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.

HPM-MVS++		87.09 5	88.92 9	84.95 4	92.61 1	87.91 36	90.23 11	76.06 3	88.85 9	81.20 5	87.33 10	87.93 9	79.47 7	88.59 7	88.23 5	90.15 30	93.60 16

v1.0		81.11 37	77.43 64	85.41 2	91.73 2	92.08 2	91.91 3	76.73 1	90.14 5	80.33 10	92.75 2	90.44 2	80.73 4	88.97 5	87.63 9	91.01 6	0.00 245

SMA-MVS		87.56 3	90.17 4	84.52 5	91.71 3	90.57 6	90.77 5	75.19 10	90.67 3	80.50 9	86.59 14	88.86 5	78.09 13	89.92 1	89.41 1	90.84 8	95.19 2

NCCC		85.34 16	86.59 21	83.88 13	91.48 4	88.88 22	89.79 13	75.54 8	86.67 18	77.94 20	76.55 32	84.99 21	78.07 14	88.04 10	87.68 8	90.46 22	93.31 17

CNVR-MVS		86.36 10	88.19 13	84.23 8	91.33 5	89.84 11	90.34 8	75.56 7	87.36 15	78.97 14	81.19 25	86.76 13	78.74 8	89.30 3	88.58 2	90.45 23	94.33 6

APDe-MVS		88.00 2	90.50 2	85.08 3	90.95 6	91.58 5	92.03 1	75.53 9	91.15 1	80.10 11	92.27 4	88.34 8	80.80 3	88.00 12	86.99 16	91.09 4	95.16 3

ESAPD		88.63 1	91.29 1	85.53 1	90.87 7	92.20 1	91.98 2	76.00 4	90.55 4	82.09 4	93.85 1	90.75 1	81.25 1	88.62 6	87.59 11	90.96 7	95.48 1

HFP-MVS		86.15 11	87.95 14	84.06 11	90.80 8	89.20 20	89.62 16	74.26 13	87.52 12	80.63 7	86.82 13	84.19 25	78.22 11	87.58 16	87.19 14	90.81 9	93.13 20

SteuartSystems-ACMMP		85.99 12	88.31 12	83.27 18	90.73 9	89.84 11	90.27 10	74.31 12	84.56 27	75.88 26	87.32 11	85.04 20	77.31 21	89.01 4	88.46 3	91.14 3	93.96 8
Skip Steuart: Steuart Systems R&D Blog.
APD-MVS		86.84 8	88.91 10	84.41 6	90.66 10	90.10 9	90.78 4	75.64 6	87.38 14	78.72 15	90.68 7	86.82 12	80.15 5	87.13 22	86.45 25	90.51 17	93.83 10
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MP-MVS		85.50 15	87.40 17	83.28 17	90.65 11	89.51 16	89.16 20	74.11 16	83.70 30	78.06 19	85.54 17	84.89 23	77.31 21	87.40 19	87.14 15	90.41 24	93.65 15
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
train_agg		84.86 21	87.21 18	82.11 24	90.59 12	85.47 53	89.81 12	73.55 22	83.95 29	73.30 34	89.84 9	87.23 11	75.61 29	86.47 31	85.46 35	89.78 33	92.06 28

MCST-MVS		85.13 19	86.62 20	83.39 15	90.55 13	89.82 13	89.29 18	73.89 20	84.38 28	76.03 25	79.01 28	85.90 17	78.47 9	87.81 14	86.11 30	92.11 1	93.29 18

zzz-MVS		85.71 13	86.88 19	84.34 7	90.54 14	87.11 40	89.77 14	74.17 15	88.54 10	83.08 2	78.60 29	86.10 15	78.11 12	87.80 15	87.46 12	90.35 26	92.56 22

DeepC-MVS_fast	78.24 3	84.27 25	85.50 27	82.85 20	90.46 15	89.24 18	87.83 29	74.24 14	84.88 23	76.23 24	75.26 35	81.05 38	77.62 18	88.02 11	87.62 10	90.69 13	92.41 24
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP_Plus		86.52 9	89.01 7	83.62 14	90.28 16	90.09 10	90.32 9	74.05 17	88.32 11	79.74 12	87.04 12	85.59 19	76.97 26	89.35 2	88.44 4	90.35 26	94.27 7

SD-MVS		86.96 6	89.45 5	84.05 12	90.13 17	89.23 19	89.77 14	74.59 11	89.17 7	80.70 6	89.93 8	89.67 3	78.47 9	87.57 17	86.79 19	90.67 14	93.76 12

ACMMPR		85.52 14	87.53 16	83.17 19	90.13 17	89.27 17	89.30 17	73.97 18	86.89 17	77.14 22	86.09 15	83.18 28	77.74 17	87.42 18	87.20 13	90.77 10	92.63 21

PGM-MVS		84.42 24	86.29 24	82.23 23	90.04 19	88.82 24	89.23 19	71.74 31	82.82 33	74.61 29	84.41 20	82.09 30	77.03 25	87.13 22	86.73 21	90.73 12	92.06 28

HSP-MVS		87.45 4	90.22 3	84.22 9	90.00 20	91.80 4	90.59 6	75.80 5	89.93 6	78.35 17	92.54 3	89.18 4	80.89 2	87.99 13	86.29 27	89.70 37	93.85 9

CSCG		85.28 18	87.68 15	82.49 22	89.95 21	91.99 3	88.82 21	71.20 33	86.41 19	79.63 13	79.26 26	88.36 7	73.94 36	86.64 29	86.67 22	91.40 2	94.41 4

mPP-MVS					89.90 22							81.29 37

TSAR-MVS + MP.		86.88 7	89.23 6	84.14 10	89.78 23	88.67 28	90.59 6	73.46 23	88.99 8	80.52 8	91.26 5	88.65 6	79.91 6	86.96 27	86.22 28	90.59 15	93.83 10

X-MVS		83.23 28	85.20 29	80.92 30	89.71 24	88.68 25	88.21 28	73.60 21	82.57 34	71.81 43	77.07 30	81.92 32	71.72 53	86.98 26	86.86 17	90.47 19	92.36 25

TSAR-MVS + ACMM		85.10 20	88.81 11	80.77 31	89.55 25	88.53 30	88.59 24	72.55 26	87.39 13	71.90 40	90.95 6	87.55 10	74.57 31	87.08 24	86.54 23	87.47 76	93.67 13

CP-MVS		84.74 23	86.43 23	82.77 21	89.48 26	88.13 35	88.64 22	73.93 19	84.92 22	76.77 23	81.94 23	83.50 26	77.29 23	86.92 28	86.49 24	90.49 18	93.14 19

CDPH-MVS		82.64 29	85.03 30	79.86 35	89.41 27	88.31 32	88.32 26	71.84 30	80.11 41	67.47 59	82.09 22	81.44 36	71.85 51	85.89 36	86.15 29	90.24 28	91.25 34

DeepC-MVS	78.47 2	84.81 22	86.03 25	83.37 16	89.29 28	90.38 8	88.61 23	76.50 2	86.25 20	77.22 21	75.12 36	80.28 40	77.59 19	88.39 8	88.17 6	91.02 5	93.66 14
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		79.74 43	78.62 56	81.05 29	89.23 29	86.06 50	84.95 46	71.96 29	79.39 44	75.51 27	63.16 80	68.84 86	76.51 27	83.55 51	82.85 50	88.13 62	86.46 68

EPNet		79.08 51	80.62 45	77.28 50	88.90 30	83.17 70	83.65 52	72.41 27	74.41 56	67.15 61	76.78 31	74.37 57	64.43 105	83.70 50	83.69 46	87.15 82	88.19 53
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	79.04 1	85.30 17	88.93 8	81.06 28	88.77 31	90.48 7	85.46 43	73.08 24	90.97 2	73.77 33	84.81 19	85.95 16	77.43 20	88.22 9	87.73 7	87.85 70	94.34 5

