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	38.91 162	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	43.18 112	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	52.95 155	68.82 55	69.00 49	38.28 169	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	43.58 101	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	43.07 117	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	41.22 147	78.02 17	56.04 30	46.65 120	59.50 73	57.50 36	69.67 63	65.27 120	72.70 132	76.67 67

TSAR-MVS + COLMAP		62.65 55	69.90 46	54.19 98	46.31 187	66.73 74	65.49 89	41.36 145	76.57 19	46.31 68	76.80 11	56.68 82	53.27 67	69.50 64	66.65 83	72.40 138	76.36 75

MVS_Test		62.40 56	66.23 55	57.94 59	59.77 76	64.77 96	66.50 77	41.76 137	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	57.21 120	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	57.21 120	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	47.38 183	65.33 89	65.35 90	36.51 180	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	37.11 175	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	42.26 134	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	55.09 135	59.86 138	64.80 93	39.55 161	58.39 58	46.21 70	60.48 49	67.82 52	49.27 100	63.53 147	63.32 143	70.64 153	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	27.23 165	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	40.83 175	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	53.24 154	64.84 95	64.47 97	40.41 155	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	42.84 119	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	55.77 128	66.75 73	63.92 99	37.04 176	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	42.84 119	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	42.84 119	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	42.64 127	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	42.49 129	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	42.47 131	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	57.16 122	66.05 86	67.17 56	43.52 103	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	57.26 118	63.28 109	66.58 76	42.42 132	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	43.11 114	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	43.11 114	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	43.10 116	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	53.68 153	45.65 68	49.51 86	31.58 142	62.91 38	68.47 47	35.85 163	68.20 78	67.28 74	74.03 110	69.24 128

Vis-MVSNet		58.48 85	65.70 58	50.06 122	53.40 153	67.20 69	60.24 116	43.32 109	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	42.33 133	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	42.73 124	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	42.81 122	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	42.77 123	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	42.68 125	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	42.65 126	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	43.15 113	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	58.40 155	59.42 117	43.64 98	48.71 97	40.25 111	57.53 58	58.55 76	52.15 90	65.42 140	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	42.67 164	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	41.48 144	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	42.49 129	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	26.49 167	57.81 57	60.05 71	34.51 167	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	42.77 160	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	50.82 171	58.79 151	61.55 106	37.86 172	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	47.44 152	46.54 186	66.73 74	56.30 133	43.28 110	50.06 81	32.99 135	62.57 42	63.26 59	33.31 173	68.25 75	67.58 71	72.20 142	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	43.63 100	38.44 193	41.44 105	38.05 185	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	48.16 178	62.78 114	66.30 79	37.93 171	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	24.31 176	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	56.62 125	69.27 54	64.77 94	42.21 135	45.66 132	37.50 121	33.08 194	57.47 81	53.33 65	65.46 139	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	41.56 142	54.17 65	48.57 61	49.18 87	46.54 145	50.44 97	61.93 160	58.82 174	68.34 167	67.28 136

CostFormer		56.57 109	59.13 112	53.60 100	57.52 94	61.12 127	66.94 60	35.95 182	53.44 67	44.68 86	55.87 64	54.44 90	48.21 106	60.37 168	58.33 177	68.27 169	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	32.89 200	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	20.52 186	52.87 74	47.33 136	36.81 160	69.12 66	69.03 59	77.56 60	69.89 117

CHOSEN 1792x2688		55.85 112	58.01 121	53.33 102	57.26 118	62.82 113	63.29 104	41.55 143	46.65 115	38.34 116	34.55 192	53.50 92	52.43 84	67.10 113	67.56 72	67.13 174	73.92 105

v7n		55.67 113	57.46 126	53.59 101	56.06 126	65.29 90	61.06 110	43.26 111	40.17 180	37.99 118	40.79 176	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	55.23 133	63.09 110	61.08 109	40.15 157	42.95 153	37.02 124	52.61 75	47.68 131	47.51 110	65.92 132	65.35 117	74.49 105	70.68 114

v148		55.58 115	57.61 125	53.20 105	54.59 144	61.86 116	61.18 108	38.70 167	44.30 141	42.25 102	47.53 115	50.24 123	48.73 102	65.15 141	62.61 153	73.79 112	71.61 108

