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	73.54 131	65.98 62	60.65 103	56.00 104	72.11 41	79.15 37	54.63 157	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 80	78.87 87	86.00 124	80.18 133

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	62.07 100	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	60.89 109	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 78	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	86.04 121	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	51.50 161	54.14 140	68.56 62	78.85 100	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	56.19 144	46.06 155	54.30 125	51.20 170	68.68 61	80.66 74	80.72 62	86.07 117	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 99	78.50 89	86.60 109	76.85 158

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	54.63 157	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	78.79 104	73.74 129	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	77.97 117	77.51 96	58.95 137	62.33 90	55.22 108	53.14 145	65.90 84	62.03 109	79.08 98	77.11 114	84.08 148	77.91 148

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 91	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	67.66 129	66.76 144	68.70 107	80.49 69	77.98 115	75.29 107	62.95 82	63.62 83	49.96 138	47.32 179	50.72 173	58.57 128	76.87 132	75.50 146	84.94 143	75.33 168
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	86.01 122	85.98 62

EPNet_dtu		68.08 121	71.00 83	64.67 146	79.64 71	68.62 175	75.05 113	63.30 77	66.36 66	45.27 159	67.40 60	66.84 82	43.64 182	75.37 143	74.98 151	81.15 161	77.44 151
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	72.61 138	61.77 104	59.66 110	63.44 67	53.26 142	59.65 101	59.16 127	76.78 134	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	60.12 126	69.72 61	64.11 64	70.53 46	73.22 55	68.90 59	80.14 84	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	61.67 106	60.68 102	55.49 105	58.48 90	65.48 85	68.85 60	76.92 131	75.55 145	87.35 71	85.46 70

EG-PatchMatch MVS		67.24 136	66.94 141	67.60 118	78.73 77	81.35 72	73.28 135	59.49 131	46.89 191	51.42 129	43.65 183	53.49 148	55.50 153	81.38 64	80.66 68	87.15 75	81.17 124

gg-mvs-nofinetune		62.55 162	65.05 159	59.62 171	78.72 78	77.61 120	70.83 149	53.63 166	39.71 203	22.04 208	36.36 196	64.32 88	47.53 174	81.16 69	79.03 86	85.00 142	77.17 153

Vis-MVSNet (Re-imp)		67.83 126	73.52 71	61.19 162	78.37 79	76.72 136	66.80 165	62.96 81	65.50 72	34.17 187	67.19 61	69.68 69	39.20 190	79.39 95	79.44 84	85.68 133	76.73 159

DI_MVS_plusplus_trai		75.13 60	76.12 65	73.96 61	78.18 80	81.55 70	80.97 53	62.54 95	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

thres600view7		67.68 128	68.43 126	66.80 131	77.90 81	78.86 102	73.84 126	62.75 84	56.07 145	44.70 163	52.85 151	52.81 157	55.58 151	80.41 75	77.77 100	86.05 119	80.28 132

thres400		67.95 123	68.62 124	67.17 125	77.90 81	78.59 107	74.27 123	62.72 86	56.34 143	45.77 157	53.00 147	53.35 152	56.46 144	80.21 83	78.43 90	85.91 128	80.43 131

thres200		67.98 122	68.55 125	67.30 123	77.89 83	78.86 102	74.18 125	62.75 84	56.35 142	46.48 153	52.98 148	53.54 146	56.46 144	80.41 75	77.97 97	86.05 119	79.78 138

tpmp4_e23		68.32 118	67.08 140	69.76 98	77.86 84	75.22 151	78.37 88	56.17 162	66.06 69	64.27 63	57.15 102	54.89 136	63.40 102	70.97 173	68.29 183	78.46 173	77.00 157

view600		67.63 132	68.36 127	66.77 132	77.84 85	78.66 105	73.74 129	62.62 93	56.04 146	44.98 160	52.86 150	52.83 156	55.48 154	80.36 79	77.75 101	85.95 127	80.02 135

tfpn200view9		68.11 120	68.72 121	67.40 121	77.83 86	78.93 100	74.28 122	62.81 83	56.64 138	46.82 151	52.65 154	53.47 150	56.59 143	80.41 75	78.43 90	86.11 115	80.52 130

view800		67.35 135	68.22 130	66.35 136	77.83 86	78.62 106	72.97 137	62.58 94	55.71 148	44.13 164	52.69 153	52.24 165	54.58 159	80.27 81	78.19 94	86.01 122	79.79 137

tfpn		66.58 139	67.18 139	65.88 138	77.82 88	78.45 109	72.07 142	62.52 96	55.35 151	43.21 168	52.54 156	46.12 189	53.68 160	80.02 85	78.23 93	85.99 125	79.55 140

Fast-Effi-MVS+		73.11 67	73.66 70	72.48 65	77.72 89	80.88 80	78.55 83	58.83 145	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 89	80.72 81	73.81 127	69.65 37	61.99 92	43.23 166	60.54 77	57.50 107	58.57 128	79.56 92	81.07 57	89.34 39	83.97 88

