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		93.16 1	95.57 1	90.36 1	88.79 52	93.57 1	97.27 1	78.23 21	95.55 1	93.00 1	93.98 18	96.01 41	87.53 1	97.69 1	96.81 1	97.33 1	95.34 4

COLMAP_ROB	85.66 2	91.85 2	95.01 2	88.16 11	88.98 51	92.86 2	95.51 19	72.17 62	94.95 4	91.27 3	94.11 17	97.77 11	84.22 8	96.49 4	95.27 5	96.79 2	93.60 12
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LTVRE_ROB	86.82 1	91.55 3	94.43 3	88.19 10	83.19 112	86.35 65	93.60 37	78.79 18	95.48 3	91.79 2	93.08 27	97.21 20	86.34 3	97.06 2	96.27 3	95.46 23	95.56 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
ACMMPR		91.30 4	92.88 11	89.46 4	91.92 11	91.61 5	96.60 5	79.46 14	90.08 31	88.53 13	89.54 70	95.57 51	84.25 7	95.24 20	94.27 12	95.97 11	93.85 8

CP-MVS		91.09 5	92.33 25	89.65 2	92.16 10	90.41 27	96.46 10	80.38 8	88.26 45	89.17 10	87.00 106	96.34 33	83.95 10	95.77 11	94.72 7	95.81 17	93.78 10

MP-MVS		90.84 6	91.95 34	89.55 3	92.92 4	90.90 19	96.56 6	79.60 11	86.83 60	88.75 12	89.00 79	94.38 85	84.01 9	94.94 24	94.34 10	95.45 24	93.24 23
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMM	80.67 7	90.67 7	92.46 19	88.57 7	91.35 22	89.93 32	96.34 11	77.36 30	90.17 29	86.88 29	87.32 100	96.63 23	83.32 13	95.79 10	94.49 9	96.19 9	92.91 26
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMMP		90.63 8	92.40 20	88.56 8	91.24 28	91.60 6	96.49 9	77.53 26	87.89 48	86.87 30	87.24 102	96.46 27	82.87 16	95.59 15	94.50 8	96.35 6	93.51 18
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
LGP-MVS_train		90.56 9	92.38 21	88.43 9	90.88 32	91.15 11	95.35 21	77.65 25	86.26 66	87.23 23	90.45 60	97.35 17	83.20 14	95.44 16	93.41 20	96.28 8	92.63 27

DVP-MVS++		90.50 10	94.18 4	86.21 27	92.52 7	90.29 28	95.29 22	76.02 41	94.24 5	82.82 54	95.84 5	97.56 15	76.82 55	93.13 38	91.20 44	93.78 45	97.01 1

PGM-MVS		90.42 11	91.58 37	89.05 5	91.77 14	91.06 13	96.51 7	78.94 16	85.41 74	87.67 18	87.02 105	95.26 61	83.62 12	95.01 23	93.94 15	95.79 19	93.40 20

DeepC-MVS	83.59 4	90.37 12	92.56 18	87.82 14	91.26 27	92.33 3	94.72 30	80.04 9	90.01 32	84.61 42	93.33 23	94.22 86	80.59 27	92.90 43	92.52 28	95.69 21	92.57 28
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		90.32 13	92.37 22	87.94 13	91.46 21	90.91 18	95.69 17	79.49 12	89.94 34	83.50 50	89.06 78	94.44 83	81.68 22	94.17 30	94.19 13	95.81 17	93.87 7

PMVS	79.51 9	90.23 14	92.67 14	87.39 20	90.16 39	88.75 42	93.64 36	75.78 44	90.00 33	83.70 47	92.97 29	92.22 112	86.13 4	97.01 3	96.79 2	94.94 28	90.96 45
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
SMA-MVS		90.13 15	92.26 27	87.64 17	91.68 16	90.44 26	95.22 24	77.34 32	90.79 23	87.80 16	90.42 61	92.05 117	79.05 35	93.89 32	93.59 18	94.77 32	94.62 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
ACMP	80.00 8	90.12 16	92.30 26	87.58 18	90.83 34	91.10 12	94.96 28	76.06 40	87.47 52	85.33 39	88.91 83	97.65 14	82.13 19	95.31 17	93.44 19	96.14 10	92.22 33
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SteuartSystems-ACMMP		90.00 17	91.73 35	87.97 12	91.21 29	90.29 28	96.51 7	78.00 23	86.33 63	85.32 40	88.23 90	94.67 77	82.08 20	95.13 22	93.88 16	94.72 35	93.59 13
Skip Steuart: Steuart Systems R&D Blog.
SD-MVS		89.91 18	92.23 30	87.19 21	91.31 24	89.79 35	94.31 32	75.34 47	89.26 37	81.79 67	92.68 32	95.08 68	83.88 11	93.10 39	92.69 25	96.54 4	93.02 24
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
ACMMP_NAP		89.86 19	91.96 33	87.42 19	91.00 30	90.08 30	96.00 15	76.61 36	89.28 35	87.73 17	90.04 63	91.80 121	78.71 38	94.36 28	93.82 17	94.48 37	94.32 6

APDe-MVS		89.85 20	92.91 10	86.29 26	90.47 38	91.34 7	96.04 14	76.41 39	91.11 17	78.50 89	93.44 22	95.82 45	81.55 23	93.16 37	91.90 38	94.77 32	93.58 15
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
OPM-MVS		89.82 21	92.24 29	86.99 22	90.86 33	89.35 38	95.07 27	75.91 43	91.16 16	86.87 30	91.07 55	97.29 18	79.13 34	93.32 35	91.99 37	94.12 40	91.49 40
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DPE-MVS		89.81 22	92.34 24	86.86 23	89.69 44	91.00 16	95.53 18	76.91 33	88.18 46	83.43 53	93.48 21	95.19 63	81.07 26	92.75 45	92.07 36	94.55 36	93.74 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
WR-MVS		89.79 23	93.66 5	85.27 37	91.32 23	88.27 46	93.49 38	79.86 10	92.75 9	75.37 104	96.86 1	98.38 5	75.10 71	95.93 8	94.07 14	96.46 5	89.39 57

TSAR-MVS + MP.		89.67 24	92.25 28	86.65 25	91.53 18	90.98 17	96.15 13	73.30 56	87.88 49	81.83 66	92.92 30	95.15 66	82.23 18	93.58 34	92.25 33	94.87 29	93.01 25
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CPTT-MVS		89.63 25	90.52 47	88.59 6	90.95 31	90.74 21	95.71 16	79.13 15	87.70 50	85.68 38	80.05 151	95.74 49	84.77 6	94.28 29	92.68 26	95.28 26	92.45 31

ACMH+	79.05 11	89.62 26	93.08 8	85.58 32	88.58 56	89.26 39	92.18 46	74.23 52	93.55 8	82.66 57	92.32 37	98.35 7	80.29 29	95.28 18	92.34 31	95.52 22	90.43 48

DVP-MVS		89.40 27	92.69 13	85.56 34	89.01 50	89.85 33	93.72 35	75.42 45	92.28 11	80.49 72	94.36 13	94.87 71	81.46 24	92.49 49	91.42 41	93.27 53	93.54 17
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
X-MVS		89.36 28	90.73 45	87.77 16	91.50 20	91.23 8	96.76 4	78.88 17	87.29 54	87.14 25	78.98 156	94.53 79	76.47 57	95.25 19	94.28 11	95.85 14	93.55 16

TSAR-MVS + ACMM		89.14 29	92.11 32	85.67 31	89.27 47	90.61 24	90.98 52	79.48 13	88.86 40	79.80 79	93.01 28	93.53 95	83.17 15	92.75 45	92.45 29	91.32 82	93.59 13

