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.

DVP-MVS++		90.50 10	94.18 4	86.21 27	92.52 7	90.29 30	95.29 22	76.02 41	94.24 5	82.82 54	95.84 5	97.56 15	76.82 57	93.13 38	91.20 44	93.78 46	97.01 1

UA-Net		89.02 33	91.44 41	86.20 28	94.88 1	89.84 36	94.76 29	77.45 28	85.41 86	74.79 120	88.83 98	88.90 172	78.67 42	96.06 7	95.45 4	96.66 3	95.58 2

LTVRE_ROB	86.82 1	91.55 3	94.43 3	88.19 10	83.19 117	86.35 67	93.60 40	78.79 18	95.48 3	91.79 2	93.08 30	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
TDRefinement		93.16 1	95.57 1	90.36 1	88.79 54	93.57 1	97.27 1	78.23 21	95.55 1	93.00 1	93.98 18	96.01 48	87.53 1	97.69 1	96.81 1	97.33 1	95.34 4

SMA-MVS		90.13 15	92.26 27	87.64 17	91.68 16	90.44 27	95.22 24	77.34 32	90.79 24	87.80 16	90.42 72	92.05 139	79.05 37	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
ACMMP_NAP		89.86 19	91.96 35	87.42 19	91.00 30	90.08 32	96.00 15	76.61 36	89.28 37	87.73 17	90.04 74	91.80 143	78.71 40	94.36 28	93.82 17	94.48 38	94.32 6

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 35	83.50 50	89.06 92	94.44 101	81.68 22	94.17 30	94.19 13	95.81 17	93.87 7

ACMMPR		91.30 4	92.88 11	89.46 4	91.92 11	91.61 5	96.60 5	79.46 14	90.08 32	88.53 13	89.54 83	95.57 62	84.25 7	95.24 20	94.27 12	95.97 11	93.85 8

anonymousdsp		85.62 62	90.53 48	79.88 94	64.64 256	76.35 165	96.28 12	53.53 237	85.63 80	81.59 69	92.81 34	97.71 12	86.88 2	94.56 25	92.83 24	96.35 6	93.84 9

CP-MVS		91.09 5	92.33 25	89.65 2	92.16 10	90.41 28	96.46 10	80.38 8	88.26 48	89.17 10	87.00 125	96.34 38	83.95 10	95.77 11	94.72 7	95.81 17	93.78 10

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 49	83.43 53	93.48 23	95.19 77	81.07 26	92.75 45	92.07 36	94.55 37	93.74 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
COLMAP_ROB	85.66 2	91.85 2	95.01 2	88.16 11	88.98 53	92.86 2	95.51 19	72.17 65	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
TSAR-MVS + ACMM		89.14 29	92.11 33	85.67 31	89.27 49	90.61 25	90.98 55	79.48 13	88.86 43	79.80 79	93.01 31	93.53 116	83.17 15	92.75 45	92.45 29	91.32 85	93.59 13

SteuartSystems-ACMMP		90.00 17	91.73 37	87.97 12	91.21 29	90.29 30	96.51 7	78.00 23	86.33 71	85.32 40	88.23 105	94.67 94	82.08 20	95.13 22	93.88 16	94.72 35	93.59 13
Skip Steuart: Steuart Systems R&D Blog.
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 92	93.44 24	95.82 56	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
X-MVS		89.36 28	90.73 47	87.77 16	91.50 20	91.23 8	96.76 4	78.88 17	87.29 59	87.14 25	78.98 185	94.53 96	76.47 59	95.25 19	94.28 11	95.85 14	93.55 16

DVP-MVS		89.40 27	92.69 13	85.56 34	89.01 52	89.85 35	93.72 38	75.42 45	92.28 11	80.49 72	94.36 13	94.87 85	81.46 24	92.49 49	91.42 41	93.27 54	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
ACMMP		90.63 8	92.40 20	88.56 8	91.24 28	91.60 6	96.49 9	77.53 26	87.89 53	86.87 30	87.24 119	96.46 31	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
CSCG		88.12 47	91.45 40	84.23 50	88.12 63	90.59 26	90.57 63	68.60 92	91.37 15	83.45 52	89.94 77	95.14 81	78.71 40	91.45 60	88.21 75	95.96 12	93.44 19

PGM-MVS		90.42 11	91.58 39	89.05 5	91.77 14	91.06 13	96.51 7	78.94 16	85.41 86	87.67 18	87.02 124	95.26 75	83.62 12	95.01 23	93.94 15	95.79 19	93.40 20

RPSCF		88.05 48	92.61 17	82.73 67	84.24 99	88.40 46	90.04 75	66.29 113	91.46 13	82.29 60	88.93 96	96.01 48	79.38 34	95.15 21	94.90 6	94.15 40	93.40 20

SED-MVS		88.96 37	92.37 22	84.99 41	88.64 57	89.65 39	95.11 25	75.98 42	90.73 25	80.15 77	94.21 15	94.51 99	76.59 58	92.94 41	91.17 45	93.46 51	93.37 22

MP-MVS		90.84 6	91.95 36	89.55 3	92.92 4	90.90 19	96.56 6	79.60 11	86.83 66	88.75 12	89.00 93	94.38 103	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.
SD-MVS		89.91 18	92.23 30	87.19 21	91.31 24	89.79 37	94.31 34	75.34 47	89.26 39	81.79 67	92.68 35	95.08 82	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
TSAR-MVS + MP.		89.67 24	92.25 28	86.65 25	91.53 18	90.98 17	96.15 13	73.30 57	87.88 54	81.83 66	92.92 33	95.15 80	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
ACMM	80.67 7	90.67 7	92.46 19	88.57 7	91.35 22	89.93 34	96.34 11	77.36 30	90.17 30	86.88 29	87.32 117	96.63 26	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
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 74	87.23 23	90.45 71	97.35 17	83.20 14	95.44 16	93.41 20	96.28 8	92.63 27

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 33	84.61 42	93.33 25	94.22 105	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
MED-MVS		88.91 39	92.21 31	85.06 40	89.33 47	90.39 29	94.13 36	75.14 48	91.00 20	76.86 104	93.91 20	94.76 90	80.32 30	92.25 50	90.58 49	94.57 36	92.56 29

aaEdge-Enhanced		88.45 44	92.03 34	84.27 49	89.33 47	90.77 21	94.55 31	72.48 63	89.22 40	76.86 104	93.91 20	95.41 68	80.41 28	92.07 51	90.28 53	91.99 76	92.56 29

SF-MVS		87.85 50	90.95 46	84.22 51	88.17 62	87.90 56	90.80 59	71.80 68	89.28 37	82.70 56	89.90 78	95.37 72	77.91 49	91.69 56	90.04 56	93.95 45	92.47 31

MSLP-MVS++		86.29 59	89.10 57	83.01 60	85.71 84	89.79 37	87.04 109	74.39 52	85.17 88	78.92 88	77.59 198	93.57 114	82.60 17	93.23 36	91.88 39	89.42 114	92.46 32

CPTT-MVS		89.63 25	90.52 49	88.59 6	90.95 31	90.74 22	95.71 16	79.13 15	87.70 55	85.68 38	80.05 176	95.74 60	84.77 6	94.28 29	92.68 26	95.28 26	92.45 33

APD-MVS		89.14 29	91.25 44	86.67 24	91.73 15	91.02 15	95.50 20	77.74 24	84.04 100	79.47 84	91.48 52	94.85 86	81.14 25	92.94 41	92.20 35	94.47 39	92.24 34
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
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 57	85.33 39	88.91 97	97.65 14	82.13 19	95.31 17	93.44 19	96.14 10	92.22 35
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
3Dnovator+	83.71 3	88.13 46	90.00 52	85.94 29	86.82 73	91.06 13	94.26 35	75.39 46	88.85 44	85.76 37	85.74 141	86.92 183	78.02 47	93.03 40	92.21 34	95.39 25	92.21 36

DeepPCF-MVS	81.61 6	87.95 49	90.29 51	85.22 38	87.48 67	90.01 33	93.79 37	73.54 55	88.93 42	83.89 45	89.40 87	90.84 154	80.26 33	90.62 74	90.19 55	92.36 72	92.03 37

SixPastTwentyTwo		89.14 29	92.19 32	85.58 32	84.62 92	82.56 96	90.53 66	71.93 67	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 38

HPM-MVS++		88.74 41	89.54 54	87.80 15	92.58 6	85.69 71	95.10 26	78.01 22	87.08 62	87.66 19	87.89 109	92.07 137	80.28 32	90.97 71	91.41 43	93.17 58	91.69 39

OMC-MVS		88.16 45	91.34 43	84.46 47	86.85 72	90.63 24	93.01 45	67.00 108	90.35 29	87.40 21	86.86 127	96.35 36	77.66 51	92.63 47	90.84 46	94.84 30	91.68 40

CNVR-MVS		86.93 53	88.98 58	84.54 45	90.11 40	87.41 60	93.23 44	73.47 56	86.31 72	82.25 61	82.96 160	92.15 135	76.04 64	91.69 56	90.69 47	92.17 75	91.64 41

