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.

ESAPD		78.19 1	83.74 1	71.72 1	79.01 1	81.38 1	83.23 2	58.63 2	83.92 4	62.44 11	87.06 2	85.82 1	64.54 3	79.39 4	77.99 7	82.44 16	90.61 1

APDe-MVS		77.58 2	82.93 2	71.35 2	77.86 2	80.55 2	83.38 1	57.61 6	85.57 1	61.11 14	86.10 4	82.98 3	64.76 2	78.29 11	76.78 18	83.40 5	90.20 2

HSP-MVS		76.78 3	82.44 3	70.19 7	75.26 10	80.22 3	80.59 7	57.85 5	84.79 3	60.84 15	88.54 1	83.43 2	66.24 1	78.21 14	76.47 20	80.34 37	85.43 26

ACMMP_Plus		76.15 4	81.17 4	70.30 5	74.09 14	79.47 5	81.59 5	57.09 9	81.38 6	63.89 5	79.02 9	80.48 12	62.24 13	80.05 3	79.12 3	82.94 9	88.64 4

HPM-MVS++		76.01 5	80.47 7	70.81 3	76.60 4	74.96 30	80.18 11	58.36 3	81.96 5	63.50 6	78.80 10	82.53 6	64.40 4	78.74 7	78.84 4	81.81 26	87.46 12

APD-MVS		75.80 6	80.90 6	69.86 11	75.42 9	78.48 11	81.43 6	57.44 7	80.45 10	59.32 21	85.28 5	80.82 11	63.96 5	76.89 24	76.08 23	81.58 32	88.30 7
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNVR-MVS		75.62 7	79.91 9	70.61 4	75.76 6	78.82 9	81.66 4	57.12 8	79.77 12	63.04 7	70.69 19	81.15 9	62.99 7	80.23 2	79.54 2	83.11 6	89.16 3

SteuartSystems-ACMMP		75.23 8	79.60 10	70.13 8	76.81 3	78.92 7	81.74 3	57.99 4	75.30 24	59.83 20	75.69 13	78.45 18	60.48 24	80.58 1	79.77 1	83.94 3	88.52 5
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + MP.		75.22 9	80.06 8	69.56 12	74.61 12	72.74 44	80.59 7	55.70 19	80.80 8	62.65 9	86.25 3	82.92 4	62.07 15	76.89 24	75.66 26	81.77 28	85.19 28

HFP-MVS		74.87 10	78.86 15	70.21 6	73.99 15	77.91 13	80.36 10	56.63 11	78.41 15	64.27 3	74.54 15	77.75 22	62.96 8	78.70 8	77.82 9	83.02 7	86.91 15

CSCG		74.68 11	79.22 11	69.40 13	75.69 8	80.01 4	79.12 18	52.83 35	79.34 13	63.99 4	70.49 20	82.02 7	60.35 26	77.48 21	77.22 15	84.38 1	87.97 10

SD-MVS		74.43 12	78.87 13	69.26 15	74.39 13	73.70 40	79.06 19	55.24 21	81.04 7	62.71 8	80.18 8	82.61 5	61.70 17	75.43 35	73.92 38	82.44 16	85.22 27

MP-MVS		74.31 13	78.87 13	68.99 16	73.49 17	78.56 10	79.25 17	56.51 12	75.33 22	60.69 17	75.30 14	79.12 17	61.81 16	77.78 18	77.93 8	82.18 22	88.06 9

NCCC		74.27 14	77.83 20	70.13 8	75.70 7	77.41 17	80.51 9	57.09 9	78.25 16	62.28 12	65.54 32	78.26 19	62.18 14	79.13 5	78.51 5	83.01 8	87.68 11

MPTG		74.25 15	77.97 19	69.91 10	73.43 18	74.06 38	79.69 13	56.44 13	80.74 9	64.98 2	68.72 25	79.98 14	62.92 9	78.24 13	77.77 11	81.99 24	86.30 17

DeepPCF-MVS	66.49 1	74.25 15	80.97 5	66.41 26	67.75 45	78.87 8	75.61 33	54.16 27	84.86 2	58.22 27	77.94 11	81.01 10	62.52 11	78.34 9	77.38 12	80.16 40	88.40 6

train_agg		73.89 17	78.25 17	68.80 18	75.25 11	72.27 46	79.75 12	56.05 16	74.87 27	58.97 22	81.83 7	79.76 15	61.05 21	77.39 22	76.01 24	81.71 29	85.61 24

DeepC-MVS	66.32 2	73.85 18	78.10 18	68.90 17	67.92 43	79.31 6	78.16 23	59.28 1	78.24 17	61.13 13	67.36 31	76.10 26	63.40 6	79.11 6	78.41 6	83.52 4	88.16 8
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 19	78.41 16	68.40 19	72.35 22	77.79 14	79.32 15	56.38 14	77.67 19	58.30 26	74.16 16	76.66 23	61.40 18	78.32 10	77.80 10	82.68 13	86.51 16

MCST-MVS		73.67 20	77.39 21	69.33 14	76.26 5	78.19 12	78.77 20	54.54 24	75.33 22	59.99 19	67.96 27	79.23 16	62.43 12	78.00 15	75.71 25	84.02 2	87.30 13

PGM-MVS		72.89 21	77.13 22	67.94 20	72.47 21	77.25 18	79.27 16	54.63 23	73.71 29	57.95 28	72.38 17	75.33 28	60.75 22	78.25 12	77.36 14	82.57 15	85.62 23

CP-MVS		72.63 22	76.95 23	67.59 21	70.67 29	75.53 28	77.95 25	56.01 17	75.65 21	58.82 23	69.16 24	76.48 24	60.46 25	77.66 19	77.20 16	81.65 30	86.97 14

TSAR-MVS + ACMM		72.56 23	79.07 12	64.96 35	73.24 19	73.16 43	78.50 21	48.80 58	79.34 13	55.32 35	85.04 6	81.49 8	58.57 32	75.06 38	73.75 39	75.35 102	85.61 24

DeepC-MVS_fast	65.08 3	72.00 24	76.11 24	67.21 23	68.93 39	77.46 15	76.54 29	54.35 25	74.92 26	58.64 25	65.18 33	74.04 36	62.62 10	77.92 16	77.02 17	82.16 23	86.21 18
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 25	75.84 25	66.59 25	70.30 33	76.85 23	78.46 22	53.95 28	73.52 30	55.56 33	70.13 21	71.36 41	58.55 33	77.00 23	76.23 22	82.71 12	85.81 22
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 26	75.82 26	66.41 26	72.97 20	77.15 19	78.14 24	54.71 22	69.88 42	53.07 50	70.98 18	74.83 30	56.95 43	76.22 28	76.57 19	82.62 14	85.09 29

X-MVS		71.18 27	75.66 27	65.96 30	71.71 24	76.96 20	77.26 27	55.88 18	72.75 32	54.48 43	64.39 36	74.47 31	54.19 55	77.84 17	77.37 13	82.21 20	85.85 21

HQP-MVS		70.88 28	75.02 28	66.05 29	71.69 25	74.47 35	77.51 26	53.17 32	72.89 31	54.88 39	70.03 22	70.48 43	57.26 39	76.02 30	75.01 30	81.78 27	86.21 18

TSAR-MVS + GP.		69.71 29	73.92 31	64.80 37	68.27 41	70.56 51	71.90 45	50.75 45	71.38 36	57.46 30	68.68 26	75.42 27	60.10 27	73.47 43	73.99 37	80.32 38	83.97 33