DU-MVS		55.41 116	59.59 105	50.54 119	54.60 142	62.97 111	57.44 124	51.80 38	48.62 101	24.31 176	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	20.59 185	51.99 78	44.73 162	34.10 170	68.58 71	68.64 63	77.66 56	70.67 115

GBi-Net		55.20 118	60.25 85	49.31 126	52.42 157	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	52.42 157	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	55.31 130	64.59 97	55.77 138	50.63 45	48.66 100	20.95 184	51.47 80	50.40 121	34.41 169	67.81 93	67.89 68	77.11 66	71.88 106

FMVSNet2		55.04 121	59.95 98	49.31 126	52.42 157	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	52.37 160	61.31 125	56.72 131	44.04 85	49.18 89	30.47 144	48.28 106	58.19 77	38.09 151	65.48 138	65.20 122	73.31 120	69.45 127

pmmvs4		54.66 123	56.07 132	53.00 107	54.63 141	57.08 162	60.43 115	44.10 83	51.69 77	40.55 109	46.55 124	44.79 161	45.95 119	62.54 152	63.66 138	72.36 140	66.20 145

FMVSNet1		54.08 124	58.68 115	48.71 138	50.90 170	61.35 124	56.73 130	43.94 89	45.91 126	29.32 154	42.72 163	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	59.04 149	60.65 111	40.11 158	46.35 117	42.77 98	49.07 88	41.07 184	51.06 96	58.62 178	58.96 173	67.00 177	67.06 137

v52		53.60 126	56.74 130	49.93 123	45.54 190	61.64 119	60.65 111	36.99 177	38.75 189	36.32 126	39.64 180	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	45.54 190	61.64 119	60.65 111	36.99 177	38.74 191	36.33 125	39.64 180	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	48.37 141	54.60 142	59.25 147	56.10 134	51.84 37	49.32 88	17.92 196	45.38 136	47.68 131	36.93 159	68.11 82	65.95 94	72.84 126	69.57 122

IterMVS		53.45 129	57.12 128	49.17 129	49.23 175	60.93 128	59.05 119	34.63 186	44.53 137	33.22 134	51.09 82	51.01 117	48.38 105	62.43 153	60.79 163	70.54 155	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	53.41 156	53.17 106	58.16 82	59.15 148	63.73 101	38.27 170	50.73 80	46.98 63	45.57 135	44.00 169	49.20 101	55.90 196	54.02 195	62.65 189	64.50 163

v748		52.93 131	55.29 139	50.19 121	51.90 164	61.31 125	56.54 132	40.05 159	39.12 187	34.82 133	39.93 179	43.83 170	43.66 126	64.26 145	63.32 143	74.15 108	75.28 84

anonymousdsp		52.84 132	57.78 123	47.06 153	40.24 206	58.95 150	53.70 152	33.54 195	36.51 200	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	43.00 159	45.06 157	37.32 154	66.38 121	64.54 127	72.71 130	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	43.00 159	45.06 157	37.32 154	66.38 121	64.54 127	72.71 130	66.54 139

CDS-MVSNet		52.42 135	57.06 129	47.02 154	53.92 151	58.30 157	55.50 140	46.47 61	42.52 163	29.38 153	49.50 85	52.85 102	28.49 188	66.70 119	66.89 79	68.34 167	62.63 172
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	48.90 135	57.39 101	60.18 132	55.60 139	43.73 95	42.93 154	27.41 163	43.35 152	45.09 156	36.61 161	66.36 123	63.92 137	72.66 133	65.78 151

thres400		52.38 137	55.51 134	48.74 137	57.49 96	60.10 135	55.45 141	43.54 102	42.90 155	26.72 166	43.34 153	45.03 160	36.61 161	66.20 129	64.53 129	72.66 133	66.43 141

EPNet_dtu		52.05 138	58.26 119	44.81 165	54.10 149	50.09 189	52.01 161	40.82 152	53.03 72	27.41 163	54.90 66	57.96 80	26.72 193	62.97 149	62.70 152	67.78 171	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	48.80 136	57.31 113	59.33 143	55.30 145	42.92 118	42.85 156	27.81 159	43.00 159	45.06 157	36.99 158	64.74 143	63.51 140	72.47 137	65.21 157

