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 56	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 53	82.76 4	90.39 19	54.21 56	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 9	79.66 29	92.67 6	64.48 1	83.13 33	82.32 3	80.89 92	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 14	81.92 8	92.57 8	63.14 5	84.35 21	79.45 11	83.37 56	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 5	80.60 24	92.68 5	63.48 3	81.43 47	80.21 9	81.95 78	90.76 63	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 26	82.01 7	92.29 10	60.35 25	82.20 41	78.32 15	80.59 93	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 27	79.95 5	80.69 18	81.09 15	92.40 9	63.06 6	79.38 62	80.21 9	80.31 97	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 35	81.03 16	89.78 23	61.16 17	80.15 57	80.44 6	84.83 42	94.19 14	70.52 17	90.70 11	87.19 22	91.71 8	87.37 32

ACMM	71.24 7	82.85 9	86.59 12	78.50 9	80.10 27	78.59 34	91.77 11	60.76 22	84.43 19	76.49 24	81.58 86	93.50 17	70.45 18	91.38 7	89.42 8	91.42 16	87.22 34
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 28	80.96 18	91.13 14	63.30 4	84.04 23	71.81 39	82.39 70	89.59 86	69.16 24	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 36	84.34 1	90.30 21	63.54 2	83.10 34	71.30 44	86.91 25	90.54 69	67.12 32	87.81 34	87.05 24	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 35	79.19 6	80.73 17	80.86 21	91.68 12	62.59 10	82.55 38	75.53 28	73.88 149	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 29	81.00 17	89.27 27	58.63 32	81.35 49	75.46 29	82.97 63	95.08 12	68.90 25	90.49 14	87.43 21	91.48 14	86.84 36
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SteuartSystems-ACMMP		82.16 14	85.55 29	78.21 10	80.48 20	79.28 30	92.65 7	61.03 19	80.55 55	77.00 22	81.80 81	90.71 64	68.73 26	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 22	77.74 15	80.13 25	80.25 26	91.01 15	60.61 23	85.54 13	78.61 14	83.21 59	86.96 125	65.95 37	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 23	80.66 22	89.48 25	56.93 36	82.50 39	67.55 68	87.05 23	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	63.57 143	70.27 78	86.48 45	62.99 7	89.00 5	80.32 7	86.25 29	91.04 55	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 7	78.96 32	88.98 31	56.44 41	89.29 3	72.39 37	93.25 1	93.86 16	63.42 52	85.46 45	81.36 58	86.96 47	94.00 1

PMVS	70.37 8	81.82 19	87.08 6	75.68 24	77.06 45	77.23 42	87.77 41	56.25 44	83.33 32	67.18 75	89.48 15	87.94 109	77.70 1	93.02 2	92.57 2	88.13 37	86.00 41
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMMP_NAP		81.79 20	85.72 25	77.21 16	79.15 33	79.68 28	91.62 13	59.66 27	83.55 29	77.74 18	83.72 53	87.34 117	65.36 38	88.61 28	87.56 20	89.73 26	89.58 6

X-MVS		81.61 21	84.73 38	77.97 11	80.31 24	81.29 12	93.53 2	62.50 11	81.41 48	77.45 19	72.04 161	90.19 78	62.50 59	90.57 13	88.87 12	91.54 10	88.73 15

OPM-MVS		81.44 22	85.68 26	76.49 20	79.27 30	78.21 37	89.84 22	58.67 31	85.25 14	76.26 25	85.28 39	92.88 26	66.03 36	87.20 37	85.40 28	88.86 31	85.58 45
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 16	83.22 3	90.55 16	55.12 51	80.87 52	67.62 67	88.01 17	92.38 31	70.61 16	86.64 39	83.10 43	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 41	82.29 5	84.41 52	62.95 8	82.99 35	64.03 92	87.71 19	89.17 92	71.98 11	88.19 30	88.10 16	86.18 57	89.95 4

APDe-MVS		81.08 25	86.12 18	75.20 26	79.25 31	80.91 19	90.38 20	57.05 35	85.83 10	66.07 80	87.34 22	91.27 48	69.45 20	85.99 43	82.55 45	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 31	76.15 21	78.58 37	80.64 23	89.77 24	57.92 33	81.66 46	73.45 32	86.84 26	89.80 84	69.33 22	85.40 46	82.91 44	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
RA-MVS		80.68 27	85.96 21	74.52 29	83.99 2	77.17 43	86.26 47	59.23 28	85.80 11	61.01 100	87.83 18	91.29 47	70.08 19	84.99 53	87.15 23	86.54 51	87.48 29

APD-MVS		80.60 28	84.63 39	75.91 23	81.22 12	81.48 10	90.49 17	58.81 30	77.54 71	67.49 70	85.90 31	89.82 83	69.43 21	86.08 42	83.80 38	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 29	82.57 52	77.96 13	81.99 8	72.76 64	90.48 18	61.31 14	80.85 53	77.90 17	81.93 79	87.01 122	68.20 28	84.15 59	85.27 30	87.85 40	86.00 41

DVP-MVS		80.31 30	85.60 28	74.15 34	76.23 49	78.39 35	86.62 43	55.79 47	86.47 9	71.32 43	90.96 7	89.02 95	69.27 23	84.62 57	81.64 55	85.66 62	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 31	86.16 16	73.14 42	73.82 63	76.41 46	83.59 56	54.82 54	87.35 6	70.86 48	86.98 24	96.27 4	66.50 33	89.17 24	83.39 40	89.26 28	83.56 51

OMC-MVS		79.95 32	85.28 30	73.74 38	72.95 66	80.10 27	87.87 38	48.13 88	84.62 18	79.42 12	80.27 98	92.49 29	64.14 46	87.25 36	85.11 31	89.92 24	87.10 35

MED-MVS		79.80 33	84.79 37	73.98 35	78.58 37	78.68 33	87.97 36	57.27 34	85.66 12	64.22 90	85.63 36	90.11 81	67.50 30	84.90 54	81.13 60	88.32 35	87.45 30

SED-MVS		79.70 34	85.16 32	73.34 40	75.83 52	78.11 38	88.77 33	56.45 40	84.85 16	69.45 59	90.70 9	88.38 102	63.16 54	85.12 51	81.28 59	86.40 54	87.63 26

MSP-MVS		79.65 35	84.28 43	74.25 32	78.92 34	81.86 9	89.07 28	60.49 24	83.85 26	70.05 54	85.12 40	90.92 61	62.99 56	81.15 79	81.64 55	83.99 72	85.42 47
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 36	84.05 44	74.17 33	74.14 60	80.88 20	89.33 26	56.24 45	82.41 40	71.58 41	82.27 72	86.47 130	66.47 34	84.80 55	84.16 36	87.26 45	87.34 33

LS3D		79.33 37	84.03 45	73.84 36	75.37 54	78.09 39	83.30 58	52.94 65	84.42 20	76.01 26	84.16 48	87.44 116	65.34 39	86.30 40	82.08 52	90.09 22	85.70 43

aaEdge-Enhanced		78.93 38	84.62 40	72.30 46	77.98 40	79.20 31	87.82 39	53.22 62	83.71 28	62.73 94	85.63 36	91.37 46	67.58 29	84.40 58	80.56 61	84.46 67	87.45 30

3Dnovator+	72.94 4	78.78 39	83.05 49	73.80 37	70.70 79	81.34 11	88.33 34	56.01 46	81.33 50	72.87 36	78.06 117	81.15 163	63.83 49	87.39 35	85.82 26	91.06 18	86.28 40

UA-Net		78.65 40	83.96 46	72.46 44	84.87 1	76.15 47	89.06 29	55.70 48	77.25 72	53.14 140	79.73 103	82.09 161	59.69 75	92.21 5	90.93 3	92.32 3	89.36 7

DeepC-MVS_fast	71.40 6	78.48 41	82.92 50	73.31 41	76.44 48	82.23 6	87.59 42	56.56 39	77.79 69	68.91 64	77.00 124	87.32 118	61.90 61	85.40 46	84.37 33	88.46 33	86.33 39
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 42	83.47 48	72.41 45	76.04 51	75.72 50	83.86 54	51.81 69	84.00 25	70.65 51	81.27 88	92.22 33	64.64 44	83.28 69	80.28 63	87.44 44	87.49 28

WR-MVS		78.32 43	86.09 20	69.25 63	76.22 50	72.33 71	85.71 49	59.02 29	86.66 7	51.41 146	92.91 2	96.76 1	53.09 114	90.21 18	85.30 29	90.05 23	78.46 79

ACMH	66.19 11	78.12 44	84.55 41	70.63 54	69.62 86	72.40 70	80.77 73	46.43 101	89.24 4	77.99 16	87.42 21	95.83 9	62.95 57	86.27 41	78.24 73	86.00 60	82.46 53
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
train_agg		77.83 45	80.47 62	74.77 28	80.92 15	69.60 79	88.87 32	56.32 43	74.03 98	71.03 46	83.67 54	87.68 112	64.75 43	83.70 61	81.85 54	86.71 49	82.73 52

