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.

MCST-MVS		85.75 4	86.99 7	84.31 1	94.07 1	92.80 2	88.15 2	79.10 1	85.66 16	70.72 23	76.50 26	80.45 12	82.17 2	88.35 1	87.49 2	91.63 2	97.65 1

HPM-MVS++		85.64 5	88.43 2	82.39 6	92.65 2	90.24 19	85.83 8	74.21 5	90.68 5	75.63 12	86.77 8	84.15 2	78.68 8	86.33 6	85.26 8	87.32 42	95.60 12

CNVR-MVS		85.96 3	87.58 5	84.06 2	92.58 3	92.40 5	87.62 3	77.77 2	88.44 8	75.93 11	79.49 19	81.97 9	81.65 3	87.04 5	86.58 3	88.79 14	97.18 3

NCCC		84.16 9	85.46 14	82.64 5	92.34 4	90.57 16	86.57 5	76.51 3	86.85 13	72.91 16	77.20 25	78.69 18	79.09 7	84.64 14	84.88 13	88.44 22	95.41 15

CSCG		82.90 13	84.52 16	81.02 11	91.85 5	93.43 1	87.14 4	74.01 8	81.96 26	76.14 9	70.84 30	82.49 6	69.71 48	82.32 32	85.18 10	87.26 44	95.40 16

QAPM		77.50 38	77.43 41	77.59 29	91.52 6	92.00 8	81.41 33	70.63 20	66.22 62	58.05 62	54.70 64	71.79 36	74.49 24	82.46 28	82.04 29	89.46 9	92.79 40

APDe-MVS		86.37 2	88.41 3	84.00 3	91.43 7	91.83 9	88.34 1	74.67 4	91.19 3	81.76 1	91.13 2	81.94 10	80.07 4	83.38 20	82.58 27	87.69 35	96.78 6

3Dnovator	70.49 5	78.42 32	76.77 47	80.35 13	91.43 7	90.27 18	81.84 29	70.79 19	72.10 49	71.95 17	50.02 80	67.86 49	77.47 11	82.89 23	84.24 15	88.61 18	89.99 66

DeepC-MVS_fast	75.41 2	81.69 17	82.10 26	81.20 10	91.04 9	87.81 41	83.42 20	74.04 7	83.77 20	71.09 21	66.88 38	72.44 30	79.48 5	85.08 10	84.97 12	88.12 31	93.78 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SteuartSystems-ACMMP		82.51 14	85.35 15	79.20 19	90.25 10	89.39 26	84.79 14	70.95 18	82.86 22	68.32 31	86.44 9	77.19 19	73.07 29	83.63 19	83.64 19	87.82 32	94.34 24
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		82.48 15	84.12 17	80.56 12	90.15 11	87.55 43	84.28 16	69.67 27	85.22 17	77.95 7	84.69 11	75.94 22	75.04 19	81.85 37	81.17 42	86.30 60	92.40 42

DeepPCF-MVS	76.94 1	83.08 12	87.77 4	77.60 28	90.11 12	90.96 13	78.48 47	72.63 15	93.10 2	65.84 35	80.67 17	81.55 11	74.80 21	85.94 8	85.39 7	83.75 147	96.77 7

OpenMVS	67.62 8	74.92 49	73.91 57	76.09 37	90.10 13	90.38 17	78.01 49	66.35 45	66.09 64	62.80 41	46.33 105	64.55 57	71.77 37	79.92 52	80.88 48	87.52 38	89.20 73

MAR-MVS		77.19 41	78.37 39	75.81 39	89.87 14	90.58 15	79.33 46	65.56 51	77.62 42	58.33 60	59.24 57	67.98 47	74.83 20	82.37 31	83.12 23	86.95 50	87.67 99
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
TSAR-MVS + ACMM		81.59 19	85.84 13	76.63 32	89.82 15	86.53 50	86.32 7	66.72 43	85.96 15	65.43 36	88.98 6	82.29 7	67.57 65	82.06 35	81.33 40	83.93 145	93.75 31

train_agg		83.35 11	86.93 9	79.17 20	89.70 16	88.41 33	85.60 12	72.89 14	86.31 14	66.58 34	90.48 3	82.24 8	73.06 30	83.10 22	82.64 26	87.21 47	95.30 17

abl_6				79.06 22	89.68 17	92.14 7	77.70 52	69.68 26	86.87 12	71.88 18	74.29 28	80.06 14	76.56 14			88.84 13	95.82 9

APD-MVS		84.83 6	87.00 6	82.30 7	89.61 18	89.21 27	86.51 6	73.64 10	90.98 4	77.99 6	89.89 4	80.04 15	79.18 6	82.00 36	81.37 39	86.88 51	95.49 14
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_Plus		83.54 10	86.37 11	80.25 14	89.57 19	90.10 21	85.27 13	71.66 16	87.38 9	73.08 15	84.23 12	80.16 13	75.31 17	84.85 12	83.64 19	86.57 55	94.21 27

HSP-MVS		86.82 1	89.95 1	83.16 4	89.38 20	91.60 11	85.63 10	74.15 6	94.20 1	75.52 13	94.99 1	83.21 4	85.96 1	87.67 3	85.88 5	88.32 24	92.13 44

AdaColmap		76.23 45	73.55 59	79.35 18	89.38 20	85.00 62	79.99 43	73.04 13	76.60 44	71.17 20	55.18 62	57.99 82	77.87 9	76.82 73	76.82 71	84.67 130	86.45 107

3Dnovator+	70.16 6	77.87 35	77.29 43	78.55 23	89.25 22	88.32 35	80.09 41	67.95 37	74.89 48	71.83 19	52.05 74	70.68 40	76.27 16	82.27 33	82.04 29	85.92 77	90.77 58

CDPH-MVS		79.39 29	82.13 25	76.19 36	89.22 23	88.34 34	84.20 17	71.00 17	79.67 36	56.97 65	77.77 22	72.24 34	68.50 58	81.33 41	82.74 24	87.23 45	92.84 38

SD-MVS		84.31 8	86.96 8	81.22 9	88.98 24	88.68 30	85.65 9	73.85 9	89.09 7	79.63 2	87.34 7	84.84 1	73.71 26	82.66 26	81.60 36	85.48 103	94.51 22

MP-MVS		80.94 20	83.49 19	77.96 25	88.48 25	88.16 37	82.82 25	69.34 29	80.79 32	69.67 27	82.35 14	77.13 20	71.60 39	80.97 46	80.96 46	85.87 84	94.06 28

ACMMPR		80.62 22	82.98 21	77.87 27	88.41 26	87.05 45	83.02 22	69.18 30	83.91 19	68.35 30	82.89 13	73.64 27	72.16 35	80.78 47	81.13 44	86.10 65	91.43 51

MSLP-MVS++		78.57 31	77.33 42	80.02 15	88.39 27	84.79 63	84.62 15	66.17 47	75.96 45	78.40 4	61.59 49	71.47 37	73.54 28	78.43 62	78.88 57	88.97 12	90.18 65

PGM-MVS		79.42 28	81.84 27	76.60 33	88.38 28	86.69 48	82.97 24	65.75 49	80.39 33	64.94 37	81.95 16	72.11 35	71.41 40	80.45 48	80.55 50	86.18 62	90.76 59

EPNet		79.28 30	82.25 23	75.83 38	88.31 29	90.14 20	79.43 45	68.07 36	81.76 28	61.26 48	77.26 24	70.08 43	70.06 46	82.43 30	82.00 31	87.82 32	92.09 45
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		79.49 24	79.84 33	79.08 21	88.26 30	92.49 3	84.12 18	70.63 20	65.27 69	69.60 29	61.29 51	66.50 51	72.75 31	88.07 2	88.03 1	89.13 11	97.22 2
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
MPTG		81.65 18	83.10 20	79.97 16	88.14 31	87.62 42	83.96 19	69.90 24	86.92 11	77.67 8	72.47 29	78.74 17	74.13 25	81.59 40	81.15 43	86.01 71	93.19 35