COLMAP_ROB	46.52 15	51.99 140	54.86 144	48.63 139	49.13 176	61.73 118	60.53 114	36.57 179	53.14 70	32.95 136	37.10 186	38.68 195	40.49 140	65.72 135	63.08 146	72.11 143	64.60 162
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	48.27 143	57.41 100	60.05 136	54.74 147	43.64 98	42.57 162	25.88 169	43.11 157	44.48 165	35.34 164	66.27 126	63.61 139	72.61 136	65.80 150

TransMVSNet (Re)		51.92 142	55.38 137	47.88 148	60.95 70	59.90 137	53.95 150	45.14 72	39.47 184	24.85 173	43.87 146	46.51 146	29.15 184	67.55 99	65.23 121	73.26 122	65.16 158

thres600view7		51.91 143	55.14 140	48.14 144	57.43 99	60.18 132	54.60 148	43.73 95	42.61 161	25.20 171	43.10 158	44.47 166	35.19 165	66.36 123	63.28 145	72.66 133	66.01 148

conf0.002		51.76 144	54.13 150	49.00 134	57.28 117	60.15 134	55.42 142	43.75 94	42.85 156	27.49 162	43.13 156	37.12 203	37.32 154	66.23 128	64.17 132	72.72 128	65.24 156

view800		51.55 145	54.89 143	47.66 151	57.37 103	59.77 140	53.62 154	43.72 97	42.22 164	24.94 172	42.80 162	43.81 171	33.94 171	66.09 130	64.38 131	72.39 139	65.14 159

pmmvs-eth3d		51.33 146	52.25 166	50.26 120	50.82 171	54.65 172	56.03 136	43.45 108	43.51 147	37.20 123	39.20 183	39.04 194	42.28 133	61.85 161	62.78 150	71.78 147	64.72 161

USDC		51.11 147	53.71 152	48.08 146	44.76 193	55.99 165	53.01 158	40.90 149	52.49 74	36.14 127	44.67 141	33.66 207	43.27 131	63.23 148	61.10 159	70.39 156	64.82 160

pm-mvs1		51.02 148	55.55 133	45.73 160	54.16 148	58.52 153	50.92 163	42.56 128	40.32 179	25.67 170	43.66 148	50.34 122	30.06 182	65.85 133	63.97 136	70.99 152	66.21 144

conf0.05thres1000		50.64 149	53.84 151	46.92 156	57.02 123	59.29 145	52.29 160	43.80 90	39.84 183	23.81 179	39.26 182	43.14 174	32.52 177	65.74 134	64.04 133	72.05 144	65.53 153

tfpn		50.58 150	53.65 154	47.00 155	57.34 109	59.31 144	52.41 159	43.76 93	41.81 168	23.86 178	42.49 165	37.80 198	32.63 176	65.68 137	64.02 135	71.99 145	64.41 164

CR-MVSNet		50.47 151	52.61 161	47.98 147	49.03 177	52.94 177	48.27 172	38.86 164	44.41 138	39.59 113	44.34 142	44.65 164	46.63 116	58.97 173	60.31 166	65.48 180	62.66 170

dps		50.42 152	51.20 178	49.51 125	55.88 127	56.07 164	53.73 151	38.89 163	43.66 144	40.36 110	45.66 133	37.63 200	45.23 121	59.05 171	56.18 180	62.94 188	60.16 181

Vis-MVSNet (Re-imp)		50.37 153	57.73 124	41.80 184	57.53 93	54.35 173	45.70 190	45.24 71	49.80 82	13.43 204	58.23 56	56.42 83	20.11 205	62.96 150	63.36 142	68.76 166	58.96 186

MDTV_nov1_ep13		50.32 154	52.43 164	47.86 149	49.87 174	54.70 171	58.10 121	34.29 188	45.59 134	37.71 119	47.44 116	47.42 135	41.86 135	58.07 181	55.21 188	65.34 182	58.56 187

tfpnnormal		50.16 155	52.19 167	47.78 150	56.86 124	58.37 156	54.15 149	44.01 88	38.35 195	25.94 168	36.10 188	37.89 197	34.50 168	65.93 131	63.42 141	71.26 150	65.28 155

