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.

xxxxxxxxxxxxxcwj		74.63 16	77.07 27	71.79 2	79.32 1	80.76 4	82.96 2	57.49 10	82.82 8	64.79 5	83.69 9	52.03 116	62.83 13	77.13 26	75.21 31	83.35 7	87.85 16

SF-MVS		77.13 7	81.70 7	71.79 2	79.32 1	80.76 4	82.96 2	57.49 10	82.82 8	64.79 5	83.69 9	84.46 4	62.83 13	77.13 26	75.21 31	83.35 7	87.85 16

SED-MVS		79.21 1	84.74 2	72.75 1	78.66 3	81.96 2	82.94 5	58.16 4	86.82 2	67.66 1	88.29 4	86.15 2	66.42 1	80.41 3	78.65 6	82.65 17	90.92 2

DVP-MVS		78.77 2	84.89 1	71.62 4	78.04 4	82.05 1	81.64 10	57.96 6	87.53 1	66.64 2	88.77 1	86.31 1	63.16 9	79.99 6	78.56 7	82.31 22	91.03 1

APDe-MVS		77.58 5	82.93 5	71.35 6	77.86 5	80.55 6	83.38 1	57.61 9	85.57 4	61.11 21	86.10 6	82.98 7	64.76 3	78.29 14	76.78 22	83.40 6	90.20 5

DPE-MVS		78.11 3	83.84 3	71.42 5	77.82 6	81.32 3	82.92 6	57.81 8	84.04 7	63.19 13	88.63 2	86.00 3	64.52 4	78.71 10	77.63 15	82.26 23	90.57 4

SteuartSystems-ACMMP		75.23 12	79.60 14	70.13 13	76.81 7	78.92 12	81.74 8	57.99 5	75.30 30	59.83 26	75.69 18	78.45 24	60.48 29	80.58 2	79.77 2	83.94 3	88.52 10
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		76.01 9	80.47 11	70.81 9	76.60 8	74.96 36	80.18 17	58.36 2	81.96 10	63.50 12	78.80 14	82.53 10	64.40 5	78.74 9	78.84 5	81.81 32	87.46 19

MCST-MVS		73.67 25	77.39 25	69.33 19	76.26 9	78.19 17	78.77 26	54.54 31	75.33 28	59.99 25	67.96 32	79.23 22	62.43 17	78.00 18	75.71 29	84.02 2	87.30 20

CNVR-MVS		75.62 11	79.91 13	70.61 10	75.76 10	78.82 14	81.66 9	57.12 13	79.77 17	63.04 14	70.69 24	81.15 15	62.99 10	80.23 4	79.54 3	83.11 9	89.16 8

NCCC		74.27 19	77.83 24	70.13 13	75.70 11	77.41 23	80.51 15	57.09 14	78.25 21	62.28 18	65.54 37	78.26 25	62.18 19	79.13 7	78.51 9	83.01 11	87.68 18

CSCG		74.68 15	79.22 15	69.40 18	75.69 12	80.01 9	79.12 24	52.83 42	79.34 18	63.99 10	70.49 25	82.02 11	60.35 31	77.48 24	77.22 19	84.38 1	87.97 15

DPM-MVS		72.80 27	75.90 30	69.19 21	75.51 13	77.68 20	81.62 11	54.83 27	75.96 26	62.06 19	63.96 43	76.58 30	58.55 38	76.66 33	76.77 23	82.60 19	83.68 43

SMA-MVS		77.32 6	82.51 6	71.26 7	75.43 14	80.19 8	82.22 7	58.26 3	84.83 6	64.36 8	78.19 15	83.46 5	63.61 7	81.00 1	80.28 1	83.66 4	89.62 6

APD-MVS		75.80 10	80.90 10	69.86 16	75.42 15	78.48 16	81.43 13	57.44 12	80.45 15	59.32 27	85.28 7	80.82 17	63.96 6	76.89 29	76.08 27	81.58 38	88.30 12
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSP-MVS		77.82 4	83.46 4	71.24 8	75.26 16	80.22 7	82.95 4	57.85 7	85.90 3	64.79 5	88.54 3	83.43 6	66.24 2	78.21 17	78.56 7	80.34 45	89.39 7

train_agg		73.89 22	78.25 21	68.80 24	75.25 17	72.27 52	79.75 18	56.05 21	74.87 33	58.97 28	81.83 11	79.76 21	61.05 26	77.39 25	76.01 28	81.71 35	85.61 31

TSAR-MVS + MP.		75.22 13	80.06 12	69.56 17	74.61 18	72.74 50	80.59 14	55.70 24	80.80 13	62.65 16	86.25 5	82.92 8	62.07 20	76.89 29	75.66 30	81.77 34	85.19 34
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SD-MVS		74.43 17	78.87 17	69.26 20	74.39 19	73.70 46	79.06 25	55.24 26	81.04 12	62.71 15	80.18 12	82.61 9	61.70 22	75.43 41	73.92 44	82.44 21	85.22 33
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
ACMMP_NAP		76.15 8	81.17 8	70.30 11	74.09 20	79.47 10	81.59 12	57.09 14	81.38 11	63.89 11	79.02 13	80.48 18	62.24 18	80.05 5	79.12 4	82.94 12	88.64 9

HFP-MVS		74.87 14	78.86 19	70.21 12	73.99 21	77.91 18	80.36 16	56.63 16	78.41 20	64.27 9	74.54 20	77.75 28	62.96 11	78.70 11	77.82 12	83.02 10	86.91 22

AdaColmap		67.89 46	68.85 56	66.77 30	73.73 22	74.30 43	75.28 40	53.58 37	70.24 46	57.59 35	51.19 99	59.19 88	60.74 28	75.33 43	73.72 46	79.69 53	77.96 67

MP-MVS		74.31 18	78.87 17	68.99 22	73.49 23	78.56 15	79.25 23	56.51 17	75.33 28	60.69 23	75.30 19	79.12 23	61.81 21	77.78 21	77.93 11	82.18 28	88.06 14
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
zzz-MVS		74.25 20	77.97 23	69.91 15	73.43 24	74.06 44	79.69 19	56.44 18	80.74 14	64.98 4	68.72 30	79.98 20	62.92 12	78.24 16	77.77 14	81.99 30	86.30 24

TSAR-MVS + ACMM		72.56 29	79.07 16	64.96 41	73.24 25	73.16 49	78.50 27	48.80 65	79.34 18	55.32 41	85.04 8	81.49 14	58.57 37	75.06 44	73.75 45	75.35 101	85.61 31

CDPH-MVS		71.47 32	75.82 32	66.41 32	72.97 26	77.15 25	78.14 30	54.71 28	69.88 48	53.07 56	70.98 23	74.83 37	56.95 51	76.22 34	76.57 24	82.62 18	85.09 35

PGM-MVS		72.89 26	77.13 26	67.94 26	72.47 27	77.25 24	79.27 22	54.63 30	73.71 35	57.95 34	72.38 22	75.33 35	60.75 27	78.25 15	77.36 18	82.57 20	85.62 30

