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 3	88.92 7	84.95 2	92.61 1	87.91 32	90.23 8	76.06 2	88.85 6	81.20 4	87.33 8	87.93 6	79.47 5	88.59 4	88.23 4	90.15 26	93.60 14

NCCC		85.34 13	86.59 18	83.88 9	91.48 2	88.88 18	89.79 10	75.54 6	86.67 14	77.94 16	76.55 28	84.99 18	78.07 10	88.04 7	87.68 7	90.46 18	93.31 15

CNVR-MVS		86.36 8	88.19 11	84.23 5	91.33 3	89.84 8	90.34 5	75.56 5	87.36 11	78.97 10	81.19 21	86.76 10	78.74 6	89.30 2	88.58 1	90.45 19	94.33 4

APDe-MVS		88.00 1	90.50 1	85.08 1	90.95 4	91.58 3	92.03 1	75.53 7	91.15 1	80.10 7	92.27 2	88.34 5	80.80 2	88.00 9	86.99 12	91.09 4	95.16 1

SteuartSystems-ACMMP		85.99 9	88.31 10	83.27 14	90.73 5	89.84 8	90.27 7	74.31 9	84.56 23	75.88 22	87.32 9	85.04 17	77.31 17	89.01 3	88.46 2	91.14 3	93.96 6
Skip Steuart: Steuart Systems R&D Blog.
APD-MVS		86.84 6	88.91 8	84.41 3	90.66 6	90.10 6	90.78 2	75.64 4	87.38 10	78.72 11	90.68 5	86.82 9	80.15 3	87.13 18	86.45 21	90.51 13	93.83 8
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MP-MVS		85.50 12	87.40 14	83.28 13	90.65 7	89.51 13	89.16 16	74.11 12	83.70 26	78.06 15	85.54 13	84.89 20	77.31 17	87.40 15	87.14 11	90.41 20	93.65 13

train_agg		84.86 18	87.21 15	82.11 20	90.59 8	85.47 47	89.81 9	73.55 18	83.95 25	73.30 30	89.84 7	87.23 8	75.61 25	86.47 27	85.46 31	89.78 29	92.06 25

MCST-MVS		85.13 16	86.62 17	83.39 11	90.55 9	89.82 10	89.29 14	73.89 16	84.38 24	76.03 21	79.01 24	85.90 14	78.47 7	87.81 11	86.11 26	92.11 1	93.29 16

MPTG		85.71 10	86.88 16	84.34 4	90.54 10	87.11 36	89.77 11	74.17 11	88.54 7	83.08 2	78.60 25	86.10 12	78.11 9	87.80 12	87.46 9	90.35 22	92.56 19

DeepC-MVS_fast	78.24 3	84.27 22	85.50 24	82.85 16	90.46 11	89.24 15	87.83 25	74.24 10	84.88 19	76.23 20	75.26 31	81.05 34	77.62 14	88.02 8	87.62 8	90.69 9	92.41 21
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 7	89.01 5	83.62 10	90.28 12	90.09 7	90.32 6	74.05 13	88.32 8	79.74 8	87.04 10	85.59 16	76.97 22	89.35 1	88.44 3	90.35 22	94.27 5

SD-MVS		86.96 4	89.45 3	84.05 8	90.13 13	89.23 16	89.77 11	74.59 8	89.17 4	80.70 5	89.93 6	89.67 1	78.47 7	87.57 13	86.79 15	90.67 10	93.76 10

ACMMPR		85.52 11	87.53 13	83.17 15	90.13 13	89.27 14	89.30 13	73.97 14	86.89 13	77.14 18	86.09 11	83.18 24	77.74 13	87.42 14	87.20 10	90.77 6	92.63 18

PGM-MVS		84.42 21	86.29 21	82.23 19	90.04 15	88.82 20	89.23 15	71.74 27	82.82 29	74.61 25	84.41 16	82.09 26	77.03 21	87.13 18	86.73 17	90.73 8	92.06 25

HSP-MVS		87.45 2	90.22 2	84.22 6	90.00 16	91.80 2	90.59 3	75.80 3	89.93 3	78.35 13	92.54 1	89.18 2	80.89 1	87.99 10	86.29 23	89.70 33	93.85 7

CSCG		85.28 15	87.68 12	82.49 18	89.95 17	91.99 1	88.82 17	71.20 29	86.41 15	79.63 9	79.26 22	88.36 4	73.94 32	86.64 25	86.67 18	91.40 2	94.41 2

mPP-MVS					89.90 18							81.29 33

TSAR-MVS + MP.		86.88 5	89.23 4	84.14 7	89.78 19	88.67 24	90.59 3	73.46 19	88.99 5	80.52 6	91.26 3	88.65 3	79.91 4	86.96 23	86.22 24	90.59 11	93.83 8

X-MVS		83.23 25	85.20 26	80.92 26	89.71 20	88.68 21	88.21 24	73.60 17	82.57 30	71.81 38	77.07 26	81.92 28	71.72 47	86.98 22	86.86 13	90.47 15	92.36 22

TSAR-MVS + ACMM		85.10 17	88.81 9	80.77 27	89.55 21	88.53 26	88.59 20	72.55 22	87.39 9	71.90 35	90.95 4	87.55 7	74.57 27	87.08 20	86.54 19	87.47 69	93.67 11

CP-MVS		84.74 20	86.43 20	82.77 17	89.48 22	88.13 31	88.64 18	73.93 15	84.92 18	76.77 19	81.94 19	83.50 22	77.29 19	86.92 24	86.49 20	90.49 14	93.14 17

CDPH-MVS		82.64 26	85.03 27	79.86 31	89.41 23	88.31 28	88.32 22	71.84 26	80.11 37	67.47 53	82.09 18	81.44 32	71.85 45	85.89 32	86.15 25	90.24 24	91.25 31

DeepC-MVS	78.47 2	84.81 19	86.03 22	83.37 12	89.29 24	90.38 5	88.61 19	76.50 1	86.25 16	77.22 17	75.12 32	80.28 36	77.59 15	88.39 5	88.17 5	91.02 5	93.66 12
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 39	78.62 50	81.05 25	89.23 25	86.06 44	84.95 41	71.96 25	79.39 40	75.51 23	63.16 71	68.84 77	76.51 23	83.55 47	82.85 46	88.13 56	86.46 60

EPNet		79.08 46	80.62 41	77.28 45	88.90 26	83.17 64	83.65 46	72.41 23	74.41 50	67.15 55	76.78 27	74.37 52	64.43 98	83.70 46	83.69 42	87.15 75	88.19 49
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	79.04 1	85.30 14	88.93 6	81.06 24	88.77 27	90.48 4	85.46 38	73.08 20	90.97 2	73.77 29	84.81 15	85.95 13	77.43 16	88.22 6	87.73 6	87.85 64	94.34 3

ACMMP		83.42 24	85.27 25	81.26 23	88.47 28	88.49 27	88.31 23	72.09 24	83.42 27	72.77 33	82.65 17	78.22 40	75.18 26	86.24 30	85.76 28	90.74 7	92.13 24
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 27	82.76 32	81.97 21	88.02 29	89.67 11	86.60 29	71.48 28	81.28 35	78.18 14	64.78 67	77.96 42	77.13 20	87.32 16	86.83 14	90.41 20	91.48 29