ACMMP		83.42 27	85.27 28	81.26 27	88.47 32	88.49 31	88.31 27	72.09 28	83.42 31	72.77 37	82.65 21	78.22 44	75.18 30	86.24 34	85.76 32	90.74 11	92.13 27
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+	75.73 4	82.40 30	82.76 35	81.97 25	88.02 33	89.67 14	86.60 33	71.48 32	81.28 39	78.18 18	64.78 75	77.96 46	77.13 24	87.32 20	86.83 18	90.41 24	91.48 32

OPM-MVS		79.68 44	79.28 53	80.15 34	87.99 34	86.77 44	88.52 25	72.72 25	64.55 87	67.65 58	67.87 65	74.33 58	74.31 34	86.37 33	85.25 37	89.73 36	89.81 45

MAR-MVS		79.21 48	80.32 49	77.92 47	87.46 35	88.15 34	83.95 51	67.48 57	74.28 57	68.25 55	64.70 76	77.04 47	72.17 47	85.42 38	85.00 39	88.22 59	87.62 60
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
HQP-MVS		81.19 36	83.27 33	78.76 42	87.40 36	85.45 54	86.95 31	70.47 36	81.31 38	66.91 62	79.24 27	76.63 49	71.67 54	84.43 44	83.78 45	89.19 46	92.05 30

abl_6				79.05 39	87.27 37	88.85 23	83.62 53	68.25 50	81.68 37	72.94 36	73.79 42	84.45 24	72.55 45			89.66 39	90.64 39

CANet		81.62 35	83.41 32	79.53 37	87.06 38	88.59 29	85.47 42	67.96 54	76.59 50	74.05 30	74.69 37	81.98 31	72.98 43	86.14 35	85.47 34	89.68 38	90.42 42

MSLP-MVS++		82.09 32	82.66 36	81.42 26	87.03 39	87.22 39	85.82 38	70.04 38	80.30 40	78.66 16	68.67 61	81.04 39	77.81 16	85.19 40	84.88 40	89.19 46	91.31 33

ACMM	72.26 8	78.86 52	78.13 57	79.71 36	86.89 40	83.40 67	86.02 36	70.50 35	75.28 53	71.49 47	63.01 81	69.26 80	73.57 38	84.11 46	83.98 44	89.76 35	87.84 57
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVS					86.63 41	88.68 25	85.00 44			71.81 43		81.92 32				90.47 19

X-MVStestdata					86.63 41	88.68 25	85.00 44			71.81 43		81.92 32				90.47 19

PHI-MVS		82.36 31	85.89 26	78.24 45	86.40 43	89.52 15	85.52 41	69.52 44	82.38 36	65.67 64	81.35 24	82.36 29	73.07 42	87.31 21	86.76 20	89.24 44	91.56 31

LGP-MVS_train		79.83 40	81.22 42	78.22 46	86.28 44	85.36 56	86.76 32	69.59 42	77.34 47	65.14 66	75.68 34	70.79 70	71.37 56	84.60 42	84.01 43	90.18 29	90.74 38

MVS_0304		81.73 34	83.86 31	79.26 38	86.22 45	89.18 21	86.41 34	67.15 58	75.28 53	70.75 50	74.59 38	83.49 27	74.42 33	87.05 25	86.34 26	90.58 16	91.08 36

CPTT-MVS		81.77 33	83.10 34	80.21 33	85.93 46	86.45 47	87.72 30	70.98 34	82.54 35	71.53 46	74.23 41	81.49 35	76.31 28	82.85 58	81.87 54	88.79 53	92.26 26

MVS_111021_HR		80.13 39	81.46 40	78.58 43	85.77 47	85.17 57	83.45 54	69.28 45	74.08 59	70.31 51	74.31 40	75.26 54	73.13 41	86.46 32	85.15 38	89.53 40	89.81 45

ACMP	73.23 7	79.79 41	80.53 46	78.94 40	85.61 48	85.68 51	85.61 40	69.59 42	77.33 48	71.00 49	74.45 39	69.16 81	71.88 49	83.15 55	83.37 48	89.92 32	90.57 41
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UA-Net		74.47 68	77.80 59	70.59 90	85.33 49	85.40 55	73.54 147	65.98 66	60.65 114	56.00 114	72.11 46	79.15 41	54.63 174	83.13 56	82.25 52	88.04 64	81.92 130

TSAR-MVS + GP.		83.69 26	86.58 22	80.32 32	85.14 50	86.96 42	84.91 47	70.25 37	84.71 26	73.91 32	85.16 18	85.63 18	77.92 15	85.44 37	85.71 33	89.77 34	92.45 23

LS3D		74.08 70	73.39 80	74.88 62	85.05 51	82.62 74	79.71 67	68.66 48	72.82 61	58.80 87	57.61 107	61.31 106	71.07 58	80.32 93	78.87 98	86.00 138	80.18 146

QAPM		78.47 53	80.22 50	76.43 55	85.03 52	86.75 45	80.62 62	66.00 65	73.77 60	65.35 65	65.54 72	78.02 45	72.69 44	83.71 49	83.36 49	88.87 52	90.41 43

OpenMVS	70.44 10	76.15 62	76.82 70	75.37 59	85.01 53	84.79 59	78.99 76	62.07 113	71.27 63	67.88 57	57.91 106	72.36 64	70.15 60	82.23 61	81.41 58	88.12 63	87.78 58

CLD-MVS		79.35 47	81.23 41	77.16 51	85.01 53	86.92 43	85.87 37	60.89 127	80.07 43	75.35 28	72.96 44	73.21 61	68.43 69	85.41 39	84.63 41	87.41 77	85.44 80
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
3Dnovator	73.76 5	79.75 42	80.52 47	78.84 41	84.94 55	87.35 37	84.43 50	65.54 68	78.29 46	73.97 31	63.00 82	75.62 53	74.07 35	85.00 41	85.34 36	90.11 31	89.04 49

PCF-MVS	73.28 6	79.42 46	80.41 48	78.26 44	84.88 56	88.17 33	86.08 35	69.85 39	75.23 55	68.43 54	68.03 64	78.38 43	71.76 52	81.26 73	80.65 74	88.56 58	91.18 35
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
casdiffmvs1		79.56 45	81.02 44	77.86 48	84.19 57	87.00 41	85.73 39	63.24 84	79.22 45	72.05 39	73.55 43	76.93 48	73.25 40	80.92 79	80.20 80	88.69 55	89.31 48

DELS-MVS		79.15 50	81.07 43	76.91 53	83.54 58	87.31 38	84.45 48	64.92 73	69.98 64	69.34 53	71.62 49	76.26 50	69.84 61	86.57 30	85.90 31	89.39 42	89.88 44
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		80.26 38	82.59 37	77.54 49	83.04 59	85.54 52	83.25 55	65.05 72	87.32 16	72.42 38	72.04 47	78.97 42	73.30 39	83.86 47	81.60 57	88.15 61	88.83 51

PLC	68.99 11	75.68 64	75.31 75	76.12 57	82.94 60	81.26 84	79.94 65	66.10 63	77.15 49	66.86 63	59.13 95	68.53 87	73.73 37	80.38 89	79.04 95	87.13 86	81.68 132
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
casdiffmvs		77.90 56	78.63 55	77.06 52	82.85 61	86.44 48	84.45 48	64.35 77	71.84 62	69.93 52	70.80 52	72.99 62	72.00 48	80.84 81	79.80 85	88.76 54	87.71 59

CNLPA		77.20 58	77.54 61	76.80 54	82.63 62	84.31 61	79.77 66	64.64 74	85.17 21	73.18 35	56.37 114	69.81 77	74.53 32	81.12 76	78.69 99	86.04 135	87.29 64

ACMH+	66.54 13	71.36 85	70.09 100	72.85 70	82.59 63	81.13 85	78.56 90	68.04 52	61.55 108	52.52 135	51.50 182	54.14 153	68.56 68	78.85 116	79.50 92	86.82 103	83.94 100

