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 5	86.99 9	84.31 2	94.07 1	92.80 3	88.15 3	79.10 1	85.66 18	70.72 25	76.50 28	80.45 14	82.17 2	88.35 1	87.49 2	91.63 2	97.65 1

ESAPD		87.78 1	90.56 1	84.53 1	92.88 2	93.82 1	88.95 1	76.05 4	92.95 3	80.32 2	93.12 2	86.87 1	80.88 4	85.54 10	84.01 18	88.09 33	97.62 2

HPM-MVS++		85.64 6	88.43 3	82.39 7	92.65 3	90.24 21	85.83 10	74.21 6	90.68 6	75.63 13	86.77 9	84.15 4	78.68 9	86.33 6	85.26 9	87.32 44	95.60 14

CNVR-MVS		85.96 4	87.58 7	84.06 3	92.58 4	92.40 6	87.62 4	77.77 2	88.44 10	75.93 12	79.49 21	81.97 11	81.65 3	87.04 5	86.58 3	88.79 14	97.18 4

NCCC		84.16 11	85.46 16	82.64 6	92.34 5	90.57 18	86.57 6	76.51 3	86.85 15	72.91 17	77.20 27	78.69 20	79.09 8	84.64 16	84.88 14	88.44 23	95.41 17

CSCG		82.90 15	84.52 18	81.02 13	91.85 6	93.43 2	87.14 5	74.01 9	81.96 28	76.14 10	70.84 32	82.49 8	69.71 52	82.32 35	85.18 11	87.26 46	95.40 18

SMA-MVS		84.91 7	87.95 5	81.36 10	91.75 7	90.84 15	86.35 8	73.36 13	90.22 7	72.81 18	80.70 18	85.67 2	76.69 15	86.06 8	86.14 5	87.20 50	96.05 10

QAPM		77.50 40	77.43 44	77.59 31	91.52 8	92.00 9	81.41 35	70.63 22	66.22 65	58.05 65	54.70 67	71.79 38	74.49 27	82.46 31	82.04 32	89.46 9	92.79 43

APDe-MVS		86.37 3	88.41 4	84.00 4	91.43 9	91.83 10	88.34 2	74.67 5	91.19 4	81.76 1	91.13 3	81.94 12	80.07 5	83.38 23	82.58 30	87.69 37	96.78 7

3Dnovator	70.49 5	78.42 34	76.77 50	80.35 15	91.43 9	90.27 20	81.84 31	70.79 21	72.10 52	71.95 19	50.02 84	67.86 51	77.47 12	82.89 26	84.24 16	88.61 18	89.99 70

DeepC-MVS_fast	75.41 2	81.69 19	82.10 28	81.20 12	91.04 11	87.81 44	83.42 22	74.04 8	83.77 22	71.09 23	66.88 40	72.44 32	79.48 6	85.08 12	84.97 13	88.12 32	93.78 32
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 16	85.35 17	79.20 21	90.25 12	89.39 29	84.79 16	70.95 20	82.86 24	68.32 33	86.44 10	77.19 21	73.07 33	83.63 21	83.64 22	87.82 34	94.34 26
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		82.48 17	84.12 19	80.56 14	90.15 13	87.55 46	84.28 18	69.67 29	85.22 19	77.95 8	84.69 12	75.94 24	75.04 22	81.85 40	81.17 45	86.30 63	92.40 45

DeepPCF-MVS	76.94 1	83.08 14	87.77 6	77.60 30	90.11 14	90.96 14	78.48 50	72.63 17	93.10 2	65.84 37	80.67 19	81.55 13	74.80 24	85.94 9	85.39 8	83.75 152	96.77 8

OpenMVS	67.62 8	74.92 52	73.91 60	76.09 39	90.10 15	90.38 19	78.01 53	66.35 48	66.09 67	62.80 43	46.33 109	64.55 60	71.77 41	79.92 55	80.88 51	87.52 40	89.20 77

MAR-MVS		77.19 44	78.37 42	75.81 41	89.87 16	90.58 17	79.33 49	65.56 54	77.62 45	58.33 63	59.24 60	67.98 49	74.83 23	82.37 34	83.12 26	86.95 53	87.67 104
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 21	85.84 15	76.63 34	89.82 17	86.53 53	86.32 9	66.72 46	85.96 17	65.43 38	88.98 7	82.29 9	67.57 69	82.06 38	81.33 43	83.93 150	93.75 33

train_agg		83.35 13	86.93 11	79.17 22	89.70 18	88.41 36	85.60 14	72.89 16	86.31 16	66.58 36	90.48 4	82.24 10	73.06 34	83.10 25	82.64 29	87.21 49	95.30 19

abl_6				79.06 24	89.68 19	92.14 8	77.70 56	69.68 28	86.87 14	71.88 20	74.29 30	80.06 16	76.56 17			88.84 13	95.82 11

APD-MVS		84.83 8	87.00 8	82.30 8	89.61 20	89.21 30	86.51 7	73.64 11	90.98 5	77.99 7	89.89 5	80.04 17	79.18 7	82.00 39	81.37 42	86.88 54	95.49 16
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_Plus		83.54 12	86.37 13	80.25 16	89.57 21	90.10 23	85.27 15	71.66 18	87.38 11	73.08 16	84.23 13	80.16 15	75.31 20	84.85 14	83.64 22	86.57 58	94.21 29

HSP-MVS		86.82 2	89.95 2	83.16 5	89.38 22	91.60 12	85.63 12	74.15 7	94.20 1	75.52 14	94.99 1	83.21 6	85.96 1	87.67 3	85.88 6	88.32 25	92.13 47

AdaColmap		76.23 48	73.55 62	79.35 20	89.38 22	85.00 66	79.99 46	73.04 15	76.60 47	71.17 22	55.18 65	57.99 85	77.87 10	76.82 77	76.82 75	84.67 135	86.45 112

3Dnovator+	70.16 6	77.87 37	77.29 46	78.55 25	89.25 24	88.32 38	80.09 44	67.95 39	74.89 51	71.83 21	52.05 77	70.68 42	76.27 19	82.27 36	82.04 32	85.92 80	90.77 61

CDPH-MVS		79.39 31	82.13 27	76.19 38	89.22 25	88.34 37	84.20 19	71.00 19	79.67 38	56.97 68	77.77 24	72.24 36	68.50 62	81.33 44	82.74 27	87.23 47	92.84 41

SD-MVS		84.31 10	86.96 10	81.22 11	88.98 26	88.68 33	85.65 11	73.85 10	89.09 9	79.63 3	87.34 8	84.84 3	73.71 29	82.66 29	81.60 39	85.48 107	94.51 24

