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		77.58 1	82.93 1	71.35 1	77.86 1	80.55 1	83.38 1	57.61 5	85.57 1	61.11 13	86.10 3	82.98 2	64.76 2	78.29 10	76.78 17	83.40 5	90.20 1

HSP-MVS		76.78 2	82.44 2	70.19 6	75.26 9	80.22 2	80.59 6	57.85 4	84.79 3	60.84 14	88.54 1	83.43 1	66.24 1	78.21 13	76.47 19	80.34 36	85.43 25

ACMMP_Plus		76.15 3	81.17 3	70.30 4	74.09 13	79.47 4	81.59 4	57.09 8	81.38 5	63.89 5	79.02 8	80.48 11	62.24 12	80.05 3	79.12 3	82.94 9	88.64 3

HPM-MVS++		76.01 4	80.47 6	70.81 2	76.60 3	74.96 29	80.18 10	58.36 2	81.96 4	63.50 6	78.80 9	82.53 5	64.40 3	78.74 6	78.84 4	81.81 25	87.46 11

APD-MVS		75.80 5	80.90 5	69.86 10	75.42 8	78.48 10	81.43 5	57.44 6	80.45 9	59.32 20	85.28 4	80.82 10	63.96 4	76.89 23	76.08 22	81.58 31	88.30 6
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNVR-MVS		75.62 6	79.91 8	70.61 3	75.76 5	78.82 8	81.66 3	57.12 7	79.77 11	63.04 7	70.69 18	81.15 8	62.99 6	80.23 2	79.54 2	83.11 6	89.16 2

SteuartSystems-ACMMP		75.23 7	79.60 9	70.13 7	76.81 2	78.92 6	81.74 2	57.99 3	75.30 23	59.83 19	75.69 12	78.45 17	60.48 23	80.58 1	79.77 1	83.94 3	88.52 4
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + MP.		75.22 8	80.06 7	69.56 11	74.61 11	72.74 43	80.59 6	55.70 18	80.80 7	62.65 9	86.25 2	82.92 3	62.07 14	76.89 23	75.66 25	81.77 27	85.19 27

HFP-MVS		74.87 9	78.86 14	70.21 5	73.99 14	77.91 12	80.36 9	56.63 10	78.41 14	64.27 3	74.54 14	77.75 21	62.96 7	78.70 7	77.82 8	83.02 7	86.91 14

CSCG		74.68 10	79.22 10	69.40 12	75.69 7	80.01 3	79.12 17	52.83 34	79.34 12	63.99 4	70.49 19	82.02 6	60.35 25	77.48 20	77.22 14	84.38 1	87.97 9

SD-MVS		74.43 11	78.87 12	69.26 14	74.39 12	73.70 39	79.06 18	55.24 20	81.04 6	62.71 8	80.18 7	82.61 4	61.70 16	75.43 34	73.92 37	82.44 16	85.22 26

MP-MVS		74.31 12	78.87 12	68.99 15	73.49 16	78.56 9	79.25 16	56.51 11	75.33 21	60.69 16	75.30 13	79.12 16	61.81 15	77.78 17	77.93 7	82.18 21	88.06 8

NCCC		74.27 13	77.83 19	70.13 7	75.70 6	77.41 16	80.51 8	57.09 8	78.25 15	62.28 11	65.54 31	78.26 18	62.18 13	79.13 4	78.51 5	83.01 8	87.68 10

MPTG		74.25 14	77.97 18	69.91 9	73.43 17	74.06 37	79.69 12	56.44 12	80.74 8	64.98 2	68.72 24	79.98 13	62.92 8	78.24 12	77.77 10	81.99 23	86.30 16

DeepPCF-MVS	66.49 1	74.25 14	80.97 4	66.41 25	67.75 44	78.87 7	75.61 32	54.16 26	84.86 2	58.22 26	77.94 10	81.01 9	62.52 10	78.34 8	77.38 11	80.16 39	88.40 5

train_agg		73.89 16	78.25 16	68.80 17	75.25 10	72.27 45	79.75 11	56.05 15	74.87 26	58.97 21	81.83 6	79.76 14	61.05 20	77.39 21	76.01 23	81.71 28	85.61 23

DeepC-MVS	66.32 2	73.85 17	78.10 17	68.90 16	67.92 42	79.31 5	78.16 22	59.28 1	78.24 16	61.13 12	67.36 30	76.10 25	63.40 5	79.11 5	78.41 6	83.52 4	88.16 7
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMPR		73.79 18	78.41 15	68.40 18	72.35 21	77.79 13	79.32 14	56.38 13	77.67 18	58.30 25	74.16 15	76.66 22	61.40 17	78.32 9	77.80 9	82.68 13	86.51 15

MCST-MVS		73.67 19	77.39 20	69.33 13	76.26 4	78.19 11	78.77 19	54.54 23	75.33 21	59.99 18	67.96 26	79.23 15	62.43 11	78.00 14	75.71 24	84.02 2	87.30 12

PGM-MVS		72.89 20	77.13 21	67.94 19	72.47 20	77.25 17	79.27 15	54.63 22	73.71 28	57.95 27	72.38 16	75.33 27	60.75 21	78.25 11	77.36 13	82.57 15	85.62 22

CP-MVS		72.63 21	76.95 22	67.59 20	70.67 28	75.53 27	77.95 24	56.01 16	75.65 20	58.82 22	69.16 23	76.48 23	60.46 24	77.66 18	77.20 15	81.65 29	86.97 13

TSAR-MVS + ACMM		72.56 22	79.07 11	64.96 34	73.24 18	73.16 42	78.50 20	48.80 57	79.34 12	55.32 34	85.04 5	81.49 7	58.57 31	75.06 37	73.75 38	75.35 101	85.61 23

DeepC-MVS_fast	65.08 3	72.00 23	76.11 23	67.21 22	68.93 38	77.46 14	76.54 28	54.35 24	74.92 25	58.64 24	65.18 32	74.04 35	62.62 9	77.92 15	77.02 16	82.16 22	86.21 17
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP		71.57 24	75.84 24	66.59 24	70.30 32	76.85 22	78.46 21	53.95 27	73.52 29	55.56 32	70.13 20	71.36 40	58.55 32	77.00 22	76.23 21	82.71 12	85.81 21
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
CDPH-MVS		71.47 25	75.82 25	66.41 25	72.97 19	77.15 18	78.14 23	54.71 21	69.88 41	53.07 49	70.98 17	74.83 29	56.95 42	76.22 27	76.57 18	82.62 14	85.09 28

X-MVS		71.18 26	75.66 26	65.96 29	71.71 23	76.96 19	77.26 26	55.88 17	72.75 31	54.48 42	64.39 35	74.47 30	54.19 54	77.84 16	77.37 12	82.21 19	85.85 20

HQP-MVS		70.88 27	75.02 27	66.05 28	71.69 24	74.47 34	77.51 25	53.17 31	72.89 30	54.88 38	70.03 21	70.48 42	57.26 38	76.02 29	75.01 29	81.78 26	86.21 17

TSAR-MVS + GP.		69.71 28	73.92 30	64.80 36	68.27 40	70.56 50	71.90 44	50.75 44	71.38 35	57.46 29	68.68 25	75.42 26	60.10 26	73.47 42	73.99 36	80.32 37	83.97 32

3Dnovator+	62.63 4	69.51 29	72.62 33	65.88 30	68.21 41	76.47 23	73.50 42	52.74 35	70.85 37	58.65 23	55.97 62	69.95 43	61.11 19	76.80 25	75.09 26	81.09 34	83.23 38