IterMVS-LS		71.69 73	72.82 76	70.37 89	77.54 91	76.34 142	75.13 112	60.46 116	61.53 98	57.57 87	64.89 66	67.33 80	66.04 90	77.09 130	77.37 110	85.48 136	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	66.71 135	77.48 92	79.54 95	73.52 132	69.20 42	61.20 100	39.76 176	58.52 88	50.11 176	51.37 168	80.26 82	80.71 66	88.97 45	83.59 95

conf0.05thres1000		66.26 141	66.77 143	65.66 139	77.45 93	78.10 110	71.85 145	62.44 98	51.47 175	43.00 169	47.92 174	51.66 168	53.40 162	79.71 88	77.97 97	85.82 129	80.56 128

TransMVSNet (Re)		64.74 151	65.66 152	63.66 153	77.40 94	75.33 149	69.86 150	62.67 92	47.63 189	41.21 175	50.01 167	52.33 162	45.31 179	79.57 91	77.69 103	85.49 135	77.07 156

TranMVSNet+NR-MVSNet		69.25 109	70.81 85	67.43 120	77.23 95	79.46 97	73.48 133	69.66 36	60.43 105	39.56 177	58.82 87	53.48 149	55.74 150	79.59 90	81.21 56	88.89 47	82.70 109

CANet_DTU		73.29 65	76.96 62	69.00 104	77.04 96	82.06 69	79.49 62	56.30 160	67.85 63	53.29 118	71.12 45	70.37 67	61.81 114	81.59 60	80.96 58	86.09 116	84.73 83

CHOSEN 1792x2688		69.20 110	69.26 110	69.13 102	76.86 97	78.93 100	77.27 99	60.12 126	61.86 94	54.42 109	42.54 186	61.61 95	66.91 71	78.55 102	78.14 96	79.23 171	83.23 100

HyFIR lowres test		69.47 106	68.94 113	70.09 94	76.77 98	82.93 65	76.63 103	60.17 122	59.00 114	54.03 112	40.54 192	65.23 86	67.89 64	76.54 137	78.30 92	85.03 141	80.07 134

UniMVSNet (Re)		69.53 103	71.90 79	66.76 133	76.42 99	80.93 77	72.59 139	68.03 49	61.75 96	41.68 174	58.34 94	57.23 115	53.27 164	79.53 93	80.62 70	88.57 51	84.90 81

DWT-MVSNet_training		67.24 136	65.96 148	68.74 105	76.15 100	74.36 157	74.37 121	56.66 158	61.82 95	60.51 72	58.23 96	49.76 178	65.07 95	70.04 181	70.39 168	79.70 168	77.11 155

gm-plane-assit		57.00 186	57.62 192	56.28 183	76.10 101	62.43 199	47.62 208	46.57 196	33.84 211	23.24 202	37.52 193	40.19 200	59.61 126	79.81 87	77.55 105	84.55 146	72.03 179

DU-MVS		69.63 98	70.91 84	68.13 112	75.99 102	79.54 95	73.81 127	69.20 42	61.20 100	43.23 166	58.52 88	53.50 147	58.57 128	79.22 96	80.45 72	87.97 59	83.97 88

Baseline_NR-MVSNet		67.53 133	68.77 119	66.09 137	75.99 102	74.75 154	72.43 140	68.41 45	61.33 99	38.33 180	51.31 162	54.13 142	56.03 147	79.22 96	78.19 94	85.37 137	82.45 111

CostFormer		68.92 112	69.58 100	68.15 111	75.98 104	76.17 144	78.22 92	51.86 176	65.80 70	61.56 70	63.57 70	62.83 92	61.85 112	70.40 180	68.67 178	79.42 169	79.62 139

tfpnnormal		64.27 154	63.64 169	65.02 142	75.84 105	75.61 146	71.24 148	62.52 96	47.79 188	42.97 170	42.65 185	44.49 193	52.66 166	78.77 101	76.86 117	84.88 144	79.29 141

tpm cat1		65.41 144	63.81 168	67.28 124	75.61 106	72.88 160	75.32 106	52.85 170	62.97 87	63.66 66	53.24 143	53.29 154	61.83 113	65.54 192	64.14 195	74.43 189	74.60 170

CDS-MVSNet		67.65 130	69.83 97	65.09 141	75.39 107	76.55 137	74.42 120	63.75 74	53.55 164	49.37 142	59.41 84	62.45 93	44.44 180	79.71 88	79.82 75	83.17 154	77.36 152
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	67.01 128	75.15 108	77.97 117	77.12 100	55.40 163	57.87 120	46.68 152	56.17 106	60.39 97	62.36 107	76.32 138	76.25 139	85.35 138	81.34 122

