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 33	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 27	93.60 14

NCCC		85.34 14	86.59 19	83.88 10	91.48 2	88.88 19	89.79 10	75.54 6	86.67 15	77.94 17	76.55 29	84.99 18	78.07 11	88.04 7	87.68 7	90.46 19	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 12	78.97 11	81.19 22	86.76 10	78.74 6	89.30 2	88.58 1	90.45 20	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 8	92.27 2	88.34 5	80.80 2	88.00 9	86.99 13	91.09 4	95.16 1

HFP-MVS		86.15 9	87.95 12	84.06 8	90.80 5	89.20 17	89.62 13	74.26 10	87.52 9	80.63 6	86.82 11	84.19 22	78.22 9	87.58 13	87.19 11	90.81 6	93.13 18

SteuartSystems-ACMMP		85.99 10	88.31 10	83.27 15	90.73 6	89.84 8	90.27 7	74.31 9	84.56 24	75.88 23	87.32 9	85.04 17	77.31 18	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 7	90.10 6	90.78 2	75.64 4	87.38 11	78.72 12	90.68 5	86.82 9	80.15 3	87.13 19	86.45 22	90.51 14	93.83 8
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MP-MVS		85.50 13	87.40 15	83.28 14	90.65 8	89.51 13	89.16 17	74.11 13	83.70 27	78.06 16	85.54 14	84.89 20	77.31 18	87.40 16	87.14 12	90.41 21	93.65 13

train_agg		84.86 19	87.21 16	82.11 21	90.59 9	85.47 48	89.81 9	73.55 19	83.95 26	73.30 31	89.84 7	87.23 8	75.61 26	86.47 28	85.46 32	89.78 30	92.06 26

MCST-MVS		85.13 17	86.62 18	83.39 12	90.55 10	89.82 10	89.29 15	73.89 17	84.38 25	76.03 22	79.01 25	85.90 14	78.47 7	87.81 11	86.11 27	92.11 1	93.29 16

MPTG		85.71 11	86.88 17	84.34 4	90.54 11	87.11 37	89.77 11	74.17 12	88.54 7	83.08 2	78.60 26	86.10 12	78.11 10	87.80 12	87.46 9	90.35 23	92.56 20

DeepC-MVS_fast	78.24 3	84.27 23	85.50 25	82.85 17	90.46 12	89.24 15	87.83 26	74.24 11	84.88 20	76.23 21	75.26 32	81.05 35	77.62 15	88.02 8	87.62 8	90.69 10	92.41 22
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 11	90.28 13	90.09 7	90.32 6	74.05 14	88.32 8	79.74 9	87.04 10	85.59 16	76.97 23	89.35 1	88.44 3	90.35 23	94.27 5

SD-MVS		86.96 4	89.45 3	84.05 9	90.13 14	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 14	86.79 16	90.67 11	93.76 10

ACMMPR		85.52 12	87.53 14	83.17 16	90.13 14	89.27 14	89.30 14	73.97 15	86.89 14	77.14 19	86.09 12	83.18 25	77.74 14	87.42 15	87.20 10	90.77 7	92.63 19

PGM-MVS		84.42 22	86.29 22	82.23 20	90.04 16	88.82 21	89.23 16	71.74 28	82.82 30	74.61 26	84.41 17	82.09 27	77.03 22	87.13 19	86.73 18	90.73 9	92.06 26

HSP-MVS		87.45 2	90.22 2	84.22 6	90.00 17	91.80 2	90.59 3	75.80 3	89.93 3	78.35 14	92.54 1	89.18 2	80.89 1	87.99 10	86.29 24	89.70 34	93.85 7

CSCG		85.28 16	87.68 13	82.49 19	89.95 18	91.99 1	88.82 18	71.20 30	86.41 16	79.63 10	79.26 23	88.36 4	73.94 33	86.64 26	86.67 19	91.40 2	94.41 2

mPP-MVS					89.90 19							81.29 34

TSAR-MVS + MP.		86.88 5	89.23 4	84.14 7	89.78 20	88.67 25	90.59 3	73.46 20	88.99 5	80.52 7	91.26 3	88.65 3	79.91 4	86.96 24	86.22 25	90.59 12	93.83 8

X-MVS		83.23 26	85.20 27	80.92 27	89.71 21	88.68 22	88.21 25	73.60 18	82.57 31	71.81 39	77.07 27	81.92 29	71.72 48	86.98 23	86.86 14	90.47 16	92.36 23

TSAR-MVS + ACMM		85.10 18	88.81 9	80.77 28	89.55 22	88.53 27	88.59 21	72.55 23	87.39 10	71.90 36	90.95 4	87.55 7	74.57 28	87.08 21	86.54 20	87.47 70	93.67 11

CP-MVS		84.74 21	86.43 21	82.77 18	89.48 23	88.13 32	88.64 19	73.93 16	84.92 19	76.77 20	81.94 20	83.50 23	77.29 20	86.92 25	86.49 21	90.49 15	93.14 17

CDPH-MVS		82.64 27	85.03 28	79.86 32	89.41 24	88.31 29	88.32 23	71.84 27	80.11 38	67.47 54	82.09 19	81.44 33	71.85 46	85.89 33	86.15 26	90.24 25	91.25 32

DeepC-MVS	78.47 2	84.81 20	86.03 23	83.37 13	89.29 25	90.38 5	88.61 20	76.50 1	86.25 17	77.22 18	75.12 33	80.28 37	77.59 16	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 40	78.62 51	81.05 26	89.23 26	86.06 45	84.95 42	71.96 26	79.39 41	75.51 24	63.16 72	68.84 78	76.51 24	83.55 48	82.85 47	88.13 57	86.46 61

EPNet		79.08 47	80.62 42	77.28 46	88.90 27	83.17 65	83.65 47	72.41 24	74.41 51	67.15 56	76.78 28	74.37 53	64.43 99	83.70 47	83.69 43	87.15 76	88.19 50
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	79.04 1	85.30 15	88.93 6	81.06 25	88.77 28	90.48 4	85.46 39	73.08 21	90.97 2	73.77 30	84.81 16	85.95 13	77.43 17	88.22 6	87.73 6	87.85 65	94.34 3

ACMMP		83.42 25	85.27 26	81.26 24	88.47 29	88.49 28	88.31 24	72.09 25	83.42 28	72.77 34	82.65 18	78.22 41	75.18 27	86.24 31	85.76 29	90.74 8	92.13 25
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 28	82.76 33	81.97 22	88.02 30	89.67 11	86.60 30	71.48 29	81.28 36	78.18 15	64.78 68	77.96 43	77.13 21	87.32 17	86.83 15	90.41 21	91.48 30

OPM-MVS		79.68 41	79.28 50	80.15 31	87.99 31	86.77 40	88.52 22	72.72 22	64.55 80	67.65 53	67.87 59	74.33 54	74.31 31	86.37 30	85.25 34	89.73 33	89.81 43

MAR-MVS		79.21 44	80.32 46	77.92 44	87.46 32	88.15 31	83.95 46	67.48 54	74.28 52	68.25 50	64.70 69	77.04 44	72.17 43	85.42 35	85.00 36	88.22 54	87.62 55
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 34	83.27 31	78.76 39	87.40 33	85.45 49	86.95 28	70.47 33	81.31 35	66.91 57	79.24 24	76.63 45	71.67 49	84.43 41	83.78 42	89.19 43	92.05 28

abl_6				79.05 36	87.27 34	88.85 20	83.62 48	68.25 47	81.68 34	72.94 33	73.79 39	84.45 21	72.55 41			89.66 36	90.64 37