MP-MVS		80.94 22	83.49 21	77.96 27	88.48 27	88.16 40	82.82 27	69.34 31	80.79 34	69.67 29	82.35 15	77.13 22	71.60 43	80.97 49	80.96 49	85.87 87	94.06 30
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMPR		80.62 24	82.98 23	77.87 29	88.41 28	87.05 48	83.02 24	69.18 32	83.91 21	68.35 32	82.89 14	73.64 29	72.16 39	80.78 50	81.13 47	86.10 68	91.43 54

MSLP-MVS++		78.57 33	77.33 45	80.02 17	88.39 29	84.79 67	84.62 17	66.17 50	75.96 48	78.40 5	61.59 52	71.47 39	73.54 31	78.43 66	78.88 61	88.97 12	90.18 69

PGM-MVS		79.42 30	81.84 29	76.60 35	88.38 30	86.69 51	82.97 26	65.75 52	80.39 35	64.94 39	81.95 17	72.11 37	71.41 44	80.45 51	80.55 53	86.18 65	90.76 62

EPNet		79.28 32	82.25 25	75.83 40	88.31 31	90.14 22	79.43 48	68.07 38	81.76 30	61.26 50	77.26 26	70.08 45	70.06 50	82.43 33	82.00 34	87.82 34	92.09 48
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		79.49 26	79.84 35	79.08 23	88.26 32	92.49 4	84.12 20	70.63 22	65.27 72	69.60 31	61.29 54	66.50 54	72.75 35	88.07 2	88.03 1	89.13 11	97.22 3
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
zzz-MVS		81.65 20	83.10 22	79.97 18	88.14 33	87.62 45	83.96 21	69.90 26	86.92 13	77.67 9	72.47 31	78.74 19	74.13 28	81.59 43	81.15 46	86.01 74	93.19 37

TSAR-MVS + MP.		84.39 9	86.58 12	81.83 9	88.09 34	86.47 54	85.63 12	73.62 12	90.13 8	79.24 4	89.67 6	82.99 7	77.72 11	81.22 45	80.92 50	86.68 57	94.66 23

X-MVS		78.16 36	80.55 33	75.38 43	87.99 35	86.27 56	81.05 39	68.98 33	78.33 40	61.07 52	75.25 29	72.27 33	67.52 70	80.03 54	80.52 54	85.66 104	91.20 56

DeepC-MVS	74.46 3	80.30 25	81.05 31	79.42 19	87.42 36	88.50 35	83.23 23	73.27 14	82.78 25	71.01 24	62.86 48	69.93 46	74.80 24	84.30 17	84.20 17	86.79 56	94.77 21
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 37							70.61 43

CP-MVS		79.44 27	81.51 30	77.02 33	86.95 38	85.96 61	82.00 29	68.44 37	81.82 29	67.39 34	77.43 25	73.68 28	71.62 42	79.56 57	79.58 56	85.73 97	92.51 44

MVS_111021_HR		77.42 41	78.40 41	76.28 36	86.95 38	90.68 16	77.41 58	70.56 25	66.21 66	62.48 46	66.17 42	63.98 61	72.08 40	82.87 27	83.15 25	88.24 28	95.71 12

CANet		80.90 23	82.93 24	78.53 26	86.83 40	92.26 7	81.19 37	66.95 43	81.60 31	69.90 28	66.93 39	74.80 26	76.79 14	84.68 15	84.77 15	89.50 8	95.50 15

CHOSEN 1792x2688		72.55 61	71.98 67	73.22 55	86.57 41	92.41 5	75.63 65	66.77 45	62.08 77	52.32 77	30.27 202	50.74 111	66.14 72	86.22 7	85.41 7	91.90 1	96.75 9

PHI-MVS		79.43 28	84.06 20	74.04 51	86.15 42	91.57 13	80.85 41	68.90 35	82.22 27	51.81 80	78.10 23	74.28 27	70.39 49	84.01 20	84.00 19	86.14 67	94.24 28

ACMMP		77.61 39	79.59 36	75.30 44	85.87 43	85.58 62	81.42 34	67.38 42	79.38 39	62.61 44	78.53 22	65.79 56	68.80 60	78.56 65	78.50 65	85.75 93	90.80 60
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 35	80.91 32	75.17 45	85.67 44	84.33 70	83.01 25	69.38 30	79.88 37	55.83 69	79.85 20	64.90 59	70.81 46	82.46 31	81.78 36	86.30 63	93.18 38

OPM-MVS		72.74 60	70.93 75	74.85 48	85.30 45	84.34 69	82.82 27	69.79 27	49.96 117	55.39 73	54.09 72	60.14 76	70.04 51	80.38 53	79.43 57	85.74 96	88.20 101

MS-PatchMatch		70.34 73	69.00 83	71.91 63	85.20 46	85.35 63	77.84 55	61.77 88	58.01 88	55.40 72	41.26 133	58.34 82	61.69 92	81.70 42	78.29 66	89.56 7	80.02 163

MVS_0304		79.43 28	82.20 26	76.20 37	84.22 47	91.79 11	81.82 32	63.81 65	76.83 46	61.71 48	66.37 41	75.52 25	76.38 18	85.54 10	85.03 12	89.28 10	94.32 27

PCF-MVS	70.85 4	75.73 49	76.55 53	74.78 49	83.67 48	88.04 43	81.47 33	70.62 24	69.24 62	57.52 66	60.59 57	69.18 47	70.65 47	77.11 74	77.65 72	84.75 133	94.01 31
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ACMM	66.70 10	70.42 69	68.49 87	72.67 58	82.85 49	77.76 141	77.70 56	64.76 59	64.61 73	60.74 56	49.29 86	53.97 101	65.86 73	74.97 97	75.57 90	84.13 148	83.29 138
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVS					82.43 50	86.27 56	75.70 63			61.07 52		72.27 33				85.67 101

X-MVStestdata					82.43 50	86.27 56	75.70 63			61.07 52		72.27 33				85.67 101

PVSNet_BlendedMVS		76.84 46	78.47 39	74.95 46	82.37 52	89.90 25	75.45 69	65.45 55	74.99 49	70.66 26	63.07 46	58.27 83	67.60 67	84.24 18	81.70 37	88.18 29	97.10 5

PVSNet_Blended		76.84 46	78.47 39	74.95 46	82.37 52	89.90 25	75.45 69	65.45 55	74.99 49	70.66 26	63.07 46	58.27 83	67.60 67	84.24 18	81.70 37	88.18 29	97.10 5