TSAR-MVS + MP.		84.39 7	86.58 10	81.83 8	88.09 32	86.47 51	85.63 10	73.62 11	90.13 6	79.24 3	89.67 5	82.99 5	77.72 10	81.22 42	80.92 47	86.68 54	94.66 21

X-MVS		78.16 34	80.55 31	75.38 41	87.99 33	86.27 53	81.05 37	68.98 31	78.33 38	61.07 50	75.25 27	72.27 31	67.52 66	80.03 51	80.52 51	85.66 100	91.20 53

DeepC-MVS	74.46 3	80.30 23	81.05 29	79.42 17	87.42 34	88.50 32	83.23 21	73.27 12	82.78 23	71.01 22	62.86 46	69.93 44	74.80 21	84.30 15	84.20 16	86.79 53	94.77 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
mPP-MVS					86.96 35							70.61 41

CP-MVS		79.44 25	81.51 28	77.02 31	86.95 36	85.96 57	82.00 27	68.44 35	81.82 27	67.39 32	77.43 23	73.68 26	71.62 38	79.56 54	79.58 52	85.73 93	92.51 41

MVS_111021_HR		77.42 39	78.40 38	76.28 34	86.95 36	90.68 14	77.41 54	70.56 23	66.21 63	62.48 44	66.17 40	63.98 58	72.08 36	82.87 24	83.15 22	88.24 27	95.71 10

CANet		80.90 21	82.93 22	78.53 24	86.83 38	92.26 6	81.19 35	66.95 41	81.60 29	69.90 26	66.93 37	74.80 24	76.79 12	84.68 13	84.77 14	89.50 8	95.50 13

CHOSEN 1792x2688		72.55 58	71.98 64	73.22 52	86.57 39	92.41 4	75.63 61	66.77 42	62.08 74	52.32 73	30.27 197	50.74 108	66.14 68	86.22 7	85.41 6	91.90 1	96.75 8

PHI-MVS		79.43 26	84.06 18	74.04 48	86.15 40	91.57 12	80.85 39	68.90 33	82.22 25	51.81 76	78.10 21	74.28 25	70.39 45	84.01 18	84.00 17	86.14 64	94.24 26

ACMMP		77.61 37	79.59 34	75.30 42	85.87 41	85.58 58	81.42 32	67.38 40	79.38 37	62.61 42	78.53 20	65.79 53	68.80 56	78.56 61	78.50 61	85.75 89	90.80 57
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
HQP-MVS		78.26 33	80.91 30	75.17 43	85.67 42	84.33 66	83.01 23	69.38 28	79.88 35	55.83 66	79.85 18	64.90 56	70.81 42	82.46 28	81.78 33	86.30 60	93.18 36

OPM-MVS		72.74 57	70.93 72	74.85 46	85.30 43	84.34 65	82.82 25	69.79 25	49.96 113	55.39 70	54.09 69	60.14 73	70.04 47	80.38 50	79.43 53	85.74 92	88.20 96

MS-PatchMatch		70.34 69	69.00 80	71.91 59	85.20 44	85.35 59	77.84 51	61.77 84	58.01 85	55.40 69	41.26 129	58.34 79	61.69 88	81.70 39	78.29 62	89.56 7	80.02 158

MVS_0304		79.43 26	82.20 24	76.20 35	84.22 45	91.79 10	81.82 30	63.81 62	76.83 43	61.71 46	66.37 39	75.52 23	76.38 15	85.54 9	85.03 11	89.28 10	94.32 25

PCF-MVS	70.85 4	75.73 46	76.55 50	74.78 47	83.67 46	88.04 40	81.47 31	70.62 22	69.24 59	57.52 63	60.59 54	69.18 45	70.65 43	77.11 70	77.65 68	84.75 128	94.01 29
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ACMM	66.70 10	70.42 65	68.49 84	72.67 54	82.85 47	77.76 136	77.70 52	64.76 56	64.61 70	60.74 54	49.29 82	53.97 98	65.86 69	74.97 92	75.57 86	84.13 143	83.29 133
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVS					82.43 48	86.27 53	75.70 59			61.07 50		72.27 31				85.67 97

X-MVStestdata					82.43 48	86.27 53	75.70 59			61.07 50		72.27 31				85.67 97

PVSNet_BlendedMVS		76.84 43	78.47 36	74.95 44	82.37 50	89.90 23	75.45 65	65.45 52	74.99 46	70.66 24	63.07 44	58.27 80	67.60 63	84.24 16	81.70 34	88.18 28	97.10 4

PVSNet_Blended		76.84 43	78.47 36	74.95 44	82.37 50	89.90 23	75.45 65	65.45 52	74.99 46	70.66 24	63.07 44	58.27 80	67.60 63	84.24 16	81.70 34	88.18 28	97.10 4

CLD-MVS		77.36 40	77.29 43	77.45 30	82.21 52	88.11 38	81.92 28	68.96 32	77.97 40	69.62 28	62.08 47	59.44 74	73.57 27	81.75 38	81.27 41	88.41 23	90.39 62
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LGP-MVS_train		72.02 61	73.18 62	70.67 63	82.13 53	80.26 111	79.58 44	63.04 69	70.09 54	51.98 74	65.06 41	55.62 92	62.49 85	75.97 84	76.32 76	84.80 127	88.93 77

MSDG		65.57 95	61.57 144	70.24 64	82.02 54	76.47 148	74.46 74	68.73 34	56.52 90	50.33 83	38.47 156	41.10 136	62.42 86	72.12 142	72.94 140	83.47 150	73.37 183

IB-MVS	64.48 11	69.02 73	68.97 81	69.09 71	81.75 55	89.01 28	64.50 148	64.91 55	56.65 89	62.59 43	47.89 89	45.23 120	51.99 149	69.18 170	81.88 32	88.77 15	92.93 37
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
canonicalmvs		77.65 36	79.59 34	75.39 40	81.52 56	89.83 25	81.32 34	60.74 96	80.05 34	66.72 33	68.43 34	65.09 54	74.72 23	78.87 58	82.73 25	87.32 42	92.16 43

CPTT-MVS		75.43 47	77.13 45	73.44 50	81.43 57	82.55 75	80.96 38	64.35 57	77.95 41	61.39 47	69.20 33	70.94 39	69.38 53	73.89 106	73.32 132	83.14 158	92.06 46

DWT-MVSNet_training		72.81 56	73.98 56	71.45 60	81.26 58	86.37 52	72.08 80	59.82 103	69.13 60	58.15 61	54.71 63	61.33 71	67.81 62	76.86 72	78.63 58	89.59 6	90.86 56

EPNet_dtu		66.17 90	70.13 76	61.54 143	81.04 59	77.39 140	68.87 126	62.50 77	69.78 55	33.51 177	63.77 43	56.22 86	37.65 194	72.20 140	72.18 148	85.69 96	79.38 160
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMP	68.86 7	72.15 60	72.25 63	72.03 57	80.96 60	80.87 96	77.93 50	64.13 59	69.29 57	60.79 53	64.04 42	53.54 99	63.91 77	73.74 110	75.27 88	84.45 135	88.98 76
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HyFIR lowres test		68.39 76	68.28 86	68.52 74	80.85 61	88.11 38	71.08 107	58.09 111	54.87 102	47.80 90	27.55 202	55.80 89	64.97 72	79.11 56	79.14 55	88.31 25	93.35 32

