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.

SMA-MVS		97.53 5	97.93 5	97.07 8	99.21 1	99.02 6	98.08 18	96.25 8	96.36 10	93.57 13	96.56 11	99.27 3	96.78 15	97.91 2	97.43 3	98.51 18	98.94 11

APDe-MVS		97.79 3	97.96 4	97.60 1	99.20 2	99.10 4	98.88 2	96.68 2	96.81 5	94.64 5	97.84 2	98.02 9	97.24 3	97.74 6	97.02 12	98.97 3	99.16 4

zzz-MVS		96.98 13	96.68 21	97.33 4	99.09 3	98.71 11	98.43 7	96.01 13	96.11 15	95.19 3	92.89 30	97.32 20	96.84 11	97.20 15	96.09 37	98.44 29	98.46 29

HPM-MVS++		97.22 9	97.40 10	97.01 9	99.08 4	98.55 22	98.19 13	96.48 5	96.02 17	93.28 18	96.26 14	98.71 6	96.76 16	97.30 13	96.25 33	98.30 46	98.68 13

ACMMP_NAP		96.93 14	97.27 12	96.53 21	99.06 5	98.95 7	98.24 12	96.06 12	95.66 19	90.96 31	95.63 21	97.71 14	96.53 19	97.66 8	96.68 18	98.30 46	98.61 18

DVP-MVS		97.93 1	98.23 1	97.58 2	99.05 6	99.31 1	98.64 4	96.62 3	97.56 1	95.08 4	96.61 10	99.64 1	97.32 1	97.91 2	97.31 6	98.77 9	99.26 1

PGM-MVS		96.16 22	96.33 26	95.95 24	99.04 7	98.63 17	98.32 11	92.76 40	93.42 45	90.49 36	96.30 13	95.31 38	96.71 17	96.46 33	96.02 38	98.38 37	98.19 38

APD-MVS		97.12 10	97.05 15	97.19 6	99.04 7	98.63 17	98.45 6	96.54 4	94.81 34	93.50 14	96.10 16	97.40 19	96.81 12	97.05 19	96.82 17	98.80 7	98.56 19
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		96.75 17	96.67 22	96.85 14	99.03 9	98.44 30	98.15 15	96.28 7	96.32 11	92.39 23	92.16 32	97.55 17	96.68 18	97.32 11	96.65 20	98.55 17	98.26 34

CNVR-MVS		97.30 8	97.41 9	97.18 7	99.02 10	98.60 19	98.15 15	96.24 10	96.12 14	94.10 9	95.54 22	97.99 10	96.99 7	97.97 1	97.17 8	98.57 16	98.50 25

MSP-MVS		97.70 4	98.09 3	97.24 5	99.00 11	99.17 3	98.76 3	96.41 6	96.91 3	93.88 12	97.72 3	99.04 5	96.93 10	97.29 14	97.31 6	98.45 28	99.23 2

ACMMPR		96.92 15	96.96 16	96.87 13	98.99 12	98.78 9	98.38 9	95.52 22	96.57 8	92.81 22	96.06 17	95.90 33	97.07 5	96.60 30	96.34 30	98.46 25	98.42 30

HFP-MVS		97.11 11	97.19 13	97.00 10	98.97 13	98.73 10	98.37 10	95.69 19	96.60 7	93.28 18	96.87 5	96.64 25	97.27 2	96.64 28	96.33 31	98.44 29	98.56 19

SteuartSystems-ACMMP		97.10 12	97.49 8	96.65 16	98.97 13	98.95 7	98.43 7	95.96 15	95.12 26	91.46 26	96.85 6	97.60 16	96.37 23	97.76 4	97.16 9	98.68 10	98.97 9
Skip Steuart: Steuart Systems R&D Blog.
X-MVS		96.07 24	96.33 26	95.77 27	98.94 15	98.66 12	97.94 22	95.41 28	95.12 26	88.03 48	93.00 29	96.06 29	95.85 25	96.65 27	96.35 28	98.47 23	98.48 26

SR-MVS					98.93 16			96.00 14				97.75 13

MP-MVS		96.56 19	96.72 20	96.37 22	98.93 16	98.48 26	98.04 19	95.55 21	94.32 38	90.95 33	95.88 19	97.02 22	96.29 24	96.77 25	96.01 39	98.47 23	98.56 19
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MCST-MVS		96.83 16	97.06 14	96.57 17	98.88 18	98.47 28	98.02 20	96.16 11	95.58 21	90.96 31	95.78 20	97.84 12	96.46 21	97.00 21	96.17 35	98.94 5	98.55 24

CP-MVS		96.68 18	96.59 24	96.77 15	98.85 19	98.58 20	98.18 14	95.51 24	95.34 23	92.94 21	95.21 25	96.25 28	96.79 14	96.44 35	95.77 42	98.35 38	98.56 19

DPE-MVS		97.83 2	98.13 2	97.48 3	98.83 20	99.19 2	98.99 1	96.70 1	96.05 16	94.39 7	98.30 1	99.47 2	97.02 6	97.75 5	97.02 12	98.98 2	99.10 7

mPP-MVS					98.76 21							95.49 36

CSCG		95.68 28	95.46 33	95.93 25	98.71 22	99.07 5	97.13 33	93.55 35	95.48 22	93.35 17	90.61 42	93.82 43	95.16 32	94.60 73	95.57 45	97.70 96	99.08 8

DeepC-MVS_fast	93.32 1	96.48 20	96.42 25	96.56 18	98.70 23	98.31 34	97.97 21	95.76 18	96.31 12	92.01 25	91.43 37	95.42 37	96.46 21	97.65 9	97.69 1	98.49 22	98.12 44
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		95.02 35	93.71 46	96.54 20	98.51 24	97.76 51	96.69 37	95.94 17	93.72 43	93.50 14	89.01 49	90.53 62	96.49 20	94.51 76	93.76 75	98.07 70	96.69 92

train_agg		96.15 23	96.64 23	95.58 31	98.44 25	98.03 42	98.14 17	95.40 29	93.90 42	87.72 53	96.26 14	98.10 8	95.75 27	96.25 40	95.45 47	98.01 76	98.47 27

CDPH-MVS		94.80 39	95.50 31	93.98 44	98.34 26	98.06 40	97.41 28	93.23 37	92.81 50	82.98 90	92.51 31	94.82 39	93.53 55	96.08 43	96.30 32	98.42 32	97.94 50

MSLP-MVS++		96.05 25	95.63 29	96.55 19	98.33 27	98.17 37	96.94 34	94.61 32	94.70 36	94.37 8	89.20 48	95.96 32	96.81 12	95.57 51	97.33 5	98.24 54	98.47 27

ACMMP		95.54 29	95.49 32	95.61 30	98.27 28	98.53 24	97.16 32	94.86 30	94.88 32	89.34 39	95.36 24	91.74 52	95.50 30	95.51 52	94.16 66	98.50 20	98.22 36
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
3Dnovator+	90.56 5	95.06 34	94.56 40	95.65 29	98.11 29	98.15 38	97.19 31	91.59 50	95.11 28	93.23 20	81.99 96	94.71 40	95.43 31	96.48 32	96.88 16	98.35 38	98.63 15