CLD-MVS		77.36 42	77.29 46	77.45 32	82.21 54	88.11 41	81.92 30	68.96 34	77.97 42	69.62 30	62.08 50	59.44 77	73.57 30	81.75 41	81.27 44	88.41 24	90.39 66
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 64	73.18 65	70.67 67	82.13 55	80.26 116	79.58 47	63.04 72	70.09 57	51.98 78	65.06 43	55.62 95	62.49 89	75.97 89	76.32 80	84.80 132	88.93 81

MSDG		65.57 100	61.57 149	70.24 68	82.02 56	76.47 153	74.46 78	68.73 36	56.52 93	50.33 87	38.47 161	41.10 141	62.42 90	72.12 147	72.94 145	83.47 155	73.37 188

IB-MVS	64.48 11	69.02 77	68.97 84	69.09 75	81.75 57	89.01 31	64.50 153	64.91 58	56.65 92	62.59 45	47.89 93	45.23 124	51.99 154	69.18 175	81.88 35	88.77 15	92.93 40
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 38	79.59 36	75.39 42	81.52 58	89.83 27	81.32 36	60.74 100	80.05 36	66.72 35	68.43 36	65.09 57	74.72 26	78.87 61	82.73 28	87.32 44	92.16 46

CPTT-MVS		75.43 50	77.13 48	73.44 53	81.43 59	82.55 79	80.96 40	64.35 60	77.95 43	61.39 49	69.20 35	70.94 41	69.38 57	73.89 111	73.32 137	83.14 163	92.06 49

DWT-MVSNet_training		72.81 59	73.98 59	71.45 64	81.26 60	86.37 55	72.08 84	59.82 108	69.13 63	58.15 64	54.71 66	61.33 74	67.81 66	76.86 76	78.63 62	89.59 6	90.86 59

casdiffmvs		77.30 43	79.53 38	74.70 50	81.15 61	89.49 28	80.82 42	60.74 100	77.66 44	59.79 60	62.47 49	66.82 53	77.17 13	83.43 22	83.84 20	88.53 21	93.02 39

EPNet_dtu		66.17 95	70.13 79	61.54 148	81.04 62	77.39 145	68.87 131	62.50 81	69.78 58	33.51 183	63.77 45	56.22 89	37.65 199	72.20 145	72.18 153	85.69 100	79.38 165
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMP	68.86 7	72.15 63	72.25 66	72.03 61	80.96 63	80.87 101	77.93 54	64.13 62	69.29 60	60.79 55	64.04 44	53.54 102	63.91 81	73.74 115	75.27 92	84.45 140	88.98 80
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HyFIR lowres test		68.39 80	68.28 90	68.52 78	80.85 64	88.11 41	71.08 112	58.09 116	54.87 106	47.80 94	27.55 207	55.80 92	64.97 76	79.11 59	79.14 59	88.31 26	93.35 34

LS3D		64.54 109	62.14 142	67.34 86	80.85 64	75.79 159	69.99 122	65.87 51	60.77 80	44.35 111	42.43 127	45.95 122	65.01 75	69.88 170	68.69 180	77.97 201	71.43 198

CNLPA		71.37 67	70.27 78	72.66 59	80.79 66	81.33 95	71.07 113	65.75 52	82.36 26	64.80 40	42.46 126	56.49 88	72.70 36	73.00 123	70.52 172	80.84 183	85.76 121

TSAR-MVS + GP.		82.27 18	85.98 14	77.94 28	80.72 67	88.25 39	81.12 38	67.71 40	87.10 12	73.31 15	85.23 11	83.68 5	76.64 16	80.43 52	81.47 41	88.15 31	95.66 13

Anonymous20240521		70.48 68	68.36 88	72.96 56	80.33 68	86.03 60	78.05 52	66.83 44	56.01 97	53.25 76	39.00 155	47.74 118	73.45 32	78.84 62	80.47 55	85.87 87	90.42 65

PLC	64.00 12	68.54 79	66.66 99	70.74 66	80.28 69	74.88 164	72.64 82	63.70 67	69.26 61	55.71 70	47.24 101	55.31 96	70.42 48	72.05 149	70.67 170	81.66 177	77.19 172
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
OMC-MVS		74.03 54	75.82 55	71.95 62	79.56 70	80.98 99	75.35 71	63.21 69	84.48 20	61.83 47	61.54 53	66.89 52	69.41 56	76.60 79	74.07 126	82.34 172	86.15 116

CostFormer		72.18 62	73.90 61	70.18 69	79.47 71	86.19 59	76.94 61	48.62 192	66.07 68	60.40 58	54.14 71	65.82 55	67.98 64	75.84 90	76.41 79	87.67 38	92.83 42

MVS_111021_LR		74.26 53	75.95 54	72.27 60	79.43 72	85.04 65	72.71 81	65.27 57	70.92 56	63.58 42	69.32 34	60.31 75	69.43 55	77.01 75	77.15 73	83.22 159	91.93 52

MVS_Test		75.22 51	76.69 51	73.51 52	79.30 73	88.82 32	80.06 45	58.74 111	69.77 59	57.50 67	59.78 59	61.35 72	75.31 20	82.07 37	83.60 24	90.13 5	91.41 55

PVSNet_Blended_VisFu		71.76 65	73.54 63	69.69 70	79.01 74	87.16 47	72.05 85	61.80 87	56.46 94	59.66 61	53.88 73	62.48 64	59.08 131	81.17 46	78.90 60	86.53 60	94.74 22

ACMH	59.42 14	61.59 154	59.22 175	64.36 111	78.92 75	78.26 134	67.65 136	67.48 41	39.81 182	30.98 191	38.25 164	34.59 195	61.37 100	70.55 162	73.47 133	79.74 192	79.59 164
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FC-MVSNet-train		68.83 78	68.29 89	69.47 71	78.35 76	79.94 117	64.72 152	66.38 47	54.96 104	54.51 74	56.75 63	47.91 117	66.91 71	75.57 94	75.75 86	85.92 80	87.12 107

tpmp4_e23		69.38 74	69.47 81	69.28 73	78.20 77	82.35 81	75.92 62	49.20 190	64.15 74	59.96 59	47.93 91	55.77 93	68.06 63	73.05 122	74.53 105	84.34 142	88.50 99

