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.

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

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
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

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

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

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

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.
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

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

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

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

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

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

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

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

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

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

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

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

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
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

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

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

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
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

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
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

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

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

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

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

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
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
CLD-MVS		79.35 43	81.23 39	77.16 47	85.01 50	86.92 39	85.87 34	60.89 112	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
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

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
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

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

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

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

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

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

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
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

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
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
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

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

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

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	86.01 124	85.98 63

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
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

DI_MVS_plusplus_trai		75.13 61	76.12 66	73.96 62	78.18 81	81.55 71	80.97 54	62.54 97	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

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

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

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

MVS_Test		75.37 59	77.13 62	73.31 64	79.07 76	81.32 74	79.98 58	60.12 130	69.72 62	64.11 65	70.53 47	73.22 56	68.90 60	80.14 86	79.48 84	87.67 67	85.50 70

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	86.04 123	87.29 58

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 82	78.87 88	86.00 126	80.18 135

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	53.21 146	56.09 129	65.85 95	78.94 101	78.50 90	86.60 110	76.85 161

CANet_DTU		73.29 66	76.96 63	69.00 105	77.04 99	82.06 70	79.49 63	56.30 164	67.85 64	53.29 119	71.12 46	70.37 68	61.81 115	81.59 61	80.96 59	86.09 118	84.73 84

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

v7		70.33 87	69.87 95	70.88 69	74.79 123	81.04 77	79.22 65	60.57 116	57.70 128	56.65 101	54.23 132	55.29 134	66.95 69	78.28 108	77.47 109	87.12 83	85.05 79

v10		70.22 89	69.76 99	70.74 75	74.79 123	80.30 93	79.22 65	59.81 133	57.71 127	56.58 102	54.22 134	55.31 132	66.95 69	78.28 108	77.47 109	87.12 83	85.07 78

v1144		69.93 98	69.36 110	70.61 80	74.89 116	80.93 78	79.11 67	60.64 114	55.97 151	55.31 108	53.85 139	54.14 142	66.54 76	78.10 110	77.44 111	87.14 79	85.09 77

v2v482		70.05 93	69.46 104	70.74 75	74.62 135	80.32 92	79.00 68	60.62 115	57.41 129	56.89 94	55.43 111	55.14 135	66.39 78	77.25 130	77.14 116	86.90 90	83.57 99

OpenMVS	70.44 10	76.15 57	76.82 64	75.37 54	85.01 50	84.79 54	78.99 69	62.07 103	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

v1141		69.96 97	69.44 107	70.58 83	74.78 125	80.50 88	78.85 70	60.30 121	56.95 134	56.74 98	54.68 123	56.26 126	65.93 92	77.38 127	76.72 133	86.88 93	83.57 99

divwei89l23v2f112		69.97 95	69.44 107	70.58 83	74.78 125	80.50 88	78.85 70	60.30 121	56.97 133	56.75 97	54.67 124	56.27 125	65.92 93	77.37 128	76.72 133	86.88 93	83.58 98

v18		70.10 91	69.52 102	70.77 74	74.66 134	77.06 130	78.84 72	58.84 148	60.01 109	59.23 77	55.06 112	57.47 109	66.34 81	77.50 125	76.75 126	86.71 104	82.77 108

v1		69.97 95	69.45 106	70.59 81	74.78 125	80.51 87	78.84 72	60.30 121	56.98 132	56.81 96	54.69 122	56.29 124	65.91 94	77.37 128	76.71 136	86.89 92	83.59 96

v6		70.35 84	69.94 92	70.83 70	74.68 131	80.62 83	78.81 74	60.16 129	58.81 116	58.17 84	55.01 113	57.31 113	66.32 84	77.53 121	76.73 132	86.82 97	83.62 93

v1neww		70.34 85	69.93 93	70.82 71	74.68 131	80.61 84	78.80 75	60.17 126	58.74 118	58.10 85	55.00 114	57.28 114	66.33 82	77.53 121	76.74 128	86.82 97	83.61 94

v7new		70.34 85	69.93 93	70.82 71	74.68 131	80.61 84	78.80 75	60.17 126	58.74 118	58.10 85	55.00 114	57.28 114	66.33 82	77.53 121	76.74 128	86.82 97	83.61 94

v16		70.07 92	69.46 104	70.79 73	74.74 129	77.08 129	78.79 77	58.86 143	59.75 110	59.15 78	54.87 119	57.33 111	66.38 79	77.61 119	76.77 121	86.81 102	82.79 106

v8		70.23 88	69.86 97	70.67 79	74.69 130	79.82 95	78.79 77	59.18 139	58.80 117	58.20 83	55.00 114	57.33 111	66.31 85	77.51 124	76.71 136	86.82 97	83.88 92

v17		70.03 94	69.43 109	70.72 77	74.75 128	77.09 128	78.78 79	58.85 145	59.53 113	58.72 81	54.87 119	57.39 110	66.38 79	77.60 120	76.75 126	86.83 96	82.80 104

v15		69.61 100	68.88 115	70.46 85	74.81 122	77.03 133	78.75 80	58.83 149	57.06 131	57.18 90	54.55 125	56.37 120	66.13 89	77.70 116	76.76 123	87.03 87	82.69 111

V14		69.59 101	68.86 116	70.45 87	74.83 121	77.04 131	78.70 81	58.83 149	56.95 134	57.08 92	54.41 126	56.34 121	66.15 86	77.77 115	76.76 123	87.08 85	82.74 109