OPM-MVS		79.68 40	79.28 49	80.15 30	87.99 30	86.77 39	88.52 21	72.72 21	64.55 79	67.65 52	67.87 58	74.33 53	74.31 30	86.37 29	85.25 33	89.73 32	89.81 42

MAR-MVS		79.21 43	80.32 45	77.92 43	87.46 31	88.15 30	83.95 45	67.48 53	74.28 51	68.25 49	64.70 68	77.04 43	72.17 42	85.42 34	85.00 35	88.22 53	87.62 54
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 33	83.27 30	78.76 38	87.40 32	85.45 48	86.95 27	70.47 32	81.31 34	66.91 56	79.24 23	76.63 44	71.67 48	84.43 40	83.78 41	89.19 42	92.05 27

abl_6				79.05 35	87.27 33	88.85 19	83.62 47	68.25 46	81.68 33	72.94 32	73.79 38	84.45 21	72.55 40			89.66 35	90.64 36

CANet		81.62 32	83.41 29	79.53 33	87.06 34	88.59 25	85.47 37	67.96 50	76.59 45	74.05 26	74.69 33	81.98 27	72.98 38	86.14 31	85.47 30	89.68 34	90.42 39

MSLP-MVS++		82.09 29	82.66 33	81.42 22	87.03 35	87.22 35	85.82 34	70.04 34	80.30 36	78.66 12	68.67 54	81.04 35	77.81 12	85.19 36	84.88 36	89.19 42	91.31 30

ACMM	72.26 8	78.86 47	78.13 51	79.71 32	86.89 36	83.40 61	86.02 32	70.50 31	75.28 47	71.49 42	63.01 72	69.26 71	73.57 34	84.11 42	83.98 40	89.76 31	87.84 52
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVS					86.63 37	88.68 21	85.00 39			71.81 38		81.92 28				90.47 15

X-MVStestdata					86.63 37	88.68 21	85.00 39			71.81 38		81.92 28				90.47 15

PHI-MVS		82.36 28	85.89 23	78.24 41	86.40 39	89.52 12	85.52 36	69.52 40	82.38 32	65.67 58	81.35 20	82.36 25	73.07 37	87.31 17	86.76 16	89.24 40	91.56 28

LGP-MVS_train		79.83 36	81.22 39	78.22 42	86.28 40	85.36 50	86.76 28	69.59 38	77.34 42	65.14 60	75.68 30	70.79 63	71.37 50	84.60 38	84.01 39	90.18 25	90.74 35

MVS_0304		81.73 31	83.86 28	79.26 34	86.22 41	89.18 17	86.41 30	67.15 54	75.28 47	70.75 45	74.59 34	83.49 23	74.42 29	87.05 21	86.34 22	90.58 12	91.08 33

CPTT-MVS		81.77 30	83.10 31	80.21 29	85.93 42	86.45 42	87.72 26	70.98 30	82.54 31	71.53 41	74.23 37	81.49 31	76.31 24	82.85 54	81.87 50	88.79 49	92.26 23

MVS_111021_HR		80.13 35	81.46 37	78.58 39	85.77 43	85.17 51	83.45 48	69.28 41	74.08 53	70.31 46	74.31 36	75.26 50	73.13 36	86.46 28	85.15 34	89.53 36	89.81 42

ACMP	73.23 7	79.79 37	80.53 42	78.94 36	85.61 44	85.68 45	85.61 35	69.59 38	77.33 43	71.00 44	74.45 35	69.16 72	71.88 43	83.15 51	83.37 44	89.92 28	90.57 38
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UA-Net		74.47 61	77.80 53	70.59 80	85.33 45	85.40 49	73.54 127	65.98 62	60.65 103	56.00 104	72.11 41	79.15 37	54.63 153	83.13 52	82.25 48	88.04 58	81.92 119

TSAR-MVS + GP.		83.69 23	86.58 19	80.32 28	85.14 46	86.96 37	84.91 42	70.25 33	84.71 22	73.91 28	85.16 14	85.63 15	77.92 11	85.44 33	85.71 29	89.77 30	92.45 20

LS3D		74.08 62	73.39 72	74.88 56	85.05 47	82.62 67	79.71 60	68.66 44	72.82 55	58.80 79	57.61 98	61.31 96	71.07 52	80.32 77	78.87 87	86.00 121	80.18 130

QAPM		78.47 48	80.22 46	76.43 49	85.03 48	86.75 40	80.62 55	66.00 61	73.77 54	65.35 59	65.54 65	78.02 41	72.69 39	83.71 45	83.36 45	88.87 48	90.41 40

OpenMVS	70.44 10	76.15 56	76.82 63	75.37 53	85.01 49	84.79 53	78.99 68	62.07 95	71.27 57	67.88 51	57.91 97	72.36 58	70.15 54	82.23 57	81.41 54	88.12 57	87.78 53

CLD-MVS		79.35 42	81.23 38	77.16 46	85.01 49	86.92 38	85.87 33	60.89 104	80.07 39	75.35 24	72.96 39	73.21 56	68.43 63	85.41 35	84.63 37	87.41 70	85.44 71
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 38	80.52 43	78.84 37	84.94 51	87.35 33	84.43 44	65.54 64	78.29 41	73.97 27	63.00 73	75.62 49	74.07 31	85.00 37	85.34 32	90.11 27	89.04 45

PCF-MVS	73.28 6	79.42 41	80.41 44	78.26 40	84.88 52	88.17 29	86.08 31	69.85 35	75.23 49	68.43 48	68.03 57	78.38 39	71.76 46	81.26 68	80.65 69	88.56 52	91.18 32
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DELS-MVS		79.15 45	81.07 40	76.91 47	83.54 53	87.31 34	84.45 43	64.92 68	69.98 58	69.34 47	71.62 44	76.26 45	69.84 55	86.57 26	85.90 27	89.39 38	89.88 41
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 34	82.59 34	77.54 44	83.04 54	85.54 46	83.25 49	65.05 67	87.32 12	72.42 34	72.04 42	78.97 38	73.30 35	83.86 43	81.60 53	88.15 55	88.83 47

PLC	68.99 11	75.68 57	75.31 67	76.12 51	82.94 55	81.26 74	79.94 58	66.10 59	77.15 44	66.86 57	59.13 86	68.53 78	73.73 33	80.38 75	79.04 85	87.13 79	81.68 121
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		77.20 52	77.54 55	76.80 48	82.63 56	84.31 55	79.77 59	64.64 69	85.17 17	73.18 31	56.37 105	69.81 68	74.53 28	81.12 70	78.69 88	86.04 118	87.29 57

ACMH+	66.54 13	71.36 75	70.09 89	72.85 64	82.59 57	81.13 75	78.56 82	68.04 48	61.55 97	52.52 124	51.50 157	54.14 140	68.56 62	78.85 96	79.50 82	86.82 96	83.94 90