Effi-MVS+		70.42 69	71.23 73	69.47 71	78.04 78	85.24 64	75.57 67	58.88 110	59.56 83	48.47 91	52.73 76	54.94 97	69.69 53	78.34 68	77.06 74	86.18 65	90.73 63

conf0.002		67.12 91	67.13 97	67.11 87	77.95 79	82.11 82	71.71 94	63.06 70	49.16 122	43.43 117	47.76 95	48.79 114	61.42 94	76.61 78	76.55 77	85.07 117	88.92 83

conf0.01		66.60 92	66.18 103	67.09 88	77.90 80	82.02 83	71.71 94	63.05 71	49.16 122	43.41 119	46.23 110	45.78 123	61.42 94	76.55 80	74.63 99	85.04 118	88.87 85

tfpn111		66.52 93	66.12 104	66.98 90	77.70 81	81.58 89	71.71 94	62.94 76	49.16 122	43.28 122	51.38 79	41.34 134	61.42 94	76.24 82	74.63 99	84.84 126	88.52 95

conf200view11		65.89 98	64.96 110	66.98 90	77.70 81	81.58 89	71.71 94	62.94 76	49.16 122	43.28 122	43.24 116	41.34 134	61.42 94	76.24 82	74.63 99	84.84 126	88.52 95

thres100view900		67.14 90	66.09 105	68.38 80	77.70 81	83.84 73	74.52 75	66.33 49	49.16 122	43.40 120	43.24 116	41.34 134	62.59 88	79.31 58	75.92 85	85.73 97	89.81 71

tfpn200view9		65.90 97	64.96 110	67.00 89	77.70 81	81.58 89	71.71 94	62.94 76	49.16 122	43.40 120	43.24 116	41.34 134	61.42 94	76.24 82	74.63 99	84.84 126	88.52 95

UA-Net		64.62 106	68.23 91	60.42 153	77.53 85	81.38 94	60.08 181	57.47 127	47.01 135	44.75 109	60.68 56	71.32 40	41.84 190	73.27 117	72.25 152	80.83 184	71.68 196

thres200		65.58 99	64.74 113	66.56 92	77.52 86	81.61 87	73.44 80	62.95 74	46.23 143	42.45 137	42.76 120	41.18 139	58.12 135	76.24 82	75.59 89	84.89 123	89.58 72

ACMH+	60.36 13	61.16 155	58.38 177	64.42 110	77.37 87	74.35 169	68.45 132	62.81 80	45.86 145	38.48 156	35.71 183	37.35 177	59.81 120	67.24 181	69.80 176	79.58 193	78.32 170

TAPA-MVS	67.10 9	71.45 66	73.47 64	69.10 74	77.04 88	80.78 102	73.81 79	62.10 82	80.80 33	51.28 81	60.91 55	63.80 63	67.98 64	74.59 100	72.42 151	82.37 171	80.97 158
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS_MVSNet		67.29 88	71.98 67	61.82 146	76.92 89	84.32 71	65.90 150	58.22 114	55.75 100	39.22 150	54.51 69	62.47 65	45.99 179	78.83 63	78.52 64	84.70 134	89.47 74

tfpn_ndepth		62.95 134	63.75 118	62.02 144	76.89 90	79.48 124	64.09 156	60.98 95	49.48 119	38.73 154	49.92 85	44.79 125	47.37 171	71.91 150	71.66 156	84.07 149	79.00 168

CANet_DTU		72.84 58	76.63 52	68.43 79	76.81 91	86.62 52	75.54 68	54.71 159	72.06 53	43.54 115	67.11 38	58.46 80	72.40 37	81.13 48	80.82 52	87.57 39	90.21 68

tpm cat1		67.47 86	67.05 98	67.98 81	76.63 92	81.51 93	74.49 77	47.65 197	61.18 79	61.12 51	42.51 125	53.02 105	64.74 79	70.11 167	71.50 158	83.22 159	89.49 73

DI_MVS_plusplus_trai		73.94 55	74.85 58	72.88 57	76.57 93	86.80 49	80.41 43	61.47 90	62.35 76	59.44 62	47.91 92	68.12 48	72.24 38	82.84 28	81.50 40	87.15 51	94.42 25

thres400		65.18 104	64.44 115	66.04 93	76.40 94	82.63 77	71.52 104	64.27 61	44.93 151	40.69 145	41.86 130	40.79 148	58.12 135	77.67 69	74.64 98	85.26 110	88.56 94

tpmrst		67.15 89	68.12 92	66.03 94	76.21 95	80.98 99	71.27 106	45.05 204	60.69 81	50.63 85	46.95 106	54.15 100	65.30 74	71.80 152	71.77 155	87.72 36	90.48 64

gg-mvs-nofinetune		62.34 137	66.19 102	57.86 171	76.15 96	88.61 34	71.18 109	41.24 221	25.74 222	13.16 226	22.91 217	63.97 62	54.52 149	85.06 13	85.25 10	90.92 3	91.78 53

thresconf0.02		63.92 116	65.18 109	62.46 139	75.91 97	80.65 110	67.51 139	63.86 64	45.00 150	33.32 184	51.38 79	51.68 107	48.34 166	75.49 95	75.13 93	85.84 92	76.91 174

EPMVS		66.21 94	67.49 95	64.73 103	75.81 98	84.20 72	68.94 130	44.37 208	61.55 78	48.07 93	49.21 88	54.87 98	62.88 86	71.82 151	71.40 162	88.28 27	79.37 166

EPP-MVSNet		67.58 84	71.10 74	63.48 125	75.71 99	83.35 74	66.85 142	57.83 119	53.02 111	41.15 142	55.82 64	67.89 50	56.01 143	74.40 102	72.92 146	83.33 157	90.30 67

view600		63.91 117	63.27 124	64.66 105	75.57 100	81.73 85	69.71 125	63.04 72	43.97 154	39.18 151	41.09 134	40.24 156	55.38 145	76.28 81	72.04 154	85.08 116	87.52 105

thres600view7		63.77 118	63.14 126	64.51 107	75.49 101	81.61 87	69.59 126	62.95 74	43.96 155	38.90 153	41.09 134	40.24 156	55.25 147	76.24 82	71.54 157	84.89 123	87.30 106

dps		64.08 112	63.22 125	65.08 97	75.27 102	79.65 121	66.68 144	46.63 202	56.94 90	55.67 71	43.96 112	43.63 130	64.00 80	69.50 174	69.82 175	82.25 173	79.02 167

MVSTER		76.92 45	79.92 34	73.42 54	74.98 103	82.97 75	78.15 51	63.41 68	78.02 41	64.41 41	67.54 37	72.80 31	71.05 45	83.29 24	83.73 21	88.53 21	91.12 57