V9		69.58 102	68.83 117	70.46 85	74.85 120	77.04 131	78.65 82	58.85 145	56.83 137	57.12 91	54.26 130	56.31 122	66.14 88	77.83 114	76.76 123	87.13 80	82.79 106

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	51.50 165	54.14 142	68.56 63	78.85 102	79.50 83	86.82 97	83.94 91

Effi-MVS+-dtu		71.82 73	71.86 81	71.78 67	78.77 77	80.47 90	78.55 84	61.67 109	60.68 103	55.49 106	58.48 91	65.48 86	68.85 61	76.92 134	75.55 149	87.35 72	85.46 71

Fast-Effi-MVS+		73.11 68	73.66 71	72.48 66	77.72 92	80.88 81	78.55 84	58.83 149	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

v1192		69.50 106	68.83 117	70.29 91	74.49 136	80.92 80	78.55 84	60.54 117	55.04 159	54.21 111	52.79 154	52.33 166	66.92 71	77.88 113	77.35 114	87.04 86	85.51 69

v12		69.54 103	68.79 119	70.41 88	74.88 117	77.03 133	78.54 87	58.85 145	56.71 138	56.87 95	54.13 135	56.23 127	66.15 86	77.89 112	76.74 128	87.17 75	82.80 104

v13		69.52 105	68.76 121	70.41 88	74.88 117	77.02 135	78.52 88	58.86 143	56.61 143	56.91 93	54.00 137	56.17 128	66.11 90	77.93 111	76.74 128	87.21 74	82.83 103

tpmp4_e23		68.32 119	67.08 144	69.76 99	77.86 85	75.22 155	78.37 89	56.17 166	66.06 70	64.27 64	57.15 103	54.89 137	63.40 103	70.97 177	68.29 187	78.46 177	77.00 160

V42		68.76 116	69.63 100	67.74 116	64.93 190	78.01 113	78.30 90	56.48 163	58.65 120	56.30 103	54.26 130	57.03 117	64.85 98	77.47 126	77.01 118	85.60 137	84.96 81

v11		69.37 108	68.65 125	70.20 92	74.87 119	76.97 136	78.29 91	58.55 153	56.38 145	56.04 104	54.02 136	54.98 136	66.47 77	78.30 107	76.91 119	86.97 88	83.02 102

diffmvs		73.13 67	75.65 67	70.19 93	74.07 139	77.17 127	78.24 92	57.45 157	72.44 57	64.02 66	69.05 51	75.92 48	64.86 97	75.18 149	75.27 151	82.47 160	84.53 85

CostFormer		68.92 113	69.58 101	68.15 112	75.98 107	76.17 148	78.22 93	51.86 180	65.80 71	61.56 71	63.57 71	62.83 93	61.85 113	70.40 184	68.67 182	79.42 173	79.62 141

v144192		69.34 109	68.68 124	70.12 94	74.06 140	80.54 86	78.08 94	60.54 117	54.99 161	54.13 112	52.92 151	52.80 162	66.73 74	77.13 132	76.72 133	87.15 76	85.63 65

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	56.19 148	46.06 158	54.30 127	51.20 174	68.68 62	80.66 75	80.72 63	86.07 119	84.45 87
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1921920		69.03 112	68.32 130	69.86 97	74.03 141	80.37 91	77.55 96	60.25 125	54.62 162	53.59 117	52.36 161	51.50 173	66.75 73	77.17 131	76.69 138	86.96 89	85.56 66

MS-PatchMatch		70.17 90	70.49 88	69.79 98	80.98 66	77.97 119	77.51 97	58.95 141	62.33 91	55.22 109	53.14 147	65.90 85	62.03 110	79.08 100	77.11 117	84.08 152	77.91 151

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

CHOSEN 1792x2688		69.20 111	69.26 111	69.13 103	76.86 100	78.93 101	77.27 100	60.12 130	61.86 95	54.42 110	42.54 190	61.61 96	66.91 72	78.55 105	78.14 98	79.23 175	83.23 101

Fast-Effi-MVS+-dtu		68.34 118	69.47 103	67.01 131	75.15 112	77.97 119	77.12 101	55.40 167	57.87 122	46.68 155	56.17 107	60.39 98	62.36 108	76.32 142	76.25 142	85.35 141	81.34 123

MVSTER		72.06 72	74.24 70	69.51 101	70.39 169	75.97 149	76.91 102	57.36 159	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

v1240		68.64 117	67.89 136	69.51 101	73.89 143	80.26 94	76.73 103	59.97 132	53.43 169	53.08 120	51.82 164	50.84 176	66.62 75	76.79 136	76.77 121	86.78 103	85.34 73

HyFIR lowres test		69.47 107	68.94 114	70.09 95	76.77 101	82.93 66	76.63 104	60.17 126	59.00 115	54.03 113	40.54 196	65.23 87	67.89 65	76.54 141	78.30 94	85.03 145	80.07 136

Vis-MVSNet		72.77 70	77.20 61	67.59 120	74.19 138	84.01 57	76.61 105	61.69 108	60.62 105	50.61 135	70.25 49	71.31 63	55.57 155	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
TDRefinement		66.09 145	65.03 164	67.31 124	69.73 173	76.75 138	75.33 106	64.55 72	60.28 107	49.72 142	45.63 185	42.83 199	60.46 123	75.75 143	75.95 145	84.08 152	78.04 150

tpm cat1		65.41 147	63.81 172	67.28 126	75.61 110	72.88 164	75.32 107	52.85 174	62.97 88	63.66 67	53.24 145	53.29 158	61.83 114	65.54 196	64.14 199	74.43 193	74.60 173