3Dnovator	90.28 7	94.70 40	94.34 43	95.11 33	98.06 30	98.21 35	96.89 35	91.03 56	94.72 35	91.45 27	82.87 87	93.10 46	94.61 37	96.24 41	97.08 11	98.63 13	98.16 40

PLC	90.69 4	94.32 42	92.99 53	95.87 26	97.91 31	96.49 83	95.95 48	94.12 33	94.94 30	94.09 10	85.90 66	90.77 59	95.58 29	94.52 75	93.32 88	97.55 104	95.00 136
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPNet		93.92 46	94.40 41	93.36 51	97.89 32	96.55 81	96.08 44	92.14 43	91.65 60	89.16 41	94.07 27	90.17 66	87.78 115	95.24 55	94.97 55	97.09 123	98.15 41
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CPTT-MVS		95.54 29	95.07 34	96.10 23	97.88 33	97.98 45	97.92 23	94.86 30	94.56 37	92.16 24	91.01 39	95.71 34	96.97 9	94.56 74	93.50 82	96.81 144	98.14 42

QAPM		94.13 44	94.33 44	93.90 45	97.82 34	98.37 33	96.47 39	90.89 57	92.73 53	85.63 74	85.35 70	93.87 42	94.17 45	95.71 50	95.90 40	98.40 34	98.42 30

DeepC-MVS	92.10 3	95.22 32	94.77 38	95.75 28	97.77 35	98.54 23	97.63 27	95.96 15	95.07 29	88.85 44	85.35 70	91.85 51	95.82 26	96.88 24	97.10 10	98.44 29	98.63 15
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OpenMVS	88.18 11	92.51 57	91.61 74	93.55 50	97.74 36	98.02 43	95.66 51	90.46 60	89.14 93	86.50 64	75.80 129	90.38 65	92.69 64	94.99 58	95.30 49	98.27 50	97.63 62

TSAR-MVS + ACMM		96.19 21	97.39 11	94.78 35	97.70 37	98.41 31	97.72 26	95.49 25	96.47 9	86.66 63	96.35 12	97.85 11	93.99 47	97.19 16	96.37 27	97.12 121	99.13 5

MAR-MVS		92.71 56	92.63 57	92.79 62	97.70 37	97.15 67	93.75 83	87.98 90	90.71 67	85.76 72	86.28 63	86.38 73	94.35 42	94.95 59	95.49 46	97.22 114	97.44 69
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
PHI-MVS		95.86 26	96.93 19	94.61 39	97.60 39	98.65 16	96.49 38	93.13 38	94.07 40	87.91 52	97.12 4	97.17 21	93.90 50	96.46 33	96.93 15	98.64 12	98.10 46

abl_6				94.78 35	97.46 40	97.99 44	95.76 49	91.80 47	93.72 43	91.25 28	91.33 38	96.47 26	94.28 44			98.14 63	97.39 71

DPM-MVS		95.07 33	94.84 36	95.34 32	97.44 41	97.49 58	97.76 25	95.52 22	94.88 32	88.92 43	87.25 55	96.44 27	94.41 39	95.78 48	96.11 36	97.99 78	95.95 116

SD-MVS		97.35 6	97.73 6	96.90 12	97.35 42	98.66 12	97.85 24	96.25 8	96.86 4	94.54 6	96.75 8	99.13 4	96.99 7	96.94 22	96.58 21	98.39 36	99.20 3
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
MVS_111021_HR		94.84 37	95.91 28	93.60 49	97.35 42	98.46 29	95.08 56	91.19 53	94.18 39	85.97 67	95.38 23	92.56 48	93.61 54	96.61 29	96.25 33	98.40 34	97.92 52

TSAR-MVS + MP.		97.31 7	97.64 7	96.92 11	97.28 44	98.56 21	98.61 5	95.48 26	96.72 6	94.03 11	96.73 9	98.29 7	97.15 4	97.61 10	96.42 25	98.96 4	99.13 5
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CANet		94.85 36	94.92 35	94.78 35	97.25 45	98.52 25	97.20 30	91.81 46	93.25 47	91.06 30	86.29 62	94.46 41	92.99 61	97.02 20	96.68 18	98.34 40	98.20 37

OMC-MVS		94.49 41	94.36 42	94.64 38	97.17 46	97.73 53	95.49 53	92.25 42	96.18 13	90.34 37	88.51 50	92.88 47	94.90 36	94.92 61	94.17 65	97.69 97	96.15 111

MVS_111021_LR		94.84 37	95.57 30	94.00 42	97.11 47	97.72 55	94.88 59	91.16 54	95.24 25	88.74 45	96.03 18	91.52 55	94.33 43	95.96 45	95.01 54	97.79 88	97.49 68

CNLPA		93.69 48	92.50 59	95.06 34	97.11 47	97.36 61	93.88 80	93.30 36	95.64 20	93.44 16	80.32 104	90.73 60	94.99 35	93.58 92	93.33 86	97.67 99	96.57 97

LS3D		91.97 63	90.98 81	93.12 57	97.03 49	97.09 70	95.33 55	95.59 20	92.47 54	79.26 109	81.60 99	82.77 94	94.39 41	94.28 78	94.23 64	97.14 120	94.45 142

TAPA-MVS	90.35 6	93.69 48	93.52 47	93.90 45	96.89 50	97.62 56	96.15 42	91.67 49	94.94 30	85.97 67	87.72 54	91.96 50	94.40 40	93.76 90	93.06 96	98.30 46	95.58 124
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DELS-MVS		93.71 47	93.47 48	94.00 42	96.82 51	98.39 32	96.80 36	91.07 55	89.51 90	89.94 38	83.80 80	89.29 67	90.95 81	97.32 11	97.65 2	98.42 32	98.32 33
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
EPNet_dtu		88.32 107	90.61 83	85.64 137	96.79 52	92.27 165	92.03 111	90.31 61	89.05 94	65.44 180	89.43 46	85.90 78	74.22 188	92.76 105	92.09 114	95.02 177	92.76 163
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSDG		90.42 85	88.25 104	92.94 60	96.67 53	94.41 109	93.96 75	92.91 39	89.59 89	86.26 65	76.74 122	80.92 108	90.43 87	92.60 110	92.08 115	97.44 109	91.41 169

DeepPCF-MVS	92.65 2	95.50 31	96.96 16	93.79 48	96.44 54	98.21 35	93.51 89	94.08 34	96.94 2	89.29 40	93.08 28	96.77 24	93.82 51	97.68 7	97.40 4	95.59 167	98.65 14

PCF-MVS	90.19 8	92.98 52	92.07 67	94.04 41	96.39 55	97.87 46	96.03 45	95.47 27	87.16 109	85.09 84	84.81 74	93.21 45	93.46 57	91.98 122	91.98 118	97.78 89	97.51 67
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_0304		94.30 43	94.68 39	93.86 47	96.33 56	98.48 26	97.41 28	91.20 52	92.75 51	86.96 60	86.03 65	93.81 44	92.64 65	96.89 23	96.54 23	98.61 14	98.24 35

