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.

UA-Net		89.02 34	91.44 40	86.20 29	94.88 1	89.84 34	94.76 30	77.45 29	85.41 73	74.79 107	88.83 78	88.90 139	78.67 40	96.06 7	95.45 4	96.66 3	95.58 2

DTE-MVSNet		88.99 36	92.77 13	84.59 44	93.31 2	88.10 49	90.96 53	83.09 2	91.38 15	76.21 96	96.03 3	98.04 9	70.78 108	95.65 14	92.32 33	93.18 59	87.84 73

zzz-MVS		90.38 12	91.35 42	89.25 5	93.08 3	86.59 65	96.45 11	79.00 16	90.23 29	89.30 10	85.87 108	94.97 66	82.54 18	95.05 23	94.83 7	95.14 27	91.94 37

mPP-MVS					93.05 4							95.77 45

MP-MVS		90.84 6	91.95 35	89.55 3	92.92 5	90.90 19	96.56 6	79.60 11	86.83 61	88.75 13	89.00 74	94.38 79	84.01 9	94.94 25	94.34 11	95.45 24	93.24 23
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PEN-MVS		88.86 40	92.92 10	84.11 54	92.92 5	88.05 51	90.83 56	82.67 5	91.04 19	74.83 106	95.97 4	98.47 4	70.38 109	95.70 13	92.43 31	93.05 63	88.78 65

HPM-MVS++		88.74 41	89.54 55	87.80 16	92.58 7	85.69 73	95.10 27	78.01 23	87.08 58	87.66 20	87.89 86	92.07 109	80.28 30	90.97 72	91.41 44	93.17 60	91.69 39

DVP-MVS++.		90.50 10	94.18 4	86.21 28	92.52 8	90.29 28	95.29 23	76.02 42	94.24 6	82.82 57	95.84 6	97.56 17	76.82 57	93.13 39	91.20 45	93.78 49	97.01 1

PS-CasMVS		89.07 33	93.23 8	84.21 52	92.44 9	88.23 48	90.54 64	82.95 3	90.50 26	75.31 104	95.80 7	98.37 7	71.16 102	96.30 5	93.32 22	92.88 64	90.11 53

CP-MVSNet		88.71 42	92.63 16	84.13 53	92.39 10	88.09 50	90.47 69	82.86 4	88.79 44	75.16 105	94.87 10	97.68 15	71.05 104	96.16 6	93.18 24	92.85 65	89.64 57

CP-MVS		91.09 5	92.33 26	89.65 2	92.16 11	90.41 27	96.46 10	80.38 8	88.26 47	89.17 11	87.00 97	96.34 32	83.95 10	95.77 11	94.72 8	95.81 17	93.78 10

ACMMPR		91.30 4	92.88 12	89.46 4	91.92 12	91.61 5	96.60 5	79.46 14	90.08 32	88.53 14	89.54 66	95.57 49	84.25 7	95.24 20	94.27 13	95.97 11	93.85 8

WR-MVS_H		88.99 36	93.28 6	83.99 55	91.92 12	89.13 40	91.95 47	83.23 1	90.14 31	71.92 126	95.85 5	98.01 11	71.83 99	95.82 9	93.19 23	93.07 62	90.83 49

SR-MVS					91.82 14			80.80 7				95.53 51

PGM-MVS		90.42 11	91.58 38	89.05 6	91.77 15	91.06 13	96.51 7	78.94 17	85.41 73	87.67 19	87.02 96	95.26 57	83.62 12	95.01 24	93.94 16	95.79 19	93.40 20

APD-MVS		89.14 30	91.25 45	86.67 25	91.73 16	91.02 15	95.50 21	77.74 25	84.04 85	79.47 84	91.48 45	94.85 68	81.14 26	92.94 42	92.20 36	94.47 41	92.24 33
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SMA-MVS		90.13 16	92.26 28	87.64 18	91.68 17	90.44 26	95.22 25	77.34 33	90.79 23	87.80 17	90.42 56	92.05 111	79.05 35	93.89 33	93.59 19	94.77 34	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
ambc			88.38 63		91.62 18	87.97 52	84.48 125		88.64 46	87.93 16	87.38 91	94.82 70	74.53 78	89.14 89	83.86 116	85.94 150	86.84 78

TSAR-MVS + MP.		89.67 25	92.25 29	86.65 26	91.53 19	90.98 17	96.15 14	73.30 57	87.88 51	81.83 69	92.92 30	95.15 61	82.23 19	93.58 35	92.25 34	94.87 31	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
train_agg		86.67 56	87.73 72	85.43 36	91.51 20	82.72 91	94.47 32	74.22 54	81.71 100	81.54 73	89.20 72	92.87 95	78.33 42	90.12 81	88.47 72	92.51 72	89.04 62

X-MVS		89.36 29	90.73 48	87.77 17	91.50 21	91.23 8	96.76 4	78.88 18	87.29 56	87.14 26	78.98 146	94.53 73	76.47 59	95.25 19	94.28 12	95.85 14	93.55 16

HFP-MVS		90.32 14	92.37 23	87.94 14	91.46 22	90.91 18	95.69 18	79.49 12	89.94 35	83.50 51	89.06 73	94.44 77	81.68 23	94.17 31	94.19 14	95.81 17	93.87 7

ACMM	80.67 7	90.67 7	92.46 20	88.57 8	91.35 23	89.93 32	96.34 12	77.36 31	90.17 30	86.88 30	87.32 92	96.63 25	83.32 13	95.79 10	94.49 10	96.19 9	92.91 26
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
WR-MVS		89.79 24	93.66 5	85.27 38	91.32 24	88.27 46	93.49 39	79.86 10	92.75 10	75.37 103	96.86 1	98.38 6	75.10 73	95.93 8	94.07 15	96.46 5	89.39 59

SD-MVS		89.91 19	92.23 31	87.19 22	91.31 25	89.79 35	94.31 33	75.34 48	89.26 39	81.79 70	92.68 32	95.08 63	83.88 11	93.10 40	92.69 26	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
XVS					91.28 26	91.23 8	96.89 2			87.14 26		94.53 73				95.84 15

X-MVStestdata					91.28 26	91.23 8	96.89 2			87.14 26		94.53 73				95.84 15

DeepC-MVS	83.59 4	90.37 13	92.56 19	87.82 15	91.26 28	92.33 3	94.72 31	80.04 9	90.01 33	84.61 43	93.33 23	94.22 80	80.59 28	92.90 45	92.52 29	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
ACMMP		90.63 8	92.40 21	88.56 9	91.24 29	91.60 6	96.49 9	77.53 27	87.89 50	86.87 31	87.24 94	96.46 27	82.87 16	95.59 15	94.50 9	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
SteuartSystems-ACMMP		90.00 18	91.73 36	87.97 13	91.21 30	90.29 28	96.51 7	78.00 24	86.33 64	85.32 41	88.23 83	94.67 71	82.08 21	95.13 22	93.88 17	94.72 36	93.59 13
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_NAP		89.86 20	91.96 34	87.42 20	91.00 31	90.08 30	96.00 16	76.61 37	89.28 36	87.73 18	90.04 58	91.80 114	78.71 38	94.36 29	93.82 18	94.48 40	94.32 6