COLMAP_ROB	62.73 15	67.66 131	66.76 148	68.70 108	80.49 70	77.98 117	75.29 108	62.95 83	63.62 84	49.96 139	47.32 183	50.72 177	58.57 129	76.87 135	75.50 150	84.94 147	75.33 171
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UGNet		72.78 69	77.67 55	67.07 130	71.65 161	83.24 63	75.20 109	63.62 76	64.93 76	56.72 99	71.82 44	73.30 55	49.02 175	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
GBi-Net		70.78 78	73.37 74	67.76 114	72.95 149	78.00 114	75.15 110	62.72 88	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	72.95 149	78.00 114	75.15 110	62.72 88	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	72.95 149	78.00 114	75.15 110	62.69 92	63.29 86	51.25 131	55.64 108	68.49 80	57.59 135	80.91 74	80.35 74	86.70 105	82.02 114

IterMVS-LS		71.69 74	72.82 77	70.37 90	77.54 94	76.34 145	75.13 113	60.46 119	61.53 99	57.57 88	64.89 67	67.33 81	66.04 91	77.09 133	77.37 113	85.48 139	85.18 76
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EPNet_dtu		68.08 123	71.00 84	64.67 149	79.64 72	68.62 179	75.05 114	63.30 78	66.36 67	45.27 162	67.40 61	66.84 83	43.64 186	75.37 147	74.98 155	81.15 165	77.44 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPP-MVSNet		74.00 64	77.41 59	70.02 96	80.53 69	83.91 58	74.99 115	62.68 93	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

FMVSNet3		70.49 82	72.90 76	67.67 118	72.88 152	77.98 117	74.96 116	62.72 88	64.13 81	51.44 127	58.37 92	69.02 74	57.43 138	79.43 96	79.57 81	86.59 111	81.81 121

FMVSNet1		68.84 114	70.47 89	66.94 132	71.35 166	77.68 122	74.71 117	62.35 102	56.93 136	49.94 140	50.01 171	64.59 88	57.07 141	81.33 66	80.72 63	86.25 114	82.00 117

GA-MVS		68.14 120	69.17 112	66.93 133	73.77 144	78.50 110	74.45 118	58.28 154	55.11 158	48.44 145	60.08 80	53.99 145	61.50 116	78.43 106	77.57 107	85.13 143	80.54 130

pmmvs4		67.89 126	67.39 141	68.48 110	71.60 163	73.57 163	74.45 118	60.98 111	64.65 78	57.97 87	54.95 117	51.73 171	61.88 112	73.78 156	75.11 153	83.99 154	77.91 151

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	54.63 160	84.51 40	83.58 44	89.48 38	86.34 62

CDS-MVSNet		67.65 132	69.83 98	65.09 144	75.39 111	76.55 140	74.42 121	63.75 75	53.55 168	49.37 143	59.41 85	62.45 94	44.44 184	79.71 90	79.82 76	83.17 158	77.36 155
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
DWT-MVSNet_training		67.24 139	65.96 152	68.74 106	76.15 103	74.36 161	74.37 122	56.66 162	61.82 96	60.51 73	58.23 97	49.76 182	65.07 96	70.04 185	70.39 172	79.70 172	77.11 158

conf200view11		68.11 121	68.72 122	67.39 123	77.83 87	78.93 101	74.28 123	62.81 84	56.64 140	46.70 154	52.65 156	53.47 152	56.59 144	80.41 76	78.43 91	86.11 116	80.53 131

tfpn200view9		68.11 121	68.72 122	67.40 122	77.83 87	78.93 101	74.28 123	62.81 84	56.64 140	46.82 152	52.65 156	53.47 152	56.59 144	80.41 76	78.43 91	86.11 116	80.52 132

thres400		67.95 125	68.62 126	67.17 127	77.90 82	78.59 109	74.27 125	62.72 88	56.34 147	45.77 160	53.00 149	53.35 156	56.46 146	80.21 85	78.43 91	85.91 130	80.43 133

v148		67.85 127	67.53 137	68.23 111	73.25 147	77.57 125	74.26 126	57.36 159	55.70 153	57.45 89	53.53 140	55.42 131	61.96 111	75.23 148	73.92 158	85.08 144	81.32 124

thres200		67.98 124	68.55 127	67.30 125	77.89 84	78.86 104	74.18 127	62.75 86	56.35 146	46.48 156	52.98 150	53.54 148	56.46 146	80.41 76	77.97 99	86.05 121	79.78 140

thres100view900		67.60 135	68.02 133	67.12 129	77.83 87	77.75 121	73.90 128	62.52 98	56.64 140	46.82 152	52.65 156	53.47 152	55.92 150	78.77 103	77.62 106	85.72 135	79.23 144

thres600view7		67.68 130	68.43 128	66.80 134	77.90 82	78.86 104	73.84 129	62.75 86	56.07 149	44.70 166	52.85 153	52.81 161	55.58 154	80.41 76	77.77 102	86.05 121	80.28 134

