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 4	88.92 8	84.95 3	92.61 1	87.91 34	90.23 9	76.06 3	88.85 7	81.20 4	87.33 9	87.93 7	79.47 6	88.59 5	88.23 4	90.15 28	93.60 15

ESAPD		88.46 1	91.07 1	85.41 1	91.73 2	92.08 1	91.91 2	76.73 1	90.14 3	80.33 8	92.75 1	90.44 1	80.73 3	88.97 4	87.63 8	91.01 6	95.48 1

NCCC		85.34 15	86.59 20	83.88 11	91.48 3	88.88 20	89.79 11	75.54 7	86.67 16	77.94 18	76.55 30	84.99 19	78.07 12	88.04 8	87.68 7	90.46 20	93.31 16

CNVR-MVS		86.36 9	88.19 12	84.23 6	91.33 4	89.84 9	90.34 6	75.56 6	87.36 13	78.97 12	81.19 23	86.76 11	78.74 7	89.30 2	88.58 1	90.45 21	94.33 5

APDe-MVS		88.00 2	90.50 2	85.08 2	90.95 5	91.58 4	92.03 1	75.53 8	91.15 1	80.10 9	92.27 3	88.34 6	80.80 2	88.00 10	86.99 14	91.09 4	95.16 2

HFP-MVS		86.15 10	87.95 13	84.06 9	90.80 6	89.20 18	89.62 14	74.26 11	87.52 10	80.63 6	86.82 12	84.19 23	78.22 10	87.58 14	87.19 12	90.81 7	93.13 19

SteuartSystems-ACMMP		85.99 11	88.31 11	83.27 16	90.73 7	89.84 9	90.27 8	74.31 10	84.56 25	75.88 24	87.32 10	85.04 18	77.31 19	89.01 3	88.46 2	91.14 3	93.96 7
Skip Steuart: Steuart Systems R&D Blog.
APD-MVS		86.84 7	88.91 9	84.41 4	90.66 8	90.10 7	90.78 3	75.64 5	87.38 12	78.72 13	90.68 6	86.82 10	80.15 4	87.13 20	86.45 23	90.51 15	93.83 9
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MP-MVS		85.50 14	87.40 16	83.28 15	90.65 9	89.51 14	89.16 18	74.11 14	83.70 28	78.06 17	85.54 15	84.89 21	77.31 19	87.40 17	87.14 13	90.41 22	93.65 14

train_agg		84.86 20	87.21 17	82.11 22	90.59 10	85.47 49	89.81 10	73.55 20	83.95 27	73.30 32	89.84 8	87.23 9	75.61 27	86.47 29	85.46 33	89.78 31	92.06 27

MCST-MVS		85.13 18	86.62 19	83.39 13	90.55 11	89.82 11	89.29 16	73.89 18	84.38 26	76.03 23	79.01 26	85.90 15	78.47 8	87.81 12	86.11 28	92.11 1	93.29 17

MPTG		85.71 12	86.88 18	84.34 5	90.54 12	87.11 38	89.77 12	74.17 13	88.54 8	83.08 2	78.60 27	86.10 13	78.11 11	87.80 13	87.46 10	90.35 24	92.56 21

DeepC-MVS_fast	78.24 3	84.27 24	85.50 26	82.85 18	90.46 13	89.24 16	87.83 27	74.24 12	84.88 21	76.23 22	75.26 33	81.05 36	77.62 16	88.02 9	87.62 9	90.69 11	92.41 23
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 8	89.01 6	83.62 12	90.28 14	90.09 8	90.32 7	74.05 15	88.32 9	79.74 10	87.04 11	85.59 17	76.97 24	89.35 1	88.44 3	90.35 24	94.27 6

SD-MVS		86.96 5	89.45 4	84.05 10	90.13 15	89.23 17	89.77 12	74.59 9	89.17 5	80.70 5	89.93 7	89.67 2	78.47 8	87.57 15	86.79 17	90.67 12	93.76 11

ACMMPR		85.52 13	87.53 15	83.17 17	90.13 15	89.27 15	89.30 15	73.97 16	86.89 15	77.14 20	86.09 13	83.18 26	77.74 15	87.42 16	87.20 11	90.77 8	92.63 20

PGM-MVS		84.42 23	86.29 23	82.23 21	90.04 17	88.82 22	89.23 17	71.74 29	82.82 31	74.61 27	84.41 18	82.09 28	77.03 23	87.13 20	86.73 19	90.73 10	92.06 27

HSP-MVS		87.45 3	90.22 3	84.22 7	90.00 18	91.80 3	90.59 4	75.80 4	89.93 4	78.35 15	92.54 2	89.18 3	80.89 1	87.99 11	86.29 25	89.70 35	93.85 8

CSCG		85.28 17	87.68 14	82.49 20	89.95 19	91.99 2	88.82 19	71.20 31	86.41 17	79.63 11	79.26 24	88.36 5	73.94 34	86.64 27	86.67 20	91.40 2	94.41 3

mPP-MVS					89.90 20							81.29 35

TSAR-MVS + MP.		86.88 6	89.23 5	84.14 8	89.78 21	88.67 26	90.59 4	73.46 21	88.99 6	80.52 7	91.26 4	88.65 4	79.91 5	86.96 25	86.22 26	90.59 13	93.83 9

X-MVS		83.23 27	85.20 28	80.92 28	89.71 22	88.68 23	88.21 26	73.60 19	82.57 32	71.81 40	77.07 28	81.92 30	71.72 49	86.98 24	86.86 15	90.47 17	92.36 24

TSAR-MVS + ACMM		85.10 19	88.81 10	80.77 29	89.55 23	88.53 28	88.59 22	72.55 24	87.39 11	71.90 37	90.95 5	87.55 8	74.57 29	87.08 22	86.54 21	87.47 71	93.67 12

CP-MVS		84.74 22	86.43 22	82.77 19	89.48 24	88.13 33	88.64 20	73.93 17	84.92 20	76.77 21	81.94 21	83.50 24	77.29 21	86.92 26	86.49 22	90.49 16	93.14 18

CDPH-MVS		82.64 28	85.03 29	79.86 33	89.41 25	88.31 30	88.32 24	71.84 28	80.11 39	67.47 55	82.09 20	81.44 34	71.85 47	85.89 34	86.15 27	90.24 26	91.25 33

DeepC-MVS	78.47 2	84.81 21	86.03 24	83.37 14	89.29 26	90.38 6	88.61 21	76.50 2	86.25 18	77.22 19	75.12 34	80.28 38	77.59 17	88.39 6	88.17 5	91.02 5	93.66 13
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 41	78.62 52	81.05 27	89.23 27	86.06 46	84.95 43	71.96 27	79.39 42	75.51 25	63.16 73	68.84 79	76.51 25	83.55 49	82.85 48	88.13 58	86.46 62

EPNet		79.08 48	80.62 43	77.28 47	88.90 28	83.17 66	83.65 48	72.41 25	74.41 52	67.15 57	76.78 29	74.37 54	64.43 100	83.70 48	83.69 44	87.15 77	88.19 51
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	79.04 1	85.30 16	88.93 7	81.06 26	88.77 29	90.48 5	85.46 40	73.08 22	90.97 2	73.77 31	84.81 17	85.95 14	77.43 18	88.22 7	87.73 6	87.85 66	94.34 4

ACMMP		83.42 26	85.27 27	81.26 25	88.47 30	88.49 29	88.31 25	72.09 26	83.42 29	72.77 35	82.65 19	78.22 42	75.18 28	86.24 32	85.76 30	90.74 9	92.13 26
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 29	82.76 34	81.97 23	88.02 31	89.67 12	86.60 31	71.48 30	81.28 37	78.18 16	64.78 69	77.96 44	77.13 22	87.32 18	86.83 16	90.41 22	91.48 31