LS3D		64.54 104	62.14 137	67.34 82	80.85 61	75.79 154	69.99 117	65.87 48	60.77 77	44.35 107	42.43 123	45.95 118	65.01 71	69.88 165	68.69 175	77.97 196	71.43 193

CNLPA		71.37 64	70.27 75	72.66 55	80.79 63	81.33 90	71.07 108	65.75 49	82.36 24	64.80 38	42.46 122	56.49 85	72.70 32	73.00 118	70.52 167	80.84 178	85.76 116

TSAR-MVS + GP.		82.27 16	85.98 12	77.94 26	80.72 64	88.25 36	81.12 36	67.71 38	87.10 10	73.31 14	85.23 10	83.68 3	76.64 13	80.43 49	81.47 38	88.15 30	95.66 11

PLC	64.00 12	68.54 75	66.66 95	70.74 62	80.28 65	74.88 159	72.64 78	63.70 64	69.26 58	55.71 67	47.24 97	55.31 93	70.42 44	72.05 144	70.67 165	81.66 172	77.19 167
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
OMC-MVS		74.03 51	75.82 52	71.95 58	79.56 66	80.98 94	75.35 67	63.21 66	84.48 18	61.83 45	61.54 50	66.89 50	69.41 52	76.60 75	74.07 121	82.34 167	86.15 111

CostFormer		72.18 59	73.90 58	70.18 65	79.47 67	86.19 56	76.94 57	48.62 187	66.07 65	60.40 56	54.14 68	65.82 52	67.98 60	75.84 85	76.41 75	87.67 36	92.83 39

MVS_111021_LR		74.26 50	75.95 51	72.27 56	79.43 68	85.04 61	72.71 77	65.27 54	70.92 53	63.58 40	69.32 32	60.31 72	69.43 51	77.01 71	77.15 69	83.22 154	91.93 49

MVS_Test		75.22 48	76.69 48	73.51 49	79.30 69	88.82 29	80.06 42	58.74 106	69.77 56	57.50 64	59.78 56	61.35 69	75.31 17	82.07 34	83.60 21	90.13 5	91.41 52

PVSNet_Blended_VisFu		71.76 62	73.54 60	69.69 66	79.01 70	87.16 44	72.05 81	61.80 83	56.46 91	59.66 58	53.88 70	62.48 61	59.08 126	81.17 43	78.90 56	86.53 57	94.74 20

ACMH	59.42 14	61.59 149	59.22 170	64.36 106	78.92 71	78.26 129	67.65 131	67.48 39	39.81 177	30.98 185	38.25 159	34.59 190	61.37 95	70.55 157	73.47 128	79.74 187	79.59 159
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FC-MVSNet-train		68.83 74	68.29 85	69.47 67	78.35 72	79.94 112	64.72 147	66.38 44	54.96 100	54.51 71	56.75 60	47.91 114	66.91 67	75.57 89	75.75 82	85.92 77	87.12 102

tpmp4_e23		69.38 70	69.47 78	69.28 69	78.20 73	82.35 77	75.92 58	49.20 185	64.15 71	59.96 57	47.93 87	55.77 90	68.06 59	73.05 117	74.53 100	84.34 137	88.50 94

Effi-MVS+		70.42 65	71.23 70	69.47 67	78.04 74	85.24 60	75.57 63	58.88 105	59.56 80	48.47 87	52.73 73	54.94 94	69.69 49	78.34 64	77.06 70	86.18 62	90.73 60

conf0.002		67.12 87	67.13 93	67.11 83	77.95 75	82.11 78	71.71 90	63.06 67	49.16 118	43.43 112	47.76 91	48.79 111	61.42 90	76.61 74	76.55 73	85.07 113	88.92 79

conf0.01		66.60 88	66.18 99	67.09 84	77.90 76	82.02 79	71.71 90	63.05 68	49.16 118	43.41 114	46.23 106	45.78 119	61.42 90	76.55 76	74.63 95	85.04 114	88.87 81

conf200view11		65.89 93	64.96 105	66.98 86	77.70 77	81.58 85	71.71 90	62.94 73	49.16 118	43.28 117	43.24 112	41.34 130	61.42 90	76.24 78	74.63 95	84.84 122	88.52 91

thres100view900		67.14 86	66.09 100	68.38 76	77.70 77	83.84 69	74.52 71	66.33 46	49.16 118	43.40 115	43.24 112	41.34 130	62.59 84	79.31 55	75.92 81	85.73 93	89.81 67

tfpn200view9		65.90 92	64.96 105	67.00 85	77.70 77	81.58 85	71.71 90	62.94 73	49.16 118	43.40 115	43.24 112	41.34 130	61.42 90	76.24 78	74.63 95	84.84 122	88.52 91

UA-Net		64.62 101	68.23 87	60.42 148	77.53 80	81.38 89	60.08 176	57.47 122	47.01 130	44.75 105	60.68 53	71.32 38	41.84 185	73.27 112	72.25 147	80.83 179	71.68 191

thres200		65.58 94	64.74 108	66.56 87	77.52 81	81.61 83	73.44 76	62.95 71	46.23 138	42.45 131	42.76 116	41.18 134	58.12 130	76.24 78	75.59 85	84.89 119	89.58 68

ACMH+	60.36 13	61.16 150	58.38 172	64.42 105	77.37 82	74.35 164	68.45 127	62.81 76	45.86 140	38.48 150	35.71 178	37.35 172	59.81 115	67.24 176	69.80 171	79.58 188	78.32 165

TAPA-MVS	67.10 9	71.45 63	73.47 61	69.10 70	77.04 83	80.78 97	73.81 75	62.10 78	80.80 31	51.28 77	60.91 52	63.80 60	67.98 60	74.59 95	72.42 146	82.37 166	80.97 153
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS_MVSNet		67.29 84	71.98 64	61.82 141	76.92 84	84.32 67	65.90 145	58.22 109	55.75 96	39.22 144	54.51 66	62.47 62	45.99 174	78.83 59	78.52 60	84.70 129	89.47 70

tfpn_ndepth		62.95 129	63.75 113	62.02 139	76.89 85	79.48 119	64.09 151	60.98 91	49.48 115	38.73 148	49.92 81	44.79 121	47.37 166	71.91 145	71.66 151	84.07 144	79.00 163

CANet_DTU		72.84 55	76.63 49	68.43 75	76.81 86	86.62 49	75.54 64	54.71 154	72.06 50	43.54 110	67.11 36	58.46 77	72.40 33	81.13 45	80.82 49	87.57 37	90.21 64

tpm cat1		67.47 82	67.05 94	67.98 77	76.63 87	81.51 88	74.49 73	47.65 192	61.18 76	61.12 49	42.51 121	53.02 102	64.74 75	70.11 162	71.50 153	83.22 154	89.49 69

DI_MVS_plusplus_trai		73.94 52	74.85 55	72.88 53	76.57 88	86.80 46	80.41 40	61.47 86	62.35 73	59.44 59	47.91 88	68.12 46	72.24 34	82.84 25	81.50 37	87.15 48	94.42 23

thres400		65.18 99	64.44 110	66.04 88	76.40 89	82.63 73	71.52 99	64.27 58	44.93 146	40.69 139	41.86 126	40.79 143	58.12 130	77.67 65	74.64 94	85.26 106	88.56 90

tpmrst		67.15 85	68.12 88	66.03 89	76.21 90	80.98 94	71.27 101	45.05 199	60.69 78	50.63 81	46.95 102	54.15 97	65.30 70	71.80 147	71.77 150	87.72 34	90.48 61

gg-mvs-nofinetune		62.34 132	66.19 98	57.86 166	76.15 91	88.61 31	71.18 104	41.24 216	25.74 217	13.16 220	22.91 212	63.97 59	54.52 144	85.06 11	85.25 9	90.92 3	91.78 50

thresconf0.02		63.92 111	65.18 104	62.46 134	75.91 92	80.65 105	67.51 134	63.86 61	45.00 145	33.32 178	51.38 76	51.68 104	48.34 161	75.49 90	75.13 89	85.84 88	76.91 169