SixPastTwentyTwo		89.14 29	92.19 31	85.58 32	84.62 89	82.56 92	90.53 63	71.93 64	91.95 12	85.89 35	94.22 14	97.25 19	85.42 5	95.73 12	91.71 40	95.08 27	91.89 36

APD-MVS		89.14 29	91.25 42	86.67 24	91.73 15	91.02 15	95.50 20	77.74 24	84.04 86	79.47 82	91.48 46	94.85 72	81.14 25	92.94 41	92.20 35	94.47 38	92.24 32
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PS-CasMVS		89.07 32	93.23 7	84.21 50	92.44 8	88.23 48	90.54 62	82.95 3	90.50 26	75.31 105	95.80 6	98.37 6	71.16 100	96.30 5	93.32 21	92.88 61	90.11 50

UA-Net		89.02 33	91.44 39	86.20 28	94.88 1	89.84 34	94.76 29	77.45 28	85.41 74	74.79 109	88.83 84	88.90 148	78.67 40	96.06 7	95.45 4	96.66 3	95.58 2

LS3D		89.02 33	91.69 36	85.91 30	89.72 43	90.81 20	92.56 45	71.69 66	90.83 22	87.24 22	89.71 68	92.07 115	78.37 42	94.43 27	92.59 27	95.86 13	91.35 41

DTE-MVSNet		88.99 35	92.77 12	84.59 43	93.31 2	88.10 49	90.96 53	83.09 2	91.38 14	76.21 98	96.03 2	98.04 8	70.78 106	95.65 14	92.32 32	93.18 56	87.84 73

WR-MVS_H		88.99 35	93.28 6	83.99 53	91.92 11	89.13 40	91.95 47	83.23 1	90.14 30	71.92 129	95.85 4	98.01 10	71.83 96	95.82 9	93.19 22	93.07 59	90.83 47

SED-MVS		88.96 37	92.37 22	84.99 40	88.64 55	89.65 37	95.11 25	75.98 42	90.73 24	80.15 77	94.21 15	94.51 82	76.59 56	92.94 41	91.17 45	93.46 50	93.37 22

ACMH	78.40 12	88.94 38	92.62 16	84.65 42	86.45 74	87.16 60	91.47 49	68.79 87	95.49 2	89.74 6	93.55 20	98.50 2	77.96 46	94.14 31	89.57 62	93.49 47	89.94 52
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PEN-MVS		88.86 39	92.92 9	84.11 52	92.92 4	88.05 51	90.83 55	82.67 5	91.04 18	74.83 108	95.97 3	98.47 3	70.38 107	95.70 13	92.43 30	93.05 60	88.78 65

HPM-MVS++		88.74 40	89.54 52	87.80 15	92.58 6	85.69 69	95.10 26	78.01 22	87.08 56	87.66 19	87.89 93	92.07 115	80.28 30	90.97 69	91.41 43	93.17 57	91.69 37

CP-MVSNet		88.71 41	92.63 15	84.13 51	92.39 9	88.09 50	90.47 66	82.86 4	88.79 42	75.16 106	94.87 9	97.68 13	71.05 102	96.16 6	93.18 23	92.85 62	89.64 55

MSP-MVS		88.51 42	91.36 40	85.19 39	90.63 36	92.01 4	95.29 22	77.52 27	90.48 27	80.21 76	90.21 62	96.08 37	76.38 59	88.30 97	91.42 41	91.12 89	91.01 44
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
OMC-MVS		88.16 43	91.34 41	84.46 46	86.85 70	90.63 23	93.01 42	67.00 103	90.35 28	87.40 21	86.86 108	96.35 31	77.66 49	92.63 47	90.84 46	94.84 30	91.68 38

3Dnovator+	83.71 3	88.13 44	90.00 50	85.94 29	86.82 71	91.06 13	94.26 33	75.39 46	88.85 41	85.76 37	85.74 119	86.92 157	78.02 45	93.03 40	92.21 34	95.39 25	92.21 34

CSCG		88.12 45	91.45 38	84.23 48	88.12 61	90.59 25	90.57 60	68.60 89	91.37 15	83.45 52	89.94 64	95.14 67	78.71 38	91.45 58	88.21 73	95.96 12	93.44 19

RPSCF		88.05 46	92.61 17	82.73 65	84.24 96	88.40 44	90.04 72	66.29 107	91.46 13	82.29 60	88.93 82	96.01 41	79.38 32	95.15 21	94.90 6	94.15 39	93.40 20

DeepPCF-MVS	81.61 6	87.95 47	90.29 49	85.22 38	87.48 65	90.01 31	93.79 34	73.54 54	88.93 39	83.89 45	89.40 73	90.84 132	80.26 31	90.62 72	90.19 53	92.36 71	92.03 35

SF-MVS		87.85 48	90.95 44	84.22 49	88.17 60	87.90 54	90.80 56	71.80 65	89.28 35	82.70 56	89.90 65	95.37 58	77.91 47	91.69 54	90.04 54	93.95 44	92.47 29

DeepC-MVS_fast	81.78 5	87.38 49	89.64 51	84.75 41	89.89 42	90.70 22	92.74 44	74.45 50	86.02 67	82.16 64	86.05 116	91.99 119	75.84 65	91.16 63	90.44 49	93.41 51	91.09 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v7n		87.11 50	90.46 48	83.19 56	85.22 85	83.69 80	90.03 73	68.20 95	91.01 19	86.71 33	94.80 10	98.46 4	77.69 48	91.10 65	85.98 93	91.30 83	88.19 69

CNVR-MVS		86.93 51	88.98 56	84.54 44	90.11 40	87.41 58	93.23 41	73.47 55	86.31 64	82.25 61	82.96 138	92.15 113	76.04 62	91.69 54	90.69 47	92.17 74	91.64 39

NCCC		86.74 52	87.97 68	85.31 36	90.64 35	87.25 59	93.27 40	74.59 49	86.50 61	83.72 46	75.92 184	92.39 109	77.08 53	91.72 53	90.68 48	92.57 67	91.30 42

train_agg		86.67 53	87.73 70	85.43 35	91.51 19	82.72 89	94.47 31	74.22 53	81.71 109	81.54 70	89.20 77	92.87 103	78.33 43	90.12 80	88.47 69	92.51 69	89.04 61

CDPH-MVS		86.66 54	88.52 59	84.48 45	89.61 45	88.27 46	92.86 43	72.69 61	80.55 127	82.71 55	86.92 107	93.32 98	75.55 67	91.00 68	89.85 56	93.47 49	89.71 54

Gipuma		86.47 55	89.25 54	83.23 55	83.88 103	78.78 127	85.35 117	68.42 91	92.69 10	89.03 11	91.94 39	96.32 35	81.80 21	94.45 26	86.86 82	90.91 90	83.69 102
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PHI-MVS		86.37 56	88.14 65	84.30 47	86.65 73	87.56 56	90.76 57	70.16 73	82.55 98	89.65 7	84.89 126	92.40 108	75.97 63	90.88 70	89.70 58	92.58 65	89.03 62

MSLP-MVS++		86.29 57	89.10 55	83.01 58	85.71 82	89.79 35	87.04 104	74.39 51	85.17 76	78.92 86	77.59 167	93.57 93	82.60 17	93.23 36	91.88 39	89.42 109	92.46 30

TAPA-MVS	78.00 13	85.88 58	88.37 61	82.96 60	84.69 87	88.62 43	90.62 58	64.22 131	89.15 38	88.05 14	78.83 158	93.71 90	76.20 61	90.11 81	88.22 72	94.00 41	89.97 51
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVS_0304		85.73 59	87.94 69	83.14 57	88.68 54	87.98 52	93.34 39	70.74 71	79.78 135	82.37 58	88.32 89	89.44 141	71.34 98	90.61 73	89.64 60	92.40 70	89.79 53

anonymousdsp		85.62 60	90.53 46	79.88 92	64.64 221	76.35 149	96.28 12	53.53 205	85.63 71	81.59 69	92.81 31	97.71 12	86.88 2	94.56 25	92.83 24	96.35 6	93.84 9