OPM-MVS		79.68 42	79.28 51	80.15 32	87.99 32	86.77 41	88.52 23	72.72 23	64.55 81	67.65 54	67.87 60	74.33 55	74.31 32	86.37 31	85.25 35	89.73 34	89.81 44

MAR-MVS		79.21 45	80.32 47	77.92 45	87.46 33	88.15 32	83.95 47	67.48 55	74.28 53	68.25 51	64.70 70	77.04 45	72.17 44	85.42 36	85.00 37	88.22 55	87.62 56
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 35	83.27 32	78.76 40	87.40 34	85.45 50	86.95 29	70.47 34	81.31 36	66.91 58	79.24 25	76.63 46	71.67 50	84.43 42	83.78 43	89.19 44	92.05 29

abl_6				79.05 37	87.27 35	88.85 21	83.62 49	68.25 48	81.68 35	72.94 34	73.79 40	84.45 22	72.55 42			89.66 37	90.64 38

CANet		81.62 34	83.41 31	79.53 35	87.06 36	88.59 27	85.47 39	67.96 52	76.59 47	74.05 28	74.69 35	81.98 29	72.98 40	86.14 33	85.47 32	89.68 36	90.42 41

MSLP-MVS++		82.09 31	82.66 35	81.42 24	87.03 37	87.22 37	85.82 36	70.04 36	80.30 38	78.66 14	68.67 56	81.04 37	77.81 14	85.19 38	84.88 38	89.19 44	91.31 32

ACMM	72.26 8	78.86 49	78.13 53	79.71 34	86.89 38	83.40 63	86.02 34	70.50 33	75.28 49	71.49 44	63.01 74	69.26 73	73.57 36	84.11 44	83.98 42	89.76 33	87.84 54
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVS					86.63 39	88.68 23	85.00 41			71.81 40		81.92 30				90.47 17

X-MVStestdata					86.63 39	88.68 23	85.00 41			71.81 40		81.92 30				90.47 17

PHI-MVS		82.36 30	85.89 25	78.24 43	86.40 41	89.52 13	85.52 38	69.52 42	82.38 34	65.67 60	81.35 22	82.36 27	73.07 39	87.31 19	86.76 18	89.24 42	91.56 30

LGP-MVS_train		79.83 38	81.22 41	78.22 44	86.28 42	85.36 52	86.76 30	69.59 40	77.34 44	65.14 62	75.68 32	70.79 65	71.37 52	84.60 40	84.01 41	90.18 27	90.74 37

MVS_0304		81.73 33	83.86 30	79.26 36	86.22 43	89.18 19	86.41 32	67.15 56	75.28 49	70.75 47	74.59 36	83.49 25	74.42 31	87.05 23	86.34 24	90.58 14	91.08 35

CPTT-MVS		81.77 32	83.10 33	80.21 31	85.93 44	86.45 44	87.72 28	70.98 32	82.54 33	71.53 43	74.23 39	81.49 33	76.31 26	82.85 56	81.87 52	88.79 51	92.26 25

MVS_111021_HR		80.13 37	81.46 39	78.58 41	85.77 45	85.17 53	83.45 50	69.28 43	74.08 55	70.31 48	74.31 38	75.26 52	73.13 38	86.46 30	85.15 36	89.53 38	89.81 44

ACMP	73.23 7	79.79 39	80.53 44	78.94 38	85.61 46	85.68 47	85.61 37	69.59 40	77.33 45	71.00 46	74.45 37	69.16 74	71.88 45	83.15 53	83.37 46	89.92 30	90.57 40
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UA-Net		74.47 63	77.80 55	70.59 82	85.33 47	85.40 51	73.54 137	65.98 64	60.65 105	56.00 106	72.11 43	79.15 39	54.63 163	83.13 54	82.25 50	88.04 60	81.92 121

TSAR-MVS + GP.		83.69 25	86.58 21	80.32 30	85.14 48	86.96 39	84.91 44	70.25 35	84.71 24	73.91 30	85.16 16	85.63 16	77.92 13	85.44 35	85.71 31	89.77 32	92.45 22

LS3D		74.08 64	73.39 74	74.88 58	85.05 49	82.62 69	79.71 62	68.66 46	72.82 57	58.80 81	57.61 100	61.31 98	71.07 54	80.32 85	78.87 89	86.00 129	80.18 136

QAPM		78.47 50	80.22 48	76.43 51	85.03 50	86.75 42	80.62 57	66.00 63	73.77 56	65.35 61	65.54 67	78.02 43	72.69 41	83.71 47	83.36 47	88.87 50	90.41 42

OpenMVS	70.44 10	76.15 58	76.82 65	75.37 55	85.01 51	84.79 55	78.99 70	62.07 106	71.27 59	67.88 53	57.91 99	72.36 60	70.15 56	82.23 59	81.41 56	88.12 59	87.78 55

CLD-MVS		79.35 44	81.23 40	77.16 48	85.01 51	86.92 40	85.87 35	60.89 119	80.07 41	75.35 26	72.96 41	73.21 58	68.43 65	85.41 37	84.63 39	87.41 72	85.44 73
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 40	80.52 45	78.84 39	84.94 53	87.35 35	84.43 46	65.54 66	78.29 43	73.97 29	63.00 75	75.62 51	74.07 33	85.00 39	85.34 34	90.11 29	89.04 47

PCF-MVS	73.28 6	79.42 43	80.41 46	78.26 42	84.88 54	88.17 31	86.08 33	69.85 37	75.23 51	68.43 50	68.03 59	78.38 41	71.76 48	81.26 70	80.65 71	88.56 54	91.18 34
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DELS-MVS		79.15 47	81.07 42	76.91 49	83.54 55	87.31 36	84.45 45	64.92 70	69.98 60	69.34 49	71.62 46	76.26 47	69.84 57	86.57 28	85.90 29	89.39 40	89.88 43
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 36	82.59 36	77.54 46	83.04 56	85.54 48	83.25 51	65.05 69	87.32 14	72.42 36	72.04 44	78.97 40	73.30 37	83.86 45	81.60 55	88.15 57	88.83 49

PLC	68.99 11	75.68 59	75.31 69	76.12 53	82.94 57	81.26 76	79.94 60	66.10 61	77.15 46	66.86 59	59.13 88	68.53 80	73.73 35	80.38 81	79.04 87	87.13 81	81.68 123
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		77.20 54	77.54 57	76.80 50	82.63 58	84.31 57	79.77 61	64.64 71	85.17 19	73.18 33	56.37 107	69.81 70	74.53 30	81.12 72	78.69 90	86.04 126	87.29 59

ACMH+	66.54 13	71.36 77	70.09 91	72.85 66	82.59 59	81.13 77	78.56 84	68.04 50	61.55 99	52.52 126	51.50 173	54.14 143	68.56 64	78.85 105	79.50 84	86.82 98	83.94 92

ACMH	65.37 14	70.71 81	70.00 92	71.54 69	82.51 60	82.47 70	77.78 96	68.13 49	56.19 153	46.06 161	54.30 133	51.20 179	68.68 63	80.66 76	80.72 64	86.07 122	84.45 88
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
canonicalmvs		79.16 46	82.37 37	75.41 54	82.33 61	86.38 45	80.80 56	63.18 80	82.90 30	67.34 56	72.79 42	76.07 48	69.62 58	83.46 52	84.41 40	89.20 43	90.60 39

MSDG		71.52 76	69.87 96	73.44 64	82.21 62	79.35 100	79.52 63	64.59 72	66.15 69	61.87 70	53.21 154	56.09 130	65.85 96	78.94 104	78.50 91	86.60 111	76.85 164