TSAR-MVS + COLMAP		73.09 57	76.86 49	68.71 76	74.97 104	82.49 80	74.51 76	61.83 86	83.16 23	49.31 90	82.22 16	51.62 108	68.94 59	78.76 64	75.52 91	82.67 167	84.23 130

diffmvs		73.50 56	75.66 56	70.97 65	74.96 105	86.71 50	77.16 60	57.42 131	71.12 55	60.43 57	57.20 62	70.40 44	68.79 61	76.11 87	76.05 83	87.10 52	92.06 49

view800		63.02 130	62.69 137	63.39 127	74.79 106	80.76 106	67.83 135	61.93 85	43.16 165	37.78 162	40.43 139	39.73 163	53.16 152	75.01 96	73.32 137	84.87 125	86.43 113

tpm		64.85 105	66.02 106	63.48 125	74.52 107	78.38 133	70.98 114	44.99 206	51.61 113	43.28 122	47.66 96	53.18 103	60.57 106	70.58 161	71.30 167	86.54 59	89.45 75

tfpn		62.54 136	62.79 133	62.25 143	74.16 108	79.86 119	66.07 149	60.97 96	42.43 170	36.41 166	39.88 143	43.76 129	51.25 159	73.85 112	74.17 122	84.67 135	85.57 124

Vis-MVSNet (Re-imp)		62.25 140	68.74 85	54.68 186	73.70 109	78.74 129	56.51 192	57.49 126	55.22 102	26.86 201	54.56 68	61.35 72	31.06 202	73.10 119	74.90 95	82.49 169	83.31 137

Fast-Effi-MVS+		67.59 83	67.56 94	67.62 84	73.67 110	81.14 98	71.12 110	54.79 158	58.88 84	50.61 86	46.70 107	47.05 119	69.12 58	76.06 88	76.44 78	86.43 61	86.65 110

IterMVS-LS		66.08 96	66.56 101	65.51 95	73.67 110	74.88 164	70.89 116	53.55 166	50.42 115	48.32 92	50.59 82	55.66 94	61.83 91	73.93 110	74.42 112	84.82 131	86.01 118
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		65.43 102	67.71 93	62.78 135	73.49 112	82.83 76	66.42 147	45.40 203	60.40 82	45.27 103	49.22 87	57.60 86	60.01 115	70.61 159	71.38 165	86.08 70	81.91 153
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
COLMAP_ROB	51.17 15	55.13 184	52.90 197	57.73 172	73.47 113	67.21 200	62.13 170	55.82 144	47.83 133	34.39 179	31.60 199	34.24 196	44.90 184	63.88 196	62.52 206	75.67 207	63.02 215
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
conf0.05thres1000		60.33 162	59.42 172	61.40 149	73.15 114	78.25 135	65.29 151	60.30 104	36.61 194	35.75 172	33.25 190	39.23 167	50.35 162	72.18 146	72.67 149	83.57 154	83.74 132

Effi-MVS+-dtu		64.58 107	64.08 116	65.16 96	73.04 115	75.17 163	70.68 118	56.23 141	54.12 109	44.71 110	47.42 97	51.10 109	63.82 82	68.08 179	66.32 192	82.47 170	86.38 114

tfpnview11		58.92 168	59.60 170	58.13 166	72.99 116	77.11 149	60.48 176	60.37 102	42.10 173	29.10 195	43.45 113	40.72 151	41.67 191	70.53 163	70.43 173	84.17 147	72.85 190

tfpn_n400		58.64 172	59.27 173	57.89 169	72.83 117	77.26 147	60.35 177	60.29 105	39.77 184	29.10 195	43.45 113	40.72 151	41.61 192	70.06 168	71.39 163	83.17 161	72.26 193

tfpnconf		58.64 172	59.27 173	57.89 169	72.83 117	77.26 147	60.35 177	60.29 105	39.77 184	29.10 195	43.45 113	40.72 151	41.61 192	70.06 168	71.39 163	83.17 161	72.26 193

tfpn1000		58.35 176	59.96 167	56.47 179	72.78 119	77.51 143	56.66 191	59.16 109	43.74 156	29.76 194	42.79 119	42.49 131	37.04 200	68.92 176	68.98 178	83.45 156	75.25 178

EG-PatchMatch MVS		58.73 171	58.03 180	59.55 158	72.32 120	80.49 112	63.44 165	55.55 148	32.49 209	38.31 157	28.87 204	37.22 178	42.84 188	74.30 108	75.70 87	84.84 126	77.14 173

TransMVSNet (Re)		57.83 177	56.90 183	58.91 163	72.26 121	74.69 167	63.57 164	61.42 91	32.30 210	32.65 186	33.97 189	35.96 187	39.17 197	73.84 114	72.84 147	84.37 141	74.69 181

CMPMVS	43.63 17	57.67 179	55.43 186	60.28 154	72.01 122	79.00 127	62.77 169	53.23 171	41.77 175	45.42 102	30.74 201	39.03 168	53.01 153	64.81 189	64.65 198	75.26 209	68.03 204
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
NR-MVSNet		61.08 157	62.09 143	59.90 155	71.96 123	75.87 157	63.60 163	61.96 83	49.31 120	27.95 198	42.76 120	33.85 199	48.82 165	74.35 105	74.05 127	85.13 112	84.45 127

PMMVS		70.37 72	75.06 57	64.90 98	71.46 124	81.88 84	64.10 155	55.64 147	71.31 54	46.69 97	70.69 33	58.56 78	69.53 54	79.03 60	75.63 88	81.96 175	88.32 100

test-LLR		68.23 81	71.61 71	64.28 116	71.37 125	81.32 96	63.98 159	61.03 93	58.62 85	42.96 127	52.74 74	61.65 70	57.74 137	75.64 92	78.09 70	88.61 18	93.21 35

test0.0.03 1		57.35 180	59.89 168	54.38 188	71.37 125	73.45 172	52.71 197	61.03 93	46.11 144	26.33 202	41.73 131	44.08 127	29.72 205	71.43 155	70.90 168	85.10 113	71.56 197

tfpnnormal		58.97 167	56.48 185	61.89 145	71.27 127	76.21 156	66.65 145	61.76 89	32.90 208	36.41 166	27.83 206	29.14 211	50.64 161	73.06 120	73.05 144	84.58 138	83.15 143

Fast-Effi-MVS+-dtu		63.05 129	64.72 114	61.11 150	71.21 128	76.81 152	70.72 117	43.13 212	52.51 112	35.34 175	46.55 108	46.36 120	61.40 99	71.57 154	71.44 160	84.84 126	87.79 103

MDTV_nov1_ep13		65.21 103	67.28 96	62.79 134	70.91 129	81.72 86	69.28 129	49.50 187	58.08 87	43.94 114	50.50 83	56.02 90	58.86 132	70.72 158	73.37 135	84.24 144	80.52 159

FMVSNet3		70.41 71	71.89 69	68.68 77	70.89 130	79.42 125	75.63 65	60.97 96	65.32 69	51.06 82	47.37 98	62.05 66	64.90 77	82.49 30	82.27 31	88.64 17	84.34 129