TSAR-MVS + COLMAP		85.51 61	88.36 62	82.19 67	86.05 79	87.69 55	90.50 65	70.60 72	86.40 62	82.33 59	89.69 69	92.52 107	74.01 81	87.53 102	86.84 83	89.63 104	87.80 74

CNLPA		85.50 62	88.58 57	81.91 71	84.55 91	87.52 57	90.89 54	63.56 141	88.18 46	84.06 44	83.85 135	91.34 129	76.46 58	91.27 60	89.00 67	91.96 75	88.88 63

UniMVSNet_ETH3D		85.39 63	91.12 43	78.71 99	90.48 37	83.72 79	81.76 146	82.41 6	93.84 6	64.43 166	95.41 7	98.76 1	63.72 148	93.63 33	89.74 57	89.47 108	82.74 114

PLC	76.06 15	85.38 64	87.46 72	82.95 61	85.79 81	88.84 41	88.86 83	68.70 88	87.06 57	83.60 48	79.02 154	90.05 138	77.37 52	90.88 70	89.66 59	93.37 52	86.74 79
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TSAR-MVS + GP.		85.32 65	87.41 74	82.89 62	90.07 41	85.69 69	89.07 81	72.99 60	82.45 99	74.52 113	85.09 123	87.67 154	79.24 33	91.11 64	90.41 50	91.45 79	89.45 56

TranMVSNet+NR-MVSNet		85.23 66	89.38 53	80.39 90	88.78 53	83.77 78	87.40 96	76.75 34	85.47 72	68.99 145	95.18 8	97.55 16	67.13 127	91.61 56	89.13 66	93.26 54	82.95 111

HQP-MVS		85.02 67	86.41 80	83.40 54	89.19 48	86.59 63	91.28 50	71.60 67	82.79 95	83.48 51	78.65 160	93.54 94	72.55 89	86.49 113	85.89 96	92.28 73	90.95 46

UniMVSNet (Re)		84.95 68	88.53 58	80.78 81	87.82 63	84.21 75	88.03 88	76.50 37	81.18 120	69.29 143	92.63 35	96.83 22	69.07 114	91.23 62	89.60 61	93.97 43	84.00 100

DU-MVS		84.88 69	88.27 64	80.92 79	88.30 57	83.59 81	87.06 102	78.35 19	80.64 125	70.49 137	92.67 33	96.91 21	68.13 119	91.79 51	89.29 65	93.20 55	83.02 108

MCST-MVS		84.79 70	86.48 78	82.83 63	87.30 67	87.03 62	90.46 67	69.33 81	83.14 92	82.21 63	81.69 147	92.14 114	75.09 72	87.27 105	84.78 107	92.58 65	89.30 58

UniMVSNet_NR-MVSNet		84.62 71	88.00 67	80.68 85	88.18 59	83.83 77	87.06 102	76.47 38	81.46 116	70.49 137	93.24 24	95.56 52	68.13 119	90.43 74	88.47 69	93.78 45	83.02 108

EG-PatchMatch MVS		84.35 72	87.55 71	80.62 86	86.38 75	82.24 94	86.75 105	64.02 136	84.24 82	78.17 92	89.38 74	95.03 70	78.78 37	89.95 82	86.33 89	89.59 105	85.65 87

AdaColmap		84.15 73	85.14 98	83.00 59	89.08 49	87.14 61	90.56 61	70.90 69	82.40 102	80.41 73	73.82 195	84.69 168	75.19 70	91.58 57	89.90 55	91.87 76	86.48 80

PCF-MVS	76.59 14	84.11 74	85.27 95	82.76 64	86.12 78	88.30 45	91.24 51	69.10 82	82.36 103	84.45 43	77.56 168	90.40 137	72.91 88	85.88 118	83.88 115	92.72 64	88.53 66
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_111021_HR		83.95 75	86.10 83	81.44 76	84.62 89	80.29 113	90.51 64	68.05 96	84.07 85	80.38 74	84.74 129	91.37 128	74.23 77	90.37 76	87.25 78	90.86 91	84.59 92

TinyColmap		83.79 76	86.12 82	81.07 78	83.42 109	81.44 101	85.42 115	68.55 90	88.71 43	89.46 8	87.60 95	92.72 104	70.34 108	89.29 87	81.94 133	89.20 110	81.12 133

EC-MVSNet		83.70 77	84.77 107	82.46 66	87.47 66	82.79 88	85.50 113	72.00 63	69.81 177	77.66 93	85.02 125	89.63 139	78.14 44	90.40 75	87.56 75	94.00 41	88.16 70

v1192		83.61 78	85.23 96	81.72 73	84.05 98	82.15 95	89.54 76	66.20 108	81.38 118	86.76 32	91.79 43	96.03 39	74.88 74	81.81 161	80.92 141	88.91 116	82.50 116

SPE-MVS-test		83.59 79	84.86 104	82.10 69	83.04 115	81.05 107	91.58 48	67.48 102	72.52 166	78.42 90	84.75 128	91.82 120	78.62 41	91.98 50	87.54 76	93.48 48	84.35 95

CS-MVS		83.57 80	84.79 106	82.14 68	83.83 104	81.48 100	87.29 97	66.54 105	72.73 165	80.05 78	84.04 133	93.12 102	80.35 28	89.50 84	86.34 88	94.76 34	86.32 83

v1240		83.57 80	84.94 102	81.97 70	84.05 98	81.27 103	89.46 78	66.06 110	81.31 119	87.50 20	91.88 42	95.46 55	76.25 60	81.16 167	80.51 145	88.52 126	82.98 110

v1921920		83.49 82	84.94 102	81.80 72	83.78 105	81.20 105	89.50 77	65.91 113	81.64 111	87.18 24	91.70 44	95.39 57	75.85 64	81.56 165	80.27 148	88.60 121	82.80 112

v144192		83.43 83	84.97 101	81.63 75	83.43 108	81.23 104	89.42 79	66.04 112	81.45 117	86.40 34	91.46 47	95.70 50	75.76 66	82.14 157	80.23 149	88.74 118	82.57 115

Vis-MVSNet		83.32 84	88.12 66	77.71 106	77.91 166	83.44 83	90.58 59	69.49 78	81.11 121	67.10 159	89.85 66	91.48 126	71.71 97	91.34 59	89.37 63	89.48 107	90.26 49
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1144		83.22 85	85.01 99	81.14 77	83.76 106	81.60 99	88.95 82	65.58 119	81.89 107	85.80 36	91.68 45	95.84 44	74.04 80	82.12 158	80.56 144	88.70 120	81.41 127

MVS_111021_LR		83.20 86	85.33 94	80.73 84	82.88 118	78.23 131	89.61 75	65.23 122	82.08 105	81.19 71	85.31 121	92.04 118	75.22 69	89.50 84	85.90 95	90.24 94	84.23 96

v10		83.17 87	85.22 97	80.78 81	83.26 111	82.99 87	88.66 85	66.49 106	79.24 139	83.60 48	91.46 47	95.47 54	74.12 78	82.60 154	80.66 142	88.53 125	84.11 99

PVSNet_Blended_VisFu		83.00 88	84.16 120	81.65 74	82.17 126	86.01 66	88.03 88	71.23 68	76.05 152	79.54 81	83.88 134	83.44 170	77.49 51	87.38 103	84.93 105	91.41 80	87.40 77

NR-MVSNet		82.89 89	87.43 73	77.59 108	83.91 102	83.59 81	87.10 101	78.35 19	80.64 125	68.85 146	92.67 33	96.50 26	54.19 191	87.19 108	88.68 68	93.16 58	82.75 113