WR-MVS		63.03 158	67.40 137	57.92 177	75.14 109	77.60 121	60.56 188	66.10 59	54.11 163	23.88 199	53.94 136	53.58 145	34.50 195	73.93 151	77.71 102	87.35 71	80.94 125

test-LLR		64.42 152	64.36 164	64.49 147	75.02 110	63.93 189	66.61 167	61.96 101	54.41 159	47.77 147	57.46 99	60.25 98	55.20 155	70.80 174	69.33 173	80.40 165	74.38 172

test0.0.03 1		58.80 182	61.58 182	55.56 185	75.02 110	68.45 176	59.58 192	61.96 101	52.74 166	29.57 191	49.75 170	54.56 138	31.46 198	71.19 168	69.77 170	75.75 182	64.57 193

v1144		69.93 97	69.36 109	70.61 79	74.89 112	80.93 77	79.11 66	60.64 111	55.97 147	55.31 107	53.85 137	54.14 140	66.54 75	78.10 107	77.44 108	87.14 78	85.09 76

v13		69.52 104	68.76 120	70.41 87	74.88 113	77.02 132	78.52 87	58.86 139	56.61 139	56.91 92	54.00 135	56.17 127	66.11 89	77.93 108	76.74 125	87.21 73	82.83 102

v12		69.54 102	68.79 118	70.41 87	74.88 113	77.03 130	78.54 86	58.85 141	56.71 136	56.87 94	54.13 133	56.23 126	66.15 85	77.89 109	76.74 125	87.17 74	82.80 103

v11		69.37 107	68.65 123	70.20 91	74.87 115	76.97 133	78.29 90	58.55 149	56.38 141	56.04 103	54.02 134	54.98 135	66.47 76	78.30 104	76.91 116	86.97 87	83.02 101

V9		69.58 101	68.83 116	70.46 84	74.85 116	77.04 128	78.65 81	58.85 141	56.83 135	57.12 90	54.26 128	56.31 121	66.14 87	77.83 111	76.76 120	87.13 79	82.79 105

V14		69.59 100	68.86 115	70.45 86	74.83 117	77.04 128	78.70 80	58.83 145	56.95 132	57.08 91	54.41 124	56.34 120	66.15 85	77.77 112	76.76 120	87.08 84	82.74 108

v15		69.61 99	68.88 114	70.46 84	74.81 118	77.03 130	78.75 79	58.83 145	57.06 129	57.18 89	54.55 123	56.37 119	66.13 88	77.70 113	76.76 120	87.03 86	82.69 110

v7		70.33 86	69.87 94	70.88 68	74.79 119	81.04 76	79.22 64	60.57 113	57.70 126	56.65 100	54.23 130	55.29 133	66.95 68	78.28 105	77.47 106	87.12 82	85.05 78

v10		70.22 88	69.76 98	70.74 74	74.79 119	80.30 92	79.22 64	59.81 129	57.71 125	56.58 101	54.22 132	55.31 131	66.95 68	78.28 105	77.47 106	87.12 82	85.07 77

v1141		69.96 96	69.44 106	70.58 82	74.78 121	80.50 87	78.85 69	60.30 117	56.95 132	56.74 97	54.68 121	56.26 125	65.93 91	77.38 124	76.72 130	86.88 92	83.57 98

divwei89l23v2f112		69.97 94	69.44 106	70.58 82	74.78 121	80.50 87	78.85 69	60.30 117	56.97 131	56.75 96	54.67 122	56.27 124	65.92 92	77.37 125	76.72 130	86.88 92	83.58 97

v1		69.97 94	69.45 105	70.59 80	74.78 121	80.51 86	78.84 71	60.30 117	56.98 130	56.81 95	54.69 120	56.29 123	65.91 93	77.37 125	76.71 133	86.89 91	83.59 95

v17		70.03 93	69.43 108	70.72 76	74.75 124	77.09 125	78.78 78	58.85 141	59.53 112	58.72 80	54.87 117	57.39 109	66.38 78	77.60 117	76.75 123	86.83 95	82.80 103

v16		70.07 91	69.46 103	70.79 72	74.74 125	77.08 126	78.79 76	58.86 139	59.75 109	59.15 77	54.87 117	57.33 110	66.38 78	77.61 116	76.77 118	86.81 101	82.79 105

v8		70.23 87	69.86 96	70.67 78	74.69 126	79.82 94	78.79 76	59.18 135	58.80 116	58.20 82	55.00 113	57.33 110	66.31 84	77.51 121	76.71 133	86.82 96	83.88 91

v1neww		70.34 84	69.93 92	70.82 70	74.68 127	80.61 83	78.80 74	60.17 122	58.74 117	58.10 84	55.00 113	57.28 113	66.33 81	77.53 118	76.74 125	86.82 96	83.61 93

v7new		70.34 84	69.93 92	70.82 70	74.68 127	80.61 83	78.80 74	60.17 122	58.74 117	58.10 84	55.00 113	57.28 113	66.33 81	77.53 118	76.74 125	86.82 96	83.61 93

v6		70.35 83	69.94 91	70.83 69	74.68 127	80.62 82	78.81 73	60.16 125	58.81 115	58.17 83	55.01 112	57.31 112	66.32 83	77.53 118	76.73 129	86.82 96	83.62 92

v18		70.10 90	69.52 101	70.77 73	74.66 130	77.06 127	78.84 71	58.84 144	60.01 108	59.23 76	55.06 111	57.47 108	66.34 80	77.50 122	76.75 123	86.71 103	82.77 107

v2v482		70.05 92	69.46 103	70.74 74	74.62 131	80.32 91	79.00 67	60.62 112	57.41 127	56.89 93	55.43 110	55.14 134	66.39 77	77.25 127	77.14 113	86.90 89	83.57 98

v1192		69.50 105	68.83 116	70.29 90	74.49 132	80.92 79	78.55 83	60.54 114	55.04 155	54.21 110	52.79 152	52.33 162	66.92 70	77.88 110	77.35 111	87.04 85	85.51 68