ACMH	65.37 14	70.71 79	70.00 90	71.54 67	82.51 58	82.47 68	77.78 94	68.13 47	56.19 141	46.06 153	54.30 125	51.20 166	68.68 61	80.66 74	80.72 62	86.07 116	84.45 86
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
canonicalmvs		79.16 44	82.37 35	75.41 52	82.33 59	86.38 43	80.80 54	63.18 78	82.90 28	67.34 54	72.79 40	76.07 46	69.62 56	83.46 50	84.41 38	89.20 41	90.60 37

MSDG		71.52 74	69.87 94	73.44 62	82.21 60	79.35 98	79.52 61	64.59 70	66.15 67	61.87 68	53.21 144	56.09 128	65.85 94	78.94 95	78.50 89	86.60 109	76.85 153

IS_MVSNet		73.33 64	77.34 59	68.65 108	81.29 61	83.47 60	74.45 117	63.58 76	65.75 71	48.49 143	67.11 62	70.61 64	54.63 153	84.51 39	83.58 43	89.48 37	86.34 61

Effi-MVS+		75.28 59	76.20 64	74.20 60	81.15 62	83.24 62	81.11 52	63.13 80	66.37 65	60.27 74	64.30 69	68.88 76	70.93 53	81.56 61	81.69 52	88.61 50	87.35 55

MVS_111021_LR		78.13 50	79.85 48	76.13 50	81.12 63	81.50 71	80.28 56	65.25 65	76.09 46	71.32 43	76.49 29	72.87 57	72.21 41	82.79 55	81.29 55	86.59 110	87.91 51

FC-MVSNet-train		72.60 70	75.07 68	69.71 99	81.10 64	78.79 101	73.74 125	65.23 66	66.10 68	53.34 117	70.36 47	63.40 91	56.92 142	81.44 62	80.96 58	87.93 60	84.46 85

MS-PatchMatch		70.17 89	70.49 87	69.79 97	80.98 65	77.97 113	77.51 96	58.95 132	62.33 90	55.22 108	53.14 145	65.90 84	62.03 109	79.08 94	77.11 110	84.08 143	77.91 143

TAPA-MVS	71.42 9	77.69 51	80.05 47	74.94 55	80.68 66	84.52 54	81.36 51	63.14 79	84.77 20	64.82 62	68.72 52	75.91 48	71.86 44	81.62 59	79.55 81	87.80 65	85.24 74
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_Blended_VisFu		76.57 53	77.90 52	75.02 54	80.56 67	86.58 41	79.24 63	66.18 58	64.81 76	68.18 50	65.61 63	71.45 60	67.05 65	84.16 41	81.80 51	88.90 46	90.92 34

EPP-MVSNet		74.00 63	77.41 58	70.02 95	80.53 68	83.91 57	74.99 114	62.68 87	65.06 74	49.77 140	68.68 53	72.09 59	63.06 104	82.49 56	80.73 61	89.12 44	88.91 46

COLMAP_ROB	62.73 15	67.66 125	66.76 140	68.70 107	80.49 69	77.98 111	75.29 107	62.95 82	63.62 83	49.96 138	47.32 175	50.72 169	58.57 128	76.87 127	75.50 141	84.94 139	75.33 163
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TSAR-MVS + COLMAP		78.34 49	81.64 36	74.48 59	80.13 70	85.01 52	81.73 50	65.93 63	84.75 21	61.68 69	85.79 12	66.27 83	71.39 49	82.91 53	80.78 60	86.01 119	85.98 62

EPNet_dtu		68.08 120	71.00 83	64.67 141	79.64 71	68.62 170	75.05 113	63.30 77	66.36 66	45.27 156	67.40 60	66.84 82	43.64 177	75.37 138	74.98 146	81.15 156	77.44 146
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		76.21 54	77.52 56	74.69 57	79.46 72	83.79 58	77.50 97	64.34 72	69.88 59	71.88 36	68.54 55	70.42 65	67.05 65	83.48 48	79.63 77	87.89 62	86.87 58

PVSNet_Blended		76.21 54	77.52 56	74.69 57	79.46 72	83.79 58	77.50 97	64.34 72	69.88 59	71.88 36	68.54 55	70.42 65	67.05 65	83.48 48	79.63 77	87.89 62	86.87 58

IB-MVS	66.94 12	71.21 76	71.66 81	70.68 77	79.18 74	82.83 66	72.61 134	61.77 99	59.66 110	63.44 67	53.26 142	59.65 101	59.16 127	76.78 129	82.11 49	87.90 61	87.33 56
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 58	77.13 61	73.31 63	79.07 75	81.32 73	79.98 57	60.12 121	69.72 61	64.11 64	70.53 46	73.22 55	68.90 59	80.14 80	79.48 83	87.67 66	85.50 69

Effi-MVS+-dtu		71.82 72	71.86 80	71.78 66	78.77 76	80.47 89	78.55 83	61.67 101	60.68 102	55.49 105	58.48 90	65.48 85	68.85 60	76.92 126	75.55 140	87.35 71	85.46 70

EG-PatchMatch MVS		67.24 132	66.94 137	67.60 118	78.73 77	81.35 72	73.28 131	59.49 126	46.89 186	51.42 129	43.65 179	53.49 147	55.50 149	81.38 64	80.66 68	87.15 75	81.17 124

gg-mvs-nofinetune		62.55 157	65.05 155	59.62 166	78.72 78	77.61 116	70.83 144	53.63 161	39.71 198	22.04 203	36.36 191	64.32 88	47.53 169	81.16 69	79.03 86	85.00 138	77.17 148

Vis-MVSNet (Re-imp)		67.83 123	73.52 71	61.19 157	78.37 79	76.72 132	66.80 160	62.96 81	65.50 72	34.17 182	67.19 61	69.68 69	39.20 185	79.39 91	79.44 84	85.68 129	76.73 154

DI_MVS_plusplus_trai		75.13 60	76.12 65	73.96 61	78.18 80	81.55 70	80.97 53	62.54 91	68.59 62	65.13 61	61.43 74	74.81 51	69.32 58	81.01 72	79.59 79	87.64 67	85.89 63

tpmp4_e23		68.32 118	67.08 136	69.76 98	77.86 81	75.22 146	78.37 88	56.17 157	66.06 69	64.27 63	57.15 102	54.89 136	63.40 102	70.97 168	68.29 178	78.46 168	77.00 152

view600		67.63 128	68.36 123	66.77 128	77.84 82	78.66 102	73.74 125	62.62 89	56.04 142	44.98 157	52.86 148	52.83 153	55.48 150	80.36 76	77.75 97	85.95 124	80.02 132

view800		67.35 131	68.22 126	66.35 132	77.83 83	78.62 103	72.97 133	62.58 90	55.71 144	44.13 160	52.69 150	52.24 161	54.58 155	80.27 78	78.19 92	86.01 119	79.79 134

tfpn		66.58 135	67.18 135	65.88 134	77.82 84	78.45 105	72.07 138	62.52 92	55.35 147	43.21 164	52.54 152	46.12 185	53.68 156	80.02 81	78.23 91	85.99 122	79.55 136

Fast-Effi-MVS+		73.11 67	73.66 70	72.48 65	77.72 85	80.88 80	78.55 83	58.83 140	65.19 73	60.36 73	59.98 81	62.42 94	71.22 51	81.66 58	80.61 71	88.20 54	84.88 82