CANet		81.62 33	83.41 30	79.53 34	87.06 35	88.59 26	85.47 38	67.96 51	76.59 46	74.05 27	74.69 34	81.98 28	72.98 39	86.14 32	85.47 31	89.68 35	90.42 40

MSLP-MVS++		82.09 30	82.66 34	81.42 23	87.03 36	87.22 36	85.82 35	70.04 35	80.30 37	78.66 13	68.67 55	81.04 36	77.81 13	85.19 37	84.88 37	89.19 43	91.31 31

ACMM	72.26 8	78.86 48	78.13 52	79.71 33	86.89 37	83.40 62	86.02 33	70.50 32	75.28 48	71.49 43	63.01 73	69.26 72	73.57 35	84.11 43	83.98 41	89.76 32	87.84 53
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVS					86.63 38	88.68 22	85.00 40			71.81 39		81.92 29				90.47 16

X-MVStestdata					86.63 38	88.68 22	85.00 40			71.81 39		81.92 29				90.47 16

PHI-MVS		82.36 29	85.89 24	78.24 42	86.40 40	89.52 12	85.52 37	69.52 41	82.38 33	65.67 59	81.35 21	82.36 26	73.07 38	87.31 18	86.76 17	89.24 41	91.56 29

LGP-MVS_train		79.83 37	81.22 40	78.22 43	86.28 41	85.36 51	86.76 29	69.59 39	77.34 43	65.14 61	75.68 31	70.79 64	71.37 51	84.60 39	84.01 40	90.18 26	90.74 36

MVS_0304		81.73 32	83.86 29	79.26 35	86.22 42	89.18 18	86.41 31	67.15 55	75.28 48	70.75 46	74.59 35	83.49 24	74.42 30	87.05 22	86.34 23	90.58 13	91.08 34

CPTT-MVS		81.77 31	83.10 32	80.21 30	85.93 43	86.45 43	87.72 27	70.98 31	82.54 32	71.53 42	74.23 38	81.49 32	76.31 25	82.85 55	81.87 51	88.79 50	92.26 24

MVS_111021_HR		80.13 36	81.46 38	78.58 40	85.77 44	85.17 52	83.45 49	69.28 42	74.08 54	70.31 47	74.31 37	75.26 51	73.13 37	86.46 29	85.15 35	89.53 37	89.81 43

ACMP	73.23 7	79.79 38	80.53 43	78.94 37	85.61 45	85.68 46	85.61 36	69.59 39	77.33 44	71.00 45	74.45 36	69.16 73	71.88 44	83.15 52	83.37 45	89.92 29	90.57 39
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UA-Net		74.47 62	77.80 54	70.59 81	85.33 46	85.40 50	73.54 135	65.98 63	60.65 104	56.00 105	72.11 42	79.15 38	54.63 161	83.13 53	82.25 49	88.04 59	81.92 120

TSAR-MVS + GP.		83.69 24	86.58 20	80.32 29	85.14 47	86.96 38	84.91 43	70.25 34	84.71 23	73.91 29	85.16 15	85.63 15	77.92 12	85.44 34	85.71 30	89.77 31	92.45 21

LS3D		74.08 63	73.39 73	74.88 57	85.05 48	82.62 68	79.71 61	68.66 45	72.82 56	58.80 80	57.61 99	61.31 97	71.07 53	80.32 83	78.87 88	86.00 127	80.18 135

QAPM		78.47 49	80.22 47	76.43 50	85.03 49	86.75 41	80.62 56	66.00 62	73.77 55	65.35 60	65.54 66	78.02 42	72.69 40	83.71 46	83.36 46	88.87 49	90.41 41

OpenMVS	70.44 10	76.15 57	76.82 64	75.37 54	85.01 50	84.79 54	78.99 69	62.07 104	71.27 58	67.88 52	57.91 98	72.36 59	70.15 55	82.23 58	81.41 55	88.12 58	87.78 54

CLD-MVS		79.35 43	81.23 39	77.16 47	85.01 50	86.92 39	85.87 34	60.89 117	80.07 40	75.35 25	72.96 40	73.21 57	68.43 64	85.41 36	84.63 38	87.41 71	85.44 72
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 39	80.52 44	78.84 38	84.94 52	87.35 34	84.43 45	65.54 65	78.29 42	73.97 28	63.00 74	75.62 50	74.07 32	85.00 38	85.34 33	90.11 28	89.04 46

PCF-MVS	73.28 6	79.42 42	80.41 45	78.26 41	84.88 53	88.17 30	86.08 32	69.85 36	75.23 50	68.43 49	68.03 58	78.38 40	71.76 47	81.26 69	80.65 70	88.56 53	91.18 33
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DELS-MVS		79.15 46	81.07 41	76.91 48	83.54 54	87.31 35	84.45 44	64.92 69	69.98 59	69.34 48	71.62 45	76.26 46	69.84 56	86.57 27	85.90 28	89.39 39	89.88 42
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 35	82.59 35	77.54 45	83.04 55	85.54 47	83.25 50	65.05 68	87.32 13	72.42 35	72.04 43	78.97 39	73.30 36	83.86 44	81.60 54	88.15 56	88.83 48

PLC	68.99 11	75.68 58	75.31 68	76.12 52	82.94 56	81.26 75	79.94 59	66.10 60	77.15 45	66.86 58	59.13 87	68.53 79	73.73 34	80.38 80	79.04 86	87.13 80	81.68 122
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		77.20 53	77.54 56	76.80 49	82.63 57	84.31 56	79.77 60	64.64 70	85.17 18	73.18 32	56.37 106	69.81 69	74.53 29	81.12 71	78.69 89	86.04 124	87.29 58

ACMH+	66.54 13	71.36 76	70.09 90	72.85 65	82.59 58	81.13 76	78.56 83	68.04 49	61.55 98	52.52 125	51.50 171	54.14 142	68.56 63	78.85 103	79.50 83	86.82 97	83.94 91

ACMH	65.37 14	70.71 80	70.00 91	71.54 68	82.51 59	82.47 69	77.78 95	68.13 48	56.19 151	46.06 159	54.30 132	51.20 178	68.68 62	80.66 75	80.72 63	86.07 120	84.45 87
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
canonicalmvs		79.16 45	82.37 36	75.41 53	82.33 60	86.38 44	80.80 55	63.18 79	82.90 29	67.34 55	72.79 41	76.07 47	69.62 57	83.46 51	84.41 39	89.20 42	90.60 38

MSDG		71.52 75	69.87 95	73.44 63	82.21 61	79.35 99	79.52 62	64.59 71	66.15 68	61.87 69	53.21 152	56.09 129	65.85 95	78.94 102	78.50 90	86.60 110	76.85 162

IS_MVSNet		73.33 65	77.34 60	68.65 109	81.29 62	83.47 61	74.45 118	63.58 77	65.75 72	48.49 144	67.11 63	70.61 65	54.63 161	84.51 40	83.58 44	89.48 38	86.34 62

Effi-MVS+		75.28 60	76.20 65	74.20 61	81.15 63	83.24 63	81.11 53	63.13 81	66.37 66	60.27 75	64.30 70	68.88 77	70.93 54	81.56 62	81.69 53	88.61 51	87.35 56

MVS_111021_LR		78.13 51	79.85 49	76.13 51	81.12 64	81.50 72	80.28 57	65.25 66	76.09 47	71.32 44	76.49 30	72.87 58	72.21 42	82.79 56	81.29 56	86.59 111	87.91 52

