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	0.00 246

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 77	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 81	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 83	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 82	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	73.54 148	65.98 66	60.65 115	56.00 115	72.11 46	79.15 41	54.63 175	83.13 56	82.25 52	88.04 64	81.92 131

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	57.61 108	61.31 106	71.07 58	80.32 93	78.87 99	86.00 139	80.18 147

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	57.91 107	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 78	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 83	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 96	68.53 87	73.73 37	80.38 89	79.04 96	87.13 87	81.68 133
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 86	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	56.37 115	69.81 77	74.53 32	81.12 76	78.69 100	86.04 136	87.29 64

ACMH+	66.54 13	71.36 86	70.09 101	72.85 70	82.59 63	81.13 85	78.56 90	68.04 52	61.55 109	52.52 136	51.50 183	54.14 154	68.56 68	78.85 117	79.50 93	86.82 104	83.94 101

ACMH	65.37 14	70.71 90	70.00 102	71.54 75	82.51 64	82.47 75	77.78 101	68.13 51	56.19 164	46.06 172	54.30 143	51.20 191	68.68 67	80.66 83	80.72 67	86.07 132	84.45 97
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	69.87 106	73.44 68	82.21 66	79.35 108	79.52 68	64.59 75	66.15 75	61.87 75	53.21 164	56.09 140	65.85 102	78.94 116	78.50 102	86.60 118	76.85 175

IS_MVSNet		73.33 73	77.34 66	68.65 119	81.29 67	83.47 66	74.45 128	63.58 82	65.75 79	48.49 155	67.11 69	70.61 72	54.63 175	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 79	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	86.59 119	87.91 56

FC-MVSNet-train		72.60 78	75.07 76	69.71 108	81.10 70	78.79 119	73.74 146	65.23 71	66.10 76	53.34 129	70.36 54	63.40 101	56.92 155	81.44 66	80.96 63	87.93 66	84.46 96

MS-PatchMatch		70.17 100	70.49 99	69.79 106	80.98 71	77.97 134	77.51 103	58.95 160	62.33 102	55.22 119	53.14 165	65.90 94	62.03 121	79.08 115	77.11 133	84.08 173	77.91 165

Anonymous202405211			72.16 90		80.85 72	81.85 78	76.88 111	65.40 69	62.89 100		46.35 202	67.99 89	62.05 120	81.15 75	80.38 78	85.97 141	84.50 95

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 92	87.80 72	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	49.77 152	68.68 60	72.09 65	63.06 113	82.49 60	80.73 66	89.12 48	88.91 50

COLMAP_ROB	62.73 15	67.66 143	66.76 163	68.70 118	80.49 76	77.98 132	75.29 117	62.95 89	63.62 94	49.96 150	47.32 201	50.72 194	58.57 140	76.87 155	75.50 170	84.94 162	75.33 185
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	62.19 118	81.29 72	80.97 62	86.74 111	86.91 65

Anonymous20231211		71.90 80	72.48 88	71.21 76	80.14 78	81.53 80	76.92 109	62.89 90	64.46 88	58.94 85	43.80 206	70.98 69	62.22 117	80.70 82	80.19 82	86.18 125	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	86.01 137	85.98 70

EPNet_dtu		68.08 134	71.00 95	64.67 163	79.64 80	68.62 200	75.05 123	63.30 83	66.36 74	45.27 176	67.40 67	66.84 92	43.64 205	75.37 168	74.98 175	81.15 185	77.44 168
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 88	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 88	87.89 68	86.87 66

IB-MVS	66.94 12	71.21 87	71.66 93	70.68 87	79.18 83	82.83 73	72.61 155	61.77 117	59.66 122	63.44 72	53.26 162	59.65 111	59.16 139	76.78 157	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	80.14 100	79.48 94	87.67 74	85.50 78

Effi-MVS+-dtu		71.82 81	71.86 92	71.78 73	78.77 85	80.47 99	78.55 91	61.67 120	60.68 114	55.49 116	58.48 100	65.48 95	68.85 66	76.92 154	75.55 169	87.35 79	85.46 79

EG-PatchMatch MVS		67.24 153	66.94 160	67.60 129	78.73 86	81.35 82	73.28 152	59.49 153	46.89 215	51.42 141	43.65 207	53.49 162	55.50 171	81.38 68	80.66 73	87.15 83	81.17 136

gg-mvs-nofinetune		62.55 185	65.05 183	59.62 194	78.72 87	77.61 138	70.83 166	53.63 189	39.71 227	22.04 231	36.36 220	64.32 98	47.53 193	81.16 74	79.03 97	85.00 160	77.17 170

Vis-MVSNet (Re-imp)		67.83 139	73.52 79	61.19 184	78.37 88	76.72 153	66.80 189	62.96 88	65.50 80	34.17 210	67.19 68	69.68 78	39.20 214	79.39 111	79.44 95	85.68 150	76.73 176

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 84	74.81 56	69.32 64	81.01 78	79.59 90	87.64 75	85.89 71

thres600view7		67.68 142	68.43 141	66.80 147	77.90 90	78.86 117	73.84 143	62.75 96	56.07 165	44.70 180	52.85 171	52.81 174	55.58 169	80.41 84	77.77 118	86.05 134	80.28 146

thres400		67.95 136	68.62 139	67.17 140	77.90 90	78.59 124	74.27 139	62.72 98	56.34 163	45.77 174	53.00 167	53.35 169	56.46 161	80.21 99	78.43 103	85.91 144	80.43 145

thres200		67.98 135	68.55 140	67.30 138	77.89 92	78.86 117	74.18 141	62.75 96	56.35 162	46.48 170	52.98 168	53.54 160	56.46 161	80.41 84	77.97 115	86.05 134	79.78 152

tpmp4_e23		68.32 130	67.08 159	69.76 107	77.86 93	75.22 174	78.37 96	56.17 185	66.06 77	64.27 69	57.15 112	54.89 148	63.40 111	70.97 197	68.29 206	78.46 196	77.00 174

view600		67.63 146	68.36 142	66.77 148	77.84 94	78.66 121	73.74 146	62.62 105	56.04 166	44.98 177	52.86 170	52.83 173	55.48 172	80.36 90	77.75 119	85.95 143	80.02 149