IS_MVSNet		73.33 66	77.34 61	68.65 110	81.29 63	83.47 62	74.45 119	63.58 78	65.75 73	48.49 145	67.11 64	70.61 66	54.63 163	84.51 41	83.58 45	89.48 39	86.34 63

Effi-MVS+		75.28 61	76.20 66	74.20 62	81.15 64	83.24 64	81.11 54	63.13 82	66.37 67	60.27 76	64.30 71	68.88 78	70.93 55	81.56 63	81.69 54	88.61 52	87.35 57

MVS_111021_LR		78.13 52	79.85 50	76.13 52	81.12 65	81.50 73	80.28 58	65.25 67	76.09 48	71.32 45	76.49 31	72.87 59	72.21 43	82.79 57	81.29 57	86.59 112	87.91 53

FC-MVSNet-train		72.60 72	75.07 70	69.71 101	81.10 66	78.79 109	73.74 135	65.23 68	66.10 70	53.34 119	70.36 49	63.40 93	56.92 144	81.44 64	80.96 60	87.93 62	84.46 87

MS-PatchMatch		70.17 91	70.49 89	69.79 99	80.98 67	77.97 122	77.51 98	58.95 149	62.33 92	55.22 110	53.14 155	65.90 86	62.03 111	79.08 103	77.11 120	84.08 160	77.91 154

TAPA-MVS	71.42 9	77.69 53	80.05 49	74.94 57	80.68 68	84.52 56	81.36 53	63.14 81	84.77 22	64.82 64	68.72 54	75.91 50	71.86 46	81.62 61	79.55 83	87.80 67	85.24 76
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_Blended_VisFu		76.57 55	77.90 54	75.02 56	80.56 69	86.58 43	79.24 65	66.18 60	64.81 78	68.18 52	65.61 65	71.45 62	67.05 67	84.16 43	81.80 53	88.90 48	90.92 36

EPP-MVSNet		74.00 65	77.41 60	70.02 97	80.53 70	83.91 59	74.99 116	62.68 96	65.06 76	49.77 142	68.68 55	72.09 61	63.06 106	82.49 58	80.73 63	89.12 46	88.91 48

COLMAP_ROB	62.73 15	67.66 133	66.76 151	68.70 109	80.49 71	77.98 120	75.29 109	62.95 84	63.62 85	49.96 140	47.32 191	50.72 182	58.57 130	76.87 142	75.50 157	84.94 151	75.33 174
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 51	81.64 38	74.48 61	80.13 72	85.01 54	81.73 52	65.93 65	84.75 23	61.68 71	85.79 14	66.27 85	71.39 51	82.91 55	80.78 62	86.01 127	85.98 64

EPNet_dtu		68.08 124	71.00 85	64.67 152	79.64 73	68.62 187	75.05 115	63.30 79	66.36 68	45.27 165	67.40 62	66.84 84	43.64 193	75.37 155	74.98 163	81.15 173	77.44 157
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		76.21 56	77.52 58	74.69 59	79.46 74	83.79 60	77.50 99	64.34 74	69.88 61	71.88 38	68.54 57	70.42 67	67.05 67	83.48 50	79.63 79	87.89 64	86.87 60

PVSNet_Blended		76.21 56	77.52 58	74.69 59	79.46 74	83.79 60	77.50 99	64.34 74	69.88 61	71.88 38	68.54 57	70.42 67	67.05 67	83.48 50	79.63 79	87.89 64	86.87 60

IB-MVS	66.94 12	71.21 78	71.66 83	70.68 79	79.18 76	82.83 68	72.61 144	61.77 110	59.66 112	63.44 69	53.26 152	59.65 103	59.16 129	76.78 144	82.11 51	87.90 63	87.33 58
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 60	77.13 63	73.31 65	79.07 77	81.32 75	79.98 59	60.12 138	69.72 63	64.11 66	70.53 48	73.22 57	68.90 61	80.14 89	79.48 85	87.67 68	85.50 71

Effi-MVS+-dtu		71.82 74	71.86 82	71.78 68	78.77 78	80.47 91	78.55 85	61.67 112	60.68 104	55.49 107	58.48 92	65.48 87	68.85 62	76.92 141	75.55 156	87.35 73	85.46 72

EG-PatchMatch MVS		67.24 142	66.94 148	67.60 120	78.73 79	81.35 74	73.28 141	59.49 143	46.89 203	51.42 131	43.65 195	53.49 151	55.50 159	81.38 66	80.66 70	87.15 77	81.17 126

gg-mvs-nofinetune		62.55 174	65.05 171	59.62 183	78.72 80	77.61 126	70.83 155	53.63 178	39.71 215	22.04 220	36.36 208	64.32 90	47.53 181	81.16 71	79.03 88	85.00 149	77.17 159

Vis-MVSNet (Re-imp)		67.83 129	73.52 73	61.19 173	78.37 81	76.72 142	66.80 176	62.96 83	65.50 74	34.17 199	67.19 63	69.68 71	39.20 202	79.39 100	79.44 86	85.68 139	76.73 165

DI_MVS_plusplus_trai		75.13 62	76.12 67	73.96 63	78.18 82	81.55 72	80.97 55	62.54 100	68.59 64	65.13 63	61.43 76	74.81 53	69.32 60	81.01 74	79.59 81	87.64 69	85.89 65

thres600view7		67.68 132	68.43 129	66.80 137	77.90 83	78.86 107	73.84 132	62.75 89	56.07 154	44.70 169	52.85 161	52.81 162	55.58 157	80.41 77	77.77 105	86.05 124	80.28 135

thres400		67.95 126	68.62 127	67.17 130	77.90 83	78.59 112	74.27 128	62.72 91	56.34 152	45.77 163	53.00 157	53.35 157	56.46 149	80.21 88	78.43 92	85.91 133	80.43 134

thres200		67.98 125	68.55 128	67.30 128	77.89 85	78.86 107	74.18 130	62.75 89	56.35 151	46.48 159	52.98 158	53.54 149	56.46 149	80.41 77	77.97 102	86.05 124	79.78 141

tpmp4_e23		68.32 120	67.08 147	69.76 100	77.86 86	75.22 162	78.37 90	56.17 174	66.06 71	64.27 65	57.15 104	54.89 138	63.40 104	70.97 185	68.29 195	78.46 185	77.00 163

view600		67.63 136	68.36 130	66.77 138	77.84 87	78.66 110	73.74 135	62.62 98	56.04 155	44.98 166	52.86 160	52.83 161	55.48 160	80.36 82	77.75 106	85.95 132	80.02 138

conf200view11		68.11 122	68.72 123	67.39 124	77.83 88	78.93 104	74.28 124	62.81 85	56.64 142	46.70 155	52.65 164	53.47 153	56.59 145	80.41 77	78.43 92	86.11 119	80.53 132

thres100view900		67.60 137	68.02 134	67.12 132	77.83 88	77.75 124	73.90 131	62.52 101	56.64 142	46.82 153	52.65 164	53.47 153	55.92 153	78.77 106	77.62 109	85.72 138	79.23 146

tfpn200view9		68.11 122	68.72 123	67.40 123	77.83 88	78.93 104	74.28 124	62.81 85	56.64 142	46.82 153	52.65 164	53.47 153	56.59 145	80.41 77	78.43 92	86.11 119	80.52 133

view800		67.35 141	68.22 133	66.35 142	77.83 88	78.62 111	72.97 143	62.58 99	55.71 157	44.13 170	52.69 163	52.24 171	54.58 165	80.27 86	78.19 99	86.01 127	79.79 140

conf0.01		67.72 131	67.99 135	67.39 124	77.82 92	78.94 102	74.28 124	62.81 85	56.64 142	46.70 155	53.33 150	48.59 192	56.59 145	80.34 83	78.43 92	86.16 118	79.67 142

tfpn		66.58 145	67.18 145	65.88 144	77.82 92	78.45 114	72.07 148	62.52 101	55.35 161	43.21 174	52.54 168	46.12 201	53.68 166	80.02 90	78.23 98	85.99 130	79.55 144

conf0.002		67.52 139	67.64 139	67.39 124	77.80 94	78.94 102	74.28 124	62.81 85	56.64 142	46.70 155	53.65 146	46.28 200	56.59 145	80.33 84	78.37 96	86.17 117	79.23 146