CANet		82.84 90	84.60 109	80.78 81	87.30 67	85.20 72	90.23 69	69.00 83	72.16 169	78.73 88	84.49 131	90.70 135	69.54 112	87.65 101	86.17 90	89.87 101	85.84 85

Baseline_NR-MVSNet		82.79 91	86.51 77	78.44 103	88.30 57	75.62 157	87.81 90	74.97 48	81.53 113	66.84 160	94.71 12	96.46 27	66.90 129	91.79 51	83.37 124	85.83 159	82.09 119

EPP-MVSNet		82.76 92	86.47 79	78.45 102	86.00 80	84.47 74	85.39 116	68.42 91	84.17 83	62.97 170	89.26 76	76.84 196	72.13 93	92.56 48	90.40 51	95.76 20	87.56 76

CLD-MVS		82.75 93	87.22 75	77.54 109	88.01 62	85.76 68	90.23 69	54.52 199	82.28 104	82.11 65	88.48 87	95.27 60	63.95 146	89.41 86	88.29 71	86.45 147	81.01 134
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Effi-MVS+		82.33 94	83.87 124	80.52 88	84.51 94	81.32 102	87.53 94	68.05 96	74.94 157	79.67 80	82.37 144	92.31 110	72.21 90	85.06 128	86.91 81	91.18 85	84.20 97

3Dnovator	79.41 10	82.21 95	86.07 84	77.71 106	79.31 149	84.61 73	87.18 99	61.02 165	85.65 70	76.11 99	85.07 124	85.38 166	70.96 104	87.22 106	86.47 85	91.66 77	88.12 72

v8		82.20 96	84.56 110	79.45 95	82.42 123	81.65 98	87.26 98	64.27 130	79.36 138	81.70 68	91.04 56	95.75 48	73.30 87	82.82 150	79.18 156	87.74 133	82.09 119

v2v482		82.20 96	84.26 116	79.81 93	82.67 122	80.18 114	87.67 92	63.96 138	81.69 110	84.73 41	91.27 53	96.33 34	72.05 94	81.94 160	79.56 153	87.79 132	78.84 153

Effi-MVS+-dtu		82.04 98	83.39 132	80.48 89	85.48 83	86.57 64	88.40 86	68.28 93	69.04 184	73.13 122	76.26 179	91.11 131	74.74 75	88.40 95	87.76 74	92.84 63	84.57 93

MAR-MVS		81.98 99	82.92 136	80.88 80	85.18 86	85.85 67	89.13 80	69.52 76	71.21 173	82.25 61	71.28 205	88.89 149	69.69 109	88.71 90	86.96 79	89.52 106	87.57 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
GeoE		81.92 100	83.87 124	79.66 94	84.64 88	79.87 115	89.75 74	65.90 114	76.12 151	75.87 101	84.62 130	92.23 111	71.96 95	86.83 110	83.60 118	89.83 102	83.81 101

IS_MVSNet		81.72 101	85.01 99	77.90 105	86.19 76	82.64 91	85.56 112	70.02 74	80.11 131	63.52 168	87.28 101	81.18 180	67.26 124	91.08 67	89.33 64	94.82 31	83.42 105

FPMVS		81.56 102	84.04 122	78.66 100	82.92 116	75.96 153	86.48 108	65.66 118	84.67 80	71.47 132	77.78 164	83.22 173	77.57 50	91.24 61	90.21 52	87.84 131	85.21 89

casdiffmvs_mvg		81.50 103	85.70 89	76.60 116	82.68 121	80.54 110	83.50 131	64.49 129	83.40 87	72.53 123	92.15 38	95.40 56	65.84 136	84.69 135	81.89 134	90.59 92	81.86 123
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DPM-MVS		81.42 104	82.11 141	80.62 86	87.54 64	85.30 71	90.18 71	68.96 84	81.00 123	79.15 84	70.45 211	83.29 172	67.67 123	82.81 151	83.46 119	90.19 95	88.48 67

Fast-Effi-MVS+		81.42 104	83.82 126	78.62 101	82.24 125	80.62 109	87.72 91	63.51 142	73.01 161	74.75 111	83.80 136	92.70 105	73.44 86	88.15 100	85.26 101	90.05 96	83.17 106

USDC		81.39 106	83.07 134	79.43 96	81.48 131	78.95 126	82.62 139	66.17 109	87.45 53	90.73 4	82.40 143	93.65 92	66.57 131	83.63 144	77.97 163	89.00 114	77.45 161

MSDG		81.39 106	84.23 118	78.09 104	82.40 124	82.47 93	85.31 119	60.91 166	79.73 136	80.26 75	86.30 112	88.27 152	69.67 110	87.20 107	84.98 104	89.97 98	80.67 136

sasdasda		81.22 108	86.04 85	75.60 122	83.17 113	83.18 85	80.29 158	65.82 116	85.97 68	67.98 154	77.74 165	91.51 124	65.17 141	88.62 92	86.15 91	91.17 86	89.09 59

canonicalmvs		81.22 108	86.04 85	75.60 122	83.17 113	83.18 85	80.29 158	65.82 116	85.97 68	67.98 154	77.74 165	91.51 124	65.17 141	88.62 92	86.15 91	91.17 86	89.09 59

thisisatest0515		81.18 110	84.32 113	77.52 110	76.73 179	74.84 164	85.06 122	61.37 162	81.05 122	73.95 115	88.79 85	89.25 145	75.49 68	85.98 117	84.78 107	92.53 68	85.56 88

viewmacassd2359aftdt		81.04 111	85.39 93	75.95 119	80.71 136	77.95 133	85.29 120	58.82 181	86.88 59	76.27 97	91.34 49	96.35 31	68.32 117	84.35 139	79.13 158	86.32 149	81.73 124

pmmvs6		80.46 112	88.34 63	71.26 156	81.96 127	77.51 138	77.54 176	68.83 86	93.72 7	55.92 191	93.94 19	98.03 9	55.94 180	89.21 88	85.61 97	87.36 137	80.38 138

QAPM		80.43 113	84.34 112	75.86 120	79.40 148	82.06 97	79.86 164	61.94 157	83.28 89	74.73 112	81.74 146	85.44 165	70.97 103	84.99 133	84.71 109	88.29 127	88.14 71

PM-MVS		80.42 114	83.63 128	76.67 114	78.04 163	72.37 179	87.14 100	60.18 171	80.13 130	71.75 130	86.12 115	93.92 89	77.08 53	86.56 112	85.12 103	85.83 159	81.18 130

viewcassd2359sk11		80.26 115	83.21 133	76.82 113	81.93 128	77.91 134	85.75 110	62.34 154	83.17 91	77.53 94	89.00 79	95.26 61	67.11 128	81.06 168	76.55 178	86.29 150	79.50 149

viewdifsd2359ckpt13		80.07 116	83.42 131	76.17 118	80.95 135	79.07 123	85.14 121	61.42 161	80.41 129	74.78 110	87.22 103	94.70 76	68.23 118	82.60 154	78.34 161	86.49 145	81.63 125

DCV-MVSNet		80.04 117	85.67 91	73.48 142	82.91 117	81.11 106	80.44 157	66.06 110	85.01 77	62.53 173	78.84 157	94.43 84	58.51 171	88.66 91	85.91 94	90.41 93	85.73 86

casdiffmvs		79.93 118	84.11 121	75.05 129	81.41 133	78.99 125	82.95 136	62.90 149	81.53 113	68.60 150	91.94 39	96.03 39	65.84 136	82.89 149	77.07 172	88.59 122	80.34 142
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewmanbaseed2359cas		79.90 119	83.96 123	75.17 128	80.25 140	77.62 137	84.62 125	58.25 185	83.22 90	74.92 107	89.50 71	95.33 59	67.20 125	83.05 146	77.84 165	85.76 161	81.18 130