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 98	78.69 100	84.87 164	88.00 55

tfpn111		68.38 128	69.23 123	67.39 133	77.83 96	78.93 113	74.28 134	62.81 91	56.64 152	46.70 165	56.24 116	53.47 164	56.59 156	80.41 84	78.43 103	86.11 128	80.53 142

conf200view11		68.11 132	68.72 135	67.39 133	77.83 96	78.93 113	74.28 134	62.81 91	56.64 152	46.70 165	52.65 174	53.47 164	56.59 156	80.41 84	78.43 103	86.11 128	80.53 142

thres100view900		67.60 147	68.02 146	67.12 142	77.83 96	77.75 136	73.90 142	62.52 108	56.64 152	46.82 163	52.65 174	53.47 164	55.92 165	78.77 118	77.62 122	85.72 149	79.23 157

tfpn200view9		68.11 132	68.72 135	67.40 132	77.83 96	78.93 113	74.28 134	62.81 91	56.64 152	46.82 163	52.65 174	53.47 164	56.59 156	80.41 84	78.43 103	86.11 128	80.52 144

view800		67.35 152	68.22 145	66.35 152	77.83 96	78.62 122	72.97 154	62.58 106	55.71 168	44.13 181	52.69 173	52.24 183	54.58 177	80.27 96	78.19 111	86.01 137	79.79 151

conf0.01		67.72 141	67.99 147	67.39 133	77.82 101	78.94 111	74.28 134	62.81 91	56.64 152	46.70 165	53.33 160	48.59 204	56.59 156	80.34 91	78.43 103	86.16 127	79.67 153

tfpn		66.58 156	67.18 157	65.88 154	77.82 101	78.45 126	72.07 159	62.52 108	55.35 172	43.21 185	52.54 178	46.12 213	53.68 178	80.02 101	78.23 110	85.99 140	79.55 155

conf0.002		67.52 149	67.64 151	67.39 133	77.80 103	78.94 111	74.28 134	62.81 91	56.64 152	46.70 165	53.65 156	46.28 212	56.59 156	80.33 92	78.37 108	86.17 126	79.23 157

Fast-Effi-MVS+		73.11 75	73.66 78	72.48 72	77.72 104	80.88 90	78.55 91	58.83 168	65.19 81	60.36 80	59.98 91	62.42 104	71.22 57	81.66 62	80.61 76	88.20 60	84.88 91

UniMVSNet_NR-MVSNet		70.59 91	72.19 89	68.72 117	77.72 104	80.72 91	73.81 144	69.65 41	61.99 104	43.23 183	60.54 87	57.50 119	58.57 140	79.56 108	81.07 61	89.34 43	83.97 99

IterMVS-LS		71.69 82	72.82 86	70.37 99	77.54 106	76.34 159	75.13 122	60.46 136	61.53 110	57.57 98	64.89 74	67.33 90	66.04 98	77.09 153	77.37 129	85.48 153	85.18 84
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NR-MVSNet		68.79 125	70.56 98	66.71 151	77.48 107	79.54 105	73.52 149	69.20 46	61.20 112	39.76 194	58.52 98	50.11 197	51.37 186	80.26 97	80.71 71	88.97 49	83.59 107

conf0.05thres1000		66.26 158	66.77 162	65.66 156	77.45 108	78.10 127	71.85 162	62.44 111	51.47 199	43.00 186	47.92 196	51.66 189	53.40 180	79.71 104	77.97 115	85.82 145	80.56 140

TransMVSNet (Re)		64.74 170	65.66 176	63.66 170	77.40 109	75.33 168	69.86 167	62.67 104	47.63 213	41.21 192	50.01 189	52.33 179	45.31 202	79.57 107	77.69 121	85.49 152	77.07 173

TranMVSNet+NR-MVSNet		69.25 120	70.81 97	67.43 131	77.23 110	79.46 107	73.48 150	69.66 40	60.43 117	39.56 195	58.82 97	53.48 163	55.74 168	79.59 106	81.21 60	88.89 51	82.70 121

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	79.18 114	78.14 113	84.63 168	87.42 61

CANet_DTU		73.29 74	76.96 69	69.00 115	77.04 112	82.06 76	79.49 69	56.30 183	67.85 70	53.29 130	71.12 51	70.37 75	61.81 126	81.59 64	80.96 63	86.09 131	84.73 92

CHOSEN 1792x2688		69.20 121	69.26 122	69.13 113	76.86 113	78.93 113	77.27 107	60.12 148	61.86 106	54.42 120	42.54 210	61.61 105	66.91 78	78.55 120	78.14 113	79.23 194	83.23 112

HyFIR lowres test		69.47 117	68.94 126	70.09 103	76.77 114	82.93 71	76.63 113	60.17 143	59.00 126	54.03 123	40.54 216	65.23 96	67.89 71	76.54 161	78.30 109	85.03 159	80.07 148