Fast-Effi-MVS+		73.11 69	73.66 72	72.48 67	77.72 95	80.88 82	78.55 85	58.83 157	65.19 75	60.36 75	59.98 83	62.42 96	71.22 53	81.66 60	80.61 73	88.20 56	84.88 84

UniMVSNet_NR-MVSNet		70.59 82	72.19 80	68.72 108	77.72 95	80.72 83	73.81 133	69.65 39	61.99 94	43.23 172	60.54 79	57.50 109	58.57 130	79.56 97	81.07 59	89.34 41	83.97 90

IterMVS-LS		71.69 75	72.82 78	70.37 91	77.54 97	76.34 148	75.13 114	60.46 126	61.53 100	57.57 89	64.89 68	67.33 82	66.04 92	77.09 140	77.37 116	85.48 142	85.18 77
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NR-MVSNet		68.79 116	70.56 88	66.71 141	77.48 98	79.54 97	73.52 138	69.20 44	61.20 102	39.76 183	58.52 90	50.11 185	51.37 174	80.26 87	80.71 68	88.97 47	83.59 97

conf0.05thres1000		66.26 147	66.77 150	65.66 145	77.45 99	78.10 115	71.85 151	62.44 104	51.47 187	43.00 175	47.92 186	51.66 177	53.40 168	79.71 93	77.97 102	85.82 134	80.56 130

TransMVSNet (Re)		64.74 159	65.66 164	63.66 159	77.40 100	75.33 157	69.86 156	62.67 97	47.63 201	41.21 181	50.01 179	52.33 167	45.31 190	79.57 96	77.69 108	85.49 141	77.07 162

TranMVSNet+NR-MVSNet		69.25 111	70.81 87	67.43 122	77.23 101	79.46 99	73.48 139	69.66 38	60.43 107	39.56 184	58.82 89	53.48 152	55.74 156	79.59 95	81.21 58	88.89 49	82.70 111

CANet_DTU		73.29 67	76.96 64	69.00 106	77.04 102	82.06 71	79.49 64	56.30 172	67.85 65	53.29 120	71.12 47	70.37 69	61.81 116	81.59 62	80.96 60	86.09 121	84.73 85

CHOSEN 1792x2688		69.20 112	69.26 112	69.13 104	76.86 103	78.93 104	77.27 101	60.12 138	61.86 96	54.42 111	42.54 198	61.61 97	66.91 73	78.55 108	78.14 101	79.23 183	83.23 102

HyFIR lowres test		69.47 108	68.94 115	70.09 96	76.77 104	82.93 67	76.63 105	60.17 133	59.00 116	54.03 114	40.54 204	65.23 88	67.89 66	76.54 148	78.30 97	85.03 148	80.07 137

UniMVSNet (Re)		69.53 105	71.90 81	66.76 139	76.42 105	80.93 79	72.59 145	68.03 51	61.75 98	41.68 180	58.34 96	57.23 117	53.27 170	79.53 98	80.62 72	88.57 53	84.90 83

thresconf0.02		64.77 158	65.90 160	63.44 161	76.37 106	75.17 165	69.51 159	61.28 113	56.98 133	39.01 186	56.24 108	48.68 191	49.78 177	77.13 138	75.61 154	84.71 155	71.53 191

tfpnview11		64.33 162	66.17 156	62.18 166	76.25 107	75.23 160	67.45 169	61.16 114	55.50 159	36.38 193	55.35 114	51.89 173	46.96 182	77.28 135	76.10 150	84.86 153	71.85 190

tfpn_n400		64.23 164	66.05 157	62.12 168	76.20 108	75.24 158	67.43 170	61.15 115	54.04 174	36.38 193	55.35 114	51.89 173	46.94 183	77.31 133	76.15 148	84.59 156	72.36 187

tfpnconf		64.23 164	66.05 157	62.12 168	76.20 108	75.24 158	67.43 170	61.15 115	54.04 174	36.38 193	55.35 114	51.89 173	46.94 183	77.31 133	76.15 148	84.59 156	72.36 187

DWT-MVSNet_training		67.24 142	65.96 159	68.74 107	76.15 110	74.36 169	74.37 123	56.66 170	61.82 97	60.51 74	58.23 98	49.76 187	65.07 97	70.04 193	70.39 180	79.70 180	77.11 161

gm-plane-assit		57.00 198	57.62 204	56.28 195	76.10 111	62.43 211	47.62 220	46.57 208	33.84 223	23.24 214	37.52 205	40.19 212	59.61 128	79.81 92	77.55 111	84.55 158	72.03 189

DU-MVS		69.63 100	70.91 86	68.13 114	75.99 112	79.54 97	73.81 133	69.20 44	61.20 102	43.23 172	58.52 90	53.50 150	58.57 130	79.22 101	80.45 74	87.97 61	83.97 90

Baseline_NR-MVSNet		67.53 138	68.77 121	66.09 143	75.99 112	74.75 166	72.43 146	68.41 47	61.33 101	38.33 188	51.31 174	54.13 145	56.03 152	79.22 101	78.19 99	85.37 143	82.45 113

CostFormer		68.92 114	69.58 102	68.15 113	75.98 114	76.17 151	78.22 94	51.86 188	65.80 72	61.56 72	63.57 72	62.83 94	61.85 114	70.40 192	68.67 190	79.42 181	79.62 143

tfpnnormal		64.27 163	63.64 181	65.02 148	75.84 115	75.61 154	71.24 154	62.52 101	47.79 200	42.97 176	42.65 197	44.49 205	52.66 172	78.77 106	76.86 123	84.88 152	79.29 145

tfpn1000		63.81 168	66.31 153	60.90 175	75.76 116	75.74 153	65.14 185	60.14 137	56.47 148	35.99 196	55.11 117	52.30 169	43.42 194	76.21 150	75.34 158	84.97 150	73.01 186

tfpn_ndepth		65.09 155	67.12 146	62.73 164	75.75 117	76.23 149	68.00 166	60.36 127	58.16 122	40.27 182	54.89 124	54.22 142	46.80 186	76.69 146	75.66 153	85.19 145	73.98 182

tpm cat1		65.41 150	63.81 180	67.28 129	75.61 118	72.88 172	75.32 108	52.85 182	62.97 89	63.66 68	53.24 153	53.29 159	61.83 115	65.54 204	64.14 207	74.43 201	74.60 176

CDS-MVSNet		67.65 134	69.83 99	65.09 147	75.39 119	76.55 143	74.42 122	63.75 76	53.55 176	49.37 144	59.41 86	62.45 95	44.44 191	79.71 93	79.82 77	83.17 166	77.36 158
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+-dtu		68.34 119	69.47 104	67.01 134	75.15 120	77.97 122	77.12 102	55.40 175	57.87 123	46.68 158	56.17 109	60.39 99	62.36 109	76.32 149	76.25 145	85.35 144	81.34 124