UniMVSNet_NR-MVSNet		70.59 81	72.19 79	68.72 107	77.72 92	80.72 82	73.81 130	69.65 38	61.99 93	43.23 169	60.54 78	57.50 108	58.57 129	79.56 94	81.07 58	89.34 40	83.97 89

DU-MVS		69.63 99	70.91 85	68.13 113	75.99 105	79.54 96	73.81 130	69.20 43	61.20 101	43.23 169	58.52 89	53.50 149	58.57 129	79.22 98	80.45 73	87.97 60	83.97 89

view600		67.63 134	68.36 129	66.77 135	77.84 86	78.66 107	73.74 132	62.62 95	56.04 150	44.98 163	52.86 152	52.83 160	55.48 157	80.36 81	77.75 103	85.95 129	80.02 137

FC-MVSNet-train		72.60 71	75.07 69	69.71 100	81.10 65	78.79 106	73.74 132	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

UA-Net		74.47 62	77.80 54	70.59 81	85.33 46	85.40 50	73.54 134	65.98 63	60.65 104	56.00 105	72.11 42	79.15 38	54.63 160	83.13 53	82.25 49	88.04 59	81.92 120

NR-MVSNet		68.79 115	70.56 87	66.71 138	77.48 95	79.54 96	73.52 135	69.20 43	61.20 101	39.76 180	58.52 89	50.11 180	51.37 171	80.26 84	80.71 67	88.97 46	83.59 96

TranMVSNet+NR-MVSNet		69.25 110	70.81 86	67.43 121	77.23 98	79.46 98	73.48 136	69.66 37	60.43 106	39.56 181	58.82 88	53.48 151	55.74 153	79.59 92	81.21 57	88.89 48	82.70 110

IterMVS		66.36 143	68.30 131	64.10 151	69.48 176	74.61 159	73.41 137	50.79 186	57.30 130	48.28 146	60.64 77	59.92 101	60.85 122	74.14 154	72.66 164	81.80 162	78.82 147
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EG-PatchMatch MVS		67.24 139	66.94 145	67.60 119	78.73 78	81.35 73	73.28 138	59.49 135	46.89 195	51.42 130	43.65 187	53.49 150	55.50 156	81.38 65	80.66 69	87.15 76	81.17 125

v7n		67.05 141	66.94 145	67.17 127	72.35 154	78.97 100	73.26 139	58.88 142	51.16 180	50.90 132	48.21 176	50.11 180	60.96 118	77.70 116	77.38 112	86.68 108	85.05 79

view800		67.35 138	68.22 132	66.35 139	77.83 87	78.62 108	72.97 140	62.58 96	55.71 152	44.13 167	52.69 155	52.24 169	54.58 162	80.27 83	78.19 96	86.01 124	79.79 139

IB-MVS	66.94 12	71.21 77	71.66 82	70.68 78	79.18 75	82.83 67	72.61 141	61.77 107	59.66 111	63.44 68	53.26 144	59.65 102	59.16 128	76.78 137	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
UniMVSNet (Re)		69.53 104	71.90 80	66.76 136	76.42 102	80.93 78	72.59 142	68.03 50	61.75 97	41.68 177	58.34 95	57.23 116	53.27 167	79.53 95	80.62 71	88.57 52	84.90 82

Baseline_NR-MVSNet		67.53 136	68.77 120	66.09 140	75.99 105	74.75 158	72.43 143	68.41 46	61.33 100	38.33 184	51.31 166	54.13 144	56.03 149	79.22 98	78.19 96	85.37 140	82.45 112

MDTV_nov1_ep13		64.37 157	65.24 159	63.37 159	68.94 178	70.81 170	72.40 144	50.29 189	60.10 108	53.91 115	60.07 81	59.15 104	57.21 139	69.43 188	67.30 189	77.47 180	69.78 188

tfpn		66.58 142	67.18 142	65.88 141	77.82 91	78.45 111	72.07 145	62.52 98	55.35 155	43.21 171	52.54 160	46.12 193	53.68 163	80.02 87	78.23 95	85.99 127	79.55 142

v52		65.23 149	66.24 150	64.06 152	61.94 194	76.42 142	72.06 146	54.30 169	49.94 184	50.04 138	47.41 181	52.42 164	60.23 125	75.71 144	76.22 143	85.78 132	85.56 66

V4		65.23 149	66.23 151	64.06 152	61.94 194	76.42 142	72.05 147	54.31 168	49.91 186	50.06 137	47.42 180	52.40 165	60.24 124	75.71 144	76.22 143	85.78 132	85.56 66

conf0.05thres1000		66.26 144	66.77 147	65.66 142	77.45 96	78.10 112	71.85 148	62.44 101	51.47 179	43.00 172	47.92 178	51.66 172	53.40 165	79.71 90	77.97 99	85.82 131	80.56 129

USDC		67.36 137	67.90 135	66.74 137	71.72 159	75.23 154	71.58 149	60.28 124	67.45 65	50.54 136	60.93 76	45.20 196	62.08 109	76.56 140	74.50 156	84.25 151	75.38 170

tpm		62.41 169	63.15 174	61.55 165	72.24 155	63.79 195	71.31 150	46.12 202	57.82 123	55.33 107	59.90 83	54.74 138	53.63 164	67.24 194	64.29 197	70.65 204	74.25 177

tfpnnormal		64.27 158	63.64 173	65.02 145	75.84 108	75.61 150	71.24 151	62.52 98	47.79 192	42.97 173	42.65 189	44.49 197	52.66 169	78.77 103	76.86 120	84.88 148	79.29 143

gg-mvs-nofinetune		62.55 166	65.05 163	59.62 175	78.72 79	77.61 123	70.83 152	53.63 170	39.71 207	22.04 212	36.36 200	64.32 89	47.53 177	81.16 70	79.03 87	85.00 146	77.17 156

