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 54	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 52	82.76 4	90.39 19	54.21 55	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 32	82.32 3	80.89 91	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 20	79.45 11	83.37 55	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 46	80.21 9	81.95 77	90.76 62	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 40	78.32 15	80.59 92	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 61	80.21 9	80.31 96	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 56	80.44 6	84.83 41	94.19 14	70.52 17	90.70 11	87.19 22	91.71 8	87.37 31

ACMM	71.24 7	82.85 9	86.59 12	78.50 9	80.10 26	78.59 34	91.77 11	60.76 22	84.43 18	76.49 24	81.58 85	93.50 17	70.45 18	91.38 7	89.42 8	91.42 16	87.22 33
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 22	71.81 39	82.39 69	89.59 85	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 33	71.30 44	86.91 24	90.54 68	67.12 31	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 37	75.53 28	73.88 147	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 48	75.46 29	82.97 62	95.08 12	68.90 24	90.49 14	87.43 21	91.48 14	86.84 35
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 54	77.00 22	81.80 80	90.71 63	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 12	78.61 14	83.21 58	86.96 124	65.95 36	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 38	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	63.57 141	70.27 76	86.48 45	62.99 7	89.00 5	80.32 7	86.25 28	91.04 54	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 51	85.46 45	81.36 57	86.96 47	94.00 1

PMVS	70.37 8	81.82 19	87.08 6	75.68 24	77.06 44	77.23 42	87.77 40	56.25 42	83.33 31	67.18 74	89.48 15	87.94 108	77.70 1	93.02 2	92.57 2	88.13 37	86.00 40
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 28	77.74 18	83.72 52	87.34 116	65.36 37	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 47	77.45 19	72.04 159	90.19 77	62.50 58	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 37	89.84 22	58.67 30	85.25 13	76.26 25	85.28 38	92.88 26	66.03 35	87.20 37	85.40 27	88.86 31	85.58 44
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 50	80.87 51	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 40	82.29 5	84.41 51	62.95 8	82.99 34	64.03 90	87.71 18	89.17 91	71.98 11	88.19 30	88.10 16	86.18 56	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 45	73.45 32	86.84 25	89.80 83	69.33 21	85.40 46	82.91 43	87.87 39	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 38	75.91 23	81.22 11	81.48 10	90.49 17	58.81 29	77.54 70	67.49 69	85.90 30	89.82 82	69.43 20	86.08 42	83.80 37	88.01 38	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 51	77.96 13	81.99 7	72.76 63	90.48 18	61.31 14	80.85 52	77.90 17	81.93 78	87.01 121	68.20 27	84.15 58	85.27 29	87.85 40	86.00 40

DVP-MVS		80.31 29	85.60 27	74.15 33	76.23 48	78.39 35	86.62 43	55.79 46	86.47 9	71.32 43	90.96 7	89.02 94	69.28 22	84.62 55	81.64 54	85.66 61	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 41	73.82 61	76.41 45	83.59 55	54.82 53	87.35 6	70.86 48	86.98 23	96.27 4	66.50 32	89.17 24	83.39 39	89.26 28	83.56 50

OMC-MVS		79.95 31	85.28 29	73.74 36	72.95 64	80.10 27	87.87 37	48.13 87	84.62 17	79.42 12	80.27 97	92.49 29	64.14 45	87.25 36	85.11 30	89.92 24	87.10 34

SED-MVS		79.70 32	85.16 31	73.34 39	75.83 51	78.11 38	88.77 33	56.45 38	84.85 15	69.45 59	90.70 9	88.38 101	63.16 53	85.12 51	81.28 58	86.40 53	87.63 26

MSP-MVS		79.65 33	84.28 42	74.25 31	78.92 33	81.86 9	89.07 28	60.49 24	83.85 25	70.05 54	85.12 39	90.92 60	62.99 55	81.15 78	81.64 54	83.99 71	85.42 46
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DeepPCF-MVS	71.57 5	79.49 34	84.05 43	74.17 32	74.14 58	80.88 20	89.33 26	56.24 43	82.41 39	71.58 41	82.27 71	86.47 129	66.47 33	84.80 53	84.16 35	87.26 45	87.34 32

MED-MVS		79.47 35	84.69 37	73.39 37	77.98 38	78.62 33	87.64 41	56.15 44	85.66 11	62.73 92	85.63 35	90.11 80	67.50 29	84.55 56	80.79 59	88.32 35	87.45 29

LS3D		79.33 36	84.03 44	73.84 34	75.37 53	78.09 39	83.30 56	52.94 64	84.42 19	76.01 26	84.16 47	87.44 115	65.34 38	86.30 40	82.08 51	90.09 22	85.70 42

aaEdge-Enhanced		78.93 37	84.62 39	72.30 45	77.98 38	79.20 31	87.82 38	53.22 61	83.71 27	62.73 92	85.63 35	91.37 46	67.58 28	84.40 57	80.56 60	84.46 66	87.45 29

3Dnovator+	72.94 4	78.78 38	83.05 48	73.80 35	70.70 77	81.34 11	88.33 34	56.01 45	81.33 49	72.87 36	78.06 116	81.15 162	63.83 48	87.39 35	85.82 25	91.06 18	86.28 39

UA-Net		78.65 39	83.96 45	72.46 43	84.87 1	76.15 46	89.06 29	55.70 47	77.25 71	53.14 138	79.73 102	82.09 160	59.69 74	92.21 5	90.93 3	92.32 3	89.36 7

DeepC-MVS_fast	71.40 6	78.48 40	82.92 49	73.31 40	76.44 47	82.23 6	87.59 42	56.56 37	77.79 68	68.91 63	77.00 123	87.32 117	61.90 60	85.40 46	84.37 32	88.46 33	86.33 38
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 41	83.47 47	72.41 44	76.04 50	75.72 49	83.86 53	51.81 68	84.00 24	70.65 51	81.27 87	92.22 33	64.64 43	83.28 68	80.28 62	87.44 44	87.49 28

WR-MVS		78.32 42	86.09 20	69.25 62	76.22 49	72.33 70	85.71 48	59.02 28	86.66 7	51.41 144	92.91 2	96.76 1	53.09 113	90.21 18	85.30 28	90.05 23	78.46 77

ACMH	66.19 11	78.12 43	84.55 40	70.63 53	69.62 84	72.40 69	80.77 71	46.43 100	89.24 4	77.99 16	87.42 20	95.83 9	62.95 56	86.27 41	78.24 72	86.00 59	82.46 52
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
train_agg		77.83 44	80.47 61	74.77 28	80.92 14	69.60 77	88.87 32	56.32 41	74.03 97	71.03 46	83.67 53	87.68 111	64.75 42	83.70 60	81.85 53	86.71 49	82.73 51

NCCC		77.82 45	80.72 60	74.43 30	79.24 31	75.72 49	88.06 35	56.36 40	79.61 58	73.22 34	67.75 174	87.05 120	63.09 54	85.62 44	84.00 36	86.62 50	85.30 47

CNVR-MVS		77.79 46	81.57 55	73.38 38	78.37 37	75.91 47	87.97 36	55.11 51	79.41 60	70.98 47	74.70 142	86.43 130	61.77 61	85.10 52	83.73 38	86.10 58	85.68 43

WR-MVS_H		77.56 47	85.88 22	67.86 66	80.54 18	74.32 58	83.23 57	61.78 12	83.47 29	47.46 170	91.81 6	95.84 8	50.50 138	90.44 16	84.37 32	83.63 76	80.89 64

RPSCF		77.56 47	84.51 41	69.46 61	65.17 119	74.36 57	79.74 78	47.45 90	84.01 23	72.89 35	77.89 117	90.67 64	65.14 40	88.25 29	89.74 7	86.38 54	86.64 37

PS-CasMVS		77.46 49	85.80 23	67.73 68	81.24 10	72.88 62	80.63 72	61.28 15	84.14 21	50.53 154	92.13 4	96.76 1	50.12 141	91.02 9	84.46 31	82.60 88	79.19 70

DTE-MVSNet		77.28 50	84.87 35	68.42 64	82.94 3	72.70 65	81.60 66	61.78 12	85.03 14	51.40 145	92.11 5	96.00 6	49.42 145	89.73 22	82.52 46	83.39 81	75.98 90