OPM-MVS		89.82 21	92.24 29	86.99 22	90.86 33	89.35 40	95.07 27	75.91 43	91.16 16	86.87 30	91.07 63	97.29 18	79.13 36	93.32 35	91.99 37	94.12 41	91.49 42
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LS3D		89.02 33	91.69 38	85.91 30	89.72 43	90.81 20	92.56 48	71.69 69	90.83 23	87.24 22	89.71 81	92.07 137	78.37 44	94.43 27	92.59 27	95.86 13	91.35 43

NCCC		86.74 54	87.97 70	85.31 36	90.64 35	87.25 61	93.27 43	74.59 50	86.50 69	83.72 46	75.92 216	92.39 131	77.08 55	91.72 55	90.68 48	92.57 68	91.30 44

DeepC-MVS_fast	81.78 5	87.38 51	89.64 53	84.75 42	89.89 42	90.70 23	92.74 47	74.45 51	86.02 76	82.16 64	86.05 138	91.99 141	75.84 67	91.16 65	90.44 50	93.41 52	91.09 45
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSP-MVS		88.51 43	91.36 42	85.19 39	90.63 36	92.01 4	95.29 22	77.52 27	90.48 28	80.21 76	90.21 73	96.08 43	76.38 61	88.30 99	91.42 41	91.12 92	91.01 46
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
PMVS	79.51 9	90.23 14	92.67 14	87.39 20	90.16 39	88.75 44	93.64 39	75.78 44	90.00 34	83.70 47	92.97 32	92.22 134	86.13 4	97.01 3	96.79 2	94.94 28	90.96 47
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
HQP-MVS		85.02 69	86.41 83	83.40 56	89.19 50	86.59 65	91.28 53	71.60 70	82.79 110	83.48 51	78.65 191	93.54 115	72.55 91	86.49 117	85.89 99	92.28 74	90.95 48

WR-MVS_H		88.99 35	93.28 6	83.99 55	91.92 11	89.13 42	91.95 50	83.23 1	90.14 31	71.92 143	95.85 4	98.01 10	71.83 98	95.82 9	93.19 22	93.07 60	90.83 49

ACMH+	79.05 11	89.62 26	93.08 8	85.58 32	88.58 58	89.26 41	92.18 49	74.23 53	93.55 8	82.66 57	92.32 41	98.35 7	80.29 31	95.28 18	92.34 31	95.52 22	90.43 50

Vis-MVSNet		83.32 87	88.12 68	77.71 114	77.91 185	83.44 86	90.58 62	69.49 81	81.11 137	67.10 180	89.85 79	91.48 148	71.71 99	91.34 61	89.37 65	89.48 112	90.26 51
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PS-CasMVS		89.07 32	93.23 7	84.21 52	92.44 8	88.23 50	90.54 65	82.95 3	90.50 27	75.31 116	95.80 6	98.37 6	71.16 103	96.30 5	93.32 21	92.88 62	90.11 52

TAPA-MVS	78.00 13	85.88 60	88.37 63	82.96 62	84.69 90	88.62 45	90.62 61	64.22 139	89.15 41	88.05 14	78.83 187	93.71 111	76.20 63	90.11 83	88.22 74	94.00 42	89.97 53
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMH	78.40 12	88.94 38	92.62 16	84.65 43	86.45 76	87.16 62	91.47 52	68.79 90	95.49 2	89.74 6	93.55 22	98.50 2	77.96 48	94.14 31	89.57 64	93.49 48	89.94 54
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MGCNet		85.73 61	87.94 71	83.14 59	88.68 56	87.98 54	93.34 42	70.74 74	79.78 152	82.37 58	88.32 104	89.44 164	71.34 100	90.61 75	89.64 62	92.40 71	89.79 55

CDPH-MVS		86.66 56	88.52 61	84.48 46	89.61 45	88.27 48	92.86 46	72.69 62	80.55 143	82.71 55	86.92 126	93.32 120	75.55 69	91.00 70	89.85 58	93.47 50	89.71 56

CP-MVSNet		88.71 42	92.63 15	84.13 53	92.39 9	88.09 52	90.47 69	82.86 4	88.79 45	75.16 117	94.87 9	97.68 13	71.05 105	96.16 6	93.18 23	92.85 63	89.64 57

TSAR-MVS + GP.		85.32 67	87.41 77	82.89 64	90.07 41	85.69 71	89.07 84	72.99 61	82.45 114	74.52 125	85.09 145	87.67 180	79.24 35	91.11 66	90.41 51	91.45 82	89.45 58

WR-MVS		89.79 23	93.66 5	85.27 37	91.32 23	88.27 48	93.49 41	79.86 10	92.75 9	75.37 115	96.86 1	98.38 5	75.10 73	95.93 8	94.07 14	96.46 5	89.39 59

MCST-MVS		84.79 72	86.48 81	82.83 65	87.30 69	87.03 64	90.46 70	69.33 84	83.14 107	82.21 63	81.69 170	92.14 136	75.09 74	87.27 107	84.78 110	92.58 66	89.30 60

sasdasda		81.22 116	86.04 89	75.60 137	83.17 118	83.18 89	80.29 179	65.82 123	85.97 77	67.98 172	77.74 196	91.51 146	65.17 158	88.62 94	86.15 94	91.17 89	89.09 61

canonicalmvs		81.22 116	86.04 89	75.60 137	83.17 118	83.18 89	80.29 179	65.82 123	85.97 77	67.98 172	77.74 196	91.51 146	65.17 158	88.62 94	86.15 94	91.17 89	89.09 61

train_agg		86.67 55	87.73 72	85.43 35	91.51 19	82.72 93	94.47 33	74.22 54	81.71 124	81.54 70	89.20 91	92.87 125	78.33 45	90.12 82	88.47 71	92.51 70	89.04 63

PHI-MVS		86.37 58	88.14 67	84.30 48	86.65 75	87.56 58	90.76 60	70.16 76	82.55 113	89.65 7	84.89 148	92.40 130	75.97 65	90.88 72	89.70 60	92.58 66	89.03 64

MGCFI-Net		79.42 141	85.64 97	72.15 168	82.80 126	82.09 100	76.92 208	65.46 128	86.31 72	57.48 213	78.15 193	91.38 149	59.10 198	88.23 101	84.47 115	91.14 91	88.88 65

CNLPA		85.50 64	88.58 59	81.91 73	84.55 94	87.52 59	90.89 57	63.56 150	88.18 49	84.06 44	83.85 157	91.34 151	76.46 60	91.27 62	89.00 69	91.96 78	88.88 65

PEN-MVS		88.86 40	92.92 9	84.11 54	92.92 4	88.05 53	90.83 58	82.67 5	91.04 18	74.83 119	95.97 3	98.47 3	70.38 111	95.70 13	92.43 30	93.05 61	88.78 67

PCF-MVS	76.59 14	84.11 76	85.27 102	82.76 66	86.12 80	88.30 47	91.24 54	69.10 85	82.36 118	84.45 43	77.56 199	90.40 159	72.91 90	85.88 122	83.88 119	92.72 65	88.53 68
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TPM-MVS					86.18 79	83.43 87	87.57 98			78.77 89	69.75 247	84.63 200	62.24 177			89.88 105	88.48 69
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
DPM-MVS		81.42 112	82.11 160	80.62 88	87.54 66	85.30 73	90.18 74	68.96 87	81.00 139	79.15 86	70.45 243	83.29 203	67.67 133	82.81 164	83.46 123	90.19 100	88.48 69

v7n		87.11 52	90.46 50	83.19 58	85.22 88	83.69 82	90.03 76	68.20 98	91.01 19	86.71 33	94.80 10	98.46 4	77.69 50	91.10 67	85.98 96	91.30 86	88.19 71

EC-MVSNet		83.70 79	84.77 116	82.46 68	87.47 68	82.79 92	85.50 126	72.00 66	69.81 209	77.66 100	85.02 147	89.63 162	78.14 46	90.40 77	87.56 77	94.00 42	88.16 72

QAPM		80.43 127	84.34 123	75.86 134	79.40 165	82.06 101	79.86 187	61.94 175	83.28 104	74.73 123	81.74 169	85.44 195	70.97 106	84.99 140	84.71 112	88.29 135	88.14 73

3Dnovator	79.41 10	82.21 100	86.07 88	77.71 114	79.31 166	84.61 75	87.18 104	61.02 184	85.65 79	76.11 109	85.07 146	85.38 196	70.96 107	87.22 109	86.47 87	91.66 80	88.12 74

DTE-MVSNet		88.99 35	92.77 12	84.59 44	93.31 2	88.10 51	90.96 56	83.09 2	91.38 14	76.21 108	96.03 2	98.04 8	70.78 109	95.65 14	92.32 32	93.18 57	87.84 75