ACMMPR		73.79 24	78.41 20	68.40 25	72.35 28	77.79 19	79.32 21	56.38 19	77.67 24	58.30 32	74.16 21	76.66 29	61.40 23	78.32 13	77.80 13	82.68 16	86.51 23

OPM-MVS		69.33 38	71.05 46	67.32 28	72.34 29	75.70 33	79.57 20	56.34 20	55.21 70	53.81 54	59.51 61	68.96 54	59.67 33	77.61 23	76.44 25	82.19 27	83.88 41

X-MVS		71.18 33	75.66 33	65.96 36	71.71 30	76.96 26	77.26 33	55.88 23	72.75 38	54.48 49	64.39 41	74.47 38	54.19 65	77.84 20	77.37 17	82.21 26	85.85 28

HQP-MVS		70.88 34	75.02 34	66.05 35	71.69 31	74.47 41	77.51 32	53.17 39	72.89 37	54.88 45	70.03 27	70.48 50	57.26 47	76.02 36	75.01 36	81.78 33	86.21 25

MAR-MVS		68.04 45	70.74 48	64.90 42	71.68 32	76.33 31	74.63 44	50.48 56	63.81 56	55.52 40	54.88 81	69.90 52	57.39 45	75.42 42	74.79 38	79.71 50	80.03 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
mPP-MVS					71.67 33							74.36 41

SR-MVS					71.46 34			54.67 29				81.54 13

CLD-MVS		67.02 50	71.57 43	61.71 52	71.01 35	74.81 38	71.62 52	38.91 153	71.86 41	60.70 22	64.97 39	67.88 61	51.88 88	76.77 32	74.98 37	76.11 91	69.75 120
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CP-MVS		72.63 28	76.95 28	67.59 27	70.67 36	75.53 34	77.95 31	56.01 22	75.65 27	58.82 29	69.16 29	76.48 31	60.46 30	77.66 22	77.20 20	81.65 36	86.97 21

ACMM	60.30 7	67.58 48	68.82 57	66.13 34	70.59 37	72.01 54	76.54 35	54.26 33	65.64 54	54.78 47	50.35 102	61.72 76	58.74 36	75.79 39	75.03 34	81.88 31	81.17 53
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVS					70.49 38	76.96 26	74.36 45			54.48 49		74.47 38				82.24 24

X-MVStestdata					70.49 38	76.96 26	74.36 45			54.48 49		74.47 38				82.24 24

ACMMP		71.57 31	75.84 31	66.59 31	70.30 40	76.85 29	78.46 28	53.95 35	73.52 36	55.56 39	70.13 26	71.36 48	58.55 38	77.00 28	76.23 26	82.71 15	85.81 29
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
abl_6				64.36 45	70.08 41	77.45 22	72.88 50	50.15 57	71.31 43	54.77 48	62.79 48	77.99 27	56.80 52			81.50 39	83.91 40

MVS_111021_HR		67.62 47	70.39 50	64.39 44	69.77 42	70.45 58	71.44 54	51.72 48	60.77 64	55.06 43	62.14 53	66.40 63	58.13 41	76.13 35	74.79 38	80.19 47	82.04 50

MSLP-MVS++		68.17 44	70.72 49	65.19 39	69.41 43	70.64 56	74.99 41	45.76 75	70.20 47	60.17 24	56.42 73	73.01 44	61.14 24	72.80 54	70.54 57	79.70 51	81.42 52

LGP-MVS_train		68.87 40	72.03 42	65.18 40	69.33 44	74.03 45	76.67 34	53.88 36	68.46 49	52.05 59	63.21 45	63.89 66	56.31 55	75.99 37	74.43 40	82.83 14	84.18 37

ACMP	61.42 5	68.72 43	71.37 44	65.64 38	69.06 45	74.45 42	75.88 38	53.30 38	68.10 50	55.74 38	61.53 56	62.29 72	56.97 50	74.70 45	74.23 42	82.88 13	84.31 36
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DeepC-MVS_fast	65.08 3	72.00 30	76.11 29	67.21 29	68.93 46	77.46 21	76.54 35	54.35 32	74.92 32	58.64 31	65.18 38	74.04 43	62.62 15	77.92 19	77.02 21	82.16 29	86.21 25
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CANet		68.77 41	73.01 38	63.83 47	68.30 47	75.19 35	73.73 48	47.90 66	63.86 55	54.84 46	67.51 34	74.36 41	57.62 42	74.22 47	73.57 48	80.56 43	82.36 47

TSAR-MVS + GP.		69.71 35	73.92 37	64.80 43	68.27 48	70.56 57	71.90 51	50.75 52	71.38 42	57.46 36	68.68 31	75.42 34	60.10 32	73.47 51	73.99 43	80.32 46	83.97 39

3Dnovator+	62.63 4	69.51 36	72.62 40	65.88 37	68.21 49	76.47 30	73.50 49	52.74 43	70.85 44	58.65 30	55.97 75	69.95 51	61.11 25	76.80 31	75.09 33	81.09 42	83.23 46

MVS_0304		69.49 37	73.96 36	64.28 46	67.92 50	76.13 32	74.90 42	47.60 67	63.29 58	54.09 53	67.44 35	76.35 32	59.53 34	75.81 38	75.03 34	81.62 37	83.70 42

DeepC-MVS	66.32 2	73.85 23	78.10 22	68.90 23	67.92 50	79.31 11	78.16 29	59.28 1	78.24 22	61.13 20	67.36 36	76.10 33	63.40 8	79.11 8	78.41 10	83.52 5	88.16 13
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepPCF-MVS	66.49 1	74.25 20	80.97 9	66.41 32	67.75 52	78.87 13	75.61 39	54.16 34	84.86 5	58.22 33	77.94 16	81.01 16	62.52 16	78.34 12	77.38 16	80.16 48	88.40 11

UA-Net		58.50 86	64.68 75	51.30 114	66.97 53	67.13 81	53.68 153	45.65 78	49.51 100	31.58 142	62.91 47	68.47 56	35.85 163	68.20 92	67.28 89	74.03 111	69.24 130

PHI-MVS		69.27 39	74.84 35	62.76 51	66.83 54	74.83 37	73.88 47	49.32 61	70.61 45	50.93 60	69.62 28	74.84 36	57.25 48	75.53 40	74.32 41	78.35 64	84.17 38

3Dnovator	60.86 6	66.99 51	70.32 51	63.11 49	66.63 55	74.52 39	71.56 53	45.76 75	67.37 52	55.00 44	54.31 86	68.19 58	58.49 40	73.97 48	73.63 47	81.22 41	80.23 55

EPNet		65.14 56	69.54 54	60.00 57	66.61 56	67.67 74	67.53 62	55.32 25	62.67 60	46.22 77	67.74 33	65.93 64	48.07 106	72.17 56	72.12 50	76.28 87	78.47 64
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OpenMVS	57.13 9	62.81 63	65.75 67	59.39 60	66.47 57	69.52 60	64.26 93	43.07 123	61.34 63	50.19 63	47.29 119	64.41 65	54.60 64	70.18 72	68.62 74	77.73 66	78.89 61