FC-MVSNet-train		72.60 71	75.07 69	69.71 100	81.10 65	78.79 107	73.74 133	65.23 67	66.10 69	53.34 118	70.36 48	63.40 92	56.92 143	81.44 63	80.96 59	87.93 61	84.46 86

MS-PatchMatch		70.17 90	70.49 88	69.79 98	80.98 66	77.97 120	77.51 97	58.95 147	62.33 91	55.22 109	53.14 153	65.90 85	62.03 110	79.08 101	77.11 118	84.08 158	77.91 152

TAPA-MVS	71.42 9	77.69 52	80.05 48	74.94 56	80.68 67	84.52 55	81.36 52	63.14 80	84.77 21	64.82 63	68.72 53	75.91 49	71.86 45	81.62 60	79.55 82	87.80 66	85.24 75
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_Blended_VisFu		76.57 54	77.90 53	75.02 55	80.56 68	86.58 42	79.24 64	66.18 59	64.81 77	68.18 51	65.61 64	71.45 61	67.05 66	84.16 42	81.80 52	88.90 47	90.92 35

EPP-MVSNet		74.00 64	77.41 59	70.02 96	80.53 69	83.91 58	74.99 115	62.68 94	65.06 75	49.77 141	68.68 54	72.09 60	63.06 105	82.49 57	80.73 62	89.12 45	88.91 47

COLMAP_ROB	62.73 15	67.66 131	66.76 149	68.70 108	80.49 70	77.98 118	75.29 108	62.95 83	63.62 84	49.96 139	47.32 189	50.72 181	58.57 129	76.87 140	75.50 155	84.94 149	75.33 172
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 50	81.64 37	74.48 60	80.13 71	85.01 53	81.73 51	65.93 64	84.75 22	61.68 70	85.79 13	66.27 84	71.39 50	82.91 54	80.78 61	86.01 125	85.98 63

EPNet_dtu		68.08 123	71.00 84	64.67 150	79.64 72	68.62 185	75.05 114	63.30 78	66.36 67	45.27 163	67.40 61	66.84 83	43.64 191	75.37 153	74.98 161	81.15 171	77.44 155
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		76.21 55	77.52 57	74.69 58	79.46 73	83.79 59	77.50 98	64.34 73	69.88 60	71.88 37	68.54 56	70.42 66	67.05 66	83.48 49	79.63 78	87.89 63	86.87 59

PVSNet_Blended		76.21 55	77.52 57	74.69 58	79.46 73	83.79 59	77.50 98	64.34 73	69.88 60	71.88 37	68.54 56	70.42 66	67.05 66	83.48 49	79.63 78	87.89 63	86.87 59

IB-MVS	66.94 12	71.21 77	71.66 82	70.68 78	79.18 75	82.83 67	72.61 142	61.77 108	59.66 111	63.44 68	53.26 150	59.65 102	59.16 128	76.78 142	82.11 50	87.90 62	87.33 57
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 59	77.13 62	73.31 64	79.07 76	81.32 74	79.98 58	60.12 136	69.72 62	64.11 65	70.53 47	73.22 56	68.90 60	80.14 87	79.48 84	87.67 67	85.50 70

Effi-MVS+-dtu		71.82 73	71.86 81	71.78 67	78.77 77	80.47 90	78.55 84	61.67 110	60.68 103	55.49 106	58.48 91	65.48 86	68.85 61	76.92 139	75.55 154	87.35 72	85.46 71

EG-PatchMatch MVS		67.24 140	66.94 146	67.60 119	78.73 78	81.35 73	73.28 139	59.49 141	46.89 201	51.42 130	43.65 193	53.49 150	55.50 157	81.38 65	80.66 69	87.15 76	81.17 125

gg-mvs-nofinetune		62.55 172	65.05 169	59.62 181	78.72 79	77.61 124	70.83 153	53.63 176	39.71 213	22.04 218	36.36 206	64.32 89	47.53 179	81.16 70	79.03 87	85.00 147	77.17 157

Vis-MVSNet (Re-imp)		67.83 128	73.52 72	61.19 171	78.37 80	76.72 140	66.80 174	62.96 82	65.50 73	34.17 197	67.19 62	69.68 70	39.20 200	79.39 98	79.44 85	85.68 137	76.73 163

DI_MVS_plusplus_trai		75.13 61	76.12 66	73.96 62	78.18 81	81.55 71	80.97 54	62.54 98	68.59 63	65.13 62	61.43 75	74.81 52	69.32 59	81.01 73	79.59 80	87.64 68	85.89 64

thres600view7		67.68 130	68.43 128	66.80 135	77.90 82	78.86 105	73.84 130	62.75 87	56.07 152	44.70 167	52.85 159	52.81 161	55.58 155	80.41 76	77.77 103	86.05 122	80.28 134

thres400		67.95 125	68.62 126	67.17 128	77.90 82	78.59 110	74.27 126	62.72 89	56.34 150	45.77 161	53.00 155	53.35 156	56.46 147	80.21 86	78.43 91	85.91 131	80.43 133

thres200		67.98 124	68.55 127	67.30 126	77.89 84	78.86 105	74.18 128	62.75 87	56.35 149	46.48 157	52.98 156	53.54 148	56.46 147	80.41 76	77.97 100	86.05 122	79.78 140

tpmp4_e23		68.32 119	67.08 145	69.76 99	77.86 85	75.22 160	78.37 89	56.17 172	66.06 70	64.27 64	57.15 103	54.89 137	63.40 103	70.97 183	68.29 193	78.46 183	77.00 161

view600		67.63 134	68.36 129	66.77 136	77.84 86	78.66 108	73.74 133	62.62 96	56.04 153	44.98 164	52.86 158	52.83 160	55.48 158	80.36 81	77.75 104	85.95 130	80.02 137

conf200view11		68.11 121	68.72 122	67.39 123	77.83 87	78.93 102	74.28 123	62.81 84	56.64 141	46.70 154	52.65 162	53.47 152	56.59 144	80.41 76	78.43 91	86.11 117	80.53 131

thres100view900		67.60 135	68.02 133	67.12 130	77.83 87	77.75 122	73.90 129	62.52 99	56.64 141	46.82 152	52.65 162	53.47 152	55.92 151	78.77 104	77.62 107	85.72 136	79.23 144

tfpn200view9		68.11 121	68.72 122	67.40 122	77.83 87	78.93 102	74.28 123	62.81 84	56.64 141	46.82 152	52.65 162	53.47 152	56.59 144	80.41 76	78.43 91	86.11 117	80.52 132

view800		67.35 139	68.22 132	66.35 140	77.83 87	78.62 109	72.97 141	62.58 97	55.71 155	44.13 168	52.69 161	52.24 170	54.58 163	80.27 84	78.19 97	86.01 125	79.79 139

tfpn		66.58 143	67.18 143	65.88 142	77.82 91	78.45 112	72.07 146	62.52 99	55.35 159	43.21 172	52.54 166	46.12 199	53.68 164	80.02 88	78.23 96	85.99 128	79.55 142

conf0.002		67.52 137	67.64 137	67.39 123	77.80 92	78.94 101	74.28 123	62.81 84	56.64 141	46.70 154	53.65 145	46.28 198	56.59 144	80.33 82	78.37 94	86.17 116	79.23 144

Fast-Effi-MVS+		73.11 68	73.66 71	72.48 66	77.72 93	80.88 81	78.55 84	58.83 155	65.19 74	60.36 74	59.98 82	62.42 95	71.22 52	81.66 59	80.61 72	88.20 55	84.88 83