NCCC		77.82 46	80.72 61	74.43 31	79.24 32	75.72 50	88.06 35	56.36 42	79.61 59	73.22 34	67.75 176	87.05 121	63.09 55	85.62 44	84.00 37	86.62 50	85.30 48

CNVR-MVS		77.79 47	81.57 56	73.38 39	78.37 39	75.91 48	87.97 36	55.11 52	79.41 61	70.98 47	74.70 143	86.43 131	61.77 62	85.10 52	83.73 39	86.10 59	85.68 44

WR-MVS_H		77.56 48	85.88 23	67.86 67	80.54 19	74.32 59	83.23 59	61.78 12	83.47 30	47.46 172	91.81 6	95.84 8	50.50 139	90.44 16	84.37 33	83.63 77	80.89 66

RPSCF		77.56 48	84.51 42	69.46 62	65.17 121	74.36 58	79.74 80	47.45 91	84.01 24	72.89 35	77.89 118	90.67 65	65.14 41	88.25 29	89.74 7	86.38 55	86.64 38

PS-CasMVS		77.46 50	85.80 24	67.73 69	81.24 11	72.88 63	80.63 74	61.28 15	84.14 22	50.53 156	92.13 4	96.76 1	50.12 142	91.02 9	84.46 32	82.60 89	79.19 72

DTE-MVSNet		77.28 51	84.87 36	68.42 65	82.94 4	72.70 66	81.60 68	61.78 12	85.03 15	51.40 147	92.11 5	96.00 6	49.42 146	89.73 22	82.52 47	83.39 82	75.98 92

SixPastTwentyTwo		77.24 52	83.65 47	69.78 59	65.14 122	64.85 105	77.44 90	47.74 90	82.76 37	68.52 65	87.65 20	93.31 19	71.68 13	89.49 23	82.41 48	88.14 36	85.05 49

CDPH-MVS		77.22 53	81.05 60	72.75 43	77.29 43	77.46 41	86.36 46	54.02 58	73.00 107	69.75 57	77.78 120	88.90 97	61.31 66	84.09 60	82.54 46	87.79 41	83.57 50

PEN-MVS		77.06 54	85.05 33	67.74 68	82.29 6	72.59 67	80.86 72	61.03 19	84.66 17	50.08 160	92.19 3	96.59 3	49.12 148	89.83 21	82.35 49	83.06 83	77.14 85

CP-MVSNet		77.01 55	85.04 34	67.65 70	81.16 13	72.72 65	80.54 75	61.18 16	82.09 42	50.41 157	90.81 8	95.89 7	50.03 143	90.86 10	84.30 35	82.56 91	78.65 78

CSCG		76.95 56	82.08 54	70.97 50	73.32 65	78.35 36	81.08 71	47.19 92	83.47 30	69.82 56	80.44 94	87.19 119	64.59 45	81.01 82	77.26 80	89.83 25	86.84 36

CNLPA		76.67 57	81.72 55	70.77 53	70.75 77	76.68 45	86.14 48	46.11 104	81.82 44	74.68 30	76.37 126	86.23 136	62.92 58	85.28 49	83.29 41	84.02 71	82.40 54

MSLP-MVS++		76.66 58	82.32 53	70.06 56	70.51 80	80.27 25	79.77 79	55.58 49	77.79 69	63.09 93	67.25 183	89.50 87	71.01 14	88.10 31	85.74 27	80.39 106	87.56 27

TSAR-MVS + COLMAP		75.85 59	81.06 58	69.77 60	71.15 73	76.90 44	82.93 61	52.43 67	79.25 63	70.13 52	82.78 64	87.00 123	60.02 71	80.30 86	79.61 67	81.95 96	81.61 62

HQP-MVS		75.81 60	78.91 69	72.18 47	77.41 42	75.38 53	84.75 50	53.35 60	76.12 82	73.32 33	69.48 166	88.07 106	57.76 83	79.42 92	78.44 70	86.48 52	85.50 46

PLC	64.88 15	75.76 61	80.22 63	70.57 55	70.46 81	77.75 40	82.01 66	48.84 82	80.74 54	70.85 49	71.32 163	84.82 148	63.69 50	84.73 56	82.35 49	87.54 42	79.80 69
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 62	79.24 67	70.86 51	67.63 94	74.09 60	83.17 60	44.75 120	81.82 44	80.83 5	65.61 200	88.04 107	61.58 63	83.21 70	80.12 64	87.17 46	81.82 58
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PHI-MVS		75.17 63	78.37 70	71.43 48	71.13 74	72.46 69	82.28 65	50.55 75	73.39 104	79.05 13	73.65 151	87.50 115	61.98 60	81.10 80	78.48 69	83.60 78	81.99 56

MGCNet		74.91 64	79.25 66	69.85 58	75.01 57	74.95 55	86.61 44	48.67 83	68.87 145	67.51 69	80.13 100	83.78 155	55.77 93	83.37 67	81.89 53	85.55 64	81.75 59

anonymousdsp		74.76 65	82.59 51	65.63 87	45.61 254	61.13 136	89.06 29	32.58 237	74.11 97	59.55 108	84.06 49	94.12 15	75.24 3	88.94 26	86.95 25	91.74 7	88.81 14

AdaColmap		74.73 66	77.57 75	71.40 49	76.90 46	75.76 49	84.54 51	53.08 64	76.20 80	66.64 78	66.06 196	78.16 189	61.32 65	85.37 48	82.20 51	85.95 61	79.27 71

v7n		74.47 67	81.06 58	66.77 76	66.98 100	67.10 82	76.76 94	45.88 106	81.98 43	67.43 71	88.38 16	95.67 10	61.38 64	80.76 84	73.49 102	82.21 94	80.06 67

PCF-MVS	65.25 14	73.99 68	76.74 80	70.79 52	71.61 72	75.33 54	83.76 55	50.40 76	74.88 86	74.50 31	67.60 177	85.36 145	58.30 81	78.61 98	74.25 97	86.15 58	81.13 65
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MCST-MVS		73.84 69	77.44 76	69.63 61	73.75 64	74.73 57	81.38 70	48.58 84	74.77 87	69.16 61	71.97 162	86.20 137	59.50 76	78.51 99	74.06 99	85.42 65	81.85 57

TSAR-MVS + GP.		73.42 70	76.31 81	70.05 57	77.15 44	71.13 74	81.59 69	54.11 57	69.84 137	58.65 112	66.20 194	78.77 183	65.29 40	83.65 62	83.14 42	83.54 79	81.47 63

Gipuma		73.40 71	79.27 65	66.55 81	63.64 142	59.35 155	70.28 148	45.92 105	83.79 27	71.78 40	84.04 50	93.07 23	68.69 27	87.90 33	76.76 82	78.98 121	69.96 139
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVS_111021_HR		72.37 72	76.12 84	68.00 66	68.55 90	64.30 113	82.93 61	48.98 81	74.25 95	65.39 84	73.59 152	84.11 153	59.48 77	82.61 73	78.38 71	82.66 88	75.59 94

TinyColmap		71.85 73	76.11 85	66.87 75	66.07 110	65.34 99	74.35 110	49.30 80	79.93 58	75.93 27	75.66 133	87.74 111	54.72 102	80.66 85	70.42 126	80.85 104	73.02 112

UniMVSNet_ETH3D		71.84 74	81.36 57	60.74 124	76.46 47	66.01 93	66.49 175	60.24 26	86.58 8	41.87 200	90.04 11	96.02 5	43.72 184	85.14 50	77.30 79	75.64 154	68.40 163

TranMVSNet+NR-MVSNet		71.66 75	79.23 68	62.83 109	72.54 69	65.64 95	74.77 108	55.27 50	75.91 84	45.50 185	89.55 13	94.25 13	45.96 173	82.74 72	77.03 81	82.96 85	69.48 147

MVS_111021_LR		71.60 76	75.21 90	67.38 71	67.42 95	62.44 124	81.73 67	46.24 102	70.89 121	66.80 77	73.19 155	84.98 146	60.09 70	81.94 76	77.77 77	82.00 95	75.29 95

EG-PatchMatch MVS		71.50 77	76.82 79	65.30 88	70.74 78	66.50 88	74.23 112	43.25 130	72.02 112	59.11 109	79.85 102	86.88 127	63.95 48	80.29 87	75.25 93	80.51 105	76.98 86

DPM-MVS		71.35 78	73.50 115	68.84 64	74.93 58	73.35 61	84.07 53	50.56 74	71.91 113	67.06 76	61.21 223	77.02 196	52.64 119	74.15 126	75.14 94	83.79 75	81.74 60