ACMH	65.37 14	70.71 89	70.00 101	71.54 75	82.51 64	82.47 75	77.78 101	68.13 51	56.19 163	46.06 171	54.30 142	51.20 190	68.68 67	80.66 83	80.72 67	86.07 131	84.45 96
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
canonicalmvs		79.16 49	82.37 38	75.41 58	82.33 65	86.38 49	80.80 60	63.18 85	82.90 32	67.34 60	72.79 45	76.07 51	69.62 62	83.46 54	84.41 42	89.20 45	90.60 40

MSDG		71.52 83	69.87 105	73.44 68	82.21 66	79.35 108	79.52 68	64.59 75	66.15 75	61.87 75	53.21 163	56.09 139	65.85 102	78.94 115	78.50 101	86.60 117	76.85 174

IS_MVSNet		73.33 73	77.34 66	68.65 118	81.29 67	83.47 66	74.45 127	63.58 82	65.75 79	48.49 154	67.11 69	70.61 72	54.63 174	84.51 43	83.58 47	89.48 41	86.34 69

Effi-MVS+		75.28 66	76.20 72	74.20 66	81.15 68	83.24 68	81.11 58	63.13 87	66.37 73	60.27 81	64.30 78	68.88 85	70.93 59	81.56 65	81.69 56	88.61 56	87.35 62

MVS_111021_LR		78.13 55	79.85 52	76.13 56	81.12 69	81.50 81	80.28 63	65.25 70	76.09 52	71.32 48	76.49 33	72.87 63	72.21 46	82.79 59	81.29 59	86.59 118	87.91 56

FC-MVSNet-train		72.60 78	75.07 76	69.71 108	81.10 70	78.79 119	73.74 145	65.23 71	66.10 76	53.34 128	70.36 54	63.40 101	56.92 154	81.44 66	80.96 63	87.93 66	84.46 95

MS-PatchMatch		70.17 99	70.49 98	69.79 106	80.98 71	77.97 133	77.51 103	58.95 159	62.33 101	55.22 118	53.14 164	65.90 94	62.03 120	79.08 114	77.11 132	84.08 172	77.91 164

Anonymous202405211			72.16 89		80.85 72	81.85 78	76.88 111	65.40 69	62.89 99		46.35 201	67.99 89	62.05 119	81.15 75	80.38 78	85.97 140	84.50 94

TAPA-MVS	71.42 9	77.69 57	80.05 51	74.94 61	80.68 73	84.52 60	81.36 57	63.14 86	84.77 24	64.82 68	68.72 59	75.91 52	71.86 50	81.62 63	79.55 91	87.80 71	85.24 83
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_Blended_VisFu		76.57 59	77.90 58	75.02 60	80.56 74	86.58 46	79.24 71	66.18 62	64.81 84	68.18 56	65.61 70	71.45 66	67.05 72	84.16 45	81.80 55	88.90 50	90.92 37

EPP-MVSNet		74.00 71	77.41 65	70.02 104	80.53 75	83.91 63	74.99 124	62.68 103	65.06 82	49.77 151	68.68 60	72.09 65	63.06 112	82.49 60	80.73 66	89.12 48	88.91 50

COLMAP_ROB	62.73 15	67.66 142	66.76 162	68.70 117	80.49 76	77.98 131	75.29 117	62.95 89	63.62 93	49.96 149	47.32 200	50.72 193	58.57 139	76.87 154	75.50 169	84.94 161	75.33 184
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		73.65 72	75.78 74	71.16 77	80.19 77	79.27 109	77.45 106	61.68 119	66.73 72	58.72 88	65.31 73	69.96 76	62.19 117	81.29 72	80.97 62	86.74 110	86.91 65

Anonymous20231211		71.90 80	72.48 87	71.21 76	80.14 78	81.53 80	76.92 109	62.89 90	64.46 88	58.94 85	43.80 205	70.98 69	62.22 116	80.70 82	80.19 82	86.18 124	85.73 72

TSAR-MVS + COLMAP		78.34 54	81.64 39	74.48 65	80.13 79	85.01 58	81.73 56	65.93 67	84.75 25	61.68 76	85.79 16	66.27 93	71.39 55	82.91 57	80.78 65	86.01 136	85.98 70

EPNet_dtu		68.08 133	71.00 94	64.67 162	79.64 80	68.62 199	75.05 123	63.30 83	66.36 74	45.27 175	67.40 67	66.84 92	43.64 204	75.37 167	74.98 174	81.15 184	77.44 167
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		76.21 60	77.52 62	74.69 63	79.46 81	83.79 64	77.50 104	64.34 78	69.88 65	71.88 41	68.54 62	70.42 73	67.05 72	83.48 52	79.63 87	87.89 68	86.87 66

PVSNet_Blended		76.21 60	77.52 62	74.69 63	79.46 81	83.79 64	77.50 104	64.34 78	69.88 65	71.88 41	68.54 62	70.42 73	67.05 72	83.48 52	79.63 87	87.89 68	86.87 66

IB-MVS	66.94 12	71.21 86	71.66 92	70.68 87	79.18 83	82.83 73	72.61 154	61.77 117	59.66 121	63.44 72	53.26 161	59.65 111	59.16 138	76.78 156	82.11 53	87.90 67	87.33 63
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
MVS_Test		75.37 65	77.13 68	73.31 69	79.07 84	81.32 83	79.98 64	60.12 148	69.72 67	64.11 70	70.53 53	73.22 60	68.90 65	80.14 99	79.48 93	87.67 73	85.50 78

Effi-MVS+-dtu		71.82 81	71.86 91	71.78 73	78.77 85	80.47 99	78.55 91	61.67 120	60.68 113	55.49 115	58.48 99	65.48 95	68.85 66	76.92 153	75.55 168	87.35 78	85.46 79

EG-PatchMatch MVS		67.24 152	66.94 159	67.60 128	78.73 86	81.35 82	73.28 151	59.49 153	46.89 214	51.42 140	43.65 206	53.49 161	55.50 170	81.38 68	80.66 73	87.15 82	81.17 135

gg-mvs-nofinetune		62.55 184	65.05 182	59.62 193	78.72 87	77.61 137	70.83 165	53.63 188	39.71 226	22.04 230	36.36 219	64.32 98	47.53 192	81.16 74	79.03 96	85.00 159	77.17 169

Vis-MVSNet (Re-imp)		67.83 138	73.52 79	61.19 183	78.37 88	76.72 152	66.80 188	62.96 88	65.50 80	34.17 209	67.19 68	69.68 78	39.20 213	79.39 110	79.44 94	85.68 149	76.73 175

DI_MVS_plusplus_trai		75.13 67	76.12 73	73.96 67	78.18 89	81.55 79	80.97 59	62.54 107	68.59 69	65.13 67	61.43 83	74.81 56	69.32 64	81.01 78	79.59 89	87.64 74	85.89 71

thres600view7		67.68 141	68.43 140	66.80 146	77.90 90	78.86 117	73.84 142	62.75 96	56.07 164	44.70 179	52.85 170	52.81 173	55.58 168	80.41 84	77.77 117	86.05 133	80.28 145

thres400		67.95 135	68.62 138	67.17 139	77.90 90	78.59 123	74.27 138	62.72 98	56.34 162	45.77 173	53.00 166	53.35 168	56.46 160	80.21 98	78.43 102	85.91 143	80.43 144

thres200		67.98 134	68.55 139	67.30 137	77.89 92	78.86 117	74.18 140	62.75 96	56.35 161	46.48 169	52.98 167	53.54 159	56.46 160	80.41 84	77.97 114	86.05 133	79.78 151

tpmp4_e23		68.32 129	67.08 158	69.76 107	77.86 93	75.22 173	78.37 96	56.17 184	66.06 77	64.27 69	57.15 111	54.89 147	63.40 110	70.97 196	68.29 205	78.46 195	77.00 173

view600		67.63 145	68.36 141	66.77 147	77.84 94	78.66 121	73.74 145	62.62 105	56.04 165	44.98 176	52.86 169	52.83 172	55.48 171	80.36 90	77.75 118	85.95 142	80.02 148

diffmvs1		76.02 63	78.90 54	72.65 71	77.84 94	82.90 72	80.67 61	60.86 129	76.16 51	63.35 73	71.50 50	74.85 55	68.35 70	80.22 97	78.69 99	84.87 163	88.00 55