DTE-MVSNet		61.85 171	64.96 161	58.22 176	74.32 133	74.39 156	61.01 187	67.85 51	51.76 174	21.91 209	53.28 141	48.17 182	37.74 191	72.22 161	76.44 136	86.52 112	78.49 145

Vis-MVSNet		72.77 69	77.20 60	67.59 119	74.19 134	84.01 56	76.61 104	61.69 105	60.62 104	50.61 134	70.25 48	71.31 62	55.57 152	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	74.07 135	77.17 124	78.24 91	57.45 153	72.44 56	64.02 65	69.05 50	75.92 47	64.86 96	75.18 145	75.27 147	82.47 156	84.53 84

v144192		69.34 108	68.68 122	70.12 93	74.06 136	80.54 85	78.08 93	60.54 114	54.99 157	54.13 111	52.92 149	52.80 158	66.73 73	77.13 129	76.72 130	87.15 75	85.63 64

v1921920		69.03 111	68.32 128	69.86 96	74.03 137	80.37 90	77.55 95	60.25 121	54.62 158	53.59 116	52.36 157	51.50 169	66.75 72	77.17 128	76.69 135	86.96 88	85.56 65

PEN-MVS		62.96 159	65.77 151	59.70 170	73.98 138	75.45 147	63.39 181	67.61 52	52.49 168	25.49 198	53.39 139	49.12 181	40.85 188	71.94 164	77.26 112	86.86 94	80.72 127

v1240		68.64 116	67.89 133	69.51 100	73.89 139	80.26 93	76.73 102	59.97 128	53.43 165	53.08 119	51.82 160	50.84 172	66.62 74	76.79 133	76.77 118	86.78 102	85.34 72

GA-MVS		68.14 119	69.17 111	66.93 130	73.77 140	78.50 108	74.45 117	58.28 150	55.11 154	48.44 144	60.08 79	53.99 143	61.50 115	78.43 103	77.57 104	85.13 139	80.54 129

pm-mvs1		65.62 143	67.42 136	63.53 154	73.66 141	76.39 141	69.66 151	60.87 110	49.73 183	43.97 165	51.24 163	57.00 117	48.16 173	79.89 86	77.84 99	84.85 145	79.82 136

dps		64.00 156	62.99 171	65.18 140	73.29 142	72.07 163	68.98 156	53.07 169	57.74 124	58.41 81	55.55 109	47.74 185	60.89 120	69.53 183	67.14 187	76.44 181	71.19 181

v148		67.85 125	67.53 134	68.23 110	73.25 143	77.57 122	74.26 124	57.36 155	55.70 149	57.45 88	53.53 138	55.42 130	61.96 110	75.23 144	73.92 154	85.08 140	81.32 123

PatchMatch-RL		67.78 127	66.65 145	69.10 103	73.01 144	72.69 161	68.49 157	61.85 103	62.93 88	60.20 75	56.83 104	50.42 174	69.52 57	75.62 142	74.46 153	81.51 159	73.62 177

GBi-Net		70.78 77	73.37 73	67.76 113	72.95 145	78.00 112	75.15 109	62.72 86	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	72.95 145	78.00 112	75.15 109	62.72 86	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	72.95 145	78.00 112	75.15 109	62.69 90	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	72.88 148	77.98 115	74.96 115	62.72 86	64.13 80	51.44 126	58.37 91	69.02 73	57.43 137	79.43 94	79.57 80	86.59 110	81.81 120

LTVRE_ROB	59.44 16	61.82 174	62.64 175	60.87 164	72.83 149	77.19 123	64.37 177	58.97 136	33.56 212	28.00 195	52.59 155	42.21 196	63.93 100	74.52 147	76.28 137	77.15 178	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		67.05 138	66.94 141	67.17 125	72.35 150	78.97 99	73.26 136	58.88 138	51.16 176	50.90 131	48.21 172	50.11 176	60.96 117	77.70 113	77.38 109	86.68 107	85.05 78

tpm		62.41 165	63.15 170	61.55 161	72.24 151	63.79 191	71.31 147	46.12 198	57.82 121	55.33 106	59.90 82	54.74 137	53.63 161	67.24 190	64.29 193	70.65 200	74.25 174

test20.03		53.93 193	56.28 193	51.19 195	72.19 152	65.83 184	53.20 200	61.08 107	42.74 197	22.08 207	37.07 195	45.76 191	24.29 211	70.44 178	69.04 175	74.31 190	63.05 197