SixPastTwentyTwo		77.24 51	83.65 46	69.78 58	65.14 120	64.85 103	77.44 88	47.74 89	82.76 36	68.52 64	87.65 19	93.31 19	71.68 13	89.49 23	82.41 47	88.14 36	85.05 48

CDPH-MVS		77.22 52	81.05 59	72.75 42	77.29 42	77.46 41	86.36 46	54.02 57	73.00 106	69.75 57	77.78 119	88.90 96	61.31 65	84.09 59	82.54 45	87.79 41	83.57 49

PEN-MVS		77.06 53	85.05 32	67.74 67	82.29 5	72.59 66	80.86 70	61.03 19	84.66 16	50.08 158	92.19 3	96.59 3	49.12 147	89.83 21	82.35 48	83.06 82	77.14 83

CP-MVSNet		77.01 54	85.04 33	67.65 69	81.16 12	72.72 64	80.54 73	61.18 16	82.09 41	50.41 155	90.81 8	95.89 7	50.03 142	90.86 10	84.30 34	82.56 90	78.65 76

CSCG		76.95 55	82.08 53	70.97 49	73.32 63	78.35 36	81.08 69	47.19 91	83.47 29	69.82 56	80.44 93	87.19 118	64.59 44	81.01 81	77.26 79	89.83 25	86.84 35

CNLPA		76.67 56	81.72 54	70.77 52	70.75 75	76.68 44	86.14 47	46.11 103	81.82 43	74.68 30	76.37 125	86.23 135	62.92 57	85.28 49	83.29 40	84.02 70	82.40 53

MSLP-MVS++		76.66 57	82.32 52	70.06 55	70.51 78	80.27 25	79.77 77	55.58 48	77.79 68	63.09 91	67.25 181	89.50 86	71.01 14	88.10 31	85.74 26	80.39 104	87.56 27

TSAR-MVS + COLMAP		75.85 58	81.06 57	69.77 59	71.15 71	76.90 43	82.93 59	52.43 66	79.25 62	70.13 52	82.78 63	87.00 122	60.02 70	80.30 85	79.61 66	81.95 94	81.61 60

HQP-MVS		75.81 59	78.91 68	72.18 46	77.41 41	75.38 52	84.75 49	53.35 59	76.12 81	73.32 33	69.48 164	88.07 105	57.76 82	79.42 91	78.44 69	86.48 51	85.50 45

PLC	64.88 15	75.76 60	80.22 62	70.57 54	70.46 79	77.75 40	82.01 64	48.84 81	80.74 53	70.85 49	71.32 161	84.82 147	63.69 49	84.73 54	82.35 48	87.54 42	79.80 67
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 61	79.24 66	70.86 50	67.63 92	74.09 59	83.17 58	44.75 119	81.82 43	80.83 5	65.61 198	88.04 106	61.58 62	83.21 69	80.12 63	87.17 46	81.82 56
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PHI-MVS		75.17 62	78.37 69	71.43 47	71.13 72	72.46 68	82.28 63	50.55 74	73.39 103	79.05 13	73.65 149	87.50 114	61.98 59	81.10 79	78.48 68	83.60 77	81.99 54

MGCNet		74.91 63	79.25 65	69.85 57	75.01 55	74.95 54	86.61 44	48.67 82	68.87 144	67.51 68	80.13 99	83.78 154	55.77 92	83.37 66	81.89 52	85.55 63	81.75 57

anonymousdsp		74.76 64	82.59 50	65.63 86	45.61 252	61.13 134	89.06 29	32.58 236	74.11 96	59.55 106	84.06 48	94.12 15	75.24 3	88.94 26	86.95 24	91.74 7	88.81 14

AdaColmap		74.73 65	77.57 74	71.40 48	76.90 45	75.76 48	84.54 50	53.08 63	76.20 79	66.64 77	66.06 194	78.16 187	61.32 64	85.37 48	82.20 50	85.95 60	79.27 69

v7n		74.47 66	81.06 57	66.77 75	66.98 98	67.10 80	76.76 92	45.88 105	81.98 42	67.43 70	88.38 16	95.67 10	61.38 63	80.76 83	73.49 101	82.21 92	80.06 65

PCF-MVS	65.25 14	73.99 67	76.74 79	70.79 51	71.61 70	75.33 53	83.76 54	50.40 75	74.88 85	74.50 31	67.60 175	85.36 144	58.30 80	78.61 97	74.25 96	86.15 57	81.13 63
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MCST-MVS		73.84 68	77.44 75	69.63 60	73.75 62	74.73 56	81.38 68	48.58 83	74.77 86	69.16 61	71.97 160	86.20 136	59.50 75	78.51 98	74.06 98	85.42 64	81.85 55

TSAR-MVS + GP.		73.42 69	76.31 80	70.05 56	77.15 43	71.13 73	81.59 67	54.11 56	69.84 136	58.65 110	66.20 192	78.77 182	65.29 39	83.65 61	83.14 41	83.54 78	81.47 61

Gipuma		73.40 70	79.27 64	66.55 80	63.64 140	59.35 153	70.28 146	45.92 104	83.79 26	71.78 40	84.04 49	93.07 23	68.69 26	87.90 33	76.76 81	78.98 119	69.96 137
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVS_111021_HR		72.37 71	76.12 83	68.00 65	68.55 88	64.30 111	82.93 59	48.98 80	74.25 94	65.39 83	73.59 150	84.11 152	59.48 76	82.61 72	78.38 70	82.66 87	75.59 92

TinyColmap		71.85 72	76.11 84	66.87 74	66.07 108	65.34 97	74.35 108	49.30 79	79.93 57	75.93 27	75.66 132	87.74 110	54.72 101	80.66 84	70.42 125	80.85 102	73.02 110

UniMVSNet_ETH3D		71.84 73	81.36 56	60.74 123	76.46 46	66.01 91	66.49 173	60.24 26	86.58 8	41.87 198	90.04 11	96.02 5	43.72 182	85.14 50	77.30 78	75.64 152	68.40 161

TranMVSNet+NR-MVSNet		71.66 74	79.23 67	62.83 108	72.54 67	65.64 93	74.77 106	55.27 49	75.91 83	45.50 183	89.55 13	94.25 13	45.96 171	82.74 71	77.03 80	82.96 84	69.48 145

MVS_111021_LR		71.60 75	75.21 89	67.38 70	67.42 93	62.44 122	81.73 65	46.24 101	70.89 120	66.80 76	73.19 153	84.98 145	60.09 69	81.94 75	77.77 76	82.00 93	75.29 93

EG-PatchMatch MVS		71.50 76	76.82 78	65.30 87	70.74 76	66.50 86	74.23 110	43.25 129	72.02 111	59.11 107	79.85 101	86.88 126	63.95 47	80.29 86	75.25 92	80.51 103	76.98 84

DPM-MVS		71.35 77	73.50 114	68.84 63	74.93 56	73.35 60	84.07 52	50.56 73	71.91 112	67.06 75	61.21 220	77.02 194	52.64 118	74.15 125	75.14 93	83.79 74	81.74 58

UniMVSNet (Re)		71.29 78	78.14 70	63.30 99	70.29 80	66.57 83	75.98 96	54.74 54	70.20 128	46.20 181	85.08 40	93.21 20	48.19 155	82.50 73	78.33 71	84.40 68	71.08 125

CLD-MVS		71.24 79	78.12 71	63.20 101	74.03 59	71.60 71	82.82 61	32.91 232	74.23 95	69.32 60	79.65 103	91.54 41	47.02 164	81.22 77	79.01 67	73.09 175	69.63 141
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Casviewmamba		71.14 80	77.09 77	64.20 93	69.71 83	66.51 85	76.15 95	46.24 101	75.99 82	57.72 116	86.62 27	91.14 49	54.30 103	76.97 105	68.87 133	81.07 99	73.52 104

CANet		71.07 81	75.09 91	66.39 81	72.57 66	71.53 72	82.38 62	47.10 92	59.81 192	59.81 105	74.97 137	84.37 151	54.25 106	79.89 89	77.64 77	82.25 91	77.40 81

v1192		71.06 82	74.87 93	66.61 77	66.38 102	65.80 92	78.27 82	45.28 110	70.19 129	70.79 50	83.37 55	91.79 39	58.76 79	70.86 173	69.02 132	80.16 108	73.08 108