MVS_0304		69.49 30	73.96 29	64.28 39	67.92 42	76.13 25	74.90 35	47.60 59	63.29 51	54.09 46	67.44 29	76.35 24	59.53 28	75.81 31	75.03 27	81.62 30	83.70 35

OPM-MVS		69.33 31	71.05 39	67.32 21	72.34 22	75.70 26	79.57 13	56.34 14	55.21 62	53.81 47	59.51 51	68.96 45	59.67 27	77.61 19	76.44 20	82.19 20	83.88 34

PHI-MVS		69.27 32	74.84 28	62.76 44	66.83 46	74.83 30	73.88 40	49.32 53	70.61 38	50.93 53	69.62 22	74.84 28	57.25 39	75.53 33	74.32 34	78.35 53	84.17 31

LGP-MVS_train		68.87 33	72.03 35	65.18 33	69.33 36	74.03 38	76.67 27	53.88 28	68.46 42	52.05 52	63.21 37	63.89 57	56.31 44	75.99 30	74.43 33	82.83 11	84.18 30

CANet		68.77 34	73.01 31	63.83 40	68.30 39	75.19 28	73.73 41	47.90 58	63.86 48	54.84 39	67.51 28	74.36 33	57.62 35	74.22 40	73.57 41	80.56 35	82.36 39

CPTT-MVS		68.76 35	73.01 31	63.81 41	65.42 53	73.66 40	76.39 30	52.08 36	72.61 32	50.33 55	60.73 48	72.65 38	59.43 29	73.32 43	72.12 43	79.19 47	85.99 19

ACMP	61.42 5	68.72 36	71.37 37	65.64 31	69.06 37	74.45 35	75.88 31	53.30 30	68.10 43	55.74 31	61.53 47	62.29 62	56.97 41	74.70 38	74.23 35	82.88 10	84.31 29
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MSLP-MVS++		68.17 37	70.72 42	65.19 32	69.41 35	70.64 49	74.99 34	45.76 65	70.20 40	60.17 17	56.42 60	73.01 36	61.14 18	72.80 45	70.54 49	79.70 41	81.42 44

MAR-MVS		68.04 38	70.74 41	64.90 35	71.68 25	76.33 24	74.63 37	50.48 48	63.81 49	55.52 33	54.88 67	69.90 44	57.39 37	75.42 35	74.79 31	79.71 40	80.03 48
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
AdaColmap		67.89 39	68.85 49	66.77 23	73.73 15	74.30 36	75.28 33	53.58 29	70.24 39	57.59 28	51.19 81	59.19 74	60.74 22	75.33 36	73.72 39	79.69 43	77.96 58

MVS_111021_HR		67.62 40	70.39 43	64.39 37	69.77 34	70.45 51	71.44 47	51.72 40	60.77 56	55.06 36	62.14 44	66.40 54	58.13 34	76.13 28	74.79 31	80.19 38	82.04 42

ACMM	60.30 7	67.58 41	68.82 50	66.13 27	70.59 29	72.01 47	76.54 28	54.26 25	65.64 47	54.78 40	50.35 83	61.72 65	58.74 30	75.79 32	75.03 27	81.88 24	81.17 45
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PCF-MVS	59.98 8	67.32 42	71.04 40	62.97 43	64.77 55	74.49 33	74.78 36	49.54 51	67.44 44	54.39 45	58.35 55	72.81 37	55.79 50	71.54 50	69.24 57	78.57 49	83.41 36
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		67.02 43	71.57 36	61.71 45	71.01 27	74.81 31	71.62 45	38.91 161	71.86 34	60.70 15	64.97 33	67.88 51	51.88 92	76.77 26	74.98 30	76.11 93	69.75 118
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
3Dnovator	60.86 6	66.99 44	70.32 44	63.11 42	66.63 47	74.52 32	71.56 46	45.76 65	67.37 45	55.00 37	54.31 71	68.19 49	58.49 33	73.97 41	73.63 40	81.22 33	80.23 47

DELS-MVS		65.87 45	70.30 45	60.71 46	64.05 62	72.68 44	70.90 48	45.43 69	57.49 59	49.05 59	64.43 34	68.66 46	55.11 52	74.31 39	73.02 42	79.70 41	81.51 43
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
canonicalmvs		65.62 46	72.06 34	58.11 55	63.94 63	71.05 48	64.49 96	43.18 111	74.08 27	47.35 62	64.17 36	71.97 39	51.17 95	71.87 48	70.74 47	78.51 51	80.56 46

QAPM		65.27 47	69.49 48	60.35 47	65.43 52	72.20 46	65.69 87	47.23 60	63.46 50	49.14 58	53.56 72	71.04 41	57.01 40	72.60 46	71.41 46	77.62 57	82.14 41

OMC-MVS		65.16 48	71.35 38	57.94 59	52.95 154	68.82 55	69.00 49	38.28 168	79.89 10	55.20 35	62.76 40	68.31 48	56.14 47	71.30 52	68.70 62	76.06 95	79.67 49

EPNet		65.14 49	69.54 47	60.00 49	66.61 48	67.67 64	67.53 53	55.32 19	62.67 53	46.22 69	67.74 27	65.93 55	48.07 108	72.17 47	72.12 43	76.28 85	78.47 55
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		63.65 50	66.92 51	59.83 50	60.03 73	73.44 41	66.33 78	48.95 55	52.20 76	50.81 54	56.07 61	60.25 70	53.56 59	73.23 44	70.01 54	79.30 45	83.24 37

Effi-MVS+		63.28 51	65.96 56	60.17 48	64.26 59	68.06 59	68.78 50	45.71 67	54.08 66	46.64 65	55.92 63	63.13 60	55.94 48	70.38 59	71.43 45	79.68 44	78.70 53

MVS_111021_LR		63.05 52	66.43 53	59.10 52	61.33 67	63.77 100	65.87 85	43.58 100	60.20 57	53.70 48	62.09 45	62.38 61	55.84 49	70.24 60	68.08 66	74.30 106	78.28 57

OpenMVS	57.13 9	62.81 53	65.75 57	59.39 51	66.47 49	69.52 53	64.26 98	43.07 116	61.34 55	50.19 56	47.29 117	64.41 56	54.60 53	70.18 61	68.62 64	77.73 55	78.89 52

CNLPA		62.78 54	66.31 54	58.65 53	58.47 81	68.41 58	65.98 84	41.22 146	78.02 17	56.04 30	46.65 120	59.50 73	57.50 36	69.67 63	65.27 120	72.70 131	76.67 67

TSAR-MVS + COLMAP		62.65 55	69.90 46	54.19 98	46.31 186	66.73 74	65.49 89	41.36 144	76.57 19	46.31 68	76.80 11	56.68 82	53.27 67	69.50 64	66.65 83	72.40 137	76.36 75

MVS_Test		62.40 56	66.23 55	57.94 59	59.77 76	64.77 96	66.50 77	41.76 136	57.26 60	49.33 57	62.68 41	67.47 53	53.50 62	68.57 72	66.25 90	76.77 70	76.58 70

DI_MVS_plusplus_trai		61.88 57	65.17 61	58.06 56	60.05 72	65.26 91	66.03 82	44.22 81	55.75 61	46.73 64	54.64 69	68.12 50	54.13 56	69.13 65	66.66 82	77.18 63	76.61 68

PVSNet_BlendedMVS		61.63 58	64.82 62	57.91 61	57.21 119	67.55 65	63.47 102	46.08 63	54.72 63	52.46 50	58.59 53	60.73 67	51.82 93	70.46 57	65.20 122	76.44 82	76.50 73

PVSNet_Blended		61.63 58	64.82 62	57.91 61	57.21 119	67.55 65	63.47 102	46.08 63	54.72 63	52.46 50	58.59 53	60.73 67	51.82 93	70.46 57	65.20 122	76.44 82	76.50 73