UniMVSNet_NR-MVSNet		70.59 81	72.19 79	68.72 107	77.72 93	80.72 82	73.81 131	69.65 38	61.99 93	43.23 170	60.54 78	57.50 108	58.57 129	79.56 95	81.07 58	89.34 40	83.97 89

IterMVS-LS		71.69 74	72.82 77	70.37 90	77.54 95	76.34 146	75.13 113	60.46 124	61.53 99	57.57 88	64.89 67	67.33 81	66.04 91	77.09 138	77.37 114	85.48 140	85.18 76
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NR-MVSNet		68.79 115	70.56 87	66.71 139	77.48 96	79.54 96	73.52 136	69.20 43	61.20 101	39.76 181	58.52 89	50.11 184	51.37 172	80.26 85	80.71 67	88.97 46	83.59 96

conf0.05thres1000		66.26 145	66.77 148	65.66 143	77.45 97	78.10 113	71.85 149	62.44 102	51.47 185	43.00 173	47.92 184	51.66 176	53.40 166	79.71 91	77.97 100	85.82 132	80.56 129

TransMVSNet (Re)		64.74 157	65.66 162	63.66 157	77.40 98	75.33 155	69.86 154	62.67 95	47.63 199	41.21 179	50.01 177	52.33 166	45.31 188	79.57 94	77.69 106	85.49 139	77.07 160

TranMVSNet+NR-MVSNet		69.25 110	70.81 86	67.43 121	77.23 99	79.46 98	73.48 137	69.66 37	60.43 106	39.56 182	58.82 88	53.48 151	55.74 154	79.59 93	81.21 57	88.89 48	82.70 110

CANet_DTU		73.29 66	76.96 63	69.00 105	77.04 100	82.06 70	79.49 63	56.30 170	67.85 64	53.29 119	71.12 46	70.37 68	61.81 115	81.59 61	80.96 59	86.09 119	84.73 84

CHOSEN 1792x2688		69.20 111	69.26 111	69.13 103	76.86 101	78.93 102	77.27 100	60.12 136	61.86 95	54.42 110	42.54 196	61.61 96	66.91 72	78.55 106	78.14 99	79.23 181	83.23 101

HyFIR lowres test		69.47 107	68.94 114	70.09 95	76.77 102	82.93 66	76.63 104	60.17 131	59.00 115	54.03 113	40.54 202	65.23 87	67.89 65	76.54 146	78.30 95	85.03 146	80.07 136

UniMVSNet (Re)		69.53 104	71.90 80	66.76 137	76.42 103	80.93 78	72.59 143	68.03 50	61.75 97	41.68 178	58.34 95	57.23 116	53.27 168	79.53 96	80.62 71	88.57 52	84.90 82

thresconf0.02		64.77 156	65.90 158	63.44 159	76.37 104	75.17 163	69.51 157	61.28 111	56.98 132	39.01 184	56.24 107	48.68 190	49.78 175	77.13 136	75.61 152	84.71 153	71.53 189

tfpnview11		64.33 160	66.17 154	62.18 164	76.25 105	75.23 158	67.45 167	61.16 112	55.50 157	36.38 191	55.35 113	51.89 172	46.96 180	77.28 133	76.10 148	84.86 151	71.85 188

tfpn_n400		64.23 162	66.05 155	62.12 166	76.20 106	75.24 156	67.43 168	61.15 113	54.04 172	36.38 191	55.35 113	51.89 172	46.94 181	77.31 131	76.15 146	84.59 154	72.36 185

tfpnconf		64.23 162	66.05 155	62.12 166	76.20 106	75.24 156	67.43 168	61.15 113	54.04 172	36.38 191	55.35 113	51.89 172	46.94 181	77.31 131	76.15 146	84.59 154	72.36 185

DWT-MVSNet_training		67.24 140	65.96 157	68.74 106	76.15 108	74.36 167	74.37 122	56.66 168	61.82 96	60.51 73	58.23 97	49.76 186	65.07 96	70.04 191	70.39 178	79.70 178	77.11 159

gm-plane-assit		57.00 196	57.62 202	56.28 193	76.10 109	62.43 209	47.62 218	46.57 206	33.84 221	23.24 212	37.52 203	40.19 210	59.61 127	79.81 90	77.55 109	84.55 156	72.03 187

DU-MVS		69.63 99	70.91 85	68.13 113	75.99 110	79.54 96	73.81 131	69.20 43	61.20 101	43.23 170	58.52 89	53.50 149	58.57 129	79.22 99	80.45 73	87.97 60	83.97 89

Baseline_NR-MVSNet		67.53 136	68.77 120	66.09 141	75.99 110	74.75 164	72.43 144	68.41 46	61.33 100	38.33 186	51.31 172	54.13 144	56.03 150	79.22 99	78.19 97	85.37 141	82.45 112

CostFormer		68.92 113	69.58 101	68.15 112	75.98 112	76.17 149	78.22 93	51.86 186	65.80 71	61.56 71	63.57 71	62.83 93	61.85 113	70.40 190	68.67 188	79.42 179	79.62 141

tfpnnormal		64.27 161	63.64 179	65.02 146	75.84 113	75.61 152	71.24 152	62.52 99	47.79 198	42.97 174	42.65 195	44.49 203	52.66 170	78.77 104	76.86 121	84.88 150	79.29 143

tfpn1000		63.81 166	66.31 151	60.90 173	75.76 114	75.74 151	65.14 183	60.14 135	56.47 146	35.99 194	55.11 116	52.30 168	43.42 192	76.21 148	75.34 156	84.97 148	73.01 184

tfpn_ndepth		65.09 153	67.12 144	62.73 162	75.75 115	76.23 147	68.00 164	60.36 125	58.16 121	40.27 180	54.89 123	54.22 141	46.80 184	76.69 144	75.66 151	85.19 143	73.98 180

tpm cat1		65.41 148	63.81 178	67.28 127	75.61 116	72.88 170	75.32 107	52.85 180	62.97 88	63.66 67	53.24 151	53.29 158	61.83 114	65.54 202	64.14 205	74.43 199	74.60 174

CDS-MVSNet		67.65 132	69.83 98	65.09 145	75.39 117	76.55 141	74.42 121	63.75 75	53.55 174	49.37 143	59.41 85	62.45 94	44.44 189	79.71 91	79.82 76	83.17 164	77.36 156
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+-dtu		68.34 118	69.47 103	67.01 132	75.15 118	77.97 120	77.12 101	55.40 173	57.87 122	46.68 156	56.17 108	60.39 98	62.36 108	76.32 147	76.25 143	85.35 142	81.34 123

WR-MVS		63.03 168	67.40 141	57.92 187	75.14 119	77.60 125	60.56 198	66.10 60	54.11 171	23.88 209	53.94 143	53.58 147	34.50 205	73.93 161	77.71 105	87.35 72	80.94 126

test-LLR		64.42 158	64.36 174	64.49 151	75.02 120	63.93 199	66.61 176	61.96 105	54.41 167	47.77 148	57.46 100	60.25 99	55.20 159	70.80 184	69.33 183	80.40 175	74.38 176

test0.0.03 1		58.80 192	61.58 192	55.56 195	75.02 120	68.45 186	59.58 202	61.96 105	52.74 176	29.57 201	49.75 180	54.56 139	31.46 208	71.19 178	69.77 180	75.75 192	64.57 203

v1144		69.93 98	69.36 110	70.61 80	74.89 122	80.93 78	79.11 67	60.64 119	55.97 154	55.31 108	53.85 144	54.14 142	66.54 76	78.10 111	77.44 112	87.14 79	85.09 77

v13		69.52 105	68.76 121	70.41 88	74.88 123	77.02 136	78.52 88	58.86 149	56.61 145	56.91 93	54.00 142	56.17 128	66.11 90	77.93 112	76.74 129	87.21 74	82.83 103

v12		69.54 103	68.79 119	70.41 88	74.88 123	77.03 134	78.54 87	58.85 151	56.71 139	56.87 95	54.13 140	56.23 127	66.15 86	77.89 113	76.74 129	87.17 75	82.80 104

v11		69.37 108	68.65 125	70.20 92	74.87 125	76.97 137	78.29 91	58.55 159	56.38 148	56.04 104	54.02 141	54.98 136	66.47 77	78.30 108	76.91 120	86.97 88	83.02 102

