83 241	48.77 220	57.26 212	28.75 242	49.99 224	38.85 200	68.78 159	75.65 189	38.30 201	60.80 218	59.78 203	55.10 235	67.07 160

MVSTER		53.08 210	56.39 216	49.21 202	47.19 232	51.08 209	60.14 196	31.74 232	40.63 249	38.97 198	55.78 231	46.74 256	42.47 179	67.29 191	62.99 189	74.73 158	70.23 128

CHOSEN 1792x2688		52.99 211	56.91 215	48.42 207	47.32 230	50.10 214	64.18 176	33.85 218	45.46 242	36.95 205	55.20 234	66.49 217	51.20 126	59.28 222	59.81 202	57.01 230	61.99 184

baseline1		52.90 212	58.38 211	46.51 219	58.87 175	50.01 215	54.17 226	40.45 166	56.81 202	29.25 241	62.72 200	58.99 231	30.25 235	65.05 197	60.57 199	66.07 211	54.54 216

CostFormer		52.59 213	55.14 220	49.61 198	52.72 214	50.40 211	66.28 166	33.78 219	52.85 210	43.43 180	66.30 181	51.37 236	41.78 182	54.92 236	51.18 232	59.68 223	58.98 206

SCA		52.47 214	53.97 225	50.71 187	46.95 235	57.79 156	60.18 195	46.89 95	51.92 213	46.71 170	60.73 210	49.97 238	47.69 153	56.39 233	52.98 229	55.82 232	48.03 235

testgi		51.94 215	61.37 203	40.94 232	58.38 181	47.03 226	65.88 168	30.49 237	70.87 118	22.64 254	57.53 227	87.59 107	18.30 253	63.01 206	54.32 226	49.93 243	49.27 229

FE-MVSNET3		51.86 216	54.48 222	48.80 204	55.08 205	53.36 196	57.15 216	34.52 209	60.20 178	34.24 219	41.26 252	47.37 248	40.03 187	61.94 214	55.90 218	71.82 179	61.36 186

usedtu_blend_shiyan5		51.33 217	54.39 223	47.77 210	55.08 205	53.36 196	50.91 239	34.52 209	60.20 178	34.24 219	41.26 252	47.37 248	40.03 187	61.94 214	55.90 218	71.82 179	60.71 191

dmvs_re		51.01 218	54.88 221	46.49 220	58.06 182	44.35 233	60.08 197	37.67 193	42.11 246	28.68 242	45.12 248	66.70 216	31.90 234	66.62 194	59.18 206	62.59 220	60.11 199

tpm cat1		50.98 219	51.28 231	50.62 189	55.74 196	49.92 216	63.13 182	38.12 187	52.38 212	47.61 160	60.11 213	44.51 259	44.86 171	51.31 245	47.49 242	54.25 236	53.24 219

RPMNet		50.92 220	50.32 234	51.62 179	50.25 221	50.23 212	59.16 202	46.70 96	46.90 237	42.39 184	48.97 241	37.23 263	41.78 182	57.30 231	56.18 216	69.44 203	55.43 212

pmmvs5		50.64 221	58.01 212	42.05 230	47.01 234	43.67 234	49.27 244	29.43 241	50.77 220	33.83 224	68.69 160	76.16 187	27.82 239	57.53 230	57.07 211	64.95 215	52.18 223

PatchT		50.55 222	53.55 227	47.05 216	37.59 253	42.26 237	50.55 241	37.56 194	46.37 240	52.60 133	66.91 176	43.54 261	48.37 147	59.15 224	59.53 204	55.62 233	57.07 208

Anonymous20231206		50.28 223	57.94 213	41.35 231	55.45 202	43.65 235	58.06 207	34.12 217	62.02 170	24.25 250	59.33 217	79.80 164	24.49 245	59.55 219	54.28 227	51.74 239	46.94 238

dps		49.71 224	51.97 229	47.07 215	52.37 217	47.00 227	53.02 231	40.52 165	44.91 243	41.23 197	64.55 191	44.27 260	40.12 186	57.71 229	51.97 231	55.14 234	53.41 218

MDTV_nov1_ep13		49.60 225	51.57 230	47.31 213	46.28 238	44.61 231	59.82 199	30.96 233	48.80 234	50.20 148	59.26 219	52.38 235	38.56 200	56.20 234	49.70 236	58.04 228	50.01 226

PatchmatchNet		48.67 226	50.10 235	46.99 217	48.29 226	41.00 238	55.54 223	38.94 177	51.38 216	45.15 177	63.22 197	48.45 244	42.83 177	53.80 241	48.50 241	51.19 242	44.37 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
blend_shiyan4		47.96 227	50.55 233	44.94 221	43.47 247	52.81 203	47.71 248	32.72 226	36.60 255	34.24 219	41.26 252	47.37 248	40.03 187	59.51 220	55.57 222	71.48 187	60.71 191

new-patchmatchnet		47.33 228	60.49 208	31.99 249	55.69 197	33.86 250	36.84 259	33.31 222	72.36 105	14.33 259	80.09 96	92.14 36	13.27 255	63.54 205	40.09 250	38.51 253	41.32 249