TAPA-MVS	54.74 10	60.85 60	66.61 52	54.12 99	47.38 182	65.33 89	65.35 90	36.51 179	75.16 24	48.82 60	54.70 68	63.51 58	53.31 66	68.36 73	64.97 125	73.37 119	74.27 103
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Fast-Effi-MVS+		60.36 61	63.35 68	56.87 81	58.70 78	65.86 87	65.08 91	37.11 174	53.00 73	45.36 78	52.12 77	56.07 87	56.27 45	71.28 53	69.42 56	78.71 48	75.69 79

Effi-MVS+-dtu		60.34 62	62.32 71	58.03 58	64.31 57	67.44 67	65.99 83	42.26 133	49.55 84	42.00 104	48.92 95	59.79 72	56.27 45	68.07 84	67.03 75	77.35 62	75.45 81

LS3D		60.20 63	61.70 72	58.45 54	64.18 60	67.77 61	67.19 55	48.84 56	61.67 54	41.27 107	45.89 131	51.81 113	54.18 55	68.78 67	66.50 88	75.03 102	69.48 124

diffmvs		59.53 64	64.04 67	54.26 97	55.09 134	59.86 137	64.80 93	39.55 160	58.39 58	46.21 70	60.48 49	67.82 52	49.27 100	63.53 146	63.32 142	70.64 152	74.89 85

EPP-MVSNet		59.39 65	65.45 59	52.32 112	60.96 69	67.70 63	58.42 120	44.75 76	49.71 83	27.23 164	59.03 52	62.20 63	43.34 130	70.71 56	69.13 58	79.25 46	79.63 50

ACMH+	53.71 12	59.26 66	60.28 84	58.06 56	64.17 61	68.46 57	67.51 54	50.93 43	52.46 75	35.83 128	40.83 174	45.12 155	52.32 88	69.88 62	69.00 60	77.59 59	76.21 76

PLC	52.09 14	59.21 67	62.47 70	55.41 94	53.24 153	64.84 95	64.47 97	40.41 154	65.92 46	44.53 91	46.19 128	55.69 88	55.33 51	68.24 77	65.30 119	74.50 104	71.09 110
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7		59.19 68	60.62 77	57.53 64	57.96 84	67.19 70	67.09 58	44.28 80	46.84 113	45.45 76	48.19 109	51.06 115	53.62 58	67.84 90	66.59 85	76.79 67	76.60 69

v10		59.17 69	60.60 78	57.50 65	57.95 85	66.73 74	67.09 58	44.11 82	46.85 112	45.42 77	48.18 111	51.07 114	53.63 57	67.84 90	66.59 85	76.79 67	76.92 65

v6		58.89 70	60.54 80	56.96 70	57.34 109	66.13 83	66.71 69	42.84 118	47.85 107	45.80 72	49.04 89	52.95 98	52.79 72	67.53 100	65.59 111	76.26 86	74.73 87

CANet_DTU		58.88 71	64.68 64	52.12 113	55.77 127	66.75 73	63.92 99	37.04 175	53.32 69	37.45 122	59.81 50	61.81 64	44.43 124	68.25 75	67.47 73	74.12 109	75.33 82

v1144		58.88 71	60.16 91	57.39 66	58.03 83	67.26 68	67.14 57	44.46 79	45.17 135	44.33 92	47.81 114	49.92 124	53.20 68	67.77 95	66.62 84	77.15 64	76.58 70

v1neww		58.88 71	60.54 80	56.94 71	57.33 111	66.13 83	66.70 71	42.84 118	47.84 108	45.74 74	49.02 91	52.93 99	52.78 73	67.53 100	65.59 111	76.26 86	74.73 87

v7new		58.88 71	60.54 80	56.94 71	57.33 111	66.13 83	66.70 71	42.84 118	47.84 108	45.74 74	49.02 91	52.93 99	52.78 73	67.53 100	65.59 111	76.26 86	74.73 87

v8		58.88 71	60.57 79	56.92 75	57.35 107	65.69 88	66.69 73	42.64 126	47.89 106	45.77 73	49.04 89	52.98 97	52.77 75	67.51 103	65.57 115	76.26 86	75.30 83

v16		58.71 76	60.20 87	56.97 69	57.35 107	63.36 108	66.67 74	42.49 128	48.69 99	46.36 67	48.87 97	52.92 101	52.82 71	67.57 98	65.58 114	76.15 92	74.38 99

v17		58.69 77	60.19 90	56.94 71	57.38 102	63.37 107	66.67 74	42.47 130	48.52 103	46.10 71	48.90 96	53.00 96	52.84 69	67.58 97	65.60 110	76.19 90	74.38 99

v2v482		58.69 77	60.12 94	57.03 68	57.16 121	66.05 86	67.17 56	43.52 102	46.33 118	45.19 79	49.46 86	51.02 116	52.51 83	67.30 110	66.03 93	76.61 78	74.62 94

v18		58.68 79	60.20 87	56.90 78	57.26 117	63.28 109	66.58 76	42.42 131	48.86 93	46.37 66	49.01 93	53.05 95	52.74 76	67.40 108	65.52 116	76.02 97	74.28 102

v1141		58.56 80	60.05 96	56.81 84	57.36 104	66.18 81	66.80 66	43.11 113	45.87 129	44.60 88	48.71 99	51.83 111	52.38 85	67.46 104	65.64 108	76.63 75	74.66 90

divwei89l23v2f112		58.56 80	60.05 96	56.81 84	57.36 104	66.18 81	66.80 66	43.11 113	45.89 128	44.60 88	48.71 99	51.84 110	52.38 85	67.45 106	65.65 105	76.63 75	74.66 90

v1		58.56 80	60.06 95	56.83 83	57.36 104	66.19 80	66.80 66	43.10 115	45.87 129	44.68 86	48.73 98	51.83 111	52.38 85	67.45 106	65.65 105	76.63 75	74.66 90

v1192		58.51 83	59.66 104	57.17 67	57.82 86	67.72 62	66.21 81	44.83 75	44.15 142	43.49 95	46.68 119	47.94 128	53.55 60	67.39 109	66.51 87	77.13 65	77.20 63

UA-Net		58.50 84	64.68 64	51.30 115	66.97 45	67.13 71	53.68 152	45.65 68	49.51 86	31.58 142	62.91 38	68.47 47	35.85 162	68.20 78	67.28 74	74.03 110	69.24 128

Vis-MVSNet		58.48 85	65.70 58	50.06 122	53.40 152	67.20 69	60.24 116	43.32 108	48.83 94	30.23 148	62.38 43	61.61 66	40.35 141	71.03 55	69.77 55	72.82 127	79.11 51
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MSDG		58.46 86	58.97 114	57.85 63	66.27 51	66.23 79	67.72 51	42.33 132	53.43 68	43.68 94	43.39 151	45.35 152	49.75 98	68.66 70	67.77 69	77.38 61	67.96 131

V9		58.45 87	59.75 99	56.92 75	57.51 95	63.49 103	66.86 61	42.73 123	46.07 124	45.05 81	48.45 104	51.99 107	52.66 79	68.04 88	65.75 100	76.72 72	74.50 96

v13		58.44 88	59.72 103	56.94 71	57.55 89	63.51 101	66.86 61	42.81 121	45.90 127	44.98 83	48.17 112	51.87 109	52.68 77	68.20 78	65.78 98	76.78 69	74.63 93

v12		58.44 88	59.74 102	56.92 75	57.54 91	63.50 102	66.84 64	42.77 122	45.96 125	44.95 84	48.31 105	51.94 108	52.67 78	68.14 81	65.75 100	76.75 71	74.55 95

