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.

MTMP										93.14 1		90.21 24

MTAPA										92.97 2		91.03 17

zzz-MVS		93.80 14	93.45 22	94.20 6	97.53 3	96.43 32	95.88 14	91.12 16	94.09 7	92.74 3	87.68 29	90.77 20	92.04 11	94.74 15	93.56 24	95.91 19	96.85 23

SD-MVS		94.53 6	95.22 5	93.73 11	95.69 31	97.03 10	95.77 17	91.95 9	94.41 4	91.35 4	94.97 4	93.34 5	91.80 15	94.72 16	93.99 16	95.82 26	98.07 4

APDe-MVS		95.23 2	95.69 2	94.70 1	97.12 11	97.81 3	97.19 1	92.83 2	95.06 2	90.98 5	96.47 1	92.77 8	93.38 1	95.34 6	94.21 12	96.68 4	98.17 3

TSAR-MVS + MP.		94.48 7	94.97 6	93.90 8	95.53 32	97.01 11	96.69 3	90.71 18	94.24 5	90.92 6	94.97 4	92.19 11	93.03 2	94.83 12	93.60 22	96.51 7	97.97 5

HPM-MVS++		94.60 4	94.91 7	94.24 4	97.86 1	96.53 28	96.14 6	92.51 4	93.87 11	90.76 7	93.45 13	93.84 2	92.62 6	95.11 9	94.08 15	95.58 40	97.48 11

MSLP-MVS++		92.02 30	91.40 32	92.75 20	96.01 27	95.88 40	93.73 35	89.00 28	89.89 39	90.31 8	81.28 49	88.85 33	91.45 19	92.88 40	94.24 11	96.00 16	96.76 26

HSP-MVS		94.83 3	95.37 3	94.21 5	96.82 20	97.94 2	96.69 3	92.37 7	93.97 8	90.29 9	96.16 3	93.71 3	92.70 5	94.80 13	93.13 32	96.37 8	97.90 6

HFP-MVS		94.02 11	94.22 14	93.78 10	97.25 8	96.85 16	95.81 15	90.94 17	94.12 6	90.29 9	94.09 10	89.98 25	92.52 8	93.94 26	93.49 27	95.87 21	97.10 19

ESAPD		95.35 1	95.97 1	94.63 2	97.35 6	97.95 1	97.09 2	93.48 1	93.91 9	90.13 11	96.41 2	95.14 1	92.88 4	95.64 3	94.53 8	96.86 2	98.21 2

APD-MVS		94.37 8	94.47 12	94.26 3	97.18 9	96.99 12	96.53 5	92.68 3	92.45 20	89.96 12	94.53 7	91.63 15	92.89 3	94.58 18	93.82 19	96.31 11	97.26 14
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
3Dnovator+	86.06 4	91.60 31	90.86 37	92.47 23	96.00 28	96.50 31	94.70 27	87.83 37	90.49 34	89.92 13	74.68 74	89.35 30	90.66 26	94.02 24	94.14 13	95.67 35	96.85 23

CNVR-MVS		94.37 8	94.65 8	94.04 7	97.29 7	97.11 8	96.00 8	92.43 6	93.45 12	89.85 14	90.92 21	93.04 6	92.59 7	95.77 2	94.82 4	96.11 15	97.42 13

SMA-MVS		94.57 5	95.23 4	93.80 9	97.56 2	97.61 5	95.92 13	92.02 8	94.43 3	89.74 15	92.16 18	92.63 9	91.78 16	95.98 1	95.57 1	95.80 27	98.25 1

CSCG		92.76 22	93.16 24	92.29 25	96.30 23	97.74 4	94.67 28	88.98 30	92.46 19	89.73 16	86.67 33	92.15 12	88.69 36	92.26 46	92.92 36	95.40 46	97.89 7

ACMMPR		93.72 15	93.94 16	93.48 14	97.07 12	96.93 13	95.78 16	90.66 20	93.88 10	89.24 17	93.53 12	89.08 32	92.24 9	93.89 28	93.50 25	95.88 20	96.73 27

CP-MVS		93.25 18	93.26 23	93.24 17	96.84 19	96.51 29	95.52 19	90.61 21	92.37 21	88.88 18	90.91 22	89.52 28	91.91 13	93.64 30	92.78 38	95.69 33	97.09 20

AdaColmap		90.29 38	88.38 50	92.53 22	96.10 26	95.19 47	92.98 41	91.40 13	89.08 42	88.65 19	78.35 60	81.44 62	91.30 24	90.81 77	90.21 65	94.72 85	93.59 71

ACMMP_Plus		93.94 12	94.49 11	93.30 16	97.03 14	97.31 7	95.96 9	91.30 14	93.41 14	88.55 20	93.00 14	90.33 22	91.43 22	95.53 4	94.41 10	95.53 42	97.47 12

NCCC		93.69 16	93.66 19	93.72 12	97.37 5	96.66 25	95.93 12	92.50 5	93.40 15	88.35 21	87.36 31	92.33 10	92.18 10	94.89 11	94.09 14	96.00 16	96.91 22

DeepC-MVS_fast	88.76 1	93.10 19	93.02 26	93.19 18	97.13 10	96.51 29	95.35 21	91.19 15	93.14 17	88.14 22	85.26 37	89.49 29	91.45 19	95.17 7	95.07 2	95.85 24	96.48 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MP-MVS		93.35 17	93.59 20	93.08 19	97.39 4	96.82 18	95.38 20	90.71 18	90.82 31	88.07 23	92.83 16	90.29 23	91.32 23	94.03 23	93.19 31	95.61 38	97.16 16
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
3Dnovator	85.17 5	90.48 37	89.90 41	91.16 32	94.88 37	95.74 41	93.82 32	85.36 51	89.28 40	87.81 24	74.34 76	87.40 42	88.56 37	93.07 36	93.74 21	96.53 6	95.71 43

CNLPA		88.40 50	87.00 62	90.03 40	93.73 49	94.28 57	89.56 69	85.81 48	91.87 25	87.55 25	69.53 108	81.49 61	89.23 31	89.45 95	88.59 101	94.31 111	93.82 69

SteuartSystems-ACMMP		94.06 10	94.65 8	93.38 15	96.97 16	97.36 6	96.12 7	91.78 10	92.05 24	87.34 26	94.42 8	90.87 19	91.87 14	95.47 5	94.59 7	96.21 13	97.77 8
Skip Steuart: Steuart Systems R&D Blog.
CANet		91.33 33	91.46 31	91.18 31	95.01 34	96.71 20	93.77 33	87.39 40	87.72 46	87.26 27	81.77 45	89.73 26	87.32 51	94.43 21	93.86 18	96.31 11	96.02 39

MCST-MVS		93.81 13	94.06 15	93.53 13	96.79 21	96.85 16	95.95 10	91.69 12	92.20 22	87.17 28	90.83 23	93.41 4	91.96 12	94.49 20	93.50 25	97.61 1	97.12 18

PLC	83.76 9	88.61 49	86.83 64	90.70 34	94.22 43	92.63 86	91.50 54	87.19 41	89.16 41	86.87 29	75.51 71	80.87 63	89.98 30	90.01 88	89.20 91	94.41 107	90.45 149
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CPTT-MVS		91.39 32	90.95 35	91.91 27	95.06 33	95.24 46	95.02 25	88.98 30	91.02 30	86.71 30	84.89 39	88.58 37	91.60 18	90.82 76	89.67 79	94.08 117	96.45 31