EPMVS		66.21 89	67.49 91	64.73 98	75.81 93	84.20 68	68.94 125	44.37 203	61.55 75	48.07 89	49.21 84	54.87 95	62.88 82	71.82 146	71.40 157	88.28 26	79.37 161

EPP-MVSNet		67.58 80	71.10 71	63.48 120	75.71 94	83.35 70	66.85 137	57.83 114	53.02 107	41.15 136	55.82 61	67.89 48	56.01 138	74.40 97	72.92 141	83.33 152	90.30 63

view600		63.91 112	63.27 119	64.66 100	75.57 95	81.73 81	69.71 120	63.04 69	43.97 149	39.18 145	41.09 130	40.24 151	55.38 140	76.28 77	72.04 149	85.08 112	87.52 100

thres600view7		63.77 113	63.14 121	64.51 102	75.49 96	81.61 83	69.59 121	62.95 71	43.96 150	38.90 147	41.09 130	40.24 151	55.25 142	76.24 78	71.54 152	84.89 119	87.30 101

dps		64.08 107	63.22 120	65.08 92	75.27 97	79.65 116	66.68 139	46.63 197	56.94 87	55.67 68	43.96 108	43.63 126	64.00 76	69.50 169	69.82 170	82.25 168	79.02 162

MVSTER		76.92 42	79.92 32	73.42 51	74.98 98	82.97 71	78.15 48	63.41 65	78.02 39	64.41 39	67.54 35	72.80 29	71.05 41	83.29 21	83.73 18	88.53 21	91.12 54

TSAR-MVS + COLMAP		73.09 54	76.86 46	68.71 72	74.97 99	82.49 76	74.51 72	61.83 82	83.16 21	49.31 86	82.22 15	51.62 105	68.94 55	78.76 60	75.52 87	82.67 162	84.23 125

diffmvs		73.50 53	75.66 53	70.97 61	74.96 100	86.71 47	77.16 56	57.42 126	71.12 52	60.43 55	57.20 59	70.40 42	68.79 57	76.11 82	76.05 79	87.10 49	92.06 46

view800		63.02 125	62.69 132	63.39 122	74.79 101	80.76 101	67.83 130	61.93 81	43.16 160	37.78 156	40.43 135	39.73 158	53.16 147	75.01 91	73.32 132	84.87 121	86.43 108

tpm		64.85 100	66.02 101	63.48 120	74.52 102	78.38 128	70.98 109	44.99 201	51.61 109	43.28 117	47.66 92	53.18 100	60.57 101	70.58 156	71.30 162	86.54 56	89.45 71

tfpn		62.54 131	62.79 128	62.25 138	74.16 103	79.86 114	66.07 144	60.97 92	42.43 165	36.41 160	39.88 139	43.76 125	51.25 154	73.85 107	74.17 117	84.67 130	85.57 119

Vis-MVSNet (Re-imp)		62.25 135	68.74 82	54.68 181	73.70 104	78.74 124	56.51 187	57.49 121	55.22 98	26.86 195	54.56 65	61.35 69	31.06 197	73.10 114	74.90 91	82.49 164	83.31 132

Fast-Effi-MVS+		67.59 79	67.56 90	67.62 80	73.67 105	81.14 93	71.12 105	54.79 153	58.88 81	50.61 82	46.70 103	47.05 115	69.12 54	76.06 83	76.44 74	86.43 58	86.65 105

IterMVS-LS		66.08 91	66.56 97	65.51 90	73.67 105	74.88 159	70.89 111	53.55 161	50.42 111	48.32 88	50.59 78	55.66 91	61.83 87	73.93 105	74.42 107	84.82 126	86.01 113
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		65.43 97	67.71 89	62.78 130	73.49 107	82.83 72	66.42 142	45.40 198	60.40 79	45.27 99	49.22 83	57.60 83	60.01 110	70.61 154	71.38 160	86.08 67	81.91 148
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
COLMAP_ROB	51.17 15	55.13 179	52.90 192	57.73 167	73.47 108	67.21 194	62.13 165	55.82 139	47.83 128	34.39 173	31.60 194	34.24 191	44.90 179	63.88 191	62.52 201	75.67 202	63.02 210
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
conf0.05thres1000		60.33 157	59.42 167	61.40 144	73.15 109	78.25 130	65.29 146	60.30 99	36.61 189	35.75 166	33.25 185	39.23 162	50.35 157	72.18 141	72.67 144	83.57 149	83.74 127

Effi-MVS+-dtu		64.58 102	64.08 111	65.16 91	73.04 110	75.17 158	70.68 113	56.23 136	54.12 105	44.71 106	47.42 93	51.10 106	63.82 78	68.08 174	66.32 187	82.47 165	86.38 109

tfpnview11		58.92 163	59.60 165	58.13 161	72.99 111	77.11 144	60.48 171	60.37 97	42.10 168	29.10 189	43.45 109	40.72 146	41.67 186	70.53 158	70.43 168	84.17 142	72.85 185

tfpn_n400		58.64 167	59.27 168	57.89 164	72.83 112	77.26 142	60.35 172	60.29 100	39.77 179	29.10 189	43.45 109	40.72 146	41.61 187	70.06 163	71.39 158	83.17 156	72.26 188

tfpnconf		58.64 167	59.27 168	57.89 164	72.83 112	77.26 142	60.35 172	60.29 100	39.77 179	29.10 189	43.45 109	40.72 146	41.61 187	70.06 163	71.39 158	83.17 156	72.26 188

tfpn1000		58.35 171	59.96 162	56.47 174	72.78 114	77.51 138	56.66 186	59.16 104	43.74 151	29.76 188	42.79 115	42.49 127	37.04 195	68.92 171	68.98 173	83.45 151	75.25 173

EG-PatchMatch MVS		58.73 166	58.03 175	59.55 153	72.32 115	80.49 107	63.44 160	55.55 143	32.49 204	38.31 151	28.87 199	37.22 173	42.84 183	74.30 103	75.70 83	84.84 122	77.14 168

TransMVSNet (Re)		57.83 172	56.90 178	58.91 158	72.26 116	74.69 162	63.57 159	61.42 87	32.30 205	32.65 180	33.97 184	35.96 182	39.17 192	73.84 109	72.84 142	84.37 136	74.69 176

CMPMVS	43.63 17	57.67 174	55.43 181	60.28 149	72.01 117	79.00 122	62.77 164	53.23 166	41.77 170	45.42 98	30.74 196	39.03 163	53.01 148	64.81 184	64.65 193	75.26 204	68.03 199
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
NR-MVSNet		61.08 152	62.09 138	59.90 150	71.96 118	75.87 152	63.60 158	61.96 79	49.31 116	27.95 192	42.76 116	33.85 194	48.82 160	74.35 100	74.05 122	85.13 108	84.45 122

PMMVS		70.37 68	75.06 54	64.90 93	71.46 119	81.88 80	64.10 150	55.64 142	71.31 51	46.69 93	70.69 31	58.56 75	69.53 50	79.03 57	75.63 84	81.96 170	88.32 95

test-LLR		68.23 77	71.61 68	64.28 111	71.37 120	81.32 91	63.98 154	61.03 89	58.62 82	42.96 121	52.74 71	61.65 67	57.74 132	75.64 87	78.09 66	88.61 18	93.21 33

test0.0.03 1		57.35 175	59.89 163	54.38 183	71.37 120	73.45 167	52.71 191	61.03 89	46.11 139	26.33 196	41.73 127	44.08 123	29.72 200	71.43 150	70.90 163	85.10 109	71.56 192

tfpnnormal		58.97 162	56.48 180	61.89 140	71.27 122	76.21 151	66.65 140	61.76 85	32.90 203	36.41 160	27.83 201	29.14 206	50.64 156	73.06 115	73.05 139	84.58 133	83.15 138