TransMVSNet (Re)		64.74 155	65.66 156	63.66 156	77.40 97	75.33 153	69.86 153	62.67 94	47.63 193	41.21 178	50.01 171	52.33 166	45.31 183	79.57 93	77.69 105	85.49 138	77.07 159

pm-mvs1		65.62 146	67.42 139	63.53 157	73.66 145	76.39 144	69.66 154	60.87 113	49.73 187	43.97 168	51.24 167	57.00 118	48.16 176	79.89 88	77.84 101	84.85 149	79.82 138

PatchmatchNet		64.21 159	64.65 166	63.69 155	71.29 167	68.66 178	69.63 155	51.70 182	63.04 87	53.77 116	59.83 84	58.34 106	60.23 125	68.54 191	66.06 194	75.56 188	68.08 192
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v748		65.12 151	65.24 159	64.98 146	69.77 172	76.45 141	69.47 156	57.06 161	49.93 185	50.70 133	47.87 179	49.50 184	57.14 140	73.64 158	75.18 152	85.75 134	84.14 88

pmmvs-eth3d		63.52 161	62.44 182	64.77 148	66.82 184	70.12 173	69.41 157	59.48 136	54.34 166	52.71 121	46.24 184	44.35 198	56.93 142	72.37 161	73.77 159	83.30 156	75.91 164

tpmrst		62.00 173	62.35 183	61.58 164	71.62 162	64.14 192	69.07 158	48.22 198	62.21 92	53.93 114	58.26 96	55.30 133	55.81 152	63.22 201	62.62 202	70.85 203	70.70 186

dps		64.00 160	62.99 175	65.18 143	73.29 146	72.07 167	68.98 159	53.07 173	57.74 126	58.41 82	55.55 110	47.74 189	60.89 121	69.53 187	67.14 191	76.44 185	71.19 185

PatchMatch-RL		67.78 129	66.65 149	69.10 104	73.01 148	72.69 165	68.49 160	61.85 106	62.93 89	60.20 76	56.83 105	50.42 178	69.52 58	75.62 146	74.46 157	81.51 163	73.62 181

TinyColmap		62.84 164	61.03 188	64.96 147	69.61 174	71.69 168	68.48 161	59.76 134	55.41 154	47.69 150	47.33 182	34.20 209	62.76 107	74.52 151	72.59 165	81.44 164	71.47 184

tfpn_ndepth		65.09 152	67.12 143	62.73 160	75.75 109	76.23 146	68.00 162	60.36 120	58.16 121	40.27 179	54.89 118	54.22 141	46.80 179	76.69 139	75.66 147	85.19 142	73.98 179

MDTV_nov1_ep13_2view		60.16 183	60.51 190	59.75 173	65.39 187	69.05 177	68.00 162	48.29 196	51.99 174	45.95 159	48.01 177	49.64 183	53.39 166	68.83 190	66.52 193	77.47 180	69.55 189

pmmvs6		62.41 169	62.88 176	61.87 163	71.38 165	75.18 157	67.76 164	59.45 137	41.64 203	42.52 176	37.33 198	52.91 159	46.87 178	77.67 118	76.26 141	83.23 157	79.18 145

RPSCF		67.64 133	71.25 83	63.43 158	61.86 196	70.73 171	67.26 165	50.86 185	74.20 53	58.91 79	67.49 60	69.33 71	64.10 100	71.41 170	68.45 186	77.61 179	77.17 156

pmmvs5		62.37 172	64.04 170	60.42 169	65.03 188	71.67 169	67.17 166	52.70 177	50.30 181	44.80 164	54.23 132	51.19 175	49.37 174	72.88 160	73.48 161	83.45 155	74.55 174

anonymousdsp		65.28 148	67.98 134	62.13 162	58.73 204	73.98 162	67.10 167	50.69 187	48.41 190	47.66 151	54.27 128	52.75 163	61.45 117	76.71 138	80.20 75	87.13 80	89.53 45

MIMVSNet		58.52 188	61.34 187	55.22 190	60.76 197	67.01 184	66.81 168	49.02 192	56.43 144	38.90 183	40.59 195	54.54 140	40.57 193	73.16 159	71.65 167	75.30 191	66.00 195

Vis-MVSNet (Re-imp)		67.83 128	73.52 72	61.19 166	78.37 80	76.72 139	66.80 169	62.96 82	65.50 73	34.17 191	67.19 62	69.68 70	39.20 194	79.39 97	79.44 85	85.68 136	76.73 162

PMMVS		65.06 153	69.17 112	60.26 171	55.25 213	63.43 196	66.71 170	43.01 211	62.41 90	50.64 134	69.44 50	67.04 82	63.29 104	74.36 153	73.54 160	82.68 159	73.99 178

test-LLR		64.42 156	64.36 168	64.49 150	75.02 114	63.93 193	66.61 171	61.96 104	54.41 163	47.77 148	57.46 100	60.25 99	55.20 158	70.80 178	69.33 177	80.40 169	74.38 175