DeepC-MVS	87.86 3	92.26 27	91.86 30	92.73 21	96.18 24	96.87 15	95.19 23	91.76 11	92.17 23	86.58 31	81.79 44	85.85 44	90.88 25	94.57 19	94.61 6	95.80 27	97.18 15
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PGM-MVS		92.76 22	93.03 25	92.45 24	97.03 14	96.67 24	95.73 18	87.92 36	90.15 38	86.53 32	92.97 15	88.33 38	91.69 17	93.62 31	93.03 33	95.83 25	96.41 33

abl_6				90.66 35	94.65 41	96.27 33	92.21 45	86.94 42	90.23 36	86.38 33	85.50 36	92.96 7	88.37 39			95.40 46	95.46 49

PVSNet_BlendedMVS		88.19 55	88.00 54	88.42 56	92.71 62	94.82 53	89.08 75	83.81 62	84.91 57	86.38 33	79.14 55	78.11 78	82.66 68	93.05 37	91.10 48	95.86 22	94.86 54

PVSNet_Blended		88.19 55	88.00 54	88.42 56	92.71 62	94.82 53	89.08 75	83.81 62	84.91 57	86.38 33	79.14 55	78.11 78	82.66 68	93.05 37	91.10 48	95.86 22	94.86 54

TSAR-MVS + GP.		92.71 24	93.91 17	91.30 30	91.96 66	96.00 37	93.43 36	87.94 35	92.53 18	86.27 36	93.57 11	91.94 13	91.44 21	93.29 33	92.89 37	96.78 3	97.15 17

ACMMP		92.03 29	92.16 28	91.87 29	95.88 29	96.55 27	94.47 30	89.49 27	91.71 27	85.26 37	91.52 20	84.48 49	90.21 28	92.82 41	91.63 45	95.92 18	96.42 32
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
CLD-MVS		88.66 47	88.52 48	88.82 51	91.37 68	94.22 58	92.82 43	82.08 94	88.27 45	85.14 38	81.86 43	78.53 77	85.93 59	91.17 60	90.61 59	95.55 41	95.00 51
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
DELS-MVS		89.71 41	89.68 42	89.74 42	93.75 48	96.22 34	93.76 34	85.84 47	82.53 63	85.05 39	78.96 57	84.24 50	84.25 65	94.91 10	94.91 3	95.78 31	96.02 39
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
OMC-MVS		90.23 39	90.40 38	90.03 40	93.45 51	95.29 45	91.89 51	86.34 46	93.25 16	84.94 40	81.72 46	86.65 43	88.90 33	91.69 53	90.27 64	94.65 90	93.95 66

DeepPCF-MVS	88.51 2	92.64 25	94.42 13	90.56 36	94.84 38	96.92 14	91.31 56	89.61 26	95.16 1	84.55 41	89.91 25	91.45 16	90.15 29	95.12 8	94.81 5	92.90 161	97.58 10

MVS_111021_LR		90.14 40	90.89 36	89.26 48	93.23 53	94.05 61	90.43 60	84.65 56	90.16 37	84.52 42	90.14 24	83.80 53	87.99 42	92.50 44	90.92 53	94.74 83	94.70 58

XVS					93.11 54	96.70 21	91.91 49			83.95 43		88.82 34				95.79 29

X-MVStestdata					93.11 54	96.70 21	91.91 49			83.95 43		88.82 34				95.79 29

X-MVS		92.36 26	92.75 27	91.90 28	96.89 17	96.70 21	95.25 22	90.48 23	91.50 29	83.95 43	88.20 27	88.82 34	89.11 32	93.75 29	93.43 28	95.75 32	96.83 25

train_agg		92.87 21	93.53 21	92.09 26	96.88 18	95.38 44	95.94 11	90.59 22	90.65 33	83.65 46	94.31 9	91.87 14	90.30 27	93.38 32	92.42 39	95.17 58	96.73 27

EPNet		89.60 42	89.91 40	89.24 49	96.45 22	93.61 66	92.95 42	88.03 34	85.74 54	83.36 47	87.29 32	83.05 56	80.98 79	92.22 47	91.85 43	93.69 147	95.58 47
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + ACMM		92.97 20	94.51 10	91.16 32	95.88 29	96.59 26	95.09 24	90.45 24	93.42 13	83.01 48	94.68 6	90.74 21	88.74 35	94.75 14	93.78 20	93.82 140	97.63 9

MVS_0304		90.88 35	91.35 33	90.34 37	93.91 46	96.79 19	94.49 29	86.54 44	86.57 50	82.85 49	81.68 47	89.70 27	87.57 48	94.64 17	93.93 17	96.67 5	96.15 37

ACMM	83.27 10	87.68 58	86.09 69	89.54 46	93.26 52	92.19 89	91.43 55	86.74 43	86.02 52	82.85 49	75.63 70	75.14 86	88.41 38	90.68 81	89.99 69	94.59 94	92.97 80
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
canonicalmvs		89.36 44	89.92 39	88.70 53	91.38 67	95.92 39	91.81 52	82.61 86	90.37 35	82.73 51	82.09 42	79.28 75	88.30 40	91.17 60	93.59 23	95.36 49	97.04 21

PVSNet_Blended_VisFu		87.40 60	87.80 56	86.92 64	92.86 58	95.40 43	88.56 87	83.45 72	79.55 88	82.26 52	74.49 75	84.03 51	79.24 129	92.97 39	91.53 46	95.15 60	96.65 29

OpenMVS	82.53 11	87.71 57	86.84 63	88.73 52	94.42 42	95.06 49	91.02 58	83.49 69	82.50 64	82.24 53	67.62 119	85.48 45	85.56 60	91.19 59	91.30 47	95.67 35	94.75 56

MVSTER		86.03 66	86.12 68	85.93 66	88.62 106	89.93 126	89.33 71	79.91 116	81.87 67	81.35 54	81.07 50	74.91 88	80.66 85	92.13 51	90.10 66	95.68 34	92.80 86

MVS_111021_HR		90.56 36	91.29 34	89.70 44	94.71 40	95.63 42	91.81 52	86.38 45	87.53 47	81.29 55	87.96 28	85.43 46	87.69 45	93.90 27	92.93 35	96.33 9	95.69 44

ACMP	83.90 8	88.32 53	88.06 53	88.62 54	92.18 64	93.98 62	91.28 57	85.24 52	86.69 49	81.23 56	85.62 35	75.13 87	87.01 54	89.83 90	89.77 77	94.79 79	95.43 50
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IB-MVS	79.09 12	82.60 90	82.19 87	83.07 100	91.08 70	93.55 67	80.90 185	81.35 101	76.56 104	80.87 57	64.81 141	69.97 107	68.87 186	85.64 146	90.06 68	95.36 49	94.74 57
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
PHI-MVS		92.05 28	93.74 18	90.08 39	94.96 35	97.06 9	93.11 40	87.71 38	90.71 32	80.78 58	92.40 17	91.03 17	87.68 46	94.32 22	94.48 9	96.21 13	96.16 36

DI_MVS_plusplus_trai		86.41 63	85.54 71	87.42 62	89.24 103	93.13 74	92.16 47	82.65 84	82.30 66	80.75 59	68.30 115	80.41 65	85.01 62	90.56 83	90.07 67	94.70 87	94.01 65

diffmvs		85.70 69	86.35 67	84.95 71	87.75 111	90.96 102	89.09 74	78.56 137	86.50 51	80.44 60	77.86 62	83.93 52	81.64 74	85.52 153	86.79 126	92.21 168	92.87 83

TAPA-MVS	84.37 7	88.91 46	88.93 46	88.89 50	93.00 57	94.85 52	92.00 48	84.84 55	91.68 28	80.05 61	79.77 53	84.56 48	88.17 41	90.11 87	89.00 97	95.30 53	92.57 96
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
QAPM		89.49 43	89.58 43	89.38 47	94.73 39	95.94 38	92.35 44	85.00 54	85.69 55	80.03 62	76.97 66	87.81 40	87.87 43	92.18 50	92.10 41	96.33 9	96.40 34