PatchMatch-RL		50.11 156	51.56 171	48.43 140	46.23 188	51.94 182	50.21 165	38.62 168	46.62 116	37.51 120	42.43 166	39.38 192	52.24 89	60.98 164	59.56 170	65.76 179	60.01 183

PatchmatchNet		49.92 157	51.29 174	48.32 142	51.83 165	51.86 183	53.38 157	37.63 174	47.90 105	40.83 108	48.54 103	45.30 153	45.19 122	56.86 185	53.99 197	61.08 193	54.57 198
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TDRefinement		49.31 158	52.44 163	45.67 161	30.44 221	59.42 142	59.24 118	39.78 160	48.76 96	31.20 143	35.73 189	29.90 211	42.81 132	64.24 146	62.59 154	70.55 154	66.43 141

test-LLR		49.28 159	50.29 182	48.10 145	55.26 131	47.16 197	49.52 166	43.48 106	39.22 185	31.98 138	43.65 149	47.93 129	41.29 138	56.80 186	55.36 186	67.08 175	61.94 173

CMPMVS	37.70 17	49.24 160	52.71 160	45.19 162	45.97 189	51.23 185	47.44 178	29.31 207	43.04 152	44.69 85	34.45 193	48.35 126	43.64 127	62.59 151	59.82 169	60.08 194	69.48 124
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PEN-MVS		49.21 161	54.32 147	43.24 176	54.33 147	59.26 146	47.04 181	51.37 42	41.67 169	9.97 214	46.22 127	41.80 178	22.97 202	60.52 166	64.03 134	73.73 113	66.75 138

PMMVS		49.20 162	54.28 149	43.28 175	34.13 215	45.70 205	48.98 169	26.09 218	46.31 119	34.92 132	55.22 65	53.47 93	47.48 111	59.43 170	59.04 172	68.05 170	60.77 178

gg-mvs-nofinetune		49.07 163	52.56 162	45.00 164	61.99 65	59.78 139	53.55 156	41.63 138	31.62 210	12.08 206	29.56 203	53.28 94	29.57 183	66.27 126	64.49 130	71.19 151	62.92 169

tpm		48.82 164	51.27 175	45.96 159	54.10 149	47.35 196	56.05 135	30.23 205	46.70 114	43.21 96	52.54 76	47.55 134	37.28 157	54.11 201	50.50 206	54.90 207	60.12 182

WR-MVS		48.78 165	55.06 142	41.45 186	55.50 129	60.40 129	43.77 199	49.99 50	41.92 166	8.10 219	45.24 139	45.56 150	17.47 207	61.57 162	64.60 126	73.85 111	66.14 147

CP-MVSNet		48.37 166	53.53 155	42.34 181	51.35 168	58.01 158	46.56 182	50.54 46	41.62 170	10.61 210	46.53 125	40.68 188	23.18 199	58.71 176	61.83 155	71.81 146	67.36 135

pmmvs6		48.35 167	51.64 169	44.51 168	51.92 163	57.94 159	49.44 168	42.17 136	34.45 203	24.62 175	28.87 207	46.90 143	29.07 186	64.60 144	63.08 146	69.83 158	65.68 152

tfpn_ndepth		48.34 168	52.27 165	43.76 170	54.35 146	56.46 163	47.24 180	40.92 148	43.45 148	21.04 183	41.16 174	43.22 173	28.90 187	61.57 162	60.65 164	70.12 157	59.34 184

PS-CasMVS		48.18 169	53.25 159	42.27 182	51.26 169	57.94 159	46.51 183	50.52 47	41.30 173	10.56 212	45.35 138	40.34 190	23.04 201	58.66 177	61.79 156	71.74 148	67.38 134

thresconf0.02		48.17 170	51.22 177	44.60 167	55.14 134	55.73 166	48.95 170	41.35 146	43.43 150	21.23 182	42.03 170	37.25 202	31.19 179	62.33 156	60.61 165	69.76 159	57.17 191