Fast-Effi-MVS+-dtu		63.05 124	64.72 109	61.11 145	71.21 123	76.81 147	70.72 112	43.13 207	52.51 108	35.34 169	46.55 104	46.36 116	61.40 94	71.57 149	71.44 155	84.84 122	87.79 98

MDTV_nov1_ep13		65.21 98	67.28 92	62.79 129	70.91 124	81.72 82	69.28 124	49.50 182	58.08 84	43.94 109	50.50 79	56.02 87	58.86 127	70.72 153	73.37 130	84.24 139	80.52 154

FMVSNet3		70.41 67	71.89 66	68.68 73	70.89 125	79.42 120	75.63 61	60.97 92	65.32 66	51.06 78	47.37 94	62.05 63	64.90 73	82.49 27	82.27 28	88.64 17	84.34 124

Vis-MVSNet		65.53 96	69.83 77	60.52 147	70.80 126	84.59 64	66.37 143	55.47 145	48.40 125	40.62 140	57.67 58	58.43 78	45.37 178	77.49 66	76.24 77	84.47 134	85.99 114
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CDS-MVSNet		64.22 105	65.89 102	62.28 137	70.05 127	80.59 106	69.91 119	57.98 112	43.53 156	46.58 94	48.22 86	50.76 107	46.45 171	75.68 86	76.08 78	82.70 161	86.34 110
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UGNet		67.57 81	71.69 67	62.76 131	69.88 128	82.58 74	66.43 141	58.64 107	54.71 103	51.87 75	61.74 48	62.01 66	45.46 177	74.78 94	74.99 90	84.24 139	91.02 55
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
GA-MVS		64.55 103	65.76 103	63.12 126	69.68 129	81.56 87	69.59 121	58.16 110	45.23 144	35.58 168	47.01 101	41.82 129	59.41 121	79.62 53	78.54 59	86.32 59	86.56 106

GBi-Net		69.21 71	70.40 73	67.81 78	69.49 130	78.65 125	74.54 68	60.97 92	65.32 66	51.06 78	47.37 94	62.05 63	63.43 79	77.49 66	78.22 63	87.37 39	83.73 128

test1		69.21 71	70.40 73	67.81 78	69.49 130	78.65 125	74.54 68	60.97 92	65.32 66	51.06 78	47.37 94	62.05 63	63.43 79	77.49 66	78.22 63	87.37 39	83.73 128

FMVSNet2		68.06 78	68.57 83	67.45 81	69.49 130	78.65 125	74.54 68	60.23 102	56.29 92	49.64 85	42.13 125	57.08 84	63.43 79	81.15 44	80.99 45	87.37 39	83.73 128

UniMVSNet_NR-MVSNet		62.30 134	63.51 115	60.89 146	69.48 133	77.83 134	64.07 152	63.94 60	50.03 112	31.17 183	44.82 107	41.12 135	51.37 151	71.02 151	74.81 93	85.30 105	84.95 120

gm-plane-assit		54.99 181	57.99 176	51.49 191	69.27 134	54.42 216	32.32 220	42.59 208	21.18 223	13.71 218	23.61 208	43.84 124	60.21 109	87.09 4	86.55 4	90.81 4	89.28 72

PatchMatch-RL		62.22 138	60.69 151	64.01 112	68.74 135	75.75 155	59.27 179	60.35 98	56.09 93	53.80 72	47.06 100	36.45 177	64.80 74	68.22 173	67.22 182	77.10 198	74.02 178

CR-MVSNet		62.31 133	64.75 107	59.47 154	68.63 136	71.29 185	67.53 132	43.18 205	55.83 94	41.40 133	41.04 132	55.85 88	57.29 135	72.76 130	73.27 135	78.77 193	83.23 136

v18		63.31 121	62.02 139	64.81 97	68.48 137	73.38 168	72.14 79	54.28 156	48.99 124	47.21 91	39.56 141	41.20 133	60.80 98	72.89 122	74.46 106	85.96 76	83.64 131

v16		63.12 123	61.78 141	64.68 99	68.45 138	73.29 169	71.86 83	54.12 157	48.36 126	47.00 92	39.30 146	41.01 137	60.67 99	72.83 128	74.40 108	86.01 71	83.24 135

v17		62.99 128	61.70 142	64.51 102	68.40 139	73.28 170	71.80 88	54.11 158	47.87 127	46.14 95	39.29 147	41.01 137	60.60 100	72.81 129	74.39 113	85.99 74	83.25 134

TranMVSNet+NR-MVSNet		60.38 156	61.30 146	59.30 155	68.34 140	75.57 157	63.38 161	63.78 63	46.74 132	27.73 193	42.56 120	36.84 175	47.66 164	70.36 160	74.59 98	84.91 118	82.46 142

v8		63.44 120	62.58 133	64.43 104	68.28 141	78.07 131	71.82 87	54.85 151	46.70 134	45.20 100	39.40 142	40.91 139	60.54 104	72.85 127	74.39 113	85.92 77	85.76 116

v6		64.09 106	63.40 116	64.90 93	68.28 141	80.78 97	71.85 84	57.64 118	46.73 133	45.18 101	39.40 142	40.89 140	60.54 104	72.86 123	74.40 108	85.92 77	88.72 86

v1neww		64.08 107	63.38 117	64.89 95	68.27 143	80.77 99	71.84 85	57.65 116	46.66 135	45.10 102	39.40 142	40.86 141	60.57 101	72.86 123	74.40 108	85.92 77	88.71 87

v7new		64.08 107	63.38 117	64.89 95	68.27 143	80.77 99	71.84 85	57.65 116	46.66 135	45.10 102	39.40 142	40.86 141	60.57 101	72.86 123	74.40 108	85.92 77	88.71 87

v1141		63.48 117	62.75 131	64.32 107	68.13 145	80.69 103	71.69 96	57.43 123	43.66 155	42.83 128	39.02 149	39.74 157	59.95 111	72.94 119	74.49 103	85.86 85	88.75 84

divwei89l23v2f112		63.48 117	62.76 130	64.32 107	68.13 145	80.68 104	71.71 90	57.43 123	43.69 153	42.84 126	39.01 150	39.75 156	59.94 112	72.93 120	74.49 103	85.86 85	88.75 84

v1		63.49 116	62.77 129	64.32 107	68.13 145	80.70 102	71.70 95	57.43 123	43.69 153	42.89 125	39.03 148	39.77 155	59.93 113	72.93 120	74.48 105	85.86 85	88.77 82

v15		62.07 139	60.70 150	63.67 117	68.09 148	73.00 171	71.27 101	53.41 162	43.70 152	43.43 112	38.77 152	39.83 153	59.87 114	72.74 132	74.25 115	85.98 75	82.61 140

V14		61.96 142	60.56 152	63.59 118	68.06 149	72.93 174	71.10 106	53.33 164	43.47 157	43.28 117	38.59 153	39.78 154	59.76 116	72.65 134	74.19 116	86.01 71	82.32 145