PCF-MVS	84.60 6	88.66 47	87.75 59	89.73 43	93.06 56	96.02 36	93.22 39	90.00 25	82.44 65	80.02 63	77.96 61	85.16 47	87.36 50	88.54 106	88.54 102	94.72 85	95.61 46
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PatchMatch-RL		83.34 83	81.36 96	85.65 67	90.33 86	89.52 137	84.36 157	81.82 97	80.87 79	79.29 64	74.04 79	62.85 140	86.05 58	88.40 108	87.04 123	92.04 170	86.77 176

MSDG		83.87 77	81.02 102	87.19 63	92.17 65	89.80 129	89.15 72	85.72 49	80.61 80	79.24 65	66.66 123	68.75 112	82.69 67	87.95 112	87.44 116	94.19 113	85.92 185

MAR-MVS		88.39 52	88.44 49	88.33 58	94.90 36	95.06 49	90.51 59	83.59 66	85.27 56	79.07 66	77.13 64	82.89 57	87.70 44	92.19 49	92.32 40	94.23 112	94.20 64
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
tpm cat1		77.78 165	75.28 193	80.70 142	87.14 121	85.84 183	85.81 138	70.40 192	77.44 100	78.80 67	63.72 145	64.01 132	76.55 145	75.60 215	75.21 215	85.51 214	85.12 190

DWT-MVSNet_training		80.51 120	78.05 150	83.39 97	88.64 105	88.33 157	86.11 136	76.33 158	79.65 86	78.64 68	69.62 106	58.89 188	80.82 80	80.50 198	82.03 193	89.77 192	87.36 170

MVS_Test		86.93 61	87.24 61	86.56 65	90.10 95	93.47 68	90.31 61	80.12 113	83.55 61	78.12 69	79.58 54	79.80 70	85.45 61	90.17 86	90.59 60	95.29 54	93.53 72

RPSCF		83.46 82	83.36 81	83.59 95	87.75 111	87.35 163	84.82 154	79.46 127	83.84 60	78.12 69	82.69 41	79.87 68	82.60 70	82.47 191	81.13 195	88.78 197	86.13 183

tpmp4_e23		79.82 132	77.96 155	82.00 110	87.59 116	86.93 167	87.81 93	72.21 186	79.99 83	78.02 71	67.83 118	64.77 124	78.74 132	79.99 200	78.90 200	87.65 202	87.29 171

UGNet		85.90 68	88.23 51	83.18 99	88.96 104	94.10 59	87.52 97	83.60 65	81.66 69	77.90 72	80.76 51	83.19 55	66.70 196	91.13 71	90.71 58	94.39 108	96.06 38
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
CDPH-MVS		91.14 34	92.01 29	90.11 38	96.18 24	96.18 35	94.89 26	88.80 32	88.76 43	77.88 73	89.18 26	87.71 41	87.29 52	93.13 35	93.31 30	95.62 37	95.84 41

CostFormer		80.94 114	80.21 117	81.79 114	87.69 114	88.58 152	87.47 99	70.66 191	80.02 82	77.88 73	73.03 89	71.40 103	78.24 135	79.96 201	79.63 197	88.82 196	88.84 155

dps		78.02 162	75.94 184	80.44 148	86.06 128	86.62 173	82.58 167	69.98 195	75.14 114	77.76 75	69.08 111	59.93 176	78.47 133	79.47 203	77.96 204	87.78 200	83.40 197

OPM-MVS		87.56 59	85.80 70	89.62 45	93.90 47	94.09 60	94.12 31	88.18 33	75.40 113	77.30 76	76.41 67	77.93 80	88.79 34	92.20 48	90.82 54	95.40 46	93.72 70

GBi-Net		84.51 73	84.80 74	84.17 84	84.20 150	89.95 123	89.70 66	80.37 107	81.17 71	75.50 77	69.63 103	79.69 72	79.75 114	90.73 78	90.72 55	95.52 43	91.71 123

test1		84.51 73	84.80 74	84.17 84	84.20 150	89.95 123	89.70 66	80.37 107	81.17 71	75.50 77	69.63 103	79.69 72	79.75 114	90.73 78	90.72 55	95.52 43	91.71 123

FMVSNet3		84.44 75	84.64 76	84.21 83	84.32 149	90.13 121	89.85 65	80.37 107	81.17 71	75.50 77	69.63 103	79.69 72	79.62 117	89.72 92	90.52 62	95.59 39	91.58 129

HQP-MVS		89.13 45	89.58 43	88.60 55	93.53 50	93.67 64	93.29 38	87.58 39	88.53 44	75.50 77	87.60 30	80.32 66	87.07 53	90.66 82	89.95 72	94.62 93	96.35 35

FMVSNet2		83.87 77	83.73 80	84.05 89	84.20 150	89.95 123	89.70 66	80.21 112	79.17 91	74.89 81	65.91 125	77.49 81	79.75 114	90.87 75	91.00 52	95.52 43	91.71 123

pmmvs4		79.99 127	78.08 146	82.22 107	83.04 181	87.16 166	84.95 151	78.80 135	78.64 94	74.53 82	64.61 142	59.41 181	79.45 127	84.13 177	84.54 177	92.53 165	88.08 162

FC-MVSNet-train		85.18 72	85.31 72	85.03 70	90.67 75	91.62 95	87.66 95	83.61 64	79.75 85	74.37 83	78.69 58	71.21 104	78.91 131	91.23 56	89.96 71	94.96 68	94.69 59

LGP-MVS_train		88.25 54	88.55 47	87.89 59	92.84 60	93.66 65	93.35 37	85.22 53	85.77 53	74.03 84	86.60 34	76.29 84	86.62 56	91.20 58	90.58 61	95.29 54	95.75 42

TSAR-MVS + COLMAP		88.40 50	89.09 45	87.60 61	92.72 61	93.92 63	92.21 45	85.57 50	91.73 26	73.72 85	91.75 19	73.22 98	87.64 47	91.49 54	89.71 78	93.73 146	91.82 118

EPP-MVSNet		86.55 62	87.76 58	85.15 69	90.52 80	94.41 56	87.24 106	82.32 91	81.79 68	73.60 86	78.57 59	82.41 58	82.07 72	91.23 56	90.39 63	95.14 61	95.48 48

IterMVS-LS		83.28 84	82.95 83	83.65 93	88.39 108	88.63 151	86.80 125	78.64 136	76.56 104	73.43 87	72.52 93	75.35 85	80.81 82	86.43 136	88.51 103	93.84 139	92.66 90
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UA-Net		86.07 65	87.78 57	84.06 87	92.85 59	95.11 48	87.73 94	84.38 57	73.22 139	73.18 88	79.99 52	89.22 31	71.47 177	93.22 34	93.03 33	94.76 82	90.69 144

Fast-Effi-MVS+		83.77 79	82.98 82	84.69 72	87.98 109	91.87 92	88.10 91	77.70 147	78.10 97	73.04 89	69.13 110	68.51 113	86.66 55	90.49 84	89.85 74	94.67 89	92.88 82

LS3D		85.96 67	84.37 77	87.81 60	94.13 44	93.27 71	90.26 62	89.00 28	84.91 57	72.84 90	71.74 94	72.47 100	87.45 49	89.53 94	89.09 94	93.20 156	89.60 151

FMVSNet1		81.64 106	80.61 114	82.84 102	82.36 192	89.20 143	88.67 84	79.58 125	70.79 156	72.63 91	58.95 188	72.26 101	79.34 128	90.73 78	90.72 55	94.47 103	91.62 127

Effi-MVS+		85.33 71	85.08 73	85.63 68	89.69 102	93.42 69	89.90 64	80.31 111	79.32 89	72.48 92	73.52 86	74.03 91	86.55 57	90.99 73	89.98 70	94.83 77	94.27 63