CPTT-MVS		89.63 26	90.52 50	88.59 7	90.95 32	90.74 21	95.71 17	79.13 15	87.70 52	85.68 39	80.05 141	95.74 47	84.77 6	94.28 30	92.68 27	95.28 26	92.45 32

LGP-MVS_train		90.56 9	92.38 22	88.43 10	90.88 33	91.15 11	95.35 22	77.65 26	86.26 66	87.23 24	90.45 55	97.35 19	83.20 14	95.44 16	93.41 21	96.28 8	92.63 27

OPM-MVS		89.82 22	92.24 30	86.99 23	90.86 34	89.35 38	95.07 28	75.91 44	91.16 17	86.87 31	91.07 51	97.29 20	79.13 34	93.32 36	91.99 38	94.12 43	91.49 42
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMP	80.00 8	90.12 17	92.30 27	87.58 19	90.83 35	91.10 12	94.96 29	76.06 41	87.47 54	85.33 40	88.91 77	97.65 16	82.13 20	95.31 17	93.44 20	96.14 10	92.22 34
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
NCCC		86.74 55	87.97 71	85.31 37	90.64 36	87.25 59	93.27 40	74.59 50	86.50 62	83.72 47	75.92 171	92.39 103	77.08 55	91.72 55	90.68 50	92.57 70	91.30 44

MSP-MVS		88.51 43	91.36 41	85.19 40	90.63 37	92.01 4	95.29 23	77.52 28	90.48 27	80.21 79	90.21 57	96.08 36	76.38 61	88.30 97	91.42 42	91.12 90	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
UniMVSNet_ETH3D		85.39 65	91.12 46	78.71 103	90.48 38	83.72 83	81.76 141	82.41 6	93.84 7	64.43 160	95.41 8	98.76 1	63.72 143	93.63 34	89.74 60	89.47 108	82.74 115

APDe-MVS		89.85 21	92.91 11	86.29 27	90.47 39	91.34 7	96.04 15	76.41 40	91.11 18	78.50 89	93.44 22	95.82 44	81.55 24	93.16 38	91.90 39	94.77 34	93.58 15

PMVS	79.51 9	90.23 15	92.67 15	87.39 21	90.16 40	88.75 42	93.64 37	75.78 45	90.00 34	83.70 48	92.97 29	92.22 106	86.13 4	97.01 3	96.79 2	94.94 30	90.96 47
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
CNVR-MVS		86.93 54	88.98 59	84.54 45	90.11 41	87.41 58	93.23 41	73.47 56	86.31 65	82.25 64	82.96 129	92.15 107	76.04 64	91.69 56	90.69 49	92.17 76	91.64 41

TSAR-MVS + GP.		85.32 67	87.41 76	82.89 65	90.07 42	85.69 73	89.07 85	72.99 59	82.45 93	74.52 110	85.09 116	87.67 145	79.24 33	91.11 67	90.41 52	91.45 82	89.45 58

DeepC-MVS_fast	81.78 5	87.38 52	89.64 54	84.75 42	89.89 43	90.70 22	92.74 44	74.45 51	86.02 67	82.16 67	86.05 106	91.99 113	75.84 67	91.16 66	90.44 51	93.41 54	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
LS3D		89.02 34	91.69 37	85.91 31	89.72 44	90.81 20	92.56 45	71.69 67	90.83 22	87.24 23	89.71 64	92.07 109	78.37 41	94.43 28	92.59 28	95.86 13	91.35 43

DPE-MVS		89.81 23	92.34 25	86.86 24	89.69 45	91.00 16	95.53 19	76.91 34	88.18 48	83.43 54	93.48 21	95.19 58	81.07 27	92.75 47	92.07 37	94.55 38	93.74 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CDPH-MVS		86.66 57	88.52 62	84.48 46	89.61 46	88.27 46	92.86 43	72.69 60	80.55 118	82.71 58	86.92 98	93.32 91	75.55 69	91.00 71	89.85 59	93.47 52	89.71 56

EPNet		79.36 124	79.44 141	79.27 102	89.51 47	77.20 137	88.35 91	77.35 32	68.27 173	74.29 111	76.31 164	79.22 173	59.63 156	85.02 128	85.45 100	86.49 142	84.61 93
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + ACMM		89.14 30	92.11 33	85.67 32	89.27 48	90.61 24	90.98 52	79.48 13	88.86 42	79.80 81	93.01 28	93.53 89	83.17 15	92.75 47	92.45 30	91.32 85	93.59 13

HQP-MVS		85.02 69	86.41 82	83.40 56	89.19 49	86.59 65	91.28 50	71.60 68	82.79 91	83.48 52	78.65 150	93.54 88	72.55 91	86.49 112	85.89 96	92.28 75	90.95 48

AdaColmap		84.15 76	85.14 97	83.00 62	89.08 50	87.14 61	90.56 63	70.90 70	82.40 94	80.41 76	73.82 182	84.69 158	75.19 72	91.58 60	89.90 58	91.87 79	86.48 80

DVP-MVS		89.40 28	92.69 14	85.56 35	89.01 51	89.85 33	93.72 36	75.42 46	92.28 12	80.49 75	94.36 14	94.87 67	81.46 25	92.49 51	91.42 42	93.27 56	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
COLMAP_ROB	85.66 2	91.85 2	95.01 2	88.16 12	88.98 52	92.86 2	95.51 20	72.17 61	94.95 5	91.27 3	94.11 17	97.77 12	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
TDRefinement		93.16 1	95.57 1	90.36 1	88.79 53	93.57 1	97.27 1	78.23 22	95.55 2	93.00 1	93.98 18	96.01 40	87.53 1	97.69 1	96.81 1	97.33 1	95.34 4

TranMVSNet+NR-MVSNet		85.23 68	89.38 56	80.39 93	88.78 54	83.77 82	87.40 99	76.75 35	85.47 71	68.99 142	95.18 9	97.55 18	67.13 128	91.61 59	89.13 68	93.26 57	82.95 112

SED-MVS		88.96 38	92.37 23	84.99 41	88.64 55	89.65 37	95.11 26	75.98 43	90.73 24	80.15 80	94.21 16	94.51 76	76.59 58	92.94 42	91.17 46	93.46 53	93.37 22

ACMH+	79.05 11	89.62 27	93.08 9	85.58 33	88.58 56	89.26 39	92.18 46	74.23 53	93.55 9	82.66 61	92.32 37	98.35 8	80.29 29	95.28 18	92.34 32	95.52 22	90.43 51

MVS_0304		84.73 73	86.19 84	83.02 60	88.32 57	86.71 64	91.55 48	70.87 71	73.79 147	82.88 56	85.13 115	93.35 90	72.55 91	88.62 92	87.69 79	91.93 78	88.05 72

DU-MVS		84.88 71	88.27 67	80.92 82	88.30 58	83.59 85	87.06 104	78.35 20	80.64 116	70.49 134	92.67 33	96.91 23	68.13 121	91.79 53	89.29 67	93.20 58	83.02 109

Baseline_NR-MVSNet		82.79 94	86.51 79	78.44 107	88.30 58	75.62 151	87.81 94	74.97 49	81.53 104	66.84 155	94.71 13	96.46 27	66.90 129	91.79 53	83.37 123	85.83 152	82.09 120