UniMVSNet (Re)		71.29 79	78.14 71	63.30 100	70.29 82	66.57 85	75.98 98	54.74 55	70.20 129	46.20 183	85.08 41	93.21 20	48.19 157	82.50 74	78.33 72	84.40 69	71.08 127

CLD-MVS		71.24 80	78.12 72	63.20 102	74.03 61	71.60 72	82.82 63	32.91 233	74.23 96	69.32 60	79.65 104	91.54 41	47.02 166	81.22 78	79.01 68	73.09 177	69.63 143
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 81	77.09 78	64.20 94	69.71 85	66.51 87	76.15 97	46.24 102	75.99 83	57.72 118	86.62 28	91.14 50	54.30 104	76.97 106	68.87 134	81.07 101	73.52 106

CANet		71.07 82	75.09 92	66.39 82	72.57 68	71.53 73	82.38 64	47.10 93	59.81 194	59.81 107	74.97 138	84.37 152	54.25 107	79.89 90	77.64 78	82.25 93	77.40 83

v1192		71.06 83	74.87 94	66.61 78	66.38 104	65.80 94	78.27 84	45.28 111	70.19 130	70.79 50	83.37 56	91.79 39	58.76 80	70.86 174	69.02 133	80.16 110	73.08 110

DU-MVS		71.03 84	77.92 73	62.98 105	70.81 75	65.48 97	73.93 116	56.76 37	69.95 135	46.77 179	85.70 34	93.49 18	46.91 167	83.47 63	77.82 76	82.72 87	69.54 144

v1240		70.94 85	74.52 98	66.76 77	66.54 103	64.40 109	77.76 87	45.29 110	70.05 133	71.45 42	83.36 58	90.96 57	60.37 68	70.50 179	68.68 136	79.14 118	73.68 105

v1921920		70.82 86	74.46 102	66.58 80	66.33 105	64.35 112	77.72 88	45.07 113	70.39 126	71.18 45	83.15 60	90.62 67	59.97 72	70.90 172	68.43 143	79.19 117	73.39 107

UniMVSNet_NR-MVSNet		70.82 86	77.44 76	63.11 104	71.75 71	66.02 92	73.93 116	55.00 53	70.90 120	46.77 179	86.68 27	91.54 41	46.91 167	81.07 81	76.32 87	84.28 70	69.54 144

PVSNet_Blended_VisFu		70.70 88	73.62 113	67.28 73	63.53 144	72.96 62	77.97 85	52.10 68	63.65 170	62.66 95	71.14 164	73.46 209	63.55 51	79.35 96	75.34 92	83.90 73	79.43 70

v144192		70.68 89	74.40 104	66.34 83	65.94 112	64.38 110	77.63 89	45.18 112	69.97 134	70.11 53	82.70 66	90.77 62	59.84 74	71.43 167	68.46 139	79.31 116	73.08 110

EC-MVSNet		70.50 90	73.32 120	67.20 74	72.07 70	66.21 90	70.86 143	50.10 77	57.66 208	60.49 106	74.97 138	79.42 177	63.32 53	79.65 91	75.46 91	86.35 56	79.87 68

FPMVS		70.46 91	74.89 93	65.28 89	69.09 88	61.42 130	77.07 92	46.92 96	76.73 77	53.53 135	67.33 180	75.07 204	67.23 31	83.41 65	81.54 57	77.86 130	78.73 76

v1144		70.45 92	74.50 101	65.73 86	65.74 115	64.88 104	77.33 91	44.16 122	70.59 125	69.63 58	83.15 60	91.42 44	57.79 82	71.29 169	68.53 138	79.72 114	71.63 123

v10		70.25 93	74.59 97	65.19 90	65.32 119	66.46 89	76.60 96	44.84 117	67.38 152	67.21 74	82.75 65	90.56 68	57.70 84	71.69 162	68.63 137	79.44 115	74.67 98

Effi-MVS+-dtu		70.10 94	73.76 112	65.82 85	70.23 83	74.92 56	79.47 81	44.49 121	56.98 213	54.34 129	64.26 205	84.78 149	59.97 72	80.96 83	80.38 62	86.44 53	74.05 103

viewdifsd2359ckpt09		70.05 95	75.98 86	63.14 103	63.10 147	66.55 86	76.63 95	46.51 99	69.53 140	56.89 121	77.65 121	89.44 90	55.48 95	77.98 104	76.58 85	80.24 108	74.13 101

SPE-MVS-test		70.00 96	72.92 123	66.59 79	66.71 102	64.06 115	80.28 77	44.82 118	58.41 200	58.08 116	73.57 153	80.94 166	63.98 47	83.29 68	75.93 89	85.65 63	74.23 99

MAR-MVS		70.00 96	72.28 136	67.34 72	69.89 84	72.57 68	80.09 78	49.49 79	60.28 187	69.03 62	59.29 232	80.79 168	54.68 103	78.39 101	76.00 88	80.87 103	78.67 77
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 98	75.50 89	63.50 98	67.73 93	63.35 119	72.92 128	44.85 116	73.46 102	62.21 96	82.98 62	92.85 27	54.29 105	74.81 121	68.84 135	79.00 120	72.21 118

Vis-MVSNet		69.95 99	77.69 74	60.91 122	60.67 165	66.71 83	77.94 86	48.58 84	69.10 143	45.78 184	80.21 99	83.58 157	53.41 113	82.92 71	80.11 65	79.08 119	81.21 64
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CS-MVS		69.56 100	72.49 132	66.14 84	66.85 101	64.10 114	72.55 129	42.91 132	58.90 197	60.78 102	72.93 158	83.21 159	66.32 35	78.77 97	72.84 109	87.45 43	76.72 87

EPP-MVSNet		69.51 101	76.17 82	61.74 118	68.38 92	66.60 84	71.77 133	46.98 94	73.60 101	41.79 201	82.06 75	69.65 220	52.51 120	83.41 65	79.94 66	89.02 29	77.94 80

3Dnovator	65.69 13	69.43 102	75.74 88	62.06 115	60.78 163	70.50 77	75.85 100	39.57 182	74.44 90	57.41 119	75.91 129	77.73 193	55.34 98	76.86 107	75.61 90	83.44 80	79.14 73

E6new		69.19 103	74.52 98	62.96 106	64.65 126	60.53 143	74.96 104	41.17 154	78.08 66	67.31 72	84.59 46	92.19 34	53.04 116	72.20 155	65.00 175	76.67 137	69.09 154

E6		69.19 103	74.52 98	62.96 106	64.65 126	60.53 143	74.96 104	41.17 154	78.08 66	67.31 72	84.59 46	92.19 34	53.04 116	72.20 155	65.00 175	76.67 137	69.09 154

Effi-MVS+		69.04 105	73.01 122	64.40 93	67.20 98	64.83 106	74.87 107	43.97 124	63.33 172	60.90 101	73.06 156	85.79 142	55.61 94	73.58 135	76.41 86	83.84 74	74.09 102

v2v482		69.01 106	73.39 119	63.89 96	63.86 136	62.99 121	75.26 103	42.05 143	70.22 128	68.46 66	82.64 67	91.61 40	55.38 96	70.89 173	66.93 161	78.30 126	68.48 162

MSDG		68.98 107	73.31 121	63.92 95	67.08 99	68.27 80	75.41 102	40.77 163	67.61 150	64.89 87	75.75 132	78.96 179	53.70 110	76.72 110	73.95 100	81.71 99	71.93 121

v8		68.77 108	73.50 115	63.26 101	63.74 140	64.47 108	74.22 113	42.07 142	67.30 154	64.89 87	82.08 74	90.23 76	56.50 91	71.85 161	66.57 164	78.14 127	72.02 119

NR-MVSNet		68.66 109	76.15 83	59.93 130	65.49 116	65.48 97	74.42 109	56.76 37	69.95 135	45.38 186	85.70 34	91.13 52	34.68 234	74.52 124	76.75 83	82.83 86	69.49 146

E4		68.56 110	73.87 109	62.38 110	64.56 128	60.30 145	74.15 114	40.68 165	77.12 74	65.86 81	83.44 55	91.14 50	52.51 120	71.34 168	64.56 178	76.49 142	69.19 152

USDC		68.53 111	71.82 145	64.68 91	63.53 144	61.87 129	70.12 150	46.98 94	77.89 68	76.58 23	68.55 172	86.88 127	50.50 139	73.73 131	65.62 167	80.39 106	68.21 166

IS_MVSNet		68.20 112	74.41 103	60.96 121	68.55 90	64.36 111	71.47 138	48.33 86	70.11 132	43.30 192	80.90 91	74.54 206	47.19 165	81.25 77	77.97 75	86.94 48	71.76 122

E5new		68.19 113	73.41 117	62.09 112	64.54 129	60.26 146	73.68 122	40.53 168	76.37 78	65.41 82	82.61 68	90.32 73	52.09 123	70.81 175	64.16 182	76.33 144	69.36 148