UniMVSNet_NR-MVSNet		70.59 80	72.19 78	68.72 106	77.72 85	80.72 81	73.81 123	69.65 37	61.99 92	43.23 162	60.54 77	57.50 107	58.57 128	79.56 88	81.07 57	89.34 39	83.97 88

IterMVS-LS		71.69 73	72.82 76	70.37 89	77.54 87	76.34 138	75.13 112	60.46 111	61.53 98	57.57 87	64.89 66	67.33 80	66.04 90	77.09 125	77.37 106	85.48 132	85.18 75
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NR-MVSNet		68.79 114	70.56 86	66.71 131	77.48 88	79.54 95	73.52 128	69.20 42	61.20 100	39.76 171	58.52 88	50.11 172	51.37 163	80.26 79	80.71 66	88.97 45	83.59 95

conf0.05thres1000		66.26 137	66.77 139	65.66 135	77.45 89	78.10 106	71.85 141	62.44 93	51.47 171	43.00 165	47.92 170	51.66 164	53.40 158	79.71 84	77.97 95	85.82 125	80.56 128

TransMVSNet (Re)		64.74 147	65.66 148	63.66 148	77.40 90	75.33 144	69.86 145	62.67 88	47.63 184	41.21 170	50.01 163	52.33 158	45.31 174	79.57 87	77.69 99	85.49 131	77.07 151

TranMVSNet+NR-MVSNet		69.25 109	70.81 85	67.43 120	77.23 91	79.46 97	73.48 129	69.66 36	60.43 105	39.56 172	58.82 87	53.48 148	55.74 147	79.59 86	81.21 56	88.89 47	82.70 109

CANet_DTU		73.29 65	76.96 62	69.00 104	77.04 92	82.06 69	79.49 62	56.30 155	67.85 63	53.29 118	71.12 45	70.37 67	61.81 114	81.59 60	80.96 58	86.09 115	84.73 83

CHOSEN 1792x2688		69.20 110	69.26 110	69.13 102	76.86 93	78.93 100	77.27 99	60.12 121	61.86 94	54.42 109	42.54 181	61.61 95	66.91 71	78.55 97	78.14 94	79.23 166	83.23 100

HyFIR lowres test		69.47 106	68.94 113	70.09 94	76.77 94	82.93 65	76.63 103	60.17 117	59.00 114	54.03 112	40.54 187	65.23 86	67.89 64	76.54 132	78.30 90	85.03 137	80.07 131

UniMVSNet (Re)		69.53 103	71.90 79	66.76 129	76.42 95	80.93 77	72.59 135	68.03 49	61.75 96	41.68 169	58.34 94	57.23 115	53.27 160	79.53 89	80.62 70	88.57 51	84.90 81

DWT-MVSNet_training		67.24 132	65.96 144	68.74 105	76.15 96	74.36 152	74.37 121	56.66 153	61.82 95	60.51 72	58.23 96	49.76 174	65.07 95	70.04 176	70.39 163	79.70 163	77.11 150

gm-plane-assit		57.00 181	57.62 187	56.28 178	76.10 97	62.43 194	47.62 203	46.57 191	33.84 206	23.24 197	37.52 188	40.19 195	59.61 126	79.81 83	77.55 101	84.55 141	72.03 174

DU-MVS		69.63 98	70.91 84	68.13 112	75.99 98	79.54 95	73.81 123	69.20 42	61.20 100	43.23 162	58.52 88	53.50 146	58.57 128	79.22 92	80.45 72	87.97 59	83.97 88

Baseline_NR-MVSNet		67.53 129	68.77 119	66.09 133	75.99 98	74.75 149	72.43 136	68.41 45	61.33 99	38.33 175	51.31 158	54.13 142	56.03 144	79.22 92	78.19 92	85.37 133	82.45 111

CostFormer		68.92 112	69.58 100	68.15 111	75.98 100	76.17 140	78.22 92	51.86 171	65.80 70	61.56 70	63.57 70	62.83 92	61.85 112	70.40 175	68.67 173	79.42 164	79.62 135

tpm cat1		65.41 140	63.81 164	67.28 122	75.61 101	72.88 155	75.32 106	52.85 165	62.97 87	63.66 66	53.24 143	53.29 151	61.83 113	65.54 187	64.14 190	74.43 184	74.60 165

CDS-MVSNet		67.65 126	69.83 97	65.09 137	75.39 102	76.55 133	74.42 120	63.75 74	53.55 160	49.37 142	59.41 84	62.45 93	44.44 175	79.71 84	79.82 75	83.17 149	77.36 147
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+-dtu		68.34 117	69.47 102	67.01 125	75.15 103	77.97 113	77.12 100	55.40 158	57.87 120	46.68 151	56.17 106	60.39 97	62.36 107	76.32 133	76.25 134	85.35 134	81.34 122

WR-MVS		63.03 153	67.40 133	57.92 172	75.14 104	77.60 117	60.56 183	66.10 59	54.11 159	23.88 194	53.94 136	53.58 145	34.50 190	73.93 146	77.71 98	87.35 71	80.94 125

test-LLR		64.42 148	64.36 160	64.49 142	75.02 105	63.93 184	66.61 162	61.96 96	54.41 155	47.77 147	57.46 99	60.25 98	55.20 151	70.80 169	69.33 168	80.40 160	74.38 167

test0.0.03 1		58.80 177	61.58 177	55.56 180	75.02 105	68.45 171	59.58 187	61.96 96	52.74 162	29.57 186	49.75 166	54.56 138	31.46 193	71.19 163	69.77 165	75.75 177	64.57 188

v1144		69.93 97	69.36 109	70.61 79	74.89 107	80.93 77	79.11 66	60.64 106	55.97 143	55.31 107	53.85 137	54.14 140	66.54 75	78.10 102	77.44 104	87.14 78	85.09 76

v13		69.52 104	68.76 120	70.41 87	74.88 108	77.02 128	78.52 87	58.86 134	56.61 138	56.91 92	54.00 135	56.17 127	66.11 89	77.93 103	76.74 120	87.21 73	82.83 102

v12		69.54 102	68.79 118	70.41 87	74.88 108	77.03 126	78.54 86	58.85 136	56.71 136	56.87 94	54.13 133	56.23 126	66.15 85	77.89 104	76.74 120	87.17 74	82.80 103

v11		69.37 107	68.65 122	70.20 91	74.87 110	76.97 129	78.29 90	58.55 144	56.38 140	56.04 103	54.02 134	54.98 135	66.47 76	78.30 99	76.91 112	86.97 87	83.02 101

V9		69.58 101	68.83 116	70.46 84	74.85 111	77.04 124	78.65 81	58.85 136	56.83 135	57.12 90	54.26 128	56.31 121	66.14 87	77.83 106	76.76 115	87.13 79	82.79 105