UniMVSNet (Re)		69.53 114	71.90 91	66.76 149	76.42 115	80.93 87	72.59 156	68.03 53	61.75 108	41.68 191	58.34 104	57.23 127	53.27 182	79.53 109	80.62 75	88.57 57	84.90 90

thresconf0.02		64.77 169	65.90 172	63.44 172	76.37 116	75.17 177	69.51 170	61.28 121	56.98 143	39.01 197	56.24 116	48.68 203	49.78 189	77.13 151	75.61 167	84.71 167	71.53 201

tfpnview11		64.33 173	66.17 168	62.18 177	76.25 117	75.23 172	67.45 181	61.16 122	55.50 170	36.38 204	55.35 124	51.89 185	46.96 194	77.28 148	76.10 163	84.86 165	71.85 200

tfpn_n400		64.23 175	66.05 169	62.12 179	76.20 118	75.24 169	67.43 182	61.15 123	54.04 186	36.38 204	55.35 124	51.89 185	46.94 195	77.31 146	76.15 161	84.59 169	72.36 197

tfpnconf		64.23 175	66.05 169	62.12 179	76.20 118	75.24 169	67.43 182	61.15 123	54.04 186	36.38 204	55.35 124	51.89 185	46.94 195	77.31 146	76.15 161	84.59 169	72.36 197

DWT-MVSNet_training		67.24 153	65.96 171	68.74 116	76.15 120	74.36 181	74.37 133	56.66 181	61.82 107	60.51 79	58.23 106	49.76 199	65.07 103	70.04 205	70.39 192	79.70 191	77.11 172

gm-plane-assit		57.00 209	57.62 216	56.28 206	76.10 121	62.43 223	47.62 232	46.57 219	33.84 235	23.24 225	37.52 217	40.19 224	59.61 138	79.81 103	77.55 124	84.55 171	72.03 199

DU-MVS		69.63 109	70.91 96	68.13 123	75.99 122	79.54 105	73.81 144	69.20 46	61.20 112	43.23 183	58.52 98	53.50 161	58.57 140	79.22 112	80.45 77	87.97 65	83.97 99

Baseline_NR-MVSNet		67.53 148	68.77 133	66.09 153	75.99 122	74.75 178	72.43 157	68.41 49	61.33 111	38.33 199	51.31 184	54.13 156	56.03 164	79.22 112	78.19 111	85.37 154	82.45 123

CostFormer		68.92 123	69.58 112	68.15 122	75.98 124	76.17 162	78.22 99	51.86 199	65.80 78	61.56 77	63.57 80	62.83 102	61.85 124	70.40 204	68.67 201	79.42 192	79.62 154

tfpnnormal		64.27 174	63.64 193	65.02 159	75.84 125	75.61 165	71.24 165	62.52 108	47.79 212	42.97 187	42.65 209	44.49 217	52.66 184	78.77 118	76.86 136	84.88 163	79.29 156

tfpn1000		63.81 179	66.31 165	60.90 186	75.76 126	75.74 164	65.14 198	60.14 147	56.47 159	35.99 207	55.11 127	52.30 181	43.42 206	76.21 163	75.34 171	84.97 161	73.01 196

tfpn_ndepth		65.09 166	67.12 158	62.73 175	75.75 127	76.23 160	68.00 178	60.36 137	58.16 132	40.27 193	54.89 134	54.22 153	46.80 198	76.69 159	75.66 166	85.19 156	73.98 193

tpm cat1		65.41 161	63.81 192	67.28 139	75.61 128	72.88 184	75.32 116	52.85 193	62.97 98	63.66 71	53.24 163	53.29 171	61.83 125	65.54 215	64.14 218	74.43 212	74.60 187

CDS-MVSNet		67.65 144	69.83 109	65.09 158	75.39 129	76.55 154	74.42 131	63.75 80	53.55 188	49.37 154	59.41 94	62.45 103	44.44 203	79.71 104	79.82 85	83.17 179	77.36 169
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+-dtu		68.34 129	69.47 114	67.01 144	75.15 130	77.97 134	77.12 108	55.40 186	57.87 133	46.68 169	56.17 118	60.39 107	62.36 116	76.32 162	76.25 158	85.35 155	81.34 134

WR-MVS		63.03 181	67.40 155	57.92 200	75.14 131	77.60 139	60.56 213	66.10 63	54.11 185	23.88 222	53.94 154	53.58 159	34.50 218	73.93 175	77.71 120	87.35 79	80.94 137

test-LLR		64.42 171	64.36 188	64.49 164	75.02 132	63.93 213	66.61 191	61.96 114	54.41 181	47.77 159	57.46 109	60.25 108	55.20 173	70.80 198	69.33 196	80.40 189	74.38 189

test0.0.03 1		58.80 205	61.58 206	55.56 208	75.02 132	68.45 201	59.58 217	61.96 114	52.74 190	29.57 214	49.75 192	54.56 151	31.46 221	71.19 192	69.77 194	75.75 205	64.57 215

v1144		69.93 108	69.36 121	70.61 89	74.89 134	80.93 87	79.11 74	60.64 130	55.97 167	55.31 118	53.85 155	54.14 154	66.54 83	78.10 126	77.44 127	87.14 86	85.09 85

v13		69.52 115	68.76 134	70.41 97	74.88 135	77.02 149	78.52 95	58.86 162	56.61 158	56.91 103	54.00 153	56.17 139	66.11 97	77.93 127	76.74 144	87.21 81	82.83 114

v12		69.54 113	68.79 131	70.41 97	74.88 135	77.03 147	78.54 94	58.85 164	56.71 150	56.87 105	54.13 151	56.23 138	66.15 93	77.89 128	76.74 144	87.17 82	82.80 115

v11		69.37 118	68.65 138	70.20 101	74.87 137	76.97 150	78.29 98	58.55 172	56.38 161	56.04 114	54.02 152	54.98 147	66.47 84	78.30 122	76.91 135	86.97 95	83.02 113

V9		69.58 112	68.83 129	70.46 94	74.85 138	77.04 145	78.65 89	58.85 164	56.83 149	57.12 101	54.26 146	56.31 133	66.14 95	77.83 130	76.76 139	87.13 87	82.79 117