V14		58.44 88	59.75 99	56.90 78	57.48 97	63.46 104	66.85 63	42.68 124	46.16 121	45.03 82	48.57 102	52.04 106	52.65 80	67.93 89	65.72 103	76.69 73	74.40 98

v15		58.43 91	59.75 99	56.88 80	57.45 98	63.44 105	66.84 64	42.65 125	46.24 120	45.07 80	48.68 101	52.07 105	52.63 81	67.84 90	65.70 104	76.65 74	74.31 101

FC-MVSNet-train		58.40 92	63.15 69	52.85 108	64.29 58	61.84 117	55.98 137	46.47 61	53.06 71	34.96 131	61.95 46	56.37 85	39.49 143	68.67 69	68.36 65	75.92 98	71.81 107

IB-MVS	54.11 11	58.36 93	60.70 76	55.62 92	58.67 79	68.02 60	61.56 105	43.15 112	46.09 122	44.06 93	44.24 143	50.99 118	48.71 103	66.70 119	70.33 50	77.60 58	78.50 54
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
IterMVS-LS		58.30 94	61.39 73	54.71 96	59.92 75	58.40 154	59.42 117	43.64 97	48.71 97	40.25 111	57.53 58	58.55 76	52.15 90	65.42 139	65.34 118	72.85 125	75.77 77
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	52.42 13	58.24 95	59.56 107	56.70 86	66.34 50	69.59 52	66.71 69	49.12 54	46.08 123	28.90 155	42.67 163	41.20 183	52.60 82	71.39 51	70.28 51	76.51 80	75.72 78
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v144192		58.23 96	59.40 110	56.87 81	57.56 88	66.89 72	65.70 86	45.01 74	44.06 143	42.88 97	46.61 121	48.09 127	53.49 63	66.94 115	65.90 96	76.61 78	77.29 61

MS-PatchMatch		58.19 97	60.20 87	55.85 91	65.17 54	64.16 98	64.82 92	41.48 143	50.95 78	42.17 103	45.38 136	56.42 83	48.08 107	68.30 74	66.70 81	73.39 118	69.46 126

v11		58.19 97	59.47 108	56.70 86	57.54 91	63.42 106	66.28 80	42.49 128	45.62 133	44.59 90	48.16 113	50.78 119	52.84 69	67.80 94	65.76 99	76.49 81	74.76 86

IS_MVSNet		57.95 99	64.26 66	50.60 117	61.62 66	65.25 92	57.18 126	45.42 70	50.79 79	26.49 166	57.81 57	60.05 71	34.51 166	71.24 54	70.20 53	78.36 52	74.44 97

v1921920		57.89 100	59.02 113	56.58 88	57.55 89	66.66 77	64.72 95	44.70 77	43.55 146	42.73 99	46.17 129	46.93 142	53.51 61	66.78 118	65.75 100	76.29 84	77.28 62

v1240		57.55 101	58.63 116	56.29 90	57.30 116	66.48 78	63.77 100	44.56 78	42.77 159	42.48 101	45.64 134	46.28 147	53.46 64	66.32 125	65.80 97	76.16 91	77.13 64

MVSTER		57.19 102	61.11 75	52.62 110	50.82 170	58.79 150	61.55 106	37.86 171	48.81 95	41.31 106	57.43 59	52.10 104	48.60 104	68.19 80	66.75 80	75.56 99	75.68 80

UGNet		57.03 103	65.25 60	47.44 151	46.54 185	66.73 74	56.30 133	43.28 109	50.06 81	32.99 135	62.57 42	63.26 59	33.31 172	68.25 75	67.58 71	72.20 141	78.29 56
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
EG-PatchMatch MVS		56.98 104	58.24 120	55.50 93	64.66 56	68.62 56	61.48 107	43.63 99	38.44 192	41.44 105	38.05 184	46.18 149	43.95 125	71.71 49	70.61 48	77.87 54	74.08 104

V42		56.97 105	60.14 92	53.28 103	48.16 177	62.78 114	66.30 79	37.93 170	47.44 110	42.68 100	48.19 109	52.59 103	51.90 91	67.46 104	65.94 95	72.72 128	76.55 72

UniMVSNet_NR-MVSNet		56.94 106	61.14 74	52.05 114	60.02 74	65.21 93	57.44 124	52.93 33	49.37 87	24.31 175	54.62 70	50.54 120	39.04 145	68.69 68	68.84 61	78.53 50	70.72 111

HyFIR lowres test		56.87 107	58.60 117	54.84 95	56.62 124	69.27 54	64.77 94	42.21 134	45.66 132	37.50 121	33.08 193	57.47 81	53.33 65	65.46 138	67.94 67	74.60 103	71.35 109

tpmp4_e23		56.84 108	57.14 127	56.49 89	62.45 64	62.05 115	67.57 52	41.56 141	54.17 65	48.57 61	49.18 87	46.54 145	50.44 97	61.93 159	58.82 173	68.34 166	67.28 136

CostFormer		56.57 109	59.13 112	53.60 100	57.52 94	61.12 127	66.94 60	35.95 181	53.44 67	44.68 86	55.87 64	54.44 90	48.21 106	60.37 167	58.33 176	68.27 168	70.33 116

Fast-Effi-MVS+-dtu		56.30 110	59.29 111	52.82 109	58.64 80	64.89 94	65.56 88	32.89 199	45.80 131	35.04 130	45.89 131	54.14 91	49.41 99	67.16 112	66.45 89	75.37 100	70.69 113

TranMVSNet+NR-MVSNet		55.87 111	60.14 92	50.88 116	59.46 77	63.82 99	57.93 122	52.98 32	48.94 92	20.52 185	52.87 74	47.33 136	36.81 159	69.12 66	69.03 59	77.56 60	69.89 117

CHOSEN 1792x2688		55.85 112	58.01 121	53.33 102	57.26 117	62.82 113	63.29 104	41.55 142	46.65 115	38.34 116	34.55 191	53.50 92	52.43 84	67.10 113	67.56 72	67.13 173	73.92 105

v7n		55.67 113	57.46 126	53.59 101	56.06 125	65.29 90	61.06 110	43.26 110	40.17 179	37.99 118	40.79 175	45.27 154	47.09 112	67.67 96	66.21 91	76.08 94	76.82 66

GA-MVS		55.67 113	58.33 118	52.58 111	55.23 132	63.09 110	61.08 109	40.15 156	42.95 153	37.02 124	52.61 75	47.68 131	47.51 110	65.92 131	65.35 117	74.49 105	70.68 114

v148		55.58 115	57.61 125	53.20 105	54.59 143	61.86 116	61.18 108	38.70 166	44.30 141	42.25 102	47.53 115	50.24 123	48.73 102	65.15 140	62.61 152	73.79 112	71.61 108

DU-MVS		55.41 116	59.59 105	50.54 119	54.60 141	62.97 111	57.44 124	51.80 38	48.62 101	24.31 175	51.99 78	47.00 141	39.04 145	68.11 82	67.75 70	76.03 96	70.72 111

NR-MVSNet		55.35 117	59.46 109	50.56 118	61.33 67	62.97 111	57.91 123	51.80 38	48.62 101	20.59 184	51.99 78	44.73 162	34.10 169	68.58 71	68.64 63	77.66 56	70.67 115

GBi-Net		55.20 118	60.25 85	49.31 126	52.42 156	61.44 121	57.03 127	44.04 85	49.18 89	30.47 144	48.28 106	58.19 77	38.22 148	68.05 85	66.96 76	73.69 114	69.65 119

test1		55.20 118	60.25 85	49.31 126	52.42 156	61.44 121	57.03 127	44.04 85	49.18 89	30.47 144	48.28 106	58.19 77	38.22 148	68.05 85	66.96 76	73.69 114	69.65 119