TSAR-MVS + COLMAP		85.51 63	88.36 64	82.19 69	86.05 81	87.69 57	90.50 68	70.60 75	86.40 70	82.33 59	89.69 82	92.52 129	74.01 83	87.53 104	86.84 85	89.63 109	87.80 76

MAR-MVS		81.98 106	82.92 152	80.88 82	85.18 89	85.85 69	89.13 83	69.52 79	71.21 205	82.25 61	71.28 237	88.89 173	69.69 114	88.71 92	86.96 81	89.52 111	87.57 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
EPP-MVSNet		82.76 95	86.47 82	78.45 106	86.00 82	84.47 76	85.39 130	68.42 94	84.17 97	62.97 198	89.26 90	76.84 228	72.13 95	92.56 48	90.40 52	95.76 20	87.56 78

PVSNet_Blended_VisFu		83.00 91	84.16 131	81.65 76	82.17 137	86.01 68	88.03 91	71.23 71	76.05 172	79.54 83	83.88 156	83.44 201	77.49 53	87.38 105	84.93 108	91.41 83	87.40 79

MVSMamba_PlusPlus		80.70 125	82.94 151	78.08 110	83.67 111	81.93 102	85.26 135	65.57 127	72.89 193	74.65 124	79.34 180	89.34 167	69.09 120	85.57 124	84.56 113	90.24 98	86.97 80

ambc			88.38 62		91.62 17	87.97 55	84.48 143		88.64 47	87.93 15	87.38 116	94.82 88	74.53 78	89.14 91	83.86 121	85.94 174	86.84 81

PLC	76.06 15	85.38 66	87.46 75	82.95 63	85.79 83	88.84 43	88.86 86	68.70 91	87.06 63	83.60 48	79.02 182	90.05 160	77.37 54	90.88 72	89.66 61	93.37 53	86.74 82
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
AdaColmap		84.15 75	85.14 106	83.00 61	89.08 51	87.14 63	90.56 64	70.90 72	82.40 117	80.41 73	73.82 227	84.69 199	75.19 72	91.58 59	89.90 57	91.87 79	86.48 83

UGNet		79.62 139	85.91 92	72.28 167	73.52 222	83.91 78	86.64 113	69.51 80	79.85 151	62.57 200	85.82 140	89.63 162	53.18 230	88.39 98	87.35 79	88.28 136	86.43 84
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
OpenMVS	75.38 16	78.44 153	81.39 164	74.99 147	80.46 154	79.85 122	79.99 184	58.31 206	77.34 165	73.85 128	77.19 202	82.33 209	68.60 125	84.67 143	81.95 139	88.72 126	86.40 85

CS-MVS		83.57 82	84.79 115	82.14 70	83.83 108	81.48 105	87.29 102	66.54 111	72.73 197	80.05 78	84.04 155	93.12 124	80.35 29	89.50 86	86.34 90	94.76 34	86.32 86

CMPMVS	55.74 18	71.56 211	76.26 202	66.08 222	68.11 240	63.91 242	63.17 261	50.52 247	68.79 217	75.49 114	70.78 242	85.67 193	63.54 168	81.58 183	77.20 188	75.63 231	85.86 87
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CANet		82.84 93	84.60 118	80.78 83	87.30 69	85.20 74	90.23 72	69.00 86	72.16 201	78.73 90	84.49 153	90.70 157	69.54 118	87.65 103	86.17 93	89.87 106	85.84 88

DCV-MVSNet		80.04 131	85.67 96	73.48 158	82.91 122	81.11 112	80.44 178	66.06 117	85.01 90	62.53 201	78.84 186	94.43 102	58.51 202	88.66 93	85.91 97	90.41 96	85.73 89

EG-PatchMatch MVS		84.35 74	87.55 73	80.62 88	86.38 77	82.24 98	86.75 112	64.02 144	84.24 96	78.17 97	89.38 88	95.03 84	78.78 39	89.95 84	86.33 91	89.59 110	85.65 90

thisisatest0515		81.18 118	84.32 124	77.52 119	76.73 200	74.84 184	85.06 138	61.37 181	81.05 138	73.95 127	88.79 99	89.25 169	75.49 70	85.98 121	84.78 110	92.53 69	85.56 91

FPMVS		81.56 109	84.04 133	78.66 103	82.92 121	75.96 170	86.48 115	65.66 125	84.67 94	71.47 147	77.78 195	83.22 204	77.57 52	91.24 63	90.21 54	87.84 140	85.21 92

tttt0517		75.86 178	76.23 203	75.42 139	75.55 211	74.06 192	82.73 157	60.31 189	69.24 212	70.24 154	79.18 181	58.79 256	72.17 93	84.49 144	83.08 130	91.54 81	84.80 93

EPNet		79.36 143	79.44 176	79.27 100	89.51 46	77.20 158	88.35 90	77.35 31	68.27 218	74.29 126	76.31 208	79.22 218	59.63 190	85.02 139	85.45 103	86.49 161	84.61 94
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_111021_HR		83.95 77	86.10 87	81.44 78	84.62 92	80.29 119	90.51 67	68.05 99	84.07 99	80.38 74	84.74 151	91.37 150	74.23 79	90.37 78	87.25 80	90.86 94	84.59 95

Effi-MVS+-dtu		82.04 103	83.39 147	80.48 91	85.48 85	86.57 66	88.40 89	68.28 96	69.04 216	73.13 135	76.26 210	91.11 153	74.74 77	88.40 97	87.76 76	92.84 64	84.57 96

IterMVS-LS		79.79 134	82.56 156	76.56 130	81.83 142	77.85 149	79.90 186	69.42 83	78.93 159	71.21 148	90.47 70	85.20 197	70.86 108	80.54 194	80.57 150	86.15 167	84.36 97
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SPE-MVS-test		83.59 81	84.86 112	82.10 71	83.04 120	81.05 113	91.58 51	67.48 106	72.52 198	78.42 93	84.75 150	91.82 142	78.62 43	91.98 52	87.54 78	93.48 49	84.35 98

MVS_111021_LR		83.20 89	85.33 101	80.73 86	82.88 124	78.23 141	89.61 78	65.23 130	82.08 120	81.19 71	85.31 143	92.04 140	75.22 71	89.50 86	85.90 98	90.24 98	84.23 99

thisisatest0530		75.54 181	75.95 207	75.05 144	75.08 216	73.56 195	82.15 164	60.31 189	69.17 213	69.32 159	79.02 182	58.78 257	72.17 93	83.88 151	83.08 130	91.30 86	84.20 100

Effi-MVS+		82.33 99	83.87 137	80.52 90	84.51 97	81.32 107	87.53 99	68.05 99	74.94 179	79.67 80	82.37 166	92.31 132	72.21 92	85.06 135	86.91 83	91.18 88	84.20 100

v10		83.17 90	85.22 105	80.78 83	83.26 116	82.99 91	88.66 88	66.49 112	79.24 157	83.60 48	91.46 53	95.47 66	74.12 80	82.60 167	80.66 149	88.53 133	84.11 102

UniMVSNet (Re)		84.95 70	88.53 60	80.78 83	87.82 65	84.21 77	88.03 91	76.50 37	81.18 136	69.29 160	92.63 39	96.83 25	69.07 121	91.23 64	89.60 63	93.97 44	84.00 103

Casviewmamba		83.46 85	87.48 74	78.78 101	85.48 85	83.45 85	87.70 96	67.34 107	86.15 75	71.52 146	93.21 27	96.37 35	70.22 113	87.27 107	82.08 137	90.40 97	83.82 104

GeoE		81.92 107	83.87 137	79.66 96	84.64 91	79.87 121	89.75 77	65.90 121	76.12 171	75.87 112	84.62 152	92.23 133	71.96 97	86.83 113	83.60 122	89.83 107	83.81 105

Gipuma		86.47 57	89.25 56	83.23 57	83.88 107	78.78 134	85.35 131	68.42 94	92.69 10	89.03 11	91.94 45	96.32 40	81.80 21	94.45 26	86.86 84	90.91 93	83.69 106
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DELS-MVS		79.71 136	83.74 142	75.01 146	79.31 166	82.68 94	84.79 140	60.06 193	75.43 176	69.09 161	86.13 136	89.38 166	67.16 141	85.12 134	83.87 120	89.65 108	83.57 107
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
CVMVSNet		75.65 179	77.62 188	73.35 161	71.95 228	69.89 219	83.04 154	60.84 186	69.12 214	68.76 164	79.92 177	78.93 220	73.64 87	81.02 190	81.01 147	81.86 217	83.43 108

IS_MVSNet		81.72 108	85.01 107	77.90 113	86.19 78	82.64 95	85.56 125	70.02 77	80.11 148	63.52 194	87.28 118	81.18 211	67.26 138	91.08 69	89.33 66	94.82 31	83.42 109

viewdifsd2359ckpt09		82.38 98	85.92 91	78.26 108	81.46 147	83.33 88	87.76 94	66.85 109	80.47 145	72.93 136	86.68 129	94.75 91	71.25 102	86.58 115	86.23 92	89.30 115	83.41 110