E5		68.19 113	73.41 117	62.09 112	64.54 129	60.26 146	73.68 122	40.53 168	76.37 78	65.41 82	82.61 68	90.32 73	52.09 123	70.81 175	64.16 182	76.33 144	69.36 148

Baseline_NR-MVSNet		68.15 115	75.12 91	60.02 129	70.81 75	55.67 183	75.88 99	53.40 59	71.25 117	43.96 190	85.88 32	92.68 28	45.76 174	83.47 63	68.34 144	70.34 209	68.58 160

GeoE		68.11 116	72.10 141	63.47 99	67.32 96	62.42 125	78.32 83	43.22 131	64.06 169	55.72 125	73.97 147	84.58 150	55.35 97	76.09 115	70.41 127	80.89 102	73.14 109

casdiffmvs_mvg		68.01 117	74.22 107	60.78 123	63.75 139	62.08 128	70.21 149	42.58 134	72.11 111	58.53 113	84.80 44	88.98 96	49.37 147	73.25 139	67.54 156	80.24 108	70.75 130
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
E3		67.77 118	72.78 126	61.92 116	64.26 131	60.09 151	73.72 121	40.40 173	74.39 92	65.10 85	81.79 82	90.27 75	51.99 125	70.42 182	63.76 190	76.04 146	68.75 158

E3new		67.76 119	72.77 127	61.92 116	64.26 131	60.10 150	73.73 120	40.41 172	74.39 92	65.08 86	81.78 83	90.23 76	51.98 126	70.43 181	63.75 191	76.04 146	68.75 158

Fast-Effi-MVS+		67.71 120	72.54 131	62.07 114	63.83 137	63.68 117	75.74 101	39.94 176	60.89 186	54.29 130	73.00 157	86.19 138	56.85 88	78.46 100	73.23 105	81.74 98	72.36 116

viewmacassd2359aftdt		67.58 121	74.22 107	59.84 132	61.60 157	59.46 154	72.40 130	35.74 212	76.19 81	62.14 97	83.74 52	90.96 57	51.94 127	73.07 140	65.37 172	75.17 159	70.72 131

MVSMamba_PlusPlus		67.34 122	71.15 153	62.89 108	66.08 109	66.04 91	73.24 126	43.69 128	59.94 193	58.73 111	67.34 179	81.03 165	53.68 111	74.26 125	71.91 114	81.93 97	77.53 82

hybridcas		67.20 123	73.79 111	59.52 134	64.79 123	61.42 130	71.75 135	39.04 183	74.57 89	53.34 137	85.47 38	89.06 94	50.57 138	72.84 147	64.11 185	77.64 133	69.05 156

viewcassd2359sk11		67.13 124	71.88 144	61.59 119	64.04 134	59.95 152	73.40 125	40.24 174	72.16 110	64.55 89	80.41 95	89.49 88	51.60 128	69.66 191	63.11 197	75.72 150	68.40 163

thisisatest0515		66.95 125	72.29 135	60.72 125	56.37 197	56.05 181	71.08 139	38.81 188	67.59 151	53.26 139	78.21 114	79.79 176	60.11 69	75.69 118	73.02 107	84.69 66	75.66 93

EPNet		66.87 126	68.89 168	64.53 92	73.97 62	61.13 136	78.46 82	61.03 19	56.78 215	53.41 136	66.91 188	70.91 215	43.49 185	76.08 116	76.68 84	76.81 135	73.73 104
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewdifsd2359ckpt13		66.73 127	72.00 143	60.58 126	62.91 149	61.21 135	72.39 132	39.64 180	68.65 146	60.54 105	78.08 116	88.54 101	52.11 122	71.52 164	65.24 174	75.88 149	70.78 129

E2		66.57 128	71.07 154	61.32 120	63.87 135	59.84 153	73.12 127	40.09 175	70.14 131	64.12 91	79.09 110	88.79 98	51.25 130	68.97 196	62.58 201	75.46 155	68.09 167

sasdasda		66.37 129	74.37 105	57.04 152	65.89 113	65.06 100	62.58 196	42.55 135	76.82 75	46.87 177	67.33 180	86.38 132	45.49 176	76.77 108	71.85 115	78.87 123	76.35 88

canonicalmvs		66.37 129	74.37 105	57.04 152	65.89 113	65.06 100	62.58 196	42.55 135	76.82 75	46.87 177	67.33 180	86.38 132	45.49 176	76.77 108	71.85 115	78.87 123	76.35 88

QAPM		66.36 131	72.76 128	58.90 138	59.57 172	65.01 102	64.05 190	41.17 154	73.09 106	56.82 122	69.42 167	77.78 192	55.07 100	73.00 143	72.07 112	76.71 136	78.96 74

viewmanbaseed2359cas		66.24 132	72.42 133	59.03 136	61.13 160	59.13 157	71.64 136	35.37 213	71.67 114	60.68 103	80.93 90	89.48 89	50.83 134	71.60 163	64.04 187	74.50 165	70.09 137

casdiffmvs		66.19 133	72.34 134	59.02 137	62.75 150	60.61 142	69.06 157	41.38 151	69.49 141	54.11 131	84.00 51	89.74 85	49.12 148	70.74 178	62.70 199	77.70 132	69.14 153
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		65.79 134	72.11 139	58.42 142	51.89 229	58.69 158	73.80 118	34.50 222	65.40 163	57.10 120	79.54 106	89.09 93	57.51 85	71.98 159	67.83 152	75.70 152	72.26 117

IterMVS-LS		65.76 135	70.85 158	59.81 133	65.33 118	57.78 165	64.63 187	48.02 89	65.65 162	51.05 151	81.31 87	77.47 194	54.94 101	69.46 192	69.36 132	74.90 163	74.95 96
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PM-MVS		65.66 136	71.25 152	59.14 135	58.92 183	54.88 198	73.66 124	38.55 191	66.12 159	49.91 162	69.87 165	86.97 124	60.61 67	76.30 113	74.75 95	73.19 175	69.83 140

UGNet		65.61 137	74.79 95	54.91 168	54.54 222	68.20 81	70.97 142	48.21 87	67.14 156	41.67 202	74.15 146	80.65 170	36.10 225	79.39 93	77.99 74	77.95 129	76.01 91
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		65.54 138	71.79 146	58.24 145	59.68 171	65.55 96	70.99 140	38.69 190	62.29 176	49.27 166	75.03 137	81.42 162	50.93 133	73.71 133	71.35 118	79.90 112	73.20 108
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		65.36 139	70.09 162	59.85 131	63.05 148	53.61 204	74.29 111	46.45 100	68.14 148	51.45 145	78.83 112	85.78 143	49.87 145	70.44 180	70.45 125	74.00 168	63.38 191

FA-MVS(training)		65.22 140	69.19 166	60.58 126	60.91 161	57.60 169	71.57 137	38.82 187	65.84 161	61.18 99	75.95 128	74.52 207	51.18 132	73.32 137	67.41 158	79.80 113	69.70 142

onestephybrid01		64.95 141	71.07 154	57.81 150	60.74 164	57.96 163	70.47 146	41.03 157	61.12 185	54.46 128	77.87 119	88.01 108	47.42 162	73.62 134	67.51 157	72.74 178	71.42 126

v148		64.92 142	70.58 161	58.32 143	59.89 169	57.08 172	66.04 179	35.27 214	69.11 142	60.66 104	79.57 105	90.93 60	53.91 109	69.81 190	62.22 203	74.14 166	65.31 182

FC-MVSNet-train		64.87 143	74.76 96	53.33 177	65.24 120	58.05 161	69.69 154	41.92 146	70.99 119	32.62 239	85.75 33	88.23 103	32.10 245	77.61 105	74.41 96	78.43 125	68.25 165

pmmvs6		64.78 144	75.82 87	51.89 185	62.41 152	57.13 171	60.24 206	45.59 108	82.90 36	34.69 224	84.83 42	93.18 21	36.22 224	76.43 112	71.13 121	72.21 185	65.12 183

viewmamba		64.77 145	71.33 150	57.11 151	59.34 177	56.66 175	69.98 151	39.86 177	64.70 165	54.75 127	80.33 96	88.09 105	46.49 170	72.71 150	66.26 166	72.23 184	70.56 133

viewdifsd2359ckpt07		64.71 146	72.11 139	56.08 160	62.59 151	56.76 174	70.41 147	32.26 240	73.93 99	51.19 148	82.32 71	90.96 57	49.92 144	69.24 194	61.27 208	70.10 211	70.27 134

OpenMVS	60.79 16	64.42 147	69.72 163	58.23 146	61.63 156	62.17 126	64.11 189	37.54 203	67.17 155	55.71 126	65.89 197	74.89 205	52.67 118	72.20 155	68.29 146	77.73 131	77.39 84