OPM-MVS		91.08 75	89.34 94	93.11 58	96.18 57	96.13 92	96.39 40	92.39 41	82.97 147	81.74 93	82.55 93	80.20 111	93.97 49	94.62 71	93.23 89	98.00 77	95.73 120

PVSNet_BlendedMVS		92.80 53	92.44 61	93.23 52	96.02 58	97.83 49	93.74 84	90.58 58	91.86 57	90.69 34	85.87 68	82.04 101	90.01 88	96.39 36	95.26 50	98.34 40	97.81 57

PVSNet_Blended		92.80 53	92.44 61	93.23 52	96.02 58	97.83 49	93.74 84	90.58 58	91.86 57	90.69 34	85.87 68	82.04 101	90.01 88	96.39 36	95.26 50	98.34 40	97.81 57

XVS					95.68 60	98.66 12	94.96 57			88.03 48		96.06 29				98.46 25

X-MVStestdata					95.68 60	98.66 12	94.96 57			88.03 48		96.06 29				98.46 25

HQP-MVS		92.39 59	92.49 60	92.29 67	95.65 62	95.94 94	95.64 52	92.12 44	92.46 55	79.65 107	91.97 34	82.68 95	92.92 63	93.47 97	92.77 101	97.74 92	98.12 44

HyFIR lowres test		87.87 109	86.42 126	89.57 96	95.56 63	96.99 73	92.37 101	84.15 130	86.64 113	77.17 116	57.65 194	83.97 85	91.08 80	92.09 120	92.44 106	97.09 123	95.16 133

ACMM	88.76 10	91.70 70	90.43 84	93.19 54	95.56 63	95.14 100	93.35 92	91.48 51	92.26 56	87.12 58	84.02 78	79.34 114	93.99 47	94.07 84	92.68 102	97.62 103	95.50 125
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP_ROB	84.39 15	87.61 111	86.03 130	89.46 97	95.54 65	94.48 106	91.77 115	90.14 62	87.16 109	75.50 121	73.41 141	76.86 132	87.33 121	90.05 154	89.76 164	96.48 148	90.46 178
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LGP-MVS_train		91.83 66	92.04 68	91.58 73	95.46 66	96.18 91	95.97 47	89.85 64	90.45 73	77.76 112	91.92 35	80.07 112	92.34 69	94.27 79	93.47 83	98.11 67	97.90 55

CHOSEN 1792x2688		88.57 104	87.82 111	89.44 98	95.46 66	96.89 76	93.74 84	85.87 112	89.63 88	77.42 115	61.38 188	83.31 89	88.80 110	93.44 98	93.16 92	95.37 172	96.95 86

PVSNet_Blended_VisFu		91.92 64	92.39 63	91.36 81	95.45 68	97.85 48	92.25 104	89.54 71	88.53 101	87.47 56	79.82 106	90.53 62	85.47 140	96.31 39	95.16 53	97.99 78	98.56 19

PatchMatch-RL		90.30 86	88.93 98	91.89 69	95.41 69	95.68 96	90.94 118	88.67 80	89.80 86	86.95 61	85.90 66	72.51 139	92.46 66	93.56 94	92.18 111	96.93 136	92.89 161

TSAR-MVS + COLMAP		92.39 59	92.31 64	92.47 63	95.35 70	96.46 85	96.13 43	92.04 45	95.33 24	80.11 105	94.95 26	77.35 129	94.05 46	94.49 77	93.08 94	97.15 118	94.53 140

ACMP	89.13 9	92.03 62	91.70 73	92.41 65	94.92 71	96.44 87	93.95 76	89.96 63	91.81 59	85.48 79	90.97 40	79.12 115	92.42 67	93.28 102	92.55 105	97.76 90	97.74 60
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UA-Net		90.81 79	92.58 58	88.74 105	94.87 72	97.44 59	92.61 98	88.22 85	82.35 150	78.93 110	85.20 72	95.61 35	79.56 175	96.52 31	96.57 22	98.23 55	94.37 143

IB-MVS	85.10 14	87.98 108	87.97 109	87.99 113	94.55 73	96.86 77	84.52 184	88.21 86	86.48 118	88.54 47	74.41 135	77.74 126	74.10 190	89.65 160	92.85 100	98.06 72	97.80 59
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
CANet_DTU		90.74 82	92.93 55	88.19 110	94.36 74	96.61 79	94.34 66	84.66 123	90.66 68	68.75 159	90.41 43	86.89 71	89.78 90	95.46 53	94.87 56	97.25 113	95.62 122

canonicalmvs		93.08 51	93.09 51	93.07 59	94.24 75	97.86 47	95.45 54	87.86 96	94.00 41	87.47 56	88.32 51	82.37 99	95.13 33	93.96 89	96.41 26	98.27 50	98.73 12

CS-MVS		93.68 50	94.33 44	92.93 61	94.15 76	98.04 41	94.43 61	87.99 88	91.64 61	87.54 54	88.22 52	92.09 49	94.56 38	96.77 25	95.85 41	98.88 6	97.71 61

UGNet		91.52 71	93.41 49	89.32 99	94.13 77	97.15 67	91.83 114	89.01 75	90.62 70	85.86 71	86.83 56	91.73 53	77.40 180	94.68 70	94.43 61	97.71 94	98.40 32
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
thres600view7		89.28 101	87.47 120	91.39 78	94.12 78	97.25 64	93.94 78	89.74 66	85.62 125	80.63 103	75.24 133	69.33 153	91.66 76	94.92 61	93.23 89	98.27 50	96.72 91

IS_MVSNet		91.87 65	93.35 50	90.14 93	94.09 79	97.73 53	93.09 95	88.12 87	88.71 97	79.98 106	84.49 75	90.63 61	87.49 119	97.07 18	96.96 14	98.07 70	97.88 56

TSAR-MVS + GP.		95.86 26	96.95 18	94.60 40	94.07 80	98.11 39	96.30 41	91.76 48	95.67 18	91.07 29	96.82 7	97.69 15	95.71 28	95.96 45	95.75 43	98.68 10	98.63 15

thres400		89.40 98	87.58 117	91.53 75	94.06 81	97.21 66	94.19 72	89.83 65	85.69 122	81.08 99	75.50 131	69.76 152	91.80 72	94.79 68	93.51 79	98.20 58	96.60 95

ACMH	85.51 13	87.31 114	86.59 124	88.14 111	93.96 82	94.51 105	89.00 154	87.99 88	81.58 152	70.15 149	78.41 113	71.78 144	90.60 85	91.30 131	91.99 117	97.17 117	96.58 96
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MS-PatchMatch		87.63 110	87.61 115	87.65 118	93.95 83	94.09 114	92.60 99	81.52 161	86.64 113	76.41 119	73.46 140	85.94 77	85.01 144	92.23 118	90.00 158	96.43 151	90.93 175

thres200		89.49 97	87.72 112	91.55 74	93.95 83	97.25 64	94.34 66	89.74 66	85.66 123	81.18 96	76.12 128	70.19 151	91.80 72	94.92 61	93.51 79	98.27 50	96.40 101