CDS-MVSNet		81.63 107	82.09 88	81.09 136	87.21 120	90.28 117	87.46 100	80.33 110	69.06 175	70.66 93	71.30 95	73.87 92	67.99 189	89.58 93	89.87 73	92.87 162	90.69 144
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CHOSEN 280x420		80.28 122	81.66 91	78.67 163	82.92 184	79.24 215	85.36 147	66.79 207	78.11 96	70.32 94	75.03 73	79.87 68	81.09 78	89.07 98	83.16 184	85.54 213	87.17 173

CHOSEN 1792x2688		82.16 96	80.91 109	83.61 94	91.14 69	92.01 91	89.55 70	79.15 131	79.87 84	70.29 95	52.51 208	72.56 99	81.39 75	88.87 101	88.17 107	90.15 189	92.37 108

IS_MVSNet		86.18 64	88.18 52	83.85 91	91.02 71	94.72 55	87.48 98	82.46 88	81.05 75	70.28 96	76.98 65	82.20 60	76.65 144	93.97 25	93.38 29	95.18 57	94.97 52

HyFIR lowres test		81.62 108	79.45 129	84.14 86	91.00 72	93.38 70	88.27 88	78.19 141	76.28 106	70.18 97	48.78 212	73.69 94	83.52 66	87.05 120	87.83 113	93.68 148	89.15 154

PMMVS		81.65 105	84.05 78	78.86 161	78.56 209	82.63 203	83.10 165	67.22 205	81.39 70	70.11 98	84.91 38	79.74 71	82.12 71	87.31 115	85.70 163	92.03 171	86.67 179

conf0.002		82.54 92	80.83 110	84.54 73	90.28 92	93.24 72	89.05 77	82.75 76	75.14 114	69.75 99	67.99 116	57.12 194	80.38 92	91.16 63	89.79 75	95.02 64	91.36 132

conf0.01		82.64 89	81.02 102	84.53 75	90.30 87	93.22 73	89.05 77	82.75 76	75.14 114	69.69 100	67.15 121	59.19 183	80.38 92	91.16 63	89.51 81	95.00 66	91.76 121

thres100view900		82.55 91	81.01 105	84.34 80	90.30 87	92.27 87	89.04 82	82.77 75	75.14 114	69.56 101	65.72 127	63.13 133	79.62 117	89.97 89	89.26 89	94.73 84	91.61 128

tfpn200view9		82.86 85	81.46 93	84.48 76	90.30 87	93.09 75	89.05 77	82.71 80	75.14 114	69.56 101	65.72 127	63.13 133	80.38 92	91.15 65	89.51 81	94.91 70	92.50 102

thres200		82.77 87	81.25 98	84.54 73	90.38 84	93.05 78	89.13 73	82.67 82	74.40 124	69.53 103	65.69 130	63.03 138	80.63 86	91.15 65	89.42 86	94.88 74	92.04 112

tfpn111		83.51 81	82.68 84	84.47 77	90.30 87	93.09 75	89.05 77	82.72 78	75.14 114	69.49 104	74.24 77	63.13 133	80.38 92	91.15 65	89.51 81	94.91 70	92.50 102

conf200view11		82.85 86	81.46 93	84.47 77	90.30 87	93.09 75	89.05 77	82.72 78	75.14 114	69.49 104	65.72 127	63.13 133	80.38 92	91.15 65	89.51 81	94.91 70	92.50 102

MDTV_nov1_ep13		79.14 145	79.49 128	78.74 162	85.40 135	86.89 168	84.32 159	70.29 193	78.85 93	69.42 106	75.37 72	73.29 97	75.64 149	80.61 197	79.48 199	87.36 203	81.91 201

v148		78.59 158	76.84 175	80.62 144	83.61 174	89.16 144	83.65 163	79.24 130	69.38 173	69.34 107	59.88 181	60.41 172	75.19 150	83.81 179	84.63 176	92.70 164	90.63 146

thres400		82.68 88	81.15 99	84.47 77	90.52 80	92.89 84	88.95 83	82.71 80	74.33 125	69.22 108	65.31 132	62.61 142	80.63 86	90.96 74	89.50 85	94.79 79	92.45 107

MS-PatchMatch		81.79 102	81.44 95	82.19 108	90.35 85	89.29 141	88.08 92	75.36 174	77.60 99	69.00 109	64.37 144	78.87 76	77.14 143	88.03 111	85.70 163	93.19 157	86.24 182

v18		79.71 137	77.98 154	81.73 118	84.02 156	86.67 169	87.37 101	76.35 157	72.61 145	68.86 110	61.35 153	62.65 141	79.94 105	85.49 155	86.21 142	93.85 138	90.92 137

v16		79.65 138	77.91 156	81.69 120	84.04 154	86.65 172	87.20 108	76.32 159	72.41 146	68.71 111	61.13 161	62.52 145	79.93 106	85.55 151	86.22 140	93.92 129	90.91 138

view600		82.51 94	81.00 106	84.27 82	90.56 79	92.95 82	88.57 85	82.57 87	74.16 128	68.70 112	65.13 135	62.15 153	80.36 97	91.15 65	88.98 98	94.87 76	92.48 105

thres600view7		82.53 93	81.02 102	84.28 81	90.61 76	93.05 78	88.57 85	82.67 82	74.12 129	68.56 113	65.09 136	62.13 154	80.40 91	91.15 65	89.02 96	94.88 74	92.59 93

v17		79.59 140	77.88 157	81.60 123	84.03 155	86.66 170	87.13 117	76.31 160	72.09 147	68.29 114	61.15 160	62.57 144	79.90 107	85.55 151	86.20 145	93.93 127	90.93 136

Vis-MVSNet		84.38 76	86.68 66	81.70 119	87.65 115	94.89 51	88.14 89	80.90 105	74.48 123	68.23 115	77.53 63	80.72 64	69.98 182	92.68 42	91.90 42	95.33 52	94.58 60
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
view800		82.38 95	80.93 107	84.06 87	90.59 78	92.96 81	88.11 90	82.44 89	73.92 130	68.10 116	65.07 137	61.64 156	80.10 103	91.17 60	89.24 90	95.01 65	92.56 97

v8		79.90 129	78.39 142	81.66 121	83.97 157	89.81 128	87.16 115	77.40 149	71.49 150	67.71 117	61.24 156	62.49 146	79.83 109	85.48 159	86.17 151	93.89 133	92.02 114

v6		80.11 124	78.47 137	82.01 109	83.97 157	90.49 108	87.19 111	79.67 121	71.59 149	67.51 118	61.26 154	62.46 148	79.81 112	85.49 155	86.18 150	93.89 133	91.86 115

tfpn		81.79 102	80.06 118	83.82 92	90.61 76	92.91 83	87.62 96	82.34 90	73.66 136	67.46 119	64.99 138	55.50 202	79.77 113	91.12 72	89.62 80	95.14 61	92.59 93

v1neww		80.09 125	78.45 139	82.00 110	83.97 157	90.49 108	87.18 112	79.67 121	71.49 150	67.44 120	61.24 156	62.41 149	79.83 109	85.49 155	86.19 147	93.88 135	91.86 115

v7new		80.09 125	78.45 139	82.00 110	83.97 157	90.49 108	87.18 112	79.67 121	71.49 150	67.44 120	61.24 156	62.41 149	79.83 109	85.49 155	86.19 147	93.88 135	91.86 115

V42		79.59 140	78.43 141	80.94 140	82.79 188	89.71 133	86.66 127	76.73 155	71.38 153	67.42 122	61.01 162	62.30 151	78.39 134	85.56 150	86.48 134	93.65 149	92.60 92

tpmrst		76.55 177	75.99 183	77.20 174	87.32 119	83.05 199	82.86 166	65.62 212	78.61 95	67.22 123	69.19 109	65.71 121	75.87 148	76.75 212	75.33 214	84.31 218	83.28 198