V9		69.58 102	68.83 117	70.46 85	74.85 126	77.04 132	78.65 82	58.85 151	56.83 138	57.12 91	54.26 135	56.31 122	66.14 88	77.83 115	76.76 124	87.13 80	82.79 106

V14		69.59 101	68.86 116	70.45 87	74.83 127	77.04 132	78.70 81	58.83 155	56.95 135	57.08 92	54.41 131	56.34 121	66.15 86	77.77 116	76.76 124	87.08 85	82.74 109

v15		69.61 100	68.88 115	70.46 85	74.81 128	77.03 134	78.75 80	58.83 155	57.06 131	57.18 90	54.55 130	56.37 120	66.13 89	77.70 117	76.76 124	87.03 87	82.69 111

v7		70.33 87	69.87 95	70.88 69	74.79 129	81.04 77	79.22 65	60.57 121	57.70 128	56.65 101	54.23 137	55.29 134	66.95 69	78.28 109	77.47 110	87.12 83	85.05 79

v10		70.22 89	69.76 99	70.74 75	74.79 129	80.30 93	79.22 65	59.81 139	57.71 127	56.58 102	54.22 139	55.31 132	66.95 69	78.28 109	77.47 110	87.12 83	85.07 78

v1141		69.96 97	69.44 107	70.58 83	74.78 131	80.50 88	78.85 70	60.30 126	56.95 135	56.74 98	54.68 128	56.26 126	65.93 92	77.38 128	76.72 134	86.88 93	83.57 99

divwei89l23v2f112		69.97 95	69.44 107	70.58 83	74.78 131	80.50 88	78.85 70	60.30 126	56.97 134	56.75 97	54.67 129	56.27 125	65.92 93	77.37 129	76.72 134	86.88 93	83.58 98

v1		69.97 95	69.45 106	70.59 81	74.78 131	80.51 87	78.84 72	60.30 126	56.98 132	56.81 96	54.69 127	56.29 124	65.91 94	77.37 129	76.71 137	86.89 92	83.59 96

v17		70.03 94	69.43 109	70.72 77	74.75 134	77.09 129	78.78 79	58.85 151	59.53 113	58.72 81	54.87 124	57.39 110	66.38 79	77.60 121	76.75 127	86.83 96	82.80 104

v16		70.07 92	69.46 104	70.79 73	74.74 135	77.08 130	78.79 77	58.86 149	59.75 110	59.15 78	54.87 124	57.33 111	66.38 79	77.61 120	76.77 122	86.81 102	82.79 106

v8		70.23 88	69.86 97	70.67 79	74.69 136	79.82 95	78.79 77	59.18 145	58.80 117	58.20 83	55.00 119	57.33 111	66.31 85	77.51 125	76.71 137	86.82 97	83.88 92

v1neww		70.34 85	69.93 93	70.82 71	74.68 137	80.61 84	78.80 75	60.17 131	58.74 118	58.10 85	55.00 119	57.28 114	66.33 82	77.53 122	76.74 129	86.82 97	83.61 94

v7new		70.34 85	69.93 93	70.82 71	74.68 137	80.61 84	78.80 75	60.17 131	58.74 118	58.10 85	55.00 119	57.28 114	66.33 82	77.53 122	76.74 129	86.82 97	83.61 94

v6		70.35 84	69.94 92	70.83 70	74.68 137	80.62 83	78.81 74	60.16 134	58.81 116	58.17 84	55.01 118	57.31 113	66.32 84	77.53 122	76.73 133	86.82 97	83.62 93

v18		70.10 91	69.52 102	70.77 74	74.66 140	77.06 131	78.84 72	58.84 154	60.01 109	59.23 77	55.06 117	57.47 109	66.34 81	77.50 126	76.75 127	86.71 104	82.77 108

v2v482		70.05 93	69.46 104	70.74 75	74.62 141	80.32 92	79.00 68	60.62 120	57.41 129	56.89 94	55.43 112	55.14 135	66.39 78	77.25 134	77.14 117	86.90 90	83.57 99

v1192		69.50 106	68.83 117	70.29 91	74.49 142	80.92 80	78.55 84	60.54 122	55.04 163	54.21 111	52.79 160	52.33 166	66.92 71	77.88 114	77.35 115	87.04 86	85.51 69

DTE-MVSNet		61.85 181	64.96 171	58.22 186	74.32 143	74.39 166	61.01 197	67.85 52	51.76 184	21.91 219	53.28 149	48.17 191	37.74 201	72.22 171	76.44 140	86.52 113	78.49 149

Vis-MVSNet		72.77 70	77.20 61	67.59 120	74.19 144	84.01 57	76.61 105	61.69 109	60.62 105	50.61 135	70.25 49	71.31 63	55.57 156	83.85 45	82.28 48	86.90 90	88.08 51
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
diffmvs		73.13 67	75.65 67	70.19 93	74.07 145	77.17 128	78.24 92	57.45 163	72.44 57	64.02 66	69.05 51	75.92 48	64.86 97	75.18 155	75.27 157	82.47 166	84.53 85

v144192		69.34 109	68.68 124	70.12 94	74.06 146	80.54 86	78.08 94	60.54 122	54.99 165	54.13 112	52.92 157	52.80 162	66.73 74	77.13 136	76.72 134	87.15 76	85.63 65

v1921920		69.03 112	68.32 130	69.86 97	74.03 147	80.37 91	77.55 96	60.25 130	54.62 166	53.59 117	52.36 167	51.50 177	66.75 73	77.17 135	76.69 139	86.96 89	85.56 66

PEN-MVS		62.96 169	65.77 161	59.70 180	73.98 148	75.45 153	63.39 191	67.61 53	52.49 178	25.49 208	53.39 147	49.12 189	40.85 198	71.94 174	77.26 116	86.86 95	80.72 128

v1240		68.64 117	67.89 136	69.51 101	73.89 149	80.26 94	76.73 103	59.97 138	53.43 175	53.08 120	51.82 170	50.84 180	66.62 75	76.79 141	76.77 122	86.78 103	85.34 73

GA-MVS		68.14 120	69.17 112	66.93 134	73.77 150	78.50 111	74.45 118	58.28 160	55.11 162	48.44 145	60.08 80	53.99 145	61.50 116	78.43 107	77.57 108	85.13 144	80.54 130

pm-mvs1		65.62 147	67.42 140	63.53 158	73.66 151	76.39 145	69.66 155	60.87 118	49.73 193	43.97 169	51.24 173	57.00 118	48.16 178	79.89 89	77.84 102	84.85 152	79.82 138

dps		64.00 165	62.99 181	65.18 144	73.29 152	72.07 173	68.98 161	53.07 179	57.74 126	58.41 82	55.55 111	47.74 194	60.89 121	69.53 193	67.14 197	76.44 191	71.19 191

v148		67.85 127	67.53 138	68.23 111	73.25 153	77.57 126	74.26 127	57.36 165	55.70 156	57.45 89	53.53 146	55.42 131	61.96 111	75.23 154	73.92 164	85.08 145	81.32 124