IterMVS-LS		79.79 120	82.56 139	76.56 117	81.83 129	77.85 135	79.90 163	69.42 80	78.93 141	71.21 133	90.47 59	85.20 167	70.86 105	80.54 173	80.57 143	86.15 151	84.36 94
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DELS-MVS		79.71 121	83.74 127	75.01 131	79.31 149	82.68 90	84.79 124	60.06 172	75.43 155	69.09 144	86.13 114	89.38 143	67.16 126	85.12 127	83.87 116	89.65 103	83.57 103
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
test1111		79.67 122	84.40 111	74.16 137	85.29 84	79.56 120	81.16 151	73.13 59	84.65 81	56.08 189	88.38 88	86.14 161	60.49 161	89.78 83	85.59 98	88.79 117	76.68 162

pmmvs-eth3d		79.64 123	82.06 142	76.83 112	80.05 142	72.64 177	87.47 95	66.59 104	80.83 124	73.50 118	89.32 75	93.20 99	67.78 121	80.78 171	81.64 137	85.58 163	76.01 164

UGNet		79.62 124	85.91 87	72.28 148	73.52 191	83.91 76	86.64 106	69.51 77	79.85 134	62.57 172	85.82 118	89.63 139	53.18 195	88.39 96	87.35 77	88.28 128	86.43 81
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
V42		79.59 125	83.59 129	74.93 134	69.61 204	77.05 145	86.59 107	55.84 192	78.42 143	77.29 95	89.84 67	95.08 68	74.12 78	83.05 146	80.11 151	86.12 152	81.59 126

MGCFI-Net		79.42 126	85.64 92	72.15 149	82.80 120	82.09 96	76.92 182	65.46 120	86.31 64	57.48 184	78.15 162	91.38 127	59.10 168	88.23 99	84.47 111	91.14 88	88.88 63

Anonymous20231211		79.37 127	85.78 88	71.89 150	82.87 119	79.66 119	78.77 173	63.93 139	83.36 88	59.39 179	90.54 57	94.66 78	56.46 178	87.38 103	84.12 113	89.92 99	80.74 135

EPNet		79.36 128	79.44 153	79.27 98	89.51 46	77.20 143	88.35 87	77.35 31	68.27 186	74.29 114	76.31 177	79.22 186	59.63 164	85.02 132	85.45 100	86.49 145	84.61 91
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v148		79.33 129	82.32 140	75.84 121	80.14 141	75.74 154	81.98 145	57.06 189	81.51 115	79.36 83	89.42 72	96.42 29	71.32 99	81.54 166	75.29 185	85.20 165	76.32 163

ECVR-MVS		79.31 130	84.20 119	73.60 139	84.55 91	80.37 111	79.63 167	73.23 57	82.64 96	55.98 190	87.50 96	86.85 158	59.61 165	90.35 77	86.46 86	88.58 123	75.26 172

FC-MVSNet-train		79.20 131	86.29 81	70.94 160	84.06 97	77.67 136	85.68 111	64.11 133	82.90 94	52.22 206	92.57 36	93.69 91	49.52 208	88.30 97	86.93 80	90.03 97	81.95 121

TransMVSNet (Re)		79.05 132	86.66 76	70.18 166	83.32 110	75.99 152	77.54 176	63.98 137	90.68 25	55.84 192	94.80 10	96.06 38	53.73 194	86.27 115	83.22 125	86.65 141	79.61 148

ETV-MVS		79.01 133	77.98 160	80.22 91	86.69 72	79.73 118	88.80 84	68.27 94	63.22 207	71.56 131	70.25 213	73.63 206	73.66 84	90.30 79	86.77 84	92.33 72	81.95 121

FA-MVS(training)		78.93 134	80.63 148	76.93 111	79.79 145	75.57 158	85.44 114	61.95 156	77.19 147	78.97 85	84.82 127	82.47 175	66.43 134	84.09 141	80.13 150	89.02 113	80.15 145

EIA-MVS		78.57 135	77.90 161	79.35 97	87.24 69	80.71 108	86.16 109	64.03 135	62.63 212	73.49 119	73.60 196	76.12 200	73.83 82	88.49 94	84.93 105	91.36 81	78.78 154

OpenMVS	75.38 16	78.44 136	81.39 145	74.99 132	80.46 138	79.85 116	79.99 161	58.31 184	77.34 146	73.85 116	77.19 171	82.33 178	68.60 116	84.67 136	81.95 132	88.72 119	86.40 82

viewdifsd2359ckpt11		78.29 137	84.30 114	71.27 154	78.48 157	74.68 169	82.25 142	55.40 195	82.45 99	60.97 178	91.34 49	96.58 25	65.48 139	85.14 125	78.70 159	85.05 168	81.21 128

viewmsd2359difaftdt		78.29 137	84.30 114	71.27 154	78.48 157	74.69 168	82.25 142	55.40 195	82.45 99	60.98 177	91.34 49	96.59 24	65.48 139	85.14 125	78.70 159	85.05 168	81.21 128

pm-mvs1		78.21 139	85.68 90	69.50 171	80.38 139	75.73 155	76.25 186	65.04 123	87.59 51	54.47 197	93.16 26	95.99 43	54.20 190	86.37 114	82.98 128	86.64 142	77.96 159

FMVSNet1		78.20 140	84.83 105	70.46 164	78.62 156	79.03 124	77.90 175	67.53 101	83.02 93	55.10 195	87.19 104	93.18 100	55.65 183	85.57 119	83.39 121	87.98 130	82.40 117

DI_MVS_pp		77.64 141	79.64 152	75.31 126	79.87 144	76.89 146	81.55 149	63.64 140	76.21 150	72.03 128	85.59 120	82.97 174	66.63 130	79.27 179	77.78 166	88.14 129	78.76 155

diffmvs_AUTHOR		77.61 142	82.84 138	71.49 153	76.16 183	74.80 165	81.22 150	57.90 187	79.89 133	68.06 153	90.49 58	94.78 75	62.29 156	81.77 162	77.04 173	83.33 181	81.14 132

IterMVS-SCA-FT		77.23 143	79.18 155	74.96 133	76.67 180	79.85 116	75.58 196	61.34 163	73.10 160	73.79 117	86.23 113	79.61 185	79.00 36	80.28 175	75.50 184	83.41 180	79.70 147

tfpnnormal		77.16 144	84.26 116	68.88 174	81.02 134	75.02 161	76.52 185	63.30 144	87.29 54	52.40 204	91.24 54	93.97 87	54.85 188	85.46 122	81.08 139	85.18 166	75.76 168

Fast-Effi-MVS+-dtu		76.92 145	77.18 166	76.62 115	79.55 146	79.17 122	84.80 123	77.40 29	64.46 202	68.75 148	70.81 209	86.57 159	63.36 153	81.74 163	81.76 135	85.86 158	75.78 167

diffmvs		76.74 146	81.61 144	71.06 158	75.64 185	74.45 170	80.68 156	57.57 188	77.48 144	67.62 158	88.95 81	93.94 88	61.98 158	79.74 176	76.18 179	82.85 182	80.50 137
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		76.72 147	79.40 154	73.60 139	78.85 155	74.99 162	79.91 162	61.56 159	69.67 178	72.44 124	85.98 117	90.78 133	63.50 151	78.30 182	75.74 182	85.33 164	80.31 143

MDA-MVSNet-bldmvs		76.51 148	82.87 137	69.09 173	50.71 232	74.72 167	84.05 129	60.27 170	81.62 112	71.16 134	88.21 91	91.58 122	69.62 111	92.78 44	77.48 169	78.75 193	73.69 179

EU-MVSNet		76.48 149	80.53 149	71.75 151	67.62 211	70.30 185	81.74 147	54.06 202	75.47 154	71.01 135	80.10 149	93.17 101	73.67 83	83.73 143	77.85 164	82.40 183	83.07 107