CLD-MVS		92.50 58	91.96 69	93.13 56	93.93 85	96.24 89	95.69 50	88.77 79	92.92 48	89.01 42	88.19 53	81.74 104	93.13 60	93.63 91	93.08 94	98.23 55	97.91 54
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thres100view900		89.36 99	87.61 115	91.39 78	93.90 86	96.86 77	94.35 65	89.66 70	85.87 120	81.15 97	76.46 124	70.38 148	91.17 78	94.09 83	93.43 85	98.13 64	96.16 110

tfpn200view9		89.55 96	87.86 110	91.53 75	93.90 86	97.26 63	94.31 68	89.74 66	85.87 120	81.15 97	76.46 124	70.38 148	91.76 74	94.92 61	93.51 79	98.28 49	96.61 94

EIA-MVS		92.72 55	92.96 54	92.44 64	93.86 88	97.76 51	93.13 94	88.65 81	89.78 87	86.68 62	86.69 59	87.57 68	93.74 52	96.07 44	95.32 48	98.58 15	97.53 66

CHOSEN 280x420		90.77 81	92.14 66	89.17 101	93.86 88	92.81 153	93.16 93	80.22 169	90.21 78	84.67 86	89.89 45	91.38 56	90.57 86	94.94 60	92.11 113	92.52 188	93.65 153

ETV-MVS		94.04 45	94.81 37	93.14 55	93.83 90	97.39 60	94.15 73	88.89 76	93.35 46	87.93 51	90.14 44	90.99 57	95.04 34	97.17 17	96.44 24	98.99 1	98.18 39

FC-MVSNet-train		90.55 83	90.19 87	90.97 84	93.78 91	95.16 99	92.11 109	88.85 77	87.64 106	83.38 89	84.36 77	78.41 120	89.53 92	94.69 69	93.15 93	98.15 61	97.92 52

Vis-MVSNet (Re-imp)		90.54 84	92.76 56	87.94 114	93.73 92	96.94 75	92.17 107	87.91 91	88.77 96	76.12 120	83.68 81	90.80 58	79.49 176	96.34 38	96.35 28	98.21 57	96.46 99

baseline1		90.81 79	90.29 85	91.42 77	93.67 93	95.86 95	93.94 78	89.69 69	89.29 92	82.85 91	82.91 86	80.30 110	89.60 91	95.05 57	94.79 58	98.80 7	93.82 151

EPP-MVSNet		92.13 61	93.06 52	91.05 83	93.66 94	97.30 62	92.18 105	87.90 92	90.24 77	83.63 87	86.14 64	90.52 64	90.76 83	94.82 66	94.38 62	98.18 60	97.98 48

ACMH+	85.75 12	87.19 115	86.02 131	88.56 106	93.42 95	94.41 109	89.91 138	87.66 100	83.45 144	72.25 136	76.42 126	71.99 143	90.78 82	89.86 155	90.94 132	97.32 110	95.11 135

MVS_Test		91.81 67	92.19 65	91.37 80	93.24 96	96.95 74	94.43 61	86.25 109	91.45 64	83.45 88	86.31 61	85.15 81	92.93 62	93.99 85	94.71 59	97.92 82	96.77 90

MVSTER		91.73 68	91.61 74	91.86 70	93.18 97	94.56 103	94.37 64	87.90 92	90.16 81	88.69 46	89.23 47	81.28 106	88.92 107	95.75 49	93.95 72	98.12 65	96.37 102

Anonymous202405211			88.00 107		93.16 98	96.38 88	93.58 87	89.34 73	87.92 105		65.04 178	83.03 91	92.07 70	92.67 107	93.33 86	96.96 131	97.63 62

casdiffmvs		91.72 69	91.16 79	92.38 66	93.16 98	97.15 67	93.95 76	89.49 72	91.58 63	86.03 66	80.75 103	80.95 107	93.16 59	95.25 54	95.22 52	98.50 20	97.23 77

tttt0517		91.01 78	91.71 72	90.19 91	92.98 100	97.07 71	91.96 113	87.63 101	90.61 71	81.42 95	86.76 58	82.26 100	89.23 99	94.86 65	93.03 98	97.90 83	97.36 72

Effi-MVS+		89.79 93	89.83 92	89.74 94	92.98 100	96.45 86	93.48 90	84.24 128	87.62 107	76.45 118	81.76 97	77.56 128	93.48 56	94.61 72	93.59 78	97.82 87	97.22 79

RPSCF		89.68 94	89.24 95	90.20 90	92.97 102	92.93 149	92.30 102	87.69 98	90.44 74	85.12 83	91.68 36	85.84 79	90.69 84	87.34 177	86.07 180	92.46 189	90.37 179

TDRefinement		84.97 142	83.39 157	86.81 126	92.97 102	94.12 113	92.18 105	87.77 97	82.78 148	71.31 141	68.43 159	68.07 159	81.10 171	89.70 159	89.03 171	95.55 169	91.62 167

thisisatest0530		91.04 77	91.74 71	90.21 89	92.93 104	97.00 72	92.06 110	87.63 101	90.74 66	81.51 94	86.81 57	82.48 96	89.23 99	94.81 67	93.03 98	97.90 83	97.33 74

DCV-MVSNet		91.24 73	91.26 77	91.22 82	92.84 105	93.44 131	93.82 81	86.75 106	91.33 65	85.61 75	84.00 79	85.46 80	91.27 77	92.91 104	93.62 77	97.02 127	98.05 47

baseline		91.19 74	91.89 70	90.38 85	92.76 106	95.04 101	93.55 88	84.54 126	92.92 48	85.71 73	86.68 60	86.96 70	89.28 98	92.00 121	92.62 104	96.46 149	96.99 84

EPMVS		85.77 130	86.24 128	85.23 142	92.76 106	93.78 121	89.91 138	73.60 191	90.19 79	74.22 124	82.18 95	78.06 122	87.55 118	85.61 186	85.38 185	93.32 183	88.48 190

DWT-MVSNet_training		86.83 117	84.44 146	89.61 95	92.75 108	93.82 119	91.66 116	82.85 144	88.57 99	87.48 55	79.00 109	64.24 180	88.82 109	85.18 187	87.50 176	94.07 180	92.79 162

diffmvs		91.37 72	91.09 80	91.70 72	92.71 109	96.47 84	94.03 74	88.78 78	92.74 52	85.43 81	83.63 82	80.37 109	91.76 74	93.39 99	93.78 74	97.50 106	97.23 77

DI_MVS_plusplus_trai		91.05 76	90.15 88	92.11 68	92.67 110	96.61 79	96.03 45	88.44 83	90.25 76	85.92 69	73.73 136	84.89 83	91.92 71	94.17 82	94.07 70	97.68 98	97.31 75