UniMVSNet (Re)		55.15 120	60.39 83	49.03 132	55.31 129	64.59 97	55.77 138	50.63 45	48.66 100	20.95 183	51.47 80	50.40 121	34.41 168	67.81 93	67.89 68	77.11 66	71.88 106

FMVSNet2		55.04 121	59.95 98	49.31 126	52.42 156	61.44 121	57.03 127	44.08 84	49.55 84	30.40 147	46.89 118	58.84 75	38.22 148	67.07 114	66.21 91	73.69 114	69.65 119

FMVSNet3		54.78 122	59.58 106	49.17 129	52.37 159	61.31 125	56.72 131	44.04 85	49.18 89	30.47 144	48.28 106	58.19 77	38.09 151	65.48 137	65.20 122	73.31 120	69.45 127

pmmvs4		54.66 123	56.07 132	53.00 107	54.63 140	57.08 161	60.43 115	44.10 83	51.69 77	40.55 109	46.55 124	44.79 161	45.95 119	62.54 151	63.66 137	72.36 139	66.20 145

FMVSNet1		54.08 124	58.68 115	48.71 137	50.90 169	61.35 124	56.73 130	43.94 89	45.91 126	29.32 154	42.72 162	56.26 86	37.70 152	68.05 85	66.96 76	73.69 114	69.50 123

DWT-MVSNet_training		53.80 125	54.31 148	53.21 104	57.65 87	59.04 148	60.65 111	40.11 157	46.35 117	42.77 98	49.07 88	41.07 184	51.06 96	58.62 177	58.96 172	67.00 176	67.06 137

v52		53.60 126	56.74 130	49.93 123	45.54 189	61.64 119	60.65 111	36.99 176	38.75 188	36.32 126	39.64 179	47.13 138	47.05 113	66.89 116	65.65 105	73.04 123	77.48 59

V4		53.60 126	56.73 131	49.93 123	45.54 189	61.64 119	60.65 111	36.99 176	38.74 190	36.33 125	39.64 179	47.12 139	47.05 113	66.89 116	65.64 108	73.04 123	77.48 59

Baseline_NR-MVSNet		53.50 128	57.89 122	48.37 140	54.60 141	59.25 146	56.10 134	51.84 37	49.32 88	17.92 195	45.38 136	47.68 131	36.93 158	68.11 82	65.95 94	72.84 126	69.57 122

IterMVS		53.45 129	57.12 128	49.17 129	49.23 174	60.93 128	59.05 119	34.63 185	44.53 137	33.22 134	51.09 82	51.01 117	48.38 105	62.43 152	60.79 162	70.54 154	69.05 129
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm cat1		53.30 130	53.41 155	53.17 106	58.16 82	59.15 147	63.73 101	38.27 169	50.73 80	46.98 63	45.57 135	44.00 169	49.20 101	55.90 195	54.02 194	62.65 188	64.50 162

v748		52.93 131	55.29 139	50.19 121	51.90 163	61.31 125	56.54 132	40.05 158	39.12 186	34.82 133	39.93 178	43.83 170	43.66 126	64.26 144	63.32 142	74.15 108	75.28 84

anonymousdsp		52.84 132	57.78 123	47.06 152	40.24 205	58.95 149	53.70 151	33.54 194	36.51 199	32.69 137	43.88 145	45.40 151	47.97 109	67.17 111	70.28 51	74.22 107	82.29 40

tfpn200view9		52.53 133	55.51 134	49.06 131	57.31 113	60.24 130	55.42 142	43.77 91	42.85 156	27.81 159	43.00 158	45.06 157	37.32 154	66.38 121	64.54 127	72.71 129	66.54 139

conf200view11		52.51 134	55.51 134	49.01 133	57.31 113	60.24 130	55.42 142	43.77 91	42.85 156	27.51 161	43.00 158	45.06 157	37.32 154	66.38 121	64.54 127	72.71 129	66.54 139

CDS-MVSNet		52.42 135	57.06 129	47.02 153	53.92 150	58.30 156	55.50 140	46.47 61	42.52 162	29.38 153	49.50 85	52.85 102	28.49 187	66.70 119	66.89 79	68.34 166	62.63 171
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thres200		52.39 136	55.37 138	48.90 134	57.39 101	60.18 132	55.60 139	43.73 94	42.93 154	27.41 162	43.35 152	45.09 156	36.61 160	66.36 123	63.92 136	72.66 132	65.78 151

thres400		52.38 137	55.51 134	48.74 136	57.49 96	60.10 134	55.45 141	43.54 101	42.90 155	26.72 165	43.34 153	45.03 160	36.61 160	66.20 128	64.53 129	72.66 132	66.43 141

EPNet_dtu		52.05 138	58.26 119	44.81 164	54.10 148	50.09 188	52.01 160	40.82 151	53.03 72	27.41 162	54.90 66	57.96 80	26.72 192	62.97 148	62.70 151	67.78 170	66.19 146
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres100view900		52.04 139	54.81 145	48.80 135	57.31 113	59.33 142	55.30 144	42.92 117	42.85 156	27.81 159	43.00 158	45.06 157	36.99 157	64.74 142	63.51 139	72.47 136	65.21 156

COLMAP_ROB	46.52 15	51.99 140	54.86 144	48.63 138	49.13 175	61.73 118	60.53 114	36.57 178	53.14 70	32.95 136	37.10 185	38.68 195	40.49 140	65.72 134	63.08 145	72.11 142	64.60 161
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
view600		51.96 141	55.13 141	48.27 142	57.41 100	60.05 135	54.74 146	43.64 97	42.57 161	25.88 168	43.11 156	44.48 165	35.34 163	66.27 126	63.61 138	72.61 135	65.80 150

TransMVSNet (Re)		51.92 142	55.38 137	47.88 147	60.95 70	59.90 136	53.95 149	45.14 72	39.47 183	24.85 172	43.87 146	46.51 146	29.15 183	67.55 99	65.23 121	73.26 122	65.16 157

thres600view7		51.91 143	55.14 140	48.14 143	57.43 99	60.18 132	54.60 147	43.73 94	42.61 160	25.20 170	43.10 157	44.47 166	35.19 164	66.36 123	63.28 144	72.66 132	66.01 148

view800		51.55 144	54.89 143	47.66 150	57.37 103	59.77 139	53.62 153	43.72 96	42.22 163	24.94 171	42.80 161	43.81 171	33.94 170	66.09 129	64.38 131	72.39 138	65.14 158

pmmvs-eth3d		51.33 145	52.25 165	50.26 120	50.82 170	54.65 171	56.03 136	43.45 107	43.51 147	37.20 123	39.20 182	39.04 194	42.28 133	61.85 160	62.78 149	71.78 146	64.72 160

USDC		51.11 146	53.71 151	48.08 145	44.76 192	55.99 164	53.01 157	40.90 148	52.49 74	36.14 127	44.67 141	33.66 206	43.27 131	63.23 147	61.10 158	70.39 155	64.82 159

pm-mvs1		51.02 147	55.55 133	45.73 159	54.16 147	58.52 152	50.92 162	42.56 127	40.32 178	25.67 169	43.66 148	50.34 122	30.06 181	65.85 132	63.97 135	70.99 151	66.21 144

conf0.05thres1000		50.64 148	53.84 150	46.92 155	57.02 122	59.29 144	52.29 159	43.80 90	39.84 182	23.81 178	39.26 181	43.14 174	32.52 176	65.74 133	64.04 132	72.05 143	65.53 153

tfpn		50.58 149	53.65 153	47.00 154	57.34 109	59.31 143	52.41 158	43.76 93	41.81 167	23.86 177	42.49 164	37.80 198	32.63 175	65.68 136	64.02 134	71.99 144	64.41 163