Fast-Effi-MVS+		81.42 112	83.82 140	78.62 104	82.24 136	80.62 115	87.72 95	63.51 151	73.01 191	74.75 122	83.80 158	92.70 127	73.44 88	88.15 102	85.26 104	90.05 101	83.17 111

EU-MVSNet		76.48 169	80.53 172	71.75 171	67.62 242	70.30 217	81.74 167	54.06 233	75.47 175	71.01 150	80.10 174	93.17 123	73.67 85	83.73 152	77.85 177	82.40 212	83.07 112

UniMVSNet_NR-MVSNet		84.62 73	88.00 69	80.68 87	88.18 61	83.83 79	87.06 107	76.47 38	81.46 131	70.49 152	93.24 26	95.56 63	68.13 128	90.43 76	88.47 71	93.78 46	83.02 113

DU-MVS		84.88 71	88.27 66	80.92 81	88.30 59	83.59 83	87.06 107	78.35 19	80.64 141	70.49 152	92.67 36	96.91 24	68.13 128	91.79 53	89.29 67	93.20 56	83.02 113

v1240		83.57 82	84.94 110	81.97 72	84.05 102	81.27 108	89.46 81	66.06 117	81.31 135	87.50 20	91.88 48	95.46 67	76.25 62	81.16 187	80.51 152	88.52 134	82.98 115

TranMVSNet+NR-MVSNet		85.23 68	89.38 55	80.39 92	88.78 55	83.77 80	87.40 101	76.75 34	85.47 84	68.99 162	95.18 8	97.55 16	67.13 142	91.61 58	89.13 68	93.26 55	82.95 116

v1921920		83.49 84	84.94 110	81.80 74	83.78 109	81.20 111	89.50 80	65.91 120	81.64 126	87.18 24	91.70 50	95.39 70	75.85 66	81.56 184	80.27 155	88.60 129	82.80 117

NR-MVSNet		82.89 92	87.43 76	77.59 116	83.91 106	83.59 83	87.10 106	78.35 19	80.64 141	68.85 163	92.67 36	96.50 29	54.19 224	87.19 111	88.68 70	93.16 59	82.75 118

UniMVSNet_ETH3D		85.39 65	91.12 45	78.71 102	90.48 37	83.72 81	81.76 166	82.41 6	93.84 6	64.43 189	95.41 7	98.76 1	63.72 166	93.63 33	89.74 59	89.47 113	82.74 119

v144192		83.43 86	84.97 109	81.63 77	83.43 113	81.23 109	89.42 82	66.04 119	81.45 132	86.40 34	91.46 53	95.70 61	75.76 68	82.14 171	80.23 156	88.74 125	82.57 120

casdiffseed414692147		82.71 97	86.24 85	78.60 105	84.08 100	81.22 110	85.85 122	66.16 116	83.98 101	76.07 110	90.85 65	97.20 21	70.51 110	85.74 123	82.14 136	88.92 121	82.56 121

v1192		83.61 80	85.23 104	81.72 75	84.05 102	82.15 99	89.54 79	66.20 114	81.38 134	86.76 32	91.79 49	96.03 46	74.88 76	81.81 178	80.92 148	88.91 123	82.50 122

FMVSNet1		78.20 159	84.83 113	70.46 184	78.62 174	79.03 131	77.90 201	67.53 105	83.02 108	55.10 224	87.19 121	93.18 122	55.65 215	85.57 124	83.39 125	87.98 139	82.40 123

IB-MVS	71.28 17	75.21 183	77.00 193	73.12 162	76.76 194	77.45 154	83.05 153	58.92 202	63.01 241	64.31 191	59.99 259	87.57 181	68.64 122	86.26 120	82.34 135	87.05 149	82.36 124
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
v8		82.20 101	84.56 119	79.45 97	82.42 131	81.65 103	87.26 103	64.27 138	79.36 156	81.70 68	91.04 64	95.75 59	73.30 89	82.82 163	79.18 163	87.74 142	82.09 125

Baseline_NR-MVSNet		82.79 94	86.51 80	78.44 107	88.30 59	75.62 176	87.81 93	74.97 49	81.53 128	66.84 182	94.71 12	96.46 31	66.90 144	91.79 53	83.37 128	85.83 177	82.09 125

ETV-MVS		79.01 148	77.98 184	80.22 93	86.69 74	79.73 125	88.80 87	68.27 97	63.22 240	71.56 145	70.25 245	73.63 238	73.66 86	90.30 81	86.77 86	92.33 73	81.95 127

FC-MVSNet-train		79.20 146	86.29 84	70.94 180	84.06 101	77.67 151	85.68 124	64.11 141	82.90 109	52.22 241	92.57 40	93.69 112	49.52 244	88.30 99	86.93 82	90.03 102	81.95 127

casdiffmvs_mvg		81.50 110	85.70 94	76.60 129	82.68 127	80.54 116	83.50 149	64.49 137	83.40 102	72.53 137	92.15 42	95.40 69	65.84 153	84.69 142	81.89 141	90.59 95	81.86 129
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewmacassd2359aftdt		81.04 121	85.39 98	75.95 133	80.71 152	77.95 147	85.29 134	58.82 203	86.88 65	76.27 107	91.34 55	96.35 36	68.32 126	84.35 146	79.13 165	86.32 165	81.73 130

viewdifsd2359ckpt13		80.07 130	83.42 146	76.17 132	80.95 150	79.07 130	85.14 136	61.42 180	80.41 146	74.78 121	87.22 120	94.70 93	68.23 127	82.60 167	78.34 172	86.49 161	81.63 131

V42		79.59 140	83.59 144	74.93 149	69.61 235	77.05 160	86.59 114	55.84 216	78.42 161	77.29 102	89.84 80	95.08 82	74.12 80	83.05 159	80.11 158	86.12 169	81.59 132

v1144		83.22 88	85.01 107	81.14 79	83.76 110	81.60 104	88.95 85	65.58 126	81.89 122	85.80 36	91.68 51	95.84 53	74.04 82	82.12 172	80.56 151	88.70 127	81.41 133

viewdifsd2359ckpt07		78.49 152	83.75 141	72.35 165	80.46 154	75.49 178	83.92 147	53.96 234	85.53 83	67.94 174	91.12 61	96.06 44	66.18 151	81.43 186	75.39 207	81.62 218	81.26 134

viewdifsd2359ckpt11		78.29 156	84.30 125	71.27 174	78.48 175	74.68 190	82.25 162	55.40 222	82.45 114	60.97 206	91.34 55	96.58 28	65.48 156	85.14 132	78.70 167	85.05 193	81.21 135

viewmsd2359difaftdt		78.29 156	84.30 125	71.27 174	78.48 175	74.69 189	82.25 162	55.40 222	82.45 114	60.98 205	91.34 55	96.59 27	65.48 156	85.14 132	78.70 167	85.05 193	81.21 135

viewmanbaseed2359cas		79.90 133	83.96 134	75.17 143	80.25 157	77.62 152	84.62 141	58.25 207	83.22 105	74.92 118	89.50 84	95.33 73	67.20 140	83.05 159	77.84 178	85.76 179	81.18 137

PM-MVS		80.42 128	83.63 143	76.67 127	78.04 182	72.37 210	87.14 105	60.18 192	80.13 147	71.75 144	86.12 137	93.92 108	77.08 55	86.56 116	85.12 106	85.83 177	81.18 137

diffmvs_AUTHOR		77.61 161	82.84 154	71.49 173	76.16 206	74.80 185	81.22 170	57.90 209	79.89 150	68.06 171	90.49 69	94.78 89	62.29 176	81.77 179	77.04 192	83.33 210	81.14 139

TinyColmap		83.79 78	86.12 86	81.07 80	83.42 114	81.44 106	85.42 129	68.55 93	88.71 46	89.46 8	87.60 111	92.72 126	70.34 112	89.29 89	81.94 140	89.20 116	81.12 140

onestephybrid01		78.35 154	82.42 158	73.60 154	78.45 177	76.56 163	83.15 151	62.05 172	74.24 184	69.57 158	87.57 112	94.27 104	63.94 164	84.24 148	79.08 166	84.43 198	81.03 141

CLD-MVS		82.75 96	87.22 78	77.54 118	88.01 64	85.76 70	90.23 72	54.52 230	82.28 119	82.11 65	88.48 101	95.27 74	63.95 163	89.41 88	88.29 73	86.45 163	81.01 142
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous20231211		79.37 142	85.78 93	71.89 170	82.87 125	79.66 126	78.77 197	63.93 147	83.36 103	59.39 207	90.54 68	94.66 95	56.46 209	87.38 105	84.12 117	89.92 104	80.74 143