UniMVSNet_NR-MVSNet		84.62 74	88.00 70	80.68 88	88.18 60	83.83 81	87.06 104	76.47 39	81.46 107	70.49 134	93.24 24	95.56 50	68.13 121	90.43 76	88.47 72	93.78 49	83.02 109

xxxxxxxxxxxxxcwj		88.03 48	91.29 44	84.22 50	88.17 61	87.90 53	90.80 57	71.80 65	89.28 36	82.70 59	89.90 60	97.72 13	77.91 47	91.69 56	90.04 56	93.95 47	92.47 29

SF-MVS		87.85 51	90.95 47	84.22 50	88.17 61	87.90 53	90.80 57	71.80 65	89.28 36	82.70 59	89.90 60	95.37 55	77.91 47	91.69 56	90.04 56	93.95 47	92.47 29

CSCG		88.12 46	91.45 39	84.23 49	88.12 63	90.59 25	90.57 62	68.60 91	91.37 16	83.45 53	89.94 59	95.14 62	78.71 38	91.45 61	88.21 76	95.96 12	93.44 19

CLD-MVS		82.75 96	87.22 77	77.54 113	88.01 64	85.76 72	90.23 72	54.52 186	82.28 96	82.11 68	88.48 81	95.27 56	63.95 141	89.41 86	88.29 74	86.45 143	81.01 128
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet (Re)		84.95 70	88.53 61	80.78 84	87.82 65	84.21 79	88.03 92	76.50 38	81.18 111	69.29 140	92.63 35	96.83 24	69.07 117	91.23 65	89.60 63	93.97 46	84.00 101

DPM-MVS		81.42 106	82.11 132	80.62 89	87.54 66	85.30 75	90.18 74	68.96 86	81.00 114	79.15 86	70.45 199	83.29 161	67.67 125	82.81 142	83.46 118	90.19 96	88.48 67

DeepPCF-MVS	81.61 6	87.95 49	90.29 52	85.22 39	87.48 67	90.01 31	93.79 35	73.54 55	88.93 41	83.89 46	89.40 68	90.84 123	80.26 31	90.62 75	90.19 55	92.36 73	92.03 36

DROMVSNet		83.70 81	84.77 105	82.46 69	87.47 68	82.79 90	85.50 114	72.00 62	69.81 164	77.66 92	85.02 118	89.63 130	78.14 43	90.40 77	87.56 80	94.00 44	88.16 69

CANet		82.84 93	84.60 107	80.78 84	87.30 69	85.20 76	90.23 72	69.00 85	72.16 156	78.73 88	84.49 123	90.70 126	69.54 114	87.65 100	86.17 91	89.87 101	85.84 86

MCST-MVS		84.79 72	86.48 80	82.83 66	87.30 69	87.03 62	90.46 70	69.33 83	83.14 88	82.21 66	81.69 137	92.14 108	75.09 74	87.27 104	84.78 107	92.58 68	89.30 60

EIA-MVS		78.57 129	77.90 149	79.35 100	87.24 71	80.71 107	86.16 111	64.03 133	62.63 200	73.49 117	73.60 183	76.12 187	73.83 84	88.49 94	84.93 105	91.36 84	78.78 145

test_part1		87.86 50	93.26 7	81.56 77	87.23 72	86.76 63	90.91 54	70.06 75	96.50 1	76.74 94	96.63 2	98.62 2	69.45 116	92.93 44	90.92 47	94.98 29	90.46 50

OMC-MVS		88.16 44	91.34 43	84.46 47	86.85 73	90.63 23	93.01 42	67.00 104	90.35 28	87.40 22	86.86 99	96.35 31	77.66 50	92.63 49	90.84 48	94.84 32	91.68 40

3Dnovator+	83.71 3	88.13 45	90.00 53	85.94 30	86.82 74	91.06 13	94.26 34	75.39 47	88.85 43	85.76 38	85.74 110	86.92 148	78.02 45	93.03 41	92.21 35	95.39 25	92.21 35

ETV-MVS		79.01 128	77.98 148	80.22 94	86.69 75	79.73 115	88.80 88	68.27 96	63.22 195	71.56 128	70.25 201	73.63 193	73.66 86	90.30 80	86.77 88	92.33 74	81.95 122

PHI-MVS		86.37 59	88.14 68	84.30 48	86.65 76	87.56 56	90.76 59	70.16 74	82.55 92	89.65 7	84.89 119	92.40 102	75.97 65	90.88 73	89.70 61	92.58 68	89.03 63

ACMH	78.40 12	88.94 39	92.62 17	84.65 43	86.45 77	87.16 60	91.47 49	68.79 89	95.49 3	89.74 6	93.55 20	98.50 3	77.96 46	94.14 32	89.57 64	93.49 51	89.94 55
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CS-MVS-test		83.73 80	84.09 117	83.31 57	86.38 78	80.24 110	85.50 114	72.00 62	65.58 183	83.11 55	84.64 121	92.52 100	78.14 43	90.40 77	88.92 70	94.71 37	86.34 83

EG-PatchMatch MVS		84.35 75	87.55 73	80.62 89	86.38 78	82.24 96	86.75 107	64.02 134	84.24 81	78.17 91	89.38 69	95.03 65	78.78 37	89.95 83	86.33 90	89.59 105	85.65 89

IS_MVSNet		81.72 104	85.01 99	77.90 109	86.19 80	82.64 93	85.56 113	70.02 76	80.11 121	63.52 162	87.28 93	81.18 168	67.26 126	91.08 70	89.33 66	94.82 33	83.42 106

PCF-MVS	76.59 14	84.11 77	85.27 94	82.76 67	86.12 81	88.30 45	91.24 51	69.10 84	82.36 95	84.45 44	77.56 156	90.40 128	72.91 90	85.88 117	83.88 114	92.72 67	88.53 66
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TSAR-MVS + COLMAP		85.51 63	88.36 65	82.19 70	86.05 82	87.69 55	90.50 67	70.60 73	86.40 63	82.33 62	89.69 65	92.52 100	74.01 83	87.53 101	86.84 87	89.63 104	87.80 74

EPP-MVSNet		82.76 95	86.47 81	78.45 106	86.00 83	84.47 78	85.39 117	68.42 93	84.17 82	62.97 164	89.26 71	76.84 183	72.13 96	92.56 50	90.40 53	95.76 20	87.56 76

PLC	76.06 15	85.38 66	87.46 74	82.95 64	85.79 84	88.84 41	88.86 87	68.70 90	87.06 59	83.60 49	79.02 144	90.05 129	77.37 53	90.88 73	89.66 62	93.37 55	86.74 79
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MSLP-MVS++		86.29 60	89.10 58	83.01 61	85.71 85	89.79 35	87.04 106	74.39 52	85.17 75	78.92 87	77.59 155	93.57 87	82.60 17	93.23 37	91.88 40	89.42 109	92.46 31

CS-MVS		83.23 87	85.14 97	81.00 81	85.59 86	79.28 119	89.80 77	63.29 143	73.02 149	75.70 101	85.28 113	92.81 96	77.09 54	91.92 52	87.93 77	94.53 39	85.76 87

Effi-MVS+-dtu		82.04 101	83.39 126	80.48 92	85.48 87	86.57 67	88.40 90	68.28 95	69.04 171	73.13 120	76.26 166	91.11 122	74.74 77	88.40 95	87.76 78	92.84 66	84.57 95