MGCFI-Net		64.40 148	73.52 114	53.76 175	65.41 117	63.86 116	58.32 217	42.38 138	77.23 73	37.76 212	68.03 174	86.11 140	39.76 205	75.70 117	67.69 155	78.96 122	76.03 90

test1111		64.34 149	71.57 147	55.90 161	67.25 97	60.24 149	66.66 172	51.63 72	73.36 105	34.69 224	75.63 134	80.67 169	39.43 208	78.17 102	71.69 117	75.71 151	61.23 201

DCV-MVSNet		64.34 149	72.84 125	54.42 171	63.79 138	62.09 127	62.50 198	42.72 133	74.32 94	41.34 207	66.96 185	88.57 100	39.18 209	75.20 120	70.35 128	77.01 134	72.37 115

ETV-MVS		64.30 151	64.76 187	63.77 97	68.59 89	62.49 123	77.02 93	45.31 109	49.27 241	50.88 152	56.23 244	59.91 244	57.12 87	80.19 89	74.23 98	83.68 76	71.03 128

viewdifsd2359ckpt11		64.18 152	72.66 129	54.28 174	59.33 178	55.48 189	68.20 161	34.30 225	69.68 138	42.09 198	82.03 76	90.43 72	48.52 152	73.95 128	65.57 168	73.27 173	71.49 124

viewmsd2359difaftdt		64.18 152	72.66 129	54.29 173	59.33 178	55.49 188	68.20 161	34.31 224	69.68 138	42.10 197	82.03 76	90.45 71	48.51 153	73.94 129	65.57 168	73.27 173	71.48 125

ECVR-MVS		63.93 154	71.52 148	55.08 166	66.19 106	61.34 132	63.84 191	51.79 70	70.75 123	34.77 222	74.70 143	81.10 164	38.92 210	79.39 93	73.43 103	75.00 161	59.92 213

Anonymous20231211		63.69 155	72.86 124	53.00 181	63.72 141	60.25 148	60.33 205	40.96 159	72.49 108	38.91 210	81.77 84	88.17 104	37.60 218	73.30 138	68.01 149	76.47 143	66.06 179

diffmvs_AUTHOR		63.60 156	71.07 154	54.90 169	56.75 195	55.35 191	67.91 166	37.08 206	66.87 157	50.84 153	81.64 85	88.73 99	45.49 176	70.02 189	64.15 184	71.31 198	70.70 132

TransMVSNet (Re)		63.49 157	73.86 110	51.39 191	64.26 131	56.07 180	61.17 201	42.23 140	78.81 65	34.80 221	85.94 30	90.63 66	34.35 238	72.73 149	67.98 150	71.50 195	64.84 184

DI_MVS_pp		63.43 158	67.54 175	58.63 139	62.34 153	58.06 160	65.75 183	42.15 141	63.05 173	53.28 138	75.88 131	75.92 201	50.18 141	68.04 199	64.20 181	78.07 128	67.65 169

EIA-MVS		63.24 159	64.16 191	62.16 111	69.30 87	63.20 120	72.40 130	40.82 162	48.31 248	51.50 144	59.63 230	62.23 236	57.33 86	78.00 103	71.94 113	81.59 100	65.82 180

Fast-Effi-MVS+-dtu		63.22 160	65.55 181	60.49 128	61.24 159	64.70 107	74.15 114	53.24 61	51.46 226	49.67 164	58.03 239	78.42 185	48.05 159	72.03 158	71.14 120	76.60 141	63.09 192

IterMVS-SCA-FT		62.67 161	68.00 172	56.45 159	56.92 194	64.92 103	57.51 223	38.12 194	59.44 196	53.62 134	74.74 142	71.60 212	64.84 42	70.24 184	65.27 173	67.70 220	69.83 140

diffmvs		62.64 162	69.66 164	54.46 170	56.19 200	55.06 194	67.36 169	36.74 209	64.18 167	50.58 155	79.54 106	87.55 114	45.13 180	68.04 199	63.20 193	70.78 204	70.02 138
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		62.58 163	67.46 176	56.89 155	59.52 173	55.90 182	64.94 185	38.83 186	57.08 212	56.55 123	76.53 125	84.49 151	47.45 161	66.95 203	62.01 204	74.04 167	69.27 150

MDA-MVSNet-bldmvs		62.46 164	72.13 138	51.19 193	34.32 268	56.10 179	68.65 159	38.85 185	69.05 144	49.50 165	78.17 115	85.43 144	51.32 129	86.67 38	67.40 159	64.46 228	62.08 195

FE-MVSNET2		62.31 165	70.75 160	52.46 182	57.40 193	55.60 185	60.43 204	40.97 158	73.41 103	42.12 196	74.36 145	91.26 49	38.76 212	71.29 169	66.83 162	75.11 160	62.50 194

dtuplus		62.30 166	68.17 171	55.45 164	59.26 180	54.38 199	66.94 171	38.05 197	62.56 174	50.39 158	76.24 127	86.29 134	46.42 171	68.97 196	61.35 207	71.64 191	67.44 170

pm-mvs1		61.97 167	72.01 142	50.25 202	60.64 166	55.23 192	58.67 215	42.44 137	74.40 91	33.63 236	81.03 89	89.86 82	34.87 233	72.93 146	67.95 151	71.28 199	62.65 193

FMVSNet1		61.92 168	71.36 149	50.90 196	57.67 192	59.29 156	59.48 212	44.14 123	70.24 127	34.72 223	75.45 136	84.94 147	36.75 221	72.33 152	68.45 140	72.66 179	68.83 157

viewmambaseed2359dif		61.91 169	67.70 174	55.15 165	58.68 186	54.97 195	66.48 176	38.16 193	61.22 184	49.79 163	75.90 130	85.95 141	46.19 172	67.70 201	60.19 212	71.60 192	67.93 168

hybridnocas07		61.87 170	69.09 167	53.44 176	55.06 218	53.58 205	66.51 173	34.96 215	61.85 179	51.08 150	78.89 111	86.78 129	42.89 188	70.11 186	63.19 194	70.14 210	69.23 151

PVSNet_BlendedMVS		61.75 171	65.07 185	57.87 148	56.27 198	60.99 139	65.81 181	43.75 126	51.27 230	54.08 132	62.12 217	78.84 181	50.67 135	71.49 165	63.91 188	76.64 139	66.86 173

PVSNet_Blended		61.75 171	65.07 185	57.87 148	56.27 198	60.99 139	65.81 181	43.75 126	51.27 230	54.08 132	62.12 217	78.84 181	50.67 135	71.49 165	63.91 188	76.64 139	66.86 173

tttt0517		61.44 173	63.85 193	58.62 140	55.20 213	55.61 184	68.80 158	38.02 198	55.70 217	50.01 161	66.93 187	48.90 255	56.69 89	73.84 130	71.10 122	82.99 84	74.89 97

tfpnnormal		61.41 174	71.33 150	49.83 205	61.73 155	54.90 197	58.52 216	41.24 152	75.20 85	32.00 244	82.13 73	87.87 110	35.63 229	72.75 148	66.30 165	69.87 212	60.14 209

IB-MVS	57.02 17	61.37 175	65.39 182	56.69 156	56.65 196	60.85 141	70.70 144	37.90 200	49.37 240	45.37 187	48.75 256	79.14 178	53.55 112	76.26 114	70.85 124	75.97 148	72.50 114
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		61.34 176	68.38 170	53.12 179	54.36 223	53.13 212	66.37 177	34.35 223	61.60 182	50.79 154	77.15 123	86.26 135	43.00 187	69.33 193	62.68 200	69.85 213	68.51 161

CANet_DTU		61.22 177	67.07 178	54.40 172	59.89 169	63.62 118	70.98 141	36.77 208	50.49 233	47.15 173	62.45 214	80.81 167	37.90 217	71.87 160	70.09 130	73.69 169	70.19 136

pmmvs4		61.12 178	64.61 188	57.04 152	60.88 162	52.15 218	70.59 145	44.82 118	61.35 183	46.91 176	72.08 160	73.27 210	46.79 169	65.06 207	67.76 153	72.28 182	60.58 205

thisisatest0530		61.02 179	63.44 200	58.19 147	54.75 220	55.09 193	68.03 165	38.02 198	55.45 218	49.06 167	66.58 191	48.69 256	56.69 89	73.07 140	71.10 122	82.60 89	74.14 100

Vis-MVSNet (Re-imp)		60.99 180	67.78 173	53.06 180	64.66 125	53.49 206	67.40 167	49.52 78	68.55 147	28.00 254	79.53 108	71.41 214	33.08 242	75.30 119	71.28 119	75.69 153	54.91 226

PatchMatch-RL		60.96 181	63.00 206	58.57 141	59.16 181	52.18 217	67.38 168	41.99 144	57.85 206	48.16 168	53.55 252	69.77 219	59.47 78	73.73 131	72.49 111	75.27 158	61.44 197