Anonymous20231211		89.82 92	88.18 105	91.74 71	92.52 111	96.09 93	93.38 91	89.30 74	88.95 95	85.90 70	64.55 182	84.39 84	92.41 68	92.24 117	93.06 96	96.93 136	97.95 49

tpmrst		83.72 160	83.45 154	84.03 158	92.21 112	91.66 177	88.74 157	73.58 192	88.14 103	72.67 133	77.37 118	72.11 142	86.34 130	82.94 195	82.05 194	90.63 198	89.86 183

CostFormer		86.78 119	86.05 129	87.62 120	92.15 113	93.20 140	91.55 117	75.83 183	88.11 104	85.29 82	81.76 97	76.22 134	87.80 114	84.45 190	85.21 186	93.12 184	93.42 156

Vis-MVSNet		89.36 99	91.49 76	86.88 125	92.10 114	97.60 57	92.16 108	85.89 111	84.21 136	75.20 122	82.58 91	87.13 69	77.40 180	95.90 47	95.63 44	98.51 18	97.36 72
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IterMVS-LS		88.60 103	88.45 100	88.78 104	92.02 115	92.44 163	92.00 112	83.57 138	86.52 116	78.90 111	78.61 112	81.34 105	89.12 102	90.68 143	93.18 91	97.10 122	96.35 103
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		85.70 131	86.65 123	84.60 149	91.79 116	93.40 132	89.27 147	73.62 190	90.19 79	72.63 134	82.74 90	81.93 103	87.64 116	84.99 188	84.29 190	92.64 187	89.00 186
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat1		84.13 153	81.99 173	86.63 129	91.74 117	91.50 180	90.68 120	75.69 184	86.12 119	85.44 80	72.39 145	70.72 146	85.16 142	80.89 198	81.56 195	91.07 196	90.71 176

USDC		86.73 120	85.96 133	87.63 119	91.64 118	93.97 116	92.76 97	84.58 125	88.19 102	70.67 146	80.10 105	67.86 160	89.43 93	91.81 123	89.77 163	96.69 146	90.05 182

SCA		86.25 122	87.52 118	84.77 146	91.59 119	93.90 117	89.11 151	73.25 195	90.38 75	72.84 132	83.26 83	83.79 87	88.49 112	86.07 184	85.56 183	93.33 182	89.67 184

gg-mvs-nofinetune		81.83 179	83.58 152	79.80 186	91.57 120	96.54 82	93.79 82	68.80 202	62.71 205	43.01 209	55.28 197	85.06 82	83.65 154	96.13 42	94.86 57	97.98 81	94.46 141

Fast-Effi-MVS+		88.56 105	87.99 108	89.22 100	91.56 121	95.21 98	92.29 103	82.69 146	86.82 111	77.73 113	76.24 127	73.39 138	93.36 58	94.22 81	93.64 76	97.65 100	96.43 100

CMPMVS	61.19 17	79.86 185	77.46 193	82.66 176	91.54 122	91.82 175	83.25 187	81.57 160	70.51 198	68.64 160	59.89 193	66.77 166	79.63 174	84.00 193	84.30 189	91.34 194	84.89 198
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ADS-MVSNet		84.08 154	84.95 141	83.05 171	91.53 123	91.75 176	88.16 161	70.70 199	89.96 85	69.51 154	78.83 110	76.97 131	86.29 131	84.08 192	84.60 188	92.13 192	88.48 190

test-LLR		86.88 116	88.28 102	85.24 141	91.22 124	92.07 169	87.41 167	83.62 136	84.58 129	69.33 155	83.00 84	82.79 92	84.24 148	92.26 115	89.81 161	95.64 165	93.44 154

test0.0.03 1		85.58 133	87.69 114	83.11 168	91.22 124	92.54 160	85.60 183	83.62 136	85.66 123	67.84 166	82.79 89	79.70 113	73.51 192	91.15 135	90.79 134	96.88 140	91.23 172

baseline2		88.97 102	89.50 93	88.36 107	91.14 126	95.30 97	90.13 132	85.17 120	87.24 108	80.80 101	84.46 76	78.44 119	85.60 137	93.54 95	91.87 119	97.31 111	95.66 121

Effi-MVS+-dtu		87.51 112	88.13 106	86.77 127	91.10 127	94.90 102	90.91 119	82.67 147	83.47 143	71.55 138	81.11 102	77.04 130	89.41 94	92.65 109	91.68 125	95.00 178	96.09 113

RPMNet		84.82 144	85.90 134	83.56 163	91.10 127	92.10 167	88.73 158	71.11 198	84.75 127	68.79 158	73.56 137	77.62 127	85.33 141	90.08 153	89.43 167	96.32 152	93.77 152

CR-MVSNet		85.48 135	86.29 127	84.53 151	91.08 129	92.10 167	89.18 149	73.30 193	84.75 127	71.08 143	73.12 144	77.91 124	86.27 132	91.48 127	90.75 137	96.27 153	93.94 148

TinyColmap		84.04 155	82.01 172	86.42 131	90.87 130	91.84 174	88.89 156	84.07 132	82.11 151	69.89 151	71.08 148	60.81 192	89.04 103	90.52 145	89.19 169	95.76 159	88.50 189

tpm		83.16 166	83.64 151	82.60 177	90.75 131	91.05 183	88.49 159	73.99 188	82.36 149	67.08 172	78.10 114	68.79 154	84.17 150	85.95 185	85.96 181	91.09 195	93.23 158

dps		85.00 141	83.21 161	87.08 123	90.73 132	92.55 159	89.34 146	75.29 185	84.94 126	87.01 59	79.27 108	67.69 161	87.27 122	84.22 191	83.56 191	92.83 186	90.25 180

MDTV_nov1_ep13		86.64 121	87.50 119	85.65 136	90.73 132	93.69 125	89.96 136	78.03 178	89.48 91	76.85 117	84.92 73	82.42 98	86.14 134	86.85 181	86.15 179	92.17 190	88.97 187

CDS-MVSNet		88.34 106	88.71 99	87.90 115	90.70 134	94.54 104	92.38 100	86.02 110	80.37 158	79.42 108	79.30 107	83.43 88	82.04 163	93.39 99	94.01 71	96.86 142	95.93 117
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		85.44 137	86.71 122	83.97 159	90.59 135	90.84 186	89.73 142	78.34 175	84.07 140	66.40 175	77.27 120	78.66 117	83.06 156	91.20 132	90.10 156	95.72 162	94.78 137

IterMVS		85.25 139	86.49 125	83.80 160	90.42 136	90.77 189	90.02 134	78.04 177	84.10 138	66.27 176	77.28 119	78.41 120	83.01 157	90.88 137	89.72 165	95.04 176	94.24 144
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+-dtu		86.25 122	87.70 113	84.56 150	90.37 137	93.70 124	90.54 122	78.14 176	83.50 142	65.37 181	81.59 100	75.83 136	86.09 136	91.70 125	91.70 123	96.88 140	95.84 118