ECVR-MVS		79.67 119	84.40 109	74.16 134	85.29 88	79.56 117	81.16 145	73.13 58	84.65 80	56.08 179	88.38 82	86.14 151	60.49 153	89.78 84	85.59 98	88.79 116	76.68 153

v7n		87.11 53	90.46 51	83.19 59	85.22 89	83.69 84	90.03 76	68.20 97	91.01 20	86.71 34	94.80 11	98.46 5	77.69 49	91.10 68	85.98 93	91.30 86	88.19 68

MAR-MVS		81.98 102	82.92 128	80.88 83	85.18 90	85.85 70	89.13 84	69.52 78	71.21 160	82.25 64	71.28 193	88.89 140	69.69 111	88.71 90	86.96 83	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
abl_6				79.30 101	84.98 91	85.78 71	90.50 67	66.88 105	77.08 135	74.02 112	73.29 186	89.34 134	68.94 118			90.49 93	85.98 84

TAPA-MVS	78.00 13	85.88 61	88.37 64	82.96 63	84.69 92	88.62 43	90.62 60	64.22 129	89.15 40	88.05 15	78.83 148	93.71 84	76.20 63	90.11 82	88.22 75	94.00 44	89.97 54
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
GeoE		81.92 103	83.87 119	79.66 97	84.64 93	79.87 112	89.75 78	65.90 115	76.12 139	75.87 99	84.62 122	92.23 105	71.96 98	86.83 109	83.60 117	89.83 102	83.81 102

SixPastTwentyTwo		89.14 30	92.19 32	85.58 33	84.62 94	82.56 94	90.53 65	71.93 64	91.95 13	85.89 36	94.22 15	97.25 21	85.42 5	95.73 12	91.71 41	95.08 28	91.89 38

MVS_111021_HR		83.95 78	86.10 86	81.44 78	84.62 94	80.29 109	90.51 66	68.05 98	84.07 84	80.38 77	84.74 120	91.37 119	74.23 79	90.37 79	87.25 82	90.86 92	84.59 94

CNLPA		85.50 64	88.58 60	81.91 72	84.55 96	87.52 57	90.89 55	63.56 139	88.18 48	84.06 45	83.85 126	91.34 120	76.46 60	91.27 63	89.00 69	91.96 77	88.88 64

Effi-MVS+		82.33 97	83.87 119	80.52 91	84.51 97	81.32 102	87.53 97	68.05 98	74.94 145	79.67 82	82.37 134	92.31 104	72.21 93	85.06 124	86.91 85	91.18 88	84.20 98

gm-plane-assit		71.56 168	69.99 183	73.39 138	84.43 98	73.21 163	90.42 71	51.36 199	84.08 83	76.00 98	91.30 48	37.09 225	59.01 158	73.65 187	70.24 186	79.09 179	60.37 200

RPSCF		88.05 47	92.61 18	82.73 68	84.24 99	88.40 44	90.04 75	66.29 108	91.46 14	82.29 63	88.93 76	96.01 40	79.38 32	95.15 21	94.90 6	94.15 42	93.40 20

FC-MVSNet-train		79.20 126	86.29 83	70.94 150	84.06 100	77.67 131	85.68 112	64.11 131	82.90 90	52.22 194	92.57 36	93.69 85	49.52 194	88.30 97	86.93 84	90.03 98	81.95 122

v1192		83.61 82	85.23 95	81.72 74	84.05 101	82.15 97	89.54 80	66.20 109	81.38 109	86.76 33	91.79 42	96.03 38	74.88 76	81.81 150	80.92 139	88.91 115	82.50 117

v1240		83.57 83	84.94 102	81.97 71	84.05 101	81.27 103	89.46 82	66.06 111	81.31 110	87.50 21	91.88 41	95.46 53	76.25 62	81.16 155	80.51 143	88.52 123	82.98 111

test20.03		69.91 172	76.20 163	62.58 188	84.01 103	67.34 183	75.67 182	65.88 116	79.98 122	40.28 212	82.65 130	89.31 135	39.63 207	77.41 171	73.28 176	69.98 196	63.40 191

Anonymous202405211			84.68 106		83.92 104	79.45 118	79.03 161	67.79 100	82.01 98		88.77 80	92.58 99	55.93 170	86.68 110	84.26 111	88.92 114	78.98 143

NR-MVSNet		82.89 92	87.43 75	77.59 112	83.91 105	83.59 85	87.10 103	78.35 20	80.64 116	68.85 143	92.67 33	96.50 26	54.19 179	87.19 107	88.68 71	93.16 61	82.75 114

Gipuma		86.47 58	89.25 57	83.23 58	83.88 106	78.78 124	85.35 118	68.42 93	92.69 11	89.03 12	91.94 38	96.32 34	81.80 22	94.45 27	86.86 86	90.91 91	83.69 103
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v1921920		83.49 84	84.94 102	81.80 73	83.78 107	81.20 105	89.50 81	65.91 114	81.64 102	87.18 25	91.70 43	95.39 54	75.85 66	81.56 153	80.27 145	88.60 120	82.80 113

v1144		83.22 88	85.01 99	81.14 79	83.76 108	81.60 100	88.95 86	65.58 119	81.89 99	85.80 37	91.68 44	95.84 43	74.04 82	82.12 147	80.56 142	88.70 119	81.41 125

Vis-MVSNet (Re-imp)		76.15 143	80.84 137	70.68 151	83.66 109	74.80 158	81.66 143	69.59 77	80.48 119	46.94 203	87.44 90	80.63 170	53.14 184	86.87 108	84.56 110	89.12 111	71.12 171

v144192		83.43 85	84.97 101	81.63 76	83.43 110	81.23 104	89.42 83	66.04 113	81.45 108	86.40 35	91.46 46	95.70 48	75.76 68	82.14 146	80.23 146	88.74 117	82.57 116

TinyColmap		83.79 79	86.12 85	81.07 80	83.42 111	81.44 101	85.42 116	68.55 92	88.71 45	89.46 8	87.60 88	92.72 97	70.34 110	89.29 87	81.94 132	89.20 110	81.12 127

TransMVSNet (Re)		79.05 127	86.66 78	70.18 156	83.32 112	75.99 146	77.54 166	63.98 135	90.68 25	55.84 181	94.80 11	96.06 37	53.73 182	86.27 114	83.22 124	86.65 138	79.61 141

v10		83.17 90	85.22 96	80.78 84	83.26 113	82.99 89	88.66 89	66.49 107	79.24 127	83.60 49	91.46 46	95.47 52	74.12 80	82.60 145	80.66 140	88.53 122	84.11 100

LTVRE_ROB	86.82 1	91.55 3	94.43 3	88.19 11	83.19 114	86.35 68	93.60 38	78.79 19	95.48 4	91.79 2	93.08 27	97.21 22	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
canonicalmvs		81.22 110	86.04 88	75.60 121	83.17 115	83.18 88	80.29 151	65.82 117	85.97 68	67.98 150	77.74 154	91.51 117	65.17 137	88.62 92	86.15 92	91.17 89	89.09 61

FPMVS		81.56 105	84.04 118	78.66 104	82.92 116	75.96 147	86.48 110	65.66 118	84.67 79	71.47 129	77.78 153	83.22 162	77.57 51	91.24 64	90.21 54	87.84 128	85.21 91

DCV-MVSNet		80.04 115	85.67 92	73.48 137	82.91 117	81.11 106	80.44 150	66.06 111	85.01 76	62.53 167	78.84 147	94.43 78	58.51 160	88.66 91	85.91 94	90.41 94	85.73 88