3Dnovator+	62.63 4	69.51 30	72.62 34	65.88 31	68.21 42	76.47 24	73.50 43	52.74 36	70.85 38	58.65 24	55.97 63	69.95 44	61.11 20	76.80 26	75.09 27	81.09 35	83.23 39

MVS_0304		69.49 31	73.96 30	64.28 40	67.92 43	76.13 26	74.90 36	47.60 60	63.29 52	54.09 47	67.44 30	76.35 25	59.53 29	75.81 32	75.03 28	81.62 31	83.70 36

OPM-MVS		69.33 32	71.05 40	67.32 22	72.34 23	75.70 27	79.57 14	56.34 15	55.21 63	53.81 48	59.51 52	68.96 46	59.67 28	77.61 20	76.44 21	82.19 21	83.88 35

PHI-MVS		69.27 33	74.84 29	62.76 45	66.83 47	74.83 31	73.88 41	49.32 54	70.61 39	50.93 54	69.62 23	74.84 29	57.25 40	75.53 34	74.32 35	78.35 54	84.17 32

LGP-MVS_train		68.87 34	72.03 36	65.18 34	69.33 37	74.03 39	76.67 28	53.88 29	68.46 43	52.05 53	63.21 38	63.89 58	56.31 45	75.99 31	74.43 34	82.83 11	84.18 31

CANet		68.77 35	73.01 32	63.83 41	68.30 40	75.19 29	73.73 42	47.90 59	63.86 49	54.84 40	67.51 29	74.36 34	57.62 36	74.22 41	73.57 42	80.56 36	82.36 40

CPTT-MVS		68.76 36	73.01 32	63.81 42	65.42 54	73.66 41	76.39 31	52.08 37	72.61 33	50.33 56	60.73 49	72.65 39	59.43 30	73.32 44	72.12 44	79.19 48	85.99 20

ACMP	61.42 5	68.72 37	71.37 38	65.64 32	69.06 38	74.45 36	75.88 32	53.30 31	68.10 44	55.74 32	61.53 48	62.29 63	56.97 42	74.70 39	74.23 36	82.88 10	84.31 30
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MSLP-MVS++		68.17 38	70.72 43	65.19 33	69.41 36	70.64 50	74.99 35	45.76 66	70.20 41	60.17 18	56.42 61	73.01 37	61.14 19	72.80 46	70.54 50	79.70 42	81.42 45

MAR-MVS		68.04 39	70.74 42	64.90 36	71.68 26	76.33 25	74.63 38	50.48 49	63.81 50	55.52 34	54.88 68	69.90 45	57.39 38	75.42 36	74.79 32	79.71 41	80.03 49
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 40	68.85 50	66.77 24	73.73 16	74.30 37	75.28 34	53.58 30	70.24 40	57.59 29	51.19 82	59.19 75	60.74 23	75.33 37	73.72 40	79.69 44	77.96 59

MVS_111021_HR		67.62 41	70.39 44	64.39 38	69.77 35	70.45 52	71.44 48	51.72 41	60.77 57	55.06 37	62.14 45	66.40 55	58.13 35	76.13 29	74.79 32	80.19 39	82.04 43

ACMM	60.30 7	67.58 42	68.82 51	66.13 28	70.59 30	72.01 48	76.54 29	54.26 26	65.64 48	54.78 41	50.35 84	61.72 66	58.74 31	75.79 33	75.03 28	81.88 25	81.17 46
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PCF-MVS	59.98 8	67.32 43	71.04 41	62.97 44	64.77 56	74.49 34	74.78 37	49.54 52	67.44 45	54.39 46	58.35 56	72.81 38	55.79 51	71.54 51	69.24 58	78.57 50	83.41 37
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		67.02 44	71.57 37	61.71 46	71.01 28	74.81 32	71.62 46	38.91 164	71.86 35	60.70 16	64.97 34	67.88 52	51.88 93	76.77 27	74.98 31	76.11 94	69.75 119
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 45	70.32 45	63.11 43	66.63 48	74.52 33	71.56 47	45.76 66	67.37 46	55.00 38	54.31 72	68.19 50	58.49 34	73.97 42	73.63 41	81.22 34	80.23 48

DELS-MVS		65.87 46	70.30 46	60.71 47	64.05 63	72.68 45	70.90 49	45.43 70	57.49 60	49.05 60	64.43 35	68.66 47	55.11 53	74.31 40	73.02 43	79.70 42	81.51 44
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 47	72.06 35	58.11 56	63.94 64	71.05 49	64.49 97	43.18 114	74.08 28	47.35 63	64.17 37	71.97 40	51.17 96	71.87 49	70.74 48	78.51 52	80.56 47

QAPM		65.27 48	69.49 49	60.35 48	65.43 53	72.20 47	65.69 88	47.23 61	63.46 51	49.14 59	53.56 73	71.04 42	57.01 41	72.60 47	71.41 47	77.62 58	82.14 42

OMC-MVS		65.16 49	71.35 39	57.94 60	52.95 157	68.82 56	69.00 50	38.28 171	79.89 11	55.20 36	62.76 41	68.31 49	56.14 48	71.30 53	68.70 63	76.06 96	79.67 50

EPNet		65.14 50	69.54 48	60.00 50	66.61 49	67.67 65	67.53 54	55.32 20	62.67 54	46.22 70	67.74 28	65.93 56	48.07 109	72.17 48	72.12 44	76.28 86	78.47 56
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		63.65 51	66.92 52	59.83 51	60.03 74	73.44 42	66.33 79	48.95 56	52.20 77	50.81 55	56.07 62	60.25 71	53.56 60	73.23 45	70.01 55	79.30 46	83.24 38

Effi-MVS+		63.28 52	65.96 57	60.17 49	64.26 60	68.06 60	68.78 51	45.71 68	54.08 67	46.64 66	55.92 64	63.13 61	55.94 49	70.38 60	71.43 46	79.68 45	78.70 54

MVS_111021_LR		63.05 53	66.43 54	59.10 53	61.33 68	63.77 101	65.87 86	43.58 103	60.20 58	53.70 49	62.09 46	62.38 62	55.84 50	70.24 61	68.08 67	74.30 107	78.28 58

OpenMVS	57.13 9	62.81 54	65.75 58	59.39 52	66.47 50	69.52 54	64.26 99	43.07 119	61.34 56	50.19 57	47.29 118	64.41 57	54.60 54	70.18 62	68.62 65	77.73 56	78.89 53

CNLPA		62.78 55	66.31 55	58.65 54	58.47 82	68.41 59	65.98 85	41.22 149	78.02 18	56.04 31	46.65 121	59.50 74	57.50 37	69.67 64	65.27 121	72.70 134	76.67 68

TSAR-MVS + COLMAP		62.65 56	69.90 47	54.19 99	46.31 189	66.73 75	65.49 90	41.36 147	76.57 20	46.31 69	76.80 12	56.68 83	53.27 68	69.50 65	66.65 84	72.40 140	76.36 76

MVS_Test		62.40 57	66.23 56	57.94 60	59.77 77	64.77 97	66.50 78	41.76 139	57.26 61	49.33 58	62.68 42	67.47 54	53.50 63	68.57 73	66.25 91	76.77 71	76.58 71

DI_MVS_plusplus_trai		61.88 58	65.17 62	58.06 57	60.05 73	65.26 92	66.03 83	44.22 82	55.75 62	46.73 65	54.64 70	68.12 51	54.13 57	69.13 66	66.66 83	77.18 64	76.61 69

PVSNet_BlendedMVS		61.63 59	64.82 63	57.91 62	57.21 122	67.55 66	63.47 103	46.08 64	54.72 64	52.46 51	58.59 54	60.73 68	51.82 94	70.46 58	65.20 123	76.44 83	76.50 74