Vis-MVSNet		65.53 101	69.83 80	60.52 152	70.80 131	84.59 68	66.37 148	55.47 150	48.40 130	40.62 146	57.67 61	58.43 81	45.37 183	77.49 70	76.24 81	84.47 139	85.99 119
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CDS-MVSNet		64.22 110	65.89 107	62.28 142	70.05 132	80.59 111	69.91 124	57.98 117	43.53 161	46.58 98	48.22 90	50.76 110	46.45 176	75.68 91	76.08 82	82.70 166	86.34 115
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UGNet		67.57 85	71.69 70	62.76 136	69.88 133	82.58 78	66.43 146	58.64 112	54.71 107	51.87 79	61.74 51	62.01 69	45.46 182	74.78 99	74.99 94	84.24 144	91.02 58
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		64.55 108	65.76 108	63.12 131	69.68 134	81.56 92	69.59 126	58.16 115	45.23 149	35.58 174	47.01 105	41.82 133	59.41 126	79.62 56	78.54 63	86.32 62	86.56 111

GBi-Net		69.21 75	70.40 76	67.81 82	69.49 135	78.65 130	74.54 72	60.97 96	65.32 69	51.06 82	47.37 98	62.05 66	63.43 83	77.49 70	78.22 67	87.37 41	83.73 133

test1		69.21 75	70.40 76	67.81 82	69.49 135	78.65 130	74.54 72	60.97 96	65.32 69	51.06 82	47.37 98	62.05 66	63.43 83	77.49 70	78.22 67	87.37 41	83.73 133

FMVSNet2		68.06 82	68.57 86	67.45 85	69.49 135	78.65 130	74.54 72	60.23 107	56.29 95	49.64 89	42.13 129	57.08 87	63.43 83	81.15 47	80.99 48	87.37 41	83.73 133

UniMVSNet_NR-MVSNet		62.30 139	63.51 120	60.89 151	69.48 138	77.83 139	64.07 157	63.94 63	50.03 116	31.17 189	44.82 111	41.12 140	51.37 156	71.02 156	74.81 97	85.30 109	84.95 125

gm-plane-assit		54.99 186	57.99 181	51.49 196	69.27 139	54.42 222	32.32 226	42.59 213	21.18 228	13.71 224	23.61 213	43.84 128	60.21 114	87.09 4	86.55 4	90.81 4	89.28 76

PatchMatch-RL		62.22 143	60.69 156	64.01 117	68.74 140	75.75 160	59.27 184	60.35 103	56.09 96	53.80 75	47.06 104	36.45 182	64.80 78	68.22 178	67.22 187	77.10 203	74.02 183

CR-MVSNet		62.31 138	64.75 112	59.47 159	68.63 141	71.29 190	67.53 137	43.18 210	55.83 98	41.40 139	41.04 136	55.85 91	57.29 140	72.76 135	73.27 140	78.77 198	83.23 141

v18		63.31 126	62.02 144	64.81 102	68.48 142	73.38 173	72.14 83	54.28 161	48.99 129	47.21 95	39.56 145	41.20 138	60.80 103	72.89 127	74.46 111	85.96 79	83.64 136

v16		63.12 128	61.78 146	64.68 104	68.45 143	73.29 174	71.86 87	54.12 162	48.36 131	47.00 96	39.30 150	41.01 142	60.67 104	72.83 133	74.40 113	86.01 74	83.24 140

v17		62.99 133	61.70 147	64.51 107	68.40 144	73.28 175	71.80 92	54.11 163	47.87 132	46.14 99	39.29 151	41.01 142	60.60 105	72.81 134	74.39 118	85.99 77	83.25 139

TranMVSNet+NR-MVSNet		60.38 161	61.30 151	59.30 160	68.34 145	75.57 162	63.38 166	63.78 66	46.74 137	27.73 199	42.56 124	36.84 180	47.66 169	70.36 165	74.59 103	84.91 122	82.46 147

v8		63.44 125	62.58 138	64.43 109	68.28 146	78.07 136	71.82 91	54.85 156	46.70 139	45.20 104	39.40 146	40.91 144	60.54 109	72.85 132	74.39 118	85.92 80	85.76 121

v6		64.09 111	63.40 121	64.90 98	68.28 146	80.78 102	71.85 88	57.64 123	46.73 138	45.18 105	39.40 146	40.89 145	60.54 109	72.86 128	74.40 113	85.92 80	88.72 90

v1neww		64.08 112	63.38 122	64.89 100	68.27 148	80.77 104	71.84 89	57.65 121	46.66 140	45.10 106	39.40 146	40.86 146	60.57 106	72.86 128	74.40 113	85.92 80	88.71 91

v7new		64.08 112	63.38 122	64.89 100	68.27 148	80.77 104	71.84 89	57.65 121	46.66 140	45.10 106	39.40 146	40.86 146	60.57 106	72.86 128	74.40 113	85.92 80	88.71 91

v1141		63.48 122	62.75 136	64.32 112	68.13 150	80.69 108	71.69 101	57.43 128	43.66 160	42.83 134	39.02 153	39.74 162	59.95 116	72.94 124	74.49 108	85.86 89	88.75 88

divwei89l23v2f112		63.48 122	62.76 135	64.32 112	68.13 150	80.68 109	71.71 94	57.43 128	43.69 158	42.84 132	39.01 154	39.75 161	59.94 117	72.93 125	74.49 108	85.86 89	88.75 88

v1		63.49 121	62.77 134	64.32 112	68.13 150	80.70 107	71.70 100	57.43 128	43.69 158	42.89 131	39.03 152	39.77 160	59.93 118	72.93 125	74.48 110	85.86 89	88.77 86

v15		62.07 144	60.70 155	63.67 122	68.09 153	73.00 176	71.27 106	53.41 167	43.70 157	43.43 117	38.77 157	39.83 158	59.87 119	72.74 137	74.25 120	85.98 78	82.61 145

V14		61.96 147	60.56 157	63.59 123	68.06 154	72.93 179	71.10 111	53.33 169	43.47 162	43.28 122	38.59 158	39.78 159	59.76 121	72.65 139	74.19 121	86.01 74	82.32 150