FC-MVSNet-test		86.15 125	89.10 97	82.71 175	89.83 138	93.18 141	87.88 164	84.69 122	86.54 115	62.18 190	82.39 94	83.31 89	74.18 189	92.52 112	91.86 120	97.50 106	93.88 150

GA-MVS		85.08 140	85.65 137	84.42 152	89.77 139	94.25 112	89.26 148	84.62 124	81.19 155	62.25 189	75.72 130	68.44 157	84.14 151	93.57 93	91.68 125	96.49 147	94.71 139

PMMVS		89.88 91	91.19 78	88.35 108	89.73 140	91.97 173	90.62 121	81.92 156	90.57 72	80.58 104	92.16 32	86.85 72	91.17 78	92.31 114	91.35 129	96.11 155	93.11 160

tfpnnormal		83.80 159	81.26 181	86.77 127	89.60 141	93.26 139	89.72 143	87.60 103	72.78 191	70.44 147	60.53 191	61.15 191	85.55 138	92.72 106	91.44 127	97.71 94	96.92 87

CVMVSNet		83.83 158	85.53 138	81.85 182	89.60 141	90.92 184	87.81 165	83.21 142	80.11 161	60.16 192	76.47 123	78.57 118	76.79 182	89.76 156	90.13 151	93.51 181	92.75 164

testgi		81.94 178	84.09 149	79.43 187	89.53 143	90.83 187	82.49 190	81.75 159	80.59 156	59.46 194	82.82 88	65.75 169	67.97 194	90.10 152	89.52 166	95.39 171	89.03 185

UniMVSNet_ETH3D		84.57 145	81.40 179	88.28 109	89.34 144	94.38 111	90.33 124	86.50 108	74.74 189	77.52 114	59.90 192	62.04 188	88.78 111	88.82 170	92.65 103	97.22 114	97.24 76

LTVRE_ROB	81.71 16	82.44 176	81.84 174	83.13 167	89.01 145	92.99 146	88.90 155	82.32 153	66.26 202	54.02 202	74.68 134	59.62 198	88.87 108	90.71 142	92.02 116	95.68 164	96.62 93
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
TAMVS		84.94 143	84.95 141	84.93 145	88.82 146	93.18 141	88.44 160	81.28 163	77.16 177	73.76 128	75.43 132	76.57 133	82.04 163	90.59 144	90.79 134	95.22 174	90.94 174

EG-PatchMatch MVS		81.70 181	81.31 180	82.15 180	88.75 147	93.81 120	87.14 170	78.89 174	71.57 194	64.12 186	61.20 190	68.46 156	76.73 183	91.48 127	90.77 136	97.28 112	91.90 166

TransMVSNet (Re)		82.67 173	80.93 184	84.69 148	88.71 148	91.50 180	87.90 163	87.15 104	71.54 196	68.24 163	63.69 184	64.67 179	78.51 179	91.65 126	90.73 139	97.64 101	92.73 165

FMVSNet3		90.19 89	90.06 91	90.34 86	88.69 149	93.85 118	94.58 60	85.78 113	90.03 82	85.56 76	77.38 115	86.13 74	89.22 101	93.29 101	94.36 63	98.20 58	95.40 130

GBi-Net		90.21 87	90.11 89	90.32 87	88.66 150	93.65 127	94.25 69	85.78 113	90.03 82	85.56 76	77.38 115	86.13 74	89.38 95	93.97 86	94.16 66	98.31 43	95.47 126

test1		90.21 87	90.11 89	90.32 87	88.66 150	93.65 127	94.25 69	85.78 113	90.03 82	85.56 76	77.38 115	86.13 74	89.38 95	93.97 86	94.16 66	98.31 43	95.47 126

FMVSNet2		89.61 95	89.14 96	90.16 92	88.66 150	93.65 127	94.25 69	85.44 117	88.57 99	84.96 85	73.53 138	83.82 86	89.38 95	94.23 80	94.68 60	98.31 43	95.47 126

PatchT		83.86 157	85.51 139	81.94 181	88.41 153	91.56 179	78.79 197	71.57 197	84.08 139	71.08 143	70.62 149	76.13 135	86.27 132	91.48 127	90.75 137	95.52 170	93.94 148

UniMVSNet (Re)		86.22 124	85.46 140	87.11 122	88.34 154	94.42 108	89.65 144	87.10 105	84.39 133	74.61 123	70.41 153	68.10 158	85.10 143	91.17 134	91.79 121	97.84 86	97.94 50

NR-MVSNet		85.46 136	84.54 145	86.52 130	88.33 155	93.78 121	90.45 123	87.87 94	84.40 131	71.61 137	70.59 150	62.09 187	82.79 159	91.75 124	91.75 122	98.10 68	97.44 69

UniMVSNet_NR-MVSNet		86.80 118	85.86 135	87.89 116	88.17 156	94.07 115	90.15 130	88.51 82	84.20 137	73.45 129	72.38 146	70.30 150	88.95 105	90.25 148	92.21 110	98.12 65	97.62 64

thisisatest0515		85.70 131	87.00 121	84.19 155	88.16 157	93.67 126	84.20 186	84.14 131	83.39 145	72.91 131	76.79 121	74.75 137	78.82 178	92.57 111	91.26 130	96.94 133	96.56 98

pm-mvs1		84.55 146	83.46 153	85.82 133	88.16 157	93.39 133	89.05 153	85.36 119	74.03 190	72.43 135	65.08 177	71.11 145	82.30 162	93.48 96	91.70 123	97.64 101	95.43 129

gm-plane-assit		77.65 190	78.50 188	76.66 191	87.96 159	85.43 201	64.70 207	74.50 186	64.15 204	51.26 204	61.32 189	58.17 200	84.11 152	95.16 56	93.83 73	97.45 108	91.41 169

test-mter		86.09 128	88.38 101	83.43 165	87.89 160	92.61 157	86.89 172	77.11 181	84.30 134	68.62 161	82.57 92	82.45 97	84.34 147	92.40 113	90.11 155	95.74 160	94.21 146

pmmvs4		86.00 129	84.28 148	88.00 112	87.80 161	92.01 172	89.94 137	84.91 121	86.79 112	80.98 100	73.41 141	66.34 168	88.12 113	89.31 163	88.90 172	96.24 154	93.20 159

TESTMET0.1,1		86.11 127	88.28 102	83.59 162	87.80 161	92.07 169	87.41 167	77.12 180	84.58 129	69.33 155	83.00 84	82.79 92	84.24 148	92.26 115	89.81 161	95.64 165	93.44 154

DU-MVS		86.12 126	84.81 143	87.66 117	87.77 163	93.78 121	90.15 130	87.87 94	84.40 131	73.45 129	70.59 150	64.82 177	88.95 105	90.14 149	92.33 107	97.76 90	97.62 64

Baseline_NR-MVSNet		85.28 138	83.42 156	87.46 121	87.77 163	90.80 188	89.90 140	87.69 98	83.93 141	74.16 125	64.72 180	66.43 167	87.48 120	90.14 149	90.83 133	97.73 93	97.11 82