V9		61.85 144	60.42 155	63.51 119	68.02 150	72.85 175	70.91 110	53.24 165	43.25 159	43.27 120	38.41 157	39.73 158	59.60 118	72.55 136	74.13 119	86.04 69	82.04 147

v2v482		63.68 114	62.85 126	64.65 101	68.01 151	80.46 108	71.90 82	57.60 119	44.26 147	42.82 129	39.80 140	38.62 167	61.56 89	73.06 115	74.86 92	86.03 70	88.90 80

v12		61.70 146	60.27 157	63.38 123	68.00 152	72.76 176	70.63 114	53.14 167	43.01 161	42.95 124	38.25 159	39.64 160	59.48 120	72.47 138	74.05 122	86.06 68	81.71 150

pm-mvs1		59.21 161	59.58 166	58.77 159	67.97 153	77.07 146	64.12 149	57.20 128	34.73 197	36.86 158	35.34 180	40.54 150	43.34 182	74.32 102	73.30 134	83.13 159	81.77 149

v13		61.60 148	60.13 160	63.31 124	67.95 154	72.67 178	70.51 115	53.05 168	42.80 162	42.96 121	38.10 164	39.57 161	59.31 123	72.36 139	73.98 124	86.10 65	81.40 152

v7		63.61 115	63.02 123	64.29 110	67.88 155	80.32 109	71.60 97	56.63 132	45.37 142	42.84 126	38.54 154	38.91 165	61.05 96	74.39 98	74.52 101	85.75 89	89.10 75

v10		63.00 126	62.22 136	63.90 115	67.88 155	77.78 135	71.59 98	54.34 155	45.37 142	42.76 130	38.53 155	38.93 164	61.05 96	74.39 98	74.52 101	85.75 89	86.04 112

v11		61.74 145	60.47 154	63.22 125	67.83 157	72.72 177	70.31 116	52.95 171	42.75 163	41.89 132	38.16 162	38.49 168	60.40 108	74.35 100	74.40 108	85.92 77	82.39 144

v1144		63.00 126	62.39 135	63.70 116	67.72 158	80.27 110	71.23 103	56.40 133	42.51 164	40.81 138	38.12 163	37.73 169	60.42 107	74.46 96	74.55 99	85.64 101	89.12 74

UniMVSNet (Re)		60.62 154	62.93 125	57.92 163	67.64 159	77.90 133	61.75 167	61.24 88	49.83 114	29.80 187	42.57 119	40.62 149	43.36 181	70.49 159	73.27 135	83.76 146	85.81 115

RPMNet		58.63 169	62.80 127	53.76 187	67.59 160	71.29 185	54.60 189	38.13 220	55.83 94	35.70 167	41.58 128	53.04 101	47.89 163	66.10 178	67.38 180	78.65 195	84.40 123

v148		62.00 141	61.19 147	62.96 127	67.46 161	79.49 118	67.87 129	57.66 115	42.30 166	45.02 104	38.20 161	38.89 166	54.77 143	69.83 166	72.60 145	84.96 115	87.01 103

IterMVS		61.87 143	63.55 114	59.90 150	67.29 162	72.20 180	67.34 135	48.56 188	47.48 129	37.86 155	47.07 99	48.27 112	54.08 145	72.12 142	73.71 125	84.30 138	83.99 126
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1192		62.25 135	61.64 143	62.96 127	66.88 163	79.72 115	69.96 118	55.77 140	41.58 171	39.42 142	37.05 169	35.96 182	60.50 106	74.30 103	74.09 120	85.24 107	88.76 83

DU-MVS		60.87 153	61.82 140	59.76 152	66.69 164	75.87 152	64.07 152	61.96 79	49.31 116	31.17 183	42.76 116	36.95 174	51.37 151	69.67 167	73.20 138	83.30 153	84.95 120

Baseline_NR-MVSNet		59.47 160	60.28 156	58.54 160	66.69 164	73.90 165	61.63 168	62.90 75	49.15 123	26.87 194	35.18 182	37.62 170	48.20 162	69.67 167	73.61 126	84.92 116	82.82 139

v144192		62.05 140	61.46 145	62.73 133	66.59 166	79.87 113	69.30 123	55.88 138	41.50 172	39.41 143	37.23 167	36.45 177	59.62 117	72.69 133	73.51 127	85.61 102	88.93 77

v1921920		61.66 147	61.10 148	62.31 136	66.32 167	79.57 117	68.41 128	55.49 144	41.03 173	38.69 149	36.64 175	35.27 188	59.60 118	73.23 113	73.41 129	85.37 104	88.51 93

TESTMET0.1,1		67.38 83	71.61 68	62.45 135	66.05 168	81.32 91	63.98 154	55.36 146	58.62 82	42.96 121	52.74 71	61.65 67	57.74 132	75.64 87	78.09 66	88.61 18	93.21 33

pmmvs4		63.14 122	62.46 134	63.94 114	66.03 169	76.40 149	66.82 138	57.60 119	56.74 88	50.26 84	40.81 134	37.51 171	59.26 124	71.75 148	71.48 154	83.68 148	82.53 141

PatchT		60.46 155	63.85 112	56.51 173	65.95 170	75.68 156	47.34 200	41.39 212	53.89 106	41.40 133	37.84 165	50.30 109	57.29 135	72.76 130	73.27 135	85.67 97	83.23 136

v1240		61.09 151	60.55 153	61.72 142	65.92 171	79.28 121	67.16 136	54.91 150	39.79 178	38.10 152	36.08 177	34.64 189	59.15 125	72.86 123	73.36 131	85.10 109	87.84 97

ADS-MVSNet		58.40 170	59.16 171	57.52 168	65.80 172	74.57 163	60.26 174	40.17 217	50.51 110	38.01 153	40.11 138	44.72 122	59.36 122	64.91 182	66.55 185	81.53 173	72.72 187

testpf		43.39 210	47.17 207	38.98 214	65.58 173	47.38 224	36.09 217	31.67 227	36.97 186	19.47 206	33.01 187	35.62 187	23.61 213	50.86 219	56.08 215	57.48 224	70.27 196

FMVSNet1		63.48 117	63.07 122	63.97 113	65.31 174	76.37 150	71.77 89	57.90 113	43.32 158	45.66 97	35.06 183	49.43 110	58.57 128	77.49 66	78.22 63	84.59 132	81.60 151

testgi		48.51 202	50.53 199	46.16 204	64.78 175	67.15 195	41.54 211	54.81 152	29.12 211	17.03 209	32.07 191	31.98 198	20.15 218	65.26 181	67.00 184	78.67 194	61.10 215

LTVRE_ROB	47.26 16	49.41 200	49.91 202	48.82 196	64.76 176	69.79 187	49.05 195	47.12 194	20.36 225	16.52 212	36.65 174	26.96 209	50.76 155	60.47 196	63.16 198	64.73 219	72.00 190
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
Anonymous20231206		52.23 192	52.80 193	51.56 190	64.70 177	69.41 188	51.01 193	58.60 108	36.63 188	22.44 202	21.80 214	31.42 201	30.52 198	66.79 177	67.83 179	82.10 169	75.73 171

USDC		59.69 159	60.03 161	59.28 156	64.04 178	71.84 183	63.15 163	55.36 146	54.90 101	35.02 172	48.34 85	29.79 205	58.16 129	70.60 155	71.33 161	79.99 185	73.42 182

WR-MVS		51.02 194	54.56 186	46.90 202	63.84 179	69.23 189	44.78 207	56.38 134	38.19 184	14.19 216	37.38 166	36.82 176	22.39 214	60.14 197	66.20 189	79.81 186	73.95 180

test20.03		47.23 206	48.69 204	45.53 206	63.28 180	64.39 201	41.01 213	56.93 131	29.16 210	15.21 215	23.90 207	30.76 204	17.51 223	64.63 185	65.26 190	79.21 192	62.71 211

pmmvs6		54.20 188	53.54 189	54.97 179	63.22 181	72.98 172	60.17 175	52.32 174	26.77 216	34.30 174	23.29 211	36.23 179	40.33 190	68.77 172	68.76 174	79.47 190	78.00 166

v7n		57.04 176	56.64 179	57.52 168	62.85 182	74.75 161	61.76 166	51.80 175	35.58 196	36.02 165	32.33 189	33.61 195	50.16 158	67.73 175	70.34 169	82.51 163	82.12 146

pmmvs5		59.72 158	60.24 158	59.11 157	62.77 183	77.33 141	63.17 162	54.00 159	40.21 176	37.23 157	40.41 136	35.99 181	51.75 150	72.55 136	72.74 143	85.72 95	82.45 143