Effi-MVS+-dtu		82.05 97	81.76 89	82.38 105	87.72 113	90.56 107	86.90 123	78.05 143	73.85 133	66.85 124	71.29 96	71.90 102	82.00 73	86.64 130	85.48 167	92.76 163	92.58 95

COLMAP_ROB	76.78 15	80.50 121	78.49 136	82.85 101	90.96 73	89.65 135	86.20 135	83.40 73	77.15 101	66.54 125	62.27 149	65.62 122	77.89 138	85.23 165	84.70 175	92.11 169	84.83 192
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v15		79.13 146	77.37 162	81.19 131	83.90 161	86.56 174	87.01 119	76.15 164	70.20 165	66.48 126	60.71 169	61.55 158	79.60 119	85.59 149	86.19 147	93.98 125	90.80 143

V9		79.08 148	77.32 166	81.14 135	83.89 164	86.52 176	86.85 124	76.06 167	70.02 168	66.42 127	60.44 174	61.52 160	79.54 121	85.68 145	86.21 142	94.08 117	90.83 140

V14		79.11 147	77.35 164	81.16 133	83.90 161	86.54 175	86.94 120	76.10 166	70.14 167	66.41 128	60.59 171	61.54 159	79.59 120	85.64 146	86.20 145	94.04 121	90.82 141

conf0.05thres1000		81.00 113	79.12 130	83.20 98	90.14 94	92.15 90	87.05 118	82.09 93	68.11 182	66.19 129	59.67 182	61.10 167	79.05 130	90.47 85	89.11 93	94.68 88	93.22 74

PatchmatchNet		78.67 157	78.85 133	78.46 165	86.85 124	86.03 180	83.77 162	68.11 202	80.88 78	66.19 129	72.90 91	73.40 96	78.06 136	79.25 205	77.71 207	87.75 201	81.75 202
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1141		79.75 135	78.04 151	81.75 115	83.89 164	90.37 113	87.20 108	79.89 118	70.23 163	66.18 131	60.92 164	61.48 161	79.54 121	85.36 161	86.17 151	93.81 141	91.76 121

divwei89l23v2f112		79.75 135	78.04 151	81.75 115	83.90 161	90.37 113	87.21 107	79.90 117	70.20 165	66.18 131	60.92 164	61.48 161	79.52 124	85.36 161	86.17 151	93.81 141	91.77 119

v12		79.03 150	77.28 167	81.06 137	83.88 168	86.49 177	86.62 128	76.02 168	69.99 169	66.18 131	60.34 177	61.44 163	79.54 121	85.70 144	86.21 142	94.11 116	90.82 141

v1		79.76 134	78.06 149	81.74 117	83.89 164	90.38 112	87.20 108	79.88 119	70.23 163	66.17 134	60.92 164	61.56 157	79.50 125	85.37 160	86.17 151	93.81 141	91.77 119

v13		78.99 152	77.25 169	81.02 138	83.87 169	86.47 179	86.60 129	75.96 170	69.87 170	66.07 135	60.25 178	61.41 164	79.49 126	85.72 143	86.22 140	94.14 115	90.84 139

pmmvs-eth3d		74.32 199	71.96 204	77.08 176	77.33 213	82.71 202	78.41 197	76.02 168	66.65 187	65.98 136	54.23 203	49.02 218	73.14 168	82.37 192	82.69 188	91.61 174	86.05 184

v2v482		79.84 130	78.07 147	81.90 113	83.75 170	90.21 120	87.17 114	79.85 120	70.65 157	65.93 137	61.93 150	60.07 174	80.82 80	85.25 164	86.71 127	93.88 135	91.70 126

ACMH+	79.08 13	81.84 101	80.06 118	83.91 90	89.92 100	90.62 106	86.21 134	83.48 71	73.88 132	65.75 138	66.38 124	65.30 123	84.63 63	85.90 141	87.25 120	93.45 151	91.13 134

v7		79.79 133	78.28 144	81.54 125	83.73 171	90.34 116	87.27 104	78.27 140	70.50 159	65.59 139	60.59 171	60.47 170	80.46 89	86.90 122	86.63 130	93.92 129	92.56 97

v10		79.62 139	78.19 145	81.28 130	83.73 171	89.69 134	87.27 104	76.86 153	70.50 159	65.46 140	60.58 173	60.47 170	80.44 90	86.91 121	86.63 130	93.93 127	92.55 99

CMPMVS	56.49 17	73.84 201	71.73 205	76.31 184	85.20 139	85.67 185	75.80 205	73.23 183	62.26 205	65.40 141	53.40 206	59.70 178	71.77 173	80.25 199	79.56 198	86.45 209	81.28 204
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CR-MVSNet		78.71 156	78.86 132	78.55 164	85.85 131	85.15 190	82.30 172	68.23 200	74.71 121	65.37 142	64.39 143	69.59 109	77.18 141	85.10 169	84.87 172	92.34 167	88.21 160

Patchmtry						85.54 188	82.30 172	68.23 200		65.37 142

PatchT		76.42 181	77.81 159	74.80 194	78.46 210	84.30 195	71.82 213	65.03 216	73.89 131	65.37 142	61.58 151	66.70 118	77.18 141	85.10 169	84.87 172	90.94 184	88.21 160

v11		79.02 151	77.36 163	80.95 139	83.89 164	86.48 178	86.53 130	75.77 172	69.69 171	65.21 145	60.36 176	60.24 173	80.32 98	87.20 117	86.54 133	93.96 126	91.02 135

UniMVSNet (Re)		81.22 109	81.08 101	81.39 126	85.35 136	91.76 93	84.93 152	82.88 74	76.13 107	65.02 146	64.94 139	63.09 137	75.17 151	87.71 113	89.04 95	94.97 67	94.88 53

CANet_DTU		85.43 70	87.72 60	82.76 103	90.95 74	93.01 80	89.99 63	75.46 173	82.67 62	64.91 147	83.14 40	80.09 67	80.68 84	92.03 52	91.03 50	94.57 96	92.08 110

tfpn_ndepth		81.77 104	82.29 86	81.15 134	89.79 101	91.71 94	85.49 145	81.63 100	79.17 91	64.76 148	73.04 88	68.14 117	70.62 180	88.72 102	87.88 111	94.63 92	87.38 169

TDRefinement		79.05 149	77.05 172	81.39 126	88.45 107	89.00 148	86.92 121	82.65 84	74.21 127	64.41 149	59.17 185	59.16 184	74.52 157	85.23 165	85.09 170	91.37 177	87.51 168

UniMVSNet_NR-MVSNet		81.87 99	81.33 97	82.50 104	85.31 137	91.30 98	85.70 139	84.25 58	75.89 108	64.21 150	66.95 122	64.65 126	80.22 99	87.07 119	89.18 92	95.27 56	94.29 61

DU-MVS		81.20 110	80.30 116	82.25 106	84.98 144	90.94 104	85.70 139	83.58 67	75.74 110	64.21 150	65.30 133	59.60 180	80.22 99	86.89 123	89.31 87	94.77 81	94.29 61

v748		76.17 186	75.10 195	77.43 171	81.60 197	88.01 158	79.02 195	76.28 161	64.47 200	64.14 152	56.55 196	56.26 199	70.40 181	82.50 190	85.77 161	93.11 158	92.15 109