GA-MVS		60.73 182	64.24 190	56.64 157	59.38 176	57.45 170	65.07 184	36.65 210	57.39 210	58.17 115	73.43 154	69.10 223	47.38 163	64.47 213	63.63 192	73.19 175	64.22 188

CVMVSNet		60.45 183	63.72 196	56.63 158	54.82 219	53.75 200	68.41 160	41.95 145	55.07 219	48.03 169	58.08 238	68.67 224	55.09 99	69.14 195	68.34 144	71.51 194	72.97 113

ET-MVSNet_ETH3D		60.33 184	62.10 211	58.27 144	58.61 187	58.05 161	68.06 163	41.20 153	51.40 227	51.10 149	64.06 206	49.42 254	50.61 137	74.72 122	70.29 129	80.05 111	66.74 175

FC-MVSNet-test		60.28 185	70.83 159	47.96 218	54.69 221	47.12 237	68.06 163	41.68 150	71.42 115	23.73 263	84.70 45	77.41 195	28.92 250	82.33 75	73.08 106	70.68 205	59.77 214

test2506		59.86 186	64.01 192	55.02 167	66.19 106	61.34 132	63.84 191	51.79 70	70.75 123	34.39 229	62.65 213	39.92 276	38.92 210	79.39 93	73.43 103	75.00 161	60.56 206

EU-MVSNet		59.77 187	66.07 179	52.42 183	47.81 240	51.86 220	62.98 195	32.28 239	62.08 177	47.10 174	59.94 228	83.42 158	53.08 115	70.06 188	63.19 194	71.26 201	71.96 120

IterMVS		59.24 188	64.45 189	53.16 178	50.98 231	61.29 134	66.51 173	32.85 234	58.17 202	46.31 182	72.58 159	70.23 217	54.26 106	64.81 210	60.24 211	68.04 219	63.81 190
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		59.15 189	62.28 208	55.49 163	52.42 227	62.59 122	71.76 134	39.74 178	50.25 235	41.92 199	62.88 211	69.16 222	55.85 92	62.77 220	67.18 160	71.27 200	61.11 202

thres600view7		58.87 190	65.91 180	50.66 198	61.27 158	56.32 177	59.88 210	40.63 166	64.88 164	32.10 243	64.82 202	69.83 218	36.72 222	72.99 144	72.55 110	73.34 171	59.97 212

FE-MVSNET		58.15 191	67.14 177	47.65 222	49.53 236	50.47 222	57.09 231	37.15 205	67.85 149	35.64 220	67.57 178	87.16 120	35.36 230	71.23 171	65.57 168	71.17 203	60.12 210

CMPMVS	45.32 18	58.10 192	65.24 184	49.76 206	47.88 239	46.86 240	48.16 259	32.82 235	58.06 203	61.35 98	59.64 229	80.00 173	47.27 164	70.15 185	64.10 186	61.08 233	77.85 81
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view		58.09 193	63.54 199	51.74 187	50.13 233	46.56 241	66.95 170	33.41 231	63.52 171	58.77 110	74.84 140	84.10 154	43.12 186	65.95 206	54.69 237	58.04 240	55.13 225

dtuonlycased		58.01 194	69.36 165	44.76 233	44.25 258	58.11 159	69.81 153	22.59 260	67.34 153	16.28 270	68.81 169	80.61 171	53.94 108	76.65 111	66.59 163	61.71 232	67.42 171

CDS-MVSNet		57.90 195	63.57 198	51.28 192	62.30 154	53.17 211	64.70 186	51.61 73	57.41 209	32.75 238	63.73 207	70.53 216	27.12 253	72.49 151	73.02 107	69.22 216	54.68 227
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		57.89 196	63.71 197	51.09 195	55.89 201	55.60 185	59.23 213	36.08 211	59.64 195	42.40 194	66.53 192	76.89 197	40.76 197	64.76 211	57.84 219	72.19 186	64.74 185

FMVSNet2		57.80 197	65.39 182	48.94 213	55.88 202	57.61 166	57.26 224	42.37 139	58.21 201	33.19 237	68.36 173	75.55 203	34.58 235	66.91 204	64.55 179	70.38 206	66.56 176

blended_shiyan6		57.28 198	63.20 204	50.37 200	55.61 207	53.68 202	57.83 221	34.81 216	61.78 181	41.66 203	66.96 185	78.89 180	39.92 203	62.76 221	56.95 224	72.40 181	61.36 198

blended_shiyan8		57.27 199	63.19 205	50.37 200	55.60 208	53.69 201	57.78 222	34.81 216	61.83 180	41.63 204	67.00 184	78.75 184	39.97 202	62.78 219	56.97 223	72.41 180	61.29 200

thres400		57.25 200	63.73 195	49.70 207	60.19 168	54.95 196	58.16 218	39.60 181	62.42 175	31.98 246	62.33 215	69.20 221	35.96 226	70.07 187	68.03 148	72.28 182	59.12 216

WB-MVS		57.14 201	71.03 157	40.93 244	47.97 238	53.10 213	52.35 246	39.66 179	79.59 60	23.31 264	89.49 14	89.23 91	32.94 243	74.68 123	61.41 206	49.32 256	49.74 240

usedtu_dtu_shiyan1		56.93 202	63.39 201	49.39 209	55.48 209	53.03 214	56.77 232	38.34 192	60.26 188	37.42 214	64.85 201	80.06 172	36.94 220	63.97 215	62.55 202	71.36 197	58.99 217

gm-plane-assit		56.76 203	57.64 225	55.73 162	66.01 111	55.45 190	74.96 104	30.54 245	73.71 100	56.04 124	81.81 80	30.91 279	43.83 182	58.77 238	54.71 236	63.02 230	48.13 247

MIMVSNet1		56.72 204	68.69 169	42.76 240	46.70 247	42.81 248	69.13 155	30.52 246	81.01 51	32.00 244	74.82 141	91.10 54	26.83 255	73.98 127	64.72 177	51.40 252	52.38 235

EPNet_dtu		56.63 205	60.77 217	51.80 186	55.47 210	44.63 242	69.83 152	38.74 189	50.27 234	47.64 170	58.01 240	72.27 211	33.71 240	68.60 198	67.72 154	65.39 224	63.86 189
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wanda-best-256-512		56.57 206	62.27 209	49.92 203	55.08 214	53.36 207	57.15 228	34.52 218	60.20 189	41.40 205	65.86 198	78.11 190	39.54 206	61.94 225	55.90 229	71.82 187	60.53 207

FE-blended-shiyan7		56.57 206	62.27 209	49.92 203	55.08 214	53.36 207	57.15 228	34.52 218	60.20 189	41.40 205	65.86 198	78.11 190	39.54 206	61.94 225	55.90 229	71.82 187	60.53 207

GBi-Net		56.54 208	63.26 202	48.70 215	55.88 202	57.61 166	57.26 224	41.75 147	49.06 242	32.37 240	61.81 219	67.02 227	34.58 235	72.33 152	68.45 140	70.38 206	66.56 176

test1		56.54 208	63.26 202	48.70 215	55.88 202	57.61 166	57.26 224	41.75 147	49.06 242	32.37 240	61.81 219	67.02 227	34.58 235	72.33 152	68.45 140	70.38 206	66.56 176

gg-mvs-nofinetune		56.45 210	61.04 215	51.10 194	63.42 146	49.40 230	53.71 241	52.52 66	74.77 87	46.93 175	77.31 122	53.88 248	26.42 257	62.51 223	57.81 220	63.60 229	51.57 238

thres200		56.35 211	62.36 207	49.34 210	58.87 184	56.32 177	55.91 233	40.63 166	58.51 199	31.34 247	58.81 236	67.31 226	35.96 226	72.99 144	65.51 171	73.34 171	57.07 220

MS-PatchMatch		56.31 212	60.22 220	51.73 188	60.53 167	55.53 187	63.41 193	37.18 204	51.34 229	37.44 213	60.53 226	62.19 237	45.52 175	64.25 214	63.17 196	66.33 221	64.55 186

tfpn200view9		56.07 213	61.85 212	49.34 210	58.57 188	56.48 176	58.01 220	40.72 164	53.23 220	31.01 248	56.41 242	66.40 232	34.18 239	73.02 142	68.06 147	73.53 170	59.35 215

usedtu_dtu_shiyan2		55.91 214	63.79 194	46.72 228	59.06 182	49.07 231	60.88 203	43.83 125	79.17 64	29.73 251	66.13 195	86.96 125	27.91 251	62.70 222	57.21 221	58.91 236	45.28 253

FMVSNet3		54.77 215	60.73 218	47.81 219	54.29 224	56.88 173	55.89 234	41.75 147	49.06 242	32.37 240	61.81 219	67.02 227	33.67 241	62.88 218	61.96 205	68.88 217	65.53 181

thres100view900		53.88 216	59.19 221	47.68 221	58.57 188	52.74 216	54.45 238	38.07 196	53.23 220	31.01 248	56.41 242	66.40 232	32.80 244	65.03 209	64.43 180	71.18 202	56.10 223