PatchT		48.08 171	51.03 179	44.64 166	42.96 200	50.12 188	40.36 207	35.09 184	43.17 151	39.59 113	42.00 171	39.96 191	46.63 116	58.97 173	60.31 166	63.21 187	62.66 170

tpmrst		48.08 171	49.88 186	45.98 158	52.71 156	48.11 194	53.62 154	33.70 193	48.70 98	39.74 112	48.96 94	46.23 148	40.29 142	50.14 210	49.28 208	55.80 204	57.71 189

DTE-MVSNet		48.03 173	53.28 158	41.91 183	54.64 140	57.50 161	44.63 197	51.66 41	41.02 175	7.97 220	46.26 126	40.90 185	20.24 204	60.45 167	62.89 149	72.33 141	63.97 165

WR-MVS_H		47.65 174	53.67 153	40.63 189	51.45 166	59.74 141	44.71 196	49.37 52	40.69 177	7.61 221	46.04 130	44.34 168	17.32 208	57.79 182	61.18 157	73.30 121	65.86 149

MDTV_nov1_ep13_2view		47.62 175	49.72 187	45.18 163	48.05 179	53.70 175	54.90 146	33.80 192	39.90 182	29.79 151	38.85 184	41.89 177	39.17 144	58.99 172	55.55 185	65.34 182	59.17 185

tfpnview11		47.58 176	51.57 170	42.92 177	54.94 136	55.30 168	46.21 184	41.58 141	42.10 165	18.54 191	42.25 167	41.54 180	27.12 190	62.29 157	61.12 158	69.15 161	56.40 195

tfpn_n400		47.56 177	51.56 171	42.90 178	54.91 137	55.28 169	46.21 184	41.59 139	41.51 171	18.54 191	42.25 167	41.54 180	27.12 190	62.41 154	61.02 160	69.05 162	56.90 193

tfpnconf		47.56 177	51.56 171	42.90 178	54.91 137	55.28 169	46.21 184	41.59 139	41.51 171	18.54 191	42.25 167	41.54 180	27.12 190	62.41 154	61.02 160	69.05 162	56.90 193

SixPastTwentyTwo		47.55 179	50.25 184	44.41 169	47.30 184	54.31 174	47.81 175	40.36 156	33.76 204	19.93 188	43.75 147	32.77 209	42.07 134	59.82 169	60.94 162	68.98 164	66.37 143

TinyColmap		47.08 180	47.56 194	46.52 157	42.35 202	53.44 176	51.77 162	40.70 153	43.44 149	31.92 140	29.78 202	23.72 222	45.04 123	61.99 159	59.54 171	67.35 173	61.03 177

pmmvs5		47.07 181	51.02 180	42.46 180	45.18 192	51.47 184	48.23 174	33.09 199	38.17 196	28.62 157	46.60 122	43.48 172	30.74 180	58.28 179	58.63 176	68.92 165	60.48 179

LTVRE_ROB	44.17 16	47.06 182	50.15 185	43.44 173	51.39 167	58.42 154	42.90 201	43.51 104	22.27 225	14.85 202	41.94 172	34.57 205	45.43 120	62.28 158	62.77 151	62.56 190	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		46.75 183	51.24 176	41.51 185	54.39 145	55.60 167	43.85 198	40.90 149	41.82 167	16.71 198	41.26 173	41.58 179	23.96 197	60.76 165	60.27 168	69.26 160	57.42 190

RPMNet		46.41 184	48.72 189	43.72 171	47.77 181	52.94 177	46.02 189	33.92 190	44.41 138	31.82 141	36.89 187	37.42 201	37.41 153	53.88 202	54.02 195	65.37 181	61.47 175

RPSCF		46.41 184	54.42 146	37.06 201	25.70 229	45.14 206	45.39 192	20.81 223	62.79 52	35.10 129	44.92 140	55.60 89	43.56 128	56.12 193	52.45 202	51.80 213	63.91 166

CVMVSNet		46.38 186	52.01 168	39.81 191	42.40 201	50.26 187	46.15 187	37.68 173	40.03 181	15.09 201	46.56 123	47.56 133	33.72 172	56.50 190	55.65 184	63.80 186	67.53 132