V14		69.59 100	68.86 115	70.45 86	74.83 112	77.04 124	78.70 80	58.83 140	56.95 132	57.08 91	54.41 124	56.34 120	66.15 85	77.77 107	76.76 115	87.08 84	82.74 108

v15		69.61 99	68.88 114	70.46 84	74.81 113	77.03 126	78.75 79	58.83 140	57.06 129	57.18 89	54.55 123	56.37 119	66.13 88	77.70 108	76.76 115	87.03 86	82.69 110

v7		70.33 86	69.87 94	70.88 68	74.79 114	81.04 76	79.22 64	60.57 108	57.70 126	56.65 100	54.23 130	55.29 133	66.95 68	78.28 100	77.47 102	87.12 82	85.05 78

v10		70.22 88	69.76 98	70.74 74	74.79 114	80.30 92	79.22 64	59.81 124	57.71 125	56.58 101	54.22 132	55.31 131	66.95 68	78.28 100	77.47 102	87.12 82	85.07 77

v1141		69.96 96	69.44 106	70.58 82	74.78 116	80.50 87	78.85 69	60.30 112	56.95 132	56.74 97	54.68 121	56.26 125	65.93 91	77.38 119	76.72 125	86.88 92	83.57 98

divwei89l23v2f112		69.97 94	69.44 106	70.58 82	74.78 116	80.50 87	78.85 69	60.30 112	56.97 131	56.75 96	54.67 122	56.27 124	65.92 92	77.37 120	76.72 125	86.88 92	83.58 97

v1		69.97 94	69.45 105	70.59 80	74.78 116	80.51 86	78.84 71	60.30 112	56.98 130	56.81 95	54.69 120	56.29 123	65.91 93	77.37 120	76.71 128	86.89 91	83.59 95

v17		70.03 93	69.43 108	70.72 76	74.75 119	77.09 121	78.78 78	58.85 136	59.53 112	58.72 80	54.87 117	57.39 109	66.38 78	77.60 112	76.75 118	86.83 95	82.80 103

v16		70.07 91	69.46 103	70.79 72	74.74 120	77.08 122	78.79 76	58.86 134	59.75 109	59.15 77	54.87 117	57.33 110	66.38 78	77.61 111	76.77 113	86.81 101	82.79 105

v8		70.23 87	69.86 96	70.67 78	74.69 121	79.82 94	78.79 76	59.18 130	58.80 116	58.20 82	55.00 113	57.33 110	66.31 84	77.51 116	76.71 128	86.82 96	83.88 91

v1neww		70.34 84	69.93 92	70.82 70	74.68 122	80.61 83	78.80 74	60.17 117	58.74 117	58.10 84	55.00 113	57.28 113	66.33 81	77.53 113	76.74 120	86.82 96	83.61 93

v7new		70.34 84	69.93 92	70.82 70	74.68 122	80.61 83	78.80 74	60.17 117	58.74 117	58.10 84	55.00 113	57.28 113	66.33 81	77.53 113	76.74 120	86.82 96	83.61 93

v6		70.35 83	69.94 91	70.83 69	74.68 122	80.62 82	78.81 73	60.16 120	58.81 115	58.17 83	55.01 112	57.31 112	66.32 83	77.53 113	76.73 124	86.82 96	83.62 92

v18		70.10 90	69.52 101	70.77 73	74.66 125	77.06 123	78.84 71	58.84 139	60.01 108	59.23 76	55.06 111	57.47 108	66.34 80	77.50 117	76.75 118	86.71 103	82.77 107

v2v482		70.05 92	69.46 103	70.74 74	74.62 126	80.32 91	79.00 67	60.62 107	57.41 127	56.89 93	55.43 110	55.14 134	66.39 77	77.25 122	77.14 109	86.90 89	83.57 98

v1192		69.50 105	68.83 116	70.29 90	74.49 127	80.92 79	78.55 83	60.54 109	55.04 151	54.21 110	52.79 149	52.33 158	66.92 70	77.88 105	77.35 107	87.04 85	85.51 68

DTE-MVSNet		61.85 166	64.96 157	58.22 171	74.32 128	74.39 151	61.01 182	67.85 51	51.76 170	21.91 204	53.28 141	48.17 178	37.74 186	72.22 156	76.44 131	86.52 112	78.49 140

Vis-MVSNet		72.77 69	77.20 60	67.59 119	74.19 129	84.01 56	76.61 104	61.69 100	60.62 104	50.61 134	70.25 48	71.31 62	55.57 148	83.85 44	82.28 47	86.90 89	88.08 50
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
diffmvs		73.13 66	75.65 66	70.19 92	74.07 130	77.17 120	78.24 91	57.45 148	72.44 56	64.02 65	69.05 50	75.92 47	64.86 96	75.18 140	75.27 142	82.47 151	84.53 84

v144192		69.34 108	68.68 121	70.12 93	74.06 131	80.54 85	78.08 93	60.54 109	54.99 153	54.13 111	52.92 147	52.80 154	66.73 73	77.13 124	76.72 125	87.15 75	85.63 64

v1921920		69.03 111	68.32 124	69.86 96	74.03 132	80.37 90	77.55 95	60.25 116	54.62 154	53.59 116	52.36 153	51.50 165	66.75 72	77.17 123	76.69 130	86.96 88	85.56 65

PEN-MVS		62.96 154	65.77 147	59.70 165	73.98 133	75.45 142	63.39 176	67.61 52	52.49 164	25.49 193	53.39 139	49.12 177	40.85 183	71.94 159	77.26 108	86.86 94	80.72 127

v1240		68.64 116	67.89 129	69.51 100	73.89 134	80.26 93	76.73 102	59.97 123	53.43 161	53.08 119	51.82 156	50.84 168	66.62 74	76.79 128	76.77 113	86.78 102	85.34 72

GA-MVS		68.14 119	69.17 111	66.93 127	73.77 135	78.50 104	74.45 117	58.28 145	55.11 150	48.44 144	60.08 79	53.99 143	61.50 115	78.43 98	77.57 100	85.13 135	80.54 129

pm-mvs1		65.62 139	67.42 132	63.53 149	73.66 136	76.39 137	69.66 146	60.87 105	49.73 179	43.97 161	51.24 159	57.00 117	48.16 168	79.89 82	77.84 96	84.85 140	79.82 133

dps		64.00 151	62.99 166	65.18 136	73.29 137	72.07 158	68.98 151	53.07 164	57.74 124	58.41 81	55.55 109	47.74 181	60.89 120	69.53 178	67.14 182	76.44 176	71.19 176

v148		67.85 122	67.53 130	68.23 110	73.25 138	77.57 118	74.26 122	57.36 150	55.70 145	57.45 88	53.53 138	55.42 130	61.96 110	75.23 139	73.92 149	85.08 136	81.32 123

PatchMatch-RL		67.78 124	66.65 141	69.10 103	73.01 139	72.69 156	68.49 152	61.85 98	62.93 88	60.20 75	56.83 104	50.42 170	69.52 57	75.62 137	74.46 148	81.51 154	73.62 172

GBi-Net		70.78 77	73.37 73	67.76 113	72.95 140	78.00 108	75.15 109	62.72 83	64.13 80	51.44 126	58.37 91	69.02 73	57.59 134	81.33 65	80.72 62	86.70 104	82.02 113

test1		70.78 77	73.37 73	67.76 113	72.95 140	78.00 108	75.15 109	62.72 83	64.13 80	51.44 126	58.37 91	69.02 73	57.59 134	81.33 65	80.72 62	86.70 104	82.02 113