TESTMET0.1,1		61.10 180	64.36 168	57.29 183	57.53 206	63.93 193	66.61 171	36.22 217	54.41 163	47.77 148	57.46 100	60.25 99	55.20 158	70.80 178	69.33 177	80.40 169	74.38 175

CVMVSNet		62.55 166	65.89 153	58.64 179	66.95 182	69.15 176	66.49 173	56.29 165	52.46 173	32.70 192	59.27 86	58.21 107	50.09 173	71.77 169	71.39 169	79.31 174	78.99 146

CR-MVSNet		64.83 154	65.54 157	64.01 154	70.64 168	69.41 174	65.97 174	52.74 175	57.81 124	52.65 122	54.27 128	56.31 122	60.92 119	72.20 166	73.09 162	81.12 166	75.69 167

Patchmtry						65.80 189	65.97 174	52.74 175		52.65 122

test-mter		60.84 181	64.62 167	56.42 186	55.99 211	64.18 191	65.39 176	34.23 219	54.39 165	46.21 157	57.40 102	59.49 103	55.86 151	71.02 176	69.65 175	80.87 168	76.20 163

FMVSNet5		57.24 189	60.02 191	53.99 194	56.45 208	62.74 200	65.27 177	47.03 199	55.14 157	39.55 182	40.88 193	53.42 155	41.83 187	72.35 162	71.10 171	73.79 195	64.50 198

CHOSEN 280x420		58.70 187	61.88 185	54.98 191	55.45 212	50.55 216	64.92 178	40.36 213	55.21 156	38.13 185	48.31 175	63.76 90	63.03 106	73.73 157	68.58 184	68.00 209	73.04 182

GG-mvs-BLEND		46.86 208	67.51 138	22.75 221	0.05 228	76.21 147	64.69 179	0.04 226	61.90 94	0.09 231	55.57 109	71.32 62	0.08 226	70.54 180	67.19 190	71.58 201	69.86 187

EPMVS		60.00 184	61.97 184	57.71 182	68.46 179	63.17 199	64.54 180	48.23 197	63.30 85	44.72 165	60.19 79	56.05 130	50.85 172	65.27 198	62.02 204	69.44 206	63.81 199

LTVRE_ROB	59.44 16	61.82 178	62.64 179	60.87 168	72.83 153	77.19 126	64.37 181	58.97 140	33.56 216	28.00 199	52.59 159	42.21 200	63.93 101	74.52 151	76.28 140	77.15 182	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
PM-MVS		60.48 182	60.94 189	59.94 172	58.85 203	66.83 185	64.27 182	51.39 183	55.03 160	48.03 147	50.00 173	40.79 203	58.26 132	69.20 189	67.13 192	78.84 176	77.60 153

TAMVS		59.58 185	62.81 178	55.81 188	66.03 186	65.64 190	63.86 183	48.74 193	49.95 183	37.07 188	54.77 121	58.54 105	44.44 184	72.29 163	71.79 166	74.70 192	66.66 194

RPMNet		61.71 179	62.88 176	60.34 170	69.51 175	69.41 174	63.48 184	49.23 190	57.81 124	45.64 161	50.51 169	50.12 179	53.13 168	68.17 193	68.49 185	81.07 167	75.62 169

PEN-MVS		62.96 163	65.77 155	59.70 174	73.98 142	75.45 151	63.39 185	67.61 53	52.49 172	25.49 202	53.39 141	49.12 185	40.85 192	71.94 168	77.26 115	86.86 95	80.72 128

CMPMVS	47.78 17	62.49 168	62.52 180	62.46 161	70.01 171	70.66 172	62.97 186	51.84 181	51.98 175	56.71 100	42.87 188	53.62 146	57.80 134	72.23 164	70.37 173	75.45 190	75.91 164
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CP-MVSNet		62.68 165	65.49 158	59.40 177	71.84 157	75.34 152	62.87 187	67.04 56	52.64 171	27.19 200	53.38 142	48.15 187	41.40 190	71.26 171	75.68 146	86.07 119	82.00 117

PS-CasMVS		62.38 171	65.06 162	59.25 178	71.73 158	75.21 156	62.77 188	66.99 57	51.94 177	26.96 201	52.00 163	47.52 190	41.06 191	71.16 174	75.60 148	85.97 128	81.97 119

SixPastTwentyTwo		61.84 176	62.45 181	61.12 167	69.20 177	72.20 166	62.03 189	57.40 158	46.54 196	38.03 186	57.14 104	41.72 201	58.12 133	69.67 186	71.58 168	81.94 161	78.30 149

WR-MVS_H		61.83 177	65.87 154	57.12 184	71.72 159	76.87 137	61.45 190	66.19 58	51.97 176	22.92 210	53.13 148	52.30 168	33.80 200	71.03 175	75.00 154	86.65 109	80.78 127

DTE-MVSNet		61.85 175	64.96 165	58.22 180	74.32 137	74.39 160	61.01 191	67.85 52	51.76 178	21.91 213	53.28 143	48.17 186	37.74 195	72.22 165	76.44 139	86.52 113	78.49 148

WR-MVS		63.03 162	67.40 140	57.92 181	75.14 113	77.60 124	60.56 192	66.10 60	54.11 167	23.88 203	53.94 138	53.58 147	34.50 199	73.93 155	77.71 104	87.35 72	80.94 126