tfpn111		68.38 127	69.23 122	67.39 132	77.83 96	78.93 113	74.28 133	62.81 91	56.64 151	46.70 164	56.24 115	53.47 163	56.59 155	80.41 84	78.43 102	86.11 127	80.53 141

conf200view11		68.11 131	68.72 134	67.39 132	77.83 96	78.93 113	74.28 133	62.81 91	56.64 151	46.70 164	52.65 173	53.47 163	56.59 155	80.41 84	78.43 102	86.11 127	80.53 141

thres100view900		67.60 146	68.02 145	67.12 141	77.83 96	77.75 135	73.90 141	62.52 108	56.64 151	46.82 162	52.65 173	53.47 163	55.92 164	78.77 117	77.62 121	85.72 148	79.23 156

tfpn200view9		68.11 131	68.72 134	67.40 131	77.83 96	78.93 113	74.28 133	62.81 91	56.64 151	46.82 162	52.65 173	53.47 163	56.59 155	80.41 84	78.43 102	86.11 127	80.52 143

view800		67.35 151	68.22 144	66.35 151	77.83 96	78.62 122	72.97 153	62.58 106	55.71 167	44.13 180	52.69 172	52.24 182	54.58 176	80.27 95	78.19 110	86.01 136	79.79 150

conf0.01		67.72 140	67.99 146	67.39 132	77.82 101	78.94 111	74.28 133	62.81 91	56.64 151	46.70 164	53.33 159	48.59 203	56.59 155	80.34 91	78.43 102	86.16 126	79.67 152

tfpn		66.58 155	67.18 156	65.88 153	77.82 101	78.45 125	72.07 158	62.52 108	55.35 171	43.21 184	52.54 177	46.12 212	53.68 177	80.02 100	78.23 109	85.99 139	79.55 154

conf0.002		67.52 148	67.64 150	67.39 132	77.80 103	78.94 111	74.28 133	62.81 91	56.64 151	46.70 164	53.65 155	46.28 211	56.59 155	80.33 92	78.37 107	86.17 125	79.23 156

Fast-Effi-MVS+		73.11 75	73.66 78	72.48 72	77.72 104	80.88 90	78.55 91	58.83 167	65.19 81	60.36 80	59.98 90	62.42 104	71.22 57	81.66 62	80.61 76	88.20 60	84.88 91

UniMVSNet_NR-MVSNet		70.59 90	72.19 88	68.72 116	77.72 104	80.72 91	73.81 143	69.65 41	61.99 103	43.23 182	60.54 86	57.50 118	58.57 139	79.56 107	81.07 61	89.34 43	83.97 98

IterMVS-LS		71.69 82	72.82 85	70.37 99	77.54 106	76.34 158	75.13 122	60.46 136	61.53 109	57.57 97	64.89 74	67.33 90	66.04 98	77.09 152	77.37 128	85.48 152	85.18 84
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NR-MVSNet		68.79 124	70.56 97	66.71 150	77.48 107	79.54 105	73.52 148	69.20 46	61.20 111	39.76 193	58.52 97	50.11 196	51.37 185	80.26 96	80.71 71	88.97 49	83.59 106

conf0.05thres1000		66.26 157	66.77 161	65.66 155	77.45 108	78.10 126	71.85 161	62.44 111	51.47 198	43.00 185	47.92 195	51.66 188	53.40 179	79.71 103	77.97 114	85.82 144	80.56 139

TransMVSNet (Re)		64.74 169	65.66 175	63.66 169	77.40 109	75.33 167	69.86 166	62.67 104	47.63 212	41.21 191	50.01 188	52.33 178	45.31 201	79.57 106	77.69 120	85.49 151	77.07 172

TranMVSNet+NR-MVSNet		69.25 119	70.81 96	67.43 130	77.23 110	79.46 107	73.48 149	69.66 40	60.43 116	39.56 194	58.82 96	53.48 162	55.74 167	79.59 105	81.21 60	88.89 51	82.70 120

diffmvs		74.38 69	76.65 71	71.74 74	77.05 111	81.86 77	79.30 70	60.54 133	69.54 68	62.16 74	69.70 56	70.74 71	66.73 80	79.18 113	78.14 112	84.63 167	87.42 61

CANet_DTU		73.29 74	76.96 69	69.00 114	77.04 112	82.06 76	79.49 69	56.30 182	67.85 70	53.29 129	71.12 51	70.37 75	61.81 125	81.59 64	80.96 63	86.09 130	84.73 92

CHOSEN 1792x2688		69.20 120	69.26 121	69.13 112	76.86 113	78.93 113	77.27 107	60.12 148	61.86 105	54.42 119	42.54 209	61.61 105	66.91 78	78.55 119	78.14 112	79.23 193	83.23 111

HyFIR lowres test		69.47 116	68.94 125	70.09 103	76.77 114	82.93 71	76.63 113	60.17 143	59.00 125	54.03 122	40.54 215	65.23 96	67.89 71	76.54 160	78.30 108	85.03 158	80.07 147

UniMVSNet (Re)		69.53 113	71.90 90	66.76 148	76.42 115	80.93 87	72.59 155	68.03 53	61.75 107	41.68 190	58.34 103	57.23 126	53.27 181	79.53 108	80.62 75	88.57 57	84.90 90

thresconf0.02		64.77 168	65.90 171	63.44 171	76.37 116	75.17 176	69.51 169	61.28 121	56.98 142	39.01 196	56.24 115	48.68 202	49.78 188	77.13 150	75.61 166	84.71 166	71.53 200

tfpnview11		64.33 172	66.17 167	62.18 176	76.25 117	75.23 171	67.45 180	61.16 122	55.50 169	36.38 203	55.35 123	51.89 184	46.96 193	77.28 147	76.10 162	84.86 164	71.85 199

tfpn_n400		64.23 174	66.05 168	62.12 178	76.20 118	75.24 168	67.43 181	61.15 123	54.04 185	36.38 203	55.35 123	51.89 184	46.94 194	77.31 145	76.15 160	84.59 168	72.36 196

tfpnconf		64.23 174	66.05 168	62.12 178	76.20 118	75.24 168	67.43 181	61.15 123	54.04 185	36.38 203	55.35 123	51.89 184	46.94 194	77.31 145	76.15 160	84.59 168	72.36 196

DWT-MVSNet_training		67.24 152	65.96 170	68.74 115	76.15 120	74.36 180	74.37 132	56.66 180	61.82 106	60.51 79	58.23 105	49.76 198	65.07 103	70.04 204	70.39 191	79.70 190	77.11 171

gm-plane-assit		57.00 208	57.62 215	56.28 205	76.10 121	62.43 222	47.62 231	46.57 218	33.84 234	23.24 224	37.52 216	40.19 223	59.61 137	79.81 102	77.55 123	84.55 170	72.03 198

DU-MVS		69.63 108	70.91 95	68.13 122	75.99 122	79.54 105	73.81 143	69.20 46	61.20 111	43.23 182	58.52 97	53.50 160	58.57 139	79.22 111	80.45 77	87.97 65	83.97 98

Baseline_NR-MVSNet		67.53 147	68.77 132	66.09 152	75.99 122	74.75 177	72.43 156	68.41 49	61.33 110	38.33 198	51.31 183	54.13 155	56.03 163	79.22 111	78.19 110	85.37 153	82.45 122