CP-MVSNet		62.68 161	65.49 154	59.40 173	71.84 153	75.34 148	62.87 183	67.04 55	52.64 167	27.19 196	53.38 140	48.15 183	41.40 186	71.26 167	75.68 143	86.07 117	82.00 116

PS-CasMVS		62.38 167	65.06 158	59.25 174	71.73 154	75.21 152	62.77 184	66.99 56	51.94 173	26.96 197	52.00 159	47.52 186	41.06 187	71.16 170	75.60 144	85.97 126	81.97 118

WR-MVS_H		61.83 173	65.87 150	57.12 180	71.72 155	76.87 134	61.45 186	66.19 57	51.97 172	22.92 206	53.13 146	52.30 164	33.80 196	71.03 171	75.00 150	86.65 108	80.78 126

USDC		67.36 134	67.90 132	66.74 134	71.72 155	75.23 150	71.58 146	60.28 120	67.45 64	50.54 135	60.93 75	45.20 192	62.08 108	76.56 136	74.50 152	84.25 147	75.38 167

UGNet		72.78 68	77.67 54	67.07 127	71.65 157	83.24 62	75.20 108	63.62 75	64.93 75	56.72 98	71.82 43	73.30 54	49.02 172	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		62.00 169	62.35 179	61.58 160	71.62 158	64.14 188	69.07 155	48.22 194	62.21 91	53.93 113	58.26 95	55.30 132	55.81 149	63.22 197	62.62 198	70.85 199	70.70 182

pmmvs4		67.89 124	67.39 138	68.48 109	71.60 159	73.57 159	74.45 117	60.98 108	64.65 77	57.97 86	54.95 116	51.73 167	61.88 111	73.78 152	75.11 149	83.99 150	77.91 148

testgi		54.39 192	57.86 190	50.35 196	71.59 160	67.24 179	54.95 198	53.25 168	43.36 196	23.78 200	44.64 182	47.87 184	24.96 207	70.45 177	68.66 179	73.60 192	62.78 198

pmmvs6		62.41 165	62.88 172	61.87 159	71.38 161	75.18 153	67.76 160	59.45 133	41.64 199	42.52 173	37.33 194	52.91 155	46.87 175	77.67 115	76.26 138	83.23 153	79.18 142

FMVSNet1		68.84 113	70.47 88	66.94 129	71.35 162	77.68 119	74.71 116	62.35 99	56.93 134	49.94 139	50.01 167	64.59 87	57.07 140	81.33 65	80.72 62	86.25 113	82.00 116

PatchmatchNet		64.21 155	64.65 162	63.69 152	71.29 163	68.66 174	69.63 152	51.70 178	63.04 86	53.77 115	59.83 83	58.34 105	60.23 124	68.54 187	66.06 190	75.56 184	68.08 188
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		64.83 150	65.54 153	64.01 151	70.64 164	69.41 170	65.97 170	52.74 171	57.81 122	52.65 121	54.27 126	56.31 121	60.92 118	72.20 162	73.09 158	81.12 162	75.69 164

MVSTER		72.06 71	74.24 69	69.51 100	70.39 165	75.97 145	76.91 101	57.36 155	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		56.36 188	57.80 191	54.67 188	70.08 166	66.39 183	60.46 189	57.54 152	49.50 185	29.30 192	33.86 202	46.64 187	35.18 194	70.44 178	68.88 177	75.47 185	68.88 187

CMPMVS	47.78 17	62.49 164	62.52 176	62.46 157	70.01 167	70.66 168	62.97 182	51.84 177	51.98 171	56.71 99	42.87 184	53.62 144	57.80 133	72.23 160	70.37 169	75.45 186	75.91 161
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v748		65.12 148	65.24 155	64.98 143	69.77 168	76.45 138	69.47 153	57.06 157	49.93 181	50.70 132	47.87 175	49.50 180	57.14 139	73.64 154	75.18 148	85.75 132	84.14 87

TDRefinement		66.09 142	65.03 160	67.31 122	69.73 169	76.75 135	75.33 105	64.55 71	60.28 106	49.72 141	45.63 181	42.83 195	60.46 122	75.75 139	75.95 142	84.08 148	78.04 147

TinyColmap		62.84 160	61.03 184	64.96 144	69.61 170	71.69 164	68.48 158	59.76 130	55.41 150	47.69 149	47.33 178	34.20 205	62.76 106	74.52 147	72.59 161	81.44 160	71.47 180

RPMNet		61.71 175	62.88 172	60.34 166	69.51 171	69.41 170	63.48 180	49.23 186	57.81 122	45.64 158	50.51 165	50.12 175	53.13 165	68.17 189	68.49 181	81.07 163	75.62 166