0.4-1-1-0.1		46.90 229	49.04 241	44.40 223	47.28 231	49.55 217	52.48 232	30.44 238	40.85 248	34.58 215	47.16 244	48.13 245	35.64 215	52.90 242	50.70 235	65.97 212	53.98 217

tpm		46.67 230	49.20 240	43.72 225	49.60 223	36.60 247	53.93 227	26.84 244	52.70 211	58.05 113	69.04 158	47.96 246	30.06 236	48.33 250	42.76 245	43.88 247	47.01 237

pmmvs3		46.64 231	54.13 224	37.90 240	31.23 258	40.68 239	49.83 243	15.34 255	46.31 241	36.34 207	53.15 239	74.40 195	36.36 210	58.43 227	56.64 215	58.32 227	49.29 228

TAMVS		46.64 231	53.62 226	38.49 238	49.56 224	36.87 244	53.16 230	25.76 246	56.33 204	22.55 255	60.72 211	61.80 225	27.12 240	59.50 221	58.33 207	52.79 238	41.82 248

test-LLR		46.01 233	45.06 252	47.11 214	59.39 167	36.72 245	51.28 236	40.95 155	36.41 256	34.45 216	46.14 245	47.02 253	38.00 202	51.78 243	48.53 239	58.60 225	48.84 231

MIMVSNet		45.83 234	53.46 228	36.94 241	45.38 245	39.50 241	52.20 234	30.68 234	57.09 199	24.53 249	55.22 233	71.54 200	21.74 249	55.81 235	51.08 233	47.11 245	43.96 242

0.3-1-1-0.015		45.78 235	47.55 244	43.71 226	46.29 237	48.64 222	51.52 235	29.70 239	39.03 253	34.24 219	44.15 250	47.37 248	35.28 218	51.31 245	49.52 237	65.23 214	52.80 220

pmnet_mix02		45.67 236	55.99 217	33.63 248	45.77 242	31.22 254	42.04 254	27.60 243	64.14 161	24.89 246	75.50 126	82.30 151	21.88 248	54.53 239	41.22 247	39.62 251	43.05 244

0.4-1-1-0.2		45.53 237	47.38 245	43.38 228	46.00 240	48.29 223	50.94 238	29.49 240	38.16 254	34.06 223	45.06 249	47.50 247	34.99 219	51.04 247	49.00 238	64.81 216	52.58 221

test0.0.03 1		45.40 238	49.55 238	40.57 235	59.39 167	44.36 232	53.37 229	40.95 155	47.14 236	19.23 256	45.49 247	60.24 228	19.24 251	54.82 237	51.98 230	51.21 241	42.82 245

PMMVS		45.37 239	49.29 239	40.79 234	27.75 259	35.07 249	50.88 240	19.88 252	39.27 251	35.78 208	50.11 240	61.29 226	42.04 180	54.13 240	55.95 217	68.43 207	49.19 230

MVS-HIRNet		44.56 240	45.52 250	43.44 227	40.98 249	31.03 255	39.52 258	36.96 199	42.80 245	44.37 178	53.80 236	60.04 229	41.85 181	47.97 252	41.08 248	56.99 231	41.95 247

test-mter		44.18 241	47.60 243	40.18 236	33.20 255	39.03 242	55.28 224	13.91 257	39.07 252	36.63 206	48.09 243	49.52 239	41.12 184	54.55 238	50.91 234	60.97 222	52.03 224

EMVS		43.85 242	49.91 236	36.77 243	45.46 244	32.70 251	44.09 251	25.33 247	57.88 193	26.62 244	58.99 221	61.14 227	42.77 178	70.26 175	38.52 254	36.38 255	29.87 255

E-PMN		43.83 243	49.81 237	36.84 242	46.09 239	31.86 253	42.77 253	25.85 245	57.76 195	25.53 245	55.50 232	62.47 221	43.77 173	70.78 169	39.51 251	37.04 254	30.79 254

tpmrst		43.31 244	46.14 248	40.02 237	47.05 233	36.48 248	48.01 247	32.17 231	49.50 227	37.26 204	63.66 196	47.04 252	31.98 233	42.00 256	40.55 249	43.64 248	43.75 243

TESTMET0.1,1		41.79 245	45.06 252	37.97 239	31.32 257	36.72 245	51.28 236	14.17 256	36.41 256	34.45 216	46.14 245	47.02 253	38.00 202	51.78 243	48.53 239	58.60 225	48.84 231

ADS-MVSNet		40.61 246	46.31 246	33.96 246	40.70 250	30.42 256	40.42 256	33.44 220	58.01 192	30.87 239	63.05 198	54.48 233	22.67 247	44.35 255	39.23 253	35.64 256	34.64 252

CHOSEN 280x420		40.24 247	44.14 255	35.69 244	32.36 256	23.58 259	50.30 242	21.21 251	40.94 247	18.84 257	32.75 257	48.65 243	48.13 151	59.16 223	55.31 223	43.28 249	48.62 233