PVSNet_BlendedMVS		76.45 150	78.12 158	74.49 135	76.76 173	78.46 128	79.65 165	63.26 145	65.42 198	73.15 120	75.05 189	88.96 146	66.51 132	82.73 152	77.66 167	87.61 134	78.60 156

PVSNet_Blended		76.45 150	78.12 158	74.49 135	76.76 173	78.46 128	79.65 165	63.26 145	65.42 198	73.15 120	75.05 189	88.96 146	66.51 132	82.73 152	77.66 167	87.61 134	78.60 156

viewmambaseed2359dif		76.20 152	80.07 150	71.68 152	76.99 171	73.91 173	80.81 154	59.23 177	74.86 158	66.65 161	86.44 110	93.44 97	62.91 154	79.19 180	73.77 188	83.49 178	78.89 152

Vis-MVSNet (Re-imp)		76.15 153	80.84 147	70.68 161	83.66 107	74.80 165	81.66 148	69.59 75	80.48 128	46.94 216	87.44 98	80.63 182	53.14 196	86.87 109	84.56 110	89.12 111	71.12 184

PatchMatch-RL		76.05 154	76.64 171	75.36 125	77.84 168	69.87 188	81.09 153	63.43 143	71.66 171	68.34 152	71.70 201	81.76 179	74.98 73	84.83 134	83.44 120	86.45 147	73.22 181

pmmvs4		75.92 155	77.48 165	74.10 138	78.21 162	70.94 182	84.06 128	64.78 125	75.13 156	68.47 151	84.12 132	83.32 171	64.74 145	75.93 194	79.14 157	84.31 172	73.77 178

FC-MVSNet-test		75.91 156	83.59 129	66.95 186	76.63 181	69.07 190	85.33 118	64.97 124	84.87 79	41.95 222	93.17 25	87.04 156	47.78 211	91.09 66	85.56 99	85.06 167	74.34 173

tttt0517		75.86 157	76.23 175	75.42 124	75.55 186	74.06 171	82.73 137	60.31 168	69.24 180	70.24 139	79.18 153	58.79 224	72.17 91	84.49 137	83.08 126	91.54 78	84.80 90

CVMVSNet		75.65 158	77.62 164	73.35 145	71.95 197	69.89 187	83.04 135	60.84 167	69.12 182	68.76 147	79.92 152	78.93 188	73.64 85	81.02 169	81.01 140	81.86 186	83.43 104

thisisatest0530		75.54 159	75.95 179	75.05 129	75.08 187	73.56 174	82.15 144	60.31 168	69.17 181	69.32 142	79.02 154	58.78 225	72.17 91	83.88 142	83.08 126	91.30 83	84.20 97

test2506		75.32 160	76.87 170	73.50 141	84.55 91	80.37 111	79.63 167	73.23 57	82.64 96	55.41 193	76.87 174	45.42 238	59.61 165	90.35 77	86.46 86	88.58 123	75.98 165

IB-MVS	71.28 17	75.21 161	77.00 168	73.12 146	76.76 173	77.45 139	83.05 134	58.92 180	63.01 208	64.31 167	59.99 227	87.57 155	68.64 115	86.26 116	82.34 131	87.05 140	82.36 118
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
CANet_DTU		75.04 162	78.45 156	71.07 157	77.27 169	77.96 132	83.88 130	58.00 186	64.11 203	68.67 149	75.65 186	88.37 151	53.92 193	82.05 159	81.11 138	84.67 170	79.88 146

FE-MVSNET		75.03 163	80.98 146	68.08 179	73.53 190	71.43 181	75.74 193	59.74 174	81.81 108	58.16 182	82.47 140	93.51 96	55.42 185	83.18 145	80.51 145	85.90 157	73.94 176

GA-MVS		75.01 164	76.39 173	73.39 143	78.37 159	75.66 156	80.03 160	58.40 183	70.51 175	75.85 102	83.24 137	76.14 199	63.75 147	77.28 186	76.62 177	83.97 174	75.30 171

ET-MVSNet_ETH3D		74.71 165	74.19 186	75.31 126	79.22 151	75.29 159	82.70 138	64.05 134	65.45 197	70.96 136	77.15 172	57.70 226	65.89 135	84.40 138	81.65 136	89.03 112	77.67 160

FMVSNet2		74.43 166	79.70 151	68.27 177	76.76 173	77.36 140	75.77 190	65.36 121	72.28 167	52.97 201	81.92 145	85.61 164	52.73 200	80.66 172	79.73 152	86.04 153	80.37 139

thres600view7		74.34 167	78.43 157	69.56 170	80.47 137	76.28 150	78.65 174	62.56 151	77.39 145	52.53 202	74.03 193	76.78 197	55.90 182	85.06 128	85.19 102	87.25 138	74.29 174

IterMVS		73.62 168	76.53 172	70.23 165	71.83 198	77.18 144	80.69 155	53.22 206	72.23 168	66.62 162	85.21 122	78.96 187	69.54 112	76.28 193	71.63 196	79.45 190	74.25 175
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MIMVSNet1		73.40 169	81.85 143	63.55 199	72.90 194	64.37 204	84.58 126	53.60 204	90.84 21	53.92 198	87.75 94	96.10 36	45.31 214	85.37 124	79.32 155	70.98 208	69.18 193

HyFIR lowres test		73.29 170	74.14 187	72.30 147	73.08 193	78.33 130	83.12 133	62.41 153	63.81 204	62.13 174	76.67 176	78.50 189	71.09 101	74.13 198	77.47 170	81.98 185	70.10 188

GBi-Net		73.17 171	77.64 162	67.95 181	76.76 173	77.36 140	75.77 190	64.57 126	62.99 209	51.83 207	76.05 180	77.76 192	52.73 200	85.57 119	83.39 121	86.04 153	80.37 139

test1		73.17 171	77.64 162	67.95 181	76.76 173	77.36 140	75.77 190	64.57 126	62.99 209	51.83 207	76.05 180	77.76 192	52.73 200	85.57 119	83.39 121	86.04 153	80.37 139

thres400		73.13 173	76.99 169	68.62 175	79.46 147	74.93 163	77.23 178	61.23 164	75.54 153	52.31 205	72.20 200	77.10 195	54.89 186	82.92 148	82.62 130	86.57 144	73.66 180

CDS-MVSNet		73.07 174	77.02 167	68.46 176	81.62 130	72.89 176	79.56 169	70.78 70	69.56 179	52.52 203	77.37 170	81.12 181	42.60 216	84.20 140	83.93 114	83.65 175	70.07 189
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MDTV_nov1_ep13_2view		72.96 175	75.59 180	69.88 167	71.15 201	64.86 203	82.31 141	54.45 200	76.30 149	78.32 91	86.52 109	91.58 122	61.35 159	76.80 187	66.83 207	71.70 201	66.26 197

WB-MVS		72.91 176	82.95 135	61.21 204	68.59 207	73.96 172	73.65 201	61.48 160	90.88 20	42.55 220	94.18 16	95.80 46	53.02 197	85.42 123	75.73 183	67.97 215	64.65 200

gg-mvs-nofinetune		72.68 177	75.21 183	69.73 168	81.48 131	69.04 191	70.48 209	76.67 35	86.92 58	67.80 157	88.06 92	64.67 214	42.12 218	77.60 184	73.65 189	79.81 188	66.57 196

thres200		72.41 178	76.00 178	68.21 178	78.28 160	76.28 150	74.94 197	62.56 151	72.14 170	51.35 210	69.59 216	76.51 198	54.89 186	85.06 128	80.51 145	87.25 138	71.92 183

tfpn200view9		72.01 179	75.40 181	68.06 180	77.97 164	76.44 148	77.04 180	62.67 150	66.81 189	50.82 211	67.30 218	75.67 202	52.46 203	85.06 128	82.64 129	87.41 136	73.86 177