CostFormer		68.92 122	69.58 111	68.15 121	75.98 124	76.17 161	78.22 99	51.86 198	65.80 78	61.56 77	63.57 79	62.83 102	61.85 123	70.40 203	68.67 200	79.42 191	79.62 153

tfpnnormal		64.27 173	63.64 192	65.02 158	75.84 125	75.61 164	71.24 164	62.52 108	47.79 211	42.97 186	42.65 208	44.49 216	52.66 183	78.77 117	76.86 135	84.88 162	79.29 155

tfpn1000		63.81 178	66.31 164	60.90 185	75.76 126	75.74 163	65.14 197	60.14 147	56.47 158	35.99 206	55.11 126	52.30 180	43.42 205	76.21 162	75.34 170	84.97 160	73.01 195

tfpn_ndepth		65.09 165	67.12 157	62.73 174	75.75 127	76.23 159	68.00 177	60.36 137	58.16 131	40.27 192	54.89 133	54.22 152	46.80 197	76.69 158	75.66 165	85.19 155	73.98 192

tpm cat1		65.41 160	63.81 191	67.28 138	75.61 128	72.88 183	75.32 116	52.85 192	62.97 97	63.66 71	53.24 162	53.29 170	61.83 124	65.54 214	64.14 217	74.43 211	74.60 186

CDS-MVSNet		67.65 143	69.83 108	65.09 157	75.39 129	76.55 153	74.42 130	63.75 80	53.55 187	49.37 153	59.41 93	62.45 103	44.44 202	79.71 103	79.82 84	83.17 178	77.36 168
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+-dtu		68.34 128	69.47 113	67.01 143	75.15 130	77.97 133	77.12 108	55.40 185	57.87 132	46.68 168	56.17 117	60.39 107	62.36 115	76.32 161	76.25 157	85.35 154	81.34 133

WR-MVS		63.03 180	67.40 154	57.92 199	75.14 131	77.60 138	60.56 212	66.10 63	54.11 184	23.88 221	53.94 153	53.58 158	34.50 217	73.93 174	77.71 119	87.35 78	80.94 136

test-LLR		64.42 170	64.36 187	64.49 163	75.02 132	63.93 212	66.61 190	61.96 114	54.41 180	47.77 158	57.46 108	60.25 108	55.20 172	70.80 197	69.33 195	80.40 188	74.38 188

test0.0.03 1		58.80 204	61.58 205	55.56 207	75.02 132	68.45 200	59.58 216	61.96 114	52.74 189	29.57 213	49.75 191	54.56 150	31.46 220	71.19 191	69.77 193	75.75 204	64.57 214

v1144		69.93 107	69.36 120	70.61 89	74.89 134	80.93 87	79.11 74	60.64 130	55.97 166	55.31 117	53.85 154	54.14 153	66.54 83	78.10 125	77.44 126	87.14 85	85.09 85

v13		69.52 114	68.76 133	70.41 97	74.88 135	77.02 148	78.52 95	58.86 161	56.61 157	56.91 102	54.00 152	56.17 138	66.11 97	77.93 126	76.74 143	87.21 80	82.83 113

v12		69.54 112	68.79 130	70.41 97	74.88 135	77.03 146	78.54 94	58.85 163	56.71 149	56.87 104	54.13 150	56.23 137	66.15 93	77.89 127	76.74 143	87.17 81	82.80 114

v11		69.37 117	68.65 137	70.20 101	74.87 137	76.97 149	78.29 98	58.55 171	56.38 160	56.04 113	54.02 151	54.98 146	66.47 84	78.30 121	76.91 134	86.97 94	83.02 112

V9		69.58 111	68.83 128	70.46 94	74.85 138	77.04 144	78.65 89	58.85 163	56.83 148	57.12 100	54.26 145	56.31 132	66.14 95	77.83 129	76.76 138	87.13 86	82.79 116

V14		69.59 110	68.86 127	70.45 96	74.83 139	77.04 144	78.70 88	58.83 167	56.95 145	57.08 101	54.41 141	56.34 131	66.15 93	77.77 130	76.76 138	87.08 91	82.74 119

v15		69.61 109	68.88 126	70.46 94	74.81 140	77.03 146	78.75 87	58.83 167	57.06 141	57.18 99	54.55 140	56.37 130	66.13 96	77.70 131	76.76 138	87.03 93	82.69 121

v7		70.33 96	69.87 105	70.88 78	74.79 141	81.04 86	79.22 72	60.57 132	57.70 138	56.65 110	54.23 147	55.29 144	66.95 75	78.28 122	77.47 124	87.12 89	85.05 87

v10		70.22 98	69.76 109	70.74 84	74.79 141	80.30 102	79.22 72	59.81 151	57.71 137	56.58 111	54.22 149	55.31 142	66.95 75	78.28 122	77.47 124	87.12 89	85.07 86

v1141		69.96 106	69.44 117	70.58 92	74.78 143	80.50 97	78.85 77	60.30 138	56.95 145	56.74 107	54.68 138	56.26 136	65.93 99	77.38 142	76.72 148	86.88 99	83.57 109

divwei89l23v2f112		69.97 104	69.44 117	70.58 92	74.78 143	80.50 97	78.85 77	60.30 138	56.97 144	56.75 106	54.67 139	56.27 135	65.92 100	77.37 143	76.72 148	86.88 99	83.58 108

v1		69.97 104	69.45 116	70.59 90	74.78 143	80.51 96	78.84 79	60.30 138	56.98 142	56.81 105	54.69 137	56.29 134	65.91 101	77.37 143	76.71 151	86.89 98	83.59 106

v17		70.03 103	69.43 119	70.72 86	74.75 146	77.09 141	78.78 86	58.85 163	59.53 123	58.72 88	54.87 134	57.39 120	66.38 86	77.60 135	76.75 141	86.83 102	82.80 114

v16		70.07 101	69.46 114	70.79 82	74.74 147	77.08 142	78.79 84	58.86 161	59.75 120	59.15 84	54.87 134	57.33 121	66.38 86	77.61 134	76.77 136	86.81 108	82.79 116

v8		70.23 97	69.86 107	70.67 88	74.69 148	79.82 104	78.79 84	59.18 156	58.80 127	58.20 92	55.00 129	57.33 121	66.31 92	77.51 139	76.71 151	86.82 103	83.88 101

v1neww		70.34 94	69.93 103	70.82 80	74.68 149	80.61 93	78.80 82	60.17 143	58.74 128	58.10 94	55.00 129	57.28 124	66.33 89	77.53 136	76.74 143	86.82 103	83.61 104

v7new		70.34 94	69.93 103	70.82 80	74.68 149	80.61 93	78.80 82	60.17 143	58.74 128	58.10 94	55.00 129	57.28 124	66.33 89	77.53 136	76.74 143	86.82 103	83.61 104

v6		70.35 93	69.94 102	70.83 79	74.68 149	80.62 92	78.81 81	60.16 146	58.81 126	58.17 93	55.01 128	57.31 123	66.32 91	77.53 136	76.73 147	86.82 103	83.62 103

v18		70.10 100	69.52 112	70.77 83	74.66 152	77.06 143	78.84 79	58.84 166	60.01 119	59.23 83	55.06 127	57.47 119	66.34 88	77.50 140	76.75 141	86.71 111	82.77 118

v2v482		70.05 102	69.46 114	70.74 84	74.62 153	80.32 101	79.00 75	60.62 131	57.41 139	56.89 103	55.43 122	55.14 145	66.39 85	77.25 148	77.14 131	86.90 96	83.57 109

v1192		69.50 115	68.83 128	70.29 100	74.49 154	80.92 89	78.55 91	60.54 133	55.04 175	54.21 120	52.79 171	52.33 178	66.92 77	77.88 128	77.35 129	87.04 92	85.51 77

DTE-MVSNet		61.85 193	64.96 184	58.22 198	74.32 155	74.39 179	61.01 211	67.85 55	51.76 197	21.91 231	53.28 160	48.17 204	37.74 214	72.22 184	76.44 154	86.52 120	78.49 161

Vis-MVSNet		72.77 77	77.20 67	67.59 129	74.19 156	84.01 62	76.61 114	61.69 118	60.62 115	50.61 145	70.25 55	71.31 68	55.57 169	83.85 48	82.28 51	86.90 96	88.08 54
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v144192		69.34 118	68.68 136	70.12 102	74.06 157	80.54 95	78.08 100	60.54 133	54.99 177	54.13 121	52.92 168	52.80 174	66.73 80	77.13 150	76.72 148	87.15 82	85.63 73

v1921920		69.03 121	68.32 142	69.86 105	74.03 158	80.37 100	77.55 102	60.25 142	54.62 179	53.59 127	52.36 178	51.50 189	66.75 79	77.17 149	76.69 153	86.96 95	85.56 74