ACMH	52.42 13	58.24 93	59.56 110	56.70 82	66.34 58	69.59 59	66.71 70	49.12 62	46.08 133	28.90 155	42.67 163	41.20 180	52.60 81	71.39 60	70.28 59	76.51 83	75.72 88
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MSDG		58.46 88	58.97 116	57.85 74	66.27 59	66.23 90	67.72 60	42.33 127	53.43 76	43.68 88	43.39 154	45.35 158	49.75 95	68.66 83	67.77 82	77.38 72	67.96 135

QAPM		65.27 54	69.49 55	60.35 55	65.43 60	72.20 53	65.69 82	47.23 68	63.46 57	49.14 66	53.56 87	71.04 49	57.01 49	72.60 55	71.41 53	77.62 68	82.14 49

CPTT-MVS		68.76 42	73.01 38	63.81 48	65.42 61	73.66 47	76.39 37	52.08 44	72.61 39	50.33 62	60.73 57	72.65 46	59.43 35	73.32 52	72.12 50	79.19 57	85.99 27

MS-PatchMatch		58.19 95	60.20 99	55.85 86	65.17 62	64.16 106	64.82 88	41.48 135	50.95 90	42.17 97	45.38 139	56.42 99	48.08 105	68.30 88	66.70 97	73.39 119	69.46 128

PCF-MVS	59.98 8	67.32 49	71.04 47	62.97 50	64.77 63	74.49 40	74.78 43	49.54 59	67.44 51	54.39 52	58.35 66	72.81 45	55.79 61	71.54 59	69.24 66	78.57 59	83.41 44
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EG-PatchMatch MVS		56.98 102	58.24 123	55.50 88	64.66 64	68.62 63	61.48 102	43.63 108	38.44 184	41.44 99	38.05 174	46.18 153	43.95 124	71.71 58	70.61 56	77.87 65	74.08 100

Effi-MVS+-dtu		60.34 74	62.32 83	58.03 69	64.31 65	67.44 78	65.99 77	42.26 128	49.55 98	42.00 98	48.92 109	59.79 86	56.27 56	68.07 96	67.03 90	77.35 73	75.45 91

FC-MVSNet-train		58.40 89	63.15 81	52.85 107	64.29 66	61.84 117	55.98 137	46.47 71	53.06 80	34.96 131	61.95 55	56.37 101	39.49 144	68.67 82	68.36 76	75.92 95	71.81 108

Effi-MVS+		63.28 60	65.96 66	60.17 56	64.26 67	68.06 68	68.78 57	45.71 77	54.08 73	46.64 74	55.92 76	63.13 70	55.94 59	70.38 70	71.43 52	79.68 54	78.70 62

CS-MVS		63.45 59	65.72 68	60.80 53	64.20 68	67.86 70	68.46 59	43.50 113	53.86 74	49.90 64	56.44 72	60.45 83	57.27 46	73.56 50	70.13 62	81.45 40	77.73 69

LS3D		60.20 75	61.70 84	58.45 65	64.18 69	67.77 71	67.19 64	48.84 64	61.67 62	41.27 102	45.89 134	51.81 117	54.18 66	68.78 80	66.50 104	75.03 103	69.48 126

ACMH+	53.71 12	59.26 78	60.28 96	58.06 67	64.17 70	68.46 64	67.51 63	50.93 51	52.46 86	35.83 128	40.83 168	45.12 162	52.32 84	69.88 73	69.00 70	77.59 70	76.21 85

DELS-MVS		65.87 52	70.30 52	60.71 54	64.05 71	72.68 51	70.90 55	45.43 79	57.49 67	49.05 68	64.43 40	68.66 55	55.11 63	74.31 46	73.02 49	79.70 51	81.51 51
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
canonicalmvs		65.62 53	72.06 41	58.11 66	63.94 72	71.05 55	64.49 91	43.18 121	74.08 34	47.35 70	64.17 42	71.97 47	51.17 91	71.87 57	70.74 55	78.51 62	80.56 54

EIA-MVS		61.53 71	63.79 79	58.89 63	63.82 73	67.61 75	65.35 85	42.15 131	49.98 95	45.66 79	57.47 70	56.62 98	56.59 54	70.91 65	69.15 67	79.78 49	74.80 94

Anonymous202405211			60.60 92		63.44 74	66.71 87	61.00 107	47.23 68	50.62 93		36.85 177	60.63 82	43.03 132	69.17 77	67.72 84	75.41 98	72.54 105

ETV-MVS		63.23 61	66.08 65	59.91 58	63.13 75	68.13 67	67.62 61	44.62 88	53.39 77	46.23 76	58.74 63	58.19 91	57.45 44	73.60 49	71.38 54	80.39 44	79.13 59

gg-mvs-nofinetune		49.07 159	52.56 159	45.00 160	61.99 76	59.78 136	53.55 155	41.63 133	31.62 200	12.08 194	29.56 194	53.28 111	29.57 178	66.27 126	64.49 131	71.19 148	62.92 165

DCV-MVSNet		59.49 76	64.00 78	54.23 93	61.81 77	64.33 105	61.42 103	43.77 102	52.85 83	38.94 116	55.62 78	62.15 74	43.24 131	69.39 76	67.66 85	76.22 89	75.97 86

casdiffmvs		64.09 57	68.13 58	59.37 61	61.81 77	68.32 66	68.48 58	44.45 91	61.95 61	49.12 67	63.04 46	69.67 53	53.83 68	70.46 67	66.06 109	78.55 60	77.43 70

Anonymous20231211		57.71 98	60.79 90	54.13 95	61.68 79	65.81 93	60.81 108	43.70 106	51.97 88	39.67 111	34.82 182	63.59 67	43.31 129	68.55 86	66.63 100	75.59 96	74.13 99

IS_MVSNet		57.95 96	64.26 77	50.60 116	61.62 80	65.25 99	57.18 125	45.42 80	50.79 91	26.49 166	57.81 68	60.05 85	34.51 167	71.24 63	70.20 61	78.36 63	74.44 96

NR-MVSNet		55.35 117	59.46 111	50.56 117	61.33 81	62.97 111	57.91 122	51.80 46	48.62 115	20.59 178	51.99 95	44.73 168	34.10 170	68.58 84	68.64 73	77.66 67	70.67 117

MVS_111021_LR		63.05 62	66.43 62	59.10 62	61.33 81	63.77 108	65.87 79	43.58 109	60.20 65	53.70 55	62.09 54	62.38 71	55.84 60	70.24 71	68.08 77	74.30 108	78.28 66

EPP-MVSNet		59.39 77	65.45 70	52.32 111	60.96 83	67.70 73	58.42 119	44.75 86	49.71 97	27.23 163	59.03 62	62.20 73	43.34 128	70.71 66	69.13 68	79.25 56	79.63 58

TransMVSNet (Re)		51.92 141	55.38 140	47.88 146	60.95 84	59.90 135	53.95 150	45.14 82	39.47 178	24.85 170	43.87 149	46.51 149	29.15 179	67.55 106	65.23 122	73.26 124	65.16 156

gm-plane-assit		44.74 179	45.95 187	43.33 168	60.88 85	46.79 193	36.97 201	32.24 191	24.15 206	11.79 195	29.26 195	32.97 201	46.64 111	65.09 140	62.95 144	71.45 145	60.42 176

baseline1		54.48 125	58.69 117	49.57 122	60.63 86	58.29 151	55.70 139	44.95 84	49.20 103	29.62 151	54.77 82	54.75 106	35.29 164	67.15 114	64.08 133	71.21 147	62.58 169