TESTMET0.1,1		46.09 187	50.29 182	41.18 187	36.91 211	47.16 197	49.52 166	20.32 224	39.22 185	31.98 138	43.65 149	47.93 129	41.29 138	56.80 186	55.36 186	67.08 175	61.94 173

test-mter		45.30 188	50.37 181	39.38 193	33.65 217	46.99 199	47.59 176	18.59 226	38.75 189	28.00 158	43.28 154	46.82 144	41.50 137	57.28 184	55.78 183	66.93 178	63.70 167

gm-plane-assit		44.74 189	45.95 196	43.33 174	60.88 71	46.79 202	36.97 211	32.24 204	24.15 221	11.79 207	29.26 206	32.97 208	46.64 115	65.09 142	62.95 148	71.45 149	60.42 180

EPMVS		44.66 190	47.86 193	40.92 188	47.97 180	44.70 207	47.58 177	33.27 196	48.11 104	29.58 152	49.65 84	44.38 167	34.65 166	51.71 205	47.90 212	52.49 212	48.57 212

PM-MVS		44.55 191	48.13 192	40.37 190	32.85 219	46.82 201	46.11 188	29.28 208	40.48 178	29.99 149	39.98 178	34.39 206	41.80 136	56.08 194	53.88 199	62.19 191	65.31 154

TAMVS		44.02 192	49.18 188	37.99 199	47.03 185	45.97 204	45.04 193	28.47 210	39.11 188	20.23 187	43.22 155	48.52 125	28.49 188	58.15 180	57.95 179	58.71 196	51.36 203

MIMVSNet		43.79 193	48.53 190	38.27 197	41.46 203	48.97 192	50.81 164	32.88 201	44.55 136	22.07 180	32.05 195	47.15 137	24.76 196	58.73 175	56.09 182	57.63 201	52.14 201

test0.0.03 1		43.15 194	46.95 195	38.72 196	55.26 131	50.56 186	42.48 202	43.48 106	38.16 197	15.11 200	35.07 191	44.69 163	16.47 210	55.95 195	54.34 194	59.54 195	49.87 210

Anonymous20231206		42.28 195	45.89 197	38.07 198	51.96 162	48.98 191	43.66 200	38.81 166	38.74 191	14.32 203	26.74 209	40.90 185	20.94 203	56.64 189	54.67 192	58.71 196	54.59 197

MVS-HIRNet		42.24 196	41.15 209	43.51 172	44.06 199	40.74 210	35.77 214	35.35 183	35.38 201	38.34 116	25.63 211	38.55 196	43.48 129	50.77 207	47.03 216	64.07 184	49.98 208

MDA-MVSNet-bldmvs		41.36 197	43.15 205	39.27 195	28.74 223	52.68 179	44.95 195	40.84 151	32.89 206	18.13 195	31.61 197	22.09 224	38.97 147	50.45 209	56.11 181	64.01 185	56.23 196

FMVSNet5		40.96 198	45.81 198	35.29 205	34.30 214	44.55 208	47.28 179	28.84 209	40.76 176	21.62 181	29.85 201	42.44 175	24.77 195	57.53 183	55.00 189	54.93 206	50.56 206

CHOSEN 280x420		40.80 199	45.05 201	35.84 204	32.95 218	29.57 225	44.98 194	23.71 221	37.54 198	18.42 194	31.36 198	47.07 140	46.41 118	56.71 188	54.65 193	48.55 218	58.47 188

LP		40.79 200	41.99 206	39.38 193	40.98 204	46.49 203	42.14 203	33.66 194	35.37 202	29.89 150	29.30 205	27.81 213	32.74 174	52.55 203	52.19 203	56.87 202	50.23 207

Anonymous20231211		40.75 201	41.57 207	39.80 192	54.71 139	52.32 181	41.42 205	45.09 73	24.45 220	6.80 222	14.58 224	23.43 223	23.08 200	56.20 192	58.74 175	67.68 172	61.31 176