PEN-MVS		62.96 181	65.77 174	59.70 192	73.98 159	75.45 165	63.39 205	67.61 56	52.49 191	25.49 220	53.39 157	49.12 201	40.85 211	71.94 187	77.26 130	86.86 101	80.72 138

v1240		68.64 126	67.89 149	69.51 110	73.89 160	80.26 103	76.73 112	59.97 150	53.43 188	53.08 130	51.82 181	50.84 192	66.62 82	76.79 155	76.77 136	86.78 109	85.34 81

GA-MVS		68.14 130	69.17 123	66.93 145	73.77 161	78.50 124	74.45 127	58.28 172	55.11 174	48.44 155	60.08 88	53.99 156	61.50 126	78.43 120	77.57 122	85.13 156	80.54 140

tttt0517		71.41 84	72.95 83	69.60 109	73.70 162	78.70 120	74.42 130	59.12 157	63.89 92	58.35 91	64.56 77	58.39 115	64.27 106	80.29 94	80.17 83	87.74 72	84.69 93

pm-mvs1		65.62 159	67.42 153	63.53 170	73.66 163	76.39 157	69.66 167	60.87 128	49.73 206	43.97 181	51.24 184	57.00 128	48.16 191	79.89 101	77.84 116	84.85 165	79.82 149

dps		64.00 177	62.99 194	65.18 156	73.29 164	72.07 186	68.98 174	53.07 191	57.74 136	58.41 90	55.55 120	47.74 207	60.89 131	69.53 206	67.14 209	76.44 203	71.19 202

v148		67.85 137	67.53 151	68.23 120	73.25 165	77.57 139	74.26 139	57.36 177	55.70 168	57.45 98	53.53 156	55.42 141	61.96 121	75.23 168	73.92 177	85.08 157	81.32 134

PatchMatch-RL		67.78 139	66.65 163	69.10 113	73.01 166	72.69 184	68.49 175	61.85 116	62.93 98	60.20 82	56.83 113	50.42 194	69.52 63	75.62 166	74.46 176	81.51 182	73.62 193

GBi-Net		70.78 87	73.37 81	67.76 123	72.95 167	78.00 128	75.15 119	62.72 98	64.13 89	51.44 137	58.37 100	69.02 82	57.59 146	81.33 69	80.72 67	86.70 112	82.02 124

test1		70.78 87	73.37 81	67.76 123	72.95 167	78.00 128	75.15 119	62.72 98	64.13 89	51.44 137	58.37 100	69.02 82	57.59 146	81.33 69	80.72 67	86.70 112	82.02 124

FMVSNet2		70.39 92	72.67 86	67.72 126	72.95 167	78.00 128	75.15 119	62.69 102	63.29 95	51.25 141	55.64 118	68.49 88	57.59 146	80.91 80	80.35 79	86.70 112	82.02 124

FMVSNet3		70.49 91	72.90 84	67.67 127	72.88 170	77.98 131	74.96 125	62.72 98	64.13 89	51.44 137	58.37 100	69.02 82	57.43 149	79.43 109	79.57 90	86.59 118	81.81 131

LTVRE_ROB	59.44 16	61.82 196	62.64 198	60.87 186	72.83 171	77.19 140	64.37 201	58.97 158	33.56 235	28.00 217	52.59 176	42.21 219	63.93 108	74.52 170	76.28 155	77.15 200	82.13 123
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
v7n		67.05 154	66.94 159	67.17 139	72.35 172	78.97 110	73.26 152	58.88 160	51.16 199	50.90 142	48.21 193	50.11 196	60.96 128	77.70 131	77.38 127	86.68 115	85.05 87

tpm		62.41 187	63.15 193	61.55 182	72.24 173	63.79 214	71.31 163	46.12 220	57.82 133	55.33 116	59.90 91	54.74 149	53.63 178	67.24 213	64.29 215	70.65 222	74.25 190

test20.03		53.93 215	56.28 216	51.19 216	72.19 174	65.83 207	53.20 223	61.08 125	42.74 220	22.08 229	37.07 218	45.76 214	24.29 233	70.44 201	69.04 197	74.31 212	63.05 218

CP-MVSNet		62.68 183	65.49 177	59.40 195	71.84 175	75.34 166	62.87 207	67.04 59	52.64 190	27.19 218	53.38 158	48.15 205	41.40 209	71.26 190	75.68 164	86.07 131	82.00 127

PS-CasMVS		62.38 189	65.06 181	59.25 196	71.73 176	75.21 174	62.77 208	66.99 60	51.94 196	26.96 219	52.00 180	47.52 208	41.06 210	71.16 193	75.60 167	85.97 140	81.97 129

WR-MVS_H		61.83 195	65.87 173	57.12 202	71.72 177	76.87 150	61.45 210	66.19 61	51.97 195	22.92 228	53.13 165	52.30 180	33.80 218	71.03 194	75.00 173	86.65 116	80.78 137

USDC		67.36 150	67.90 148	66.74 149	71.72 177	75.23 171	71.58 162	60.28 141	67.45 71	50.54 146	60.93 84	45.20 215	62.08 118	76.56 159	74.50 175	84.25 171	75.38 183

UGNet		72.78 76	77.67 60	67.07 142	71.65 179	83.24 68	75.20 118	63.62 81	64.93 83	56.72 108	71.82 48	73.30 59	49.02 190	81.02 77	80.70 72	86.22 123	88.67 52
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
tpmrst		62.00 191	62.35 202	61.58 181	71.62 180	64.14 211	69.07 173	48.22 216	62.21 102	53.93 123	58.26 104	55.30 143	55.81 166	63.22 219	62.62 220	70.85 221	70.70 203

pmmvs4		67.89 136	67.39 155	68.48 119	71.60 181	73.57 182	74.45 127	60.98 126	64.65 85	57.97 96	54.95 132	51.73 187	61.88 122	73.78 175	75.11 172	83.99 174	77.91 164

testgi		54.39 214	57.86 213	50.35 217	71.59 182	67.24 203	54.95 221	53.25 190	43.36 219	23.78 222	44.64 204	47.87 206	24.96 229	70.45 200	68.66 201	73.60 214	62.78 219

pmmvs6		62.41 187	62.88 195	61.87 180	71.38 183	75.18 175	67.76 179	59.45 155	41.64 222	42.52 189	37.33 217	52.91 171	46.87 196	77.67 133	76.26 156	83.23 177	79.18 158

FMVSNet1		68.84 123	70.47 99	66.94 144	71.35 184	77.68 136	74.71 126	62.35 112	56.93 147	49.94 150	50.01 188	64.59 97	57.07 152	81.33 69	80.72 67	86.25 122	82.00 127

PatchmatchNet		64.21 176	64.65 185	63.69 168	71.29 185	68.66 198	69.63 168	51.70 200	63.04 96	53.77 125	59.83 92	58.34 116	60.23 135	68.54 210	66.06 212	75.56 206	68.08 209
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		64.83 167	65.54 176	64.01 167	70.64 186	69.41 194	65.97 193	52.74 193	57.81 134	52.65 132	54.27 143	56.31 132	60.92 129	72.20 185	73.09 181	81.12 185	75.69 180

MVSTER		72.06 79	74.24 77	69.51 110	70.39 187	75.97 162	76.91 110	57.36 177	64.64 86	61.39 78	68.86 58	63.76 99	63.46 109	81.44 66	79.70 86	87.56 75	85.31 82

Anonymous20231206		56.36 210	57.80 214	54.67 210	70.08 188	66.39 206	60.46 213	57.54 174	49.50 208	29.30 214	33.86 225	46.64 209	35.18 216	70.44 201	68.88 199	75.47 207	68.88 208

thisisatest0515		67.40 149	68.78 131	65.80 154	70.02 189	75.24 168	69.36 172	57.37 176	54.94 178	53.67 126	55.53 121	54.85 148	58.00 144	78.19 124	78.91 97	86.39 121	83.78 102