SixPastTwentyTwo		83.12 168	83.44 155	82.74 174	87.71 165	93.11 145	82.30 191	82.33 152	79.24 166	64.33 184	78.77 111	62.75 183	84.11 152	88.11 172	87.89 174	95.70 163	94.21 146

TranMVSNet+NR-MVSNet		85.57 134	84.41 147	86.92 124	87.67 166	93.34 134	90.31 126	88.43 84	83.07 146	70.11 150	69.99 156	65.28 172	86.96 124	89.73 157	92.27 108	98.06 72	97.17 81

WR-MVS		83.14 167	83.38 158	82.87 173	87.55 167	93.29 136	86.36 177	84.21 129	80.05 162	66.41 174	66.91 165	66.92 165	75.66 186	88.96 168	90.56 142	97.05 125	96.96 85

v8		84.45 151	83.30 160	85.80 134	87.53 168	92.95 147	90.31 126	82.46 151	80.46 157	71.43 139	66.99 164	67.16 163	86.14 134	89.26 164	90.22 150	96.94 133	96.06 114

WR-MVS_H		82.86 172	82.66 166	83.10 169	87.44 169	93.33 135	85.71 182	83.20 143	77.36 176	68.20 164	66.37 168	65.23 173	76.05 185	89.35 161	90.13 151	97.99 78	96.89 88

v148		83.61 161	82.10 170	85.37 138	87.34 170	92.94 148	87.48 166	85.72 116	78.92 167	73.87 127	65.71 174	64.69 178	81.78 167	87.82 173	89.35 168	96.01 156	95.26 132

v10		84.18 152	83.17 162	85.37 138	87.34 170	92.68 155	90.32 125	81.33 162	79.93 165	69.23 157	66.33 169	65.74 170	87.03 123	90.84 138	90.38 145	96.97 129	96.29 107

v2v482		84.51 147	83.05 163	86.20 132	87.25 172	93.28 137	90.22 128	85.40 118	79.94 164	69.78 152	67.74 161	65.15 174	87.57 117	89.12 166	90.55 143	96.97 129	95.60 123

CP-MVSNet		83.11 169	82.15 169	84.23 154	87.20 173	92.70 154	86.42 176	83.53 139	77.83 174	67.67 167	66.89 167	60.53 194	82.47 160	89.23 165	90.65 141	98.08 69	97.20 80

v1144		84.03 156	82.88 164	85.37 138	87.17 174	93.15 144	90.18 129	83.31 141	78.83 168	67.85 165	65.99 171	64.99 175	86.79 126	90.75 140	90.33 147	96.90 138	96.15 111

V42		84.48 149	83.36 159	85.79 135	87.14 175	93.28 137	90.03 133	83.98 133	80.30 159	71.20 142	66.90 166	67.17 162	85.55 138	89.35 161	90.27 148	96.82 143	96.27 108

pmmvs5		83.37 164	82.68 165	84.18 156	87.13 176	93.18 141	86.74 173	82.08 155	76.48 181	67.28 170	71.26 147	62.70 184	84.71 145	90.77 139	90.12 154	97.15 118	94.24 144

FMVSNet1		87.33 113	86.00 132	88.89 102	87.13 176	92.83 152	93.08 96	84.46 127	81.35 154	82.20 92	66.33 169	77.96 123	88.96 104	93.97 86	94.16 66	97.54 105	95.38 131

PS-CasMVS		82.53 174	81.54 177	83.68 161	87.08 178	92.54 160	86.20 178	83.46 140	76.46 182	65.73 179	65.71 174	59.41 199	81.61 168	89.06 167	90.55 143	98.03 74	97.07 83

our_test_3					86.93 179	89.77 190	81.61 192

PEN-MVS		82.49 175	81.58 176	83.56 163	86.93 179	92.05 171	86.71 174	83.84 134	76.94 179	64.68 183	67.24 162	60.11 195	81.17 170	87.78 174	90.70 140	98.02 75	96.21 109

v1192		83.56 162	82.35 167	84.98 143	86.84 181	92.84 150	90.01 135	82.70 145	78.54 169	66.48 173	64.88 179	62.91 182	86.91 125	90.72 141	90.25 149	96.94 133	96.32 105

v144192		83.48 163	82.23 168	84.94 144	86.65 182	92.84 150	89.63 145	82.48 150	77.87 173	67.36 169	65.33 176	63.50 181	86.51 128	89.72 158	89.99 159	97.03 126	96.35 103

DTE-MVSNet		81.76 180	81.04 182	82.60 177	86.63 183	91.48 182	85.97 180	83.70 135	76.45 183	62.44 188	67.16 163	59.98 196	78.98 177	87.15 178	89.93 160	97.88 85	95.12 134

v1921920		83.30 165	82.09 171	84.70 147	86.59 184	92.67 156	89.82 141	82.23 154	78.32 170	65.76 178	64.64 181	62.35 185	86.78 127	90.34 147	90.02 157	97.02 127	96.31 106

v1240		82.88 171	81.66 175	84.29 153	86.46 185	92.52 162	89.06 152	81.82 158	77.16 177	65.09 182	64.17 183	61.50 189	86.36 129	90.12 151	90.13 151	96.95 132	96.04 115

anonymousdsp		84.51 147	85.85 136	82.95 172	86.30 186	93.51 130	85.77 181	80.38 168	78.25 172	63.42 187	73.51 139	72.20 141	84.64 146	93.21 103	92.16 112	97.19 116	98.14 42

pmmvs6		80.90 182	78.77 187	83.38 166	85.84 187	91.61 178	86.01 179	82.54 149	64.17 203	70.43 148	54.14 201	67.06 164	80.73 172	90.50 146	89.17 170	94.74 179	94.75 138

MVS-HIRNet		78.16 188	77.57 192	78.83 188	85.83 188	87.76 195	76.67 198	70.22 200	75.82 186	67.39 168	55.61 196	70.52 147	81.96 165	86.67 182	85.06 187	90.93 197	81.58 201

test20.03		76.41 192	78.49 189	73.98 194	85.64 189	87.50 196	75.89 199	80.71 167	70.84 197	51.07 205	68.06 160	61.40 190	54.99 203	88.28 171	87.20 177	95.58 168	86.15 194

v7n		82.25 177	81.54 177	83.07 170	85.55 190	92.58 158	86.68 175	81.10 166	76.54 180	65.97 177	62.91 185	60.56 193	82.36 161	91.07 136	90.35 146	96.77 145	96.80 89

N_pmnet		77.55 191	76.68 194	78.56 189	85.43 191	87.30 198	78.84 196	81.88 157	78.30 171	60.61 191	61.46 187	62.15 186	74.03 191	82.04 196	80.69 198	90.59 199	84.81 199

Anonymous20231206		78.09 189	78.11 190	78.07 190	85.19 192	89.17 191	80.99 193	81.24 165	75.46 187	58.25 196	54.78 200	59.90 197	66.73 197	88.94 169	88.26 173	96.01 156	90.25 180