V9		61.85 149	60.42 160	63.51 124	68.02 155	72.85 180	70.91 115	53.24 170	43.25 164	43.27 126	38.41 162	39.73 163	59.60 123	72.55 141	74.13 124	86.04 72	82.04 152

v2v482		63.68 119	62.85 131	64.65 106	68.01 156	80.46 113	71.90 86	57.60 124	44.26 152	42.82 135	39.80 144	38.62 172	61.56 93	73.06 120	74.86 96	86.03 73	88.90 84

v12		61.70 151	60.27 162	63.38 128	68.00 157	72.76 181	70.63 119	53.14 172	43.01 166	42.95 130	38.25 164	39.64 165	59.48 125	72.47 143	74.05 127	86.06 71	81.71 155

pm-mvs1		59.21 166	59.58 171	58.77 164	67.97 158	77.07 151	64.12 154	57.20 133	34.73 202	36.86 164	35.34 185	40.54 155	43.34 187	74.32 107	73.30 139	83.13 164	81.77 154

v13		61.60 153	60.13 165	63.31 129	67.95 159	72.67 183	70.51 120	53.05 173	42.80 167	42.96 127	38.10 169	39.57 166	59.31 128	72.36 144	73.98 129	86.10 68	81.40 157

v7		63.61 120	63.02 128	64.29 115	67.88 160	80.32 114	71.60 102	56.63 137	45.37 147	42.84 132	38.54 159	38.91 170	61.05 101	74.39 103	74.52 106	85.75 93	89.10 79

v10		63.00 131	62.22 141	63.90 120	67.88 160	77.78 140	71.59 103	54.34 160	45.37 147	42.76 136	38.53 160	38.93 169	61.05 101	74.39 103	74.52 106	85.75 93	86.04 117

v11		61.74 150	60.47 159	63.22 130	67.83 162	72.72 182	70.31 121	52.95 176	42.75 168	41.89 138	38.16 167	38.49 173	60.40 113	74.35 105	74.40 113	85.92 80	82.39 149

v1144		63.00 131	62.39 140	63.70 121	67.72 163	80.27 115	71.23 108	56.40 138	42.51 169	40.81 144	38.12 168	37.73 174	60.42 112	74.46 101	74.55 104	85.64 105	89.12 78

UniMVSNet (Re)		60.62 159	62.93 130	57.92 168	67.64 164	77.90 138	61.75 172	61.24 92	49.83 118	29.80 193	42.57 123	40.62 154	43.36 186	70.49 164	73.27 140	83.76 151	85.81 120

RPMNet		58.63 174	62.80 132	53.76 192	67.59 165	71.29 190	54.60 195	38.13 225	55.83 98	35.70 173	41.58 132	53.04 104	47.89 168	66.10 183	67.38 185	78.65 200	84.40 128

v148		62.00 146	61.19 152	62.96 132	67.46 166	79.49 123	67.87 134	57.66 120	42.30 171	45.02 108	38.20 166	38.89 171	54.77 148	69.83 171	72.60 150	84.96 119	87.01 108

IterMVS		61.87 148	63.55 119	59.90 155	67.29 167	72.20 185	67.34 140	48.56 193	47.48 134	37.86 161	47.07 103	48.27 115	54.08 150	72.12 147	73.71 130	84.30 143	83.99 131
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1192		62.25 140	61.64 148	62.96 132	66.88 168	79.72 120	69.96 123	55.77 145	41.58 176	39.42 148	37.05 174	35.96 187	60.50 111	74.30 108	74.09 125	85.24 111	88.76 87

DU-MVS		60.87 158	61.82 145	59.76 157	66.69 169	75.87 157	64.07 157	61.96 83	49.31 120	31.17 189	42.76 120	36.95 179	51.37 156	69.67 172	73.20 143	83.30 158	84.95 125

Baseline_NR-MVSNet		59.47 165	60.28 161	58.54 165	66.69 169	73.90 170	61.63 173	62.90 79	49.15 128	26.87 200	35.18 187	37.62 175	48.20 167	69.67 172	73.61 131	84.92 120	82.82 144

v144192		62.05 145	61.46 150	62.73 138	66.59 171	79.87 118	69.30 128	55.88 143	41.50 177	39.41 149	37.23 172	36.45 182	59.62 122	72.69 138	73.51 132	85.61 106	88.93 81

v1921920		61.66 152	61.10 153	62.31 141	66.32 172	79.57 122	68.41 133	55.49 149	41.03 178	38.69 155	36.64 180	35.27 193	59.60 123	73.23 118	73.41 134	85.37 108	88.51 98

TESTMET0.1,1		67.38 87	71.61 71	62.45 140	66.05 173	81.32 96	63.98 159	55.36 151	58.62 85	42.96 127	52.74 74	61.65 70	57.74 137	75.64 92	78.09 70	88.61 18	93.21 35

pmmvs4		63.14 127	62.46 139	63.94 119	66.03 174	76.40 154	66.82 143	57.60 124	56.74 91	50.26 88	40.81 138	37.51 176	59.26 129	71.75 153	71.48 159	83.68 153	82.53 146

PatchT		60.46 160	63.85 117	56.51 178	65.95 175	75.68 161	47.34 206	41.39 217	53.89 110	41.40 139	37.84 170	50.30 112	57.29 140	72.76 135	73.27 140	85.67 101	83.23 141

v1240		61.09 156	60.55 158	61.72 147	65.92 176	79.28 126	67.16 141	54.91 155	39.79 183	38.10 158	36.08 182	34.64 194	59.15 130	72.86 128	73.36 136	85.10 113	87.84 102

ADS-MVSNet		58.40 175	59.16 176	57.52 173	65.80 177	74.57 168	60.26 179	40.17 222	50.51 114	38.01 159	40.11 142	44.72 126	59.36 127	64.91 187	66.55 190	81.53 178	72.72 192

testpf		43.39 215	47.17 212	38.98 219	65.58 178	47.38 230	36.09 223	31.67 232	36.97 191	19.47 212	33.01 192	35.62 192	23.61 218	50.86 224	56.08 220	57.48 229	70.27 201

FMVSNet1		63.48 122	63.07 127	63.97 118	65.31 179	76.37 155	71.77 93	57.90 118	43.32 163	45.66 101	35.06 188	49.43 113	58.57 133	77.49 70	78.22 67	84.59 137	81.60 156

testgi		48.51 207	50.53 204	46.16 210	64.78 180	67.15 201	41.54 217	54.81 157	29.12 216	17.03 215	32.07 196	31.98 203	20.15 223	65.26 186	67.00 189	78.67 199	61.10 220

LTVRE_ROB	47.26 16	49.41 205	49.91 207	48.82 201	64.76 181	69.79 193	49.05 201	47.12 199	20.36 230	16.52 218	36.65 179	26.96 214	50.76 160	60.47 201	63.16 203	64.73 224	72.00 195
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		52.23 197	52.80 198	51.56 195	64.70 182	69.41 194	51.01 199	58.60 113	36.63 193	22.44 208	21.80 219	31.42 206	30.52 203	66.79 182	67.83 184	82.10 174	75.73 176

USDC		59.69 164	60.03 166	59.28 161	64.04 183	71.84 188	63.15 168	55.36 151	54.90 105	35.02 178	48.34 89	29.79 210	58.16 134	70.60 160	71.33 166	79.99 190	73.42 187