CMPMVS	47.78 17	62.49 186	62.52 199	62.46 175	70.01 190	70.66 192	62.97 206	51.84 199	51.98 194	56.71 109	42.87 207	53.62 157	57.80 145	72.23 183	70.37 192	75.45 208	75.91 177
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v748		65.12 164	65.24 178	64.98 159	69.77 191	76.45 154	69.47 170	57.06 179	49.93 204	50.70 143	47.87 196	49.50 200	57.14 151	73.64 177	75.18 171	85.75 147	84.14 97

TDRefinement		66.09 158	65.03 183	67.31 136	69.73 192	76.75 151	75.33 115	64.55 76	60.28 117	49.72 152	45.63 203	42.83 218	60.46 133	75.75 163	75.95 163	84.08 172	78.04 163

TinyColmap		62.84 182	61.03 207	64.96 160	69.61 193	71.69 187	68.48 176	59.76 152	55.41 170	47.69 160	47.33 199	34.20 228	62.76 114	74.52 170	72.59 184	81.44 183	71.47 201

RPMNet		61.71 197	62.88 195	60.34 188	69.51 194	69.41 194	63.48 204	49.23 208	57.81 134	45.64 174	50.51 186	50.12 195	53.13 182	68.17 212	68.49 203	81.07 186	75.62 182

IterMVS		66.36 156	68.30 143	64.10 164	69.48 195	74.61 178	73.41 150	50.79 204	57.30 140	48.28 156	60.64 85	59.92 110	60.85 132	74.14 173	72.66 183	81.80 181	78.82 160
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SixPastTwentyTwo		61.84 194	62.45 200	61.12 184	69.20 196	72.20 185	62.03 209	57.40 175	46.54 215	38.03 200	57.14 112	41.72 220	58.12 143	69.67 205	71.58 187	81.94 180	78.30 162

MDTV_nov1_ep13		64.37 171	65.24 178	63.37 173	68.94 197	70.81 190	72.40 157	50.29 207	60.10 118	53.91 124	60.07 89	59.15 113	57.21 150	69.43 207	67.30 207	77.47 198	69.78 205

EPMVS		60.00 202	61.97 203	57.71 200	68.46 198	63.17 218	64.54 200	48.23 215	63.30 94	44.72 178	60.19 87	56.05 140	50.85 186	65.27 216	62.02 222	69.44 224	63.81 216

our_test_3					67.93 199	70.99 189	66.89 186

FC-MVSNet-test		56.90 209	65.20 180	47.21 220	66.98 200	63.20 217	49.11 229	58.60 170	59.38 124	11.50 240	65.60 71	56.68 129	24.66 232	71.17 192	71.36 189	72.38 217	69.02 207

CVMVSNet		62.55 184	65.89 172	58.64 197	66.95 201	69.15 196	66.49 192	56.29 183	52.46 192	32.70 210	59.27 94	58.21 117	50.09 187	71.77 188	71.39 188	79.31 192	78.99 159

FPMVS		51.87 218	50.00 223	54.07 211	66.83 202	57.25 225	60.25 214	50.91 202	50.25 201	34.36 208	36.04 222	32.02 230	41.49 208	58.98 231	56.07 231	70.56 223	59.36 225

pmmvs-eth3d		63.52 179	62.44 201	64.77 161	66.82 203	70.12 193	69.41 171	59.48 154	54.34 183	52.71 131	46.24 202	44.35 217	56.93 153	72.37 180	73.77 178	83.30 176	75.91 177

testpf		47.41 221	48.47 228	46.18 221	66.30 204	50.67 234	48.15 230	42.60 230	37.10 230	28.75 215	40.97 211	39.01 225	30.82 221	52.95 236	53.74 235	60.46 234	64.87 213

TAMVS		59.58 203	62.81 197	55.81 206	66.03 205	65.64 209	63.86 203	48.74 211	49.95 202	37.07 202	54.77 136	58.54 114	44.44 202	72.29 182	71.79 185	74.70 210	66.66 211

MDTV_nov1_ep13_2view		60.16 201	60.51 209	59.75 191	65.39 206	69.05 197	68.00 177	48.29 214	51.99 193	45.95 172	48.01 194	49.64 199	53.39 180	68.83 209	66.52 211	77.47 198	69.55 206

pmmvs5		62.37 190	64.04 189	60.42 187	65.03 207	71.67 188	67.17 184	52.70 195	50.30 200	44.80 177	54.23 147	51.19 191	49.37 189	72.88 179	73.48 180	83.45 175	74.55 187

ambc			53.42 218		64.99 208	63.36 216	49.96 227		47.07 213	37.12 201	28.97 229	16.36 243	41.82 207	75.10 169	67.34 206	71.55 220	75.72 179

V42		68.76 125	69.63 110	67.74 125	64.93 209	78.01 127	78.30 97	56.48 181	58.65 130	56.30 112	54.26 145	57.03 127	64.85 104	77.47 141	77.01 133	85.60 150	84.96 89

PMVS	39.38 18	46.06 226	43.30 232	49.28 219	62.93 210	38.75 241	41.88 234	53.50 189	33.33 236	35.46 207	28.90 230	31.01 233	33.04 219	58.61 232	54.63 234	68.86 225	57.88 228
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new-patchmatchnet		46.97 224	49.47 224	44.05 226	62.82 211	56.55 226	45.35 232	52.01 197	42.47 221	17.04 236	35.73 223	35.21 227	21.84 238	61.27 224	54.83 233	65.26 232	60.26 222

ADS-MVSNet		55.94 211	58.01 212	53.54 215	62.48 212	58.48 224	59.12 217	46.20 219	59.65 122	42.88 187	52.34 179	53.31 169	46.31 199	62.00 223	60.02 227	64.23 233	60.24 224

v52		65.23 162	66.24 165	64.06 165	61.94 213	76.42 155	72.06 159	54.30 187	49.94 203	50.04 148	47.41 198	52.42 176	60.23 135	75.71 164	76.22 158	85.78 145	85.56 74

V4		65.23 162	66.23 166	64.06 165	61.94 213	76.42 155	72.05 160	54.31 186	49.91 205	50.06 147	47.42 197	52.40 177	60.24 134	75.71 164	76.22 158	85.78 145	85.56 74

RPSCF		67.64 144	71.25 93	63.43 172	61.86 215	70.73 191	67.26 183	50.86 203	74.20 58	58.91 86	67.49 66	69.33 79	64.10 107	71.41 189	68.45 204	77.61 197	77.17 169

MIMVSNet		58.52 206	61.34 206	55.22 208	60.76 216	67.01 204	66.81 187	49.02 210	56.43 159	38.90 197	40.59 214	54.54 151	40.57 212	73.16 178	71.65 186	75.30 209	66.00 212

PatchT		61.97 192	64.04 189	59.55 194	60.49 217	67.40 202	56.54 219	48.65 212	56.69 150	52.65 132	51.10 185	52.14 183	60.92 129	72.20 185	73.09 181	78.03 196	75.69 180

N_pmnet		47.35 222	50.13 222	44.11 225	59.98 218	51.64 233	51.86 224	44.80 225	49.58 207	20.76 232	40.65 213	40.05 224	29.64 222	59.84 229	55.15 232	57.63 235	54.00 232

1111		43.08 228	44.02 231	41.98 227	59.22 219	49.27 237	41.48 235	45.63 222	35.01 231	23.06 226	28.60 231	30.15 234	27.22 224	60.42 227	57.97 229	55.27 238	46.74 234

.test1245		30.81 235	29.14 238	32.77 234	59.22 219	49.27 237	41.48 235	45.63 222	35.01 231	23.06 226	28.60 231	30.15 234	27.22 224	60.42 227	0.10 242	0.01 246	0.43 243

MVS-HIRNet		54.41 213	52.10 221	57.11 203	58.99 221	56.10 227	49.68 228	49.10 209	46.18 216	52.15 136	33.18 226	46.11 213	56.10 162	63.19 220	59.70 228	76.64 202	60.25 223