CR-MVSNet		50.47 150	52.61 160	47.98 146	49.03 176	52.94 176	48.27 171	38.86 163	44.41 138	39.59 113	44.34 142	44.65 164	46.63 116	58.97 172	60.31 165	65.48 179	62.66 169

dps		50.42 151	51.20 177	49.51 125	55.88 126	56.07 163	53.73 150	38.89 162	43.66 144	40.36 110	45.66 133	37.63 200	45.23 121	59.05 170	56.18 179	62.94 187	60.16 180

Vis-MVSNet (Re-imp)		50.37 152	57.73 124	41.80 183	57.53 93	54.35 172	45.70 189	45.24 71	49.80 82	13.43 203	58.23 56	56.42 83	20.11 204	62.96 149	63.36 141	68.76 165	58.96 185

MDTV_nov1_ep13		50.32 153	52.43 163	47.86 148	49.87 173	54.70 170	58.10 121	34.29 187	45.59 134	37.71 119	47.44 116	47.42 135	41.86 135	58.07 180	55.21 187	65.34 181	58.56 186

tfpnnormal		50.16 154	52.19 166	47.78 149	56.86 123	58.37 155	54.15 148	44.01 88	38.35 194	25.94 167	36.10 187	37.89 197	34.50 167	65.93 130	63.42 140	71.26 149	65.28 155

PatchMatch-RL		50.11 155	51.56 170	48.43 139	46.23 187	51.94 181	50.21 164	38.62 167	46.62 116	37.51 120	42.43 165	39.38 192	52.24 89	60.98 163	59.56 169	65.76 178	60.01 182

PatchmatchNet		49.92 156	51.29 173	48.32 141	51.83 164	51.86 182	53.38 156	37.63 173	47.90 105	40.83 108	48.54 103	45.30 153	45.19 122	56.86 184	53.99 196	61.08 192	54.57 197
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TDRefinement		49.31 157	52.44 162	45.67 160	30.44 220	59.42 141	59.24 118	39.78 159	48.76 96	31.20 143	35.73 188	29.90 210	42.81 132	64.24 145	62.59 153	70.55 153	66.43 141

test-LLR		49.28 158	50.29 181	48.10 144	55.26 130	47.16 196	49.52 165	43.48 105	39.22 184	31.98 138	43.65 149	47.93 129	41.29 138	56.80 185	55.36 185	67.08 174	61.94 172

CMPMVS	37.70 17	49.24 159	52.71 159	45.19 161	45.97 188	51.23 184	47.44 177	29.31 206	43.04 152	44.69 85	34.45 192	48.35 126	43.64 127	62.59 150	59.82 168	60.08 193	69.48 124
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PEN-MVS		49.21 160	54.32 147	43.24 175	54.33 146	59.26 145	47.04 180	51.37 42	41.67 168	9.97 213	46.22 127	41.80 178	22.97 201	60.52 165	64.03 133	73.73 113	66.75 138

PMMVS		49.20 161	54.28 149	43.28 174	34.13 214	45.70 204	48.98 168	26.09 217	46.31 119	34.92 132	55.22 65	53.47 93	47.48 111	59.43 169	59.04 171	68.05 169	60.77 177

gg-mvs-nofinetune		49.07 162	52.56 161	45.00 163	61.99 65	59.78 138	53.55 155	41.63 137	31.62 209	12.08 205	29.56 202	53.28 94	29.57 182	66.27 126	64.49 130	71.19 150	62.92 168

tpm		48.82 163	51.27 174	45.96 158	54.10 148	47.35 195	56.05 135	30.23 204	46.70 114	43.21 96	52.54 76	47.55 134	37.28 156	54.11 200	50.50 205	54.90 206	60.12 181

WR-MVS		48.78 164	55.06 142	41.45 185	55.50 128	60.40 129	43.77 198	49.99 50	41.92 165	8.10 218	45.24 139	45.56 150	17.47 206	61.57 161	64.60 126	73.85 111	66.14 147

CP-MVSNet		48.37 165	53.53 154	42.34 180	51.35 167	58.01 157	46.56 181	50.54 46	41.62 169	10.61 209	46.53 125	40.68 188	23.18 198	58.71 175	61.83 154	71.81 145	67.36 135

pmmvs6		48.35 166	51.64 168	44.51 167	51.92 162	57.94 158	49.44 167	42.17 135	34.45 202	24.62 174	28.87 206	46.90 143	29.07 185	64.60 143	63.08 145	69.83 157	65.68 152

tfpn_ndepth		48.34 167	52.27 164	43.76 169	54.35 145	56.46 162	47.24 179	40.92 147	43.45 148	21.04 182	41.16 173	43.22 173	28.90 186	61.57 161	60.65 163	70.12 156	59.34 183

PS-CasMVS		48.18 168	53.25 158	42.27 181	51.26 168	57.94 158	46.51 182	50.52 47	41.30 172	10.56 211	45.35 138	40.34 190	23.04 200	58.66 176	61.79 155	71.74 147	67.38 134

thresconf0.02		48.17 169	51.22 176	44.60 166	55.14 133	55.73 165	48.95 169	41.35 145	43.43 150	21.23 181	42.03 169	37.25 202	31.19 178	62.33 155	60.61 164	69.76 158	57.17 190

PatchT		48.08 170	51.03 178	44.64 165	42.96 199	50.12 187	40.36 206	35.09 183	43.17 151	39.59 113	42.00 170	39.96 191	46.63 116	58.97 172	60.31 165	63.21 186	62.66 169

tpmrst		48.08 170	49.88 185	45.98 157	52.71 155	48.11 193	53.62 153	33.70 192	48.70 98	39.74 112	48.96 94	46.23 148	40.29 142	50.14 209	49.28 207	55.80 203	57.71 188

DTE-MVSNet		48.03 172	53.28 157	41.91 182	54.64 139	57.50 160	44.63 196	51.66 41	41.02 174	7.97 219	46.26 126	40.90 185	20.24 203	60.45 166	62.89 148	72.33 140	63.97 164

WR-MVS_H		47.65 173	53.67 152	40.63 188	51.45 165	59.74 140	44.71 195	49.37 52	40.69 176	7.61 220	46.04 130	44.34 168	17.32 207	57.79 181	61.18 156	73.30 121	65.86 149

MDTV_nov1_ep13_2view		47.62 174	49.72 186	45.18 162	48.05 178	53.70 174	54.90 145	33.80 191	39.90 181	29.79 151	38.85 183	41.89 177	39.17 144	58.99 171	55.55 184	65.34 181	59.17 184

tfpnview11		47.58 175	51.57 169	42.92 176	54.94 135	55.30 167	46.21 183	41.58 140	42.10 164	18.54 190	42.25 166	41.54 180	27.12 189	62.29 156	61.12 157	69.15 160	56.40 194

tfpn_n400		47.56 176	51.56 170	42.90 177	54.91 136	55.28 168	46.21 183	41.59 138	41.51 170	18.54 190	42.25 166	41.54 180	27.12 189	62.41 153	61.02 159	69.05 161	56.90 192

tfpnconf		47.56 176	51.56 170	42.90 177	54.91 136	55.28 168	46.21 183	41.59 138	41.51 170	18.54 190	42.25 166	41.54 180	27.12 189	62.41 153	61.02 159	69.05 161	56.90 192

SixPastTwentyTwo		47.55 178	50.25 183	44.41 168	47.30 183	54.31 173	47.81 174	40.36 155	33.76 203	19.93 187	43.75 147	32.77 208	42.07 134	59.82 168	60.94 161	68.98 163	66.37 143