WR-MVS		63.03 170	67.40 143	57.92 189	75.14 121	77.60 127	60.56 200	66.10 61	54.11 173	23.88 211	53.94 144	53.58 148	34.50 207	73.93 163	77.71 107	87.35 73	80.94 127

test-LLR		64.42 160	64.36 176	64.49 153	75.02 122	63.93 201	66.61 178	61.96 107	54.41 169	47.77 149	57.46 101	60.25 100	55.20 161	70.80 186	69.33 185	80.40 177	74.38 178

test0.0.03 1		58.80 194	61.58 194	55.56 197	75.02 122	68.45 188	59.58 204	61.96 107	52.74 178	29.57 203	49.75 182	54.56 140	31.46 210	71.19 180	69.77 182	75.75 194	64.57 205

v1144		69.93 99	69.36 111	70.61 81	74.89 124	80.93 79	79.11 68	60.64 121	55.97 156	55.31 109	53.85 145	54.14 143	66.54 77	78.10 113	77.44 114	87.14 80	85.09 78

v13		69.52 106	68.76 122	70.41 89	74.88 125	77.02 138	78.52 89	58.86 151	56.61 147	56.91 94	54.00 143	56.17 129	66.11 91	77.93 114	76.74 131	87.21 75	82.83 104

v12		69.54 104	68.79 120	70.41 89	74.88 125	77.03 136	78.54 88	58.85 153	56.71 140	56.87 96	54.13 141	56.23 128	66.15 87	77.89 115	76.74 131	87.17 76	82.80 105

v11		69.37 109	68.65 126	70.20 93	74.87 127	76.97 139	78.29 92	58.55 161	56.38 150	56.04 105	54.02 142	54.98 137	66.47 78	78.30 110	76.91 122	86.97 89	83.02 103

V9		69.58 103	68.83 118	70.46 86	74.85 128	77.04 134	78.65 83	58.85 153	56.83 139	57.12 92	54.26 136	56.31 123	66.14 89	77.83 117	76.76 126	87.13 81	82.79 107

V14		69.59 102	68.86 117	70.45 88	74.83 129	77.04 134	78.70 82	58.83 157	56.95 136	57.08 93	54.41 132	56.34 122	66.15 87	77.77 118	76.76 126	87.08 86	82.74 110

v15		69.61 101	68.88 116	70.46 86	74.81 130	77.03 136	78.75 81	58.83 157	57.06 132	57.18 91	54.55 131	56.37 121	66.13 90	77.70 119	76.76 126	87.03 88	82.69 112

v7		70.33 88	69.87 96	70.88 70	74.79 131	81.04 78	79.22 66	60.57 123	57.70 129	56.65 102	54.23 138	55.29 135	66.95 70	78.28 111	77.47 112	87.12 84	85.05 80

v10		70.22 90	69.76 100	70.74 76	74.79 131	80.30 94	79.22 66	59.81 141	57.71 128	56.58 103	54.22 140	55.31 133	66.95 70	78.28 111	77.47 112	87.12 84	85.07 79

v1141		69.96 98	69.44 108	70.58 84	74.78 133	80.50 89	78.85 71	60.30 128	56.95 136	56.74 99	54.68 129	56.26 127	65.93 93	77.38 130	76.72 136	86.88 94	83.57 100

divwei89l23v2f112		69.97 96	69.44 108	70.58 84	74.78 133	80.50 89	78.85 71	60.30 128	56.97 135	56.75 98	54.67 130	56.27 126	65.92 94	77.37 131	76.72 136	86.88 94	83.58 99

v1		69.97 96	69.45 107	70.59 82	74.78 133	80.51 88	78.84 73	60.30 128	56.98 133	56.81 97	54.69 128	56.29 125	65.91 95	77.37 131	76.71 139	86.89 93	83.59 97

v17		70.03 95	69.43 110	70.72 78	74.75 136	77.09 131	78.78 80	58.85 153	59.53 114	58.72 82	54.87 125	57.39 111	66.38 80	77.60 123	76.75 129	86.83 97	82.80 105

v16		70.07 93	69.46 105	70.79 74	74.74 137	77.08 132	78.79 78	58.86 151	59.75 111	59.15 79	54.87 125	57.33 112	66.38 80	77.61 122	76.77 124	86.81 103	82.79 107

v8		70.23 89	69.86 98	70.67 80	74.69 138	79.82 96	78.79 78	59.18 147	58.80 118	58.20 84	55.00 120	57.33 112	66.31 86	77.51 127	76.71 139	86.82 98	83.88 93

v1neww		70.34 86	69.93 94	70.82 72	74.68 139	80.61 85	78.80 76	60.17 133	58.74 119	58.10 86	55.00 120	57.28 115	66.33 83	77.53 124	76.74 131	86.82 98	83.61 95

v7new		70.34 86	69.93 94	70.82 72	74.68 139	80.61 85	78.80 76	60.17 133	58.74 119	58.10 86	55.00 120	57.28 115	66.33 83	77.53 124	76.74 131	86.82 98	83.61 95

v6		70.35 85	69.94 93	70.83 71	74.68 139	80.62 84	78.81 75	60.16 136	58.81 117	58.17 85	55.01 119	57.31 114	66.32 85	77.53 124	76.73 135	86.82 98	83.62 94

v18		70.10 92	69.52 103	70.77 75	74.66 142	77.06 133	78.84 73	58.84 156	60.01 110	59.23 78	55.06 118	57.47 110	66.34 82	77.50 128	76.75 129	86.71 105	82.77 109

v2v482		70.05 94	69.46 105	70.74 76	74.62 143	80.32 93	79.00 69	60.62 122	57.41 130	56.89 95	55.43 113	55.14 136	66.39 79	77.25 136	77.14 119	86.90 91	83.57 100

v1192		69.50 107	68.83 118	70.29 92	74.49 144	80.92 81	78.55 85	60.54 124	55.04 165	54.21 112	52.79 162	52.33 167	66.92 72	77.88 116	77.35 117	87.04 87	85.51 70

DTE-MVSNet		61.85 183	64.96 173	58.22 188	74.32 145	74.39 168	61.01 199	67.85 53	51.76 186	21.91 221	53.28 151	48.17 193	37.74 203	72.22 173	76.44 142	86.52 114	78.49 151

Vis-MVSNet		72.77 71	77.20 62	67.59 121	74.19 146	84.01 58	76.61 106	61.69 111	60.62 106	50.61 136	70.25 50	71.31 64	55.57 158	83.85 46	82.28 49	86.90 91	88.08 52
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
diffmvs		73.13 68	75.65 68	70.19 94	74.07 147	77.17 130	78.24 93	57.45 165	72.44 58	64.02 67	69.05 52	75.92 49	64.86 98	75.18 157	75.27 159	82.47 168	84.53 86

v144192		69.34 110	68.68 125	70.12 95	74.06 148	80.54 87	78.08 95	60.54 124	54.99 167	54.13 113	52.92 159	52.80 163	66.73 75	77.13 138	76.72 136	87.15 77	85.63 66

v1921920		69.03 113	68.32 131	69.86 98	74.03 149	80.37 92	77.55 97	60.25 132	54.62 168	53.59 118	52.36 169	51.50 178	66.75 74	77.17 137	76.69 141	86.96 90	85.56 67

PEN-MVS		62.96 171	65.77 163	59.70 182	73.98 150	75.45 155	63.39 193	67.61 54	52.49 180	25.49 210	53.39 148	49.12 190	40.85 200	71.94 176	77.26 118	86.86 96	80.72 129

v1240		68.64 118	67.89 138	69.51 102	73.89 151	80.26 95	76.73 104	59.97 140	53.43 177	53.08 121	51.82 172	50.84 181	66.62 76	76.79 143	76.77 124	86.78 104	85.34 74