PM-MVS		60.48 200	60.94 208	59.94 190	58.85 222	66.83 205	64.27 202	51.39 201	55.03 176	48.03 157	50.00 190	40.79 222	58.26 142	69.20 208	67.13 210	78.84 194	77.60 166

anonymousdsp		65.28 161	67.98 147	62.13 177	58.73 223	73.98 181	67.10 185	50.69 205	48.41 209	47.66 161	54.27 143	52.75 175	61.45 127	76.71 157	80.20 80	87.13 86	89.53 47

LP		53.62 216	53.43 217	53.83 213	58.51 224	62.59 221	57.31 218	46.04 221	47.86 210	42.69 188	36.08 221	36.86 226	46.53 198	64.38 217	64.25 216	71.92 218	62.00 221

TESTMET0.1,1		61.10 198	64.36 187	57.29 201	57.53 225	63.93 212	66.61 190	36.22 235	54.41 180	47.77 158	57.46 108	60.25 108	55.20 172	70.80 197	69.33 195	80.40 188	74.38 188

EU-MVSNet		54.63 212	58.69 211	49.90 218	56.99 226	62.70 220	56.41 220	50.64 206	45.95 217	23.14 225	50.42 187	46.51 210	36.63 215	65.51 215	64.85 214	75.57 205	74.91 185

FMVSNet5		57.24 207	60.02 210	53.99 212	56.45 227	62.74 219	65.27 196	47.03 217	55.14 173	39.55 195	40.88 212	53.42 167	41.83 206	72.35 181	71.10 190	73.79 213	64.50 215

test2356		47.20 223	48.62 227	45.54 223	56.38 228	54.89 229	50.62 225	45.08 224	38.65 227	23.40 223	36.23 220	31.10 232	29.31 223	62.76 221	62.49 221	68.48 226	54.23 231

testus		45.61 227	49.06 226	41.59 228	56.13 229	55.28 228	43.51 233	39.64 233	37.74 228	18.23 234	35.52 224	31.28 231	24.69 231	62.46 222	62.90 219	67.33 228	58.26 227

test-mter		60.84 199	64.62 186	56.42 204	55.99 230	64.18 210	65.39 195	34.23 237	54.39 182	46.21 170	57.40 110	59.49 112	55.86 165	71.02 195	69.65 194	80.87 187	76.20 176

CHOSEN 280x420		58.70 205	61.88 204	54.98 209	55.45 231	50.55 235	64.92 198	40.36 231	55.21 172	38.13 199	48.31 192	63.76 99	63.03 113	73.73 176	68.58 202	68.00 227	73.04 194

PMMVS		65.06 166	69.17 123	60.26 189	55.25 232	63.43 215	66.71 189	43.01 229	62.41 100	50.64 144	69.44 57	67.04 91	63.29 111	74.36 172	73.54 179	82.68 179	73.99 191

testmv		42.58 229	44.36 229	40.49 229	54.63 233	52.76 231	41.21 237	44.37 226	28.83 237	12.87 237	27.16 234	25.03 238	23.01 234	60.83 225	61.13 223	66.88 229	54.81 229

test1235678		42.57 230	44.36 229	40.49 229	54.63 233	52.75 232	41.21 237	44.37 226	28.82 238	12.87 237	27.15 235	25.01 239	23.01 234	60.83 225	61.13 223	66.88 229	54.81 229

no-one		36.35 233	37.59 235	34.91 232	46.13 235	49.89 236	27.99 242	43.56 228	20.91 242	7.03 243	14.64 240	15.50 244	18.92 239	42.95 237	60.20 226	65.84 231	59.03 226

Gipuma		36.38 232	35.80 236	37.07 231	45.76 236	33.90 242	29.81 241	48.47 213	39.91 225	18.02 235	8.00 244	8.14 246	25.14 228	59.29 230	61.02 225	55.19 239	40.31 236
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs3		47.65 220	49.08 225	45.99 222	44.61 237	54.79 230	50.04 226	31.95 240	33.91 233	29.90 212	30.37 227	33.53 229	46.31 199	63.50 218	63.67 218	73.14 216	63.77 217

MIMVSNet1		49.27 219	53.25 219	44.62 224	44.61 237	61.52 223	53.61 222	52.18 196	41.62 223	18.68 233	28.14 233	41.58 221	25.50 227	68.46 211	69.04 197	73.15 215	62.37 220

test12356		35.10 234	38.50 234	31.13 235	44.14 239	43.70 240	32.27 240	34.42 236	26.51 240	9.47 241	25.22 237	20.34 240	10.86 241	53.47 234	56.15 230	55.59 237	44.11 235

MDA-MVSNet-bldmvs		53.37 217	53.01 220	53.79 214	43.67 240	67.95 201	59.69 215	57.92 173	43.69 218	32.41 211	41.47 210	27.89 237	52.38 184	56.97 233	65.99 213	76.68 201	67.13 210

E-PMN		21.77 237	18.24 240	25.89 236	40.22 241	19.58 245	12.46 246	39.87 232	18.68 244	6.71 244	9.57 241	4.31 249	22.36 237	19.89 242	27.28 240	33.73 241	28.34 240

EMVS		20.98 238	17.15 241	25.44 237	39.51 242	19.37 246	12.66 245	39.59 234	19.10 243	6.62 245	9.27 242	4.40 248	22.43 236	17.99 243	24.40 241	31.81 242	25.53 241

new_pmnet		38.40 231	42.64 233	33.44 233	37.54 243	45.00 239	36.60 239	32.72 239	40.27 224	12.72 239	29.89 228	28.90 236	24.78 230	53.17 235	52.90 236	56.31 236	48.34 233

PMMVS2		25.60 236	29.75 237	20.76 239	28.00 244	30.93 243	23.10 243	29.18 241	23.14 241	1.46 247	18.23 239	16.54 242	5.08 242	40.22 238	41.40 238	37.76 240	37.79 238

tmp_tt				14.50 241	14.68 245	7.17 248	10.46 248	2.21 243	37.73 229	28.71 216	25.26 236	16.98 241	4.37 243	31.49 239	29.77 239	26.56 243

MVE	19.12 19	20.47 239	23.27 239	17.20 240	12.66 246	25.41 244	10.52 247	34.14 238	14.79 245	6.53 246	8.79 243	4.68 247	16.64 240	29.49 240	41.63 237	22.73 244	38.11 237
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GG-mvs-BLEND		46.86 225	67.51 152	22.75 238	0.05 247	76.21 160	64.69 199	0.04 244	61.90 104	0.09 248	55.57 119	71.32 67	0.08 244	70.54 199	67.19 208	71.58 219	69.86 204

testmvs		0.09 240	0.15 242	0.02 242	0.01 248	0.02 249	0.05 250	0.01 245	0.11 246	0.01 249	0.26 246	0.01 250	0.06 246	0.10 244	0.10 242	0.01 246	0.43 243

sosnet-low-res		0.00 242	0.00 244	0.00 244	0.00 249	0.00 251	0.00 252	0.00 247	0.00 248	0.00 250	0.00 247	0.00 251	0.00 247	0.00 246	0.00 245	0.00 249	0.00 245

sosnet		0.00 242	0.00 244	0.00 244	0.00 249	0.00 251	0.00 252	0.00 247	0.00 248	0.00 250	0.00 247	0.00 251	0.00 247	0.00 246	0.00 245	0.00 249	0.00 245

test123		0.09 240	0.14 243	0.02 242	0.00 249	0.02 249	0.02 251	0.01 245	0.09 247	0.00 250	0.30 245	0.00 251	0.08 244	0.03 245	0.09 244	0.01 246	0.45 242

MTAPA										83.48 1		86.45 14

MTMP										82.66 3		84.91 22

Patchmatch-RL test							2.85 249

NP-MVS									80.10 42

Patchmtry						65.80 208	65.97 193	52.74 193		52.65 132

DeepMVS_CX						18.74 247	18.55 244	8.02 242	26.96 239	7.33 242	23.81 238	13.05 245	25.99 226	25.17 241		22.45 245	36.25 239

High-resolution multi-view benchmark