TinyColmap		47.08 179	47.56 193	46.52 156	42.35 201	53.44 175	51.77 161	40.70 152	43.44 149	31.92 140	29.78 201	23.72 221	45.04 123	61.99 158	59.54 170	67.35 172	61.03 176

pmmvs5		47.07 180	51.02 179	42.46 179	45.18 191	51.47 183	48.23 173	33.09 198	38.17 195	28.62 157	46.60 122	43.48 172	30.74 179	58.28 178	58.63 175	68.92 164	60.48 178

LTVRE_ROB	44.17 16	47.06 181	50.15 184	43.44 172	51.39 166	58.42 153	42.90 200	43.51 103	22.27 224	14.85 201	41.94 171	34.57 204	45.43 120	62.28 157	62.77 150	62.56 189	68.83 130
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
tfpn1000		46.75 182	51.24 175	41.51 184	54.39 144	55.60 166	43.85 197	40.90 148	41.82 166	16.71 197	41.26 172	41.58 179	23.96 196	60.76 164	60.27 167	69.26 159	57.42 189

RPMNet		46.41 183	48.72 188	43.72 170	47.77 180	52.94 176	46.02 188	33.92 189	44.41 138	31.82 141	36.89 186	37.42 201	37.41 153	53.88 201	54.02 194	65.37 180	61.47 174

RPSCF		46.41 183	54.42 146	37.06 200	25.70 228	45.14 205	45.39 191	20.81 222	62.79 52	35.10 129	44.92 140	55.60 89	43.56 128	56.12 192	52.45 201	51.80 212	63.91 165

CVMVSNet		46.38 185	52.01 167	39.81 190	42.40 200	50.26 186	46.15 186	37.68 172	40.03 180	15.09 200	46.56 123	47.56 133	33.72 171	56.50 189	55.65 183	63.80 185	67.53 132

TESTMET0.1,1		46.09 186	50.29 181	41.18 186	36.91 210	47.16 196	49.52 165	20.32 223	39.22 184	31.98 138	43.65 149	47.93 129	41.29 138	56.80 185	55.36 185	67.08 174	61.94 172

test-mter		45.30 187	50.37 180	39.38 192	33.65 216	46.99 198	47.59 175	18.59 225	38.75 188	28.00 158	43.28 154	46.82 144	41.50 137	57.28 183	55.78 182	66.93 177	63.70 166

gm-plane-assit		44.74 188	45.95 195	43.33 173	60.88 71	46.79 201	36.97 210	32.24 203	24.15 220	11.79 206	29.26 205	32.97 207	46.64 115	65.09 141	62.95 147	71.45 148	60.42 179

EPMVS		44.66 189	47.86 192	40.92 187	47.97 179	44.70 206	47.58 176	33.27 195	48.11 104	29.58 152	49.65 84	44.38 167	34.65 165	51.71 204	47.90 211	52.49 211	48.57 211

PM-MVS		44.55 190	48.13 191	40.37 189	32.85 218	46.82 200	46.11 187	29.28 207	40.48 177	29.99 149	39.98 177	34.39 205	41.80 136	56.08 193	53.88 198	62.19 190	65.31 154

TAMVS		44.02 191	49.18 187	37.99 198	47.03 184	45.97 203	45.04 192	28.47 209	39.11 187	20.23 186	43.22 155	48.52 125	28.49 187	58.15 179	57.95 178	58.71 195	51.36 202

MIMVSNet		43.79 192	48.53 189	38.27 196	41.46 202	48.97 191	50.81 163	32.88 200	44.55 136	22.07 179	32.05 194	47.15 137	24.76 195	58.73 174	56.09 181	57.63 200	52.14 200

test0.0.03 1		43.15 193	46.95 194	38.72 195	55.26 130	50.56 185	42.48 201	43.48 105	38.16 196	15.11 199	35.07 190	44.69 163	16.47 209	55.95 194	54.34 193	59.54 194	49.87 209

Anonymous20231206		42.28 194	45.89 196	38.07 197	51.96 161	48.98 190	43.66 199	38.81 165	38.74 190	14.32 202	26.74 208	40.90 185	20.94 202	56.64 188	54.67 191	58.71 195	54.59 196

MVS-HIRNet		42.24 195	41.15 208	43.51 171	44.06 198	40.74 209	35.77 213	35.35 182	35.38 200	38.34 116	25.63 210	38.55 196	43.48 129	50.77 206	47.03 215	64.07 183	49.98 207

MDA-MVSNet-bldmvs		41.36 196	43.15 204	39.27 194	28.74 222	52.68 178	44.95 194	40.84 150	32.89 205	18.13 194	31.61 196	22.09 223	38.97 147	50.45 208	56.11 180	64.01 184	56.23 195

FMVSNet5		40.96 197	45.81 197	35.29 204	34.30 213	44.55 207	47.28 178	28.84 208	40.76 175	21.62 180	29.85 200	42.44 175	24.77 194	57.53 182	55.00 188	54.93 205	50.56 205

CHOSEN 280x420		40.80 198	45.05 200	35.84 203	32.95 217	29.57 224	44.98 193	23.71 220	37.54 197	18.42 193	31.36 197	47.07 140	46.41 118	56.71 187	54.65 192	48.55 217	58.47 187

LP		40.79 199	41.99 205	39.38 192	40.98 203	46.49 202	42.14 202	33.66 193	35.37 201	29.89 150	29.30 204	27.81 212	32.74 173	52.55 202	52.19 202	56.87 201	50.23 206

Anonymous20231211		40.75 200	41.57 206	39.80 191	54.71 138	52.32 180	41.42 204	45.09 73	24.45 219	6.80 221	14.58 223	23.43 222	23.08 199	56.20 191	58.74 174	67.68 171	61.31 175

ADS-MVSNet		40.67 201	43.38 203	37.50 199	44.36 194	39.79 212	42.09 203	32.67 201	44.34 140	28.87 156	40.76 176	40.37 189	30.22 180	48.34 219	45.87 217	46.81 220	44.21 215

EU-MVSNet		40.63 202	45.65 198	34.78 205	39.11 206	46.94 199	40.02 207	34.03 188	33.50 204	10.37 212	35.57 189	37.80 198	23.65 197	51.90 203	50.21 206	61.49 191	63.62 167

test20.03		40.38 203	44.20 201	35.92 202	53.73 151	49.05 189	38.54 208	43.49 104	32.55 206	9.54 214	27.88 207	39.12 193	12.24 220	56.28 190	54.69 190	57.96 199	49.83 210

FC-MVSNet-test		39.65 204	48.35 190	29.49 211	44.43 193	39.28 213	30.23 220	40.44 153	43.59 145	3.12 231	53.00 73	42.03 176	10.02 227	55.09 197	54.77 189	48.66 216	50.71 204

testgi		38.71 205	43.64 202	32.95 207	52.30 160	48.63 192	35.59 214	35.05 184	31.58 210	9.03 217	30.29 198	40.75 187	11.19 225	55.30 196	53.47 199	54.53 208	45.48 213

FPMVS		38.36 206	40.41 209	35.97 201	38.92 207	39.85 211	45.50 190	25.79 218	41.13 173	18.70 189	30.10 199	24.56 216	31.86 177	49.42 214	46.80 216	55.04 204	51.03 203

GG-mvs-BLEND		36.62 207	53.39 156	17.06 225	0.01 233	58.61 151	48.63 170	0.01 231	47.13 111	0.02 236	43.98 144	60.64 69	0.03 231	54.92 199	51.47 204	53.64 209	56.99 191