MVS_111021_LR		83.20 89	85.33 93	80.73 87	82.88 118	78.23 128	89.61 79	65.23 121	82.08 97	81.19 74	85.31 112	92.04 112	75.22 71	89.50 85	85.90 95	90.24 95	84.23 97

Anonymous20231211		79.37 123	85.78 90	71.89 144	82.87 119	79.66 116	78.77 163	63.93 137	83.36 86	59.39 171	90.54 53	94.66 72	56.46 167	87.38 102	84.12 112	89.92 100	80.74 129

v2v482		82.20 99	84.26 112	79.81 96	82.67 120	80.18 111	87.67 96	63.96 136	81.69 101	84.73 42	91.27 49	96.33 33	72.05 97	81.94 149	79.56 149	87.79 129	78.84 144

v8		82.20 99	84.56 108	79.45 98	82.42 121	81.65 99	87.26 100	64.27 128	79.36 126	81.70 71	91.04 52	95.75 46	73.30 89	82.82 141	79.18 152	87.74 130	82.09 120

MSDG		81.39 108	84.23 114	78.09 108	82.40 122	82.47 95	85.31 120	60.91 161	79.73 124	80.26 78	86.30 102	88.27 143	69.67 112	87.20 106	84.98 104	89.97 99	80.67 130

Fast-Effi-MVS+		81.42 106	83.82 121	78.62 105	82.24 123	80.62 108	87.72 95	63.51 140	73.01 150	74.75 108	83.80 127	92.70 98	73.44 88	88.15 99	85.26 101	90.05 97	83.17 107

PVSNet_Blended_VisFu		83.00 91	84.16 115	81.65 75	82.17 124	86.01 69	88.03 92	71.23 69	76.05 140	79.54 83	83.88 125	83.44 159	77.49 52	87.38 102	84.93 105	91.41 83	87.40 77

pmmvs6		80.46 112	88.34 66	71.26 146	81.96 125	77.51 132	77.54 166	68.83 88	93.72 8	55.92 180	93.94 19	98.03 10	55.94 169	89.21 88	85.61 97	87.36 134	80.38 132

IterMVS-LS		79.79 117	82.56 130	76.56 118	81.83 126	77.85 130	79.90 155	69.42 82	78.93 129	71.21 130	90.47 54	85.20 157	70.86 107	80.54 160	80.57 141	86.15 145	84.36 96
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		73.07 162	77.02 155	68.46 166	81.62 127	72.89 164	79.56 159	70.78 72	69.56 166	52.52 191	77.37 158	81.12 169	42.60 202	84.20 134	83.93 113	83.65 165	70.07 176
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
gg-mvs-nofinetune		72.68 164	75.21 170	69.73 158	81.48 128	69.04 178	70.48 195	76.67 36	86.92 60	67.80 152	88.06 85	64.67 201	42.12 204	77.60 170	73.65 175	79.81 176	66.57 183

USDC		81.39 108	83.07 127	79.43 99	81.48 128	78.95 123	82.62 136	66.17 110	87.45 55	90.73 4	82.40 133	93.65 86	66.57 131	83.63 137	77.97 155	89.00 113	77.45 152

casdiffmvs		79.93 116	84.11 116	75.05 126	81.41 130	78.99 122	82.95 133	62.90 148	81.53 104	68.60 147	91.94 38	96.03 38	65.84 135	82.89 140	77.07 163	88.59 121	80.34 136

tfpnnormal		77.16 135	84.26 112	68.88 164	81.02 131	75.02 154	76.52 173	63.30 142	87.29 56	52.40 192	91.24 50	93.97 81	54.85 176	85.46 121	81.08 137	85.18 158	75.76 158

thres600view7		74.34 155	78.43 145	69.56 160	80.47 132	76.28 144	78.65 164	62.56 150	77.39 133	52.53 190	74.03 180	76.78 184	55.90 171	85.06 124	85.19 102	87.25 135	74.29 162

OpenMVS	75.38 16	78.44 130	81.39 136	74.99 129	80.46 133	79.85 113	79.99 153	58.31 175	77.34 134	73.85 114	77.19 159	82.33 166	68.60 120	84.67 131	81.95 131	88.72 118	86.40 82

pm-mvs1		78.21 131	85.68 91	69.50 161	80.38 134	75.73 149	76.25 174	65.04 122	87.59 53	54.47 185	93.16 26	95.99 42	54.20 178	86.37 113	82.98 127	86.64 139	77.96 150

v148		79.33 125	82.32 131	75.84 120	80.14 135	75.74 148	81.98 140	57.06 178	81.51 106	79.36 85	89.42 67	96.42 29	71.32 101	81.54 154	75.29 172	85.20 157	76.32 154

pmmvs-eth3d		79.64 120	82.06 133	76.83 115	80.05 136	72.64 165	87.47 98	66.59 106	80.83 115	73.50 116	89.32 70	93.20 92	67.78 123	80.78 158	81.64 135	85.58 155	76.01 155

testgi		68.20 181	76.05 164	59.04 194	79.99 137	67.32 184	81.16 145	51.78 197	84.91 77	39.36 213	73.42 184	95.19 58	32.79 213	76.54 177	70.40 185	69.14 199	64.55 187

DI_MVS_plusplus_trai		77.64 133	79.64 140	75.31 124	79.87 138	76.89 140	81.55 144	63.64 138	76.21 138	72.03 125	85.59 111	82.97 163	66.63 130	79.27 166	77.78 157	88.14 126	78.76 146

Fast-Effi-MVS+-dtu		76.92 136	77.18 154	76.62 117	79.55 139	79.17 120	84.80 122	77.40 30	64.46 190	68.75 145	70.81 197	86.57 149	63.36 148	81.74 151	81.76 133	85.86 151	75.78 157

thres400		73.13 161	76.99 157	68.62 165	79.46 140	74.93 156	77.23 168	61.23 159	75.54 141	52.31 193	72.20 188	77.10 182	54.89 174	82.92 139	82.62 129	86.57 141	73.66 167

QAPM		80.43 113	84.34 110	75.86 119	79.40 141	82.06 98	79.86 156	61.94 154	83.28 87	74.73 109	81.74 136	85.44 155	70.97 105	84.99 129	84.71 109	88.29 124	88.14 70

DELS-MVS		79.71 118	83.74 122	75.01 128	79.31 142	82.68 92	84.79 123	60.06 167	75.43 143	69.09 141	86.13 104	89.38 133	67.16 127	85.12 123	83.87 115	89.65 103	83.57 104
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
3Dnovator	79.41 10	82.21 98	86.07 87	77.71 110	79.31 142	84.61 77	87.18 101	61.02 160	85.65 69	76.11 97	85.07 117	85.38 156	70.96 106	87.22 105	86.47 89	91.66 80	88.12 71