CVMVSNet		54.92 183	58.16 173	51.13 192	62.61 184	68.44 191	55.45 188	52.38 173	42.28 167	21.45 203	47.10 98	46.10 117	37.96 193	64.42 187	63.81 195	76.92 200	75.01 175

TAMVS		58.86 164	60.91 149	56.47 174	62.38 185	77.57 137	58.97 181	52.98 169	38.76 183	36.17 163	42.26 124	47.94 113	46.45 171	70.23 161	70.79 164	81.86 171	78.82 164

DTE-MVSNet		49.82 198	51.92 197	47.37 201	61.75 186	64.38 202	45.89 206	57.33 127	36.11 192	12.79 221	36.87 171	31.93 200	25.73 209	58.01 199	65.22 191	80.75 180	70.93 195

PEN-MVS		51.04 193	52.94 191	48.82 196	61.45 187	66.00 197	48.68 197	57.20 128	36.87 187	15.36 214	36.98 170	32.72 197	28.77 204	57.63 202	66.37 186	81.44 175	74.00 179

v748		55.19 178	54.63 184	55.85 176	61.44 188	72.97 173	58.72 182	51.62 176	34.48 199	36.39 162	32.09 190	33.05 196	45.48 176	61.85 194	67.87 178	81.45 174	80.08 157

V42		62.86 130	62.97 124	62.74 132	60.84 189	78.99 123	71.46 100	57.13 130	46.85 131	44.28 108	38.87 151	40.73 145	57.63 134	72.60 135	74.14 118	85.09 111	88.63 89

MDTV_nov1_ep13_2view		54.47 187	54.61 185	54.30 186	60.50 190	73.82 166	57.92 183	43.38 204	39.43 182	32.51 181	33.23 186	34.05 192	47.26 167	62.36 192	66.21 188	84.24 139	73.19 184

LP		48.21 203	46.65 209	50.03 193	60.39 191	63.86 205	48.73 196	38.71 219	35.60 195	32.99 179	23.31 210	24.95 216	40.07 191	57.73 200	61.56 203	79.29 191	59.51 216

MVS-HIRNet		53.86 189	53.02 190	54.85 180	60.30 192	72.36 179	44.63 208	42.20 210	39.45 181	43.47 111	21.66 215	34.00 193	55.47 139	65.42 180	67.16 183	83.02 160	71.08 194

CHOSEN 280x420		62.23 137	66.57 96	57.17 171	59.88 193	68.92 190	61.20 170	42.28 209	54.17 104	39.57 141	47.78 90	64.97 55	62.68 83	73.85 107	69.52 172	77.43 197	86.75 104

TinyColmap		52.66 191	50.09 201	55.65 177	59.72 194	64.02 204	57.15 185	52.96 170	40.28 175	32.51 181	32.42 188	20.97 220	56.65 137	63.95 188	65.15 192	74.91 205	63.87 207

FC-MVSNet-test		47.24 205	54.37 187	38.93 215	59.49 195	58.25 212	34.48 219	53.36 163	45.66 141	6.66 230	50.62 77	42.02 128	16.62 224	58.39 198	61.21 204	62.99 220	64.40 206

test-mter		64.06 110	69.24 79	58.01 162	59.07 196	77.40 139	59.13 180	48.11 190	55.64 97	39.18 145	51.56 75	58.54 76	55.38 140	73.52 111	76.00 80	87.22 46	92.05 48

WR-MVS_H		49.62 199	52.63 194	46.11 205	58.80 197	67.58 193	46.14 205	54.94 148	36.51 190	13.63 219	36.75 173	35.67 186	22.10 215	56.43 206	62.76 199	81.06 177	72.73 186

CP-MVSNet		50.57 195	52.60 195	48.21 199	58.77 198	65.82 198	48.17 198	56.29 135	37.41 185	16.59 211	37.14 168	31.95 199	29.21 201	56.60 205	63.71 196	80.22 183	75.56 172

PS-CasMVS		50.17 196	52.02 196	48.02 200	58.60 199	65.54 199	48.04 199	56.19 137	36.42 191	16.42 213	35.68 179	31.33 202	28.85 203	56.42 207	63.54 197	80.01 184	75.18 174

SixPastTwentyTwo		49.11 201	49.22 203	48.99 195	58.54 200	64.14 203	47.18 201	47.75 191	31.15 207	24.42 198	41.01 133	26.55 210	44.04 180	54.76 213	58.70 208	71.99 212	68.21 197

TDRefinement		52.70 190	51.02 198	54.66 182	57.41 201	65.06 200	61.47 169	54.94 148	44.03 148	33.93 175	30.13 198	27.57 208	46.17 173	61.86 193	62.48 202	74.01 208	66.06 203

pmmvs-eth3d		55.20 177	53.95 188	56.65 172	57.34 202	67.77 192	57.54 184	53.74 160	40.93 174	41.09 137	31.19 195	29.10 207	49.07 159	65.54 179	67.28 181	81.14 176	75.81 170

FPMVS		39.11 216	36.39 220	42.28 208	55.97 203	45.94 225	46.23 204	41.57 211	35.73 194	22.61 200	23.46 209	19.82 222	28.32 207	43.57 221	40.67 224	58.96 222	45.54 221

MIMVSNet		57.78 173	59.71 164	55.53 178	54.79 204	77.10 145	63.89 156	45.02 200	46.59 137	36.79 159	28.36 200	40.77 144	45.84 175	74.97 92	76.58 72	86.87 52	73.60 181

N_pmnet		47.67 204	47.00 208	48.45 198	54.72 205	62.78 206	46.95 202	51.25 177	36.01 193	26.09 197	26.59 205	25.93 215	35.50 196	55.67 209	59.01 206	76.22 201	63.04 209

test2356		46.29 207	47.37 206	45.03 207	54.38 206	57.99 213	42.03 210	50.32 179	30.78 208	16.65 210	27.40 203	23.70 217	29.86 199	61.20 195	64.31 194	76.93 199	66.22 202

Anonymous20231211		40.44 215	39.25 216	41.84 209	54.29 207	57.29 214	41.10 212	49.06 186	17.67 228	10.15 225	10.63 227	16.79 226	25.15 211	52.14 215	56.70 213	71.30 213	63.51 208

v52		54.79 184	55.15 182	54.36 185	54.07 208	72.13 181	59.84 177	49.39 183	34.50 198	35.08 171	31.63 193	35.74 184	47.21 169	63.90 189	67.92 176	80.59 181	80.23 155

V4		54.78 185	55.14 183	54.37 184	54.07 208	72.13 181	59.83 178	49.39 183	34.46 200	35.11 170	31.64 192	35.72 185	47.22 168	63.90 189	67.92 176	80.59 181	80.23 155

anonymousdsp		54.99 181	57.24 177	52.36 188	53.82 210	71.75 184	51.49 192	48.14 189	33.74 201	33.66 176	38.34 158	36.13 180	47.54 165	64.53 186	70.60 166	79.53 189	85.59 118

testus		42.30 211	43.69 210	40.67 213	53.21 211	53.50 217	31.81 221	49.96 180	27.06 214	11.55 223	25.67 206	19.00 223	25.20 210	55.34 210	62.59 200	72.31 211	62.69 212

new-patchmatchnet		42.21 212	42.97 212	41.33 211	53.05 212	59.89 209	39.38 214	49.61 181	28.26 213	12.10 222	22.17 213	21.54 219	19.22 219	50.96 218	56.04 216	74.61 207	61.92 213