DU-MVS		71.03 83	77.92 72	62.98 104	70.81 73	65.48 95	73.93 114	56.76 35	69.95 134	46.77 177	85.70 33	93.49 18	46.91 165	83.47 62	77.82 75	82.72 86	69.54 142

v1240		70.94 84	74.52 97	66.76 76	66.54 101	64.40 107	77.76 85	45.29 109	70.05 132	71.45 42	83.36 57	90.96 56	60.37 67	70.50 178	68.68 135	79.14 116	73.68 103

v1921920		70.82 85	74.46 101	66.58 79	66.33 103	64.35 110	77.72 86	45.07 112	70.39 125	71.18 45	83.15 59	90.62 66	59.97 71	70.90 171	68.43 142	79.19 115	73.39 105

UniMVSNet_NR-MVSNet		70.82 85	77.44 75	63.11 103	71.75 69	66.02 90	73.93 114	55.00 52	70.90 119	46.77 177	86.68 26	91.54 41	46.91 165	81.07 80	76.32 86	84.28 69	69.54 142

PVSNet_Blended_VisFu		70.70 87	73.62 112	67.28 72	63.53 142	72.96 61	77.97 83	52.10 67	63.65 168	62.66 94	71.14 162	73.46 206	63.55 50	79.35 95	75.34 91	83.90 72	79.43 68

v144192		70.68 88	74.40 103	66.34 82	65.94 110	64.38 108	77.63 87	45.18 111	69.97 133	70.11 53	82.70 65	90.77 61	59.84 73	71.43 166	68.46 138	79.31 114	73.08 108

EC-MVSNet		70.50 89	73.32 119	67.20 73	72.07 68	66.21 88	70.86 141	50.10 76	57.66 206	60.49 104	74.97 137	79.42 176	63.32 52	79.65 90	75.46 90	86.35 55	79.87 66

FPMVS		70.46 90	74.89 92	65.28 88	69.09 86	61.42 128	77.07 90	46.92 95	76.73 76	53.53 133	67.33 178	75.07 201	67.23 30	83.41 64	81.54 56	77.86 128	78.73 74

v1144		70.45 91	74.50 100	65.73 85	65.74 113	64.88 102	77.33 89	44.16 121	70.59 124	69.63 58	83.15 59	91.42 44	57.79 81	71.29 168	68.53 137	79.72 112	71.63 121

v10		70.25 92	74.59 96	65.19 89	65.32 117	66.46 87	76.60 94	44.84 116	67.38 151	67.21 73	82.75 64	90.56 67	57.70 83	71.69 161	68.63 136	79.44 113	74.67 96

Effi-MVS+-dtu		70.10 93	73.76 111	65.82 84	70.23 81	74.92 55	79.47 79	44.49 120	56.98 211	54.34 127	64.26 203	84.78 148	59.97 71	80.96 82	80.38 61	86.44 52	74.05 101

viewdifsd2359ckpt09		70.05 94	75.98 85	63.14 102	63.10 145	66.55 84	76.63 93	46.51 98	69.53 139	56.89 119	77.65 120	89.44 89	55.48 94	77.98 103	76.58 84	80.24 106	74.13 99

SPE-MVS-test		70.00 95	72.92 122	66.59 78	66.71 100	64.06 113	80.28 75	44.82 117	58.41 198	58.08 114	73.57 151	80.94 165	63.98 46	83.29 67	75.93 88	85.65 62	74.23 97

MAR-MVS		70.00 95	72.28 135	67.34 71	69.89 82	72.57 67	80.09 76	49.49 78	60.28 185	69.03 62	59.29 229	80.79 167	54.68 102	78.39 100	76.00 87	80.87 101	78.67 75
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
casdiffseed414692147		69.96 97	75.50 88	63.50 97	67.73 91	63.35 117	72.92 126	44.85 115	73.46 101	62.21 95	82.98 61	92.85 27	54.29 104	74.81 120	68.84 134	79.00 118	72.21 116

Vis-MVSNet		69.95 98	77.69 73	60.91 121	60.67 163	66.71 81	77.94 84	48.58 83	69.10 142	45.78 182	80.21 98	83.58 156	53.41 112	82.92 70	80.11 64	79.08 117	81.21 62
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CS-MVS		69.56 99	72.49 131	66.14 83	66.85 99	64.10 112	72.55 127	42.91 131	58.90 195	60.78 100	72.93 156	83.21 158	66.32 34	78.77 96	72.84 108	87.45 43	76.72 85

EPP-MVSNet		69.51 100	76.17 81	61.74 117	68.38 90	66.60 82	71.77 131	46.98 93	73.60 100	41.79 199	82.06 74	69.65 217	52.51 119	83.41 64	79.94 65	89.02 29	77.94 78

3Dnovator	65.69 13	69.43 101	75.74 87	62.06 114	60.78 161	70.50 75	75.85 98	39.57 181	74.44 89	57.41 117	75.91 128	77.73 191	55.34 97	76.86 106	75.61 89	83.44 79	79.14 71

E6new		69.19 102	74.52 97	62.96 105	64.65 124	60.53 141	74.96 102	41.17 153	78.08 65	67.31 71	84.59 45	92.19 34	53.04 115	72.20 154	65.00 174	76.67 135	69.09 152

E6		69.19 102	74.52 97	62.96 105	64.65 124	60.53 141	74.96 102	41.17 153	78.08 65	67.31 71	84.59 45	92.19 34	53.04 115	72.20 154	65.00 174	76.67 135	69.09 152

Effi-MVS+		69.04 104	73.01 121	64.40 92	67.20 96	64.83 104	74.87 105	43.97 123	63.33 170	60.90 99	73.06 154	85.79 141	55.61 93	73.58 134	76.41 85	83.84 73	74.09 100

v2v482		69.01 105	73.39 118	63.89 95	63.86 134	62.99 119	75.26 101	42.05 142	70.22 127	68.46 65	82.64 66	91.61 40	55.38 95	70.89 172	66.93 160	78.30 124	68.48 160

MSDG		68.98 106	73.31 120	63.92 94	67.08 97	68.27 78	75.41 100	40.77 162	67.61 149	64.89 86	75.75 131	78.96 178	53.70 109	76.72 109	73.95 99	81.71 97	71.93 119

v8		68.77 107	73.50 114	63.26 100	63.74 138	64.47 106	74.22 111	42.07 141	67.30 153	64.89 86	82.08 73	90.23 75	56.50 90	71.85 160	66.57 163	78.14 125	72.02 117

NR-MVSNet		68.66 108	76.15 82	59.93 129	65.49 114	65.48 95	74.42 107	56.76 35	69.95 134	45.38 184	85.70 33	91.13 51	34.68 232	74.52 123	76.75 82	82.83 85	69.49 144

E4		68.56 109	73.87 108	62.38 109	64.56 126	60.30 143	74.15 112	40.68 164	77.12 73	65.86 80	83.44 54	91.14 49	52.51 119	71.34 167	64.56 177	76.49 140	69.19 150

USDC		68.53 110	71.82 144	64.68 90	63.53 142	61.87 127	70.12 148	46.98 93	77.89 67	76.58 23	68.55 170	86.88 126	50.50 138	73.73 130	65.62 166	80.39 104	68.21 164

IS_MVSNet		68.20 111	74.41 102	60.96 120	68.55 88	64.36 109	71.47 136	48.33 85	70.11 131	43.30 190	80.90 90	74.54 203	47.19 163	81.25 76	77.97 74	86.94 48	71.76 120

E5new		68.19 112	73.41 116	62.09 111	64.54 127	60.26 144	73.68 120	40.53 167	76.37 77	65.41 81	82.61 67	90.32 72	52.09 122	70.81 174	64.16 181	76.33 142	69.36 146

E5		68.19 112	73.41 116	62.09 111	64.54 127	60.26 144	73.68 120	40.53 167	76.37 77	65.41 81	82.61 67	90.32 72	52.09 122	70.81 174	64.16 181	76.33 142	69.36 146

Baseline_NR-MVSNet		68.15 114	75.12 90	60.02 128	70.81 73	55.67 181	75.88 97	53.40 58	71.25 116	43.96 188	85.88 31	92.68 28	45.76 172	83.47 62	68.34 143	70.34 207	68.58 158