ADS-MVSNet		40.67 202	43.38 204	37.50 200	44.36 195	39.79 213	42.09 204	32.67 202	44.34 140	28.87 156	40.76 177	40.37 189	30.22 181	48.34 220	45.87 218	46.81 221	44.21 216

EU-MVSNet		40.63 203	45.65 199	34.78 206	39.11 207	46.94 200	40.02 208	34.03 189	33.50 205	10.37 213	35.57 190	37.80 198	23.65 198	51.90 204	50.21 207	61.49 192	63.62 168

test20.03		40.38 204	44.20 202	35.92 203	53.73 152	49.05 190	38.54 209	43.49 105	32.55 207	9.54 215	27.88 208	39.12 193	12.24 221	56.28 191	54.69 191	57.96 200	49.83 211

FC-MVSNet-test		39.65 205	48.35 191	29.49 212	44.43 194	39.28 214	30.23 221	40.44 154	43.59 145	3.12 232	53.00 73	42.03 176	10.02 228	55.09 198	54.77 190	48.66 217	50.71 205

testgi		38.71 206	43.64 203	32.95 208	52.30 161	48.63 193	35.59 215	35.05 185	31.58 211	9.03 218	30.29 199	40.75 187	11.19 226	55.30 197	53.47 200	54.53 209	45.48 214

FPMVS		38.36 207	40.41 210	35.97 202	38.92 208	39.85 212	45.50 191	25.79 219	41.13 174	18.70 190	30.10 200	24.56 217	31.86 178	49.42 215	46.80 217	55.04 205	51.03 204

GG-mvs-BLEND		36.62 208	53.39 157	17.06 226	0.01 234	58.61 152	48.63 171	0.01 232	47.13 111	0.02 237	43.98 144	60.64 69	0.03 232	54.92 200	51.47 205	53.64 210	56.99 192

MIMVSNet1		35.51 209	41.41 208	28.63 214	27.53 225	43.36 209	38.09 210	33.82 191	32.01 208	6.77 223	21.63 219	35.43 204	11.97 223	55.05 199	53.99 197	53.59 211	48.36 213

pmmvs3		35.10 210	38.47 211	31.17 210	26.37 228	40.47 211	34.51 217	18.09 227	24.75 219	16.88 197	23.05 215	26.69 215	32.69 175	50.73 208	51.60 204	58.46 199	51.98 202

testpf		34.85 211	36.16 216	33.31 207	47.49 182	35.56 221	36.85 212	32.31 203	23.08 222	15.63 199	29.39 204	29.48 212	19.62 206	41.38 223	41.07 222	47.95 219	53.18 199

PMVS	27.84 18	33.81 212	35.28 217	32.09 209	34.13 215	24.81 228	32.51 218	26.48 217	26.41 218	19.37 189	23.76 214	24.02 221	25.18 194	50.78 206	47.24 215	54.89 208	49.95 209
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test2356		33.40 213	36.53 214	29.76 211	37.51 210	38.39 216	34.68 216	27.35 212	27.88 213	10.61 210	25.54 212	24.44 218	17.15 209	49.99 212	48.32 210	51.24 214	41.16 220

new-patchmatchnet		33.24 214	37.20 212	28.62 215	44.32 196	38.26 218	29.68 224	36.05 181	31.97 209	6.33 224	26.59 210	27.33 214	11.12 227	50.08 211	41.05 223	44.23 222	45.15 215

N_pmnet		32.67 215	36.85 213	27.79 216	40.55 205	32.13 224	35.80 213	26.79 216	37.24 199	9.10 216	32.02 196	30.94 210	16.30 211	47.22 221	41.21 221	38.21 224	37.21 221