CR-MVSNet		53.82 217	55.40 229	51.98 184	51.57 230	50.23 224	45.00 262	44.97 114	46.90 250	52.60 141	67.91 175	46.99 269	48.37 154	59.15 236	59.53 215	69.38 215	57.07 220

baseline2		53.55 218	55.19 230	51.62 189	55.27 212	51.95 219	60.89 202	34.23 226	46.69 252	42.47 193	53.56 251	50.01 251	45.33 179	64.63 212	61.22 209	71.56 193	58.28 219

test20.03		53.49 219	60.95 216	44.78 232	64.73 124	47.25 236	61.58 200	43.30 129	65.86 160	22.85 265	66.87 190	79.85 174	22.99 259	62.38 224	56.95 224	53.25 249	47.46 249

baseline		53.46 220	61.55 213	44.01 235	45.83 252	48.77 232	57.26 224	28.75 252	49.99 236	38.85 211	68.78 170	75.65 202	38.30 214	60.80 229	59.78 214	55.10 247	67.07 172

MVSTER		53.08 221	56.39 227	49.21 212	47.19 243	51.08 221	60.14 208	31.74 242	40.63 262	38.97 209	55.78 245	46.74 270	42.47 191	67.29 202	62.99 198	74.73 164	70.23 135

CHOSEN 1792x2688		52.99 222	56.91 226	48.42 217	47.32 241	50.10 226	64.18 188	33.85 228	45.46 255	36.95 216	55.20 248	66.49 231	51.20 131	59.28 234	59.81 213	57.01 242	61.99 196

baseline1		52.90 223	58.38 222	46.51 229	58.87 184	50.01 227	54.17 239	40.45 171	56.81 214	29.25 252	62.72 212	58.99 245	30.25 248	65.05 208	60.57 210	66.07 222	54.54 228

CostFormer		52.59 224	55.14 231	49.61 208	52.72 225	50.40 223	66.28 178	33.78 229	52.85 222	43.43 191	66.30 193	51.37 250	41.78 194	54.92 248	51.18 244	59.68 235	58.98 218

SCA		52.47 225	53.97 237	50.71 197	46.95 246	57.79 164	60.18 207	46.89 97	51.92 225	46.71 181	60.73 224	49.97 252	47.69 160	56.39 245	52.98 241	55.82 244	48.03 248

testgi		51.94 226	61.37 214	40.94 243	58.38 190	47.03 238	65.88 180	30.49 247	70.87 122	22.64 266	57.53 241	87.59 113	18.30 266	63.01 217	54.32 238	49.93 255	49.27 242

FE-MVSNET3		51.86 227	54.48 234	48.80 214	55.08 214	53.36 207	57.15 228	34.52 218	60.20 189	34.24 230	41.26 266	47.37 262	40.03 199	61.94 225	55.90 229	71.82 187	61.36 198

usedtu_blend_shiyan5		51.33 228	54.39 235	47.77 220	55.08 214	53.36 207	50.91 252	34.52 218	60.20 189	34.24 230	41.26 266	47.37 262	40.03 199	61.94 225	55.90 229	71.82 187	60.71 203

dmvs_re		51.01 229	54.88 233	46.49 230	58.06 191	44.35 245	60.08 209	37.67 201	42.11 259	28.68 253	45.12 262	66.70 230	31.90 247	66.62 205	59.18 217	62.59 231	60.11 211

tpm cat1		50.98 230	51.28 243	50.62 199	55.74 205	49.92 228	63.13 194	38.12 194	52.38 224	47.61 171	60.11 227	44.51 273	44.86 181	51.31 257	47.49 254	54.25 248	53.24 232

RPMNet		50.92 231	50.32 246	51.62 189	50.25 232	50.23 224	59.16 214	46.70 98	46.90 250	42.39 195	48.97 255	37.23 277	41.78 194	57.30 243	56.18 227	69.44 214	55.43 224

pmmvs5		50.64 232	58.01 223	42.05 241	47.01 245	43.67 246	49.27 257	29.43 251	50.77 232	33.83 235	68.69 171	76.16 200	27.82 252	57.53 242	57.07 222	64.95 226	52.18 236

PatchT		50.55 233	53.55 239	47.05 226	37.59 266	42.26 249	50.55 254	37.56 202	46.37 253	52.60 141	66.91 188	43.54 275	48.37 154	59.15 236	59.53 215	55.62 245	57.07 220

Anonymous20231206		50.28 234	57.94 224	41.35 242	55.45 211	43.65 247	58.06 219	34.12 227	62.02 178	24.25 262	59.33 231	79.80 175	24.49 258	59.55 231	54.28 239	51.74 251	46.94 251

dps		49.71 235	51.97 241	47.07 225	52.37 228	47.00 239	53.02 244	40.52 170	44.91 256	41.23 208	64.55 203	44.27 274	40.12 198	57.71 241	51.97 243	55.14 246	53.41 231

MDTV_nov1_ep13		49.60 236	51.57 242	47.31 223	46.28 249	44.61 243	59.82 211	30.96 243	48.80 247	50.20 159	59.26 233	52.38 249	38.56 213	56.20 246	49.70 248	58.04 240	50.01 239

PatchmatchNet		48.67 237	50.10 247	46.99 227	48.29 237	41.00 250	55.54 235	38.94 184	51.38 228	45.15 188	63.22 209	48.45 258	42.83 189	53.80 253	48.50 253	51.19 254	44.37 254
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
blend_shiyan4		47.96 238	50.55 245	44.94 231	43.47 259	52.81 215	47.71 261	32.72 236	36.60 268	34.24 230	41.26 266	47.37 262	40.03 199	59.51 232	55.57 233	71.48 196	60.71 203

new-patchmatchnet		47.33 239	60.49 219	31.99 261	55.69 206	33.86 263	36.84 273	33.31 232	72.36 109	14.33 272	80.09 101	92.14 36	13.27 269	63.54 216	40.09 262	38.51 266	41.32 262

dtuonly		47.04 240	55.08 232	37.67 252	36.58 267	38.07 255	54.74 237	20.55 263	48.83 246	27.31 255	62.23 216	67.56 225	39.92 203	60.00 230	55.14 235	46.90 258	53.71 230

0.4-1-1-0.1		46.90 241	49.04 253	44.40 234	47.28 242	49.55 229	52.48 245	30.44 248	40.85 261	34.58 226	47.16 258	48.13 259	35.64 228	52.90 254	50.70 247	65.97 223	53.98 229

tpm		46.67 242	49.20 252	43.72 236	49.60 234	36.60 260	53.93 240	26.84 254	52.70 223	58.05 117	69.04 168	47.96 260	30.06 249	48.33 263	42.76 257	43.88 260	47.01 250

pmmvs3		46.64 243	54.13 236	37.90 251	31.23 272	40.68 251	49.83 256	15.34 267	46.31 254	36.34 218	53.15 253	74.40 208	36.36 223	58.43 239	56.64 226	58.32 239	49.29 241

TAMVS		46.64 243	53.62 238	38.49 249	49.56 235	36.87 257	53.16 243	25.76 256	56.33 216	22.55 267	60.72 225	61.80 239	27.12 253	59.50 233	58.33 218	52.79 250	41.82 261

test-LLR		46.01 245	45.06 264	47.11 224	59.39 174	36.72 258	51.28 249	40.95 160	36.41 269	34.45 227	46.14 259	47.02 267	38.00 215	51.78 255	48.53 251	58.60 237	48.84 244

MIMVSNet		45.83 246	53.46 240	36.94 253	45.38 256	39.50 253	52.20 247	30.68 244	57.09 211	24.53 261	55.22 247	71.54 213	21.74 262	55.81 247	51.08 245	47.11 257	43.96 255

0.3-1-1-0.015		45.78 247	47.55 256	43.71 237	46.29 248	48.64 234	51.52 248	29.70 249	39.03 266	34.24 230	44.15 264	47.37 262	35.28 231	51.31 257	49.52 249	65.23 225	52.80 233

pmnet_mix02		45.67 248	55.99 228	33.63 260	45.77 253	31.22 267	42.04 267	27.60 253	64.14 168	24.89 258	75.50 135	82.30 160	21.88 261	54.53 251	41.22 259	39.62 264	43.05 257

0.4-1-1-0.2		45.53 249	47.38 257	43.38 239	46.00 251	48.29 235	50.94 251	29.49 250	38.16 267	34.06 234	45.06 263	47.50 261	34.99 232	51.04 259	49.00 250	64.81 227	52.58 234

test0.0.03 1		45.40 250	49.55 250	40.57 246	59.39 174	44.36 244	53.37 242	40.95 160	47.14 249	19.23 268	45.49 261	60.24 242	19.24 264	54.82 249	51.98 242	51.21 253	42.82 258