FMVSNet5		58.86 164	60.24 158	57.25 170	52.66 213	66.25 196	63.77 157	52.86 172	57.85 86	37.92 154	36.12 176	52.22 103	51.37 151	70.88 152	71.43 156	84.92 116	66.91 201

ambc			42.30 213		50.36 214	49.51 221	35.47 218		32.04 206	23.53 199	17.36 220	8.95 232	29.06 202	64.88 183	56.26 214	61.29 221	67.12 200

1111		38.93 217	38.98 217	38.86 216	50.10 215	50.42 219	29.52 222	38.00 221	22.67 221	17.99 207	17.40 218	26.26 212	28.72 205	54.86 211	58.20 209	68.82 217	43.08 224

.test1245		25.86 224	24.56 226	27.39 225	50.10 215	50.42 219	29.52 222	38.00 221	22.67 221	17.99 207	17.40 218	26.26 212	28.72 205	54.86 211	0.05 231	0.01 235	0.24 233

EU-MVSNet		44.84 208	47.85 205	41.32 212	49.26 217	56.59 215	43.07 209	47.64 193	33.03 202	13.82 217	36.78 172	30.99 203	24.37 212	53.80 214	55.57 217	69.78 214	68.21 197

testmv		37.40 218	37.95 218	36.76 218	48.97 218	49.33 222	28.65 225	46.74 195	18.34 226	7.68 228	16.80 223	14.47 228	19.18 220	51.72 216	56.93 211	69.36 215	58.09 217

test1235678		37.40 218	37.94 219	36.76 218	48.97 218	49.30 223	28.65 225	46.73 196	18.33 227	7.68 228	16.79 224	14.46 229	19.18 220	51.72 216	56.92 212	69.36 215	58.07 218

RPSCF		55.07 180	58.06 174	51.57 189	48.87 220	58.95 210	53.68 190	41.26 215	62.42 72	45.88 96	54.38 67	54.26 96	53.75 146	57.15 203	53.53 219	66.01 218	65.75 204

PMVS	27.44 18	32.08 221	29.07 223	35.60 220	48.33 221	24.79 230	26.97 227	41.34 213	20.45 224	22.50 201	17.11 222	18.64 224	20.44 217	41.99 224	38.06 225	54.02 227	42.44 225
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PM-MVS		50.11 197	50.38 200	49.80 194	47.23 222	62.08 208	50.91 194	44.84 202	41.90 169	36.10 164	35.22 181	26.05 214	46.83 170	57.64 201	55.42 218	72.90 209	74.32 177

pmmvs3		41.86 213	42.29 214	41.36 210	39.80 223	52.66 218	38.93 216	35.85 226	23.40 220	20.22 205	19.30 216	20.84 221	40.56 189	55.98 208	58.79 207	72.80 210	65.03 205

test12356		29.92 222	31.49 222	28.08 222	38.46 224	37.74 228	21.36 228	40.17 217	16.83 229	5.61 232	15.66 226	11.48 230	6.60 230	42.01 223	51.23 220	56.29 225	45.52 222

MDA-MVSNet-bldmvs		44.15 209	42.27 215	46.34 203	38.34 225	62.31 207	46.28 203	55.74 141	29.83 209	20.98 204	27.11 204	16.45 227	41.98 184	41.11 225	57.47 210	74.72 206	61.65 214

no-one		26.96 223	26.51 224	27.49 224	37.87 226	39.14 227	17.12 230	41.31 214	12.02 231	3.68 234	8.04 229	8.42 233	10.67 228	28.11 227	45.96 223	54.27 226	43.89 223

MIMVSNet1		40.84 214	43.46 211	37.79 217	32.14 227	58.92 211	39.24 215	50.83 178	27.00 215	11.29 224	16.76 225	26.53 211	17.75 222	57.14 204	61.12 205	75.46 203	56.78 219

Gipuma		24.91 225	24.61 225	25.26 226	31.47 228	21.59 231	18.06 229	37.53 223	25.43 218	10.03 226	4.18 233	4.25 235	14.85 225	43.20 222	47.03 221	39.62 229	26.55 229
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		15.08 227	11.65 229	19.08 227	28.73 229	12.31 234	6.95 235	36.87 225	10.71 233	3.63 235	5.13 230	2.22 238	13.81 227	11.34 231	18.50 229	24.49 231	21.32 230

EMVS		14.40 228	10.71 230	18.70 228	28.15 230	12.09 235	7.06 234	36.89 224	11.00 232	3.56 236	4.95 231	2.27 237	13.91 226	10.13 232	16.06 230	22.63 232	18.51 231

new_pmnet		33.19 220	35.52 221	30.47 221	27.55 231	45.31 226	29.29 224	30.92 228	29.00 212	9.88 227	18.77 217	17.64 225	26.77 208	44.07 220	45.98 222	58.41 223	47.87 220

PMMVS2		20.45 226	22.31 227	18.27 229	20.52 232	26.73 229	14.85 232	28.43 230	13.69 230	0.79 237	10.35 228	9.10 231	3.83 232	27.64 228	32.87 226	41.17 228	35.81 226

tmp_tt				16.09 230	13.07 233	8.12 236	13.61 233	2.08 232	55.09 99	30.10 186	40.26 137	22.83 218	5.35 231	29.91 226	25.25 228	32.33 230

MVE	15.98 19	14.37 229	16.36 228	12.04 231	7.72 234	20.24 232	5.90 236	29.05 229	8.28 234	3.92 233	4.72 232	2.42 236	9.57 229	18.89 230	31.46 227	16.07 234	28.53 228
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GG-mvs-BLEND		54.54 186	77.58 40	27.67 223	0.03 235	90.09 22	77.20 55	0.02 233	66.83 61	0.05 238	59.90 55	73.33 28	0.04 233	78.40 63	79.30 54	88.65 16	95.20 18

ESAPD		0.00 232	0.00 233	0.00 234	0.00 236	0.00 239	0.00 240	0.00 235	0.00 237	0.00 239	0.00 236	0.00 240	0.00 236	0.00 235	0.00 234	0.00 237	0.00 235

sosnet-low-res		0.00 232	0.00 233	0.00 234	0.00 236	0.00 239	0.00 240	0.00 235	0.00 237	0.00 239	0.00 236	0.00 240	0.00 236	0.00 235	0.00 234	0.00 237	0.00 235

sosnet		0.00 232	0.00 233	0.00 234	0.00 236	0.00 239	0.00 240	0.00 235	0.00 237	0.00 239	0.00 236	0.00 240	0.00 236	0.00 235	0.00 234	0.00 237	0.00 235

testmvs		0.05 230	0.08 231	0.01 232	0.00 236	0.01 237	0.03 238	0.01 234	0.05 235	0.00 239	0.14 235	0.01 239	0.03 235	0.05 233	0.05 231	0.01 235	0.24 233

test123		0.05 230	0.08 231	0.01 232	0.00 236	0.01 237	0.01 239	0.00 235	0.05 235	0.00 239	0.16 234	0.00 240	0.04 233	0.02 234	0.05 231	0.00 237	0.26 232

MTAPA										78.32 5		79.42 16

MTMP										76.04 10		76.65 21

Patchmatch-RL test							2.17 237

NP-MVS									81.60 29

Patchmtry						78.06 132	67.53 132	43.18 205		41.40 133

DeepMVS_CX						19.81 233	17.01 231	10.02 231	23.61 219	5.85 231	17.21 221	8.03 234	21.13 216	22.60 229		21.42 233	30.01 227

High-resolution multi-view benchmark