MSDG		81.39 114	84.23 129	78.09 109	82.40 132	82.47 97	85.31 133	60.91 185	79.73 153	80.26 75	86.30 133	88.27 177	69.67 115	87.20 110	84.98 107	89.97 103	80.67 144

diffmvs		76.74 165	81.61 163	71.06 178	75.64 210	74.45 191	80.68 177	57.57 210	77.48 163	67.62 177	88.95 95	93.94 107	61.98 178	79.74 199	76.18 198	82.85 211	80.50 145
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
hybridcas		80.80 122	85.25 103	75.61 136	82.91 122	79.79 124	85.07 137	61.72 176	85.56 82	68.49 168	92.67 36	95.38 71	67.22 139	84.31 147	78.61 169	88.24 137	80.42 146

E5new		81.18 118	84.50 120	77.29 120	82.38 134	78.21 142	86.06 118	62.76 162	86.68 67	78.24 95	90.75 66	95.93 51	67.54 134	82.06 173	77.51 183	86.77 153	80.40 147

E5		81.18 118	84.50 120	77.29 120	82.38 134	78.21 142	86.06 118	62.76 162	86.68 67	78.24 95	90.75 66	95.93 51	67.54 134	82.06 173	77.51 183	86.77 153	80.40 147

pmmvs6		80.46 126	88.34 65	71.26 176	81.96 140	77.51 153	77.54 202	68.83 89	93.72 7	55.92 220	93.94 19	98.03 9	55.94 212	89.21 90	85.61 100	87.36 146	80.38 149

GBi-Net		73.17 197	77.64 186	67.95 209	76.76 194	77.36 155	75.77 221	64.57 134	62.99 242	51.83 242	76.05 212	77.76 224	52.73 236	85.57 124	83.39 125	86.04 171	80.37 150

test1		73.17 197	77.64 186	67.95 209	76.76 194	77.36 155	75.77 221	64.57 134	62.99 242	51.83 242	76.05 212	77.76 224	52.73 236	85.57 124	83.39 125	86.04 171	80.37 150

FMVSNet2		74.43 188	79.70 174	68.27 205	76.76 194	77.36 155	75.77 221	65.36 129	72.28 199	52.97 236	81.92 167	85.61 194	52.73 236	80.66 193	79.73 159	86.04 171	80.37 150

casdiffmvs		79.93 132	84.11 132	75.05 144	81.41 148	78.99 132	82.95 156	62.90 161	81.53 128	68.60 167	91.94 45	96.03 46	65.84 153	82.89 162	77.07 191	88.59 130	80.34 153
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_Test		76.72 166	79.40 177	73.60 154	78.85 172	74.99 182	79.91 185	61.56 178	69.67 210	72.44 138	85.98 139	90.78 155	63.50 169	78.30 206	75.74 203	85.33 184	80.31 154

viewmamba		78.33 155	82.83 155	73.07 163	77.55 188	75.72 174	82.97 155	60.76 187	78.06 162	70.14 155	89.47 85	94.50 100	63.04 172	83.55 155	78.24 173	83.99 201	80.28 155

E4		81.47 111	84.83 113	77.55 117	82.40 132	78.25 140	86.41 116	62.92 160	87.20 61	78.63 91	91.12 61	96.50 29	68.00 130	82.58 169	77.96 176	86.93 152	80.22 156

MVSTER		68.08 226	69.73 228	66.16 220	66.33 252	70.06 218	75.71 225	52.36 241	55.18 260	58.64 210	70.23 246	56.72 267	57.34 206	79.68 200	76.03 200	86.61 157	80.20 157

FA-MVS(training)		78.93 149	80.63 169	76.93 124	79.79 162	75.57 177	85.44 128	61.95 174	77.19 166	78.97 87	84.82 149	82.47 206	66.43 150	84.09 150	80.13 157	89.02 119	80.15 158

E6new		81.99 104	85.39 98	78.02 111	82.48 129	78.47 135	87.03 110	63.34 153	87.93 51	79.62 81	92.12 43	97.12 22	68.62 123	83.40 156	78.53 170	87.05 149	80.13 159

E6		81.99 104	85.39 98	78.02 111	82.48 129	78.47 135	87.03 110	63.34 153	87.93 51	79.62 81	92.12 43	97.12 22	68.62 123	83.40 156	78.53 170	87.05 149	80.13 159

CANet_DTU		75.04 184	78.45 180	71.07 177	77.27 190	77.96 146	83.88 148	58.00 208	64.11 236	68.67 166	75.65 218	88.37 175	53.92 226	82.05 175	81.11 145	84.67 196	79.88 161

E3new		80.80 122	83.95 135	77.13 122	82.13 138	78.06 144	86.04 120	62.57 165	85.02 89	77.97 99	89.98 76	95.83 54	67.49 137	81.75 180	77.19 189	86.56 159	79.82 162

E3		80.80 122	83.95 135	77.13 122	82.13 138	78.05 145	86.03 121	62.56 166	85.00 91	77.99 98	89.99 75	95.83 54	67.50 136	81.75 180	77.19 189	86.56 159	79.81 163

IterMVS-SCA-FT		77.23 162	79.18 178	74.96 148	76.67 201	79.85 122	75.58 228	61.34 182	73.10 190	73.79 129	86.23 135	79.61 217	79.00 38	80.28 198	75.50 206	83.41 209	79.70 164

hybridnocas07		76.05 174	81.19 165	70.05 188	74.83 218	72.76 202	80.26 181	56.12 215	75.67 173	67.35 178	88.47 102	93.87 110	59.44 194	81.83 177	76.14 199	82.29 213	79.61 165

TransMVSNet (Re)		79.05 147	86.66 79	70.18 187	83.32 115	75.99 169	77.54 202	63.98 145	90.68 26	55.84 221	94.80 10	96.06 44	53.73 228	86.27 119	83.22 129	86.65 155	79.61 165

viewcassd2359sk11		80.26 129	83.21 148	76.82 126	81.93 141	77.91 148	85.75 123	62.34 170	83.17 106	77.53 101	89.00 93	95.26 75	67.11 143	81.06 189	76.55 197	86.29 166	79.50 167

FMVSNet3		71.40 213	75.20 214	66.97 214	75.00 217	76.59 162	74.29 231	64.57 134	62.99 242	51.83 242	76.05 212	77.76 224	51.49 241	76.58 216	77.03 193	84.62 197	79.43 168

E2		79.77 135	82.52 157	76.56 130	81.77 143	77.80 150	85.49 127	62.14 171	81.45 132	77.16 103	88.03 108	94.73 92	66.75 145	80.40 196	76.02 201	86.07 170	79.22 169

hybrid		75.61 180	80.58 170	69.81 190	74.36 220	72.39 209	80.17 182	55.48 221	75.16 177	67.30 179	87.14 122	93.52 117	59.56 193	81.16 187	75.66 205	82.01 215	79.03 170

Anonymous202405211			84.68 117		83.92 105	79.45 128	79.03 195	67.79 101	82.01 121		88.77 100	92.58 128	55.93 213	86.68 114	84.26 116	88.92 121	78.98 171

viewmambaseed2359dif		76.20 172	80.07 173	71.68 172	76.99 192	73.91 194	80.81 175	59.23 198	74.86 180	66.65 183	86.44 131	93.44 119	62.91 174	79.19 203	73.77 213	83.49 207	78.89 172

v2v482		82.20 101	84.26 127	79.81 95	82.67 128	80.18 120	87.67 97	63.96 146	81.69 125	84.73 41	91.27 59	96.33 39	72.05 96	81.94 176	79.56 160	87.79 141	78.84 173

EIA-MVS		78.57 151	77.90 185	79.35 99	87.24 71	80.71 114	86.16 117	64.03 143	62.63 245	73.49 132	73.60 228	76.12 232	73.83 84	88.49 96	84.93 108	91.36 84	78.78 174

DI_MVS_pp		77.64 160	79.64 175	75.31 141	79.87 161	76.89 161	81.55 169	63.64 149	76.21 169	72.03 142	85.59 142	82.97 205	66.63 146	79.27 202	77.78 180	88.14 138	78.76 175

dtuplus		76.59 167	80.58 170	71.94 169	77.50 189	73.54 196	81.21 171	59.20 199	76.13 170	67.10 180	86.78 128	93.90 109	63.03 173	80.39 197	74.68 209	83.59 206	78.65 176

PVSNet_BlendedMVS		76.45 170	78.12 182	74.49 150	76.76 194	78.46 137	79.65 188	63.26 156	65.42 230	73.15 133	75.05 221	88.96 170	66.51 148	82.73 165	77.66 181	87.61 143	78.60 177

PVSNet_Blended		76.45 170	78.12 182	74.49 150	76.76 194	78.46 137	79.65 188	63.26 156	65.42 230	73.15 133	75.05 221	88.96 170	66.51 148	82.73 165	77.66 181	87.61 143	78.60 177

baseline		69.33 219	75.37 212	62.28 235	66.54 250	66.67 235	73.95 233	48.07 250	66.10 224	59.26 209	82.45 163	86.30 188	54.44 222	74.42 225	73.25 216	71.42 241	78.43 179

pm-mvs1		78.21 158	85.68 95	69.50 196	80.38 156	75.73 173	76.25 214	65.04 131	87.59 56	54.47 226	93.16 29	95.99 50	54.20 223	86.37 118	82.98 132	86.64 156	77.96 180