GeoE		68.11 115	72.10 140	63.47 98	67.32 94	62.42 123	78.32 81	43.22 130	64.06 167	55.72 123	73.97 146	84.58 149	55.35 96	76.09 114	70.41 126	80.89 100	73.14 107

casdiffmvs_mvg		68.01 116	74.22 106	60.78 122	63.75 137	62.08 126	70.21 147	42.58 133	72.11 110	58.53 111	84.80 43	88.98 95	49.37 146	73.25 138	67.54 155	80.24 106	70.75 128
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		67.77 117	72.78 125	61.92 115	64.26 129	60.09 149	73.72 119	40.40 172	74.39 91	65.10 84	81.79 81	90.27 74	51.99 124	70.42 181	63.76 189	76.04 144	68.75 156

E3new		67.76 118	72.77 126	61.92 115	64.26 129	60.10 148	73.73 118	40.41 171	74.39 91	65.08 85	81.78 82	90.23 75	51.98 125	70.43 180	63.75 190	76.04 144	68.75 156

Fast-Effi-MVS+		67.71 119	72.54 130	62.07 113	63.83 135	63.68 115	75.74 99	39.94 175	60.89 184	54.29 128	73.00 155	86.19 137	56.85 87	78.46 99	73.23 104	81.74 96	72.36 114

viewmacassd2359aftdt		67.58 120	74.22 106	59.84 131	61.60 155	59.46 152	72.40 128	35.74 211	76.19 80	62.14 96	83.74 51	90.96 56	51.94 126	73.07 139	65.37 171	75.17 157	70.72 129

MVSMamba_PlusPlus		67.34 121	71.15 152	62.89 107	66.08 107	66.04 89	73.24 124	43.69 127	59.94 191	58.73 109	67.34 177	81.03 164	53.68 110	74.26 124	71.91 113	81.93 95	77.53 80

hybridcas		67.20 122	73.79 110	59.52 133	64.79 121	61.42 128	71.75 133	39.04 182	74.57 88	53.34 135	85.47 37	89.06 93	50.57 137	72.84 146	64.11 184	77.64 131	69.05 154

viewcassd2359sk11		67.13 123	71.88 143	61.59 118	64.04 132	59.95 150	73.40 123	40.24 173	72.16 109	64.55 88	80.41 94	89.49 87	51.60 127	69.66 190	63.11 196	75.72 148	68.40 161

thisisatest0515		66.95 124	72.29 134	60.72 124	56.37 195	56.05 179	71.08 137	38.81 187	67.59 150	53.26 137	78.21 113	79.79 175	60.11 68	75.69 117	73.02 106	84.69 65	75.66 91

EPNet		66.87 125	68.89 167	64.53 91	73.97 60	61.13 134	78.46 80	61.03 19	56.78 213	53.41 134	66.91 186	70.91 212	43.49 183	76.08 115	76.68 83	76.81 133	73.73 102
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewdifsd2359ckpt13		66.73 126	72.00 142	60.58 125	62.91 147	61.21 133	72.39 130	39.64 179	68.65 145	60.54 103	78.08 115	88.54 100	52.11 121	71.52 163	65.24 173	75.88 147	70.78 127

E2		66.57 127	71.07 153	61.32 119	63.87 133	59.84 151	73.12 125	40.09 174	70.14 130	64.12 89	79.09 109	88.79 97	51.25 129	68.97 195	62.58 200	75.46 153	68.09 165

sasdasda		66.37 128	74.37 104	57.04 151	65.89 111	65.06 98	62.58 194	42.55 134	76.82 74	46.87 175	67.33 178	86.38 131	45.49 174	76.77 107	71.85 114	78.87 121	76.35 86

canonicalmvs		66.37 128	74.37 104	57.04 151	65.89 111	65.06 98	62.58 194	42.55 134	76.82 74	46.87 175	67.33 178	86.38 131	45.49 174	76.77 107	71.85 114	78.87 121	76.35 86

QAPM		66.36 130	72.76 127	58.90 137	59.57 170	65.01 100	64.05 188	41.17 153	73.09 105	56.82 120	69.42 165	77.78 190	55.07 99	73.00 142	72.07 111	76.71 134	78.96 72

viewmanbaseed2359cas		66.24 131	72.42 132	59.03 135	61.13 158	59.13 155	71.64 134	35.37 212	71.67 113	60.68 101	80.93 89	89.48 88	50.83 133	71.60 162	64.04 186	74.50 163	70.09 135

casdiffmvs		66.19 132	72.34 133	59.02 136	62.75 148	60.61 140	69.06 155	41.38 150	69.49 140	54.11 129	84.00 50	89.74 84	49.12 147	70.74 177	62.70 198	77.70 130	69.14 151
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		65.79 133	72.11 138	58.42 141	51.89 227	58.69 156	73.80 116	34.50 221	65.40 161	57.10 118	79.54 105	89.09 92	57.51 84	71.98 158	67.83 151	75.70 150	72.26 115

IterMVS-LS		65.76 134	70.85 157	59.81 132	65.33 116	57.78 163	64.63 185	48.02 88	65.65 160	51.05 149	81.31 86	77.47 192	54.94 100	69.46 191	69.36 131	74.90 161	74.95 94
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PM-MVS		65.66 135	71.25 151	59.14 134	58.92 181	54.88 196	73.66 122	38.55 190	66.12 157	49.91 160	69.87 163	86.97 123	60.61 66	76.30 112	74.75 94	73.19 173	69.83 138

UGNet		65.61 136	74.79 94	54.91 167	54.54 220	68.20 79	70.97 140	48.21 86	67.14 155	41.67 200	74.15 145	80.65 169	36.10 223	79.39 92	77.99 73	77.95 127	76.01 89
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		65.54 137	71.79 145	58.24 144	59.68 169	65.55 94	70.99 138	38.69 189	62.29 174	49.27 164	75.03 136	81.42 161	50.93 132	73.71 132	71.35 117	79.90 110	73.20 106
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		65.36 138	70.09 161	59.85 130	63.05 146	53.61 202	74.29 109	46.45 99	68.14 147	51.45 143	78.83 111	85.78 142	49.87 144	70.44 179	70.45 124	74.00 166	63.38 189

FA-MVS(training)		65.22 139	69.19 165	60.58 125	60.91 159	57.60 167	71.57 135	38.82 186	65.84 159	61.18 98	75.95 127	74.52 204	51.18 131	73.32 136	67.41 157	79.80 111	69.70 140

onestephybrid01		64.95 140	71.07 153	57.81 149	60.74 162	57.96 161	70.47 144	41.03 156	61.12 183	54.46 126	77.87 118	88.01 107	47.42 160	73.62 133	67.51 156	72.74 176	71.42 124

v148		64.92 141	70.58 160	58.32 142	59.89 167	57.09 170	66.04 177	35.27 213	69.11 141	60.66 102	79.57 104	90.93 59	53.91 108	69.81 189	62.22 202	74.14 164	65.31 180

FC-MVSNet-train		64.87 142	74.76 95	53.33 176	65.24 118	58.05 159	69.69 152	41.92 145	70.99 118	32.62 237	85.75 32	88.23 102	32.10 243	77.61 104	74.41 95	78.43 123	68.25 163

pmmvs6		64.78 143	75.82 86	51.89 184	62.41 150	57.13 169	60.24 204	45.59 107	82.90 35	34.69 222	84.83 41	93.18 21	36.22 222	76.43 111	71.13 120	72.21 183	65.12 181

viewmamba		64.77 144	71.33 149	57.11 150	59.34 175	56.66 173	69.98 149	39.86 176	64.70 163	54.75 125	80.33 95	88.09 104	46.49 168	72.71 149	66.26 165	72.23 182	70.56 131

viewdifsd2359ckpt07		64.71 145	72.11 138	56.08 159	62.59 149	56.76 172	70.41 145	32.26 239	73.93 98	51.19 146	82.32 70	90.96 56	49.92 143	69.24 193	61.27 207	70.10 209	70.27 132

OpenMVS	60.79 16	64.42 146	69.72 162	58.23 145	61.63 154	62.17 124	64.11 187	37.54 202	67.17 154	55.71 124	65.89 195	74.89 202	52.67 117	72.20 154	68.29 145	77.73 129	77.39 82