GA-MVS		68.14 121	69.17 113	66.93 136	73.77 152	78.50 113	74.45 119	58.28 162	55.11 164	48.44 146	60.08 81	53.99 146	61.50 117	78.43 109	77.57 110	85.13 146	80.54 131

pm-mvs1		65.62 149	67.42 142	63.53 160	73.66 153	76.39 147	69.66 157	60.87 120	49.73 195	43.97 171	51.24 175	57.00 119	48.16 180	79.89 91	77.84 104	84.85 154	79.82 139

dps		64.00 167	62.99 183	65.18 146	73.29 154	72.07 175	68.98 163	53.07 181	57.74 127	58.41 83	55.55 112	47.74 196	60.89 122	69.53 195	67.14 199	76.44 193	71.19 193

v148		67.85 128	67.53 140	68.23 112	73.25 155	77.57 128	74.26 129	57.36 167	55.70 158	57.45 90	53.53 147	55.42 132	61.96 112	75.23 156	73.92 166	85.08 147	81.32 125

PatchMatch-RL		67.78 130	66.65 152	69.10 105	73.01 156	72.69 173	68.49 164	61.85 109	62.93 90	60.20 77	56.83 106	50.42 183	69.52 59	75.62 154	74.46 165	81.51 171	73.62 184

GBi-Net		70.78 79	73.37 75	67.76 115	72.95 157	78.00 117	75.15 111	62.72 91	64.13 82	51.44 128	58.37 93	69.02 75	57.59 136	81.33 67	80.72 64	86.70 106	82.02 115

test1		70.78 79	73.37 75	67.76 115	72.95 157	78.00 117	75.15 111	62.72 91	64.13 82	51.44 128	58.37 93	69.02 75	57.59 136	81.33 67	80.72 64	86.70 106	82.02 115

FMVSNet2		70.39 84	72.67 79	67.72 118	72.95 157	78.00 117	75.15 111	62.69 95	63.29 87	51.25 132	55.64 110	68.49 81	57.59 136	80.91 75	80.35 75	86.70 106	82.02 115

FMVSNet3		70.49 83	72.90 77	67.67 119	72.88 160	77.98 120	74.96 117	62.72 91	64.13 82	51.44 128	58.37 93	69.02 75	57.43 139	79.43 99	79.57 82	86.59 112	81.81 122

LTVRE_ROB	59.44 16	61.82 186	62.64 187	60.87 176	72.83 161	77.19 129	64.37 189	58.97 148	33.56 224	28.00 207	52.59 167	42.21 208	63.93 102	74.52 159	76.28 143	77.15 190	82.13 114
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		67.05 144	66.94 148	67.17 130	72.35 162	78.97 101	73.26 142	58.88 150	51.16 188	50.90 133	48.21 184	50.11 185	60.96 119	77.70 119	77.38 115	86.68 109	85.05 80

tpm		62.41 177	63.15 182	61.55 172	72.24 163	63.79 203	71.31 153	46.12 210	57.82 124	55.33 108	59.90 84	54.74 139	53.63 167	67.24 202	64.29 205	70.65 212	74.25 180

test20.03		53.93 205	56.28 205	51.19 207	72.19 164	65.83 196	53.20 212	61.08 117	42.74 209	22.08 219	37.07 207	45.76 203	24.29 223	70.44 190	69.04 187	74.31 202	63.05 209

CP-MVSNet		62.68 173	65.49 166	59.40 185	71.84 165	75.34 156	62.87 195	67.04 57	52.64 179	27.19 208	53.38 149	48.15 194	41.40 198	71.26 179	75.68 152	86.07 122	82.00 118

PS-CasMVS		62.38 179	65.06 170	59.25 186	71.73 166	75.21 163	62.77 196	66.99 58	51.94 185	26.96 209	52.00 171	47.52 197	41.06 199	71.16 182	75.60 155	85.97 131	81.97 120

WR-MVS_H		61.83 185	65.87 162	57.12 192	71.72 167	76.87 140	61.45 198	66.19 59	51.97 184	22.92 218	53.13 156	52.30 169	33.80 208	71.03 183	75.00 162	86.65 110	80.78 128

USDC		67.36 140	67.90 137	66.74 140	71.72 167	75.23 160	71.58 152	60.28 131	67.45 66	50.54 137	60.93 77	45.20 204	62.08 110	76.56 147	74.50 164	84.25 159	75.38 173

UGNet		72.78 70	77.67 56	67.07 133	71.65 169	83.24 64	75.20 110	63.62 77	64.93 77	56.72 100	71.82 45	73.30 56	49.02 179	81.02 73	80.70 69	86.22 116	88.67 50
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		62.00 181	62.35 191	61.58 171	71.62 170	64.14 200	69.07 162	48.22 206	62.21 93	53.93 115	58.26 97	55.30 134	55.81 155	63.22 209	62.62 210	70.85 211	70.70 194

pmmvs4		67.89 127	67.39 144	68.48 111	71.60 171	73.57 171	74.45 119	60.98 118	64.65 79	57.97 88	54.95 123	51.73 176	61.88 113	73.78 164	75.11 161	83.99 162	77.91 154

testgi		54.39 204	57.86 202	50.35 208	71.59 172	67.24 191	54.95 210	53.25 180	43.36 208	23.78 212	44.64 194	47.87 195	24.96 219	70.45 189	68.66 191	73.60 204	62.78 210

pmmvs6		62.41 177	62.88 184	61.87 170	71.38 173	75.18 164	67.76 168	59.45 145	41.64 211	42.52 179	37.33 206	52.91 160	46.87 185	77.67 121	76.26 144	83.23 165	79.18 148

FMVSNet1		68.84 115	70.47 90	66.94 135	71.35 174	77.68 125	74.71 118	62.35 105	56.93 138	49.94 141	50.01 179	64.59 89	57.07 142	81.33 67	80.72 64	86.25 115	82.00 118

PatchmatchNet		64.21 166	64.65 174	63.69 158	71.29 175	68.66 186	69.63 158	51.70 190	63.04 88	53.77 117	59.83 85	58.34 107	60.23 126	68.54 199	66.06 202	75.56 196	68.08 200
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		64.83 157	65.54 165	64.01 157	70.64 176	69.41 182	65.97 181	52.74 183	57.81 125	52.65 123	54.27 134	56.31 123	60.92 120	72.20 174	73.09 170	81.12 174	75.69 170

MVSTER		72.06 73	74.24 71	69.51 102	70.39 177	75.97 152	76.91 103	57.36 167	64.64 80	61.39 73	68.86 53	63.76 91	63.46 103	81.44 64	79.70 78	87.56 70	85.31 75

Anonymous20231206		56.36 200	57.80 203	54.67 200	70.08 178	66.39 195	60.46 201	57.54 164	49.50 197	29.30 204	33.86 214	46.64 198	35.18 206	70.44 190	68.88 189	75.47 197	68.88 199

CMPMVS	47.78 17	62.49 176	62.52 188	62.46 165	70.01 179	70.66 180	62.97 194	51.84 189	51.98 183	56.71 101	42.87 196	53.62 147	57.80 135	72.23 172	70.37 181	75.45 198	75.91 167
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v748		65.12 154	65.24 167	64.98 149	69.77 180	76.45 144	69.47 160	57.06 169	49.93 193	50.70 134	47.87 187	49.50 189	57.14 141	73.64 166	75.18 160	85.75 137	84.14 89

TDRefinement		66.09 148	65.03 172	67.31 127	69.73 181	76.75 141	75.33 107	64.55 73	60.28 108	49.72 143	45.63 193	42.83 207	60.46 124	75.75 151	75.95 151	84.08 160	78.04 153