1111		31.35 216	33.52 220	28.83 213	44.28 197	32.44 222	31.71 219	33.25 197	27.87 214	10.92 208	22.18 217	24.05 219	15.89 212	49.03 218	44.09 219	36.94 226	34.96 222

testus		31.33 217	36.31 215	25.52 220	37.55 209	38.40 215	25.87 225	23.58 222	26.46 217	5.97 225	24.15 213	24.92 216	12.44 220	49.14 217	48.21 211	47.73 220	42.86 217

testmv		30.97 218	34.42 218	26.95 217	36.49 212	37.38 219	29.80 222	27.28 213	22.34 223	4.72 226	20.63 221	20.64 225	13.22 218	49.86 214	47.74 213	50.20 215	42.36 218

test1235678		30.97 218	34.42 218	26.95 217	36.49 212	37.38 219	29.79 223	27.28 213	22.33 224	4.72 226	20.62 222	20.64 225	13.22 218	49.87 213	47.74 213	50.20 215	42.36 218

no-one		29.19 220	31.89 221	26.05 219	30.96 220	38.33 217	21.54 226	29.86 206	15.84 229	3.56 229	11.28 228	13.03 230	14.44 217	38.96 224	52.83 201	55.96 203	52.92 200

Gipuma		25.87 221	26.91 224	24.66 221	28.98 222	20.17 229	20.46 228	34.62 187	29.55 212	9.10 216	4.91 232	5.31 234	15.76 214	49.37 216	49.10 209	39.03 223	29.95 225
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test12356		23.91 222	28.47 222	18.60 223	26.80 227	28.30 226	20.92 227	19.76 225	19.89 226	2.88 234	18.48 223	16.57 228	4.05 229	42.34 222	41.93 220	37.21 225	31.75 223

new_pmnet		23.19 223	28.17 223	17.37 224	17.03 230	24.92 227	19.66 229	16.16 229	27.05 216	4.42 228	20.77 220	19.20 227	12.19 222	37.71 225	36.38 224	34.77 227	31.17 224

.test1245		22.44 224	22.23 225	22.67 222	44.28 197	32.44 222	31.71 219	33.25 197	27.87 214	10.92 208	22.18 217	24.05 219	15.89 212	49.03 218	0.01 230	0.00 234	0.06 232

PMMVS2		15.84 225	19.68 226	11.35 228	15.74 231	16.95 230	13.31 230	17.64 228	16.08 228	0.36 236	13.12 225	11.47 231	1.69 231	28.82 226	27.24 226	19.38 230	24.09 227

E-PMN		15.09 226	13.19 228	17.30 225	27.80 224	12.62 232	7.81 232	27.54 211	14.62 231	3.19 230	6.89 229	2.52 237	15.09 215	15.93 228	20.22 227	22.38 228	19.53 228

EMVS		14.49 227	12.45 229	16.87 227	27.02 226	12.56 233	8.13 231	27.19 215	15.05 230	3.14 231	6.69 230	2.67 236	15.08 216	14.60 230	18.05 228	20.67 229	17.56 230

MVE	12.28 19	13.53 228	15.72 227	10.96 229	7.39 232	15.71 231	6.05 233	23.73 220	10.29 233	3.01 233	5.77 231	3.41 235	11.91 224	20.11 227	29.79 225	13.67 231	24.98 226
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.01 229	0.02 230	0.00 231	0.00 235	0.00 236	0.01 237	0.00 233	0.01 234	0.00 238	0.03 234	0.00 238	0.01 233	0.01 232	0.01 230	0.00 234	0.06 232

test123		0.01 229	0.02 230	0.00 231	0.00 235	0.00 236	0.00 238	0.00 233	0.01 234	0.00 238	0.04 233	0.00 238	0.01 233	0.00 233	0.01 230	0.00 234	0.07 231

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

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

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

ambc			45.54 200		50.66 173	52.63 180	40.99 206		38.36 194	24.67 174	22.62 216	13.94 229	29.14 185	65.71 136	58.06 178	58.60 198	67.43 133

MTAPA										65.14 1		80.20 12

MTMP										62.63 10		78.04 19

Patchmatch-RL test							1.04 236

tmp_tt				5.40 230	3.97 233	2.35 235	3.26 235	0.44 231	17.56 227	12.09 205	11.48 227	7.14 232	1.98 230	15.68 229	15.49 229	10.69 232

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						47.61 195	48.27 172	38.86 164		39.59 113

DeepMVS_CX						6.95 234	5.98 234	2.25 230	11.73 232	2.07 235	11.85 226	5.43 233	11.75 225	11.40 231		8.10 233	18.38 229

High-resolution multi-view benchmark