MGCFI-Net		64.40 147	73.52 113	53.76 174	65.41 115	63.86 114	58.32 215	42.38 137	77.23 72	37.76 210	68.03 172	86.11 139	39.76 203	75.70 116	67.69 154	78.96 120	76.03 88

test1111		64.34 148	71.57 146	55.90 160	67.25 95	60.24 147	66.66 170	51.63 71	73.36 104	34.69 222	75.63 133	80.67 168	39.43 206	78.17 101	71.69 116	75.71 149	61.23 199

DCV-MVSNet		64.34 148	72.84 124	54.42 170	63.79 136	62.09 125	62.50 196	42.72 132	74.32 93	41.34 205	66.96 183	88.57 99	39.18 207	75.20 119	70.35 127	77.01 132	72.37 113

ETV-MVS		64.30 150	64.76 186	63.77 96	68.59 87	62.49 121	77.02 91	45.31 108	49.27 239	50.88 150	56.23 241	59.91 241	57.12 86	80.19 88	74.23 97	83.68 75	71.03 126

viewdifsd2359ckpt11		64.18 151	72.66 128	54.28 173	59.33 176	55.48 187	68.20 159	34.30 224	69.68 137	42.09 196	82.03 75	90.43 71	48.52 150	73.95 127	65.57 167	73.27 171	71.49 122

viewmsd2359difaftdt		64.18 151	72.66 128	54.29 172	59.33 176	55.49 186	68.20 159	34.31 223	69.68 137	42.10 195	82.03 75	90.45 70	48.51 151	73.94 128	65.57 167	73.27 171	71.48 123

ECVR-MVS		63.93 153	71.52 147	55.08 165	66.19 104	61.34 130	63.84 189	51.79 69	70.75 122	34.77 220	74.70 142	81.10 163	38.92 208	79.39 92	73.43 102	75.00 159	59.92 211

Anonymous20231211		63.69 154	72.86 123	53.00 180	63.72 139	60.25 146	60.33 203	40.96 158	72.49 107	38.91 208	81.77 83	88.17 103	37.60 216	73.30 137	68.01 148	76.47 141	66.06 177

diffmvs_AUTHOR		63.60 155	71.07 153	54.90 168	56.75 193	55.35 189	67.91 164	37.08 205	66.87 156	50.84 151	81.64 84	88.73 98	45.49 174	70.02 188	64.15 183	71.31 196	70.70 130

TransMVSNet (Re)		63.49 156	73.86 109	51.39 190	64.26 129	56.07 178	61.17 199	42.23 139	78.81 64	34.80 219	85.94 29	90.63 65	34.35 236	72.73 148	67.98 149	71.50 193	64.84 182

DI_MVS_pp		63.43 157	67.54 174	58.63 138	62.34 151	58.06 158	65.75 181	42.15 140	63.05 171	53.28 136	75.88 130	75.92 198	50.18 140	68.04 198	64.20 180	78.07 126	67.65 167

EIA-MVS		63.24 158	64.16 190	62.16 110	69.30 85	63.20 118	72.40 128	40.82 161	48.31 246	51.50 142	59.63 227	62.23 233	57.33 85	78.00 102	71.94 112	81.59 98	65.82 178

Fast-Effi-MVS+-dtu		63.22 159	65.55 180	60.49 127	61.24 157	64.70 105	74.15 112	53.24 60	51.46 224	49.67 162	58.03 236	78.42 184	48.05 157	72.03 157	71.14 119	76.60 139	63.09 190

IterMVS-SCA-FT		62.67 160	68.00 171	56.45 158	56.92 192	64.92 101	57.51 221	38.12 193	59.44 194	53.62 132	74.74 141	71.60 209	64.84 41	70.24 183	65.27 172	67.70 218	69.83 138

diffmvs		62.64 161	69.66 163	54.46 169	56.19 198	55.06 192	67.36 167	36.74 208	64.18 165	50.58 153	79.54 105	87.55 113	45.13 178	68.04 198	63.20 192	70.78 202	70.02 136
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		62.58 162	67.46 175	56.89 154	59.52 171	55.90 180	64.94 183	38.83 185	57.08 210	56.55 121	76.53 124	84.49 150	47.45 159	66.95 202	62.01 203	74.04 165	69.27 148

MDA-MVSNet-bldmvs		62.46 163	72.13 137	51.19 192	34.32 265	56.10 177	68.65 157	38.85 184	69.05 143	49.50 163	78.17 114	85.43 143	51.32 128	86.67 38	67.40 158	64.46 226	62.08 193

FE-MVSNET2		62.31 164	70.75 159	52.46 181	57.40 191	55.60 183	60.43 202	40.97 157	73.41 102	42.12 194	74.36 144	91.26 48	38.76 210	71.29 168	66.83 161	75.11 158	62.50 192

dtuplus		62.30 165	68.17 170	55.45 163	59.26 178	54.38 197	66.94 169	38.05 196	62.56 172	50.39 156	76.24 126	86.29 133	46.42 169	68.97 195	61.35 206	71.64 189	67.44 168

pm-mvs1		61.97 166	72.01 141	50.25 201	60.64 164	55.23 190	58.67 213	42.44 136	74.40 90	33.63 234	81.03 88	89.86 81	34.87 231	72.93 145	67.95 150	71.28 197	62.65 191

FMVSNet1		61.92 167	71.36 148	50.90 195	57.67 190	59.29 154	59.48 210	44.14 122	70.24 126	34.72 221	75.45 135	84.94 146	36.75 219	72.33 151	68.45 139	72.66 177	68.83 155

viewmambaseed2359dif		61.91 168	67.70 173	55.15 164	58.68 184	54.97 193	66.48 174	38.16 192	61.22 182	49.79 161	75.90 129	85.95 140	46.19 170	67.70 200	60.19 211	71.60 190	67.93 166

hybridnocas07		61.87 169	69.09 166	53.44 175	55.06 216	53.58 203	66.51 171	34.96 214	61.85 177	51.08 148	78.89 110	86.78 128	42.89 186	70.11 185	63.19 193	70.14 208	69.23 149

PVSNet_BlendedMVS		61.75 170	65.07 184	57.87 147	56.27 196	60.99 137	65.81 179	43.75 125	51.27 228	54.08 130	62.12 215	78.84 180	50.67 134	71.49 164	63.91 187	76.64 137	66.86 171

PVSNet_Blended		61.75 170	65.07 184	57.87 147	56.27 196	60.99 137	65.81 179	43.75 125	51.27 228	54.08 130	62.12 215	78.84 180	50.67 134	71.49 164	63.91 187	76.64 137	66.86 171

tttt0517		61.44 172	63.85 192	58.62 139	55.20 211	55.61 182	68.80 156	38.02 197	55.70 215	50.01 159	66.93 185	48.90 252	56.69 88	73.84 129	71.10 121	82.99 83	74.89 95

tfpnnormal		61.41 173	71.33 149	49.83 204	61.73 153	54.90 195	58.52 214	41.24 151	75.20 84	32.00 242	82.13 72	87.87 109	35.63 227	72.75 147	66.30 164	69.87 210	60.14 207

IB-MVS	57.02 17	61.37 174	65.39 181	56.69 155	56.65 194	60.85 139	70.70 142	37.90 199	49.37 238	45.37 185	48.75 253	79.14 177	53.55 111	76.26 113	70.85 123	75.97 146	72.50 112
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
hybrid		61.34 175	68.38 169	53.12 178	54.36 221	53.13 210	66.37 175	34.35 222	61.60 180	50.79 152	77.15 122	86.26 134	43.00 185	69.33 192	62.68 199	69.85 211	68.51 159

CANet_DTU		61.22 176	67.07 177	54.40 171	59.89 167	63.62 116	70.98 139	36.77 207	50.49 231	47.15 171	62.45 212	80.81 166	37.90 215	71.87 159	70.09 129	73.69 167	70.19 134

pmmvs4		61.12 177	64.61 187	57.04 151	60.88 160	52.15 216	70.59 143	44.82 117	61.35 181	46.91 174	72.08 158	73.27 207	46.79 167	65.06 206	67.76 152	72.28 180	60.58 203

thisisatest0530		61.02 178	63.44 199	58.19 146	54.75 218	55.09 191	68.03 163	38.02 197	55.45 216	49.06 165	66.58 189	48.69 253	56.69 88	73.07 139	71.10 121	82.60 88	74.14 98