IterMVS		66.36 140	68.30 129	64.10 148	69.48 172	74.61 155	73.41 134	50.79 182	57.30 128	48.28 145	60.64 76	59.92 100	60.85 121	74.14 150	72.66 160	81.80 158	78.82 144
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SixPastTwentyTwo		61.84 172	62.45 177	61.12 163	69.20 173	72.20 162	62.03 185	57.40 154	46.54 192	38.03 182	57.14 103	41.72 197	58.12 132	69.67 182	71.58 164	81.94 157	78.30 146

MDTV_nov1_ep13		64.37 153	65.24 155	63.37 156	68.94 174	70.81 166	72.40 141	50.29 185	60.10 107	53.91 114	60.07 80	59.15 103	57.21 138	69.43 184	67.30 185	77.47 176	69.78 184

EPMVS		60.00 180	61.97 180	57.71 178	68.46 175	63.17 195	64.54 176	48.23 193	63.30 84	44.72 162	60.19 78	56.05 129	50.85 169	65.27 194	62.02 200	69.44 202	63.81 195

Anonymous20231211		51.46 197	50.59 199	52.46 194	67.30 176	66.70 182	55.00 197	59.22 134	29.96 214	17.62 214	19.11 216	28.74 214	35.72 193	66.42 191	69.52 172	79.92 167	73.71 176

FC-MVSNet-test		56.90 187	65.20 157	47.21 199	66.98 177	63.20 194	49.11 206	58.60 148	59.38 113	11.50 219	65.60 64	56.68 118	24.66 210	71.17 169	71.36 166	72.38 195	69.02 186

CVMVSNet		62.55 162	65.89 149	58.64 175	66.95 178	69.15 172	66.49 169	56.29 161	52.46 169	32.70 188	59.27 85	58.21 106	50.09 170	71.77 165	71.39 165	79.31 170	78.99 143

FPMVS		51.87 196	50.00 201	54.07 189	66.83 179	57.25 202	60.25 190	50.91 180	50.25 178	34.36 186	36.04 199	32.02 207	41.49 185	58.98 209	56.07 209	70.56 201	59.36 204

pmmvs-eth3d		63.52 157	62.44 178	64.77 145	66.82 180	70.12 169	69.41 154	59.48 132	54.34 162	52.71 120	46.24 180	44.35 194	56.93 141	72.37 157	73.77 155	83.30 152	75.91 161

testpf		47.41 200	48.47 206	46.18 200	66.30 181	50.67 211	48.15 207	42.60 208	37.10 207	28.75 193	40.97 188	39.01 202	30.82 199	52.95 214	53.74 213	60.46 212	64.87 192

TAMVS		59.58 181	62.81 174	55.81 184	66.03 182	65.64 186	63.86 179	48.74 189	49.95 179	37.07 184	54.77 119	58.54 104	44.44 180	72.29 159	71.79 162	74.70 188	66.66 190

MDTV_nov1_ep13_2view		60.16 179	60.51 186	59.75 169	65.39 183	69.05 173	68.00 159	48.29 192	51.99 170	45.95 156	48.01 173	49.64 179	53.39 163	68.83 186	66.52 189	77.47 176	69.55 185

pmmvs5		62.37 168	64.04 166	60.42 165	65.03 184	71.67 165	67.17 162	52.70 173	50.30 177	44.80 161	54.23 130	51.19 171	49.37 171	72.88 156	73.48 157	83.45 151	74.55 171

ambc			53.42 195		64.99 185	63.36 193	49.96 204		47.07 190	37.12 183	28.97 206	16.36 221	41.82 184	75.10 146	67.34 184	71.55 198	75.72 163

V42		68.76 115	69.63 99	67.74 115	64.93 186	78.01 111	78.30 89	56.48 159	58.65 119	56.30 102	54.26 128	57.03 116	64.85 97	77.47 123	77.01 115	85.60 134	84.96 80

PMVS	39.38 18	46.06 205	43.30 210	49.28 198	62.93 187	38.75 218	41.88 211	53.50 167	33.33 213	35.46 185	28.90 207	31.01 210	33.04 197	58.61 210	54.63 212	68.86 203	57.88 207
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new-patchmatchnet		46.97 203	49.47 202	44.05 205	62.82 188	56.55 203	45.35 209	52.01 175	42.47 198	17.04 215	35.73 200	35.21 204	21.84 216	61.27 202	54.83 211	65.26 210	60.26 201

ADS-MVSNet		55.94 189	58.01 189	53.54 193	62.48 189	58.48 201	59.12 193	46.20 197	59.65 111	42.88 171	52.34 158	53.31 153	46.31 177	62.00 201	60.02 205	64.23 211	60.24 203

v52		65.23 146	66.24 146	64.06 149	61.94 190	76.42 139	72.06 143	54.30 165	49.94 180	50.04 137	47.41 177	52.42 160	60.23 124	75.71 140	76.22 140	85.78 130	85.56 65

V4		65.23 146	66.23 147	64.06 149	61.94 190	76.42 139	72.05 144	54.31 164	49.91 182	50.06 136	47.42 176	52.40 161	60.24 123	75.71 140	76.22 140	85.78 130	85.56 65