ET-MVSNet_ETH3D		74.71 187	74.19 216	75.31 141	79.22 168	75.29 179	82.70 158	64.05 142	65.45 229	70.96 151	77.15 203	57.70 258	65.89 152	84.40 145	81.65 143	89.03 118	77.67 181

dtuonlycased		72.06 208	81.13 166	61.48 238	66.59 248	76.01 168	84.21 144	41.25 259	79.57 154	31.88 268	81.89 168	89.95 161	69.64 116	85.52 128	77.35 187	75.27 233	77.61 182

gbinet_0.2-2-1-0.02		73.88 190	76.94 195	70.31 185	76.23 205	74.72 187	77.93 200	57.54 211	72.77 196	64.37 190	80.14 173	85.20 197	60.60 181	76.92 212	71.41 223	85.16 187	77.45 183

USDC		81.39 114	83.07 149	79.43 98	81.48 145	78.95 133	82.62 159	66.17 115	87.45 58	90.73 4	82.40 165	93.65 113	66.57 147	83.63 154	77.97 175	89.00 120	77.45 183

test1111		79.67 137	84.40 122	74.16 152	85.29 87	79.56 127	81.16 172	73.13 60	84.65 95	56.08 218	88.38 103	86.14 189	60.49 182	89.78 85	85.59 101	88.79 124	76.68 185

v148		79.33 144	82.32 159	75.84 135	80.14 158	75.74 172	81.98 165	57.06 212	81.51 130	79.36 85	89.42 86	96.42 33	71.32 101	81.54 185	75.29 208	85.20 185	76.32 186

FE-MVSNET2		78.59 150	83.83 139	72.48 164	78.67 173	75.81 171	79.06 194	63.78 148	85.63 80	65.66 187	87.12 123	96.22 41	59.04 199	83.72 153	82.07 138	88.67 128	76.26 187

pmmvs-eth3d		79.64 138	82.06 161	76.83 125	80.05 159	72.64 207	87.47 100	66.59 110	80.83 140	73.50 131	89.32 89	93.20 121	67.78 131	80.78 192	81.64 144	85.58 183	76.01 188

test2506		75.32 182	76.87 196	73.50 157	84.55 94	80.37 117	79.63 190	73.23 58	82.64 111	55.41 222	76.87 205	45.42 276	59.61 191	90.35 79	86.46 88	88.58 131	75.98 189

MS-PatchMatch		71.18 214	73.99 218	67.89 211	77.16 191	71.76 212	77.18 205	56.38 214	67.35 219	55.04 225	74.63 223	75.70 233	62.38 175	76.62 215	75.97 202	79.22 226	75.90 190

Fast-Effi-MVS+-dtu		76.92 164	77.18 191	76.62 128	79.55 163	79.17 129	84.80 139	77.40 29	64.46 235	68.75 165	70.81 241	86.57 187	63.36 171	81.74 182	81.76 142	85.86 176	75.78 191

tfpnnormal		77.16 163	84.26 127	68.88 202	81.02 149	75.02 181	76.52 213	63.30 155	87.29 59	52.40 239	91.24 60	93.97 106	54.85 221	85.46 129	81.08 146	85.18 186	75.76 192

EPNet_dtu		71.90 210	73.03 222	70.59 182	78.28 179	61.64 246	82.44 160	64.12 140	63.26 239	69.74 156	71.47 235	82.41 207	51.89 240	78.83 204	78.01 174	77.07 229	75.60 193
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
dmvs_re		68.11 225	70.60 226	65.21 227	77.91 185	63.73 243	76.72 209	59.65 196	55.93 257	47.79 252	59.79 260	79.91 216	49.72 243	82.48 170	76.98 194	79.48 223	75.41 194

GA-MVS		75.01 186	76.39 199	73.39 159	78.37 178	75.66 175	80.03 183	58.40 205	70.51 207	75.85 113	83.24 159	76.14 231	63.75 165	77.28 211	76.62 196	83.97 202	75.30 195

ECVR-MVS		79.31 145	84.20 130	73.60 154	84.55 94	80.37 117	79.63 190	73.23 58	82.64 111	55.98 219	87.50 113	86.85 184	59.61 191	90.35 79	86.46 88	88.58 131	75.26 196

FC-MVSNet-test		75.91 177	83.59 144	66.95 215	76.63 202	69.07 223	85.33 132	64.97 132	84.87 93	41.95 259	93.17 28	87.04 182	47.78 247	91.09 68	85.56 102	85.06 188	74.34 197

thres600view7		74.34 189	78.43 181	69.56 194	80.47 153	76.28 166	78.65 198	62.56 166	77.39 164	52.53 237	74.03 225	76.78 229	55.90 214	85.06 135	85.19 105	87.25 147	74.29 198

blended_shiyan6		73.23 195	76.38 200	69.56 194	75.93 207	73.03 199	76.58 211	55.73 218	74.84 181	63.74 193	79.66 179	86.74 185	59.75 185	75.14 222	70.97 225	85.65 182	74.26 199

FE-MVSNET3		67.68 227	67.80 235	67.53 212	75.29 212	72.66 203	75.85 216	55.31 224	73.43 186	53.98 228	53.29 264	56.81 262	59.69 186	74.34 226	69.81 230	85.06 188	74.26 199

IterMVS		73.62 191	76.53 198	70.23 186	71.83 229	77.18 159	80.69 176	53.22 238	72.23 200	66.62 184	85.21 144	78.96 219	69.54 118	76.28 219	71.63 221	79.45 224	74.25 201
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
blended_shiyan8		73.23 195	76.36 201	69.57 193	75.91 208	73.04 198	76.56 212	55.74 217	74.84 181	63.75 192	79.69 178	86.62 186	59.80 184	75.17 221	71.00 224	85.67 181	74.20 202

FE-MVSNET		75.03 185	80.98 167	68.08 207	73.53 221	71.43 213	75.74 224	59.74 195	81.81 123	58.16 211	82.47 162	93.51 118	55.42 217	83.18 158	80.51 152	85.90 175	73.94 203

tfpn200view9		72.01 209	75.40 211	68.06 208	77.97 183	76.44 164	77.04 206	62.67 164	66.81 221	50.82 246	67.30 250	75.67 234	52.46 239	85.06 135	82.64 133	87.41 145	73.86 204

pmmvs4		75.92 176	77.48 190	74.10 153	78.21 181	70.94 214	84.06 145	64.78 133	75.13 178	68.47 169	84.12 154	83.32 202	64.74 162	75.93 220	79.14 164	84.31 199	73.77 205

MDA-MVSNet-bldmvs		76.51 168	82.87 153	69.09 198	50.71 270	74.72 187	84.05 146	60.27 191	81.62 127	71.16 149	88.21 106	91.58 144	69.62 117	92.78 44	77.48 185	78.75 228	73.69 206

thres400		73.13 200	76.99 194	68.62 203	79.46 164	74.93 183	77.23 204	61.23 183	75.54 174	52.31 240	72.20 232	77.10 227	54.89 219	82.92 161	82.62 134	86.57 158	73.66 207

usedtu_blend_shiyan5		67.09 229	67.69 236	66.40 218	75.29 212	72.66 203	69.07 254	55.31 224	73.43 186	53.98 228	53.29 264	56.81 262	59.69 186	74.34 226	69.81 230	85.06 188	73.46 208

blend_shiyan4		63.43 237	63.66 248	63.17 231	62.30 259	71.99 211	65.44 258	52.82 240	48.52 268	53.98 228	53.29 264	56.81 262	59.69 186	71.98 239	69.57 235	84.81 195	73.46 208

wanda-best-256-512		72.50 205	75.48 209	69.03 200	75.29 212	72.66 203	75.85 216	55.31 224	73.43 186	63.41 195	78.69 188	86.04 190	59.27 195	74.34 226	69.81 230	85.06 188	73.37 210

FE-blended-shiyan7		72.50 205	75.48 209	69.03 200	75.29 212	72.66 203	75.85 216	55.31 224	73.43 186	63.41 195	78.69 188	86.04 190	59.27 195	74.34 226	69.81 230	85.06 188	73.37 210

PatchMatch-RL		76.05 174	76.64 197	75.36 140	77.84 187	69.87 220	81.09 174	63.43 152	71.66 203	68.34 170	71.70 233	81.76 210	74.98 75	84.83 141	83.44 124	86.45 163	73.22 212

CHOSEN 1792x2688		68.80 221	71.09 224	66.13 221	69.11 237	68.89 225	78.98 196	54.68 228	61.63 247	56.69 215	71.56 234	78.39 222	67.69 132	72.13 237	72.01 220	69.63 248	73.02 213

usedtu_dtu_shiyan1		73.59 192	77.49 189	69.05 199	76.40 204	72.84 201	75.67 226	60.47 188	74.12 185	59.35 208	79.02 182	88.33 176	56.25 211	77.46 209	77.81 179	86.14 168	72.84 214

thres200		72.41 207	76.00 206	68.21 206	78.28 179	76.28 166	74.94 229	62.56 166	72.14 202	51.35 245	69.59 248	76.51 230	54.89 219	85.06 135	80.51 152	87.25 147	71.92 215