PVSNet_Blended		61.63 59	64.82 63	57.91 62	57.21 122	67.55 66	63.47 103	46.08 64	54.72 64	52.46 51	58.59 54	60.73 68	51.82 94	70.46 58	65.20 123	76.44 83	76.50 74

TAPA-MVS	54.74 10	60.85 61	66.61 53	54.12 100	47.38 185	65.33 90	65.35 91	36.51 182	75.16 25	48.82 61	54.70 69	63.51 59	53.31 67	68.36 74	64.97 126	73.37 120	74.27 104
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Fast-Effi-MVS+		60.36 62	63.35 69	56.87 82	58.70 79	65.86 88	65.08 92	37.11 177	53.00 74	45.36 79	52.12 78	56.07 88	56.27 46	71.28 54	69.42 57	78.71 49	75.69 80

Effi-MVS+-dtu		60.34 63	62.32 72	58.03 59	64.31 58	67.44 68	65.99 84	42.26 136	49.55 85	42.00 105	48.92 96	59.79 73	56.27 46	68.07 85	67.03 76	77.35 63	75.45 82

LS3D		60.20 64	61.70 73	58.45 55	64.18 61	67.77 62	67.19 56	48.84 57	61.67 55	41.27 108	45.89 132	51.81 114	54.18 56	68.78 68	66.50 89	75.03 103	69.48 125

diffmvs		59.53 65	64.04 68	54.26 98	55.09 137	59.86 140	64.80 94	39.55 163	58.39 59	46.21 71	60.48 50	67.82 53	49.27 101	63.53 149	63.32 145	70.64 155	74.89 86

EPP-MVSNet		59.39 66	65.45 60	52.32 113	60.96 70	67.70 64	58.42 121	44.75 77	49.71 84	27.23 167	59.03 53	62.20 64	43.34 131	70.71 57	69.13 59	79.25 47	79.63 51

ACMH+	53.71 12	59.26 67	60.28 85	58.06 57	64.17 62	68.46 58	67.51 55	50.93 44	52.46 76	35.83 129	40.83 177	45.12 156	52.32 89	69.88 63	69.00 61	77.59 60	76.21 77

PLC	52.09 14	59.21 68	62.47 71	55.41 95	53.24 156	64.84 96	64.47 98	40.41 157	65.92 47	44.53 92	46.19 129	55.69 89	55.33 52	68.24 78	65.30 120	74.50 105	71.09 111
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7		59.19 69	60.62 78	57.53 65	57.96 85	67.19 71	67.09 59	44.28 81	46.84 114	45.45 77	48.19 110	51.06 116	53.62 59	67.84 91	66.59 86	76.79 68	76.60 70

v10		59.17 70	60.60 79	57.50 66	57.95 86	66.73 75	67.09 59	44.11 83	46.85 113	45.42 78	48.18 112	51.07 115	53.63 58	67.84 91	66.59 86	76.79 68	76.92 66

v6		58.89 71	60.54 81	56.96 71	57.34 110	66.13 84	66.71 70	42.84 121	47.85 108	45.80 73	49.04 90	52.95 99	52.79 73	67.53 101	65.59 112	76.26 87	74.73 88

CANet_DTU		58.88 72	64.68 65	52.12 114	55.77 130	66.75 74	63.92 100	37.04 178	53.32 70	37.45 123	59.81 51	61.81 65	44.43 125	68.25 76	67.47 74	74.12 110	75.33 83

v1144		58.88 72	60.16 92	57.39 67	58.03 84	67.26 69	67.14 58	44.46 80	45.17 136	44.33 93	47.81 115	49.92 125	53.20 69	67.77 96	66.62 85	77.15 65	76.58 71

v1neww		58.88 72	60.54 81	56.94 72	57.33 112	66.13 84	66.70 72	42.84 121	47.84 109	45.74 75	49.02 92	52.93 100	52.78 74	67.53 101	65.59 112	76.26 87	74.73 88

v7new		58.88 72	60.54 81	56.94 72	57.33 112	66.13 84	66.70 72	42.84 121	47.84 109	45.74 75	49.02 92	52.93 100	52.78 74	67.53 101	65.59 112	76.26 87	74.73 88

v8		58.88 72	60.57 80	56.92 76	57.35 108	65.69 89	66.69 74	42.64 129	47.89 107	45.77 74	49.04 90	52.98 98	52.77 76	67.51 104	65.57 116	76.26 87	75.30 84

v16		58.71 77	60.20 88	56.97 70	57.35 108	63.36 109	66.67 75	42.49 131	48.69 100	46.36 68	48.87 98	52.92 102	52.82 72	67.57 99	65.58 115	76.15 93	74.38 100

v17		58.69 78	60.19 91	56.94 72	57.38 103	63.37 108	66.67 75	42.47 133	48.52 104	46.10 72	48.90 97	53.00 97	52.84 70	67.58 98	65.60 111	76.19 91	74.38 100

v2v482		58.69 78	60.12 95	57.03 69	57.16 124	66.05 87	67.17 57	43.52 105	46.33 119	45.19 80	49.46 87	51.02 117	52.51 84	67.30 111	66.03 94	76.61 79	74.62 95

v18		58.68 80	60.20 88	56.90 79	57.26 120	63.28 110	66.58 77	42.42 134	48.86 94	46.37 67	49.01 94	53.05 96	52.74 77	67.40 109	65.52 117	76.02 98	74.28 103

v1141		58.56 81	60.05 97	56.81 85	57.36 105	66.18 82	66.80 67	43.11 116	45.87 130	44.60 89	48.71 100	51.83 112	52.38 86	67.46 105	65.64 109	76.63 76	74.66 91

divwei89l23v2f112		58.56 81	60.05 97	56.81 85	57.36 105	66.18 82	66.80 67	43.11 116	45.89 129	44.60 89	48.71 100	51.84 111	52.38 86	67.45 107	65.65 106	76.63 76	74.66 91

v1		58.56 81	60.06 96	56.83 84	57.36 105	66.19 81	66.80 67	43.10 118	45.87 130	44.68 87	48.73 99	51.83 112	52.38 86	67.45 107	65.65 106	76.63 76	74.66 91

v1192		58.51 84	59.66 105	57.17 68	57.82 87	67.72 63	66.21 82	44.83 76	44.15 143	43.49 96	46.68 120	47.94 129	53.55 61	67.39 110	66.51 88	77.13 66	77.20 64

UA-Net		58.50 85	64.68 65	51.30 116	66.97 46	67.13 72	53.68 155	45.65 69	49.51 87	31.58 143	62.91 39	68.47 48	35.85 165	68.20 79	67.28 75	74.03 111	69.24 129

Vis-MVSNet		58.48 86	65.70 59	50.06 123	53.40 155	67.20 70	60.24 117	43.32 111	48.83 95	30.23 149	62.38 44	61.61 67	40.35 142	71.03 56	69.77 56	72.82 128	79.11 52
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MSDG		58.46 87	58.97 115	57.85 64	66.27 52	66.23 80	67.72 52	42.33 135	53.43 69	43.68 95	43.39 152	45.35 153	49.75 99	68.66 71	67.77 70	77.38 62	67.96 132

V9		58.45 88	59.75 100	56.92 76	57.51 96	63.49 104	66.86 62	42.73 126	46.07 125	45.05 82	48.45 105	51.99 108	52.66 80	68.04 89	65.75 101	76.72 73	74.50 97

v13		58.44 89	59.72 104	56.94 72	57.55 90	63.51 102	66.86 62	42.81 124	45.90 128	44.98 84	48.17 113	51.87 110	52.68 78	68.20 79	65.78 99	76.78 70	74.63 94

v12		58.44 89	59.74 103	56.92 76	57.54 92	63.50 103	66.84 65	42.77 125	45.96 126	44.95 85	48.31 106	51.94 109	52.67 79	68.14 82	65.75 101	76.75 72	74.55 96