DI_MVS_plusplus_trai		61.88 67	65.17 72	58.06 67	60.05 87	65.26 97	66.03 76	44.22 92	55.75 69	46.73 72	54.64 84	68.12 59	54.13 67	69.13 78	66.66 98	77.18 75	76.61 78

PVSNet_Blended_VisFu		63.65 58	66.92 59	59.83 59	60.03 88	73.44 48	66.33 73	48.95 63	52.20 87	50.81 61	56.07 74	60.25 84	53.56 70	73.23 53	70.01 63	79.30 55	83.24 45

UniMVSNet_NR-MVSNet		56.94 104	61.14 87	52.05 113	60.02 89	65.21 100	57.44 123	52.93 41	49.37 101	24.31 173	54.62 85	50.54 123	39.04 146	68.69 81	68.84 71	78.53 61	70.72 113

IterMVS-LS		58.30 92	61.39 86	54.71 91	59.92 90	58.40 148	59.42 113	43.64 107	48.71 112	40.25 109	57.53 69	58.55 90	52.15 86	65.42 138	65.34 119	72.85 126	75.77 87
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_Test		62.40 66	66.23 64	57.94 70	59.77 91	64.77 103	66.50 72	41.76 132	57.26 68	49.33 65	62.68 50	67.47 62	53.50 73	68.57 85	66.25 106	76.77 80	76.58 79

TranMVSNet+NR-MVSNet		55.87 111	60.14 101	50.88 115	59.46 92	63.82 107	57.93 121	52.98 40	48.94 107	20.52 179	52.87 89	47.33 140	36.81 160	69.12 79	69.03 69	77.56 71	69.89 119

Fast-Effi-MVS+		60.36 73	63.35 80	56.87 80	58.70 93	65.86 92	65.08 87	37.11 166	53.00 82	45.36 81	52.12 94	56.07 103	56.27 56	71.28 62	69.42 65	78.71 58	75.69 89

IB-MVS	54.11 11	58.36 91	60.70 91	55.62 87	58.67 94	68.02 69	61.56 100	43.15 122	46.09 132	44.06 87	44.24 146	50.99 122	48.71 100	66.70 120	70.33 58	77.60 69	78.50 63
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
Fast-Effi-MVS+-dtu		56.30 109	59.29 113	52.82 108	58.64 95	64.89 101	65.56 83	32.89 188	45.80 135	35.04 130	45.89 134	54.14 108	49.41 96	67.16 113	66.45 105	75.37 100	70.69 115

CNLPA		62.78 64	66.31 63	58.65 64	58.47 96	68.41 65	65.98 78	41.22 138	78.02 23	56.04 37	46.65 122	59.50 87	57.50 43	69.67 74	65.27 121	72.70 132	76.67 77

tpm cat1		53.30 130	53.41 152	53.17 104	58.16 97	59.15 142	63.73 96	38.27 161	50.73 92	46.98 71	45.57 138	44.00 174	49.20 97	55.90 186	54.02 185	62.65 177	64.50 160

v1144		58.88 81	60.16 100	57.39 76	58.03 98	67.26 79	67.14 66	44.46 90	45.17 138	44.33 86	47.81 116	49.92 127	53.20 79	67.77 102	66.62 101	77.15 76	76.58 79

v10		59.17 80	60.60 92	57.50 75	57.95 99	66.73 84	67.09 68	44.11 93	46.85 126	45.42 80	48.18 115	51.07 119	53.63 69	67.84 100	66.59 102	76.79 79	76.92 75

v1192		58.51 85	59.66 106	57.17 77	57.82 100	67.72 72	66.21 75	44.83 85	44.15 146	43.49 89	46.68 121	47.94 131	53.55 71	67.39 109	66.51 103	77.13 77	77.20 73

v144192		58.23 94	59.40 112	56.87 80	57.56 101	66.89 82	65.70 80	45.01 83	44.06 147	42.88 91	46.61 123	48.09 130	53.49 74	66.94 118	65.90 113	76.61 81	77.29 71

v1921920		57.89 97	59.02 115	56.58 83	57.55 102	66.66 88	64.72 90	44.70 87	43.55 150	42.73 92	46.17 131	46.93 144	53.51 72	66.78 119	65.75 115	76.29 86	77.28 72

Vis-MVSNet (Re-imp)		50.37 149	57.73 129	41.80 175	57.53 103	54.35 165	45.70 183	45.24 81	49.80 96	13.43 192	58.23 67	56.42 99	20.11 194	62.96 146	63.36 140	68.76 156	58.96 181

CostFormer		56.57 107	59.13 114	53.60 98	57.52 104	61.12 124	66.94 69	35.95 171	53.44 75	44.68 84	55.87 77	54.44 107	48.21 103	60.37 159	58.33 166	68.27 158	70.33 118

thres400		52.38 136	55.51 138	48.74 133	57.49 105	60.10 134	55.45 142	43.54 110	42.90 157	26.72 165	43.34 156	45.03 166	36.61 161	66.20 128	64.53 130	72.66 133	66.43 142

thres600view7		51.91 142	55.14 142	48.14 142	57.43 106	60.18 132	54.60 148	43.73 104	42.61 161	25.20 169	43.10 159	44.47 171	35.19 165	66.36 123	63.28 141	72.66 133	66.01 149

thres200		52.39 135	55.37 141	48.90 131	57.39 107	60.18 132	55.60 140	43.73 104	42.93 156	27.41 161	43.35 155	45.09 163	36.61 161	66.36 123	63.92 136	72.66 133	65.78 151

baseline2		55.89 110	57.82 126	53.64 97	57.36 108	61.09 125	59.75 112	40.45 145	47.38 124	41.26 103	51.23 98	46.90 145	48.11 104	65.63 135	64.38 132	74.90 104	68.16 134

v8		58.88 81	60.57 94	56.92 79	57.35 109	65.69 94	66.69 71	42.64 125	47.89 121	45.77 78	49.04 107	52.98 112	52.77 80	67.51 107	65.57 116	76.26 88	75.30 93

thres100view900		52.04 139	54.81 145	48.80 132	57.31 110	59.33 139	55.30 144	42.92 124	42.85 158	27.81 159	43.00 160	45.06 164	36.99 158	64.74 141	63.51 138	72.47 136	65.21 155

tfpn200view9		52.53 133	55.51 138	49.06 129	57.31 110	60.24 131	55.42 143	43.77 102	42.85 158	27.81 159	43.00 160	45.06 164	37.32 156	66.38 122	64.54 129	72.71 131	66.54 141

v1240		57.55 99	58.63 119	56.29 84	57.30 112	66.48 89	63.77 95	44.56 89	42.77 160	42.48 94	45.64 137	46.28 151	53.46 75	66.32 125	65.80 114	76.16 90	77.13 74