MIMVSNet1		35.51 208	41.41 207	28.63 213	27.53 224	43.36 208	38.09 209	33.82 190	32.01 207	6.77 222	21.63 218	35.43 203	11.97 222	55.05 198	53.99 196	53.59 210	48.36 212

pmmvs3		35.10 209	38.47 210	31.17 209	26.37 227	40.47 210	34.51 216	18.09 226	24.75 218	16.88 196	23.05 214	26.69 214	32.69 174	50.73 207	51.60 203	58.46 198	51.98 201

testpf		34.85 210	36.16 215	33.31 206	47.49 181	35.56 220	36.85 211	32.31 202	23.08 221	15.63 198	29.39 203	29.48 211	19.62 205	41.38 222	41.07 221	47.95 218	53.18 198

PMVS	27.84 18	33.81 211	35.28 216	32.09 208	34.13 214	24.81 227	32.51 217	26.48 216	26.41 217	19.37 188	23.76 213	24.02 220	25.18 193	50.78 205	47.24 214	54.89 207	49.95 208
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test2356		33.40 212	36.53 213	29.76 210	37.51 209	38.39 215	34.68 215	27.35 211	27.88 212	10.61 209	25.54 211	24.44 217	17.15 208	49.99 211	48.32 209	51.24 213	41.16 219

new-patchmatchnet		33.24 213	37.20 211	28.62 214	44.32 195	38.26 217	29.68 223	36.05 180	31.97 208	6.33 223	26.59 209	27.33 213	11.12 226	50.08 210	41.05 222	44.23 221	45.15 214

N_pmnet		32.67 214	36.85 212	27.79 215	40.55 204	32.13 223	35.80 212	26.79 215	37.24 198	9.10 215	32.02 195	30.94 209	16.30 210	47.22 220	41.21 220	38.21 223	37.21 220

1111		31.35 215	33.52 219	28.83 212	44.28 196	32.44 221	31.71 218	33.25 196	27.87 213	10.92 207	22.18 216	24.05 218	15.89 211	49.03 217	44.09 218	36.94 225	34.96 221

testus		31.33 216	36.31 214	25.52 219	37.55 208	38.40 214	25.87 224	23.58 221	26.46 216	5.97 224	24.15 212	24.92 215	12.44 219	49.14 216	48.21 210	47.73 219	42.86 216

testmv		30.97 217	34.42 217	26.95 216	36.49 211	37.38 218	29.80 221	27.28 212	22.34 222	4.72 225	20.63 220	20.64 224	13.22 217	49.86 213	47.74 212	50.20 214	42.36 217

test1235678		30.97 217	34.42 217	26.95 216	36.49 211	37.38 218	29.79 222	27.28 212	22.33 223	4.72 225	20.62 221	20.64 224	13.22 217	49.87 212	47.74 212	50.20 214	42.36 217

no-one		29.19 219	31.89 220	26.05 218	30.96 219	38.33 216	21.54 225	29.86 205	15.84 228	3.56 228	11.28 227	13.03 229	14.44 216	38.96 223	52.83 200	55.96 202	52.92 199

Gipuma		25.87 220	26.91 223	24.66 220	28.98 221	20.17 228	20.46 227	34.62 186	29.55 211	9.10 215	4.91 231	5.31 233	15.76 213	49.37 215	49.10 208	39.03 222	29.95 224
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test12356		23.91 221	28.47 221	18.60 222	26.80 226	28.30 225	20.92 226	19.76 224	19.89 225	2.88 233	18.48 222	16.57 227	4.05 228	42.34 221	41.93 219	37.21 224	31.75 222

new_pmnet		23.19 222	28.17 222	17.37 223	17.03 229	24.92 226	19.66 228	16.16 228	27.05 215	4.42 227	20.77 219	19.20 226	12.19 221	37.71 224	36.38 223	34.77 226	31.17 223

.test1245		22.44 223	22.23 224	22.67 221	44.28 196	32.44 221	31.71 218	33.25 196	27.87 213	10.92 207	22.18 216	24.05 218	15.89 211	49.03 217	0.01 229	0.00 233	0.06 231

PMMVS2		15.84 224	19.68 225	11.35 227	15.74 230	16.95 229	13.31 229	17.64 227	16.08 227	0.36 235	13.12 224	11.47 230	1.69 230	28.82 225	27.24 225	19.38 229	24.09 226

E-PMN		15.09 225	13.19 227	17.30 224	27.80 223	12.62 231	7.81 231	27.54 210	14.62 230	3.19 229	6.89 228	2.52 236	15.09 214	15.93 227	20.22 226	22.38 227	19.53 227

EMVS		14.49 226	12.45 228	16.87 226	27.02 225	12.56 232	8.13 230	27.19 214	15.05 229	3.14 230	6.69 229	2.67 235	15.08 215	14.60 229	18.05 227	20.67 228	17.56 229

MVE	12.28 19	13.53 227	15.72 226	10.96 228	7.39 231	15.71 230	6.05 232	23.73 219	10.29 232	3.01 232	5.77 230	3.41 234	11.91 223	20.11 226	29.79 224	13.67 230	24.98 225
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.01 228	0.02 229	0.00 230	0.00 234	0.00 235	0.01 236	0.00 232	0.01 233	0.00 237	0.03 233	0.00 237	0.01 232	0.01 231	0.01 229	0.00 233	0.06 231

test123		0.01 228	0.02 229	0.00 230	0.00 234	0.00 235	0.00 237	0.00 232	0.01 233	0.00 237	0.04 232	0.00 237	0.01 232	0.00 232	0.01 229	0.00 233	0.07 230

ESAPD		0.00 230	0.00 231	0.00 230	0.00 234	0.00 235	0.00 237	0.00 232	0.00 235	0.00 237	0.00 234	0.00 237	0.00 234	0.00 232	0.00 232	0.00 233	0.00 233

sosnet-low-res		0.00 230	0.00 231	0.00 230	0.00 234	0.00 235	0.00 237	0.00 232	0.00 235	0.00 237	0.00 234	0.00 237	0.00 234	0.00 232	0.00 232	0.00 233	0.00 233

sosnet		0.00 230	0.00 231	0.00 230	0.00 234	0.00 235	0.00 237	0.00 232	0.00 235	0.00 237	0.00 234	0.00 237	0.00 234	0.00 232	0.00 232	0.00 233	0.00 233

ambc			45.54 199		50.66 172	52.63 179	40.99 205		38.36 193	24.67 173	22.62 215	13.94 228	29.14 184	65.71 135	58.06 177	58.60 197	67.43 133

MTAPA										65.14 1		80.20 12

MTMP										62.63 10		78.04 19

Patchmatch-RL test							1.04 235

tmp_tt				5.40 229	3.97 232	2.35 234	3.26 234	0.44 230	17.56 226	12.09 204	11.48 226	7.14 231	1.98 229	15.68 228	15.49 228	10.69 231

XVS					70.49 30	76.96 19	74.36 38			54.48 42		74.47 30				82.24 17

X-MVStestdata					70.49 30	76.96 19	74.36 38			54.48 42		74.47 30				82.24 17

abl_6				64.36 38	70.08 33	77.45 15	72.88 43	50.15 49	71.31 36	54.77 41	62.79 39	77.99 20	56.80 43			81.50 32	83.91 33

mPP-MVS					71.67 26							74.36 33

NP-MVS									72.00 33

Patchmtry						47.61 194	48.27 171	38.86 163		39.59 113

DeepMVS_CX						6.95 233	5.98 233	2.25 229	11.73 231	2.07 234	11.85 225	5.43 232	11.75 224	11.40 230		8.10 232	18.38 228

High-resolution multi-view benchmark