ET-MVSNet_ETH3D		74.71 153	74.19 173	75.31 124	79.22 144	75.29 152	82.70 135	64.05 132	65.45 185	70.96 133	77.15 160	57.70 213	65.89 134	84.40 133	81.65 134	89.03 112	77.67 151

test-LLR		62.15 197	59.46 213	65.29 184	79.07 145	52.66 208	69.46 201	62.93 146	50.76 217	53.81 187	63.11 211	58.91 209	52.87 185	66.54 206	62.34 198	73.59 184	61.87 196

test0.0.03 1		61.79 199	65.33 195	57.65 197	79.07 145	64.09 192	68.51 204	62.93 146	61.59 203	33.71 216	61.58 213	71.58 197	33.43 212	70.95 195	68.68 190	68.26 201	58.82 203

baseline1		69.62 174	73.55 177	65.02 186	78.95 147	70.39 171	71.38 194	62.03 153	70.97 161	47.95 202	78.47 151	68.19 199	47.77 198	79.65 165	76.94 165	82.05 172	70.27 174

MVS_Test		76.72 138	79.40 142	73.60 136	78.85 148	74.99 155	79.91 154	61.56 156	69.67 165	72.44 121	85.98 107	90.78 124	63.50 146	78.30 168	75.74 170	85.33 156	80.31 137

FMVSNet1		78.20 132	84.83 104	70.46 154	78.62 149	79.03 121	77.90 165	67.53 103	83.02 89	55.10 183	87.19 95	93.18 93	55.65 172	85.57 118	83.39 120	87.98 127	82.40 118

GA-MVS		75.01 152	76.39 160	73.39 138	78.37 150	75.66 150	80.03 152	58.40 174	70.51 162	75.85 100	83.24 128	76.14 186	63.75 142	77.28 172	76.62 166	83.97 164	75.30 160

thres200		72.41 165	76.00 165	68.21 168	78.28 151	76.28 144	74.94 184	62.56 150	72.14 157	51.35 198	69.59 203	76.51 185	54.89 174	85.06 124	80.51 143	87.25 135	71.92 170

EPNet_dtu		71.90 167	73.03 179	70.59 152	78.28 151	61.64 196	82.44 137	64.12 130	63.26 194	69.74 137	71.47 191	82.41 164	51.89 191	78.83 167	78.01 154	77.07 181	75.60 159
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pmmvs4		75.92 145	77.48 153	74.10 135	78.21 153	70.94 169	84.06 126	64.78 124	75.13 144	68.47 148	84.12 124	83.32 160	64.74 140	75.93 180	79.14 153	84.31 162	73.77 165

PM-MVS		80.42 114	83.63 123	76.67 116	78.04 154	72.37 167	87.14 102	60.18 166	80.13 120	71.75 127	86.12 105	93.92 83	77.08 55	86.56 111	85.12 103	85.83 152	81.18 126

thres100view900		69.86 173	72.97 180	66.24 177	77.97 155	72.49 166	73.29 188	59.12 170	66.81 176	50.82 199	67.30 205	75.67 189	50.54 193	78.24 169	79.40 150	85.71 154	70.88 172

tfpn200view9		72.01 166	75.40 168	68.06 169	77.97 155	76.44 142	77.04 170	62.67 149	66.81 176	50.82 199	67.30 205	75.67 189	52.46 190	85.06 124	82.64 128	87.41 133	73.86 164

Vis-MVSNet		83.32 86	88.12 69	77.71 110	77.91 157	83.44 87	90.58 61	69.49 80	81.11 112	67.10 154	89.85 62	91.48 118	71.71 100	91.34 62	89.37 65	89.48 107	90.26 52
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PatchMatch-RL		76.05 144	76.64 158	75.36 123	77.84 158	69.87 175	81.09 147	63.43 141	71.66 158	68.34 149	71.70 189	81.76 167	74.98 75	84.83 130	83.44 119	86.45 143	73.22 168

CANet_DTU		75.04 151	78.45 144	71.07 147	77.27 159	77.96 129	83.88 128	58.00 176	64.11 191	68.67 146	75.65 173	88.37 142	53.92 181	82.05 148	81.11 136	84.67 160	79.88 139

MS-PatchMatch		71.18 171	73.99 175	67.89 172	77.16 160	71.76 168	77.18 169	56.38 180	67.35 174	55.04 184	74.63 178	75.70 188	62.38 149	76.62 175	75.97 169	79.22 178	75.90 156

new-patchmatchnet		62.59 196	73.79 176	49.53 210	76.98 161	53.57 206	53.46 218	54.64 185	85.43 72	28.81 217	91.94 38	96.41 30	25.28 215	76.80 173	53.66 213	57.99 211	58.69 204

GBi-Net		73.17 159	77.64 150	67.95 170	76.76 162	77.36 134	75.77 178	64.57 125	62.99 197	51.83 195	76.05 167	77.76 179	52.73 187	85.57 118	83.39 120	86.04 147	80.37 133

PVSNet_BlendedMVS		76.45 141	78.12 146	74.49 132	76.76 162	78.46 125	79.65 157	63.26 144	65.42 186	73.15 118	75.05 176	88.96 137	66.51 132	82.73 143	77.66 158	87.61 131	78.60 147

PVSNet_Blended		76.45 141	78.12 146	74.49 132	76.76 162	78.46 125	79.65 157	63.26 144	65.42 186	73.15 118	75.05 176	88.96 137	66.51 132	82.73 143	77.66 158	87.61 131	78.60 147

test1		73.17 159	77.64 150	67.95 170	76.76 162	77.36 134	75.77 178	64.57 125	62.99 197	51.83 195	76.05 167	77.76 179	52.73 187	85.57 118	83.39 120	86.04 147	80.37 133

FMVSNet2		74.43 154	79.70 139	68.27 167	76.76 162	77.36 134	75.77 178	65.36 120	72.28 154	52.97 189	81.92 135	85.61 154	52.73 187	80.66 159	79.73 148	86.04 147	80.37 133

IB-MVS	71.28 17	75.21 150	77.00 156	73.12 141	76.76 162	77.45 133	83.05 131	58.92 172	63.01 196	64.31 161	59.99 214	87.57 146	68.64 119	86.26 115	82.34 130	87.05 137	82.36 119
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
thisisatest0515		81.18 111	84.32 111	77.52 114	76.73 168	74.84 157	85.06 121	61.37 157	81.05 113	73.95 113	88.79 79	89.25 136	75.49 70	85.98 116	84.78 107	92.53 71	85.56 90

IterMVS-SCA-FT		77.23 134	79.18 143	74.96 130	76.67 169	79.85 113	75.58 183	61.34 158	73.10 148	73.79 115	86.23 103	79.61 172	79.00 36	80.28 162	75.50 171	83.41 169	79.70 140

FC-MVSNet-test		75.91 146	83.59 124	66.95 175	76.63 170	69.07 177	85.33 119	64.97 123	84.87 78	41.95 208	93.17 25	87.04 147	47.78 197	91.09 69	85.56 99	85.06 159	74.34 161

Anonymous20231206		67.28 183	73.41 178	60.12 193	76.45 171	63.61 194	74.21 186	56.52 179	76.35 136	42.23 207	75.81 172	90.47 127	41.51 205	74.52 181	69.97 187	69.83 197	63.17 192

baseline2		68.71 179	68.34 188	69.14 162	75.69 172	69.70 176	76.60 172	55.53 183	60.13 205	62.07 169	66.76 207	60.35 206	60.77 152	76.53 178	74.03 174	84.19 163	70.88 172

diffmvs		76.74 137	81.61 135	71.06 148	75.64 173	74.45 160	80.68 149	57.57 177	77.48 132	67.62 153	88.95 75	93.94 82	61.98 150	79.74 163	76.18 167	82.85 170	80.50 131

tttt0517		75.86 147	76.23 162	75.42 122	75.55 174	74.06 161	82.73 134	60.31 163	69.24 167	70.24 136	79.18 143	58.79 211	72.17 94	84.49 132	83.08 125	91.54 81	84.80 92

thisisatest0530		75.54 149	75.95 166	75.05 126	75.08 175	73.56 162	82.15 139	60.31 163	69.17 168	69.32 139	79.02 144	58.78 212	72.17 94	83.88 135	83.08 125	91.30 86	84.20 98