EPMVS		77.53 168	78.07 147	76.90 178	86.89 123	84.91 193	82.18 175	66.64 208	81.00 76	64.11 153	72.75 92	69.68 108	74.42 159	79.36 204	78.13 203	87.14 206	80.68 207

v1144		79.38 144	77.83 158	81.18 132	83.62 173	90.23 118	87.15 116	78.35 139	69.13 174	64.02 154	60.20 179	59.41 181	80.14 102	86.78 126	86.57 132	93.81 141	92.53 101

tpm		76.30 185	76.05 182	76.59 180	86.97 122	83.01 200	83.83 161	67.06 206	71.83 148	63.87 155	69.56 107	62.88 139	73.41 166	79.79 202	78.59 201	84.41 217	86.68 177

pm-mvs1		78.51 160	77.75 160	79.40 157	84.83 147	89.30 140	83.55 164	79.38 128	62.64 204	63.68 156	58.73 190	64.68 125	70.78 179	89.79 91	87.84 112	94.17 114	91.28 133

USDC		80.69 116	79.89 122	81.62 122	86.48 125	89.11 146	86.53 130	78.86 133	81.15 74	63.48 157	72.98 90	59.12 186	81.16 77	87.10 118	85.01 171	93.23 155	84.77 193

Baseline_NR-MVSNet		79.84 130	78.37 143	81.55 124	84.98 144	86.66 170	85.06 150	83.49 69	75.57 112	63.31 158	58.22 192	60.97 168	78.00 137	86.89 123	87.13 121	94.47 103	93.15 76

v144192		78.81 154	77.22 170	80.67 143	82.95 182	89.79 130	86.40 132	77.42 148	68.26 181	63.13 159	59.50 183	58.13 190	80.08 104	85.93 140	86.08 156	94.06 119	92.83 85

MVS-HIRNet		68.83 207	66.39 211	71.68 203	77.58 211	75.52 219	66.45 219	65.05 215	62.16 206	62.84 160	44.76 220	56.60 198	71.96 172	78.04 208	75.06 216	86.18 211	72.56 220

test-LLR		79.47 143	79.84 123	79.03 160	87.47 117	82.40 206	81.24 180	78.05 143	73.72 134	62.69 161	73.76 80	74.42 89	73.49 164	84.61 173	82.99 186	91.25 179	87.01 174

TESTMET0.1,1		77.78 165	79.84 123	75.38 190	80.86 202	82.40 206	81.24 180	62.72 221	73.72 134	62.69 161	73.76 80	74.42 89	73.49 164	84.61 173	82.99 186	91.25 179	87.01 174

v1192		78.94 153	77.33 165	80.82 141	83.25 177	89.90 127	86.91 122	77.72 146	68.63 179	62.61 163	59.17 185	57.53 192	80.62 88	86.89 123	86.47 135	93.79 145	92.75 89

Vis-MVSNet (Re-imp)		83.65 80	86.81 65	79.96 154	90.46 83	92.71 85	84.84 153	82.00 95	80.93 77	62.44 164	76.29 68	82.32 59	65.54 199	92.29 45	91.66 44	94.49 102	91.47 130

ACMH	78.52 14	81.86 100	80.45 115	83.51 96	90.51 82	91.22 99	85.62 142	84.23 59	70.29 162	62.21 165	69.04 112	64.05 131	84.48 64	87.57 114	88.45 104	94.01 123	92.54 100
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test-mter		77.79 164	80.02 120	75.18 191	81.18 201	82.85 201	80.52 188	62.03 222	73.62 137	62.16 166	73.55 82	73.83 93	73.81 162	84.67 172	83.34 183	91.37 177	88.31 159

tfpn1000		81.03 112	81.70 90	80.25 152	90.18 93	91.35 96	83.96 160	81.15 104	78.00 98	62.11 167	73.37 87	65.75 120	69.17 185	88.68 104	87.44 116	94.93 69	87.29 171

TinyColmap		76.73 173	73.95 199	79.96 154	85.16 141	85.64 186	82.34 171	78.19 141	70.63 158	62.06 168	60.69 170	49.61 216	80.81 82	85.12 168	83.69 182	91.22 181	82.27 200

PM-MVS		74.17 200	73.10 200	75.41 189	76.07 216	82.53 204	77.56 201	71.69 187	71.04 154	61.92 169	61.23 159	47.30 219	74.82 155	81.78 194	79.80 196	90.42 186	88.05 163

pmmvs6		74.83 196	72.89 202	77.09 175	82.11 193	87.50 162	80.88 186	76.97 152	52.79 222	61.91 170	46.66 215	60.49 169	69.28 184	86.74 129	85.46 168	91.39 176	90.56 147

v1921920		78.57 159	76.99 173	80.41 151	82.93 183	89.63 136	86.38 133	77.14 151	68.31 180	61.80 171	58.89 189	56.79 195	80.19 101	86.50 135	86.05 158	94.02 122	92.76 88

V4		76.55 177	75.89 185	77.32 172	79.95 205	88.50 153	81.07 183	73.62 181	65.47 197	61.71 172	56.31 197	58.87 189	74.28 161	83.48 182	85.62 165	93.28 153	92.98 79

tfpnnormal		77.46 169	74.86 196	80.49 147	86.34 127	88.92 149	84.33 158	81.26 102	61.39 208	61.70 173	51.99 209	53.66 209	74.84 154	88.63 105	87.38 119	94.50 101	92.08 110

v52		76.55 177	75.89 185	77.31 173	79.94 206	88.49 154	81.07 183	73.62 181	65.49 196	61.66 174	56.29 198	58.90 187	74.30 160	83.47 183	85.62 165	93.28 153	92.99 78

FMVSNet5		75.50 194	76.07 180	74.83 193	76.16 215	81.19 209	81.34 178	70.21 194	73.20 140	61.59 175	58.97 187	68.33 115	68.50 187	85.87 142	85.85 160	91.18 182	79.11 211

thresconf0.02		81.14 111	80.93 107	81.39 126	90.01 99	91.31 97	86.79 126	82.28 92	76.97 103	61.46 176	74.24 77	62.08 155	72.98 169	88.70 103	87.90 109	94.81 78	85.28 188

pmmvs5		76.93 172	76.33 178	77.62 169	81.97 194	88.40 156	81.32 179	74.35 178	65.42 198	61.42 177	63.07 147	57.95 191	73.23 167	85.60 148	85.35 169	93.41 152	88.55 157

NR-MVSNet		80.25 123	79.98 121	80.56 145	85.20 139	90.94 104	85.65 141	83.58 67	75.74 110	61.36 178	65.30 133	56.75 196	72.38 170	88.46 107	88.80 99	95.16 59	93.87 68

IterMVS		78.79 155	79.71 126	77.71 168	85.26 138	85.91 182	84.54 156	69.84 197	73.38 138	61.25 179	70.53 100	70.35 105	74.43 158	85.21 167	83.80 181	90.95 183	88.77 156
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v7n		77.22 170	76.23 179	78.38 166	81.89 195	89.10 147	82.24 174	76.36 156	65.96 192	61.21 180	56.56 195	55.79 201	75.07 153	86.55 131	86.68 128	93.52 150	92.95 81

v1240		78.15 161	76.53 176	80.04 153	82.85 187	89.48 139	85.61 143	76.77 154	67.05 184	61.18 181	58.37 191	56.16 200	79.89 108	86.11 139	86.08 156	93.92 129	92.47 106

TAMVS		76.42 181	77.16 171	75.56 188	83.05 180	85.55 187	80.58 187	71.43 188	65.40 199	61.04 182	67.27 120	69.22 111	67.99 189	84.88 171	84.78 174	89.28 195	83.01 199

Anonymous20240521		77.55 167	78.71 134	76.20 185	82.88 185	89.76 131	77.36 202	79.11 132	68.90 176	60.75 183	61.50 152	62.59 143	63.06 202	86.52 133	87.81 114	94.06 119	93.89 67

TranMVSNet+NR-MVSNet		80.52 119	79.84 123	81.33 129	84.92 146	90.39 111	85.53 144	84.22 60	74.27 126	60.68 184	64.93 140	59.96 175	77.48 140	86.75 128	89.28 88	95.12 63	93.29 73