CHOSEN 1792x2688		55.85 112	58.01 124	53.33 100	57.26 113	62.82 113	63.29 99	41.55 134	46.65 128	38.34 117	34.55 183	53.50 109	52.43 83	67.10 115	67.56 87	67.13 162	73.92 102

PVSNet_BlendedMVS		61.63 69	64.82 73	57.91 72	57.21 114	67.55 76	63.47 97	46.08 73	54.72 71	52.46 57	58.59 64	60.73 79	51.82 89	70.46 67	65.20 123	76.44 84	76.50 82

PVSNet_Blended		61.63 69	64.82 73	57.91 72	57.21 114	67.55 76	63.47 97	46.08 73	54.72 71	52.46 57	58.59 64	60.73 79	51.82 89	70.46 67	65.20 123	76.44 84	76.50 82

v2v482		58.69 84	60.12 103	57.03 78	57.16 116	66.05 91	67.17 65	43.52 111	46.33 130	45.19 82	49.46 106	51.02 120	52.51 82	67.30 110	66.03 110	76.61 81	74.62 95

tfpnnormal		50.16 151	52.19 163	47.78 148	56.86 117	58.37 149	54.15 149	44.01 99	38.35 186	25.94 167	36.10 178	37.89 193	34.50 168	65.93 130	63.42 139	71.26 146	65.28 154

diffmvs		61.64 68	66.55 61	55.90 85	56.63 118	63.71 109	67.13 67	41.27 137	59.49 66	46.70 73	63.93 44	68.01 60	50.46 92	67.30 110	65.51 117	73.24 125	77.87 68

HyFIR lowres test		56.87 105	58.60 120	54.84 90	56.62 119	69.27 61	64.77 89	42.21 129	45.66 136	37.50 123	33.08 185	57.47 96	53.33 76	65.46 137	67.94 78	74.60 105	71.35 110

v7n		55.67 113	57.46 131	53.59 99	56.06 120	65.29 96	61.06 106	43.26 120	40.17 175	37.99 120	40.79 169	45.27 161	47.09 110	67.67 104	66.21 107	76.08 92	76.82 76

dps		50.42 148	51.20 169	49.51 123	55.88 121	56.07 161	53.73 151	38.89 154	43.66 148	40.36 108	45.66 136	37.63 195	45.23 119	59.05 162	56.18 170	62.94 176	60.16 177

CANet_DTU		58.88 81	64.68 75	52.12 112	55.77 122	66.75 83	63.92 94	37.04 167	53.32 78	37.45 124	59.81 59	61.81 75	44.43 123	68.25 89	67.47 88	74.12 110	75.33 92

WR-MVS		48.78 161	55.06 143	41.45 176	55.50 123	60.40 130	43.77 191	49.99 58	41.92 164	8.10 204	45.24 142	45.56 156	17.47 195	61.57 154	64.60 128	73.85 112	66.14 148

UniMVSNet (Re)		55.15 121	60.39 95	49.03 130	55.31 124	64.59 104	55.77 138	50.63 53	48.66 114	20.95 177	51.47 97	50.40 124	34.41 169	67.81 101	67.89 79	77.11 78	71.88 107

test-LLR		49.28 155	50.29 173	48.10 143	55.26 125	47.16 188	49.52 164	43.48 115	39.22 179	31.98 138	43.65 152	47.93 132	41.29 139	56.80 177	55.36 176	67.08 163	61.94 170

test0.0.03 1		43.15 184	46.95 186	38.72 184	55.26 125	50.56 177	42.48 194	43.48 115	38.16 188	15.11 188	35.07 181	44.69 169	16.47 197	55.95 185	54.34 184	59.54 184	49.87 198

GA-MVS		55.67 113	58.33 121	52.58 110	55.23 127	63.09 110	61.08 105	40.15 149	42.95 155	37.02 126	52.61 91	47.68 135	47.51 108	65.92 131	65.35 118	74.49 107	70.68 116

DTE-MVSNet		48.03 167	53.28 154	41.91 174	54.64 128	57.50 157	44.63 190	51.66 49	41.02 170	7.97 205	46.26 128	40.90 181	20.24 193	60.45 158	62.89 145	72.33 139	63.97 161

pmmvs4		54.66 124	56.07 135	53.00 105	54.63 129	57.08 159	60.43 110	44.10 94	51.69 89	40.55 106	46.55 126	44.79 167	45.95 116	62.54 148	63.66 137	72.36 138	66.20 146

DU-MVS		55.41 116	59.59 107	50.54 118	54.60 130	62.97 111	57.44 123	51.80 46	48.62 115	24.31 173	51.99 95	47.00 143	39.04 146	68.11 94	67.75 83	76.03 94	70.72 113

Baseline_NR-MVSNet		53.50 128	57.89 125	48.37 140	54.60 130	59.25 141	56.10 133	51.84 45	49.32 102	17.92 186	45.38 139	47.68 135	36.93 159	68.11 94	65.95 111	72.84 127	69.57 124

v148		55.58 115	57.61 130	53.20 102	54.59 132	61.86 116	61.18 104	38.70 158	44.30 145	42.25 96	47.53 117	50.24 126	48.73 99	65.15 139	62.61 149	73.79 113	71.61 109

PEN-MVS		49.21 157	54.32 147	43.24 170	54.33 133	59.26 140	47.04 177	51.37 50	41.67 166	9.97 199	46.22 129	41.80 179	22.97 191	60.52 157	64.03 134	73.73 114	66.75 140

pm-mvs1		51.02 145	55.55 137	45.73 155	54.16 134	58.52 146	50.92 160	42.56 126	40.32 174	25.67 168	43.66 151	50.34 125	30.06 177	65.85 132	63.97 135	70.99 149	66.21 145

EPNet_dtu		52.05 138	58.26 122	44.81 161	54.10 135	50.09 180	52.01 158	40.82 141	53.03 81	27.41 161	54.90 80	57.96 95	26.72 184	62.97 145	62.70 148	67.78 160	66.19 147
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm		48.82 160	51.27 168	45.96 154	54.10 135	47.35 187	56.05 134	30.23 192	46.70 127	43.21 90	52.54 92	47.55 138	37.28 157	54.11 191	50.50 194	54.90 194	60.12 178

CDS-MVSNet		52.42 134	57.06 133	47.02 151	53.92 137	58.30 150	55.50 141	46.47 71	42.52 162	29.38 153	49.50 105	52.85 113	28.49 182	66.70 120	66.89 94	68.34 157	62.63 168
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test20.03		40.38 192	44.20 193	35.92 191	53.73 138	49.05 181	38.54 199	43.49 114	32.55 197	9.54 200	27.88 197	39.12 189	12.24 202	56.28 182	54.69 181	57.96 189	49.83 199

Vis-MVSNet		58.48 87	65.70 69	50.06 121	53.40 139	67.20 80	60.24 111	43.32 118	48.83 109	30.23 148	62.38 52	61.61 77	40.35 142	71.03 64	69.77 64	72.82 128	79.11 60
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PLC	52.09 14	59.21 79	62.47 82	55.41 89	53.24 140	64.84 102	64.47 92	40.41 147	65.92 53	44.53 85	46.19 130	55.69 104	55.33 62	68.24 91	65.30 120	74.50 106	71.09 111
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
thisisatest0530		56.68 106	59.68 105	53.19 103	52.97 141	60.96 127	59.41 114	40.51 143	48.26 118	41.06 104	52.67 90	46.30 150	49.78 93	67.66 105	67.83 80	75.39 99	74.07 101