V14		58.44 89	59.75 100	56.90 79	57.48 98	63.46 105	66.85 64	42.68 127	46.16 122	45.03 83	48.57 103	52.04 107	52.65 81	67.93 90	65.72 104	76.69 74	74.40 99

v15		58.43 92	59.75 100	56.88 81	57.45 99	63.44 106	66.84 65	42.65 128	46.24 121	45.07 81	48.68 102	52.07 106	52.63 82	67.84 91	65.70 105	76.65 75	74.31 102

FC-MVSNet-train		58.40 93	63.15 70	52.85 109	64.29 59	61.84 118	55.98 138	46.47 62	53.06 72	34.96 132	61.95 47	56.37 86	39.49 144	68.67 70	68.36 66	75.92 99	71.81 108

IB-MVS	54.11 11	58.36 94	60.70 77	55.62 93	58.67 80	68.02 61	61.56 106	43.15 115	46.09 123	44.06 94	44.24 144	50.99 119	48.71 104	66.70 120	70.33 51	77.60 59	78.50 55
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 95	61.39 74	54.71 97	59.92 76	58.40 157	59.42 118	43.64 100	48.71 98	40.25 112	57.53 59	58.55 77	52.15 91	65.42 142	65.34 119	72.85 126	75.77 78
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	52.42 13	58.24 96	59.56 108	56.70 87	66.34 51	69.59 53	66.71 70	49.12 55	46.08 124	28.90 156	42.67 166	41.20 184	52.60 83	71.39 52	70.28 52	76.51 81	75.72 79
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v144192		58.23 97	59.40 111	56.87 82	57.56 89	66.89 73	65.70 87	45.01 75	44.06 144	42.88 98	46.61 122	48.09 128	53.49 64	66.94 116	65.90 97	76.61 79	77.29 62

MS-PatchMatch		58.19 98	60.20 88	55.85 92	65.17 55	64.16 99	64.82 93	41.48 146	50.95 79	42.17 104	45.38 137	56.42 84	48.08 108	68.30 75	66.70 82	73.39 119	69.46 127

v11		58.19 98	59.47 109	56.70 87	57.54 92	63.42 107	66.28 81	42.49 131	45.62 134	44.59 91	48.16 114	50.78 120	52.84 70	67.80 95	65.76 100	76.49 82	74.76 87

IS_MVSNet		57.95 100	64.26 67	50.60 118	61.62 67	65.25 93	57.18 127	45.42 71	50.79 80	26.49 169	57.81 58	60.05 72	34.51 169	71.24 55	70.20 54	78.36 53	74.44 98

v1921920		57.89 101	59.02 114	56.58 89	57.55 90	66.66 78	64.72 96	44.70 78	43.55 147	42.73 100	46.17 130	46.93 143	53.51 62	66.78 119	65.75 101	76.29 85	77.28 63

v1240		57.55 102	58.63 117	56.29 91	57.30 117	66.48 79	63.77 101	44.56 79	42.77 162	42.48 102	45.64 135	46.28 148	53.46 65	66.32 126	65.80 98	76.16 92	77.13 65

MVSTER		57.19 103	61.11 76	52.62 111	50.82 173	58.79 153	61.55 107	37.86 174	48.81 96	41.31 107	57.43 60	52.10 105	48.60 105	68.19 81	66.75 81	75.56 100	75.68 81

UGNet		57.03 104	65.25 61	47.44 154	46.54 188	66.73 75	56.30 134	43.28 112	50.06 82	32.99 136	62.57 43	63.26 60	33.31 175	68.25 76	67.58 72	72.20 144	78.29 57
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		56.98 105	58.24 121	55.50 94	64.66 57	68.62 57	61.48 108	43.63 102	38.44 195	41.44 106	38.05 187	46.18 150	43.95 126	71.71 50	70.61 49	77.87 55	74.08 105

V42		56.97 106	60.14 93	53.28 104	48.16 180	62.78 115	66.30 80	37.93 173	47.44 111	42.68 101	48.19 110	52.59 104	51.90 92	67.46 105	65.94 96	72.72 129	76.55 73

UniMVSNet_NR-MVSNet		56.94 107	61.14 75	52.05 115	60.02 75	65.21 94	57.44 125	52.93 34	49.37 88	24.31 178	54.62 71	50.54 121	39.04 146	68.69 69	68.84 62	78.53 51	70.72 112

HyFIR lowres test		56.87 108	58.60 118	54.84 96	56.62 127	69.27 55	64.77 95	42.21 137	45.66 133	37.50 122	33.08 196	57.47 82	53.33 66	65.46 141	67.94 68	74.60 104	71.35 110

tpmp4_e23		56.84 109	57.14 128	56.49 90	62.45 65	62.05 116	67.57 53	41.56 144	54.17 66	48.57 62	49.18 88	46.54 146	50.44 98	61.93 162	58.82 176	68.34 169	67.28 137

CostFormer		56.57 110	59.13 113	53.60 101	57.52 95	61.12 128	66.94 61	35.95 184	53.44 68	44.68 87	55.87 65	54.44 91	48.21 107	60.37 170	58.33 179	68.27 171	70.33 117

Fast-Effi-MVS+-dtu		56.30 111	59.29 112	52.82 110	58.64 81	64.89 95	65.56 89	32.89 202	45.80 132	35.04 131	45.89 132	54.14 92	49.41 100	67.16 113	66.45 90	75.37 101	70.69 114

TranMVSNet+NR-MVSNet		55.87 112	60.14 93	50.88 117	59.46 78	63.82 100	57.93 123	52.98 33	48.94 93	20.52 188	52.87 75	47.33 137	36.81 162	69.12 67	69.03 60	77.56 61	69.89 118

CHOSEN 1792x2688		55.85 113	58.01 122	53.33 103	57.26 120	62.82 114	63.29 105	41.55 145	46.65 116	38.34 117	34.55 194	53.50 93	52.43 85	67.10 114	67.56 73	67.13 176	73.92 106

v7n		55.67 114	57.46 127	53.59 102	56.06 128	65.29 91	61.06 111	43.26 113	40.17 182	37.99 119	40.79 178	45.27 155	47.09 113	67.67 97	66.21 92	76.08 95	76.82 67

GA-MVS		55.67 114	58.33 119	52.58 112	55.23 135	63.09 111	61.08 110	40.15 159	42.95 154	37.02 125	52.61 76	47.68 132	47.51 111	65.92 134	65.35 118	74.49 106	70.68 115

v148		55.58 116	57.61 126	53.20 106	54.59 146	61.86 117	61.18 109	38.70 169	44.30 142	42.25 103	47.53 116	50.24 124	48.73 103	65.15 143	62.61 155	73.79 113	71.61 109

DU-MVS		55.41 117	59.59 106	50.54 120	54.60 144	62.97 112	57.44 125	51.80 39	48.62 102	24.31 178	51.99 79	47.00 142	39.04 146	68.11 83	67.75 71	76.03 97	70.72 112

NR-MVSNet		55.35 118	59.46 110	50.56 119	61.33 68	62.97 112	57.91 124	51.80 39	48.62 102	20.59 187	51.99 79	44.73 163	34.10 172	68.58 72	68.64 64	77.66 57	70.67 116