RPMNet		77.07 171	77.63 161	76.42 181	85.56 134	85.15 190	81.37 177	65.27 214	74.71 121	60.29 185	63.71 146	66.59 119	73.64 163	82.71 188	82.12 191	92.38 166	88.39 158

EPNet_dtu		81.98 98	83.82 79	79.83 156	94.10 45	85.97 181	87.29 103	84.08 61	80.61 80	59.96 186	81.62 48	77.19 82	62.91 204	87.21 116	86.38 137	90.66 185	87.77 167
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ADS-MVSNet		74.53 198	75.69 189	73.17 201	81.57 199	80.71 211	79.27 193	63.03 220	79.27 90	59.94 187	67.86 117	68.32 116	71.08 178	77.33 209	76.83 210	84.12 220	79.53 208

EG-PatchMatch MVS		76.40 183	75.47 191	77.48 170	85.86 130	90.22 119	82.45 169	73.96 180	59.64 213	59.60 188	52.75 207	62.20 152	68.44 188	88.23 109	87.50 115	94.55 99	87.78 166

Fast-Effi-MVS+-dtu		79.95 128	80.69 113	79.08 159	86.36 126	89.14 145	85.85 137	72.28 185	72.85 144	59.32 189	70.43 101	68.42 114	77.57 139	86.14 138	86.44 136	93.11 158	91.39 131

tfpn_n400		80.63 117	80.79 111	80.43 149	90.02 97	91.08 100	85.34 148	81.79 98	72.93 142	59.27 190	73.54 83	64.40 127	71.61 175	89.05 99	88.21 105	94.56 97	86.32 180

tfpnconf		80.63 117	80.79 111	80.43 149	90.02 97	91.08 100	85.34 148	81.79 98	72.93 142	59.27 190	73.54 83	64.40 127	71.61 175	89.05 99	88.21 105	94.56 97	86.32 180

tfpnview11		80.84 115	81.10 100	80.54 146	90.10 95	90.96 102	85.44 146	81.84 96	75.77 109	59.27 190	73.54 83	64.40 127	71.69 174	89.16 97	87.97 108	94.91 70	85.92 185

MDTV_nov1_ep13_2view		73.21 202	72.91 201	73.56 200	80.01 203	84.28 196	78.62 196	66.43 209	68.64 178	59.12 193	60.39 175	59.69 179	69.81 183	78.82 207	77.43 209	87.36 203	81.11 206

MIMVSNet		74.69 197	75.60 190	73.62 199	76.02 217	85.31 189	81.21 182	67.43 203	71.02 155	59.07 194	54.48 200	64.07 130	66.14 198	86.52 133	86.64 129	91.83 172	81.17 205

TransMVSNet (Re)		76.57 176	75.16 194	78.22 167	85.60 133	87.24 164	82.46 168	81.23 103	59.80 212	59.05 195	57.07 194	59.14 185	66.60 197	88.09 110	86.82 124	94.37 109	87.95 165

anonymousdsp		77.94 163	79.00 131	76.71 179	79.03 207	87.83 160	79.58 190	72.87 184	65.80 193	58.86 196	65.82 126	62.48 147	75.99 147	86.77 127	88.66 100	93.92 129	95.68 45

GA-MVS		79.52 142	79.71 126	79.30 158	85.68 132	90.36 115	84.55 155	78.44 138	70.47 161	57.87 197	68.52 114	61.38 165	76.21 146	89.40 96	87.89 110	93.04 160	89.96 150

SixPastTwentyTwo		76.02 188	75.72 188	76.36 182	83.38 175	87.54 161	75.50 206	76.22 162	65.50 195	57.05 198	70.64 98	53.97 208	74.54 156	80.96 196	82.12 191	91.44 175	89.35 153

LP		68.35 208	67.23 209	69.67 207	77.49 212	79.38 214	72.84 212	61.37 223	66.94 185	55.08 199	47.00 214	50.35 214	65.16 200	75.61 214	76.03 211	86.08 212	75.28 217

PMVS	50.48 18	55.81 225	51.93 226	60.33 221	72.90 222	49.34 235	48.78 232	69.51 198	43.49 229	54.25 200	36.26 229	41.04 226	39.71 229	65.07 228	60.70 229	76.85 227	67.58 226
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
CP-MVSNet		76.36 184	76.41 177	76.32 183	82.73 189	88.64 150	79.39 191	79.62 124	67.21 183	53.70 201	60.72 168	55.22 204	67.91 191	83.52 181	86.34 138	94.55 99	93.19 75

FPMVS		63.63 216	60.08 222	67.78 210	80.01 203	71.50 224	72.88 211	69.41 199	61.82 207	53.11 202	45.12 218	42.11 223	50.86 216	66.69 226	63.84 228	80.41 224	69.46 225

PS-CasMVS		75.90 190	75.86 187	75.96 186	82.59 190	88.46 155	79.23 194	79.56 126	66.00 191	52.77 203	59.48 184	54.35 207	67.14 194	83.37 184	86.23 139	94.47 103	93.10 77

WR-MVS_H		75.84 191	76.93 174	74.57 197	82.86 186	89.50 138	78.34 198	79.36 129	66.90 186	52.51 204	60.20 179	59.71 177	59.73 206	83.61 180	85.77 161	94.65 90	92.84 84

WR-MVS		76.63 175	78.02 153	75.02 192	84.14 153	89.76 131	78.34 198	80.64 106	69.56 172	52.32 205	61.26 154	61.24 166	60.66 205	84.45 175	87.07 122	93.99 124	92.77 87

ambc			61.92 218		70.98 227	73.54 221	63.64 223		60.06 211	52.23 206	38.44 223	19.17 238	57.12 207	82.33 193	75.03 217	83.21 221	84.89 191

PEN-MVS		76.02 188	76.07 180	75.95 187	83.17 179	87.97 159	79.65 189	80.07 115	66.57 188	51.45 207	60.94 163	55.47 203	66.81 195	82.72 187	86.80 125	94.59 94	92.03 113

tmp_tt				32.73 233	43.96 238	21.15 241	26.71 238	8.99 236	65.67 194	51.39 208	56.01 199	42.64 222	11.76 236	56.60 230	50.81 232	53.55 235

CVMVSNet		76.70 174	78.46 138	74.64 196	83.34 176	84.48 194	81.83 176	74.58 175	68.88 177	51.23 209	69.77 102	70.05 106	67.49 192	84.27 176	83.81 180	89.38 194	87.96 164

test0.0.03 1		76.03 187	78.51 135	73.12 202	87.47 117	85.13 192	76.32 204	78.05 143	73.19 141	50.98 210	70.64 98	69.28 110	55.53 208	85.33 163	84.38 178	90.39 187	81.63 203

Anonymous20231206		70.80 204	70.59 207	71.04 204	81.60 197	82.49 205	74.64 208	75.87 171	64.17 201	49.27 211	44.85 219	53.59 210	54.68 211	83.07 185	82.34 190	90.17 188	83.65 196

testpf		63.91 214	65.23 213	62.38 218	81.32 200	69.95 226	62.71 225	54.16 230	61.29 209	48.73 212	57.31 193	52.50 211	50.97 215	67.50 225	68.86 226	76.36 228	79.21 210

DTE-MVSNet		75.14 195	75.44 192	74.80 194	83.18 178	87.19 165	78.25 200	80.11 114	66.05 190	48.31 213	60.88 167	54.67 205	64.54 201	82.57 189	86.17 151	94.43 106	90.53 148

MDA-MVSNet-bldmvs		66.22 211	64.49 214	68.24 209	61.67 230	82.11 208	70.07 215	76.16 163	59.14 214	47.94 214	54.35 202	35.82 231	67.33 193	64.94 229	75.68 213	86.30 210	79.36 209