FMVSNet3		71.40 170	75.20 171	66.97 174	75.00 176	76.59 141	74.29 185	64.57 125	62.99 197	51.83 195	76.05 167	77.76 179	51.49 192	76.58 176	77.03 164	84.62 161	79.43 142

tpm cat1		64.79 190	62.74 204	67.17 173	74.61 177	65.91 188	76.18 175	59.32 169	64.88 189	66.41 157	71.21 194	53.56 221	59.17 157	61.53 212	58.16 206	67.33 202	63.95 188

UGNet		79.62 121	85.91 89	72.28 143	73.52 178	83.91 80	86.64 108	69.51 79	79.85 123	62.57 166	85.82 109	89.63 130	53.18 183	88.39 96	87.35 81	88.28 125	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
our_test_3					73.27 179	70.91 170	83.26 129

HyFIR lowres test		73.29 158	74.14 174	72.30 142	73.08 180	78.33 127	83.12 130	62.41 152	63.81 192	62.13 168	76.67 163	78.50 176	71.09 103	74.13 184	77.47 161	81.98 173	70.10 175

MIMVSNet1		73.40 157	81.85 134	63.55 187	72.90 181	64.37 191	84.58 124	53.60 191	90.84 21	53.92 186	87.75 87	96.10 35	45.31 200	85.37 122	79.32 151	70.98 195	69.18 180

CostFormer		66.81 185	66.94 191	66.67 176	72.79 182	68.25 180	79.55 160	55.57 182	65.52 184	62.77 165	76.98 161	60.09 207	56.73 166	65.69 208	62.35 197	72.59 187	69.71 177

CR-MVSNet		69.56 175	68.34 188	70.99 149	72.78 183	67.63 181	64.47 207	67.74 101	59.93 206	72.30 122	80.10 139	56.77 215	65.04 138	71.64 192	72.91 178	83.61 167	69.40 178

CVMVSNet		75.65 148	77.62 152	73.35 140	71.95 184	69.89 174	83.04 132	60.84 162	69.12 169	68.76 144	79.92 142	78.93 175	73.64 87	81.02 156	81.01 138	81.86 174	83.43 105

IterMVS		73.62 156	76.53 159	70.23 155	71.83 185	77.18 138	80.69 148	53.22 193	72.23 155	66.62 156	85.21 114	78.96 174	69.54 114	76.28 179	71.63 182	79.45 177	74.25 163
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
RPMNet		67.02 184	63.99 199	70.56 153	71.55 186	67.63 181	75.81 176	69.44 81	59.93 206	63.24 163	64.32 209	47.51 224	59.68 155	70.37 197	69.64 188	83.64 166	68.49 181

dps		65.14 187	64.50 197	65.89 182	71.41 187	65.81 189	71.44 193	61.59 155	58.56 209	61.43 170	75.45 174	52.70 222	58.06 162	69.57 199	64.65 195	71.39 192	64.77 186

MDTV_nov1_ep13_2view		72.96 163	75.59 167	69.88 157	71.15 188	64.86 190	82.31 138	54.45 187	76.30 137	78.32 90	86.52 100	91.58 115	61.35 151	76.80 173	66.83 193	71.70 188	66.26 184

TAMVS		63.02 191	69.30 185	55.70 201	70.12 189	56.89 202	69.63 199	45.13 205	70.23 163	38.00 214	77.79 152	75.15 191	42.60 202	74.48 182	72.81 180	68.70 200	57.75 207

tpm		62.79 193	63.25 201	62.26 191	70.09 190	53.78 205	71.65 192	47.31 203	65.72 182	76.70 95	80.62 138	56.40 218	48.11 196	64.20 210	58.54 204	59.70 208	63.47 190

V42		79.59 122	83.59 124	74.93 131	69.61 191	77.05 139	86.59 109	55.84 181	78.42 131	77.29 93	89.84 63	95.08 63	74.12 80	83.05 138	80.11 147	86.12 146	81.59 124

PatchmatchNet		64.81 189	63.74 200	66.06 181	69.21 192	58.62 200	73.16 189	60.01 168	65.92 180	66.19 158	76.27 165	59.09 208	60.45 154	66.58 205	61.47 203	67.33 202	58.24 205
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CHOSEN 1792x2688		68.80 178	71.09 181	66.13 179	69.11 193	68.89 179	78.98 162	54.68 184	61.63 202	56.69 176	71.56 190	78.39 177	67.69 124	72.13 191	72.01 181	69.63 198	73.02 169

MIMVSNet		63.02 191	69.02 186	56.01 199	68.20 194	59.26 199	70.01 198	53.79 190	71.56 159	41.26 211	71.38 192	82.38 165	36.38 209	71.43 194	67.32 192	66.45 204	59.83 202

CMPMVS	55.74 18	71.56 168	76.26 161	66.08 180	68.11 195	63.91 193	63.17 209	50.52 201	68.79 172	75.49 102	70.78 198	85.67 153	63.54 145	81.58 152	77.20 162	75.63 182	85.86 85
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
SCA		68.54 180	67.52 190	69.73 158	67.79 196	75.04 153	76.96 171	68.94 87	66.41 178	67.86 151	74.03 180	60.96 204	65.55 136	68.99 200	65.67 194	71.30 193	61.54 199

EU-MVSNet		76.48 140	80.53 138	71.75 145	67.62 197	70.30 172	81.74 142	54.06 189	75.47 142	71.01 132	80.10 139	93.17 94	73.67 85	83.73 136	77.85 156	82.40 171	83.07 108

tpmrst		59.42 201	60.02 211	58.71 195	67.56 198	53.10 207	66.99 205	51.88 196	63.80 193	57.68 174	76.73 162	56.49 217	48.73 195	56.47 216	55.55 209	59.43 209	58.02 206

pmmvs5		68.91 177	74.35 172	62.56 189	67.45 199	66.78 185	71.70 191	51.47 198	67.17 175	56.25 178	82.41 132	88.59 141	47.21 199	73.21 190	74.23 173	81.30 175	68.03 182

MDTV_nov1_ep13		64.96 188	64.77 196	65.18 185	67.08 200	62.46 195	75.80 177	51.10 200	62.27 201	69.74 137	74.12 179	62.65 202	55.64 173	68.19 202	62.16 201	71.70 188	61.57 198

E-PMN		59.07 203	62.79 203	54.72 202	67.01 201	47.81 215	60.44 213	43.40 206	72.95 151	44.63 205	70.42 200	73.17 194	58.73 159	80.97 157	51.98 214	54.14 214	42.26 216

pmnet_mix02		62.60 195	70.81 182	53.02 206	66.56 202	50.44 212	62.81 210	46.84 204	79.13 128	43.76 206	87.45 89	90.75 125	39.85 206	70.48 196	57.09 207	58.27 210	60.32 201

baseline		69.33 176	75.37 169	62.28 190	66.54 203	66.67 186	73.95 187	48.07 202	66.10 179	59.26 172	82.45 131	86.30 150	54.44 177	74.42 183	73.25 177	71.42 191	78.43 149