GBi-Net		55.20 119	60.25 86	49.31 127	52.42 159	61.44 122	57.03 128	44.04 86	49.18 90	30.47 145	48.28 107	58.19 78	38.22 149	68.05 86	66.96 77	73.69 115	69.65 120

test1		55.20 119	60.25 86	49.31 127	52.42 159	61.44 122	57.03 128	44.04 86	49.18 90	30.47 145	48.28 107	58.19 78	38.22 149	68.05 86	66.96 77	73.69 115	69.65 120

UniMVSNet (Re)		55.15 121	60.39 84	49.03 133	55.31 132	64.59 98	55.77 139	50.63 46	48.66 101	20.95 186	51.47 81	50.40 122	34.41 171	67.81 94	67.89 69	77.11 67	71.88 107

FMVSNet2		55.04 122	59.95 99	49.31 127	52.42 159	61.44 122	57.03 128	44.08 85	49.55 85	30.40 148	46.89 119	58.84 76	38.22 149	67.07 115	66.21 92	73.69 115	69.65 120

FMVSNet3		54.78 123	59.58 107	49.17 130	52.37 162	61.31 126	56.72 132	44.04 86	49.18 90	30.47 145	48.28 107	58.19 78	38.09 152	65.48 140	65.20 123	73.31 121	69.45 128

pmmvs4		54.66 124	56.07 133	53.00 108	54.63 143	57.08 164	60.43 116	44.10 84	51.69 78	40.55 110	46.55 125	44.79 162	45.95 120	62.54 154	63.66 140	72.36 142	66.20 146

FMVSNet1		54.08 125	58.68 116	48.71 140	50.90 172	61.35 125	56.73 131	43.94 90	45.91 127	29.32 155	42.72 165	56.26 87	37.70 153	68.05 86	66.96 77	73.69 115	69.50 124

DWT-MVSNet_training		53.80 126	54.31 150	53.21 105	57.65 88	59.04 151	60.65 112	40.11 160	46.35 118	42.77 99	49.07 89	41.07 185	51.06 97	58.62 180	58.96 175	67.00 179	67.06 138

v52		53.60 127	56.74 131	49.93 124	45.54 192	61.64 120	60.65 112	36.99 179	38.75 191	36.32 127	39.64 182	47.13 139	47.05 114	66.89 117	65.65 106	73.04 124	77.48 60

V4		53.60 127	56.73 132	49.93 124	45.54 192	61.64 120	60.65 112	36.99 179	38.74 193	36.33 126	39.64 182	47.12 140	47.05 114	66.89 117	65.64 109	73.04 124	77.48 60

Baseline_NR-MVSNet		53.50 129	57.89 123	48.37 143	54.60 144	59.25 149	56.10 135	51.84 38	49.32 89	17.92 198	45.38 137	47.68 132	36.93 161	68.11 83	65.95 95	72.84 127	69.57 123

IterMVS		53.45 130	57.12 129	49.17 130	49.23 177	60.93 129	59.05 120	34.63 188	44.53 138	33.22 135	51.09 83	51.01 118	48.38 106	62.43 155	60.79 165	70.54 157	69.05 130
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm cat1		53.30 131	53.41 158	53.17 107	58.16 83	59.15 150	63.73 102	38.27 172	50.73 81	46.98 64	45.57 136	44.00 170	49.20 102	55.90 198	54.02 197	62.65 191	64.50 165

v748		52.93 132	55.29 140	50.19 122	51.90 166	61.31 126	56.54 133	40.05 161	39.12 189	34.82 134	39.93 181	43.83 171	43.66 127	64.26 147	63.32 145	74.15 109	75.28 85

anonymousdsp		52.84 133	57.78 124	47.06 155	40.24 208	58.95 152	53.70 154	33.54 197	36.51 202	32.69 138	43.88 146	45.40 152	47.97 110	67.17 112	70.28 52	74.22 108	82.29 41

tfpn200view9		52.53 134	55.51 135	49.06 132	57.31 114	60.24 131	55.42 143	43.77 92	42.85 157	27.81 160	43.00 161	45.06 158	37.32 155	66.38 122	64.54 128	72.71 132	66.54 140

conf200view11		52.51 135	55.51 135	49.01 134	57.31 114	60.24 131	55.42 143	43.77 92	42.85 157	27.51 162	43.00 161	45.06 158	37.32 155	66.38 122	64.54 128	72.71 132	66.54 140

CDS-MVSNet		52.42 136	57.06 130	47.02 156	53.92 153	58.30 159	55.50 141	46.47 62	42.52 165	29.38 154	49.50 86	52.85 103	28.49 190	66.70 120	66.89 80	68.34 169	62.63 174
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thres200		52.39 137	55.37 139	48.90 137	57.39 102	60.18 133	55.60 140	43.73 97	42.93 155	27.41 165	43.35 153	45.09 157	36.61 163	66.36 124	63.92 139	72.66 135	65.78 152

thres400		52.38 138	55.51 135	48.74 139	57.49 97	60.10 137	55.45 142	43.54 104	42.90 156	26.72 168	43.34 154	45.03 161	36.61 163	66.20 131	64.53 131	72.66 135	66.43 142

EPNet_dtu		52.05 139	58.26 120	44.81 167	54.10 151	50.09 191	52.01 163	40.82 154	53.03 73	27.41 165	54.90 67	57.96 81	26.72 195	62.97 151	62.70 154	67.78 173	66.19 147
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres100view900		52.04 140	54.81 146	48.80 138	57.31 114	59.33 145	55.30 147	42.92 120	42.85 157	27.81 160	43.00 161	45.06 158	36.99 160	64.74 145	63.51 142	72.47 139	65.21 159

conf0.01		52.02 141	54.62 147	49.00 135	57.30 117	60.17 135	55.42 143	43.76 94	42.85 157	27.49 163	43.12 158	39.71 193	37.32 155	66.26 129	64.54 128	72.72 129	65.66 154

COLMAP_ROB	46.52 15	51.99 142	54.86 145	48.63 141	49.13 178	61.73 119	60.53 115	36.57 181	53.14 71	32.95 137	37.10 188	38.68 197	40.49 141	65.72 137	63.08 148	72.11 145	64.60 164
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
view600		51.96 143	55.13 142	48.27 145	57.41 101	60.05 138	54.74 149	43.64 100	42.57 164	25.88 171	43.11 159	44.48 166	35.34 166	66.27 127	63.61 141	72.61 138	65.80 151

TransMVSNet (Re)		51.92 144	55.38 138	47.88 150	60.95 71	59.90 139	53.95 152	45.14 73	39.47 186	24.85 175	43.87 147	46.51 147	29.15 186	67.55 100	65.23 122	73.26 123	65.16 160

thres600view7		51.91 145	55.14 141	48.14 146	57.43 100	60.18 133	54.60 150	43.73 97	42.61 163	25.20 173	43.10 160	44.47 167	35.19 167	66.36 124	63.28 147	72.66 135	66.01 149

conf0.002		51.76 146	54.13 152	49.00 135	57.28 119	60.15 136	55.42 143	43.75 96	42.85 157	27.49 163	43.13 157	37.12 205	37.32 155	66.23 130	64.17 134	72.72 129	65.24 158

view800		51.55 147	54.89 144	47.66 153	57.37 104	59.77 142	53.62 156	43.72 99	42.22 166	24.94 174	42.80 164	43.81 172	33.94 173	66.09 132	64.38 133	72.39 141	65.14 161

pmmvs-eth3d		51.33 148	52.25 168	50.26 121	50.82 173	54.65 174	56.03 137	43.45 110	43.51 148	37.20 124	39.20 185	39.04 196	42.28 134	61.85 163	62.78 152	71.78 149	64.72 163