EPNet_dtu		71.90 180	73.03 192	70.59 162	78.28 160	61.64 210	82.44 140	64.12 132	63.26 206	69.74 140	71.47 203	82.41 176	51.89 204	78.83 181	78.01 162	77.07 194	75.60 169
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
gm-plane-assit		71.56 181	69.99 197	73.39 143	84.43 95	73.21 175	90.42 68	51.36 212	84.08 84	76.00 100	91.30 52	37.09 239	59.01 169	73.65 201	70.24 200	79.09 192	60.37 214

CMPMVS	55.74 18	71.56 181	76.26 174	66.08 191	68.11 209	63.91 206	63.17 223	50.52 214	68.79 185	75.49 103	70.78 210	85.67 163	63.54 150	81.58 164	77.20 171	75.63 195	85.86 84
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet3		71.40 183	75.20 184	66.97 185	75.00 188	76.59 147	74.29 198	64.57 126	62.99 209	51.83 207	76.05 180	77.76 192	51.49 205	76.58 190	77.03 174	84.62 171	79.43 150

MS-PatchMatch		71.18 184	73.99 188	67.89 183	77.16 170	71.76 180	77.18 179	56.38 191	67.35 187	55.04 196	74.63 191	75.70 201	62.38 155	76.62 189	75.97 181	79.22 191	75.90 166

test20.03		69.91 185	76.20 176	62.58 200	84.01 100	67.34 196	75.67 195	65.88 115	79.98 132	40.28 226	82.65 139	89.31 144	39.63 221	77.41 185	73.28 190	69.98 209	63.40 205

thres100view900		69.86 186	72.97 193	66.24 188	77.97 164	72.49 178	73.29 202	59.12 178	66.81 189	50.82 211	67.30 218	75.67 202	50.54 206	78.24 183	79.40 154	85.71 162	70.88 185

baseline1		69.62 187	73.55 190	65.02 198	78.95 154	70.39 184	71.38 208	62.03 155	70.97 174	47.95 214	78.47 161	68.19 212	47.77 212	79.65 178	76.94 176	82.05 184	70.27 187

CR-MVSNet		69.56 188	68.34 202	70.99 159	72.78 196	67.63 194	64.47 221	67.74 99	59.93 218	72.30 125	80.10 149	56.77 228	65.04 143	71.64 206	72.91 192	83.61 177	69.40 191

baseline		69.33 189	75.37 182	62.28 202	66.54 217	66.67 199	73.95 200	48.07 215	66.10 192	59.26 180	82.45 141	86.30 160	54.44 189	74.42 197	73.25 191	71.42 204	78.43 158

pmmvs5		68.91 190	74.35 185	62.56 201	67.45 213	66.78 198	71.70 205	51.47 211	67.17 188	56.25 188	82.41 142	88.59 150	47.21 213	73.21 204	74.23 186	81.30 187	68.03 195

CHOSEN 1792x2688		68.80 191	71.09 194	66.13 190	69.11 206	68.89 192	78.98 172	54.68 197	61.63 214	56.69 186	71.56 202	78.39 190	67.69 122	72.13 205	72.01 195	69.63 211	73.02 182

baseline2		68.71 192	68.34 202	69.14 172	75.69 184	69.70 189	76.60 184	55.53 194	60.13 217	62.07 175	66.76 220	60.35 219	60.77 160	76.53 192	74.03 187	84.19 173	70.88 185

SCA		68.54 193	67.52 204	69.73 168	67.79 210	75.04 160	76.96 181	68.94 85	66.41 191	67.86 156	74.03 193	60.96 217	65.55 138	68.99 214	65.67 208	71.30 206	61.54 213

testgi		68.20 194	76.05 177	59.04 207	79.99 143	67.32 197	81.16 151	51.78 210	84.91 78	39.36 227	73.42 197	95.19 63	32.79 227	76.54 191	70.40 199	69.14 212	64.55 201

dmvs_re		68.11 195	70.60 196	65.21 196	77.91 166	63.73 207	76.72 183	59.65 175	55.93 224	47.79 215	59.79 228	79.91 184	49.72 207	82.48 156	76.98 175	79.48 189	75.41 170

MVSTER		68.08 196	69.73 198	66.16 189	66.33 219	70.06 186	75.71 194	52.36 208	55.18 227	58.64 181	70.23 214	56.72 229	57.34 175	79.68 177	76.03 180	86.61 143	80.20 144

Anonymous20231206		67.28 197	73.41 191	60.12 206	76.45 182	63.61 208	74.21 199	56.52 190	76.35 148	42.23 221	75.81 185	90.47 136	41.51 219	74.52 195	69.97 201	69.83 210	63.17 206

RPMNet		67.02 198	63.99 213	70.56 163	71.55 199	67.63 194	75.81 188	69.44 79	59.93 218	63.24 169	64.32 222	47.51 237	59.68 163	70.37 211	69.64 202	83.64 176	68.49 194

CostFormer		66.81 199	66.94 205	66.67 187	72.79 195	68.25 193	79.55 170	55.57 193	65.52 196	62.77 171	76.98 173	60.09 220	56.73 177	65.69 222	62.35 211	72.59 200	69.71 190

PatchT		66.25 200	66.76 206	65.67 194	55.87 227	60.75 211	70.17 210	59.00 179	59.80 220	72.30 125	78.68 159	54.12 233	65.04 143	71.64 206	72.91 192	71.63 203	69.40 191

dps		65.14 201	64.50 211	65.89 193	71.41 200	65.81 202	71.44 207	61.59 158	58.56 221	61.43 176	75.45 187	52.70 235	58.06 173	69.57 213	64.65 209	71.39 205	64.77 199

MDTV_nov1_ep13		64.96 202	64.77 210	65.18 197	67.08 214	62.46 209	75.80 189	51.10 213	62.27 213	69.74 140	74.12 192	62.65 215	55.64 184	68.19 216	62.16 215	71.70 201	61.57 212

PatchmatchNet		64.81 203	63.74 214	66.06 192	69.21 205	58.62 214	73.16 203	60.01 173	65.92 193	66.19 164	76.27 178	59.09 221	60.45 162	66.58 219	61.47 217	67.33 216	58.24 219
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat1		64.79 204	62.74 218	67.17 184	74.61 189	65.91 201	76.18 187	59.32 176	64.88 201	66.41 163	71.21 206	53.56 234	59.17 167	61.53 226	58.16 220	67.33 216	63.95 202

MIMVSNet		63.02 205	69.02 200	56.01 212	68.20 208	59.26 213	70.01 212	53.79 203	71.56 172	41.26 225	71.38 204	82.38 177	36.38 223	71.43 208	67.32 206	66.45 218	59.83 216

TAMVS		63.02 205	69.30 199	55.70 214	70.12 202	56.89 216	69.63 213	45.13 218	70.23 176	38.00 228	77.79 163	75.15 204	42.60 216	74.48 196	72.81 194	68.70 213	57.75 221

tpm		62.79 207	63.25 215	62.26 203	70.09 203	53.78 219	71.65 206	47.31 216	65.72 195	76.70 96	80.62 148	56.40 231	48.11 210	64.20 224	58.54 218	59.70 222	63.47 204

pmmvs3		62.72 208	68.71 201	55.74 213	50.74 231	57.10 215	70.05 211	28.82 228	61.57 216	57.39 185	71.19 207	85.73 162	53.96 192	73.36 203	69.43 203	73.47 199	62.55 208

pmnet_mix02		62.60 209	70.81 195	53.02 219	66.56 216	50.44 226	62.81 224	46.84 217	79.13 140	43.76 219	87.45 97	90.75 134	39.85 220	70.48 210	57.09 221	58.27 224	60.32 215

new-patchmatchnet		62.59 210	73.79 189	49.53 223	76.98 172	53.57 220	53.46 232	54.64 198	85.43 73	28.81 231	91.94 39	96.41 30	25.28 229	76.80 187	53.66 227	57.99 225	58.69 218

test-LLR		62.15 211	59.46 227	65.29 195	79.07 152	52.66 222	69.46 215	62.93 147	50.76 230	53.81 199	63.11 224	58.91 222	52.87 198	66.54 220	62.34 212	73.59 197	61.87 210