TinyColmap		62.84 172	61.03 196	64.96 150	69.61 182	71.69 176	68.48 165	59.76 142	55.41 160	47.69 151	47.33 190	34.20 217	62.76 108	74.52 159	72.59 173	81.44 172	71.47 192

RPMNet		61.71 187	62.88 184	60.34 178	69.51 183	69.41 182	63.48 192	49.23 198	57.81 125	45.64 164	50.51 177	50.12 184	53.13 171	68.17 201	68.49 193	81.07 175	75.62 172

IterMVS		66.36 146	68.30 132	64.10 154	69.48 184	74.61 167	73.41 140	50.79 194	57.30 131	48.28 147	60.64 78	59.92 102	60.85 123	74.14 162	72.66 172	81.80 170	78.82 150
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SixPastTwentyTwo		61.84 184	62.45 189	61.12 174	69.20 185	72.20 174	62.03 197	57.40 166	46.54 204	38.03 190	57.14 105	41.72 209	58.12 134	69.67 194	71.58 176	81.94 169	78.30 152

MDTV_nov1_ep13		64.37 161	65.24 167	63.37 163	68.94 186	70.81 178	72.40 147	50.29 197	60.10 109	53.91 116	60.07 82	59.15 105	57.21 140	69.43 196	67.30 197	77.47 188	69.78 196

EPMVS		60.00 192	61.97 192	57.71 190	68.46 187	63.17 207	64.54 188	48.23 205	63.30 86	44.72 168	60.19 80	56.05 131	50.85 175	65.27 206	62.02 212	69.44 214	63.81 207

Anonymous20231211		51.46 209	50.59 211	52.46 206	67.30 188	66.70 194	55.00 209	59.22 146	29.96 226	17.62 226	19.11 228	28.74 226	35.72 205	66.42 203	69.52 184	79.92 179	73.71 183

FC-MVSNet-test		56.90 199	65.20 169	47.21 211	66.98 189	63.20 206	49.11 218	58.60 160	59.38 115	11.50 231	65.60 66	56.68 120	24.66 222	71.17 181	71.36 178	72.38 207	69.02 198

CVMVSNet		62.55 174	65.89 161	58.64 187	66.95 190	69.15 184	66.49 180	56.29 173	52.46 181	32.70 200	59.27 87	58.21 108	50.09 176	71.77 177	71.39 177	79.31 182	78.99 149

FPMVS		51.87 208	50.00 213	54.07 201	66.83 191	57.25 214	60.25 202	50.91 192	50.25 190	34.36 198	36.04 211	32.02 219	41.49 197	58.98 221	56.07 221	70.56 213	59.36 216

pmmvs-eth3d		63.52 169	62.44 190	64.77 151	66.82 192	70.12 181	69.41 161	59.48 144	54.34 172	52.71 122	46.24 192	44.35 206	56.93 143	72.37 169	73.77 167	83.30 164	75.91 167

testpf		47.41 212	48.47 218	46.18 212	66.30 193	50.67 223	48.15 219	42.60 220	37.10 219	28.75 205	40.97 200	39.01 214	30.82 211	52.95 226	53.74 225	60.46 224	64.87 204

TAMVS		59.58 193	62.81 186	55.81 196	66.03 194	65.64 198	63.86 191	48.74 201	49.95 191	37.07 192	54.77 127	58.54 106	44.44 191	72.29 171	71.79 174	74.70 200	66.66 202

MDTV_nov1_ep13_2view		60.16 191	60.51 198	59.75 181	65.39 195	69.05 185	68.00 166	48.29 204	51.99 182	45.95 162	48.01 185	49.64 188	53.39 169	68.83 198	66.52 201	77.47 188	69.55 197

pmmvs5		62.37 180	64.04 178	60.42 177	65.03 196	71.67 177	67.17 173	52.70 185	50.30 189	44.80 167	54.23 138	51.19 180	49.37 178	72.88 168	73.48 169	83.45 163	74.55 177

ambc			53.42 207		64.99 197	63.36 205	49.96 216		47.07 202	37.12 191	28.97 218	16.36 233	41.82 196	75.10 158	67.34 196	71.55 210	75.72 169

V42		68.76 117	69.63 101	67.74 117	64.93 198	78.01 116	78.30 91	56.48 171	58.65 121	56.30 104	54.26 136	57.03 118	64.85 99	77.47 129	77.01 121	85.60 140	84.96 82

PMVS	39.38 18	46.06 217	43.30 222	49.28 210	62.93 199	38.75 230	41.88 223	53.50 179	33.33 225	35.46 197	28.90 219	31.01 222	33.04 209	58.61 222	54.63 224	68.86 215	57.88 219
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new-patchmatchnet		46.97 215	49.47 214	44.05 217	62.82 200	56.55 215	45.35 221	52.01 187	42.47 210	17.04 227	35.73 212	35.21 216	21.84 228	61.27 214	54.83 223	65.26 222	60.26 213

ADS-MVSNet		55.94 201	58.01 201	53.54 205	62.48 201	58.48 213	59.12 205	46.20 209	59.65 113	42.88 177	52.34 170	53.31 158	46.31 188	62.00 213	60.02 217	64.23 223	60.24 215

v52		65.23 152	66.24 154	64.06 155	61.94 202	76.42 145	72.06 149	54.30 177	49.94 192	50.04 139	47.41 189	52.42 165	60.23 126	75.71 152	76.22 146	85.78 135	85.56 67

V4		65.23 152	66.23 155	64.06 155	61.94 202	76.42 145	72.05 150	54.31 176	49.91 194	50.06 138	47.42 188	52.40 166	60.24 125	75.71 152	76.22 146	85.78 135	85.56 67

RPSCF		67.64 135	71.25 84	63.43 162	61.86 204	70.73 179	67.26 172	50.86 193	74.20 54	58.91 80	67.49 61	69.33 72	64.10 101	71.41 178	68.45 194	77.61 187	77.17 159

MIMVSNet		58.52 196	61.34 195	55.22 198	60.76 205	67.01 192	66.81 175	49.02 200	56.43 149	38.90 187	40.59 203	54.54 141	40.57 201	73.16 167	71.65 175	75.30 199	66.00 203

PatchT		61.97 182	64.04 178	59.55 184	60.49 206	67.40 190	56.54 207	48.65 202	56.69 141	52.65 123	51.10 176	52.14 172	60.92 120	72.20 174	73.09 170	78.03 186	75.69 170

N_pmnet		47.35 213	50.13 212	44.11 216	59.98 207	51.64 222	51.86 213	44.80 215	49.58 196	20.76 222	40.65 202	40.05 213	29.64 212	59.84 219	55.15 222	57.63 225	54.00 223

1111		43.08 219	44.02 221	41.98 218	59.22 208	49.27 226	41.48 224	45.63 212	35.01 220	23.06 216	28.60 220	30.15 223	27.22 214	60.42 217	57.97 219	55.27 228	46.74 225

.test1245		30.81 226	29.14 228	32.77 225	59.22 208	49.27 226	41.48 224	45.63 212	35.01 220	23.06 216	28.60 220	30.15 223	27.22 214	60.42 217	0.10 232	0.01 236	0.43 234

MVS-HIRNet		54.41 203	52.10 210	57.11 193	58.99 210	56.10 216	49.68 217	49.10 199	46.18 205	52.15 127	33.18 215	46.11 202	56.10 151	63.19 210	59.70 218	76.64 192	60.25 214

PM-MVS		60.48 190	60.94 197	59.94 180	58.85 211	66.83 193	64.27 190	51.39 191	55.03 166	48.03 148	50.00 181	40.79 211	58.26 133	69.20 197	67.13 200	78.84 184	77.60 156

anonymousdsp		65.28 151	67.98 136	62.13 167	58.73 212	73.98 170	67.10 174	50.69 195	48.41 198	47.66 152	54.27 134	52.75 164	61.45 118	76.71 145	80.20 76	87.13 81	89.53 46