PMMVS		45.37 251	49.29 251	40.79 245	27.75 273	35.07 262	50.88 253	19.88 264	39.27 264	35.78 219	50.11 254	61.29 240	42.04 192	54.13 252	55.95 228	68.43 218	49.19 243

MVS-HIRNet		44.56 252	45.52 262	43.44 238	40.98 262	31.03 268	39.52 271	36.96 207	42.80 258	44.37 189	53.80 250	60.04 243	41.85 193	47.97 265	41.08 260	56.99 243	41.95 260

test-mter		44.18 253	47.60 255	40.18 247	33.20 269	39.03 254	55.28 236	13.91 269	39.07 265	36.63 217	48.09 257	49.52 253	41.12 196	54.55 250	50.91 246	60.97 234	52.03 237

EMVS		43.85 254	49.91 248	36.77 255	45.46 255	32.70 264	44.09 264	25.33 257	57.88 205	26.62 256	58.99 235	61.14 241	42.77 190	70.26 183	38.52 266	36.38 268	29.87 268

E-PMN		43.83 255	49.81 249	36.84 254	46.09 250	31.86 266	42.77 266	25.85 255	57.76 207	25.53 257	55.50 246	62.47 235	43.77 183	70.78 177	39.51 263	37.04 267	30.79 267

tpmrst		43.31 256	46.14 260	40.02 248	47.05 244	36.48 261	48.01 260	32.17 241	49.50 239	37.26 215	63.66 208	47.04 266	31.98 246	42.00 269	40.55 261	43.64 261	43.75 256

TESTMET0.1,1		41.79 257	45.06 264	37.97 250	31.32 271	36.72 258	51.28 249	14.17 268	36.41 269	34.45 227	46.14 259	47.02 267	38.00 215	51.78 255	48.53 251	58.60 237	48.84 244

ADS-MVSNet		40.61 258	46.31 258	33.96 258	40.70 263	30.42 269	40.42 269	33.44 230	58.01 204	30.87 250	63.05 210	54.48 247	22.67 260	44.35 268	39.23 265	35.64 269	34.64 265

CHOSEN 280x420		40.24 259	44.14 267	35.69 256	32.36 270	23.58 273	50.30 255	21.21 262	40.94 260	18.84 269	32.75 271	48.65 257	48.13 158	59.16 235	55.31 234	43.28 262	48.62 246

EPMVS		40.11 260	44.96 266	34.44 257	41.55 261	32.65 265	41.74 268	32.39 238	49.89 238	24.83 259	64.44 204	46.38 271	26.57 256	44.75 267	39.47 264	39.59 265	37.16 264

FMVSNet5		39.83 261	45.08 263	33.71 259	39.24 264	39.56 252	48.77 258	23.55 259	39.45 263	24.55 260	33.73 270	44.57 272	20.97 263	58.27 240	54.23 240	45.16 259	45.77 252

N_pmnet		39.50 262	51.01 244	26.09 263	44.48 257	25.59 271	40.20 270	21.49 261	64.20 166	7.98 277	73.86 150	76.67 199	13.66 267	50.17 260	36.69 269	28.71 272	29.86 269

MVE	28.01 19	35.86 263	43.56 268	26.88 262	22.33 275	19.75 275	30.85 276	23.88 258	49.90 237	10.48 274	43.64 265	61.87 238	48.99 150	47.26 266	42.15 258	24.76 273	40.37 263
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet		35.76 264	45.64 261	24.22 264	38.59 265	25.83 270	31.87 275	19.24 265	49.06 242	9.01 275	54.34 249	64.73 234	12.46 270	49.21 262	44.91 255	34.17 270	31.41 266

PMMVS2		34.11 265	48.55 254	17.26 265	25.45 274	20.72 274	35.08 274	16.26 266	58.71 198	4.16 279	59.22 234	78.40 186	3.65 274	57.24 244	38.31 267	18.94 275	27.28 271

GG-mvs-BLEND		31.54 266	46.27 259	14.37 266	0.07 284	48.65 233	42.97 265	0.08 278	44.04 257	1.21 282	39.77 269	57.94 246	0.15 280	48.19 264	42.82 256	41.70 263	42.46 259

test_method		13.28 267	15.83 269	10.30 267	1.05 280	2.18 280	15.40 277	2.23 272	22.43 271	13.84 273	22.00 273	33.14 278	9.78 271	17.80 271	9.93 271	19.50 274	3.31 277

VLMVS_CLIP		8.71 268	10.25 271	6.90 269	18.57 276	7.95 277	1.73 281	7.14 270	8.77 275	2.23 280	11.00 275	9.82 282	9.03 272	10.51 273	8.62 273	2.71 278	15.09 273

MVS_clip		7.23 269	11.04 270	2.78 270	4.95 278	4.38 278	1.40 282	1.30 273	10.87 274	0.37 284	15.29 274	13.65 281	7.24 273	9.55 274	8.87 272	1.43 279	14.65 274

MVS_baseline		1.95 270	3.24 272	0.46 272	0.58 281	0.23 283	0.08 286	0.00 279	2.89 276	0.00 286	3.70 277	4.08 284	2.06 275	1.11 276	1.26 274	0.01 283	9.40 275

VLMVS		1.29 271	1.82 273	0.66 271	1.29 279	0.84 281	0.36 283	0.35 275	2.02 277	0.44 283	1.69 278	2.68 285	1.15 277	1.12 275	0.90 275	0.39 280	3.51 276

test123		0.53 272	0.60 275	0.46 272	0.22 282	0.25 282	0.33 285	0.13 277	0.66 278	1.37 281	1.10 279	0.00 287	0.43 278	0.68 277	0.61 276	0.26 281	0.88 278

testmvs		0.47 273	0.69 274	0.21 274	0.17 283	0.17 284	0.35 284	0.16 276	0.66 278	0.18 285	1.05 280	0.99 286	0.27 279	0.62 278	0.54 277	0.15 282	0.77 279

uanet_test		0.00 274	0.00 276	0.00 275	0.00 285	0.00 285	0.00 287	0.00 279	0.00 280	0.00 286	0.00 281	0.00 287	0.00 281	0.00 279	0.00 278	0.00 284	0.00 280

sosnet-low-res		0.00 274	0.00 276	0.00 275	0.00 285	0.00 285	0.00 287	0.00 279	0.00 280	0.00 286	0.00 281	0.00 287	0.00 281	0.00 279	0.00 278	0.00 284	0.00 280

sosnet		0.00 274	0.00 276	0.00 275	0.00 285	0.00 285	0.00 287	0.00 279	0.00 280	0.00 286	0.00 281	0.00 287	0.00 281	0.00 279	0.00 278	0.00 284	0.00 280

ACM-MVS					75.22 55	70.53 76	83.32 57		66.82 158	69.00 63	61.79 222	78.25 187	48.80 151			82.49 92	82.38 55

PatchmatchNet2					42.50 260	24.33 272	39.02 272
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1												76.74 198	13.63 268	49.76 261	36.71 268	28.75 271	29.80 270
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3										8.05 276	73.92 148
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
TestfortrainingZip							87.82 39	53.22 62		58.49 114						84.46 67

TPM-MVS					72.72 67	70.92 75	80.38 76			66.22 79	58.35 237	78.23 188	48.26 156			83.40 81	81.74 60
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												83.64 156

SR-MVS					81.31 10			62.63 9				91.11 53

Anonymous202405211			72.22 137		66.19 106	61.09 138	62.23 199	45.87 107	71.25 117		79.33 109	86.16 139	37.36 219	73.54 136	69.84 131	75.45 156	64.32 187

our_test_3					52.72 225	53.66 203	69.11 156

ambc			79.96 64		74.57 59	75.48 52	73.75 119		80.32 56	72.34 38	78.46 113	92.41 30	59.05 79	80.24 88	73.95 100	75.41 157	78.85 75

MTAPA										80.26 8		90.53 70

MTMP										82.07 4		91.00 56

Patchmatch-RL test							2.05 280

tmp_tt				7.47 268	8.89 277	3.32 279	4.35 279	1.14 274	15.58 273	15.76 271	8.50 276	5.90 283	2.00 276	20.02 270	21.51 270	12.70 276

XVS					80.47 21	81.29 12	93.33 3			77.45 19		90.19 78				91.52 11

X-MVStestdata					80.47 21	81.29 12	93.33 3			77.45 19		90.19 78				91.52 11

mPP-MVS					82.97 3							92.12 37

NP-MVS									71.39 116

Patchmtry						37.73 256	45.00 262	44.97 114		52.60 141

DeepMVS_CX						8.52 276	9.75 278	3.19 271	16.70 272	5.02 278	23.06 272	19.33 280	18.69 265	13.75 272		11.34 277	25.07 272

High-resolution multi-view benchmark