V14		69.59 111	68.86 128	70.45 96	74.83 139	77.04 145	78.70 88	58.83 168	56.95 146	57.08 102	54.41 142	56.34 132	66.15 93	77.77 131	76.76 139	87.08 92	82.74 120

v15		69.61 110	68.88 127	70.46 94	74.81 140	77.03 147	78.75 87	58.83 168	57.06 142	57.18 100	54.55 141	56.37 131	66.13 96	77.70 132	76.76 139	87.03 94	82.69 122

v7		70.33 97	69.87 106	70.88 78	74.79 141	81.04 86	79.22 72	60.57 132	57.70 139	56.65 111	54.23 148	55.29 145	66.95 75	78.28 123	77.47 125	87.12 90	85.05 87

v10		70.22 99	69.76 110	70.74 84	74.79 141	80.30 102	79.22 72	59.81 151	57.71 138	56.58 112	54.22 150	55.31 143	66.95 75	78.28 123	77.47 125	87.12 90	85.07 86

v1141		69.96 107	69.44 118	70.58 92	74.78 143	80.50 97	78.85 77	60.30 138	56.95 146	56.74 108	54.68 139	56.26 137	65.93 99	77.38 143	76.72 149	86.88 100	83.57 110

divwei89l23v2f112		69.97 105	69.44 118	70.58 92	74.78 143	80.50 97	78.85 77	60.30 138	56.97 145	56.75 107	54.67 140	56.27 136	65.92 100	77.37 144	76.72 149	86.88 100	83.58 109

v1		69.97 105	69.45 117	70.59 90	74.78 143	80.51 96	78.84 79	60.30 138	56.98 143	56.81 106	54.69 138	56.29 135	65.91 101	77.37 144	76.71 152	86.89 99	83.59 107

v17		70.03 104	69.43 120	70.72 86	74.75 146	77.09 142	78.78 86	58.85 164	59.53 124	58.72 88	54.87 135	57.39 121	66.38 86	77.60 136	76.75 142	86.83 103	82.80 115

v16		70.07 102	69.46 115	70.79 82	74.74 147	77.08 143	78.79 84	58.86 162	59.75 121	59.15 84	54.87 135	57.33 122	66.38 86	77.61 135	76.77 137	86.81 109	82.79 117

v8		70.23 98	69.86 108	70.67 88	74.69 148	79.82 104	78.79 84	59.18 156	58.80 128	58.20 93	55.00 130	57.33 122	66.31 92	77.51 140	76.71 152	86.82 104	83.88 102

v1neww		70.34 95	69.93 104	70.82 80	74.68 149	80.61 93	78.80 82	60.17 143	58.74 129	58.10 95	55.00 130	57.28 125	66.33 89	77.53 137	76.74 144	86.82 104	83.61 105

v7new		70.34 95	69.93 104	70.82 80	74.68 149	80.61 93	78.80 82	60.17 143	58.74 129	58.10 95	55.00 130	57.28 125	66.33 89	77.53 137	76.74 144	86.82 104	83.61 105

v6		70.35 94	69.94 103	70.83 79	74.68 149	80.62 92	78.81 81	60.16 146	58.81 127	58.17 94	55.01 129	57.31 124	66.32 91	77.53 137	76.73 148	86.82 104	83.62 104

v18		70.10 101	69.52 113	70.77 83	74.66 152	77.06 144	78.84 79	58.84 167	60.01 120	59.23 83	55.06 128	57.47 120	66.34 88	77.50 141	76.75 142	86.71 112	82.77 119

v2v482		70.05 103	69.46 115	70.74 84	74.62 153	80.32 101	79.00 75	60.62 131	57.41 140	56.89 104	55.43 123	55.14 146	66.39 85	77.25 149	77.14 132	86.90 97	83.57 110

v1192		69.50 116	68.83 129	70.29 100	74.49 154	80.92 89	78.55 91	60.54 133	55.04 176	54.21 121	52.79 172	52.33 179	66.92 77	77.88 129	77.35 130	87.04 93	85.51 77

DTE-MVSNet		61.85 194	64.96 185	58.22 199	74.32 155	74.39 180	61.01 212	67.85 55	51.76 198	21.91 232	53.28 161	48.17 205	37.74 215	72.22 185	76.44 155	86.52 121	78.49 162

Vis-MVSNet		72.77 77	77.20 67	67.59 130	74.19 156	84.01 62	76.61 114	61.69 118	60.62 116	50.61 146	70.25 55	71.31 68	55.57 170	83.85 48	82.28 51	86.90 97	88.08 54
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v144192		69.34 119	68.68 137	70.12 102	74.06 157	80.54 95	78.08 100	60.54 133	54.99 178	54.13 122	52.92 169	52.80 175	66.73 80	77.13 151	76.72 149	87.15 83	85.63 73

v1921920		69.03 122	68.32 143	69.86 105	74.03 158	80.37 100	77.55 102	60.25 142	54.62 180	53.59 128	52.36 179	51.50 190	66.75 79	77.17 150	76.69 154	86.96 96	85.56 74

PEN-MVS		62.96 182	65.77 175	59.70 193	73.98 159	75.45 166	63.39 206	67.61 56	52.49 192	25.49 221	53.39 158	49.12 202	40.85 212	71.94 188	77.26 131	86.86 102	80.72 139

v1240		68.64 127	67.89 150	69.51 111	73.89 160	80.26 103	76.73 112	59.97 150	53.43 189	53.08 131	51.82 182	50.84 193	66.62 82	76.79 156	76.77 137	86.78 110	85.34 81

thisisatest0530		71.48 84	73.01 83	69.70 109	73.83 161	78.62 122	74.53 127	59.12 157	64.13 89	58.63 90	64.60 77	58.63 114	64.27 106	80.28 95	80.17 83	87.82 71	84.64 94