PatchMatch-RL		67.78 129	66.65 150	69.10 104	73.01 154	72.69 171	68.49 162	61.85 107	62.93 89	60.20 76	56.83 105	50.42 182	69.52 58	75.62 152	74.46 163	81.51 169	73.62 182

GBi-Net		70.78 78	73.37 74	67.76 114	72.95 155	78.00 115	75.15 110	62.72 89	64.13 81	51.44 127	58.37 92	69.02 74	57.59 135	81.33 66	80.72 63	86.70 105	82.02 114

test1		70.78 78	73.37 74	67.76 114	72.95 155	78.00 115	75.15 110	62.72 89	64.13 81	51.44 127	58.37 92	69.02 74	57.59 135	81.33 66	80.72 63	86.70 105	82.02 114

FMVSNet2		70.39 83	72.67 78	67.72 117	72.95 155	78.00 115	75.15 110	62.69 93	63.29 86	51.25 131	55.64 109	68.49 80	57.59 135	80.91 74	80.35 74	86.70 105	82.02 114

FMVSNet3		70.49 82	72.90 76	67.67 118	72.88 158	77.98 118	74.96 116	62.72 89	64.13 81	51.44 127	58.37 92	69.02 74	57.43 138	79.43 97	79.57 81	86.59 111	81.81 121

LTVRE_ROB	59.44 16	61.82 184	62.64 185	60.87 174	72.83 159	77.19 127	64.37 187	58.97 146	33.56 222	28.00 205	52.59 165	42.21 206	63.93 101	74.52 157	76.28 141	77.15 188	82.13 113
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		67.05 142	66.94 146	67.17 128	72.35 160	78.97 100	73.26 140	58.88 148	51.16 186	50.90 132	48.21 182	50.11 184	60.96 118	77.70 117	77.38 113	86.68 108	85.05 79

tpm		62.41 175	63.15 180	61.55 170	72.24 161	63.79 201	71.31 151	46.12 208	57.82 123	55.33 107	59.90 83	54.74 138	53.63 165	67.24 200	64.29 203	70.65 210	74.25 178

test20.03		53.93 203	56.28 203	51.19 205	72.19 162	65.83 194	53.20 210	61.08 115	42.74 207	22.08 217	37.07 205	45.76 201	24.29 221	70.44 188	69.04 185	74.31 200	63.05 207

CP-MVSNet		62.68 171	65.49 164	59.40 183	71.84 163	75.34 154	62.87 193	67.04 56	52.64 177	27.19 206	53.38 148	48.15 192	41.40 196	71.26 177	75.68 150	86.07 120	82.00 117

PS-CasMVS		62.38 177	65.06 168	59.25 184	71.73 164	75.21 161	62.77 194	66.99 57	51.94 183	26.96 207	52.00 169	47.52 195	41.06 197	71.16 180	75.60 153	85.97 129	81.97 119

WR-MVS_H		61.83 183	65.87 160	57.12 190	71.72 165	76.87 138	61.45 196	66.19 58	51.97 182	22.92 216	53.13 154	52.30 168	33.80 206	71.03 181	75.00 160	86.65 109	80.78 127

USDC		67.36 138	67.90 135	66.74 138	71.72 165	75.23 158	71.58 150	60.28 129	67.45 65	50.54 136	60.93 76	45.20 202	62.08 109	76.56 145	74.50 162	84.25 157	75.38 171

UGNet		72.78 69	77.67 55	67.07 131	71.65 167	83.24 63	75.20 109	63.62 76	64.93 76	56.72 99	71.82 44	73.30 55	49.02 177	81.02 72	80.70 68	86.22 115	88.67 49
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		62.00 179	62.35 189	61.58 169	71.62 168	64.14 198	69.07 160	48.22 204	62.21 92	53.93 114	58.26 96	55.30 133	55.81 153	63.22 207	62.62 208	70.85 209	70.70 192

pmmvs4		67.89 126	67.39 142	68.48 110	71.60 169	73.57 169	74.45 118	60.98 116	64.65 78	57.97 87	54.95 122	51.73 175	61.88 112	73.78 162	75.11 159	83.99 160	77.91 152

testgi		54.39 202	57.86 200	50.35 206	71.59 170	67.24 189	54.95 208	53.25 178	43.36 206	23.78 210	44.64 192	47.87 193	24.96 217	70.45 187	68.66 189	73.60 202	62.78 208

pmmvs6		62.41 175	62.88 182	61.87 168	71.38 171	75.18 162	67.76 166	59.45 143	41.64 209	42.52 177	37.33 204	52.91 159	46.87 183	77.67 119	76.26 142	83.23 163	79.18 146

FMVSNet1		68.84 114	70.47 89	66.94 133	71.35 172	77.68 123	74.71 117	62.35 103	56.93 137	49.94 140	50.01 177	64.59 88	57.07 141	81.33 66	80.72 63	86.25 114	82.00 117

PatchmatchNet		64.21 164	64.65 172	63.69 156	71.29 173	68.66 184	69.63 156	51.70 188	63.04 87	53.77 116	59.83 84	58.34 106	60.23 125	68.54 197	66.06 200	75.56 194	68.08 198
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		64.83 155	65.54 163	64.01 155	70.64 174	69.41 180	65.97 179	52.74 181	57.81 124	52.65 122	54.27 133	56.31 122	60.92 119	72.20 172	73.09 168	81.12 172	75.69 168

MVSTER		72.06 72	74.24 70	69.51 101	70.39 175	75.97 150	76.91 102	57.36 165	64.64 79	61.39 72	68.86 52	63.76 90	63.46 102	81.44 63	79.70 77	87.56 69	85.31 74

Anonymous20231206		56.36 198	57.80 201	54.67 198	70.08 176	66.39 193	60.46 199	57.54 162	49.50 195	29.30 202	33.86 212	46.64 196	35.18 204	70.44 188	68.88 187	75.47 195	68.88 197

CMPMVS	47.78 17	62.49 174	62.52 186	62.46 163	70.01 177	70.66 178	62.97 192	51.84 187	51.98 181	56.71 100	42.87 194	53.62 146	57.80 134	72.23 170	70.37 179	75.45 196	75.91 165
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v748		65.12 152	65.24 165	64.98 147	69.77 178	76.45 142	69.47 158	57.06 167	49.93 191	50.70 133	47.87 185	49.50 188	57.14 140	73.64 164	75.18 158	85.75 135	84.14 88

TDRefinement		66.09 146	65.03 170	67.31 125	69.73 179	76.75 139	75.33 106	64.55 72	60.28 107	49.72 142	45.63 191	42.83 205	60.46 123	75.75 149	75.95 149	84.08 158	78.04 151

TinyColmap		62.84 170	61.03 194	64.96 148	69.61 180	71.69 174	68.48 163	59.76 140	55.41 158	47.69 150	47.33 188	34.20 215	62.76 107	74.52 157	72.59 171	81.44 170	71.47 190

RPMNet		61.71 185	62.88 182	60.34 176	69.51 181	69.41 180	63.48 190	49.23 196	57.81 124	45.64 162	50.51 175	50.12 183	53.13 169	68.17 199	68.49 191	81.07 173	75.62 170