FMVSNet2		70.39 82	72.67 77	67.72 116	72.95 140	78.00 108	75.15 109	62.69 86	63.29 85	51.25 130	55.64 107	68.49 79	57.59 134	80.91 73	80.35 73	86.70 104	82.02 113

FMVSNet3		70.49 81	72.90 75	67.67 117	72.88 143	77.98 111	74.96 115	62.72 83	64.13 80	51.44 126	58.37 91	69.02 73	57.43 137	79.43 90	79.57 80	86.59 110	81.81 120

LTVRE_ROB	59.44 16	61.82 169	62.64 170	60.87 159	72.83 144	77.19 119	64.37 172	58.97 131	33.56 207	28.00 190	52.59 151	42.21 191	63.93 100	74.52 142	76.28 132	77.15 173	82.13 112
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		67.05 134	66.94 137	67.17 123	72.35 145	78.97 99	73.26 132	58.88 133	51.16 172	50.90 131	48.21 168	50.11 172	60.96 117	77.70 108	77.38 105	86.68 107	85.05 78

tpm		62.41 160	63.15 165	61.55 156	72.24 146	63.79 186	71.31 143	46.12 193	57.82 121	55.33 106	59.90 82	54.74 137	53.63 157	67.24 185	64.29 188	70.65 195	74.25 169

test20.03		53.93 188	56.28 188	51.19 190	72.19 147	65.83 179	53.20 195	61.08 102	42.74 192	22.08 202	37.07 190	45.76 187	24.29 206	70.44 173	69.04 170	74.31 185	63.05 192

CP-MVSNet		62.68 156	65.49 150	59.40 168	71.84 148	75.34 143	62.87 178	67.04 55	52.64 163	27.19 191	53.38 140	48.15 179	41.40 181	71.26 162	75.68 138	86.07 116	82.00 116

PS-CasMVS		62.38 162	65.06 154	59.25 169	71.73 149	75.21 147	62.77 179	66.99 56	51.94 169	26.96 192	52.00 155	47.52 182	41.06 182	71.16 165	75.60 139	85.97 123	81.97 118

WR-MVS_H		61.83 168	65.87 146	57.12 175	71.72 150	76.87 130	61.45 181	66.19 57	51.97 168	22.92 201	53.13 146	52.30 160	33.80 191	71.03 166	75.00 145	86.65 108	80.78 126

USDC		67.36 130	67.90 128	66.74 130	71.72 150	75.23 145	71.58 142	60.28 115	67.45 64	50.54 135	60.93 75	45.20 188	62.08 108	76.56 131	74.50 147	84.25 142	75.38 162

UGNet		72.78 68	77.67 54	67.07 124	71.65 152	83.24 62	75.20 108	63.62 75	64.93 75	56.72 98	71.82 43	73.30 54	49.02 167	81.02 71	80.70 67	86.22 114	88.67 48
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		62.00 164	62.35 174	61.58 155	71.62 153	64.14 183	69.07 150	48.22 189	62.21 91	53.93 113	58.26 95	55.30 132	55.81 146	63.22 192	62.62 193	70.85 194	70.70 177

pmmvs4		67.89 121	67.39 134	68.48 109	71.60 154	73.57 154	74.45 117	60.98 103	64.65 77	57.97 86	54.95 116	51.73 163	61.88 111	73.78 147	75.11 144	83.99 145	77.91 143

testgi		54.39 187	57.86 185	50.35 191	71.59 155	67.24 174	54.95 193	53.25 163	43.36 191	23.78 195	44.64 178	47.87 180	24.96 202	70.45 172	68.66 174	73.60 187	62.78 193

pmmvs6		62.41 160	62.88 167	61.87 154	71.38 156	75.18 148	67.76 155	59.45 128	41.64 194	42.52 168	37.33 189	52.91 152	46.87 170	77.67 110	76.26 133	83.23 148	79.18 137

FMVSNet1		68.84 113	70.47 88	66.94 126	71.35 157	77.68 115	74.71 116	62.35 94	56.93 134	49.94 139	50.01 163	64.59 87	57.07 140	81.33 65	80.72 62	86.25 113	82.00 116

PatchmatchNet		64.21 150	64.65 158	63.69 147	71.29 158	68.66 169	69.63 147	51.70 173	63.04 86	53.77 115	59.83 83	58.34 105	60.23 124	68.54 182	66.06 185	75.56 179	68.08 183
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		64.83 146	65.54 149	64.01 146	70.64 159	69.41 165	65.97 165	52.74 166	57.81 122	52.65 121	54.27 126	56.31 121	60.92 118	72.20 157	73.09 153	81.12 157	75.69 159

MVSTER		72.06 71	74.24 69	69.51 100	70.39 160	75.97 141	76.91 101	57.36 150	64.64 78	61.39 71	68.86 51	63.76 89	63.46 101	81.44 62	79.70 76	87.56 68	85.31 73

Anonymous20231206		56.36 183	57.80 186	54.67 183	70.08 161	66.39 178	60.46 184	57.54 147	49.50 181	29.30 187	33.86 197	46.64 183	35.18 189	70.44 173	68.88 172	75.47 180	68.88 182

CMPMVS	47.78 17	62.49 159	62.52 171	62.46 152	70.01 162	70.66 163	62.97 177	51.84 172	51.98 167	56.71 99	42.87 180	53.62 144	57.80 133	72.23 155	70.37 164	75.45 181	75.91 156
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v748		65.12 144	65.24 151	64.98 138	69.77 163	76.45 134	69.47 148	57.06 152	49.93 177	50.70 132	47.87 171	49.50 176	57.14 139	73.64 149	75.18 143	85.75 128	84.14 87

TDRefinement		66.09 138	65.03 156	67.31 121	69.73 164	76.75 131	75.33 105	64.55 71	60.28 106	49.72 141	45.63 177	42.83 190	60.46 122	75.75 134	75.95 137	84.08 143	78.04 142

TinyColmap		62.84 155	61.03 179	64.96 139	69.61 165	71.69 159	68.48 153	59.76 125	55.41 146	47.69 149	47.33 174	34.20 200	62.76 106	74.52 142	72.59 156	81.44 155	71.47 175

RPMNet		61.71 170	62.88 167	60.34 161	69.51 166	69.41 165	63.48 175	49.23 181	57.81 122	45.64 155	50.51 161	50.12 171	53.13 161	68.17 184	68.49 176	81.07 158	75.62 161

IterMVS		66.36 136	68.30 125	64.10 143	69.48 167	74.61 150	73.41 130	50.79 177	57.30 128	48.28 145	60.64 76	59.92 100	60.85 121	74.14 145	72.66 155	81.80 153	78.82 139
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SixPastTwentyTwo		61.84 167	62.45 172	61.12 158	69.20 168	72.20 157	62.03 180	57.40 149	46.54 187	38.03 177	57.14 103	41.72 192	58.12 132	69.67 177	71.58 159	81.94 152	78.30 141

MDTV_nov1_ep13		64.37 149	65.24 151	63.37 151	68.94 169	70.81 161	72.40 137	50.29 180	60.10 107	53.91 114	60.07 80	59.15 103	57.21 138	69.43 179	67.30 180	77.47 171	69.78 179