N_pmnet		54.95 210	65.90 193	42.18 211	66.37 204	43.86 218	57.92 215	39.79 210	79.54 125	17.24 222	86.31 101	87.91 144	25.44 214	64.68 209	51.76 215	46.33 217	47.23 214

MVSTER		68.08 182	69.73 184	66.16 178	66.33 205	70.06 173	75.71 181	52.36 195	55.18 214	58.64 173	70.23 202	56.72 216	57.34 164	79.68 164	76.03 168	86.61 140	80.20 138

EMVS		58.97 204	62.63 205	54.70 203	66.26 206	48.71 213	61.74 211	42.71 207	72.80 153	46.00 204	73.01 187	71.66 195	57.91 163	80.41 161	50.68 216	53.55 215	41.11 217

anonymousdsp		85.62 62	90.53 49	79.88 95	64.64 207	76.35 143	96.28 13	53.53 192	85.63 70	81.59 72	92.81 31	97.71 14	86.88 2	94.56 26	92.83 25	96.35 6	93.84 9

EPMVS		56.62 207	59.77 212	52.94 207	62.41 208	50.55 211	60.66 212	52.83 194	65.15 188	41.80 209	77.46 157	57.28 214	42.68 201	59.81 214	54.82 210	57.23 212	53.35 210

FMVSNet5		56.37 208	60.14 210	51.98 209	60.83 209	59.58 198	66.85 206	42.37 208	52.68 216	41.33 210	47.09 217	54.68 219	35.28 210	73.88 185	70.77 184	65.24 205	62.26 195

ADS-MVSNet		56.89 206	61.09 207	52.00 208	59.48 210	48.10 214	58.02 214	54.37 188	72.82 152	49.19 201	75.32 175	65.97 200	37.96 208	59.34 215	54.66 211	52.99 216	51.42 212

new_pmnet		52.29 211	63.16 202	39.61 213	58.89 211	44.70 217	48.78 220	34.73 213	65.88 181	17.85 221	73.42 184	80.00 171	23.06 216	67.00 204	62.28 200	54.36 213	48.81 213

MVS-HIRNet		59.74 200	58.74 216	60.92 192	57.74 212	45.81 216	56.02 216	58.69 173	55.69 212	65.17 159	70.86 196	71.66 195	56.75 165	61.11 213	53.74 212	71.17 194	52.28 211

PatchT		66.25 186	66.76 192	65.67 183	55.87 213	60.75 197	70.17 196	59.00 171	59.80 208	72.30 122	78.68 149	54.12 220	65.04 138	71.64 192	72.91 178	71.63 190	69.40 178

test-mter		59.39 202	61.59 206	56.82 198	53.21 214	54.82 204	73.12 190	26.57 217	53.19 215	56.31 177	64.71 208	60.47 205	56.36 168	68.69 201	64.27 196	75.38 183	65.00 185

CHOSEN 280x420		56.32 209	58.85 215	53.36 205	51.63 215	39.91 219	69.12 203	38.61 211	56.29 211	36.79 215	48.84 216	62.59 203	63.39 147	73.61 188	67.66 191	60.61 206	63.07 193

TESTMET0.1,1		57.21 205	59.46 213	54.60 204	50.95 216	52.66 208	69.46 201	26.91 216	50.76 217	53.81 187	63.11 211	58.91 209	52.87 185	66.54 206	62.34 198	73.59 184	61.87 196

pmmvs3		62.72 194	68.71 187	55.74 200	50.74 217	57.10 201	70.05 197	28.82 215	61.57 204	57.39 175	71.19 195	85.73 152	53.96 180	73.36 189	69.43 189	73.47 186	62.55 194

MDA-MVSNet-bldmvs		76.51 139	82.87 129	69.09 163	50.71 218	74.72 159	84.05 127	60.27 165	81.62 103	71.16 131	88.21 84	91.58 115	69.62 113	92.78 46	77.48 160	78.75 180	73.69 166

PMMVS		61.98 198	65.61 194	57.74 196	45.03 219	51.76 210	69.54 200	35.05 212	55.49 213	55.32 182	68.23 204	78.39 177	58.09 161	70.21 198	71.56 183	83.42 168	63.66 189

PMMVS2		48.13 213	64.06 198	29.55 214	44.06 220	36.69 220	51.95 219	29.97 214	74.75 146	8.90 224	76.02 170	91.24 121	7.53 218	73.78 186	55.91 208	34.87 219	40.01 218

MVE	41.12 19	51.80 212	60.92 208	41.16 212	35.21 221	34.14 221	48.45 221	41.39 209	69.11 170	19.53 220	63.33 210	73.80 192	63.56 144	67.19 203	61.51 202	38.85 218	57.38 208
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				13.54 217	16.73 222	6.42 223	8.49 224	2.36 220	28.69 221	27.44 218	18.40 220	13.51 227	3.70 219	33.23 217	36.26 217	22.54 222

test_method		22.69 215	26.99 217	17.67 216	2.13 223	4.31 224	27.50 222	4.53 219	37.94 219	24.52 219	36.20 219	51.40 223	15.26 217	29.86 218	17.09 218	32.07 220	12.16 219

test123		1.06 216	1.41 218	0.64 218	0.39 224	0.48 225	0.52 227	0.25 222	1.11 223	1.37 226	2.01 222	1.98 228	0.87 220	1.43 220	1.27 219	0.46 224	1.62 221

testmvs		0.93 217	1.37 219	0.41 219	0.36 225	0.36 226	0.62 226	0.39 221	1.48 222	0.18 227	2.41 221	1.31 229	0.41 221	1.25 221	1.08 220	0.48 223	1.68 220

GG-mvs-BLEND		41.63 214	60.36 209	19.78 215	0.14 226	66.04 187	55.66 217	0.17 223	57.64 210	2.42 225	51.82 215	69.42 198	0.28 222	64.11 211	58.29 205	60.02 207	55.18 209

uanet_test		0.00 218	0.00 220	0.00 220	0.00 227	0.00 227	0.00 228	0.00 224	0.00 224	0.00 228	0.00 223	0.00 230	0.00 223	0.00 222	0.00 221	0.00 225	0.00 222

sosnet-low-res		0.00 218	0.00 220	0.00 220	0.00 227	0.00 227	0.00 228	0.00 224	0.00 224	0.00 228	0.00 223	0.00 230	0.00 223	0.00 222	0.00 221	0.00 225	0.00 222

sosnet		0.00 218	0.00 220	0.00 220	0.00 227	0.00 227	0.00 228	0.00 224	0.00 224	0.00 228	0.00 223	0.00 230	0.00 223	0.00 222	0.00 221	0.00 225	0.00 222

RE-MVS-def										87.10 29

9.14												89.43 132

MTAPA										89.37 9		94.85 68

MTMP										90.54 5		95.16 60

Patchmatch-RL test							4.13 225

NP-MVS									78.65 130

Patchmtry						56.88 203	64.47 207	67.74 101		72.30 122

DeepMVS_CX						17.78 222	20.40 223	6.69 218	31.41 220	9.80 223	38.61 218	34.88 226	33.78 211	28.41 219		23.59 221	45.77 215

High-resolution multi-view benchmark