LP		53.62 206	53.43 206	53.83 203	58.51 213	62.59 210	57.31 206	46.04 211	47.86 199	42.69 178	36.08 210	36.86 215	46.53 187	64.38 207	64.25 206	71.92 208	62.00 212

TESTMET0.1,1		61.10 188	64.36 176	57.29 191	57.53 214	63.93 201	66.61 178	36.22 225	54.41 169	47.77 149	57.46 101	60.25 100	55.20 161	70.80 186	69.33 185	80.40 177	74.38 178

EU-MVSNet		54.63 202	58.69 200	49.90 209	56.99 215	62.70 209	56.41 208	50.64 196	45.95 206	23.14 215	50.42 178	46.51 199	36.63 204	65.51 205	64.85 204	75.57 195	74.91 175

FMVSNet5		57.24 197	60.02 199	53.99 202	56.45 216	62.74 208	65.27 184	47.03 207	55.14 163	39.55 185	40.88 201	53.42 156	41.83 195	72.35 170	71.10 179	73.79 203	64.50 206

test2356		47.20 214	48.62 217	45.54 214	56.38 217	54.89 218	50.62 214	45.08 214	38.65 216	23.40 213	36.23 209	31.10 221	29.31 213	62.76 211	62.49 211	68.48 216	54.23 222

testus		45.61 218	49.06 216	41.59 219	56.13 218	55.28 217	43.51 222	39.64 223	37.74 217	18.23 224	35.52 213	31.28 220	24.69 221	62.46 212	62.90 209	67.33 218	58.26 218

test-mter		60.84 189	64.62 175	56.42 194	55.99 219	64.18 199	65.39 183	34.23 227	54.39 171	46.21 160	57.40 103	59.49 104	55.86 154	71.02 184	69.65 183	80.87 176	76.20 166

CHOSEN 280x420		58.70 195	61.88 193	54.98 199	55.45 220	50.55 224	64.92 186	40.36 221	55.21 162	38.13 189	48.31 183	63.76 91	63.03 107	73.73 165	68.58 192	68.00 217	73.04 185

PMMVS		65.06 156	69.17 113	60.26 179	55.25 221	63.43 204	66.71 177	43.01 219	62.41 91	50.64 135	69.44 51	67.04 83	63.29 105	74.36 161	73.54 168	82.68 167	73.99 181

testmv		42.58 220	44.36 219	40.49 220	54.63 222	52.76 220	41.21 226	44.37 216	28.83 227	12.87 228	27.16 223	25.03 228	23.01 224	60.83 215	61.13 213	66.88 219	54.81 220

test1235678		42.57 221	44.36 219	40.49 220	54.63 222	52.75 221	41.21 226	44.37 216	28.82 228	12.87 228	27.15 224	25.01 229	23.01 224	60.83 215	61.13 213	66.88 219	54.81 220

no-one		36.35 224	37.59 225	34.91 223	46.13 224	49.89 225	27.99 231	43.56 218	20.91 232	7.03 234	14.64 230	15.50 234	18.92 229	42.95 227	60.20 216	65.84 221	59.03 217

Gipuma		36.38 223	35.80 226	37.07 222	45.76 225	33.90 231	29.81 230	48.47 203	39.91 214	18.02 225	8.00 234	8.14 236	25.14 218	59.29 220	61.02 215	55.19 229	40.31 227
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs3		47.65 211	49.08 215	45.99 213	44.61 226	54.79 219	50.04 215	31.95 230	33.91 222	29.90 202	30.37 216	33.53 218	46.31 188	63.50 208	63.67 208	73.14 206	63.77 208

MIMVSNet1		49.27 210	53.25 208	44.62 215	44.61 226	61.52 212	53.61 211	52.18 186	41.62 212	18.68 223	28.14 222	41.58 210	25.50 217	68.46 200	69.04 187	73.15 205	62.37 211

test12356		35.10 225	38.50 224	31.13 226	44.14 228	43.70 229	32.27 229	34.42 226	26.51 230	9.47 232	25.22 226	20.34 230	10.86 231	53.47 224	56.15 220	55.59 227	44.11 226

MDA-MVSNet-bldmvs		53.37 207	53.01 209	53.79 204	43.67 229	67.95 189	59.69 203	57.92 163	43.69 207	32.41 201	41.47 199	27.89 227	52.38 173	56.97 223	65.99 203	76.68 191	67.13 201

E-PMN		21.77 228	18.24 230	25.89 227	40.22 230	19.58 234	12.46 235	39.87 222	18.68 234	6.71 235	9.57 231	4.31 239	22.36 227	19.89 232	27.28 230	33.73 231	28.34 231

EMVS		20.98 229	17.15 231	25.44 228	39.51 231	19.37 235	12.66 234	39.59 224	19.10 233	6.62 236	9.27 232	4.40 238	22.43 226	17.99 233	24.40 231	31.81 232	25.53 232

new_pmnet		38.40 222	42.64 223	33.44 224	37.54 232	45.00 228	36.60 228	32.72 229	40.27 213	12.72 230	29.89 217	28.90 225	24.78 220	53.17 225	52.90 226	56.31 226	48.34 224

PMMVS2		25.60 227	29.75 227	20.76 230	28.00 233	30.93 232	23.10 232	29.18 231	23.14 231	1.46 238	18.23 229	16.54 232	5.08 232	40.22 228	41.40 228	37.76 230	37.79 229

tmp_tt				14.50 232	14.68 234	7.17 237	10.46 237	2.21 233	37.73 218	28.71 206	25.26 225	16.98 231	4.37 233	31.49 229	29.77 229	26.56 233

MVE	19.12 19	20.47 230	23.27 229	17.20 231	12.66 235	25.41 233	10.52 236	34.14 228	14.79 235	6.53 237	8.79 233	4.68 237	16.64 230	29.49 230	41.63 227	22.73 234	38.11 228
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GG-mvs-BLEND		46.86 216	67.51 141	22.75 229	0.05 236	76.21 150	64.69 187	0.04 234	61.90 95	0.09 239	55.57 111	71.32 63	0.08 234	70.54 188	67.19 198	71.58 209	69.86 195

testmvs		0.09 231	0.15 232	0.02 233	0.01 237	0.02 238	0.05 239	0.01 235	0.11 236	0.01 240	0.26 236	0.01 240	0.06 236	0.10 234	0.10 232	0.01 236	0.43 234

sosnet-low-res		0.00 233	0.00 234	0.00 235	0.00 238	0.00 240	0.00 241	0.00 237	0.00 238	0.00 241	0.00 237	0.00 241	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

sosnet		0.00 233	0.00 234	0.00 235	0.00 238	0.00 240	0.00 241	0.00 237	0.00 238	0.00 241	0.00 237	0.00 241	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

test123		0.09 231	0.14 233	0.02 233	0.00 238	0.02 238	0.02 240	0.01 235	0.09 237	0.00 241	0.30 235	0.00 241	0.08 234	0.03 235	0.09 234	0.01 236	0.45 233

MTAPA										83.48 1		86.45 12

MTMP										82.66 3		84.91 20

Patchmatch-RL test							2.85 238

NP-MVS									80.10 40

Patchmtry						65.80 197	65.97 181	52.74 183		52.65 123

DeepMVS_CX						18.74 236	18.55 233	8.02 232	26.96 229	7.33 233	23.81 227	13.05 235	25.99 216	25.17 231		22.45 235	36.25 230

High-resolution multi-view benchmark