USDC		51.11 149	53.71 154	48.08 148	44.76 195	55.99 167	53.01 160	40.90 151	52.49 75	36.14 128	44.67 142	33.66 209	43.27 132	63.23 150	61.10 161	70.39 158	64.82 162

pm-mvs1		51.02 150	55.55 134	45.73 162	54.16 150	58.52 155	50.92 165	42.56 130	40.32 181	25.67 172	43.66 149	50.34 123	30.06 184	65.85 135	63.97 138	70.99 154	66.21 145

conf0.05thres1000		50.64 151	53.84 153	46.92 158	57.02 125	59.29 147	52.29 162	43.80 91	39.84 185	23.81 181	39.26 184	43.14 175	32.52 179	65.74 136	64.04 135	72.05 146	65.53 155

tfpn		50.58 152	53.65 156	47.00 157	57.34 110	59.31 146	52.41 161	43.76 94	41.81 170	23.86 180	42.49 167	37.80 200	32.63 178	65.68 139	64.02 137	71.99 147	64.41 166

CR-MVSNet		50.47 153	52.61 163	47.98 149	49.03 179	52.94 179	48.27 174	38.86 166	44.41 139	39.59 114	44.34 143	44.65 165	46.63 117	58.97 175	60.31 168	65.48 182	62.66 172

dps		50.42 154	51.20 180	49.51 126	55.88 129	56.07 166	53.73 153	38.89 165	43.66 145	40.36 111	45.66 134	37.63 202	45.23 122	59.05 173	56.18 182	62.94 190	60.16 183

Vis-MVSNet (Re-imp)		50.37 155	57.73 125	41.80 186	57.53 94	54.35 175	45.70 192	45.24 72	49.80 83	13.43 206	58.23 57	56.42 84	20.11 207	62.96 152	63.36 144	68.76 168	58.96 188

MDTV_nov1_ep13		50.32 156	52.43 166	47.86 151	49.87 176	54.70 173	58.10 122	34.29 190	45.59 135	37.71 120	47.44 117	47.42 136	41.86 136	58.07 183	55.21 190	65.34 184	58.56 189

tfpnnormal		50.16 157	52.19 169	47.78 152	56.86 126	58.37 158	54.15 151	44.01 89	38.35 197	25.94 170	36.10 190	37.89 199	34.50 170	65.93 133	63.42 143	71.26 152	65.28 157

PatchMatch-RL		50.11 158	51.56 173	48.43 142	46.23 190	51.94 184	50.21 167	38.62 170	46.62 117	37.51 121	42.43 168	39.38 194	52.24 90	60.98 166	59.56 172	65.76 181	60.01 185

PatchmatchNet		49.92 159	51.29 176	48.32 144	51.83 167	51.86 185	53.38 159	37.63 176	47.90 106	40.83 109	48.54 104	45.30 154	45.19 123	56.86 187	53.99 199	61.08 195	54.57 200
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TDRefinement		49.31 160	52.44 165	45.67 163	30.44 223	59.42 144	59.24 119	39.78 162	48.76 97	31.20 144	35.73 191	29.90 213	42.81 133	64.24 148	62.59 156	70.55 156	66.43 142

test-LLR		49.28 161	50.29 184	48.10 147	55.26 133	47.16 199	49.52 168	43.48 108	39.22 187	31.98 139	43.65 150	47.93 130	41.29 139	56.80 188	55.36 188	67.08 177	61.94 175

CMPMVS	37.70 17	49.24 162	52.71 162	45.19 164	45.97 191	51.23 187	47.44 180	29.31 209	43.04 153	44.69 86	34.45 195	48.35 127	43.64 128	62.59 153	59.82 171	60.08 196	69.48 125
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PEN-MVS		49.21 163	54.32 149	43.24 178	54.33 149	59.26 148	47.04 183	51.37 43	41.67 171	9.97 216	46.22 128	41.80 179	22.97 204	60.52 168	64.03 136	73.73 114	66.75 139

PMMVS		49.20 164	54.28 151	43.28 177	34.13 217	45.70 207	48.98 171	26.09 220	46.31 120	34.92 133	55.22 66	53.47 94	47.48 112	59.43 172	59.04 174	68.05 172	60.77 180

gg-mvs-nofinetune		49.07 165	52.56 164	45.00 166	61.99 66	59.78 141	53.55 158	41.63 140	31.62 212	12.08 208	29.56 205	53.28 95	29.57 185	66.27 127	64.49 132	71.19 153	62.92 171

tpm		48.82 166	51.27 177	45.96 161	54.10 151	47.35 198	56.05 136	30.23 207	46.70 115	43.21 97	52.54 77	47.55 135	37.28 159	54.11 203	50.50 208	54.90 209	60.12 184

WR-MVS		48.78 167	55.06 143	41.45 188	55.50 131	60.40 130	43.77 201	49.99 51	41.92 168	8.10 221	45.24 140	45.56 151	17.47 209	61.57 164	64.60 127	73.85 112	66.14 148

CP-MVSNet		48.37 168	53.53 157	42.34 183	51.35 170	58.01 160	46.56 184	50.54 47	41.62 172	10.61 212	46.53 126	40.68 189	23.18 201	58.71 178	61.83 157	71.81 148	67.36 136

pmmvs6		48.35 169	51.64 171	44.51 170	51.92 165	57.94 161	49.44 170	42.17 138	34.45 205	24.62 177	28.87 209	46.90 144	29.07 188	64.60 146	63.08 148	69.83 160	65.68 153

tfpn_ndepth		48.34 170	52.27 167	43.76 172	54.35 148	56.46 165	47.24 182	40.92 150	43.45 149	21.04 185	41.16 176	43.22 174	28.90 189	61.57 164	60.65 166	70.12 159	59.34 186

PS-CasMVS		48.18 171	53.25 161	42.27 184	51.26 171	57.94 161	46.51 185	50.52 48	41.30 175	10.56 214	45.35 139	40.34 191	23.04 203	58.66 179	61.79 158	71.74 150	67.38 135

thresconf0.02		48.17 172	51.22 179	44.60 169	55.14 136	55.73 168	48.95 172	41.35 148	43.43 151	21.23 184	42.03 172	37.25 204	31.19 181	62.33 158	60.61 167	69.76 161	57.17 193

PatchT		48.08 173	51.03 181	44.64 168	42.96 202	50.12 190	40.36 209	35.09 186	43.17 152	39.59 114	42.00 173	39.96 192	46.63 117	58.97 175	60.31 168	63.21 189	62.66 172

tpmrst		48.08 173	49.88 188	45.98 160	52.71 158	48.11 196	53.62 156	33.70 195	48.70 99	39.74 113	48.96 95	46.23 149	40.29 143	50.14 212	49.28 210	55.80 206	57.71 191

DTE-MVSNet		48.03 175	53.28 160	41.91 185	54.64 142	57.50 163	44.63 199	51.66 42	41.02 177	7.97 222	46.26 127	40.90 186	20.24 206	60.45 169	62.89 151	72.33 143	63.97 167

WR-MVS_H		47.65 176	53.67 155	40.63 191	51.45 168	59.74 143	44.71 198	49.37 53	40.69 179	7.61 223	46.04 131	44.34 169	17.32 210	57.79 184	61.18 159	73.30 122	65.86 150