Vis-MVSNet (Re-imp)		60.99 179	67.78 172	53.06 179	64.66 123	53.49 204	67.40 165	49.52 77	68.55 146	28.00 252	79.53 107	71.41 211	33.08 240	75.30 118	71.28 118	75.69 151	54.91 224

PatchMatch-RL		60.96 180	63.00 205	58.57 140	59.16 179	52.18 215	67.38 166	41.99 143	57.85 204	48.16 166	53.55 249	69.77 216	59.47 77	73.73 130	72.49 110	75.27 156	61.44 195

GA-MVS		60.73 181	64.24 189	56.64 156	59.38 174	57.45 168	65.07 182	36.65 209	57.39 208	58.17 113	73.43 152	69.10 220	47.38 161	64.47 212	63.63 191	73.19 173	64.22 186

CVMVSNet		60.45 182	63.72 195	56.63 157	54.82 217	53.75 198	68.41 158	41.95 144	55.07 217	48.03 167	58.08 235	68.67 221	55.09 98	69.14 194	68.34 143	71.51 192	72.97 111

ET-MVSNet_ETH3D		60.33 183	62.10 210	58.27 143	58.61 185	58.05 159	68.06 161	41.20 152	51.40 225	51.10 147	64.06 204	49.42 251	50.61 136	74.72 121	70.29 128	80.05 109	66.74 173

FC-MVSNet-test		60.28 184	70.83 158	47.96 217	54.69 219	47.12 235	68.06 161	41.68 149	71.42 114	23.73 261	84.70 44	77.41 193	28.92 248	82.33 74	73.08 105	70.68 203	59.77 212

test2506		59.86 185	64.01 191	55.02 166	66.19 104	61.34 130	63.84 189	51.79 69	70.75 122	34.39 227	62.65 211	39.92 273	38.92 208	79.39 92	73.43 102	75.00 159	60.56 204

EU-MVSNet		59.77 186	66.07 178	52.42 182	47.81 238	51.86 218	62.98 193	32.28 238	62.08 175	47.10 172	59.94 225	83.42 157	53.08 114	70.06 187	63.19 193	71.26 199	71.96 118

IterMVS		59.24 187	64.45 188	53.16 177	50.98 229	61.29 132	66.51 171	32.85 233	58.17 200	46.31 180	72.58 157	70.23 214	54.26 105	64.81 209	60.24 210	68.04 217	63.81 188
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		59.15 188	62.28 207	55.49 162	52.42 225	62.59 120	71.76 132	39.74 177	50.25 233	41.92 197	62.88 209	69.16 219	55.85 91	62.77 219	67.18 159	71.27 198	61.11 200

thres600view7		58.87 189	65.91 179	50.66 197	61.27 156	56.32 175	59.88 208	40.63 165	64.88 162	32.10 241	64.82 200	69.83 215	36.72 220	72.99 143	72.55 109	73.34 169	59.97 210

FE-MVSNET		58.15 190	67.14 176	47.65 221	49.53 234	50.47 220	57.09 229	37.15 204	67.85 148	35.64 218	67.57 176	87.16 119	35.36 228	71.23 170	65.57 167	71.17 201	60.12 208

CMPMVS	45.32 18	58.10 191	65.24 183	49.76 205	47.88 237	46.86 238	48.16 257	32.82 234	58.06 201	61.35 97	59.64 226	80.00 172	47.27 162	70.15 184	64.10 185	61.08 231	77.85 79
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view		58.09 192	63.54 198	51.74 186	50.13 231	46.56 239	66.95 168	33.41 230	63.52 169	58.77 108	74.84 139	84.10 153	43.12 184	65.95 205	54.69 236	58.04 238	55.13 223

dtuonlycased		58.01 193	69.36 164	44.76 232	44.25 256	58.11 157	69.81 151	22.59 259	67.34 152	16.28 268	68.81 167	80.61 170	53.94 107	76.65 110	66.59 162	61.71 230	67.42 169

CDS-MVSNet		57.90 194	63.57 197	51.28 191	62.30 152	53.17 209	64.70 184	51.61 72	57.41 207	32.75 236	63.73 205	70.53 213	27.12 251	72.49 150	73.02 106	69.22 214	54.68 225
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		57.89 195	63.71 196	51.09 194	55.89 199	55.60 183	59.23 211	36.08 210	59.64 193	42.40 192	66.53 190	76.89 195	40.76 195	64.76 210	57.84 218	72.19 184	64.74 183

FMVSNet2		57.80 196	65.39 181	48.94 212	55.88 200	57.61 164	57.26 222	42.37 138	58.21 199	33.19 235	68.36 171	75.55 200	34.58 233	66.91 203	64.55 178	70.38 204	66.56 174

blended_shiyan6		57.28 197	63.20 203	50.37 199	55.61 205	53.68 200	57.83 219	34.81 215	61.78 179	41.66 201	66.96 183	78.89 179	39.92 201	62.76 220	56.95 223	72.40 179	61.36 196

blended_shiyan8		57.27 198	63.19 204	50.37 199	55.60 206	53.69 199	57.78 220	34.81 215	61.83 178	41.63 202	67.00 182	78.75 183	39.97 200	62.78 218	56.97 222	72.41 178	61.29 198

thres400		57.25 199	63.73 194	49.70 206	60.19 166	54.95 194	58.16 216	39.60 180	62.42 173	31.98 244	62.33 213	69.20 218	35.96 224	70.07 186	68.03 147	72.28 180	59.12 214

WB-MVS		57.14 200	71.03 156	40.93 243	47.97 236	53.10 211	52.35 244	39.66 178	79.59 59	23.31 262	89.49 14	89.23 90	32.94 241	74.68 122	61.41 205	49.32 254	49.74 238

usedtu_dtu_shiyan1		56.93 201	63.39 200	49.39 208	55.48 207	53.03 212	56.77 230	38.34 191	60.26 186	37.42 212	64.85 199	80.06 171	36.94 218	63.97 214	62.55 201	71.36 195	58.99 215

gm-plane-assit		56.76 202	57.64 224	55.73 161	66.01 109	55.45 188	74.96 102	30.54 244	73.71 99	56.04 122	81.81 79	30.91 276	43.83 180	58.77 237	54.71 235	63.02 228	48.13 245

MIMVSNet1		56.72 203	68.69 168	42.76 239	46.70 245	42.81 246	69.13 153	30.52 245	81.01 50	32.00 242	74.82 140	91.10 53	26.83 253	73.98 126	64.72 176	51.40 250	52.38 233

EPNet_dtu		56.63 204	60.77 216	51.80 185	55.47 208	44.63 240	69.83 150	38.74 188	50.27 232	47.64 168	58.01 237	72.27 208	33.71 238	68.60 197	67.72 153	65.39 222	63.86 187
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wanda-best-256-512		56.57 205	62.27 208	49.92 202	55.08 212	53.36 205	57.15 226	34.52 217	60.20 187	41.40 203	65.86 196	78.11 188	39.54 204	61.94 224	55.90 228	71.82 185	60.53 205

FE-blended-shiyan7		56.57 205	62.27 208	49.92 202	55.08 212	53.36 205	57.15 226	34.52 217	60.20 187	41.40 203	65.86 196	78.11 188	39.54 204	61.94 224	55.90 228	71.82 185	60.53 205

GBi-Net		56.54 207	63.26 201	48.70 214	55.88 200	57.61 164	57.26 222	41.75 146	49.06 240	32.37 238	61.81 217	67.02 224	34.58 233	72.33 151	68.45 139	70.38 204	66.56 174

test1		56.54 207	63.26 201	48.70 214	55.88 200	57.61 164	57.26 222	41.75 146	49.06 240	32.37 238	61.81 217	67.02 224	34.58 233	72.33 151	68.45 139	70.38 204	66.56 174

gg-mvs-nofinetune		56.45 209	61.04 214	51.10 193	63.42 144	49.40 228	53.71 239	52.52 65	74.77 86	46.93 173	77.31 121	53.88 245	26.42 255	62.51 222	57.81 219	63.60 227	51.57 236

thres200		56.35 210	62.36 206	49.34 209	58.87 182	56.32 175	55.91 231	40.63 165	58.51 197	31.34 245	58.81 233	67.31 223	35.96 224	72.99 143	65.51 170	73.34 169	57.07 218