OMC-MVS		65.16 55	71.35 45	57.94 70	52.95 142	68.82 62	69.00 56	38.28 160	79.89 16	55.20 42	62.76 49	68.31 57	56.14 58	71.30 61	68.70 72	76.06 93	79.67 57

tpmrst		48.08 165	49.88 177	45.98 153	52.71 143	48.11 185	53.62 154	33.70 184	48.70 113	39.74 110	48.96 108	46.23 152	40.29 143	50.14 199	49.28 196	55.80 191	57.71 184

tttt0517		56.53 108	59.59 107	52.95 106	52.66 144	60.99 126	59.21 116	40.51 143	47.89 121	40.40 107	52.50 93	46.04 154	49.78 93	67.75 103	67.83 80	75.15 102	74.17 98

GBi-Net		55.20 118	60.25 97	49.31 124	52.42 145	61.44 119	57.03 126	44.04 96	49.18 104	30.47 144	48.28 111	58.19 91	38.22 149	68.05 97	66.96 91	73.69 115	69.65 121

test1		55.20 118	60.25 97	49.31 124	52.42 145	61.44 119	57.03 126	44.04 96	49.18 104	30.47 144	48.28 111	58.19 91	38.22 149	68.05 97	66.96 91	73.69 115	69.65 121

FMVSNet2		55.04 122	59.95 104	49.31 124	52.42 145	61.44 119	57.03 126	44.08 95	49.55 98	30.40 147	46.89 120	58.84 89	38.22 149	67.07 116	66.21 107	73.69 115	69.65 121

FMVSNet3		54.78 123	59.58 109	49.17 127	52.37 148	61.31 123	56.72 131	44.04 96	49.18 104	30.47 144	48.28 111	58.19 91	38.09 152	65.48 136	65.20 123	73.31 122	69.45 129

testgi		38.71 194	43.64 194	32.95 195	52.30 149	48.63 184	35.59 204	35.05 174	31.58 201	9.03 203	30.29 190	40.75 183	11.19 207	55.30 187	53.47 190	54.53 196	45.48 202

Anonymous20231206		42.28 185	45.89 188	38.07 186	51.96 150	48.98 182	43.66 192	38.81 157	38.74 183	14.32 191	26.74 198	40.90 181	20.94 192	56.64 180	54.67 182	58.71 185	54.59 188

pmmvs6		48.35 163	51.64 165	44.51 163	51.92 151	57.94 155	49.44 166	42.17 130	34.45 193	24.62 172	28.87 196	46.90 145	29.07 181	64.60 142	63.08 142	69.83 153	65.68 152

PatchmatchNet		49.92 153	51.29 167	48.32 141	51.83 152	51.86 174	53.38 156	37.63 165	47.90 120	40.83 105	48.54 110	45.30 159	45.19 120	56.86 176	53.99 187	61.08 182	54.57 189
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WR-MVS_H		47.65 168	53.67 150	40.63 179	51.45 153	59.74 137	44.71 189	49.37 60	40.69 172	7.61 206	46.04 132	44.34 173	17.32 196	57.79 173	61.18 154	73.30 123	65.86 150

LTVRE_ROB	44.17 16	47.06 173	50.15 176	43.44 167	51.39 154	58.42 147	42.90 193	43.51 112	22.27 207	14.85 190	41.94 167	34.57 198	45.43 117	62.28 151	62.77 147	62.56 179	68.83 132
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
CP-MVSNet		48.37 162	53.53 151	42.34 172	51.35 155	58.01 154	46.56 178	50.54 54	41.62 167	10.61 196	46.53 127	40.68 184	23.18 189	58.71 167	61.83 151	71.81 142	67.36 139

PS-CasMVS		48.18 164	53.25 155	42.27 173	51.26 156	57.94 155	46.51 179	50.52 55	41.30 168	10.56 197	45.35 141	40.34 186	23.04 190	58.66 168	61.79 152	71.74 144	67.38 138

our_test_3					51.15 157	57.31 158	55.12 145

SCA		50.99 146	53.22 156	48.40 139	51.07 158	56.78 160	50.25 162	39.05 152	48.31 117	41.38 100	49.54 104	46.70 148	46.00 115	58.31 169	56.28 169	62.65 177	56.60 186

UniMVSNet_ETH3D		52.62 132	55.98 136	48.70 135	51.04 159	60.71 129	56.87 129	46.74 70	42.52 162	26.96 164	42.50 164	45.95 155	37.87 153	66.22 127	65.15 126	72.74 129	68.78 133

IterMVS-SCA-FT		52.18 137	57.75 128	45.68 156	51.01 160	62.06 115	55.10 146	34.75 175	44.85 139	32.86 136	51.13 100	51.22 118	48.74 98	62.47 149	61.51 153	51.61 201	71.02 112

FMVSNet1		54.08 126	58.68 118	48.71 134	50.90 161	61.35 122	56.73 130	43.94 100	45.91 134	29.32 154	42.72 162	56.26 102	37.70 154	68.05 97	66.96 91	73.69 115	69.50 125

pmmvs-eth3d		51.33 143	52.25 162	50.26 120	50.82 162	54.65 164	56.03 135	43.45 117	43.51 151	37.20 125	39.20 172	39.04 190	42.28 134	61.85 153	62.78 146	71.78 143	64.72 158

MVSTER		57.19 100	61.11 88	52.62 109	50.82 162	58.79 144	61.55 101	37.86 163	48.81 110	41.31 101	57.43 71	52.10 115	48.60 101	68.19 93	66.75 96	75.56 97	75.68 90

ambc			45.54 191		50.66 164	52.63 172	40.99 196		38.36 185	24.67 171	22.62 203	13.94 212	29.14 180	65.71 134	58.06 167	58.60 187	67.43 137

thisisatest0515		53.85 127	56.84 134	50.37 119	50.25 165	58.17 152	55.99 136	39.90 150	41.88 165	38.16 119	45.91 133	45.30 159	44.58 122	66.15 129	66.89 94	73.36 121	73.57 104

baseline		55.19 120	60.88 89	48.55 137	49.87 166	58.10 153	58.70 118	34.75 175	52.82 84	39.48 115	60.18 58	60.86 78	45.41 118	61.05 155	60.74 158	63.10 175	72.41 106

MDTV_nov1_ep13		50.32 150	52.43 161	47.86 147	49.87 166	54.70 163	58.10 120	34.29 179	45.59 137	37.71 121	47.44 118	47.42 139	41.86 136	58.07 172	55.21 178	65.34 169	58.56 182