EPMVS		60.00 175	61.97 175	57.71 173	68.46 170	63.17 190	64.54 171	48.23 188	63.30 84	44.72 159	60.19 78	56.05 129	50.85 164	65.27 189	62.02 195	69.44 197	63.81 190

Anonymous20231211		51.46 192	50.59 194	52.46 189	67.30 171	66.70 177	55.00 192	59.22 129	29.96 209	17.62 209	19.11 211	28.74 209	35.72 188	66.42 186	69.52 167	79.92 162	73.71 171

FC-MVSNet-test		56.90 182	65.20 153	47.21 194	66.98 172	63.20 189	49.11 201	58.60 143	59.38 113	11.50 214	65.60 64	56.68 118	24.66 205	71.17 164	71.36 161	72.38 190	69.02 181

CVMVSNet		62.55 157	65.89 145	58.64 170	66.95 173	69.15 167	66.49 164	56.29 156	52.46 165	32.70 183	59.27 85	58.21 106	50.09 165	71.77 160	71.39 160	79.31 165	78.99 138

FPMVS		51.87 191	50.00 196	54.07 184	66.83 174	57.25 197	60.25 185	50.91 175	50.25 174	34.36 181	36.04 194	32.02 202	41.49 180	58.98 204	56.07 204	70.56 196	59.36 199

pmmvs-eth3d		63.52 152	62.44 173	64.77 140	66.82 175	70.12 164	69.41 149	59.48 127	54.34 158	52.71 120	46.24 176	44.35 189	56.93 141	72.37 152	73.77 150	83.30 147	75.91 156

testpf		47.41 195	48.47 201	46.18 195	66.30 176	50.67 206	48.15 202	42.60 203	37.10 202	28.75 188	40.97 183	39.01 197	30.82 194	52.95 209	53.74 208	60.46 207	64.87 187

TAMVS		59.58 176	62.81 169	55.81 179	66.03 177	65.64 181	63.86 174	48.74 184	49.95 175	37.07 179	54.77 119	58.54 104	44.44 175	72.29 154	71.79 157	74.70 183	66.66 185

MDTV_nov1_ep13_2view		60.16 174	60.51 181	59.75 164	65.39 178	69.05 168	68.00 154	48.29 187	51.99 166	45.95 154	48.01 169	49.64 175	53.39 159	68.83 181	66.52 184	77.47 171	69.55 180

pmmvs5		62.37 163	64.04 162	60.42 160	65.03 179	71.67 160	67.17 157	52.70 168	50.30 173	44.80 158	54.23 130	51.19 167	49.37 166	72.88 151	73.48 152	83.45 146	74.55 166

ambc			53.42 190		64.99 180	63.36 188	49.96 199		47.07 185	37.12 178	28.97 201	16.36 216	41.82 179	75.10 141	67.34 179	71.55 193	75.72 158

V42		68.76 115	69.63 99	67.74 115	64.93 181	78.01 107	78.30 89	56.48 154	58.65 119	56.30 102	54.26 128	57.03 116	64.85 97	77.47 118	77.01 111	85.60 130	84.96 80

PMVS	39.38 18	46.06 200	43.30 205	49.28 193	62.93 182	38.75 213	41.88 206	53.50 162	33.33 208	35.46 180	28.90 202	31.01 205	33.04 192	58.61 205	54.63 207	68.86 198	57.88 202
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new-patchmatchnet		46.97 198	49.47 197	44.05 200	62.82 183	56.55 198	45.35 204	52.01 170	42.47 193	17.04 210	35.73 195	35.21 199	21.84 211	61.27 197	54.83 206	65.26 205	60.26 196

ADS-MVSNet		55.94 184	58.01 184	53.54 188	62.48 184	58.48 196	59.12 188	46.20 192	59.65 111	42.88 166	52.34 154	53.31 150	46.31 172	62.00 196	60.02 200	64.23 206	60.24 198

v52		65.23 142	66.24 142	64.06 144	61.94 185	76.42 135	72.06 139	54.30 160	49.94 176	50.04 137	47.41 173	52.42 156	60.23 124	75.71 135	76.22 135	85.78 126	85.56 65

V4		65.23 142	66.23 143	64.06 144	61.94 185	76.42 135	72.05 140	54.31 159	49.91 178	50.06 136	47.42 172	52.40 157	60.24 123	75.71 135	76.22 135	85.78 126	85.56 65

RPSCF		67.64 127	71.25 82	63.43 150	61.86 187	70.73 162	67.26 156	50.86 176	74.20 52	58.91 78	67.49 59	69.33 70	64.10 99	71.41 161	68.45 177	77.61 170	77.17 148

MIMVSNet		58.52 179	61.34 178	55.22 181	60.76 188	67.01 175	66.81 159	49.02 183	56.43 139	38.90 174	40.59 186	54.54 139	40.57 184	73.16 150	71.65 158	75.30 182	66.00 186

PatchT		61.97 165	64.04 162	59.55 167	60.49 189	67.40 173	56.54 190	48.65 185	56.69 137	52.65 121	51.10 160	52.14 162	60.92 118	72.20 157	73.09 153	78.03 169	75.69 159

N_pmnet		47.35 196	50.13 195	44.11 199	59.98 190	51.64 205	51.86 196	44.80 198	49.58 180	20.76 205	40.65 185	40.05 196	29.64 195	59.84 202	55.15 205	57.63 208	54.00 206

1111		43.08 202	44.02 204	41.98 201	59.22 191	49.27 209	41.48 207	45.63 195	35.01 203	23.06 199	28.60 203	30.15 206	27.22 197	60.42 200	57.97 202	55.27 211	46.74 208

.test1245		30.81 209	29.14 211	32.77 208	59.22 191	49.27 209	41.48 207	45.63 195	35.01 203	23.06 199	28.60 203	30.15 206	27.22 197	60.42 200	0.10 215	0.01 219	0.43 217

MVS-HIRNet		54.41 186	52.10 193	57.11 176	58.99 193	56.10 199	49.68 200	49.10 182	46.18 188	52.15 125	33.18 198	46.11 186	56.10 143	63.19 193	59.70 201	76.64 175	60.25 197

PM-MVS		60.48 173	60.94 180	59.94 163	58.85 194	66.83 176	64.27 173	51.39 174	55.03 152	48.03 146	50.00 165	40.79 194	58.26 131	69.20 180	67.13 183	78.84 167	77.60 145

anonymousdsp		65.28 141	67.98 127	62.13 153	58.73 195	73.98 153	67.10 158	50.69 178	48.41 182	47.66 150	54.27 126	52.75 155	61.45 116	76.71 130	80.20 74	87.13 79	89.53 44

LP		53.62 189	53.43 189	53.83 186	58.51 196	62.59 193	57.31 189	46.04 194	47.86 183	42.69 167	36.08 193	36.86 198	46.53 171	64.38 190	64.25 189	71.92 191	62.00 195

TESTMET0.1,1		61.10 171	64.36 160	57.29 174	57.53 197	63.93 184	66.61 162	36.22 208	54.41 155	47.77 147	57.46 99	60.25 98	55.20 151	70.80 169	69.33 168	80.40 160	74.38 167