N_pmnet		66.85 210	66.63 210	67.11 212	78.73 208	74.66 220	70.53 214	71.07 189	66.46 189	46.54 215	51.68 210	51.91 213	55.48 209	74.68 217	72.38 221	80.29 225	74.65 218

testgi		71.92 203	74.20 198	69.27 208	84.58 148	83.06 198	73.40 209	74.39 177	64.04 202	46.17 216	68.90 113	57.15 193	48.89 219	84.07 178	83.08 185	88.18 199	79.09 212

LTVRE_ROB	74.41 16	75.78 192	74.72 197	77.02 177	85.88 129	89.22 142	82.44 170	77.17 150	50.57 224	45.45 217	65.44 131	52.29 212	81.25 76	85.50 154	87.42 118	89.94 191	92.62 91
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
pmmvs3		61.89 219	61.74 219	62.06 219	64.30 228	70.83 225	64.22 221	52.14 232	48.78 226	44.47 218	41.67 222	41.70 224	63.03 203	76.06 213	76.02 212	84.18 219	77.14 215

new-patchmatchnet		63.80 215	63.31 217	64.37 215	76.49 214	75.99 218	63.73 222	70.99 190	57.27 216	43.08 219	45.86 217	43.80 220	45.13 224	73.20 220	70.68 225	86.80 208	76.34 216

EU-MVSNet		69.98 206	72.30 203	67.28 211	75.67 218	79.39 213	73.12 210	69.94 196	63.59 203	42.80 220	62.93 148	56.71 197	55.07 210	79.13 206	78.55 202	87.06 207	85.82 187

MIMVSNet1		65.00 212	66.24 212	63.55 217	58.41 234	80.01 212	69.00 216	74.03 179	55.81 220	41.88 221	36.81 228	49.48 217	47.89 220	81.32 195	82.40 189	90.08 190	77.88 213

gm-plane-assit		70.29 205	70.65 206	69.88 206	85.03 142	78.50 216	58.41 227	65.47 213	50.39 225	40.88 222	49.60 211	50.11 215	75.14 152	91.43 55	89.78 76	94.32 110	84.73 194

test20.03		68.31 209	70.05 208	66.28 213	82.41 191	80.84 210	67.35 218	76.11 165	58.44 215	40.80 223	53.77 204	54.54 206	42.28 225	83.07 185	81.96 194	88.73 198	77.76 214

test2356		63.96 213	64.10 216	63.78 216	74.71 219	71.55 223	65.83 220	67.38 204	57.11 217	40.41 224	53.58 205	41.13 225	49.35 218	77.00 211	77.57 208	85.01 216	70.79 221

1111		57.32 222	57.20 223	57.46 222	71.89 225	67.50 230	52.34 228	58.78 225	46.57 227	39.69 225	37.38 226	38.78 227	46.37 221	74.15 218	74.36 220	75.70 229	61.66 228

.test1245		41.43 230	38.48 231	44.88 228	71.89 225	67.50 230	52.34 228	58.78 225	46.57 227	39.69 225	37.38 226	38.78 227	46.37 221	74.15 218	1.18 235	0.20 239	3.76 237

Anonymous20231211		62.95 218	60.42 221	65.89 214	74.22 220	78.37 217	67.66 217	74.47 176	40.37 232	39.59 227	27.51 231	38.26 230	52.13 213	75.39 216	77.89 206	87.28 205	85.16 189

testus		63.31 217	64.48 215	61.94 220	73.99 221	71.99 222	63.56 224	63.25 219	57.01 218	39.41 228	54.38 201	38.73 229	46.24 223	77.01 210	77.93 205	85.20 215	74.29 219

FC-MVSNet-test		76.53 180	81.62 92	70.58 205	84.99 143	85.73 184	74.81 207	78.85 134	77.00 102	39.13 229	75.90 69	73.50 95	54.08 212	86.54 132	85.99 159	91.65 173	86.68 177

gg-mvs-nofinetune		75.64 193	77.26 168	73.76 198	87.92 110	92.20 88	87.32 102	64.67 217	51.92 223	35.35 230	46.44 216	77.05 83	71.97 171	92.64 43	91.02 51	95.34 51	89.53 152

new_pmnet		59.28 220	61.47 220	56.73 225	61.66 231	68.29 227	59.57 226	54.91 228	60.83 210	34.38 231	44.66 221	43.65 221	49.90 217	71.66 223	71.56 224	79.94 226	69.67 224

Gipuma		49.17 227	47.05 228	51.65 226	59.67 233	48.39 236	41.98 235	63.47 218	55.64 221	33.33 232	14.90 234	13.78 239	41.34 226	69.31 224	72.30 222	70.11 232	55.00 232
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testmv		56.62 223	56.41 224	56.86 223	71.92 223	67.58 228	52.17 230	65.69 210	40.60 230	28.53 233	37.90 224	31.52 232	40.10 227	72.64 221	74.73 218	82.78 222	69.91 222

test1235678		56.61 224	56.40 225	56.86 223	71.92 223	67.58 228	52.17 230	65.69 210	40.58 231	28.52 234	37.89 225	31.49 233	40.10 227	72.64 221	74.72 219	82.78 222	69.90 223

DeepMVS_CX						48.31 237	48.03 233	26.08 235	56.42 219	25.77 235	47.51 213	31.31 234	51.30 214	48.49 232		53.61 234	61.52 229

test12356		50.02 226	51.22 227	48.61 227	63.00 229	60.15 233	47.60 234	56.49 227	38.02 233	24.74 236	36.14 230	25.93 235	24.79 232	66.19 227	71.68 223	75.07 230	60.44 230

MVE	30.17 19	30.88 232	33.52 232	27.80 235	23.78 239	39.16 238	18.69 241	46.90 234	21.88 238	15.39 237	14.37 236	7.31 242	24.41 233	41.63 234	56.22 231	37.64 238	54.07 233
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
no-one		44.14 228	43.91 230	44.40 229	59.91 232	61.10 232	34.07 237	60.09 224	27.71 235	14.44 238	19.11 233	19.28 237	23.90 234	47.36 233	66.69 227	73.98 231	66.11 227

E-PMN		31.40 231	26.80 233	36.78 231	51.39 236	29.96 239	20.20 239	54.17 229	25.93 237	12.75 239	14.73 235	8.58 241	34.10 231	27.36 235	37.83 233	48.07 236	43.18 234

EMVS		30.49 233	25.44 234	36.39 232	51.47 235	29.89 240	20.17 240	54.00 231	26.49 236	12.02 240	13.94 237	8.84 240	34.37 230	25.04 236	34.37 234	46.29 237	39.53 235

PMMVS2		41.68 229	44.74 229	38.10 230	46.97 237	52.32 234	40.63 236	48.08 233	35.51 234	7.36 241	26.86 232	24.64 236	16.72 235	55.24 231	59.03 230	68.85 233	59.59 231

GG-mvs-BLEND		57.56 221	82.61 85	28.34 234	0.22 240	90.10 122	79.37 192	0.14 238	79.56 87	0.40 242	71.25 97	83.40 54	0.30 239	86.27 137	83.87 179	89.59 193	83.83 195

testmvs		1.03 234	1.63 235	0.34 236	0.09 241	0.35 242	0.61 243	0.16 237	1.49 239	0.10 243	3.15 238	0.15 243	0.86 238	1.32 237	1.18 235	0.20 239	3.76 237

test123		0.87 235	1.40 236	0.25 237	0.03 242	0.25 243	0.35 244	0.08 239	1.21 240	0.05 244	2.84 239	0.03 244	0.89 237	0.43 238	1.16 237	0.13 241	3.87 236

sosnet-low-res		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

sosnet		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

our_test_3					81.81 196	83.96 197	76.61 203

Patchmatch-RL test							8.55 242

mPP-MVS					97.06 13							88.08 39

NP-MVS									87.47 48

High-resolution multi-view benchmark