MDTV_nov1_ep13_2view		47.62 177	49.72 189	45.18 165	48.05 181	53.70 177	54.90 148	33.80 194	39.90 184	29.79 152	38.85 186	41.89 178	39.17 145	58.99 174	55.55 187	65.34 184	59.17 187

tfpnview11		47.58 178	51.57 172	42.92 179	54.94 138	55.30 170	46.21 186	41.58 143	42.10 167	18.54 193	42.25 169	41.54 181	27.12 192	62.29 159	61.12 160	69.15 163	56.40 197

tfpn_n400		47.56 179	51.56 173	42.90 180	54.91 139	55.28 171	46.21 186	41.59 141	41.51 173	18.54 193	42.25 169	41.54 181	27.12 192	62.41 156	61.02 162	69.05 164	56.90 195

tfpnconf		47.56 179	51.56 173	42.90 180	54.91 139	55.28 171	46.21 186	41.59 141	41.51 173	18.54 193	42.25 169	41.54 181	27.12 192	62.41 156	61.02 162	69.05 164	56.90 195

SixPastTwentyTwo		47.55 181	50.25 186	44.41 171	47.30 186	54.31 176	47.81 177	40.36 158	33.76 206	19.93 190	43.75 148	32.77 211	42.07 135	59.82 171	60.94 164	68.98 166	66.37 144

TinyColmap		47.08 182	47.56 196	46.52 159	42.35 204	53.44 178	51.77 164	40.70 155	43.44 150	31.92 141	29.78 204	23.72 224	45.04 124	61.99 161	59.54 173	67.35 175	61.03 179

pmmvs5		47.07 183	51.02 182	42.46 182	45.18 194	51.47 186	48.23 176	33.09 201	38.17 198	28.62 158	46.60 123	43.48 173	30.74 182	58.28 181	58.63 178	68.92 167	60.48 181

LTVRE_ROB	44.17 16	47.06 184	50.15 187	43.44 175	51.39 169	58.42 156	42.90 203	43.51 106	22.27 227	14.85 204	41.94 174	34.57 207	45.43 121	62.28 160	62.77 153	62.56 192	68.83 131
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		46.75 185	51.24 178	41.51 187	54.39 147	55.60 169	43.85 200	40.90 151	41.82 169	16.71 200	41.26 175	41.58 180	23.96 199	60.76 167	60.27 170	69.26 162	57.42 192

RPMNet		46.41 186	48.72 191	43.72 173	47.77 183	52.94 179	46.02 191	33.92 192	44.41 139	31.82 142	36.89 189	37.42 203	37.41 154	53.88 204	54.02 197	65.37 183	61.47 177

RPSCF		46.41 186	54.42 148	37.06 203	25.70 231	45.14 208	45.39 194	20.81 225	62.79 53	35.10 130	44.92 141	55.60 90	43.56 129	56.12 195	52.45 204	51.80 215	63.91 168

CVMVSNet		46.38 188	52.01 170	39.81 193	42.40 203	50.26 189	46.15 189	37.68 175	40.03 183	15.09 203	46.56 124	47.56 134	33.72 174	56.50 192	55.65 186	63.80 188	67.53 133

TESTMET0.1,1		46.09 189	50.29 184	41.18 189	36.91 213	47.16 199	49.52 168	20.32 226	39.22 187	31.98 139	43.65 150	47.93 130	41.29 139	56.80 188	55.36 188	67.08 177	61.94 175

test-mter		45.30 190	50.37 183	39.38 195	33.65 219	46.99 201	47.59 178	18.59 228	38.75 191	28.00 159	43.28 155	46.82 145	41.50 138	57.28 186	55.78 185	66.93 180	63.70 169

gm-plane-assit		44.74 191	45.95 198	43.33 176	60.88 72	46.79 204	36.97 213	32.24 206	24.15 223	11.79 209	29.26 208	32.97 210	46.64 116	65.09 144	62.95 150	71.45 151	60.42 182

EPMVS		44.66 192	47.86 195	40.92 190	47.97 182	44.70 209	47.58 179	33.27 198	48.11 105	29.58 153	49.65 85	44.38 168	34.65 168	51.71 207	47.90 214	52.49 214	48.57 214

PM-MVS		44.55 193	48.13 194	40.37 192	32.85 221	46.82 203	46.11 190	29.28 210	40.48 180	29.99 150	39.98 180	34.39 208	41.80 137	56.08 196	53.88 201	62.19 193	65.31 156

TAMVS		44.02 194	49.18 190	37.99 201	47.03 187	45.97 206	45.04 195	28.47 212	39.11 190	20.23 189	43.22 156	48.52 126	28.49 190	58.15 182	57.95 181	58.71 198	51.36 205

MIMVSNet		43.79 195	48.53 192	38.27 199	41.46 205	48.97 194	50.81 166	32.88 203	44.55 137	22.07 182	32.05 197	47.15 138	24.76 198	58.73 177	56.09 184	57.63 203	52.14 203

test0.0.03 1		43.15 196	46.95 197	38.72 198	55.26 133	50.56 188	42.48 204	43.48 108	38.16 199	15.11 202	35.07 193	44.69 164	16.47 212	55.95 197	54.34 196	59.54 197	49.87 212

Anonymous20231206		42.28 197	45.89 199	38.07 200	51.96 164	48.98 193	43.66 202	38.81 168	38.74 193	14.32 205	26.74 211	40.90 186	20.94 205	56.64 191	54.67 194	58.71 198	54.59 199

MVS-HIRNet		42.24 198	41.15 211	43.51 174	44.06 201	40.74 212	35.77 216	35.35 185	35.38 203	38.34 117	25.63 213	38.55 198	43.48 130	50.77 209	47.03 218	64.07 186	49.98 210

MDA-MVSNet-bldmvs		41.36 199	43.15 207	39.27 197	28.74 225	52.68 181	44.95 197	40.84 153	32.89 208	18.13 197	31.61 199	22.09 226	38.97 148	50.45 211	56.11 183	64.01 187	56.23 198

FMVSNet5		40.96 200	45.81 200	35.29 207	34.30 216	44.55 210	47.28 181	28.84 211	40.76 178	21.62 183	29.85 203	42.44 176	24.77 197	57.53 185	55.00 191	54.93 208	50.56 208

CHOSEN 280x420		40.80 201	45.05 203	35.84 206	32.95 220	29.57 227	44.98 196	23.71 223	37.54 200	18.42 196	31.36 200	47.07 141	46.41 119	56.71 190	54.65 195	48.55 220	58.47 190

LP		40.79 202	41.99 208	39.38 195	40.98 206	46.49 205	42.14 205	33.66 196	35.37 204	29.89 151	29.30 207	27.81 215	32.74 176	52.55 205	52.19 205	56.87 204	50.23 209

Anonymous20231211		40.75 203	41.57 209	39.80 194	54.71 141	52.32 183	41.42 207	45.09 74	24.45 222	6.80 224	14.58 226	23.43 225	23.08 202	56.20 194	58.74 177	67.68 174	61.31 178

ADS-MVSNet		40.67 204	43.38 206	37.50 202	44.36 197	39.79 215	42.09 206	32.67 204	44.34 141	28.87 157	40.76 179	40.37 190	30.22 183	48.34 222	45.87 220	46.81 223	44.21 218

EU-MVSNet		40.63 205	45.65 201	34.78 208	39.11 209	46.94 202	40.02 210	34.03 191	33.50 207	10.37 215	35.57 192	37.80 200	23.65 200	51.90 206	50.21 209	61.49 194	63.62 170

test20.03		40.38 206	44.20 204	35.92 205	53.73 154	49.05 192	38.54 211	43.49 107	32.55 209	9.54 217	27.88 210	39.12 195	12.24 223	56.28 193	54.69 193	57.96 202	49.83 213

FC-MVSNet-test		39.65 207	48.35 193	29.49 214	44.43 196	39.28 216	30.23 223	40.44 156	43.59 146	3.12 234	53.00 74	42.03 177	10.02 230	55.09 200	54.77 192	48.66 219	50.71 207

testgi		38.71 208	43.64 205	32.95 210	52.30 163	48.63 195	35.59 217	35.05 187	31.58 213	9.03 220	30.29 201	40.75 188	11.19 228	55.30 199	53.47 202	54.53 211	45.48 216