PMMVS		61.98 212	65.61 208	57.74 209	45.03 233	51.76 224	69.54 214	35.05 225	55.49 226	55.32 194	68.23 217	78.39 190	58.09 172	70.21 212	71.56 197	83.42 179	63.66 203

test0.0.03 1		61.79 213	65.33 209	57.65 210	79.07 152	64.09 205	68.51 218	62.93 147	61.59 215	33.71 230	61.58 226	71.58 210	33.43 226	70.95 209	68.68 204	68.26 214	58.82 217

MVS-HIRNet		59.74 214	58.74 230	60.92 205	57.74 226	45.81 230	56.02 230	58.69 182	55.69 225	65.17 165	70.86 208	71.66 208	56.75 176	61.11 227	53.74 226	71.17 207	52.28 225

tpmrst		59.42 215	60.02 225	58.71 208	67.56 212	53.10 221	66.99 219	51.88 209	63.80 205	57.68 183	76.73 175	56.49 230	48.73 209	56.47 230	55.55 223	59.43 223	58.02 220

test-mter		59.39 216	61.59 220	56.82 211	53.21 228	54.82 218	73.12 204	26.57 230	53.19 228	56.31 187	64.71 221	60.47 218	56.36 179	68.69 215	64.27 210	75.38 196	65.00 198

E-PMN		59.07 217	62.79 217	54.72 215	67.01 215	47.81 229	60.44 227	43.40 219	72.95 162	44.63 218	70.42 212	73.17 207	58.73 170	80.97 170	51.98 228	54.14 228	42.26 230

EMVS		58.97 218	62.63 219	54.70 216	66.26 220	48.71 227	61.74 225	42.71 220	72.80 164	46.00 217	73.01 199	71.66 208	57.91 174	80.41 174	50.68 230	53.55 229	41.11 231

TESTMET0.1,1		57.21 219	59.46 227	54.60 217	50.95 230	52.66 222	69.46 215	26.91 229	50.76 230	53.81 199	63.11 224	58.91 222	52.87 198	66.54 220	62.34 212	73.59 197	61.87 210

ADS-MVSNet		56.89 220	61.09 221	52.00 221	59.48 224	48.10 228	58.02 228	54.37 201	72.82 163	49.19 213	75.32 188	65.97 213	37.96 222	59.34 229	54.66 225	52.99 230	51.42 226

EPMVS		56.62 221	59.77 226	52.94 220	62.41 222	50.55 225	60.66 226	52.83 207	65.15 200	41.80 223	77.46 169	57.28 227	42.68 215	59.81 228	54.82 224	57.23 226	53.35 224

FMVSNet5		56.37 222	60.14 224	51.98 222	60.83 223	59.58 212	66.85 220	42.37 221	52.68 229	41.33 224	47.09 231	54.68 232	35.28 224	73.88 199	70.77 198	65.24 219	62.26 209

CHOSEN 280x420		56.32 223	58.85 229	53.36 218	51.63 229	39.91 233	69.12 217	38.61 224	56.29 223	36.79 229	48.84 230	62.59 216	63.39 152	73.61 202	67.66 205	60.61 220	63.07 207

N_pmnet		54.95 224	65.90 207	42.18 224	66.37 218	43.86 232	57.92 229	39.79 223	79.54 137	17.24 236	86.31 111	87.91 153	25.44 228	64.68 223	51.76 229	46.33 231	47.23 228

new_pmnet		52.29 225	63.16 216	39.61 226	58.89 225	44.70 231	48.78 234	34.73 226	65.88 194	17.85 235	73.42 197	80.00 183	23.06 230	67.00 218	62.28 214	54.36 227	48.81 227

MVE	41.12 19	51.80 226	60.92 222	41.16 225	35.21 235	34.14 235	48.45 235	41.39 222	69.11 183	19.53 234	63.33 223	73.80 205	63.56 149	67.19 217	61.51 216	38.85 232	57.38 222
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		48.13 227	64.06 212	29.55 227	44.06 234	36.69 234	51.95 233	29.97 227	74.75 159	8.90 238	76.02 183	91.24 130	7.53 232	73.78 200	55.91 222	34.87 233	40.01 232

GG-mvs-BLEND		41.63 228	60.36 223	19.78 228	0.14 240	66.04 200	55.66 231	0.17 236	57.64 222	2.42 239	51.82 229	69.42 211	0.28 236	64.11 225	58.29 219	60.02 221	55.18 223

test_method		22.69 229	26.99 231	17.67 229	2.13 237	4.31 238	27.50 236	4.53 232	37.94 232	24.52 233	36.20 233	51.40 236	15.26 231	29.86 232	17.09 232	32.07 234	12.16 233

test123		1.06 230	1.41 232	0.64 231	0.39 238	0.48 239	0.52 241	0.25 235	1.11 236	1.37 240	2.01 236	1.98 242	0.87 234	1.43 234	1.27 233	0.46 238	1.62 235

testmvs		0.93 231	1.37 233	0.41 232	0.36 239	0.36 240	0.62 240	0.39 234	1.48 235	0.18 241	2.41 235	1.31 243	0.41 235	1.25 235	1.08 234	0.48 237	1.68 234

uanet_test		0.00 232	0.00 234	0.00 233	0.00 241	0.00 241	0.00 242	0.00 237	0.00 237	0.00 242	0.00 237	0.00 244	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

sosnet-low-res		0.00 232	0.00 234	0.00 233	0.00 241	0.00 241	0.00 242	0.00 237	0.00 237	0.00 242	0.00 237	0.00 244	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

sosnet		0.00 232	0.00 234	0.00 233	0.00 241	0.00 241	0.00 242	0.00 237	0.00 237	0.00 242	0.00 237	0.00 244	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

TPM-MVS					86.18 77	83.43 84	87.57 93			78.77 87	69.75 215	84.63 169	62.24 157			89.88 100	88.48 67
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										87.10 28

9.14												89.43 142

SR-MVS					91.82 13			80.80 7				95.53 53

Anonymous202405211			84.68 108		83.92 101	79.45 121	79.03 171	67.79 98	82.01 106		88.77 86	92.58 106	55.93 181	86.68 111	84.26 112	88.92 115	78.98 151

our_test_3					73.27 192	70.91 183	83.26 132

ambc			88.38 60		91.62 17	87.97 53	84.48 127		88.64 44	87.93 15	87.38 99	94.82 74	74.53 76	89.14 89	83.86 117	85.94 156	86.84 78

MTAPA										89.37 9		94.85 72

MTMP										90.54 5		95.16 65

Patchmatch-RL test							4.13 239

tmp_tt				13.54 230	16.73 236	6.42 237	8.49 238	2.36 233	28.69 234	27.44 232	18.40 234	13.51 241	3.70 233	33.23 231	36.26 231	22.54 236

XVS					91.28 25	91.23 8	96.89 2			87.14 25		94.53 79				95.84 15

X-MVStestdata					91.28 25	91.23 8	96.89 2			87.14 25		94.53 79				95.84 15

mPP-MVS					93.05 3							95.77 47

NP-MVS									78.65 142

Patchmtry						56.88 217	64.47 221	67.74 99		72.30 125

DeepMVS_CX						17.78 236	20.40 237	6.69 231	31.41 233	9.80 237	38.61 232	34.88 240	33.78 225	28.41 233		23.59 235	45.77 229

High-resolution multi-view benchmark