GA-MVS		68.14 131	69.17 124	66.93 146	73.77 162	78.50 125	74.45 128	58.28 173	55.11 175	48.44 156	60.08 89	53.99 157	61.50 127	78.43 121	77.57 123	85.13 157	80.54 141

tttt0517		71.41 85	72.95 84	69.60 110	73.70 163	78.70 120	74.42 131	59.12 157	63.89 93	58.35 92	64.56 78	58.39 116	64.27 106	80.29 94	80.17 83	87.74 73	84.69 93

pm-mvs1		65.62 160	67.42 154	63.53 171	73.66 164	76.39 158	69.66 168	60.87 128	49.73 207	43.97 182	51.24 185	57.00 129	48.16 192	79.89 102	77.84 117	84.85 166	79.82 150

dps		64.00 178	62.99 195	65.18 157	73.29 165	72.07 187	68.98 175	53.07 192	57.74 137	58.41 91	55.55 121	47.74 208	60.89 132	69.53 207	67.14 210	76.44 204	71.19 203

v148		67.85 138	67.53 152	68.23 121	73.25 166	77.57 140	74.26 140	57.36 178	55.70 169	57.45 99	53.53 157	55.42 142	61.96 122	75.23 169	73.92 178	85.08 158	81.32 135

PatchMatch-RL		67.78 140	66.65 164	69.10 114	73.01 167	72.69 185	68.49 176	61.85 116	62.93 99	60.20 82	56.83 114	50.42 195	69.52 63	75.62 167	74.46 177	81.51 183	73.62 194

GBi-Net		70.78 88	73.37 81	67.76 124	72.95 168	78.00 129	75.15 119	62.72 98	64.13 89	51.44 138	58.37 101	69.02 82	57.59 147	81.33 69	80.72 67	86.70 113	82.02 125

test1		70.78 88	73.37 81	67.76 124	72.95 168	78.00 129	75.15 119	62.72 98	64.13 89	51.44 138	58.37 101	69.02 82	57.59 147	81.33 69	80.72 67	86.70 113	82.02 125

FMVSNet2		70.39 93	72.67 87	67.72 127	72.95 168	78.00 129	75.15 119	62.69 102	63.29 96	51.25 142	55.64 119	68.49 88	57.59 147	80.91 80	80.35 79	86.70 113	82.02 125

FMVSNet3		70.49 92	72.90 85	67.67 128	72.88 171	77.98 132	74.96 125	62.72 98	64.13 89	51.44 138	58.37 101	69.02 82	57.43 150	79.43 110	79.57 91	86.59 119	81.81 132

LTVRE_ROB	59.44 16	61.82 197	62.64 199	60.87 187	72.83 172	77.19 141	64.37 202	58.97 159	33.56 236	28.00 218	52.59 177	42.21 220	63.93 109	74.52 171	76.28 156	77.15 201	82.13 124
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		67.05 155	66.94 160	67.17 140	72.35 173	78.97 110	73.26 153	58.88 161	51.16 200	50.90 143	48.21 194	50.11 197	60.96 129	77.70 132	77.38 128	86.68 116	85.05 87

tpm		62.41 188	63.15 194	61.55 183	72.24 174	63.79 215	71.31 164	46.12 221	57.82 134	55.33 117	59.90 92	54.74 150	53.63 179	67.24 214	64.29 216	70.65 223	74.25 191

test20.03		53.93 216	56.28 217	51.19 217	72.19 175	65.83 208	53.20 224	61.08 125	42.74 221	22.08 230	37.07 219	45.76 215	24.29 234	70.44 202	69.04 198	74.31 213	63.05 219

CP-MVSNet		62.68 184	65.49 178	59.40 196	71.84 176	75.34 167	62.87 208	67.04 59	52.64 191	27.19 219	53.38 159	48.15 206	41.40 210	71.26 191	75.68 165	86.07 132	82.00 128

PS-CasMVS		62.38 190	65.06 182	59.25 197	71.73 177	75.21 175	62.77 209	66.99 60	51.94 197	26.96 220	52.00 181	47.52 209	41.06 211	71.16 194	75.60 168	85.97 141	81.97 130

WR-MVS_H		61.83 196	65.87 174	57.12 203	71.72 178	76.87 151	61.45 211	66.19 61	51.97 196	22.92 229	53.13 166	52.30 181	33.80 219	71.03 195	75.00 174	86.65 117	80.78 138

USDC		67.36 151	67.90 149	66.74 150	71.72 178	75.23 172	71.58 163	60.28 141	67.45 71	50.54 147	60.93 85	45.20 216	62.08 119	76.56 160	74.50 176	84.25 172	75.38 184

UGNet		72.78 76	77.67 60	67.07 143	71.65 180	83.24 68	75.20 118	63.62 81	64.93 83	56.72 109	71.82 48	73.30 59	49.02 191	81.02 77	80.70 72	86.22 124	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		62.00 192	62.35 203	61.58 182	71.62 181	64.14 212	69.07 174	48.22 217	62.21 103	53.93 124	58.26 105	55.30 144	55.81 167	63.22 220	62.62 221	70.85 222	70.70 204

pmmvs4		67.89 137	67.39 156	68.48 120	71.60 182	73.57 183	74.45 128	60.98 126	64.65 85	57.97 97	54.95 133	51.73 188	61.88 123	73.78 176	75.11 173	83.99 175	77.91 165

testgi		54.39 215	57.86 214	50.35 218	71.59 183	67.24 204	54.95 222	53.25 191	43.36 220	23.78 223	44.64 205	47.87 207	24.96 230	70.45 201	68.66 202	73.60 215	62.78 220

pmmvs6		62.41 188	62.88 196	61.87 181	71.38 184	75.18 176	67.76 180	59.45 155	41.64 223	42.52 190	37.33 218	52.91 172	46.87 197	77.67 134	76.26 157	83.23 178	79.18 159

FMVSNet1		68.84 124	70.47 100	66.94 145	71.35 185	77.68 137	74.71 126	62.35 112	56.93 148	49.94 151	50.01 189	64.59 97	57.07 153	81.33 69	80.72 67	86.25 123	82.00 128