IterMVS		53.45 129	57.12 132	49.17 127	49.23 168	60.93 128	59.05 117	34.63 177	44.53 141	33.22 133	51.09 101	51.01 121	48.38 102	62.43 150	60.79 157	70.54 151	69.05 131
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
COLMAP_ROB	46.52 15	51.99 140	54.86 144	48.63 136	49.13 169	61.73 118	60.53 109	36.57 168	53.14 79	32.95 135	37.10 175	38.68 191	40.49 141	65.72 133	63.08 142	72.11 141	64.60 159
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CR-MVSNet		50.47 147	52.61 158	47.98 145	49.03 170	52.94 169	48.27 169	38.86 155	44.41 142	39.59 112	44.34 145	44.65 170	46.63 112	58.97 164	60.31 159	65.48 167	62.66 166

V42		56.97 103	60.14 101	53.28 101	48.16 171	62.78 114	66.30 74	37.93 162	47.44 123	42.68 93	48.19 114	52.59 114	51.90 87	67.46 108	65.94 112	72.72 130	76.55 81

MDTV_nov1_ep13_2view		47.62 169	49.72 178	45.18 159	48.05 172	53.70 167	54.90 147	33.80 183	39.90 177	29.79 150	38.85 173	41.89 178	39.17 145	58.99 163	55.55 175	65.34 169	59.17 180

EPMVS		44.66 180	47.86 184	40.92 178	47.97 173	44.70 197	47.58 174	33.27 186	48.11 119	29.58 152	49.65 103	44.38 172	34.65 166	51.71 194	47.90 198	52.49 199	48.57 200

RPMNet		46.41 174	48.72 180	43.72 165	47.77 174	52.94 169	46.02 182	33.92 181	44.41 142	31.82 141	36.89 176	37.42 196	37.41 155	53.88 192	54.02 185	65.37 168	61.47 172

TAPA-MVS	54.74 10	60.85 72	66.61 60	54.12 96	47.38 175	65.33 95	65.35 85	36.51 169	75.16 31	48.82 69	54.70 83	63.51 68	53.31 77	68.36 87	64.97 127	73.37 120	74.27 97
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SixPastTwentyTwo		47.55 170	50.25 175	44.41 164	47.30 176	54.31 166	47.81 172	40.36 148	33.76 194	19.93 181	43.75 150	32.77 202	42.07 135	59.82 160	60.94 156	68.98 154	66.37 144

TAMVS		44.02 182	49.18 179	37.99 187	47.03 177	45.97 194	45.04 186	28.47 196	39.11 181	20.23 180	43.22 158	48.52 128	28.49 182	58.15 171	57.95 168	58.71 185	51.36 192

UGNet		57.03 101	65.25 71	47.44 149	46.54 178	66.73 84	56.30 132	43.28 119	50.06 94	32.99 134	62.57 51	63.26 69	33.31 172	68.25 89	67.58 86	72.20 140	78.29 65
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
TSAR-MVS + COLMAP		62.65 65	69.90 53	54.19 94	46.31 179	66.73 84	65.49 84	41.36 136	76.57 25	46.31 75	76.80 17	56.68 97	53.27 78	69.50 75	66.65 99	72.40 137	76.36 84

PatchMatch-RL		50.11 152	51.56 166	48.43 138	46.23 180	51.94 173	50.21 163	38.62 159	46.62 129	37.51 122	42.43 165	39.38 188	52.24 85	60.98 156	59.56 162	65.76 166	60.01 179

CMPMVS	37.70 17	49.24 156	52.71 157	45.19 158	45.97 181	51.23 176	47.44 175	29.31 193	43.04 154	44.69 83	34.45 184	48.35 129	43.64 125	62.59 147	59.82 161	60.08 183	69.48 126
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs5		47.07 172	51.02 171	42.46 171	45.18 182	51.47 175	48.23 171	33.09 187	38.17 187	28.62 157	46.60 124	43.48 175	30.74 175	58.28 170	58.63 165	68.92 155	60.48 175

USDC		51.11 144	53.71 149	48.08 144	44.76 183	55.99 162	53.01 157	40.90 139	52.49 85	36.14 127	44.67 144	33.66 200	43.27 130	63.23 144	61.10 155	70.39 152	64.82 157

FC-MVSNet-test		39.65 193	48.35 182	29.49 198	44.43 184	39.28 204	30.23 207	40.44 146	43.59 149	3.12 212	53.00 88	42.03 177	10.02 209	55.09 188	54.77 180	48.66 202	50.71 194

ADS-MVSNet		40.67 190	43.38 195	37.50 188	44.36 185	39.79 203	42.09 195	32.67 190	44.34 144	28.87 156	40.76 170	40.37 185	30.22 176	48.34 203	45.87 202	46.81 204	44.21 204

new-patchmatchnet		33.24 200	37.20 201	28.62 200	44.32 186	38.26 205	29.68 208	36.05 170	31.97 199	6.33 208	26.59 199	27.33 205	11.12 208	50.08 200	41.05 204	44.23 205	45.15 203

MVS-HIRNet		42.24 186	41.15 198	43.51 166	44.06 187	40.74 200	35.77 203	35.35 172	35.38 192	38.34 117	25.63 200	38.55 192	43.48 127	50.77 196	47.03 200	64.07 171	49.98 196

PatchT		48.08 165	51.03 170	44.64 162	42.96 188	50.12 179	40.36 197	35.09 173	43.17 153	39.59 112	42.00 166	39.96 187	46.63 112	58.97 164	60.31 159	63.21 174	62.66 166

CVMVSNet		46.38 176	52.01 164	39.81 181	42.40 189	50.26 178	46.15 180	37.68 164	40.03 176	15.09 189	46.56 125	47.56 137	33.72 171	56.50 181	55.65 174	63.80 173	67.53 136

TinyColmap		47.08 171	47.56 185	46.52 152	42.35 190	53.44 168	51.77 159	40.70 142	43.44 152	31.92 140	29.78 193	23.72 209	45.04 121	61.99 152	59.54 163	67.35 161	61.03 173

ET-MVSNet_ETH3D		58.38 90	61.57 85	54.67 92	42.15 191	65.26 97	65.70 80	43.82 101	48.84 108	42.34 95	59.76 60	47.76 134	56.68 53	67.02 117	68.60 75	77.33 74	73.73 103

MIMVSNet		43.79 183	48.53 181	38.27 185	41.46 192	48.97 183	50.81 161	32.88 189	44.55 140	22.07 175	32.05 186	47.15 141	24.76 187	58.73 166	56.09 172	57.63 190	52.14 190

N_pmnet		32.67 201	36.85 202	27.79 201	40.55 193	32.13 206	35.80 202	26.79 199	37.24 190	9.10 201	32.02 187	30.94 203	16.30 198	47.22 204	41.21 203	38.21 207	37.21 205

anonymousdsp		52.84 131	57.78 127	47.06 150	40.24 194	58.95 143	53.70 152	33.54 185	36.51 191	32.69 137	43.88 148	45.40 157	47.97 107	67.17 112	70.28 59	74.22 109	82.29 48

EU-MVSNet		40.63 191	45.65 190	34.78 194	39.11 195	46.94 191	40.02 198	34.03 180	33.50 195	10.37 198	35.57 180	37.80 194	23.65 188	51.90 193	50.21 195	61.49 181	63.62 164