IterMVS		66.36 144	68.30 131	64.10 152	69.48 182	74.61 165	73.41 138	50.79 192	57.30 130	48.28 146	60.64 77	59.92 101	60.85 122	74.14 160	72.66 170	81.80 168	78.82 148
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SixPastTwentyTwo		61.84 182	62.45 187	61.12 172	69.20 183	72.20 172	62.03 195	57.40 164	46.54 202	38.03 188	57.14 104	41.72 207	58.12 133	69.67 192	71.58 174	81.94 167	78.30 150

MDTV_nov1_ep13		64.37 159	65.24 165	63.37 161	68.94 184	70.81 176	72.40 145	50.29 195	60.10 108	53.91 115	60.07 81	59.15 104	57.21 139	69.43 194	67.30 195	77.47 186	69.78 194

EPMVS		60.00 190	61.97 190	57.71 188	68.46 185	63.17 205	64.54 186	48.23 203	63.30 85	44.72 166	60.19 79	56.05 130	50.85 173	65.27 204	62.02 210	69.44 212	63.81 205

Anonymous20231211		51.46 207	50.59 209	52.46 204	67.30 186	66.70 192	55.00 207	59.22 144	29.96 224	17.62 224	19.11 226	28.74 224	35.72 203	66.42 201	69.52 182	79.92 177	73.71 181

FC-MVSNet-test		56.90 197	65.20 167	47.21 209	66.98 187	63.20 204	49.11 216	58.60 158	59.38 114	11.50 229	65.60 65	56.68 119	24.66 220	71.17 179	71.36 176	72.38 205	69.02 196

CVMVSNet		62.55 172	65.89 159	58.64 185	66.95 188	69.15 182	66.49 178	56.29 171	52.46 179	32.70 198	59.27 86	58.21 107	50.09 174	71.77 175	71.39 175	79.31 180	78.99 147

FPMVS		51.87 206	50.00 211	54.07 199	66.83 189	57.25 212	60.25 200	50.91 190	50.25 188	34.36 196	36.04 209	32.02 217	41.49 195	58.98 219	56.07 219	70.56 211	59.36 214

pmmvs-eth3d		63.52 167	62.44 188	64.77 149	66.82 190	70.12 179	69.41 159	59.48 142	54.34 170	52.71 121	46.24 190	44.35 204	56.93 142	72.37 167	73.77 165	83.30 162	75.91 165

testpf		47.41 210	48.47 216	46.18 210	66.30 191	50.67 221	48.15 217	42.60 218	37.10 217	28.75 203	40.97 198	39.01 212	30.82 209	52.95 224	53.74 223	60.46 222	64.87 202

TAMVS		59.58 191	62.81 184	55.81 194	66.03 192	65.64 196	63.86 189	48.74 199	49.95 189	37.07 190	54.77 126	58.54 105	44.44 189	72.29 169	71.79 172	74.70 198	66.66 200

MDTV_nov1_ep13_2view		60.16 189	60.51 196	59.75 179	65.39 193	69.05 183	68.00 164	48.29 202	51.99 180	45.95 160	48.01 183	49.64 187	53.39 167	68.83 196	66.52 199	77.47 186	69.55 195

pmmvs5		62.37 178	64.04 176	60.42 175	65.03 194	71.67 175	67.17 171	52.70 183	50.30 187	44.80 165	54.23 137	51.19 179	49.37 176	72.88 166	73.48 167	83.45 161	74.55 175

ambc			53.42 205		64.99 195	63.36 203	49.96 214		47.07 200	37.12 189	28.97 216	16.36 231	41.82 194	75.10 156	67.34 194	71.55 208	75.72 167

V42		68.76 116	69.63 100	67.74 116	64.93 196	78.01 114	78.30 90	56.48 169	58.65 120	56.30 103	54.26 135	57.03 117	64.85 98	77.47 127	77.01 119	85.60 138	84.96 81

PMVS	39.38 18	46.06 215	43.30 220	49.28 208	62.93 197	38.75 228	41.88 221	53.50 177	33.33 223	35.46 195	28.90 217	31.01 220	33.04 207	58.61 220	54.63 222	68.86 213	57.88 217
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new-patchmatchnet		46.97 213	49.47 212	44.05 215	62.82 198	56.55 213	45.35 219	52.01 185	42.47 208	17.04 225	35.73 210	35.21 214	21.84 226	61.27 212	54.83 221	65.26 220	60.26 211

ADS-MVSNet		55.94 199	58.01 199	53.54 203	62.48 199	58.48 211	59.12 203	46.20 207	59.65 112	42.88 175	52.34 168	53.31 157	46.31 186	62.00 211	60.02 215	64.23 221	60.24 213

v52		65.23 150	66.24 152	64.06 153	61.94 200	76.42 143	72.06 147	54.30 175	49.94 190	50.04 138	47.41 187	52.42 164	60.23 125	75.71 150	76.22 144	85.78 133	85.56 66

V4		65.23 150	66.23 153	64.06 153	61.94 200	76.42 143	72.05 148	54.31 174	49.91 192	50.06 137	47.42 186	52.40 165	60.24 124	75.71 150	76.22 144	85.78 133	85.56 66

RPSCF		67.64 133	71.25 83	63.43 160	61.86 202	70.73 177	67.26 170	50.86 191	74.20 53	58.91 79	67.49 60	69.33 71	64.10 100	71.41 176	68.45 192	77.61 185	77.17 157

MIMVSNet		58.52 194	61.34 193	55.22 196	60.76 203	67.01 190	66.81 173	49.02 198	56.43 147	38.90 185	40.59 201	54.54 140	40.57 199	73.16 165	71.65 173	75.30 197	66.00 201

PatchT		61.97 180	64.04 176	59.55 182	60.49 204	67.40 188	56.54 205	48.65 200	56.69 140	52.65 122	51.10 174	52.14 171	60.92 119	72.20 172	73.09 168	78.03 184	75.69 168

N_pmnet		47.35 211	50.13 210	44.11 214	59.98 205	51.64 220	51.86 211	44.80 213	49.58 194	20.76 220	40.65 200	40.05 211	29.64 210	59.84 217	55.15 220	57.63 223	54.00 221

1111		43.08 217	44.02 219	41.98 216	59.22 206	49.27 224	41.48 222	45.63 210	35.01 218	23.06 214	28.60 218	30.15 221	27.22 212	60.42 215	57.97 217	55.27 226	46.74 223

.test1245		30.81 224	29.14 226	32.77 223	59.22 206	49.27 224	41.48 222	45.63 210	35.01 218	23.06 214	28.60 218	30.15 221	27.22 212	60.42 215	0.10 230	0.01 234	0.43 232

MVS-HIRNet		54.41 201	52.10 208	57.11 191	58.99 208	56.10 214	49.68 215	49.10 197	46.18 203	52.15 126	33.18 213	46.11 200	56.10 149	63.19 208	59.70 216	76.64 190	60.25 212

PM-MVS		60.48 188	60.94 195	59.94 178	58.85 209	66.83 191	64.27 188	51.39 189	55.03 164	48.03 147	50.00 179	40.79 209	58.26 132	69.20 195	67.13 198	78.84 182	77.60 154

anonymousdsp		65.28 149	67.98 134	62.13 165	58.73 210	73.98 168	67.10 172	50.69 193	48.41 196	47.66 151	54.27 133	52.75 163	61.45 117	76.71 143	80.20 75	87.13 80	89.53 45

LP		53.62 204	53.43 204	53.83 201	58.51 211	62.59 208	57.31 204	46.04 209	47.86 197	42.69 176	36.08 208	36.86 213	46.53 185	64.38 205	64.25 204	71.92 206	62.00 210