RPSCF		67.64 131	71.25 82	63.43 155	61.86 192	70.73 167	67.26 161	50.86 181	74.20 52	58.91 78	67.49 59	69.33 70	64.10 99	71.41 166	68.45 182	77.61 175	77.17 153

MIMVSNet		58.52 184	61.34 183	55.22 186	60.76 193	67.01 180	66.81 164	49.02 188	56.43 140	38.90 179	40.59 191	54.54 139	40.57 189	73.16 155	71.65 163	75.30 187	66.00 191

PatchT		61.97 170	64.04 166	59.55 172	60.49 194	67.40 178	56.54 195	48.65 190	56.69 137	52.65 121	51.10 164	52.14 166	60.92 118	72.20 162	73.09 158	78.03 174	75.69 164

N_pmnet		47.35 201	50.13 200	44.11 204	59.98 195	51.64 210	51.86 201	44.80 203	49.58 184	20.76 210	40.65 190	40.05 201	29.64 200	59.84 207	55.15 210	57.63 213	54.00 211

1111		43.08 207	44.02 209	41.98 206	59.22 196	49.27 214	41.48 212	45.63 200	35.01 208	23.06 204	28.60 208	30.15 211	27.22 202	60.42 205	57.97 207	55.27 216	46.74 213

.test1245		30.81 214	29.14 216	32.77 213	59.22 196	49.27 214	41.48 212	45.63 200	35.01 208	23.06 204	28.60 208	30.15 211	27.22 202	60.42 205	0.10 220	0.01 224	0.43 222

MVS-HIRNet		54.41 191	52.10 198	57.11 181	58.99 198	56.10 204	49.68 205	49.10 187	46.18 193	52.15 125	33.18 203	46.11 190	56.10 146	63.19 198	59.70 206	76.64 180	60.25 202

PM-MVS		60.48 178	60.94 185	59.94 168	58.85 199	66.83 181	64.27 178	51.39 179	55.03 156	48.03 146	50.00 169	40.79 199	58.26 131	69.20 185	67.13 188	78.84 172	77.60 150

anonymousdsp		65.28 145	67.98 131	62.13 158	58.73 200	73.98 158	67.10 163	50.69 183	48.41 186	47.66 150	54.27 126	52.75 159	61.45 116	76.71 135	80.20 74	87.13 79	89.53 44

LP		53.62 194	53.43 194	53.83 191	58.51 201	62.59 198	57.31 194	46.04 199	47.86 187	42.69 172	36.08 198	36.86 203	46.53 176	64.38 195	64.25 194	71.92 196	62.00 200

TESTMET0.1,1		61.10 176	64.36 164	57.29 179	57.53 202	63.93 189	66.61 167	36.22 213	54.41 159	47.77 147	57.46 99	60.25 98	55.20 155	70.80 174	69.33 173	80.40 165	74.38 172

EU-MVSNet		54.63 190	58.69 188	49.90 197	56.99 203	62.70 197	56.41 196	50.64 184	45.95 194	23.14 203	50.42 166	46.51 188	36.63 192	65.51 193	64.85 192	75.57 183	74.91 169

FMVSNet5		57.24 185	60.02 187	53.99 190	56.45 204	62.74 196	65.27 173	47.03 195	55.14 153	39.55 178	40.88 189	53.42 151	41.83 183	72.35 158	71.10 167	73.79 191	64.50 194

test2356		47.20 202	48.62 205	45.54 202	56.38 205	54.89 206	50.62 202	45.08 202	38.65 204	23.40 201	36.23 197	31.10 209	29.31 201	62.76 199	62.49 199	68.48 204	54.23 210

testus		45.61 206	49.06 204	41.59 207	56.13 206	55.28 205	43.51 210	39.64 211	37.74 205	18.23 212	35.52 201	31.28 208	24.69 209	62.46 200	62.90 197	67.33 206	58.26 206

test-mter		60.84 177	64.62 163	56.42 182	55.99 207	64.18 187	65.39 172	34.23 215	54.39 161	46.21 154	57.40 101	59.49 102	55.86 148	71.02 172	69.65 171	80.87 164	76.20 160

CHOSEN 280x420		58.70 183	61.88 181	54.98 187	55.45 208	50.55 212	64.92 174	40.36 209	55.21 152	38.13 181	48.31 171	63.76 89	63.03 105	73.73 153	68.58 180	68.00 205	73.04 178

PMMVS		65.06 149	69.17 111	60.26 167	55.25 209	63.43 192	66.71 166	43.01 207	62.41 89	50.64 133	69.44 49	67.04 81	63.29 103	74.36 149	73.54 156	82.68 155	73.99 175

testmv		42.58 208	44.36 207	40.49 208	54.63 210	52.76 208	41.21 214	44.37 204	28.83 215	12.87 216	27.16 211	25.03 216	23.01 212	60.83 203	61.13 201	66.88 207	54.81 208

test1235678		42.57 209	44.36 207	40.49 208	54.63 210	52.75 209	41.21 214	44.37 204	28.82 216	12.87 216	27.15 212	25.01 217	23.01 212	60.83 203	61.13 201	66.88 207	54.81 208

no-one		36.35 212	37.59 213	34.91 211	46.13 212	49.89 213	27.99 219	43.56 206	20.91 220	7.03 222	14.64 218	15.50 222	18.92 217	42.95 215	60.20 204	65.84 209	59.03 205