FPMVS		38.36 209	40.41 212	35.97 204	38.92 210	39.85 214	45.50 193	25.79 221	41.13 176	18.70 192	30.10 202	24.56 219	31.86 180	49.42 217	46.80 219	55.04 207	51.03 206

GG-mvs-BLEND		36.62 210	53.39 159	17.06 228	0.01 236	58.61 154	48.63 173	0.01 234	47.13 112	0.02 239	43.98 145	60.64 70	0.03 234	54.92 202	51.47 207	53.64 212	56.99 194

MIMVSNet1		35.51 211	41.41 210	28.63 216	27.53 227	43.36 211	38.09 212	33.82 193	32.01 210	6.77 225	21.63 221	35.43 206	11.97 225	55.05 201	53.99 199	53.59 213	48.36 215

pmmvs3		35.10 212	38.47 213	31.17 212	26.37 230	40.47 213	34.51 219	18.09 229	24.75 221	16.88 199	23.05 217	26.69 217	32.69 177	50.73 210	51.60 206	58.46 201	51.98 204

testpf		34.85 213	36.16 218	33.31 209	47.49 184	35.56 223	36.85 214	32.31 205	23.08 224	15.63 201	29.39 206	29.48 214	19.62 208	41.38 225	41.07 224	47.95 221	53.18 201

PMVS	27.84 18	33.81 214	35.28 219	32.09 211	34.13 217	24.81 230	32.51 220	26.48 219	26.41 220	19.37 191	23.76 216	24.02 223	25.18 196	50.78 208	47.24 217	54.89 210	49.95 211
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test2356		33.40 215	36.53 216	29.76 213	37.51 212	38.39 218	34.68 218	27.35 214	27.88 215	10.61 212	25.54 214	24.44 220	17.15 211	49.99 214	48.32 212	51.24 216	41.16 222

new-patchmatchnet		33.24 216	37.20 214	28.62 217	44.32 198	38.26 220	29.68 226	36.05 183	31.97 211	6.33 226	26.59 212	27.33 216	11.12 229	50.08 213	41.05 225	44.23 224	45.15 217

N_pmnet		32.67 217	36.85 215	27.79 218	40.55 207	32.13 226	35.80 215	26.79 218	37.24 201	9.10 218	32.02 198	30.94 212	16.30 213	47.22 223	41.21 223	38.21 226	37.21 223

1111		31.35 218	33.52 222	28.83 215	44.28 199	32.44 224	31.71 221	33.25 199	27.87 216	10.92 210	22.18 219	24.05 221	15.89 214	49.03 220	44.09 221	36.94 228	34.96 224

testus		31.33 219	36.31 217	25.52 222	37.55 211	38.40 217	25.87 227	23.58 224	26.46 219	5.97 227	24.15 215	24.92 218	12.44 222	49.14 219	48.21 213	47.73 222	42.86 219

testmv		30.97 220	34.42 220	26.95 219	36.49 214	37.38 221	29.80 224	27.28 215	22.34 225	4.72 228	20.63 223	20.64 227	13.22 220	49.86 216	47.74 215	50.20 217	42.36 220

test1235678		30.97 220	34.42 220	26.95 219	36.49 214	37.38 221	29.79 225	27.28 215	22.33 226	4.72 228	20.62 224	20.64 227	13.22 220	49.87 215	47.74 215	50.20 217	42.36 220

no-one		29.19 222	31.89 223	26.05 221	30.96 222	38.33 219	21.54 228	29.86 208	15.84 231	3.56 231	11.28 230	13.03 232	14.44 219	38.96 226	52.83 203	55.96 205	52.92 202

Gipuma		25.87 223	26.91 226	24.66 223	28.98 224	20.17 231	20.46 230	34.62 189	29.55 214	9.10 218	4.91 234	5.31 236	15.76 216	49.37 218	49.10 211	39.03 225	29.95 227
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test12356		23.91 224	28.47 224	18.60 225	26.80 229	28.30 228	20.92 229	19.76 227	19.89 228	2.88 236	18.48 225	16.57 230	4.05 231	42.34 224	41.93 222	37.21 227	31.75 225

new_pmnet		23.19 225	28.17 225	17.37 226	17.03 232	24.92 229	19.66 231	16.16 231	27.05 218	4.42 230	20.77 222	19.20 229	12.19 224	37.71 227	36.38 226	34.77 229	31.17 226

.test1245		22.44 226	22.23 227	22.67 224	44.28 199	32.44 224	31.71 221	33.25 199	27.87 216	10.92 210	22.18 219	24.05 221	15.89 214	49.03 220	0.01 232	0.00 236	0.06 234

PMMVS2		15.84 227	19.68 228	11.35 230	15.74 233	16.95 232	13.31 232	17.64 230	16.08 230	0.36 238	13.12 227	11.47 233	1.69 233	28.82 228	27.24 228	19.38 232	24.09 229

E-PMN		15.09 228	13.19 230	17.30 227	27.80 226	12.62 234	7.81 234	27.54 213	14.62 233	3.19 232	6.89 231	2.52 239	15.09 217	15.93 230	20.22 229	22.38 230	19.53 230

EMVS		14.49 229	12.45 231	16.87 229	27.02 228	12.56 235	8.13 233	27.19 217	15.05 232	3.14 233	6.69 232	2.67 238	15.08 218	14.60 232	18.05 230	20.67 231	17.56 232

MVE	12.28 19	13.53 230	15.72 229	10.96 231	7.39 234	15.71 233	6.05 235	23.73 222	10.29 235	3.01 235	5.77 233	3.41 237	11.91 226	20.11 229	29.79 227	13.67 233	24.98 228
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.01 231	0.02 232	0.00 233	0.00 237	0.00 238	0.01 239	0.00 235	0.01 236	0.00 240	0.03 236	0.00 240	0.01 235	0.01 234	0.01 232	0.00 236	0.06 234

test123		0.01 231	0.02 232	0.00 233	0.00 237	0.00 238	0.00 240	0.00 235	0.01 236	0.00 240	0.04 235	0.00 240	0.01 235	0.00 235	0.01 232	0.00 236	0.07 233

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

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

ambc			45.54 202		50.66 175	52.63 182	40.99 208		38.36 196	24.67 176	22.62 218	13.94 231	29.14 187	65.71 138	58.06 180	58.60 200	67.43 134

MTAPA										65.14 1		80.20 13

MTMP										62.63 10		78.04 20

Patchmatch-RL test							1.04 238

tmp_tt				5.40 232	3.97 235	2.35 237	3.26 237	0.44 233	17.56 229	12.09 207	11.48 229	7.14 234	1.98 232	15.68 231	15.49 231	10.69 234

XVS					70.49 31	76.96 20	74.36 39			54.48 43		74.47 31				82.24 18

X-MVStestdata					70.49 31	76.96 20	74.36 39			54.48 43		74.47 31				82.24 18

abl_6				64.36 39	70.08 34	77.45 16	72.88 44	50.15 50	71.31 37	54.77 42	62.79 40	77.99 21	56.80 44			81.50 33	83.91 34

mPP-MVS					71.67 27							74.36 34

NP-MVS									72.00 34

Patchmtry						47.61 197	48.27 174	38.86 166		39.59 114

DeepMVS_CX						6.95 236	5.98 236	2.25 232	11.73 234	2.07 237	11.85 228	5.43 235	11.75 227	11.40 233		8.10 235	18.38 231

High-resolution multi-view benchmark