EU-MVSNet		54.63 185	58.69 183	49.90 192	56.99 198	62.70 192	56.41 191	50.64 179	45.95 189	23.14 198	50.42 162	46.51 184	36.63 187	65.51 188	64.85 187	75.57 178	74.91 164

FMVSNet5		57.24 180	60.02 182	53.99 185	56.45 199	62.74 191	65.27 168	47.03 190	55.14 149	39.55 173	40.88 184	53.42 149	41.83 178	72.35 153	71.10 162	73.79 186	64.50 189

test2356		47.20 197	48.62 200	45.54 197	56.38 200	54.89 201	50.62 197	45.08 197	38.65 199	23.40 196	36.23 192	31.10 204	29.31 196	62.76 194	62.49 194	68.48 199	54.23 205

testus		45.61 201	49.06 199	41.59 202	56.13 201	55.28 200	43.51 205	39.64 206	37.74 200	18.23 207	35.52 196	31.28 203	24.69 204	62.46 195	62.90 192	67.33 201	58.26 201

test-mter		60.84 172	64.62 159	56.42 177	55.99 202	64.18 182	65.39 167	34.23 210	54.39 157	46.21 152	57.40 101	59.49 102	55.86 145	71.02 167	69.65 166	80.87 159	76.20 155

CHOSEN 280x420		58.70 178	61.88 176	54.98 182	55.45 203	50.55 207	64.92 169	40.36 204	55.21 148	38.13 176	48.31 167	63.76 89	63.03 105	73.73 148	68.58 175	68.00 200	73.04 173

PMMVS		65.06 145	69.17 111	60.26 162	55.25 204	63.43 187	66.71 161	43.01 202	62.41 89	50.64 133	69.44 49	67.04 81	63.29 103	74.36 144	73.54 151	82.68 150	73.99 170

testmv		42.58 203	44.36 202	40.49 203	54.63 205	52.76 203	41.21 209	44.37 199	28.83 210	12.87 211	27.16 206	25.03 211	23.01 207	60.83 198	61.13 196	66.88 202	54.81 203

test1235678		42.57 204	44.36 202	40.49 203	54.63 205	52.75 204	41.21 209	44.37 199	28.82 211	12.87 211	27.15 207	25.01 212	23.01 207	60.83 198	61.13 196	66.88 202	54.81 203

no-one		36.35 207	37.59 208	34.91 206	46.13 207	49.89 208	27.99 214	43.56 201	20.91 215	7.03 217	14.64 213	15.50 217	18.92 212	42.95 210	60.20 199	65.84 204	59.03 200

Gipuma		36.38 206	35.80 209	37.07 205	45.76 208	33.90 214	29.81 213	48.47 186	39.91 197	18.02 208	8.00 217	8.14 219	25.14 201	59.29 203	61.02 198	55.19 212	40.31 210
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs3		47.65 194	49.08 198	45.99 196	44.61 209	54.79 202	50.04 198	31.95 213	33.91 205	29.90 185	30.37 199	33.53 201	46.31 172	63.50 191	63.67 191	73.14 189	63.77 191

MIMVSNet1		49.27 193	53.25 191	44.62 198	44.61 209	61.52 195	53.61 194	52.18 169	41.62 195	18.68 206	28.14 205	41.58 193	25.50 200	68.46 183	69.04 170	73.15 188	62.37 194

test12356		35.10 208	38.50 207	31.13 209	44.14 211	43.70 212	32.27 212	34.42 209	26.51 213	9.47 215	25.22 209	20.34 213	10.86 214	53.47 207	56.15 203	55.59 210	44.11 209

MDA-MVSNet-bldmvs		53.37 190	53.01 192	53.79 187	43.67 212	67.95 172	59.69 186	57.92 146	43.69 190	32.41 184	41.47 182	27.89 210	52.38 162	56.97 206	65.99 186	76.68 174	67.13 184

E-PMN		21.77 211	18.24 213	25.89 210	40.22 213	19.58 217	12.46 218	39.87 205	18.68 217	6.71 218	9.57 214	4.31 222	22.36 210	19.89 215	27.28 213	33.73 214	28.34 214

EMVS		20.98 212	17.15 214	25.44 211	39.51 214	19.37 218	12.66 217	39.59 207	19.10 216	6.62 219	9.27 215	4.40 221	22.43 209	17.99 216	24.40 214	31.81 215	25.53 215

new_pmnet		38.40 205	42.64 206	33.44 207	37.54 215	45.00 211	36.60 211	32.72 212	40.27 196	12.72 213	29.89 200	28.90 208	24.78 203	53.17 208	52.90 209	56.31 209	48.34 207

PMMVS2		25.60 210	29.75 210	20.76 213	28.00 216	30.93 215	23.10 215	29.18 214	23.14 214	1.46 221	18.23 212	16.54 215	5.08 215	40.22 211	41.40 211	37.76 213	37.79 212

tmp_tt				14.50 215	14.68 217	7.17 220	10.46 220	2.21 216	37.73 201	28.71 189	25.26 208	16.98 214	4.37 216	31.49 212	29.77 212	26.56 216

MVE	19.12 19	20.47 213	23.27 212	17.20 214	12.66 218	25.41 216	10.52 219	34.14 211	14.79 218	6.53 220	8.79 216	4.68 220	16.64 213	29.49 213	41.63 210	22.73 217	38.11 211
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GG-mvs-BLEND		46.86 199	67.51 131	22.75 212	0.05 219	76.21 139	64.69 170	0.04 217	61.90 93	0.09 222	55.57 108	71.32 61	0.08 217	70.54 171	67.19 181	71.58 192	69.86 178

testmvs		0.09 214	0.15 215	0.02 216	0.01 220	0.02 221	0.05 222	0.01 218	0.11 219	0.01 223	0.26 219	0.01 223	0.06 219	0.10 217	0.10 215	0.01 219	0.43 217

sosnet-low-res		0.00 216	0.00 217	0.00 218	0.00 221	0.00 223	0.00 224	0.00 220	0.00 221	0.00 224	0.00 220	0.00 224	0.00 220	0.00 219	0.00 218	0.00 222	0.00 219

sosnet		0.00 216	0.00 217	0.00 218	0.00 221	0.00 223	0.00 224	0.00 220	0.00 221	0.00 224	0.00 220	0.00 224	0.00 220	0.00 219	0.00 218	0.00 222	0.00 219

test123		0.09 214	0.14 216	0.02 216	0.00 221	0.02 221	0.02 223	0.01 218	0.09 220	0.00 224	0.30 218	0.00 224	0.08 217	0.03 218	0.09 217	0.01 219	0.45 216

MTAPA										83.48 1		86.45 11

MTMP										82.66 3		84.91 19

Patchmatch-RL test							2.85 221

NP-MVS									80.10 38

Patchmtry						65.80 180	65.97 165	52.74 166		52.65 121

DeepMVS_CX						18.74 219	18.55 216	8.02 215	26.96 212	7.33 216	23.81 210	13.05 218	25.99 199	25.17 214		22.45 218	36.25 213

High-resolution multi-view benchmark