Vis-MVSNet (Re-imp)		76.15 173	80.84 168	70.68 181	83.66 112	74.80 185	81.66 168	69.59 78	80.48 144	46.94 253	87.44 115	80.63 213	53.14 231	86.87 112	84.56 113	89.12 117	71.12 216

thres100view900		69.86 216	72.97 223	66.24 219	77.97 183	72.49 208	73.29 235	59.12 200	66.81 221	50.82 246	67.30 250	75.67 234	50.54 242	78.24 207	79.40 161	85.71 180	70.88 217

baseline2		68.71 222	68.34 233	69.14 197	75.69 209	69.70 221	76.60 210	55.53 220	60.13 250	62.07 203	66.76 252	60.35 251	60.77 180	76.53 218	74.03 212	84.19 200	70.88 217

baseline1		69.62 217	73.55 220	65.02 229	78.95 171	70.39 216	71.38 241	62.03 173	70.97 206	47.95 251	78.47 192	68.19 244	47.77 248	79.65 201	76.94 195	82.05 214	70.27 219

HyFIR lowres test		73.29 194	74.14 217	72.30 166	73.08 224	78.33 139	83.12 152	62.41 169	63.81 237	62.13 202	76.67 207	78.50 221	71.09 104	74.13 230	77.47 186	81.98 216	70.10 220

CDS-MVSNet		73.07 201	77.02 192	68.46 204	81.62 144	72.89 200	79.56 192	70.78 73	69.56 211	52.52 238	77.37 201	81.12 212	42.60 253	84.20 149	83.93 118	83.65 203	70.07 221
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CostFormer		66.81 231	66.94 238	66.67 216	72.79 226	68.25 226	79.55 193	55.57 219	65.52 228	62.77 199	76.98 204	60.09 252	56.73 208	65.69 256	62.35 248	72.59 237	69.71 222

CR-MVSNet		69.56 218	68.34 233	70.99 179	72.78 227	67.63 228	64.47 259	67.74 102	59.93 251	72.30 139	80.10 174	56.77 266	65.04 160	71.64 240	72.91 217	83.61 205	69.40 223

PatchT		66.25 232	66.76 239	65.67 225	55.87 265	60.75 247	70.17 244	59.00 201	59.80 253	72.30 139	78.68 190	54.12 271	65.04 160	71.64 240	72.91 217	71.63 240	69.40 223

MIMVSNet1		73.40 193	81.85 162	63.55 230	72.90 225	64.37 240	84.58 142	53.60 236	90.84 22	53.92 232	87.75 110	96.10 42	45.31 251	85.37 131	79.32 162	70.98 245	69.18 225

RPMNet		67.02 230	63.99 246	70.56 183	71.55 230	67.63 228	75.81 219	69.44 82	59.93 251	63.24 197	64.32 254	47.51 275	59.68 189	70.37 245	69.64 234	83.64 204	68.49 226

pmmvs5		68.91 220	74.35 215	62.56 234	67.45 244	66.78 233	71.70 238	51.47 244	67.17 220	56.25 217	82.41 164	88.59 174	47.21 249	73.21 236	74.23 211	81.30 219	68.03 227

0.4-1-1-0.1		62.35 245	62.12 254	62.60 232	66.85 247	68.23 227	70.78 242	49.40 248	52.78 262	54.44 227	59.25 261	57.42 259	53.76 227	65.41 257	64.40 244	80.41 220	67.37 228

gg-mvs-nofinetune		72.68 204	75.21 213	69.73 191	81.48 145	69.04 224	70.48 243	76.67 35	86.92 64	67.80 176	88.06 107	64.67 246	42.12 255	77.60 208	73.65 214	79.81 221	66.57 229

MDTV_nov1_ep13_2view		72.96 202	75.59 208	69.88 189	71.15 232	64.86 239	82.31 161	54.45 231	76.30 168	78.32 94	86.52 130	91.58 144	61.35 179	76.80 213	66.83 241	71.70 238	66.26 230

0.3-1-1-0.015		61.14 249	60.59 258	61.78 237	65.65 254	67.14 232	69.76 247	48.31 249	51.00 264	53.98 228	56.11 263	56.81 262	53.29 229	63.79 262	63.19 246	79.66 222	66.07 231

0.4-1-1-0.2		60.88 250	60.45 259	61.38 239	65.29 255	66.73 234	69.11 253	48.01 251	50.14 267	53.73 235	57.22 262	57.01 261	52.91 233	63.57 263	62.64 247	79.23 225	65.82 232

test-mter		59.39 253	61.59 255	56.82 247	53.21 266	54.82 255	73.12 237	26.57 268	53.19 261	56.31 216	64.71 253	60.47 250	56.36 210	68.69 249	64.27 245	75.38 232	65.00 233

dps		65.14 233	64.50 244	65.89 224	71.41 231	65.81 238	71.44 240	61.59 177	58.56 254	61.43 204	75.45 219	52.70 273	58.06 204	69.57 247	64.65 243	71.39 242	64.77 234

WB-MVS		72.91 203	82.95 150	61.21 240	68.59 238	73.96 193	73.65 234	61.48 179	90.88 21	42.55 257	94.18 16	95.80 57	53.02 232	85.42 130	75.73 204	67.97 252	64.65 235

testgi		68.20 224	76.05 205	59.04 243	79.99 160	67.32 231	81.16 172	51.78 243	84.91 92	39.36 264	73.42 229	95.19 77	32.79 264	76.54 217	70.40 227	69.14 249	64.55 236

dtuonly		62.71 242	68.55 232	55.89 249	58.38 263	55.27 254	74.41 230	36.47 262	64.61 234	48.30 250	76.18 211	80.16 214	54.95 218	71.99 238	67.49 239	62.86 257	64.12 237

tpm cat1		64.79 236	62.74 252	67.17 213	74.61 219	65.91 237	76.18 215	59.32 197	64.88 233	66.41 185	71.21 238	53.56 272	59.17 197	61.53 264	58.16 257	67.33 253	63.95 238

PMMVS		61.98 247	65.61 241	57.74 245	45.03 271	51.76 261	69.54 249	35.05 263	55.49 259	55.32 223	68.23 249	78.39 222	58.09 203	70.21 246	71.56 222	83.42 208	63.66 239

tpm		62.79 240	63.25 249	62.26 236	70.09 234	53.78 256	71.65 239	47.31 252	65.72 227	76.70 106	80.62 172	56.40 269	48.11 246	64.20 260	58.54 255	59.70 260	63.47 240

test20.03		69.91 215	76.20 204	62.58 233	84.01 104	67.34 230	75.67 226	65.88 122	79.98 149	40.28 263	82.65 161	89.31 168	39.63 258	77.41 210	73.28 215	69.98 246	63.40 241

usedtu_dtu_shiyan2		73.14 199	78.83 179	66.49 217	80.89 151	69.55 222	78.12 199	67.67 104	89.65 36	49.76 248	80.90 171	95.49 65	45.72 250	78.37 205	74.56 210	76.81 230	63.31 242

Anonymous20231206		67.28 228	73.41 221	60.12 242	76.45 203	63.61 244	74.21 232	56.52 213	76.35 167	42.23 258	75.81 217	90.47 158	41.51 256	74.52 223	69.97 229	69.83 247	63.17 243

CHOSEN 280x420		56.32 260	58.85 266	53.36 255	51.63 267	39.91 271	69.12 252	38.61 261	56.29 256	36.79 266	48.84 268	62.59 248	63.39 170	73.61 234	67.66 238	60.61 258	63.07 244

pmmvs3		62.72 241	68.71 231	55.74 250	50.74 269	57.10 251	70.05 245	28.82 266	61.57 249	57.39 214	71.19 239	85.73 192	53.96 225	73.36 235	69.43 236	73.47 236	62.55 245

FMVSNet5		56.37 259	60.14 261	51.98 259	60.83 260	59.58 248	66.85 257	42.37 257	52.68 263	41.33 261	47.09 269	54.68 270	35.28 261	73.88 231	70.77 226	65.24 256	62.26 246

test-LLR		62.15 246	59.46 264	65.29 226	79.07 169	52.66 259	69.46 250	62.93 158	50.76 265	53.81 233	63.11 256	58.91 254	52.87 234	66.54 254	62.34 249	73.59 234	61.87 247

TESTMET0.1,1		57.21 256	59.46 264	54.60 254	50.95 268	52.66 259	69.46 250	26.91 267	50.76 265	53.81 233	63.11 256	58.91 254	52.87 234	66.54 254	62.34 249	73.59 234	61.87 247

MDTV_nov1_ep13		64.96 234	64.77 243	65.18 228	67.08 245	62.46 245	75.80 220	51.10 246	62.27 246	69.74 156	74.12 224	62.65 247	55.64 216	68.19 250	62.16 252	71.70 238	61.57 249