Gipuma		36.38 211	35.80 214	37.07 210	45.76 213	33.90 219	29.81 218	48.47 191	39.91 202	18.02 213	8.00 222	8.14 224	25.14 206	59.29 208	61.02 203	55.19 217	40.31 215
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs3		47.65 199	49.08 203	45.99 201	44.61 214	54.79 207	50.04 203	31.95 218	33.91 210	29.90 190	30.37 204	33.53 206	46.31 177	63.50 196	63.67 196	73.14 194	63.77 196

MIMVSNet1		49.27 198	53.25 196	44.62 203	44.61 214	61.52 200	53.61 199	52.18 174	41.62 200	18.68 211	28.14 210	41.58 198	25.50 205	68.46 188	69.04 175	73.15 193	62.37 199

test12356		35.10 213	38.50 212	31.13 214	44.14 216	43.70 217	32.27 217	34.42 214	26.51 218	9.47 220	25.22 214	20.34 218	10.86 219	53.47 212	56.15 208	55.59 215	44.11 214

MDA-MVSNet-bldmvs		53.37 195	53.01 197	53.79 192	43.67 217	67.95 177	59.69 191	57.92 151	43.69 195	32.41 189	41.47 187	27.89 215	52.38 167	56.97 211	65.99 191	76.68 179	67.13 189

E-PMN		21.77 216	18.24 218	25.89 215	40.22 218	19.58 222	12.46 223	39.87 210	18.68 222	6.71 223	9.57 219	4.31 227	22.36 215	19.89 220	27.28 218	33.73 219	28.34 219

EMVS		20.98 217	17.15 219	25.44 216	39.51 219	19.37 223	12.66 222	39.59 212	19.10 221	6.62 224	9.27 220	4.40 226	22.43 214	17.99 221	24.40 219	31.81 220	25.53 220

new_pmnet		38.40 210	42.64 211	33.44 212	37.54 220	45.00 216	36.60 216	32.72 217	40.27 201	12.72 218	29.89 205	28.90 213	24.78 208	53.17 213	52.90 214	56.31 214	48.34 212

PMMVS2		25.60 215	29.75 215	20.76 218	28.00 221	30.93 220	23.10 220	29.18 219	23.14 219	1.46 226	18.23 217	16.54 220	5.08 220	40.22 216	41.40 216	37.76 218	37.79 217

tmp_tt				14.50 220	14.68 222	7.17 225	10.46 225	2.21 221	37.73 206	28.71 194	25.26 213	16.98 219	4.37 221	31.49 217	29.77 217	26.56 221

MVE	19.12 19	20.47 218	23.27 217	17.20 219	12.66 223	25.41 221	10.52 224	34.14 216	14.79 223	6.53 225	8.79 221	4.68 225	16.64 218	29.49 218	41.63 215	22.73 222	38.11 216
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GG-mvs-BLEND		46.86 204	67.51 135	22.75 217	0.05 224	76.21 143	64.69 175	0.04 222	61.90 93	0.09 227	55.57 108	71.32 61	0.08 222	70.54 176	67.19 186	71.58 197	69.86 183

testmvs		0.09 219	0.15 220	0.02 221	0.01 225	0.02 226	0.05 227	0.01 223	0.11 224	0.01 228	0.26 224	0.01 228	0.06 224	0.10 222	0.10 220	0.01 224	0.43 222

ESAPD		0.00 221	0.00 222	0.00 223	0.00 226	0.00 228	0.00 229	0.00 225	0.00 226	0.00 229	0.00 225	0.00 229	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet-low-res		0.00 221	0.00 222	0.00 223	0.00 226	0.00 228	0.00 229	0.00 225	0.00 226	0.00 229	0.00 225	0.00 229	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet		0.00 221	0.00 222	0.00 223	0.00 226	0.00 228	0.00 229	0.00 225	0.00 226	0.00 229	0.00 225	0.00 229	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

test123		0.09 219	0.14 221	0.02 221	0.00 226	0.02 226	0.02 228	0.01 223	0.09 225	0.00 229	0.30 223	0.00 229	0.08 222	0.03 223	0.09 222	0.01 224	0.45 221

MTAPA										83.48 1		86.45 11

MTMP										82.66 3		84.91 19

Patchmatch-RL test							2.85 226

NP-MVS									80.10 38

Patchmtry						65.80 185	65.97 170	52.74 171		52.65 121

DeepMVS_CX						18.74 224	18.55 221	8.02 220	26.96 217	7.33 221	23.81 215	13.05 223	25.99 204	25.17 219		22.45 223	36.25 218

High-resolution multi-view benchmark