MS-PatchMatch		56.31 211	60.22 219	51.73 187	60.53 165	55.53 185	63.41 191	37.18 203	51.34 227	37.44 211	60.53 223	62.19 234	45.52 173	64.25 213	63.17 195	66.33 219	64.56 184

tfpn200view9		56.07 212	61.85 211	49.34 209	58.57 186	56.48 174	58.01 218	40.72 163	53.23 218	31.01 246	56.41 239	66.40 229	34.18 237	73.02 141	68.06 146	73.53 168	59.35 213

usedtu_dtu_shiyan2		55.91 213	63.79 193	46.72 227	59.06 180	49.07 229	60.88 201	43.83 124	79.17 63	29.73 249	66.13 193	86.96 124	27.91 249	62.70 221	57.21 220	58.91 234	45.28 251

FMVSNet3		54.77 214	60.73 217	47.81 218	54.29 222	56.88 171	55.89 232	41.75 146	49.06 240	32.37 238	61.81 217	67.02 224	33.67 239	62.88 217	61.96 204	68.88 215	65.53 179

thres100view900		53.88 215	59.19 220	47.68 220	58.57 186	52.74 214	54.45 236	38.07 195	53.23 218	31.01 246	56.41 239	66.40 229	32.80 242	65.03 208	64.43 179	71.18 200	56.10 221

CR-MVSNet		53.82 216	55.40 228	51.98 183	51.57 228	50.23 222	45.00 260	44.97 113	46.90 248	52.60 139	67.91 173	46.99 266	48.37 152	59.15 235	59.53 214	69.38 213	57.07 218

baseline2		53.55 217	55.19 229	51.62 188	55.27 210	51.95 217	60.89 200	34.23 225	46.69 250	42.47 191	53.56 248	50.01 248	45.33 177	64.63 211	61.22 208	71.56 191	58.28 217

test20.03		53.49 218	60.95 215	44.78 231	64.73 122	47.25 234	61.58 198	43.30 128	65.86 158	22.85 263	66.87 188	79.85 173	22.99 257	62.38 223	56.95 223	53.25 247	47.46 247

baseline		53.46 219	61.55 212	44.01 234	45.83 250	48.77 230	57.26 222	28.75 251	49.99 234	38.85 209	68.78 168	75.65 199	38.30 212	60.80 228	59.78 213	55.10 245	67.07 170

MVSTER		53.08 220	56.39 226	49.21 211	47.19 241	51.08 219	60.14 206	31.74 241	40.63 260	38.97 207	55.78 242	46.74 267	42.47 189	67.29 201	62.99 197	74.73 162	70.23 133

CHOSEN 1792x2688		52.99 221	56.91 225	48.42 216	47.32 239	50.10 224	64.18 186	33.85 227	45.46 253	36.95 214	55.20 245	66.49 228	51.20 130	59.28 233	59.81 212	57.01 240	61.99 194

baseline1		52.90 222	58.38 221	46.51 228	58.87 182	50.01 225	54.17 237	40.45 170	56.81 212	29.25 250	62.72 210	58.99 242	30.25 246	65.05 207	60.57 209	66.07 220	54.54 226

CostFormer		52.59 223	55.14 230	49.61 207	52.72 223	50.40 221	66.28 176	33.78 228	52.85 220	43.43 189	66.30 191	51.37 247	41.78 192	54.92 247	51.18 243	59.68 233	58.98 216

SCA		52.47 224	53.97 236	50.71 196	46.95 244	57.79 162	60.18 205	46.89 96	51.92 223	46.71 179	60.73 221	49.97 249	47.69 158	56.39 244	52.98 240	55.82 242	48.03 246

testgi		51.94 225	61.37 213	40.94 242	58.38 188	47.03 236	65.88 178	30.49 246	70.87 121	22.64 264	57.53 238	87.59 112	18.30 264	63.01 216	54.32 237	49.93 253	49.27 240

FE-MVSNET3		51.86 226	54.48 233	48.80 213	55.08 212	53.36 205	57.15 226	34.52 217	60.20 187	34.24 228	41.26 263	47.37 259	40.03 197	61.94 224	55.90 228	71.82 185	61.36 196

usedtu_blend_shiyan5		51.33 227	54.39 234	47.77 219	55.08 212	53.36 205	50.91 250	34.52 217	60.20 187	34.24 228	41.26 263	47.37 259	40.03 197	61.94 224	55.90 228	71.82 185	60.71 201

dmvs_re		51.01 228	54.88 232	46.49 229	58.06 189	44.35 243	60.08 207	37.67 200	42.11 257	28.68 251	45.12 259	66.70 227	31.90 245	66.62 204	59.18 216	62.59 229	60.11 209

tpm cat1		50.98 229	51.28 242	50.62 198	55.74 203	49.92 226	63.13 192	38.12 193	52.38 222	47.61 169	60.11 224	44.51 270	44.86 179	51.31 256	47.49 253	54.25 246	53.24 230

RPMNet		50.92 230	50.32 245	51.62 188	50.25 230	50.23 222	59.16 212	46.70 97	46.90 248	42.39 193	48.97 252	37.23 274	41.78 192	57.30 242	56.18 226	69.44 212	55.43 222

pmmvs5		50.64 231	58.01 222	42.05 240	47.01 243	43.67 244	49.27 255	29.43 250	50.77 230	33.83 233	68.69 169	76.16 197	27.82 250	57.53 241	57.07 221	64.95 224	52.18 234

PatchT		50.55 232	53.55 238	47.05 225	37.59 263	42.26 247	50.55 252	37.56 201	46.37 251	52.60 139	66.91 186	43.54 272	48.37 152	59.15 235	59.53 214	55.62 243	57.07 218

Anonymous20231206		50.28 233	57.94 223	41.35 241	55.45 209	43.65 245	58.06 217	34.12 226	62.02 176	24.25 260	59.33 228	79.80 174	24.49 256	59.55 230	54.28 238	51.74 249	46.94 249

dps		49.71 234	51.97 240	47.07 224	52.37 226	47.00 237	53.02 242	40.52 169	44.91 254	41.23 206	64.55 201	44.27 271	40.12 196	57.71 240	51.97 242	55.14 244	53.41 229

MDTV_nov1_ep13		49.60 235	51.57 241	47.31 222	46.28 247	44.61 241	59.82 209	30.96 242	48.80 245	50.20 157	59.26 230	52.38 246	38.56 211	56.20 245	49.70 247	58.04 238	50.01 237

PatchmatchNet		48.67 236	50.10 246	46.99 226	48.29 235	41.00 248	55.54 233	38.94 183	51.38 226	45.15 186	63.22 207	48.45 255	42.83 187	53.80 252	48.50 252	51.19 252	44.37 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
blend_shiyan4		47.96 237	50.55 244	44.94 230	43.47 257	52.81 213	47.71 259	32.72 235	36.60 266	34.24 228	41.26 263	47.37 259	40.03 197	59.51 231	55.57 232	71.48 194	60.71 201

new-patchmatchnet		47.33 238	60.49 218	31.99 260	55.69 204	33.86 261	36.84 270	33.31 231	72.36 108	14.33 270	80.09 100	92.14 36	13.27 266	63.54 215	40.09 261	38.51 264	41.32 260

dtuonly		47.04 239	55.08 231	37.67 251	36.58 264	38.07 253	54.74 235	20.55 262	48.83 244	27.31 253	62.23 214	67.56 222	39.92 201	60.00 229	55.14 234	46.90 256	53.71 228

0.4-1-1-0.1		46.90 240	49.04 252	44.40 233	47.28 240	49.55 227	52.48 243	30.44 247	40.85 259	34.58 224	47.16 255	48.13 256	35.64 226	52.90 253	50.70 246	65.97 221	53.98 227

tpm		46.67 241	49.20 251	43.72 235	49.60 232	36.60 258	53.93 238	26.84 253	52.70 221	58.05 115	69.04 166	47.96 257	30.06 247	48.33 261	42.76 256	43.88 258	47.01 248

pmmvs3		46.64 242	54.13 235	37.90 250	31.23 269	40.68 249	49.83 254	15.34 266	46.31 252	36.34 216	53.15 250	74.40 205	36.36 221	58.43 238	56.64 225	58.32 237	49.29 239