SCA		68.54 223	67.52 237	69.73 191	67.79 241	75.04 180	76.96 207	68.94 88	66.41 223	67.86 175	74.03 225	60.96 249	65.55 155	68.99 248	65.67 242	71.30 243	61.54 250

gm-plane-assit		71.56 211	69.99 227	73.39 159	84.43 98	73.21 197	90.42 71	51.36 245	84.08 98	76.00 111	91.30 58	37.09 277	59.01 200	73.65 233	70.24 228	79.09 227	60.37 251

pmnet_mix02		62.60 243	70.81 225	53.02 256	66.56 249	50.44 263	62.81 262	46.84 253	79.13 158	43.76 256	87.45 114	90.75 156	39.85 257	70.48 244	57.09 258	58.27 262	60.32 252

MIMVSNet		63.02 238	69.02 230	56.01 248	68.20 239	59.26 249	70.01 246	53.79 235	71.56 204	41.26 262	71.38 236	82.38 208	36.38 260	71.43 242	67.32 240	66.45 255	59.83 253

test0.0.03 1		61.79 248	65.33 242	57.65 246	79.07 169	64.09 241	68.51 255	62.93 158	61.59 248	33.71 267	61.58 258	71.58 242	33.43 263	70.95 243	68.68 237	68.26 251	58.82 254

new-patchmatchnet		62.59 244	73.79 219	49.53 260	76.98 193	53.57 257	53.46 271	54.64 229	85.43 85	28.81 269	91.94 45	96.41 34	25.28 267	76.80 213	53.66 264	57.99 263	58.69 255

PatchmatchNet		64.81 235	63.74 247	66.06 223	69.21 236	58.62 250	73.16 236	60.01 194	65.92 225	66.19 186	76.27 209	59.09 253	60.45 183	66.58 253	61.47 254	67.33 253	58.24 256
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		59.42 252	60.02 262	58.71 244	67.56 243	53.10 258	66.99 256	51.88 242	63.80 238	57.68 212	76.73 206	56.49 268	48.73 245	56.47 268	55.55 260	59.43 261	58.02 257

TAMVS		63.02 238	69.30 229	55.70 251	70.12 233	56.89 252	69.63 248	45.13 254	70.23 208	38.00 265	77.79 194	75.15 236	42.60 253	74.48 224	72.81 219	68.70 250	57.75 258

MVE	41.12 19	51.80 263	60.92 257	41.16 262	35.21 273	34.14 273	48.45 274	41.39 258	69.11 215	19.53 272	63.33 255	73.80 237	63.56 167	67.19 251	61.51 253	38.85 271	57.38 259
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GG-mvs-BLEND		41.63 265	60.36 260	19.78 265	0.14 282	66.04 236	55.66 270	0.17 277	57.64 255	2.42 279	51.82 267	69.42 243	0.28 278	64.11 261	58.29 256	60.02 259	55.18 260

EPMVS		56.62 258	59.77 263	52.94 257	62.41 258	50.55 262	60.66 264	52.83 239	65.15 232	41.80 260	77.46 200	57.28 260	42.68 252	59.81 266	54.82 261	57.23 264	53.35 261

MVS-HIRNet		59.74 251	58.74 267	60.92 241	57.74 264	45.81 267	56.02 269	58.69 204	55.69 258	65.17 188	70.86 240	71.66 240	56.75 207	61.11 265	53.74 263	71.17 244	52.28 262

ADS-MVSNet		56.89 257	61.09 256	52.00 258	59.48 261	48.10 265	58.02 266	54.37 232	72.82 194	49.19 249	75.32 220	65.97 245	37.96 259	59.34 267	54.66 262	52.99 268	51.42 263

new_pmnet		52.29 262	63.16 250	39.61 263	58.89 262	44.70 268	48.78 273	34.73 264	65.88 226	17.85 273	73.42 229	80.00 215	23.06 268	67.00 252	62.28 251	54.36 265	48.81 264

N_pmnet		54.95 261	65.90 240	42.18 261	66.37 251	43.86 269	57.92 267	39.79 260	79.54 155	17.24 275	86.31 132	87.91 179	25.44 265	64.68 258	51.76 267	46.33 270	47.23 265

PatchmatchNet1												87.99 178	25.44 265	64.23 259	51.81 266	46.37 269	47.19 266
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
DeepMVS_CX						17.78 274	20.40 276	6.69 270	31.41 270	9.80 276	38.61 270	34.88 278	33.78 262	28.41 271		23.59 274	45.77 267

E-PMN		59.07 254	62.79 251	54.72 252	67.01 246	47.81 266	60.44 265	43.40 255	72.95 192	44.63 255	70.42 244	73.17 239	58.73 201	80.97 191	51.98 265	54.14 266	42.26 268

EMVS		58.97 255	62.63 253	54.70 253	66.26 253	48.71 264	61.74 263	42.71 256	72.80 195	46.00 254	73.01 231	71.66 240	57.91 205	80.41 195	50.68 268	53.55 267	41.11 269

PMMVS2		48.13 264	64.06 245	29.55 264	44.06 272	36.69 272	51.95 272	29.97 265	74.75 183	8.90 277	76.02 215	91.24 152	7.53 272	73.78 232	55.91 259	34.87 272	40.01 270

MVS_clip		13.15 268	20.01 269	5.15 269	9.47 276	8.55 276	2.73 280	2.62 272	19.66 272	0.76 282	26.96 272	24.20 279	12.53 271	17.90 273	16.55 271	2.80 277	26.23 271

VLMVS_CLIP		15.19 267	17.84 270	12.09 268	31.85 274	14.34 275	3.33 279	13.23 269	15.35 273	3.95 278	18.75 273	17.87 280	14.99 270	18.62 272	15.68 272	5.20 276	24.28 272

MVS_baseline		3.67 269	6.07 271	0.86 271	1.13 279	0.44 281	0.17 284	0.00 278	5.57 274	0.00 284	6.81 275	7.78 282	3.86 273	2.15 275	2.53 273	0.02 281	17.25 273

test_method		22.69 266	26.99 268	17.67 266	2.13 278	4.31 278	27.50 275	4.53 271	37.94 269	24.52 271	36.20 271	51.40 274	15.26 269	29.86 270	17.09 270	32.07 273	12.16 274

VLMVS		2.47 270	3.49 272	1.28 270	2.52 277	1.70 279	0.71 281	0.70 274	3.87 275	0.83 281	3.23 276	5.07 283	2.15 275	2.21 274	1.81 274	0.75 278	6.54 275

testmvs		0.93 272	1.37 274	0.41 273	0.36 281	0.36 282	0.62 282	0.39 275	1.48 276	0.18 283	2.41 277	1.31 285	0.41 277	1.25 277	1.08 276	0.48 279	1.68 276

test123		1.06 271	1.41 273	0.64 272	0.39 280	0.48 280	0.52 283	0.25 276	1.11 277	1.37 280	2.01 278	1.98 284	0.87 276	1.43 276	1.27 275	0.46 280	1.62 277

uanet_test		0.00 273	0.00 275	0.00 274	0.00 283	0.00 283	0.00 285	0.00 278	0.00 278	0.00 284	0.00 279	0.00 286	0.00 279	0.00 278	0.00 277	0.00 282	0.00 278

sosnet-low-res		0.00 273	0.00 275	0.00 274	0.00 283	0.00 283	0.00 285	0.00 278	0.00 278	0.00 284	0.00 279	0.00 286	0.00 279	0.00 278	0.00 277	0.00 282	0.00 278

sosnet		0.00 273	0.00 275	0.00 274	0.00 283	0.00 283	0.00 285	0.00 278	0.00 278	0.00 284	0.00 279	0.00 286	0.00 279	0.00 278	0.00 277	0.00 282	0.00 278

PatchmatchNet2					64.26 257	41.70 270	56.82 268
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3										17.36 274	86.27 134
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
TestfortrainingZip							94.55 31	72.48 63		73.73 130						91.99 76

RE-MVS-def										87.10 28

9.14												89.43 165

SR-MVS					91.82 13			80.80 7				95.53 64

our_test_3					73.27 223	70.91 215	83.26 150

MTAPA										89.37 9		94.85 86

MTMP										90.54 5		95.16 79

Patchmatch-RL test							4.13 278

tmp_tt				13.54 267	16.73 275	6.42 277	8.49 277	2.36 273	28.69 271	27.44 270	18.40 274	13.51 281	3.70 274	33.23 269	36.26 269	22.54 275

XVS					91.28 25	91.23 8	96.89 2			87.14 25		94.53 96				95.84 15

X-MVStestdata					91.28 25	91.23 8	96.89 2			87.14 25		94.53 96				95.84 15

mPP-MVS					93.05 3							95.77 58

NP-MVS									78.65 160

Patchmtry						56.88 253	64.47 259	67.74 102		72.30 139

High-resolution multi-view benchmark