PatchmatchNet		64.21 177	64.65 186	63.69 169	71.29 186	68.66 199	69.63 169	51.70 201	63.04 97	53.77 126	59.83 93	58.34 117	60.23 136	68.54 211	66.06 213	75.56 207	68.08 210
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		64.83 168	65.54 177	64.01 168	70.64 187	69.41 195	65.97 194	52.74 194	57.81 135	52.65 133	54.27 144	56.31 133	60.92 130	72.20 186	73.09 182	81.12 186	75.69 181

MVSTER		72.06 79	74.24 77	69.51 111	70.39 188	75.97 163	76.91 110	57.36 178	64.64 86	61.39 78	68.86 58	63.76 99	63.46 110	81.44 66	79.70 87	87.56 76	85.31 82

Anonymous20231206		56.36 211	57.80 215	54.67 211	70.08 189	66.39 207	60.46 214	57.54 175	49.50 209	29.30 215	33.86 226	46.64 210	35.18 217	70.44 202	68.88 200	75.47 208	68.88 209

thisisatest0515		67.40 150	68.78 132	65.80 155	70.02 190	75.24 169	69.36 173	57.37 177	54.94 179	53.67 127	55.53 122	54.85 149	58.00 145	78.19 125	78.91 98	86.39 122	83.78 103

CMPMVS	47.78 17	62.49 187	62.52 200	62.46 176	70.01 191	70.66 193	62.97 207	51.84 200	51.98 195	56.71 110	42.87 208	53.62 158	57.80 146	72.23 184	70.37 193	75.45 209	75.91 178
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v748		65.12 165	65.24 179	64.98 160	69.77 192	76.45 155	69.47 171	57.06 180	49.93 205	50.70 144	47.87 197	49.50 201	57.14 152	73.64 178	75.18 172	85.75 148	84.14 98

TDRefinement		66.09 159	65.03 184	67.31 137	69.73 193	76.75 152	75.33 115	64.55 76	60.28 118	49.72 153	45.63 204	42.83 219	60.46 134	75.75 164	75.95 164	84.08 173	78.04 164

TinyColmap		62.84 183	61.03 208	64.96 161	69.61 194	71.69 188	68.48 177	59.76 152	55.41 171	47.69 161	47.33 200	34.20 229	62.76 115	74.52 171	72.59 185	81.44 184	71.47 202

RPMNet		61.71 198	62.88 196	60.34 189	69.51 195	69.41 195	63.48 205	49.23 209	57.81 135	45.64 175	50.51 187	50.12 196	53.13 183	68.17 213	68.49 204	81.07 187	75.62 183

IterMVS		66.36 157	68.30 144	64.10 165	69.48 196	74.61 179	73.41 151	50.79 205	57.30 141	48.28 157	60.64 86	59.92 110	60.85 133	74.14 174	72.66 184	81.80 182	78.82 161
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SixPastTwentyTwo		61.84 195	62.45 201	61.12 185	69.20 197	72.20 186	62.03 210	57.40 176	46.54 216	38.03 201	57.14 113	41.72 221	58.12 144	69.67 206	71.58 188	81.94 181	78.30 163

MDTV_nov1_ep13		64.37 172	65.24 179	63.37 174	68.94 198	70.81 191	72.40 158	50.29 208	60.10 119	53.91 125	60.07 90	59.15 113	57.21 151	69.43 208	67.30 208	77.47 199	69.78 206

EPMVS		60.00 203	61.97 204	57.71 201	68.46 199	63.17 219	64.54 201	48.23 216	63.30 95	44.72 179	60.19 88	56.05 141	50.85 187	65.27 217	62.02 223	69.44 225	63.81 217

our_test_3					67.93 200	70.99 190	66.89 187

FC-MVSNet-test		56.90 210	65.20 181	47.21 221	66.98 201	63.20 218	49.11 230	58.60 171	59.38 125	11.50 241	65.60 71	56.68 130	24.66 233	71.17 193	71.36 190	72.38 218	69.02 208

CVMVSNet		62.55 185	65.89 173	58.64 198	66.95 202	69.15 197	66.49 193	56.29 184	52.46 193	32.70 211	59.27 95	58.21 118	50.09 188	71.77 189	71.39 189	79.31 193	78.99 160

FPMVS		51.87 219	50.00 224	54.07 212	66.83 203	57.25 226	60.25 215	50.91 203	50.25 202	34.36 209	36.04 223	32.02 231	41.49 209	58.98 232	56.07 232	70.56 224	59.36 226

pmmvs-eth3d		63.52 180	62.44 202	64.77 162	66.82 204	70.12 194	69.41 172	59.48 154	54.34 184	52.71 132	46.24 203	44.35 218	56.93 154	72.37 181	73.77 179	83.30 177	75.91 178

testpf		47.41 222	48.47 229	46.18 222	66.30 205	50.67 235	48.15 231	42.60 231	37.10 231	28.75 216	40.97 212	39.01 226	30.82 222	52.95 237	53.74 236	60.46 235	64.87 214

TAMVS		59.58 204	62.81 198	55.81 207	66.03 206	65.64 210	63.86 204	48.74 212	49.95 203	37.07 203	54.77 137	58.54 115	44.44 203	72.29 183	71.79 186	74.70 211	66.66 212

MDTV_nov1_ep13_2view		60.16 202	60.51 210	59.75 192	65.39 207	69.05 198	68.00 178	48.29 215	51.99 194	45.95 173	48.01 195	49.64 200	53.39 181	68.83 210	66.52 212	77.47 199	69.55 207

pmmvs5		62.37 191	64.04 190	60.42 188	65.03 208	71.67 189	67.17 185	52.70 196	50.30 201	44.80 178	54.23 148	51.19 192	49.37 190	72.88 180	73.48 181	83.45 176	74.55 188

ambc			53.42 219		64.99 209	63.36 217	49.96 228		47.07 214	37.12 202	28.97 230	16.36 244	41.82 208	75.10 170	67.34 207	71.55 221	75.72 180