EPMVS		40.11 248	44.96 254	34.44 245	41.55 248	32.65 252	41.74 255	32.39 228	49.89 226	24.83 247	64.44 192	46.38 257	26.57 243	44.75 254	39.47 252	39.59 252	37.16 251

FMVSNet5		39.83 249	45.08 251	33.71 247	39.24 251	39.56 240	48.77 245	23.55 249	39.45 250	24.55 248	33.73 256	44.57 258	20.97 250	58.27 228	54.23 228	45.16 246	45.77 239

N_pmnet		39.50 250	51.01 232	26.09 251	44.48 246	25.59 258	40.20 257	21.49 250	64.20 159	7.98 263	73.86 140	76.67 186	13.66 254	50.17 248	36.69 256	28.71 258	29.86 256

MVE	28.01 19	35.86 251	43.56 256	26.88 250	22.33 261	19.75 261	30.85 262	23.88 248	49.90 225	10.48 261	43.64 251	61.87 224	48.99 144	47.26 253	42.15 246	24.76 259	40.37 250
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet		35.76 252	45.64 249	24.22 252	38.59 252	25.83 257	31.87 261	19.24 253	49.06 230	9.01 262	54.34 235	64.73 220	12.46 256	49.21 249	44.91 243	34.17 257	31.41 253

PMMVS2		34.11 253	48.55 242	17.26 253	25.45 260	20.72 260	35.08 260	16.26 254	58.71 186	4.16 265	59.22 220	78.40 175	3.65 258	57.24 232	38.31 255	18.94 261	27.28 257

GG-mvs-BLEND		31.54 254	46.27 247	14.37 254	0.07 266	48.65 221	42.97 252	0.08 263	44.04 244	1.21 267	39.77 255	57.94 232	0.15 262	48.19 251	42.82 244	41.70 250	42.46 246

test_method		13.28 255	15.83 257	10.30 255	1.05 263	2.18 264	15.40 263	2.23 259	22.43 258	13.84 260	22.00 259	33.14 264	9.78 257	17.80 258	9.93 258	19.50 260	3.31 259

test123		0.53 256	0.60 259	0.46 257	0.22 264	0.25 265	0.33 268	0.13 262	0.66 261	1.37 266	1.10 261	0.00 269	0.43 260	0.68 260	0.61 259	0.26 264	0.88 260

testmvs		0.47 257	0.69 258	0.21 258	0.17 265	0.17 266	0.35 267	0.16 261	0.66 261	0.18 268	1.05 262	0.99 268	0.27 261	0.62 261	0.54 260	0.15 265	0.77 261

uanet_test		0.00 258	0.00 260	0.00 259	0.00 267	0.00 267	0.00 269	0.00 264	0.00 263	0.00 269	0.00 263	0.00 269	0.00 263	0.00 262	0.00 261	0.00 266	0.00 262

sosnet-low-res		0.00 258	0.00 260	0.00 259	0.00 267	0.00 267	0.00 269	0.00 264	0.00 263	0.00 269	0.00 263	0.00 269	0.00 263	0.00 262	0.00 261	0.00 266	0.00 262

sosnet		0.00 258	0.00 260	0.00 259	0.00 267	0.00 267	0.00 269	0.00 264	0.00 263	0.00 269	0.00 263	0.00 269	0.00 263	0.00 262	0.00 261	0.00 266	0.00 262

TestfortrainingZip							87.82 38	53.22 60		58.49 110						84.46 65

TPM-MVS					72.72 64	70.92 73	80.38 73			66.22 78	58.35 223	78.23 176	48.26 149			83.40 79	81.74 57
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										70.04 55

9.14												83.64 147

SR-MVS					81.31 9			62.63 9				91.11 51

Anonymous202405211			72.22 133		66.19 102	61.09 132	62.23 187	45.87 104	71.25 113		79.33 104	86.16 130	37.36 206	73.54 130	69.84 128	75.45 150	64.32 175

our_test_3					52.72 214	53.66 193	69.11 147

ambc			79.96 62		74.57 56	75.48 50	73.75 115		80.32 54	72.34 38	78.46 107	92.41 30	59.05 77	80.24 86	73.95 98	75.41 151	78.85 72

MTAPA										80.26 8		90.53 68

MTMP										82.07 4		91.00 54

Patchmatch-RL test							2.05 266

tmp_tt				7.47 256	8.89 262	3.32 263	4.35 265	1.14 260	15.58 260	15.76 258	8.50 260	5.90 267	2.00 259	20.02 257	21.51 257	12.70 262

XVS					80.47 20	81.29 12	93.33 3			77.45 19		90.19 76				91.52 11

X-MVStestdata					80.47 20	81.29 12	93.33 3			77.45 19		90.19 76				91.52 11

mPP-MVS					82.97 2							92.12 37

NP-MVS									71.39 112

Patchmtry						37.73 243	45.00 249	44.97 111		52.60 133

DeepMVS_CX						8.52 262	9.75 264	3.19 258	16.70 259	5.02 264	23.06 258	19.33 266	18.69 252	13.75 259		11.34 263	25.07 258

High-resolution multi-view benchmark