Anonymous20231206		56.36 192	57.80 195	54.67 192	70.08 170	66.39 187	60.46 193	57.54 156	49.50 189	29.30 196	33.86 206	46.64 191	35.18 198	70.44 182	68.88 181	75.47 189	68.88 191

FPMVS		51.87 200	50.00 205	54.07 193	66.83 183	57.25 206	60.25 194	50.91 184	50.25 182	34.36 190	36.04 203	32.02 211	41.49 189	58.98 213	56.07 213	70.56 205	59.36 208

MDA-MVSNet-bldmvs		53.37 199	53.01 201	53.79 196	43.67 221	67.95 181	59.69 195	57.92 155	43.69 199	32.41 193	41.47 191	27.89 219	52.38 170	56.97 215	65.99 195	76.68 183	67.13 193

test0.0.03 1		58.80 186	61.58 186	55.56 189	75.02 114	68.45 180	59.58 196	61.96 104	52.74 170	29.57 195	49.75 174	54.56 139	31.46 202	71.19 172	69.77 174	75.75 186	64.57 197

ADS-MVSNet		55.94 193	58.01 193	53.54 197	62.48 193	58.48 205	59.12 197	46.20 201	59.65 112	42.88 174	52.34 162	53.31 157	46.31 181	62.00 205	60.02 209	64.23 215	60.24 207

LP		53.62 198	53.43 198	53.83 195	58.51 205	62.59 202	57.31 198	46.04 203	47.86 191	42.69 175	36.08 202	36.86 207	46.53 180	64.38 199	64.25 198	71.92 200	62.00 204

PatchT		61.97 174	64.04 170	59.55 176	60.49 198	67.40 182	56.54 199	48.65 194	56.69 139	52.65 122	51.10 168	52.14 170	60.92 119	72.20 166	73.09 162	78.03 178	75.69 167

EU-MVSNet		54.63 194	58.69 192	49.90 201	56.99 207	62.70 201	56.41 200	50.64 188	45.95 198	23.14 207	50.42 170	46.51 192	36.63 196	65.51 197	64.85 196	75.57 187	74.91 172

Anonymous20231211		51.46 201	50.59 203	52.46 198	67.30 180	66.70 186	55.00 201	59.22 138	29.96 218	17.62 218	19.11 220	28.74 218	35.72 197	66.42 195	69.52 176	79.92 171	73.71 180

testgi		54.39 196	57.86 194	50.35 200	71.59 164	67.24 183	54.95 202	53.25 172	43.36 200	23.78 204	44.64 186	47.87 188	24.96 211	70.45 181	68.66 183	73.60 196	62.78 202

MIMVSNet1		49.27 202	53.25 200	44.62 207	44.61 218	61.52 204	53.61 203	52.18 178	41.62 204	18.68 215	28.14 214	41.58 202	25.50 209	68.46 192	69.04 179	73.15 197	62.37 203

test20.03		53.93 197	56.28 197	51.19 199	72.19 156	65.83 188	53.20 204	61.08 110	42.74 201	22.08 211	37.07 199	45.76 195	24.29 215	70.44 182	69.04 179	74.31 194	63.05 201

N_pmnet		47.35 205	50.13 204	44.11 208	59.98 199	51.64 214	51.86 205	44.80 207	49.58 188	20.76 214	40.65 194	40.05 205	29.64 204	59.84 211	55.15 214	57.63 217	54.00 215

test2356		47.20 206	48.62 209	45.54 206	56.38 209	54.89 210	50.62 206	45.08 206	38.65 208	23.40 205	36.23 201	31.10 213	29.31 205	62.76 203	62.49 203	68.48 208	54.23 214

pmmvs3		47.65 203	49.08 207	45.99 205	44.61 218	54.79 211	50.04 207	31.95 222	33.91 214	29.90 194	30.37 208	33.53 210	46.31 181	63.50 200	63.67 200	73.14 198	63.77 200

ambc			53.42 199		64.99 189	63.36 197	49.96 208		47.07 194	37.12 187	28.97 210	16.36 225	41.82 188	75.10 150	67.34 188	71.55 202	75.72 166

MVS-HIRNet		54.41 195	52.10 202	57.11 185	58.99 202	56.10 208	49.68 209	49.10 191	46.18 197	52.15 126	33.18 207	46.11 194	56.10 148	63.19 202	59.70 210	76.64 184	60.25 206

FC-MVSNet-test		56.90 191	65.20 161	47.21 203	66.98 181	63.20 198	49.11 210	58.60 152	59.38 114	11.50 223	65.60 65	56.68 119	24.66 214	71.17 173	71.36 170	72.38 199	69.02 190

testpf		47.41 204	48.47 210	46.18 204	66.30 185	50.67 215	48.15 211	42.60 212	37.10 211	28.75 197	40.97 192	39.01 206	30.82 203	52.95 218	53.74 217	60.46 216	64.87 196

gm-plane-assit		57.00 190	57.62 196	56.28 187	76.10 104	62.43 203	47.62 212	46.57 200	33.84 215	23.24 206	37.52 197	40.19 204	59.61 127	79.81 89	77.55 108	84.55 150	72.03 183

new-patchmatchnet		46.97 207	49.47 206	44.05 209	62.82 192	56.55 207	45.35 213	52.01 179	42.47 202	17.04 219	35.73 204	35.21 208	21.84 220	61.27 206	54.83 215	65.26 214	60.26 205

testus		45.61 210	49.06 208	41.59 211	56.13 210	55.28 209	43.51 214	39.64 215	37.74 209	18.23 216	35.52 205	31.28 212	24.69 213	62.46 204	62.90 201	67.33 210	58.26 210