V42		68.76 126	69.63 111	67.74 126	64.93 210	78.01 128	78.30 97	56.48 182	58.65 131	56.30 113	54.26 146	57.03 128	64.85 104	77.47 142	77.01 134	85.60 151	84.96 89

PMVS	39.38 18	46.06 227	43.30 233	49.28 220	62.93 211	38.75 242	41.88 235	53.50 190	33.33 237	35.46 208	28.90 231	31.01 234	33.04 220	58.61 233	54.63 235	68.86 226	57.88 229
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new-patchmatchnet		46.97 225	49.47 225	44.05 227	62.82 212	56.55 227	45.35 233	52.01 198	42.47 222	17.04 237	35.73 224	35.21 228	21.84 239	61.27 225	54.83 234	65.26 233	60.26 223

ADS-MVSNet		55.94 212	58.01 213	53.54 216	62.48 213	58.48 225	59.12 218	46.20 220	59.65 123	42.88 188	52.34 180	53.31 170	46.31 200	62.00 224	60.02 228	64.23 234	60.24 225

v52		65.23 163	66.24 166	64.06 166	61.94 214	76.42 156	72.06 160	54.30 188	49.94 204	50.04 149	47.41 199	52.42 177	60.23 136	75.71 165	76.22 159	85.78 146	85.56 74

V4		65.23 163	66.23 167	64.06 166	61.94 214	76.42 156	72.05 161	54.31 187	49.91 206	50.06 148	47.42 198	52.40 178	60.24 135	75.71 165	76.22 159	85.78 146	85.56 74

RPSCF		67.64 145	71.25 94	63.43 173	61.86 216	70.73 192	67.26 184	50.86 204	74.20 58	58.91 86	67.49 66	69.33 79	64.10 108	71.41 190	68.45 205	77.61 198	77.17 170

MIMVSNet		58.52 207	61.34 207	55.22 209	60.76 217	67.01 205	66.81 188	49.02 211	56.43 160	38.90 198	40.59 215	54.54 152	40.57 213	73.16 179	71.65 187	75.30 210	66.00 213

PatchT		61.97 193	64.04 190	59.55 195	60.49 218	67.40 203	56.54 220	48.65 213	56.69 151	52.65 133	51.10 186	52.14 184	60.92 130	72.20 186	73.09 182	78.03 197	75.69 181

N_pmnet		47.35 223	50.13 223	44.11 226	59.98 219	51.64 234	51.86 225	44.80 226	49.58 208	20.76 233	40.65 214	40.05 225	29.64 223	59.84 230	55.15 233	57.63 236	54.00 233

1111		43.08 229	44.02 232	41.98 228	59.22 220	49.27 238	41.48 236	45.63 223	35.01 232	23.06 227	28.60 232	30.15 235	27.22 225	60.42 228	57.97 230	55.27 239	46.74 235

.test1245		30.81 236	29.14 239	32.77 235	59.22 220	49.27 238	41.48 236	45.63 223	35.01 232	23.06 227	28.60 232	30.15 235	27.22 225	60.42 228	0.10 243	0.01 247	0.43 244

MVS-HIRNet		54.41 214	52.10 222	57.11 204	58.99 222	56.10 228	49.68 229	49.10 210	46.18 217	52.15 137	33.18 227	46.11 214	56.10 163	63.19 221	59.70 229	76.64 203	60.25 224

PM-MVS		60.48 201	60.94 209	59.94 191	58.85 223	66.83 206	64.27 203	51.39 202	55.03 177	48.03 158	50.00 191	40.79 223	58.26 143	69.20 209	67.13 211	78.84 195	77.60 167

anonymousdsp		65.28 162	67.98 148	62.13 178	58.73 224	73.98 182	67.10 186	50.69 206	48.41 210	47.66 162	54.27 144	52.75 176	61.45 128	76.71 158	80.20 80	87.13 87	89.53 47

LP		53.62 217	53.43 218	53.83 214	58.51 225	62.59 222	57.31 219	46.04 222	47.86 211	42.69 189	36.08 222	36.86 227	46.53 199	64.38 218	64.25 217	71.92 219	62.00 222

TESTMET0.1,1		61.10 199	64.36 188	57.29 202	57.53 226	63.93 213	66.61 191	36.22 236	54.41 181	47.77 159	57.46 109	60.25 108	55.20 173	70.80 198	69.33 196	80.40 189	74.38 189

EU-MVSNet		54.63 213	58.69 212	49.90 219	56.99 227	62.70 221	56.41 221	50.64 207	45.95 218	23.14 226	50.42 188	46.51 211	36.63 216	65.51 216	64.85 215	75.57 206	74.91 186

FMVSNet5		57.24 208	60.02 211	53.99 213	56.45 228	62.74 220	65.27 197	47.03 218	55.14 174	39.55 196	40.88 213	53.42 168	41.83 207	72.35 182	71.10 191	73.79 214	64.50 216

test2356		47.20 224	48.62 228	45.54 224	56.38 229	54.89 230	50.62 226	45.08 225	38.65 228	23.40 224	36.23 221	31.10 233	29.31 224	62.76 222	62.49 222	68.48 227	54.23 232

testus		45.61 228	49.06 227	41.59 229	56.13 230	55.28 229	43.51 234	39.64 234	37.74 229	18.23 235	35.52 225	31.28 232	24.69 232	62.46 223	62.90 220	67.33 229	58.26 228

test-mter		60.84 200	64.62 187	56.42 205	55.99 231	64.18 211	65.39 196	34.23 238	54.39 183	46.21 171	57.40 111	59.49 112	55.86 166	71.02 196	69.65 195	80.87 188	76.20 177