MDTV_nov1_ep13_2view		80.43 183	80.94 183	79.84 185	84.82 193	90.87 185	84.23 185	73.80 189	80.28 160	64.33 184	70.05 155	68.77 155	79.67 173	84.83 189	83.50 192	92.17 190	88.25 192

FPMVS		69.87 198	67.10 201	73.10 196	84.09 194	78.35 206	79.40 195	76.41 182	71.92 192	57.71 197	54.06 202	50.04 205	56.72 201	71.19 202	68.70 203	84.25 204	75.43 204

EU-MVSNet		78.43 187	80.25 185	76.30 192	83.81 195	87.27 199	80.99 193	79.52 171	76.01 184	54.12 201	70.44 152	64.87 176	67.40 196	86.23 183	85.54 184	91.95 193	91.41 169

FMVSNet5		84.47 150	84.72 144	84.18 156	83.30 196	88.43 193	88.09 162	79.42 172	84.25 135	74.14 126	73.15 143	78.74 116	83.65 154	91.19 133	91.19 131	96.46 149	86.07 195

MIMVSNet		82.97 170	84.00 150	81.77 183	82.23 197	92.25 166	87.40 169	72.73 196	81.48 153	69.55 153	68.79 158	72.42 140	81.82 166	92.23 118	92.25 109	96.89 139	88.61 188

PM-MVS		80.29 184	79.30 186	81.45 184	81.91 198	88.23 194	82.61 189	79.01 173	79.99 163	67.15 171	69.07 157	51.39 204	82.92 158	87.55 176	85.59 182	95.08 175	93.28 157

pmmvs-eth3d		79.78 186	77.58 191	82.34 179	81.57 199	87.46 197	82.92 188	81.28 163	75.33 188	71.34 140	61.88 186	52.41 203	81.59 169	87.56 175	86.90 178	95.36 173	91.48 168

new-patchmatchnet		72.32 195	71.09 198	73.74 195	81.17 200	84.86 202	72.21 204	77.48 179	68.32 200	54.89 200	55.10 198	49.31 207	63.68 200	79.30 199	76.46 201	93.03 185	84.32 200

ET-MVSNet_ETH3D		89.93 90	90.84 82	88.87 103	79.60 201	96.19 90	94.43 61	86.56 107	90.63 69	80.75 102	90.71 41	77.78 125	93.73 53	91.36 130	93.45 84	98.15 61	95.77 119

PMVS	56.77 18	61.27 200	58.64 202	64.35 200	75.66 202	54.60 210	53.62 209	74.23 187	53.69 207	58.37 195	44.27 205	49.38 206	44.16 206	69.51 204	65.35 205	80.07 206	73.66 205
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		72.29 196	73.25 196	71.16 199	75.35 203	81.38 203	73.72 203	69.27 201	75.97 185	49.84 206	56.27 195	56.12 202	69.08 193	81.73 197	80.86 197	89.72 202	80.44 202

ambc			67.96 200		73.69 204	79.79 205	73.82 202		71.61 193	59.80 193	46.00 203	20.79 212	66.15 198	86.92 180	80.11 199	89.13 203	90.50 177

pmmvs3		71.13 197	71.06 199	71.21 198	73.54 205	80.19 204	71.69 205	64.86 204	62.04 206	52.10 203	54.92 199	48.00 208	75.03 187	83.75 194	83.24 193	90.04 201	85.27 196

MDA-MVSNet-bldmvs		73.81 193	72.56 197	75.28 193	72.52 206	88.87 192	74.95 201	82.67 147	71.57 194	55.02 199	65.96 172	42.84 210	76.11 184	70.61 203	81.47 196	90.38 200	86.59 193

tmp_tt				50.24 203	68.55 207	46.86 212	48.90 211	18.28 210	86.51 117	68.32 162	70.19 154	65.33 171	26.69 209	74.37 201	66.80 204	70.72 209

Gipuma		58.52 201	56.17 203	61.27 201	67.14 208	58.06 209	52.16 210	68.40 203	69.00 199	45.02 208	22.79 207	20.57 213	55.11 202	76.27 200	79.33 200	79.80 207	67.16 207
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		73.19 194	73.70 195	72.60 197	65.42 209	86.69 200	75.56 200	79.65 170	67.87 201	55.30 198	45.24 204	56.41 201	63.79 199	86.98 179	87.66 175	95.85 158	85.04 197

PMMVS2		53.68 202	55.72 204	51.30 202	58.84 210	67.02 208	54.23 208	60.97 207	47.50 208	19.42 211	34.81 206	31.97 211	30.88 208	65.84 205	69.99 202	83.47 205	72.92 206

EMVS		39.04 205	34.32 207	44.54 205	58.25 211	39.35 213	27.61 213	62.55 206	35.99 209	16.40 213	20.04 210	14.77 214	44.80 204	33.12 209	44.10 208	57.61 211	52.89 210

E-PMN		40.00 203	35.74 206	44.98 204	57.69 212	39.15 214	28.05 212	62.70 205	35.52 210	17.78 212	20.90 208	14.36 215	44.47 205	35.89 208	47.86 207	59.15 210	56.47 209

MVE	39.81 19	39.52 204	41.58 205	37.11 206	33.93 213	49.06 211	26.45 214	54.22 208	29.46 211	24.15 210	20.77 209	10.60 216	34.42 207	51.12 207	65.27 206	49.49 212	64.81 208
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		4.35 206	6.54 208	1.79 208	0.60 214	1.82 215	3.06 216	0.95 211	7.22 212	0.88 215	12.38 211	1.25 217	3.87 211	6.09 210	5.58 209	1.40 213	11.42 212

GG-mvs-BLEND		62.84 199	90.21 86	30.91 207	0.57 215	94.45 107	86.99 171	0.34 213	88.71 97	0.98 214	81.55 101	91.58 54	0.86 212	92.66 108	91.43 128	95.73 161	91.11 173

test123		3.48 207	5.31 209	1.34 209	0.20 216	1.52 216	2.17 217	0.58 212	6.13 213	0.31 216	9.85 212	0.31 218	3.90 210	2.65 211	5.28 210	0.87 214	11.46 211

sosnet-low-res		0.00 208	0.00 210	0.00 210	0.00 217	0.00 217	0.00 218	0.00 214	0.00 214	0.00 217	0.00 213	0.00 219	0.00 213	0.00 212	0.00 211	0.00 215	0.00 213

sosnet		0.00 208	0.00 210	0.00 210	0.00 217	0.00 217	0.00 218	0.00 214	0.00 214	0.00 217	0.00 213	0.00 219	0.00 213	0.00 212	0.00 211	0.00 215	0.00 213

test_part1																	98.96 10

MTAPA										95.36 2		97.46 18

MTMP										95.70 1		96.90 23

Patchmatch-RL test							18.47 215

NP-MVS									91.63 62

Patchmtry						92.39 164	89.18 149	73.30 193		71.08 143

DeepMVS_CX						71.82 207	68.37 206	48.05 209	77.38 175	46.88 207	65.77 173	47.03 209	67.48 195	64.27 206		76.89 208	76.72 203

High-resolution multi-view benchmark