PMVS	39.38 18	46.06 209	43.30 214	49.28 202	62.93 191	38.75 222	41.88 215	53.50 171	33.33 217	35.46 189	28.90 211	31.01 214	33.04 201	58.61 214	54.63 216	68.86 207	57.88 211
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1111		43.08 211	44.02 213	41.98 210	59.22 200	49.27 218	41.48 216	45.63 204	35.01 212	23.06 208	28.60 212	30.15 215	27.22 206	60.42 209	57.97 211	55.27 220	46.74 217

.test1245		30.81 218	29.14 220	32.77 217	59.22 200	49.27 218	41.48 216	45.63 204	35.01 212	23.06 208	28.60 212	30.15 215	27.22 206	60.42 209	0.10 224	0.01 228	0.43 226

testmv		42.58 212	44.36 211	40.49 212	54.63 214	52.76 212	41.21 218	44.37 208	28.83 219	12.87 220	27.16 215	25.03 220	23.01 216	60.83 207	61.13 205	66.88 211	54.81 212

test1235678		42.57 213	44.36 211	40.49 212	54.63 214	52.75 213	41.21 218	44.37 208	28.82 220	12.87 220	27.15 216	25.01 221	23.01 216	60.83 207	61.13 205	66.88 211	54.81 212

new_pmnet		38.40 214	42.64 215	33.44 216	37.54 224	45.00 220	36.60 220	32.72 221	40.27 205	12.72 222	29.89 209	28.90 217	24.78 212	53.17 217	52.90 218	56.31 218	48.34 216

test12356		35.10 217	38.50 216	31.13 218	44.14 220	43.70 221	32.27 221	34.42 218	26.51 222	9.47 224	25.22 218	20.34 222	10.86 223	53.47 216	56.15 212	55.59 219	44.11 218

Gipuma		36.38 215	35.80 218	37.07 214	45.76 217	33.90 223	29.81 222	48.47 195	39.91 206	18.02 217	8.00 226	8.14 228	25.14 210	59.29 212	61.02 207	55.19 221	40.31 219
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
no-one		36.35 216	37.59 217	34.91 215	46.13 216	49.89 217	27.99 223	43.56 210	20.91 224	7.03 226	14.64 222	15.50 226	18.92 221	42.95 219	60.20 208	65.84 213	59.03 209

PMMVS2		25.60 219	29.75 219	20.76 222	28.00 225	30.93 224	23.10 224	29.18 223	23.14 223	1.46 230	18.23 221	16.54 224	5.08 224	40.22 220	41.40 220	37.76 222	37.79 221

DeepMVS_CX						18.74 228	18.55 225	8.02 224	26.96 221	7.33 225	23.81 219	13.05 227	25.99 208	25.17 223		22.45 227	36.25 222

EMVS		20.98 221	17.15 223	25.44 220	39.51 223	19.37 227	12.66 226	39.59 216	19.10 225	6.62 228	9.27 224	4.40 230	22.43 218	17.99 225	24.40 223	31.81 224	25.53 224

E-PMN		21.77 220	18.24 222	25.89 219	40.22 222	19.58 226	12.46 227	39.87 214	18.68 226	6.71 227	9.57 223	4.31 231	22.36 219	19.89 224	27.28 222	33.73 223	28.34 223

MVE	19.12 19	20.47 222	23.27 221	17.20 223	12.66 227	25.41 225	10.52 228	34.14 220	14.79 227	6.53 229	8.79 225	4.68 229	16.64 222	29.49 222	41.63 219	22.73 226	38.11 220
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				14.50 224	14.68 226	7.17 229	10.46 229	2.21 225	37.73 210	28.71 198	25.26 217	16.98 223	4.37 225	31.49 221	29.77 221	26.56 225

Patchmatch-RL test							2.85 230

testmvs		0.09 223	0.15 224	0.02 225	0.01 229	0.02 230	0.05 231	0.01 227	0.11 228	0.01 232	0.26 228	0.01 232	0.06 228	0.10 226	0.10 224	0.01 228	0.43 226

test123		0.09 223	0.14 225	0.02 225	0.00 230	0.02 230	0.02 232	0.01 227	0.09 229	0.00 233	0.30 227	0.00 233	0.08 226	0.03 227	0.09 226	0.01 228	0.45 225

test11111		0.00 225	0.00 226	0.00 227	0.00 230	0.00 232	0.00 233	0.00 229	0.00 230	0.00 233	0.00 229	0.00 233	0.00 229	0.00 228	0.00 227	0.00 231	0.00 228

sosnet-low-res		0.00 225	0.00 226	0.00 227	0.00 230	0.00 232	0.00 233	0.00 229	0.00 230	0.00 233	0.00 229	0.00 233	0.00 229	0.00 228	0.00 227	0.00 231	0.00 228

sosnet		0.00 225	0.00 226	0.00 227	0.00 230	0.00 232	0.00 233	0.00 229	0.00 230	0.00 233	0.00 229	0.00 233	0.00 229	0.00 228	0.00 227	0.00 231	0.00 228

MTAPA										83.48 1		86.45 11

MTMP										82.66 3		84.91 19

mPP-MVS					89.90 19							81.29 34

NP-MVS									80.10 39

High-resolution multi-view benchmark