CHOSEN 280x420		58.70 206	61.88 205	54.98 210	55.45 232	50.55 236	64.92 199	40.36 232	55.21 173	38.13 200	48.31 193	63.76 99	63.03 114	73.73 177	68.58 203	68.00 228	73.04 195

PMMVS		65.06 167	69.17 124	60.26 190	55.25 233	63.43 216	66.71 190	43.01 230	62.41 101	50.64 145	69.44 57	67.04 91	63.29 112	74.36 173	73.54 180	82.68 180	73.99 192

testmv		42.58 230	44.36 230	40.49 230	54.63 234	52.76 232	41.21 238	44.37 227	28.83 238	12.87 238	27.16 235	25.03 239	23.01 235	60.83 226	61.13 224	66.88 230	54.81 230

test1235678		42.57 231	44.36 230	40.49 230	54.63 234	52.75 233	41.21 238	44.37 227	28.82 239	12.87 238	27.15 236	25.01 240	23.01 235	60.83 226	61.13 224	66.88 230	54.81 230

no-one		36.35 234	37.59 236	34.91 233	46.13 236	49.89 237	27.99 243	43.56 229	20.91 243	7.03 244	14.64 241	15.50 245	18.92 240	42.95 238	60.20 227	65.84 232	59.03 227

Gipuma		36.38 233	35.80 237	37.07 232	45.76 237	33.90 243	29.81 242	48.47 214	39.91 226	18.02 236	8.00 245	8.14 247	25.14 229	59.29 231	61.02 226	55.19 240	40.31 237
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs3		47.65 221	49.08 226	45.99 223	44.61 238	54.79 231	50.04 227	31.95 241	33.91 234	29.90 213	30.37 228	33.53 230	46.31 200	63.50 219	63.67 219	73.14 217	63.77 218

MIMVSNet1		49.27 220	53.25 220	44.62 225	44.61 238	61.52 224	53.61 223	52.18 197	41.62 224	18.68 234	28.14 234	41.58 222	25.50 228	68.46 212	69.04 198	73.15 216	62.37 221

test12356		35.10 235	38.50 235	31.13 236	44.14 240	43.70 241	32.27 241	34.42 237	26.51 241	9.47 242	25.22 238	20.34 241	10.86 242	53.47 235	56.15 231	55.59 238	44.11 236

MDA-MVSNet-bldmvs		53.37 218	53.01 221	53.79 215	43.67 241	67.95 202	59.69 216	57.92 174	43.69 219	32.41 212	41.47 211	27.89 238	52.38 185	56.97 234	65.99 214	76.68 202	67.13 211

E-PMN		21.77 238	18.24 241	25.89 237	40.22 242	19.58 246	12.46 247	39.87 233	18.68 245	6.71 245	9.57 242	4.31 250	22.36 238	19.89 243	27.28 241	33.73 242	28.34 241

EMVS		20.98 239	17.15 242	25.44 238	39.51 243	19.37 247	12.66 246	39.59 235	19.10 244	6.62 246	9.27 243	4.40 249	22.43 237	17.99 244	24.40 242	31.81 243	25.53 242

new_pmnet		38.40 232	42.64 234	33.44 234	37.54 244	45.00 240	36.60 240	32.72 240	40.27 225	12.72 240	29.89 229	28.90 237	24.78 231	53.17 236	52.90 237	56.31 237	48.34 234

PMMVS2		25.60 237	29.75 238	20.76 240	28.00 245	30.93 244	23.10 244	29.18 242	23.14 242	1.46 248	18.23 240	16.54 243	5.08 243	40.22 239	41.40 239	37.76 241	37.79 239

tmp_tt				14.50 242	14.68 246	7.17 249	10.46 249	2.21 244	37.73 230	28.71 217	25.26 237	16.98 242	4.37 244	31.49 240	29.77 240	26.56 244

MVE	19.12 19	20.47 240	23.27 240	17.20 241	12.66 247	25.41 245	10.52 248	34.14 239	14.79 246	6.53 247	8.79 244	4.68 248	16.64 241	29.49 241	41.63 238	22.73 245	38.11 238
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GG-mvs-BLEND		46.86 226	67.51 153	22.75 239	0.05 248	76.21 161	64.69 200	0.04 245	61.90 105	0.09 249	55.57 120	71.32 67	0.08 245	70.54 200	67.19 209	71.58 220	69.86 205

testmvs		0.09 241	0.15 243	0.02 243	0.01 249	0.02 250	0.05 251	0.01 246	0.11 247	0.01 250	0.26 247	0.01 251	0.06 247	0.10 245	0.10 243	0.01 247	0.43 244

sosnet-low-res		0.00 243	0.00 245	0.00 245	0.00 250	0.00 252	0.00 253	0.00 248	0.00 249	0.00 251	0.00 248	0.00 252	0.00 248	0.00 247	0.00 246	0.00 250	0.00 246

sosnet		0.00 243	0.00 245	0.00 245	0.00 250	0.00 252	0.00 253	0.00 248	0.00 249	0.00 251	0.00 248	0.00 252	0.00 248	0.00 247	0.00 246	0.00 250	0.00 246

test123		0.09 241	0.14 244	0.02 243	0.00 250	0.02 250	0.02 252	0.01 246	0.09 248	0.00 251	0.30 246	0.00 252	0.08 245	0.03 246	0.09 245	0.01 247	0.45 243

MTAPA										83.48 1		86.45 14

MTMP										82.66 3		84.91 22

Patchmatch-RL test							2.85 250

NP-MVS									80.10 42

Patchmtry						65.80 209	65.97 194	52.74 194		52.65 133

DeepMVS_CX						18.74 248	18.55 245	8.02 243	26.96 240	7.33 243	23.81 239	13.05 246	25.99 227	25.17 242		22.45 246	36.25 240

High-resolution multi-view benchmark