TESTMET0.1,1		61.10 186	64.36 174	57.29 189	57.53 212	63.93 199	66.61 176	36.22 223	54.41 167	47.77 148	57.46 100	60.25 99	55.20 159	70.80 184	69.33 183	80.40 175	74.38 176

EU-MVSNet		54.63 200	58.69 198	49.90 207	56.99 213	62.70 207	56.41 206	50.64 194	45.95 204	23.14 213	50.42 176	46.51 197	36.63 202	65.51 203	64.85 202	75.57 193	74.91 173

FMVSNet5		57.24 195	60.02 197	53.99 200	56.45 214	62.74 206	65.27 182	47.03 205	55.14 161	39.55 183	40.88 199	53.42 155	41.83 193	72.35 168	71.10 177	73.79 201	64.50 204

test2356		47.20 212	48.62 215	45.54 212	56.38 215	54.89 216	50.62 212	45.08 212	38.65 214	23.40 211	36.23 207	31.10 219	29.31 211	62.76 209	62.49 209	68.48 214	54.23 220

testus		45.61 216	49.06 214	41.59 217	56.13 216	55.28 215	43.51 220	39.64 221	37.74 215	18.23 222	35.52 211	31.28 218	24.69 219	62.46 210	62.90 207	67.33 216	58.26 216

test-mter		60.84 187	64.62 173	56.42 192	55.99 217	64.18 197	65.39 181	34.23 225	54.39 169	46.21 158	57.40 102	59.49 103	55.86 152	71.02 182	69.65 181	80.87 174	76.20 164

CHOSEN 280x420		58.70 193	61.88 191	54.98 197	55.45 218	50.55 222	64.92 184	40.36 219	55.21 160	38.13 187	48.31 181	63.76 90	63.03 106	73.73 163	68.58 190	68.00 215	73.04 183

PMMVS		65.06 154	69.17 112	60.26 177	55.25 219	63.43 202	66.71 175	43.01 217	62.41 90	50.64 134	69.44 50	67.04 82	63.29 104	74.36 159	73.54 166	82.68 165	73.99 179

testmv		42.58 218	44.36 217	40.49 218	54.63 220	52.76 218	41.21 224	44.37 214	28.83 225	12.87 226	27.16 221	25.03 226	23.01 222	60.83 213	61.13 211	66.88 217	54.81 218

test1235678		42.57 219	44.36 217	40.49 218	54.63 220	52.75 219	41.21 224	44.37 214	28.82 226	12.87 226	27.15 222	25.01 227	23.01 222	60.83 213	61.13 211	66.88 217	54.81 218

no-one		36.35 222	37.59 223	34.91 221	46.13 222	49.89 223	27.99 229	43.56 216	20.91 230	7.03 232	14.64 228	15.50 232	18.92 227	42.95 225	60.20 214	65.84 219	59.03 215

Gipuma		36.38 221	35.80 224	37.07 220	45.76 223	33.90 229	29.81 228	48.47 201	39.91 212	18.02 223	8.00 232	8.14 234	25.14 216	59.29 218	61.02 213	55.19 227	40.31 225
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs3		47.65 209	49.08 213	45.99 211	44.61 224	54.79 217	50.04 213	31.95 228	33.91 220	29.90 200	30.37 214	33.53 216	46.31 186	63.50 206	63.67 206	73.14 204	63.77 206

MIMVSNet1		49.27 208	53.25 206	44.62 213	44.61 224	61.52 210	53.61 209	52.18 184	41.62 210	18.68 221	28.14 220	41.58 208	25.50 215	68.46 198	69.04 185	73.15 203	62.37 209

test12356		35.10 223	38.50 222	31.13 224	44.14 226	43.70 227	32.27 227	34.42 224	26.51 228	9.47 230	25.22 224	20.34 228	10.86 229	53.47 222	56.15 218	55.59 225	44.11 224

MDA-MVSNet-bldmvs		53.37 205	53.01 207	53.79 202	43.67 227	67.95 187	59.69 201	57.92 161	43.69 205	32.41 199	41.47 197	27.89 225	52.38 171	56.97 221	65.99 201	76.68 189	67.13 199

E-PMN		21.77 226	18.24 228	25.89 225	40.22 228	19.58 232	12.46 233	39.87 220	18.68 232	6.71 233	9.57 229	4.31 237	22.36 225	19.89 230	27.28 228	33.73 229	28.34 229

EMVS		20.98 227	17.15 229	25.44 226	39.51 229	19.37 233	12.66 232	39.59 222	19.10 231	6.62 234	9.27 230	4.40 236	22.43 224	17.99 231	24.40 229	31.81 230	25.53 230

new_pmnet		38.40 220	42.64 221	33.44 222	37.54 230	45.00 226	36.60 226	32.72 227	40.27 211	12.72 228	29.89 215	28.90 223	24.78 218	53.17 223	52.90 224	56.31 224	48.34 222

PMMVS2		25.60 225	29.75 225	20.76 228	28.00 231	30.93 230	23.10 230	29.18 229	23.14 229	1.46 236	18.23 227	16.54 230	5.08 230	40.22 226	41.40 226	37.76 228	37.79 227

tmp_tt				14.50 230	14.68 232	7.17 235	10.46 235	2.21 231	37.73 216	28.71 204	25.26 223	16.98 229	4.37 231	31.49 227	29.77 227	26.56 231

MVE	19.12 19	20.47 228	23.27 227	17.20 229	12.66 233	25.41 231	10.52 234	34.14 226	14.79 233	6.53 235	8.79 231	4.68 235	16.64 228	29.49 228	41.63 225	22.73 232	38.11 226
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GG-mvs-BLEND		46.86 214	67.51 139	22.75 227	0.05 234	76.21 148	64.69 185	0.04 232	61.90 94	0.09 237	55.57 110	71.32 62	0.08 232	70.54 186	67.19 196	71.58 207	69.86 193

testmvs		0.09 229	0.15 230	0.02 231	0.01 235	0.02 236	0.05 237	0.01 233	0.11 234	0.01 238	0.26 234	0.01 238	0.06 234	0.10 232	0.10 230	0.01 234	0.43 232

ESAPD		0.00 231	0.00 232	0.00 233	0.00 236	0.00 238	0.00 239	0.00 235	0.00 236	0.00 239	0.00 235	0.00 239	0.00 235	0.00 234	0.00 233	0.00 237	0.00 234

sosnet-low-res		0.00 231	0.00 232	0.00 233	0.00 236	0.00 238	0.00 239	0.00 235	0.00 236	0.00 239	0.00 235	0.00 239	0.00 235	0.00 234	0.00 233	0.00 237	0.00 234

sosnet		0.00 231	0.00 232	0.00 233	0.00 236	0.00 238	0.00 239	0.00 235	0.00 236	0.00 239	0.00 235	0.00 239	0.00 235	0.00 234	0.00 233	0.00 237	0.00 234

test123		0.09 229	0.14 231	0.02 231	0.00 236	0.02 236	0.02 238	0.01 233	0.09 235	0.00 239	0.30 233	0.00 239	0.08 232	0.03 233	0.09 232	0.01 234	0.45 231

MTAPA										83.48 1		86.45 11

MTMP										82.66 3		84.91 19

Patchmatch-RL test							2.85 236

NP-MVS									80.10 39

Patchmtry						65.80 195	65.97 179	52.74 181		52.65 122

DeepMVS_CX						18.74 234	18.55 231	8.02 230	26.96 227	7.33 231	23.81 225	13.05 233	25.99 214	25.17 229		22.45 233	36.25 228

High-resolution multi-view benchmark