TAMVS		46.64 242	53.62 237	38.49 248	49.56 233	36.87 255	53.16 241	25.76 255	56.33 214	22.55 265	60.72 222	61.80 236	27.12 251	59.50 232	58.33 217	52.79 248	41.82 259

test-LLR		46.01 244	45.06 263	47.11 223	59.39 172	36.72 256	51.28 247	40.95 159	36.41 267	34.45 225	46.14 256	47.02 264	38.00 213	51.78 254	48.53 250	58.60 235	48.84 242

MIMVSNet		45.83 245	53.46 239	36.94 252	45.38 254	39.50 251	52.20 245	30.68 243	57.09 209	24.53 259	55.22 244	71.54 210	21.74 260	55.81 246	51.08 244	47.11 255	43.96 253

0.3-1-1-0.015		45.78 246	47.55 255	43.71 236	46.29 246	48.64 232	51.52 246	29.70 248	39.03 264	34.24 228	44.15 261	47.37 259	35.28 229	51.31 256	49.52 248	65.23 223	52.80 231

pmnet_mix02		45.67 247	55.99 227	33.63 259	45.77 251	31.22 265	42.04 265	27.60 252	64.14 166	24.89 256	75.50 134	82.30 159	21.88 259	54.53 250	41.22 258	39.62 262	43.05 255

0.4-1-1-0.2		45.53 248	47.38 256	43.38 238	46.00 249	48.29 233	50.94 249	29.49 249	38.16 265	34.06 232	45.06 260	47.50 258	34.99 230	51.04 258	49.00 249	64.81 225	52.58 232

test0.0.03 1		45.40 249	49.55 249	40.57 245	59.39 172	44.36 242	53.37 240	40.95 159	47.14 247	19.23 266	45.49 258	60.24 239	19.24 262	54.82 248	51.98 241	51.21 251	42.82 256

PMMVS		45.37 250	49.29 250	40.79 244	27.75 270	35.07 260	50.88 251	19.88 263	39.27 262	35.78 217	50.11 251	61.29 237	42.04 190	54.13 251	55.95 227	68.43 216	49.19 241

MVS-HIRNet		44.56 251	45.52 261	43.44 237	40.98 259	31.03 266	39.52 269	36.96 206	42.80 256	44.37 187	53.80 247	60.04 240	41.85 191	47.97 263	41.08 259	56.99 241	41.95 258

test-mter		44.18 252	47.60 254	40.18 246	33.20 266	39.03 252	55.28 234	13.91 268	39.07 263	36.63 215	48.09 254	49.52 250	41.12 194	54.55 249	50.91 245	60.97 232	52.03 235

EMVS		43.85 253	49.91 247	36.77 254	45.46 253	32.70 262	44.09 262	25.33 256	57.88 203	26.62 254	58.99 232	61.14 238	42.77 188	70.26 182	38.52 265	36.38 266	29.87 266

E-PMN		43.83 254	49.81 248	36.84 253	46.09 248	31.86 264	42.77 264	25.85 254	57.76 205	25.53 255	55.50 243	62.47 232	43.77 181	70.78 176	39.51 262	37.04 265	30.79 265

tpmrst		43.31 255	46.14 259	40.02 247	47.05 242	36.48 259	48.01 258	32.17 240	49.50 237	37.26 213	63.66 206	47.04 263	31.98 244	42.00 267	40.55 260	43.64 259	43.75 254

TESTMET0.1,1		41.79 256	45.06 263	37.97 249	31.32 268	36.72 256	51.28 247	14.17 267	36.41 267	34.45 225	46.14 256	47.02 264	38.00 213	51.78 254	48.53 250	58.60 235	48.84 242

ADS-MVSNet		40.61 257	46.31 257	33.96 257	40.70 260	30.42 267	40.42 267	33.44 229	58.01 202	30.87 248	63.05 208	54.48 244	22.67 258	44.35 266	39.23 264	35.64 267	34.64 263

CHOSEN 280x420		40.24 258	44.14 266	35.69 255	32.36 267	23.58 270	50.30 253	21.21 261	40.94 258	18.84 267	32.75 268	48.65 254	48.13 156	59.16 234	55.31 233	43.28 260	48.62 244

EPMVS		40.11 259	44.96 265	34.44 256	41.55 258	32.65 263	41.74 266	32.39 237	49.89 236	24.83 257	64.44 202	46.38 268	26.57 254	44.75 265	39.47 263	39.59 263	37.16 262

FMVSNet5		39.83 260	45.08 262	33.71 258	39.24 261	39.56 250	48.77 256	23.55 258	39.45 261	24.55 258	33.73 267	44.57 269	20.97 261	58.27 239	54.23 239	45.16 257	45.77 250

N_pmnet		39.50 261	51.01 243	26.09 262	44.48 255	25.59 269	40.20 268	21.49 260	64.20 164	7.98 274	73.86 148	76.67 196	13.66 265	50.17 259	36.69 267	28.71 269	29.86 267

MVE	28.01 19	35.86 262	43.56 267	26.88 261	22.33 272	19.75 272	30.85 273	23.88 257	49.90 235	10.48 272	43.64 262	61.87 235	48.99 149	47.26 264	42.15 257	24.76 270	40.37 261
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet		35.76 263	45.64 260	24.22 263	38.59 262	25.83 268	31.87 272	19.24 264	49.06 240	9.01 273	54.34 246	64.73 231	12.46 267	49.21 260	44.91 254	34.17 268	31.41 264

PMMVS2		34.11 264	48.55 253	17.26 264	25.45 271	20.72 271	35.08 271	16.26 265	58.71 196	4.16 276	59.22 231	78.40 185	3.65 269	57.24 243	38.31 266	18.94 272	27.28 268

GG-mvs-BLEND		31.54 265	46.27 258	14.37 265	0.07 277	48.65 231	42.97 263	0.08 274	44.04 255	1.21 278	39.77 266	57.94 243	0.15 273	48.19 262	42.82 255	41.70 261	42.46 257

test_method		13.28 266	15.83 268	10.30 266	1.05 274	2.18 275	15.40 274	2.23 270	22.43 269	13.84 271	22.00 270	33.14 275	9.78 268	17.80 269	9.93 269	19.50 271	3.31 270

test123		0.53 267	0.60 270	0.46 268	0.22 275	0.25 276	0.33 279	0.13 273	0.66 272	1.37 277	1.10 272	0.00 280	0.43 271	0.68 271	0.61 270	0.26 275	0.88 271

testmvs		0.47 268	0.69 269	0.21 269	0.17 276	0.17 277	0.35 278	0.16 272	0.66 272	0.18 279	1.05 273	0.99 279	0.27 272	0.62 272	0.54 271	0.15 276	0.77 272

uanet_test		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 280	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

sosnet-low-res		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 280	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

sosnet		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 280	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

TestfortrainingZip							87.82 38	53.22 61		58.49 112						84.46 66

TPM-MVS					72.72 65	70.92 74	80.38 74			66.22 78	58.35 234	78.23 186	48.26 154			83.40 80	81.74 58
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												83.64 155

SR-MVS					81.31 9			62.63 9				91.11 52

Anonymous202405211			72.22 136		66.19 104	61.09 136	62.23 197	45.87 106	71.25 116		79.33 108	86.16 138	37.36 217	73.54 135	69.84 130	75.45 154	64.32 185

our_test_3					52.72 223	53.66 201	69.11 154

ambc			79.96 63		74.57 57	75.48 51	73.75 117		80.32 55	72.34 38	78.46 112	92.41 30	59.05 78	80.24 87	73.95 99	75.41 155	78.85 73

MTAPA										80.26 8		90.53 69

MTMP										82.07 4		91.00 55

Patchmatch-RL test							2.05 277

tmp_tt				7.47 267	8.89 273	3.32 274	4.35 276	1.14 271	15.58 271	15.76 269	8.50 271	5.90 278	2.00 270	20.02 268	21.51 268	12.70 273

XVS					80.47 20	81.29 12	93.33 3			77.45 19		90.19 77				91.52 11

X-MVStestdata					80.47 20	81.29 12	93.33 3			77.45 19		90.19 77				91.52 11

mPP-MVS					82.97 2							92.12 37

NP-MVS									71.39 115

Patchmtry						37.73 254	45.00 260	44.97 113		52.60 139

DeepMVS_CX						8.52 273	9.75 275	3.19 269	16.70 270	5.02 275	23.06 269	19.33 277	18.69 263	13.75 270		11.34 274	25.07 269

High-resolution multi-view benchmark