FPMVS		38.36 195	40.41 199	35.97 190	38.92 196	39.85 202	45.50 184	25.79 202	41.13 169	18.70 183	30.10 191	24.56 207	31.86 174	49.42 201	46.80 201	55.04 192	51.03 193

TESTMET0.1,1		46.09 177	50.29 173	41.18 177	36.91 197	47.16 188	49.52 164	20.32 206	39.22 179	31.98 138	43.65 152	47.93 132	41.29 139	56.80 177	55.36 176	67.08 163	61.94 170

FMVSNet5		40.96 188	45.81 189	35.29 193	34.30 198	44.55 198	47.28 176	28.84 195	40.76 171	21.62 176	29.85 192	42.44 176	24.77 186	57.53 174	55.00 179	54.93 193	50.56 195

PMMVS		49.20 158	54.28 148	43.28 169	34.13 199	45.70 195	48.98 167	26.09 201	46.31 131	34.92 132	55.22 79	53.47 110	47.48 109	59.43 161	59.04 164	68.05 159	60.77 174

PMVS	27.84 18	33.81 199	35.28 203	32.09 196	34.13 199	24.81 209	32.51 206	26.48 200	26.41 204	19.37 182	23.76 201	24.02 208	25.18 185	50.78 195	47.24 199	54.89 195	49.95 197
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test-mter		45.30 178	50.37 172	39.38 182	33.65 201	46.99 190	47.59 173	18.59 207	38.75 182	28.00 158	43.28 157	46.82 147	41.50 138	57.28 175	55.78 173	66.93 165	63.70 163

CHOSEN 280x420		40.80 189	45.05 192	35.84 192	32.95 202	29.57 207	44.98 187	23.71 204	37.54 189	18.42 184	31.36 189	47.07 142	46.41 114	56.71 179	54.65 183	48.55 203	58.47 183

PM-MVS		44.55 181	48.13 183	40.37 180	32.85 203	46.82 192	46.11 181	29.28 194	40.48 173	29.99 149	39.98 171	34.39 199	41.80 137	56.08 184	53.88 189	62.19 180	65.31 153

TDRefinement		49.31 154	52.44 160	45.67 157	30.44 204	59.42 138	59.24 115	39.78 151	48.76 111	31.20 143	35.73 179	29.90 204	42.81 133	64.24 143	62.59 150	70.55 150	66.43 142

Gipuma		25.87 202	26.91 205	24.66 202	28.98 205	20.17 210	20.46 209	34.62 178	29.55 202	9.10 201	4.91 212	5.31 216	15.76 199	49.37 202	49.10 197	39.03 206	29.95 207
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet-bldmvs		41.36 187	43.15 196	39.27 183	28.74 206	52.68 171	44.95 188	40.84 140	32.89 196	18.13 185	31.61 188	22.09 210	38.97 148	50.45 198	56.11 171	64.01 172	56.23 187

E-PMN		15.09 205	13.19 208	17.30 204	27.80 207	12.62 213	7.81 213	27.54 197	14.62 211	3.19 210	6.89 209	2.52 219	15.09 200	15.93 208	20.22 208	22.38 209	19.53 210

MIMVSNet1		35.51 197	41.41 197	28.63 199	27.53 208	43.36 199	38.09 200	33.82 182	32.01 198	6.77 207	21.63 204	35.43 197	11.97 204	55.05 189	53.99 187	53.59 198	48.36 201

EMVS		14.49 206	12.45 209	16.87 206	27.02 209	12.56 214	8.13 212	27.19 198	15.05 210	3.14 211	6.69 210	2.67 218	15.08 201	14.60 210	18.05 209	20.67 210	17.56 212

pmmvs3		35.10 198	38.47 200	31.17 197	26.37 210	40.47 201	34.51 205	18.09 208	24.75 205	16.88 187	23.05 202	26.69 206	32.69 173	50.73 197	51.60 192	58.46 188	51.98 191

RPSCF		46.41 174	54.42 146	37.06 189	25.70 211	45.14 196	45.39 185	20.81 205	62.79 59	35.10 129	44.92 143	55.60 105	43.56 126	56.12 183	52.45 191	51.80 200	63.91 162

new_pmnet		23.19 203	28.17 204	17.37 203	17.03 212	24.92 208	19.66 210	16.16 210	27.05 203	4.42 209	20.77 205	19.20 211	12.19 203	37.71 205	36.38 205	34.77 208	31.17 206

PMMVS2		15.84 204	19.68 206	11.35 207	15.74 213	16.95 211	13.31 211	17.64 209	16.08 209	0.36 215	13.12 206	11.47 213	1.69 211	28.82 206	27.24 207	19.38 211	24.09 209

MVE	12.28 19	13.53 207	15.72 207	10.96 208	7.39 214	15.71 212	6.05 214	23.73 203	10.29 213	3.01 213	5.77 211	3.41 217	11.91 205	20.11 207	29.79 206	13.67 212	24.98 208
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				5.40 209	3.97 215	2.35 216	3.26 216	0.44 212	17.56 208	12.09 193	11.48 208	7.14 214	1.98 210	15.68 209	15.49 210	10.69 213

GG-mvs-BLEND		36.62 196	53.39 153	17.06 205	0.01 216	58.61 145	48.63 168	0.01 213	47.13 125	0.02 216	43.98 147	60.64 81	0.03 212	54.92 190	51.47 193	53.64 197	56.99 185

uanet_test		0.00 210	0.00 212	0.00 210	0.00 217	0.00 217	0.00 219	0.00 214	0.00 216	0.00 217	0.00 215	0.00 220	0.00 215	0.00 213	0.00 213	0.00 215	0.00 215

sosnet-low-res		0.00 210	0.00 212	0.00 210	0.00 217	0.00 217	0.00 219	0.00 214	0.00 216	0.00 217	0.00 215	0.00 220	0.00 215	0.00 213	0.00 213	0.00 215	0.00 215

sosnet		0.00 210	0.00 212	0.00 210	0.00 217	0.00 217	0.00 219	0.00 214	0.00 216	0.00 217	0.00 215	0.00 220	0.00 215	0.00 213	0.00 213	0.00 215	0.00 215

testmvs		0.01 208	0.02 210	0.00 210	0.00 217	0.00 217	0.01 218	0.00 214	0.01 214	0.00 217	0.03 214	0.00 220	0.01 213	0.01 212	0.01 211	0.00 215	0.06 214

test123		0.01 208	0.02 210	0.00 210	0.00 217	0.00 217	0.00 219	0.00 214	0.01 214	0.00 217	0.04 213	0.00 220	0.01 213	0.00 213	0.01 211	0.00 215	0.07 213

9.14												81.81 12

test_part1																	90.62 3

MTAPA										65.14 3		80.20 19

MTMP										62.63 17		78.04 26

Patchmatch-RL test							1.04 217

NP-MVS									72.00 40

Patchmtry						47.61 186	48.27 169	38.86 155		39.59 112

DeepMVS_CX						6.95 215	5.98 215	2.25 211	11.73 212	2.07 214	11.85 207	5.43 215	11.75 206	11.40 211		8.10 214	18.38 211

High-resolution multi-view benchmark