WR-MVS		51.02 199	54.56 191	46.90 208	63.84 184	69.23 195	44.78 213	56.38 139	38.19 189	14.19 222	37.38 171	36.82 181	22.39 219	60.14 202	66.20 194	79.81 191	73.95 185

our_test_3					63.32 185	71.07 192	55.90 193

test20.03		47.23 211	48.69 209	45.53 212	63.28 186	64.39 207	41.01 219	56.93 136	29.16 215	15.21 221	23.90 212	30.76 209	17.51 228	64.63 190	65.26 195	79.21 197	62.71 216

pmmvs6		54.20 193	53.54 194	54.97 184	63.22 187	72.98 177	60.17 180	52.32 179	26.77 221	34.30 180	23.29 216	36.23 184	40.33 195	68.77 177	68.76 179	79.47 195	78.00 171

v7n		57.04 181	56.64 184	57.52 173	62.85 188	74.75 166	61.76 171	51.80 180	35.58 201	36.02 171	32.33 194	33.61 200	50.16 163	67.73 180	70.34 174	82.51 168	82.12 151

pmmvs5		59.72 163	60.24 163	59.11 162	62.77 189	77.33 146	63.17 167	54.00 164	40.21 181	37.23 163	40.41 140	35.99 186	51.75 155	72.55 141	72.74 148	85.72 99	82.45 148

CVMVSNet		54.92 188	58.16 178	51.13 197	62.61 190	68.44 197	55.45 194	52.38 178	42.28 172	21.45 209	47.10 102	46.10 121	37.96 198	64.42 192	63.81 200	76.92 205	75.01 180

TAMVS		58.86 169	60.91 154	56.47 179	62.38 191	77.57 142	58.97 186	52.98 174	38.76 188	36.17 169	42.26 128	47.94 116	46.45 176	70.23 166	70.79 169	81.86 176	78.82 169

DTE-MVSNet		49.82 203	51.92 202	47.37 207	61.75 192	64.38 208	45.89 212	57.33 132	36.11 197	12.79 227	36.87 176	31.93 205	25.73 214	58.01 204	65.22 196	80.75 185	70.93 200

PEN-MVS		51.04 198	52.94 196	48.82 201	61.45 193	66.00 203	48.68 203	57.20 133	36.87 192	15.36 220	36.98 175	32.72 202	28.77 209	57.63 207	66.37 191	81.44 180	74.00 184

v748		55.19 183	54.63 189	55.85 181	61.44 194	72.97 178	58.72 187	51.62 181	34.48 204	36.39 168	32.09 195	33.05 201	45.48 181	61.85 199	67.87 183	81.45 179	80.08 162

V42		62.86 135	62.97 129	62.74 137	60.84 195	78.99 128	71.46 105	57.13 135	46.85 136	44.28 112	38.87 156	40.73 150	57.63 139	72.60 140	74.14 123	85.09 115	88.63 93

MDTV_nov1_ep13_2view		54.47 192	54.61 190	54.30 191	60.50 196	73.82 171	57.92 188	43.38 209	39.43 187	32.51 187	33.23 191	34.05 197	47.26 172	62.36 197	66.21 193	84.24 144	73.19 189

LP		48.21 208	46.65 214	50.03 198	60.39 197	63.86 211	48.73 202	38.71 224	35.60 200	32.99 185	23.31 215	24.95 221	40.07 196	57.73 205	61.56 208	79.29 196	59.51 221

MVS-HIRNet		53.86 194	53.02 195	54.85 185	60.30 198	72.36 184	44.63 214	42.20 215	39.45 186	43.47 116	21.66 220	34.00 198	55.47 144	65.42 185	67.16 188	83.02 165	71.08 199

CHOSEN 280x420		62.23 142	66.57 100	57.17 176	59.88 199	68.92 196	61.20 175	42.28 214	54.17 108	39.57 147	47.78 94	64.97 58	62.68 87	73.85 112	69.52 177	77.43 202	86.75 109

TinyColmap		52.66 196	50.09 206	55.65 182	59.72 200	64.02 210	57.15 190	52.96 175	40.28 180	32.51 187	32.42 193	20.97 225	56.65 142	63.95 193	65.15 197	74.91 210	63.87 212

FC-MVSNet-test		47.24 210	54.37 192	38.93 220	59.49 201	58.25 218	34.48 225	53.36 168	45.66 146	6.66 235	50.62 81	42.02 132	16.62 229	58.39 203	61.21 209	62.99 225	64.40 211

test-mter		64.06 115	69.24 82	58.01 167	59.07 202	77.40 144	59.13 185	48.11 195	55.64 101	39.18 151	51.56 78	58.54 79	55.38 145	73.52 116	76.00 84	87.22 48	92.05 51

WR-MVS_H		49.62 204	52.63 199	46.11 211	58.80 203	67.58 199	46.14 211	54.94 153	36.51 195	13.63 225	36.75 178	35.67 191	22.10 220	56.43 211	62.76 204	81.06 182	72.73 191

CP-MVSNet		50.57 200	52.60 200	48.21 204	58.77 204	65.82 204	48.17 204	56.29 140	37.41 190	16.59 217	37.14 173	31.95 204	29.21 206	56.60 210	63.71 201	80.22 188	75.56 177

PS-CasMVS		50.17 201	52.02 201	48.02 205	58.60 205	65.54 205	48.04 205	56.19 142	36.42 196	16.42 219	35.68 184	31.33 207	28.85 208	56.42 212	63.54 202	80.01 189	75.18 179

SixPastTwentyTwo		49.11 206	49.22 208	48.99 200	58.54 206	64.14 209	47.18 207	47.75 196	31.15 212	24.42 204	41.01 137	26.55 215	44.04 185	54.76 218	58.70 213	71.99 217	68.21 202

TDRefinement		52.70 195	51.02 203	54.66 187	57.41 207	65.06 206	61.47 174	54.94 153	44.03 153	33.93 181	30.13 203	27.57 213	46.17 178	61.86 198	62.48 207	74.01 213	66.06 208

pmmvs-eth3d		55.20 182	53.95 193	56.65 177	57.34 208	67.77 198	57.54 189	53.74 165	40.93 179	41.09 143	31.19 200	29.10 212	49.07 164	65.54 184	67.28 186	81.14 181	75.81 175

FPMVS		39.11 221	36.39 225	42.28 214	55.97 209	45.94 231	46.23 210	41.57 216	35.73 199	22.61 206	23.46 214	19.82 227	28.32 212	43.57 226	40.67 229	58.96 227	45.54 226

MIMVSNet		57.78 178	59.71 169	55.53 183	54.79 210	77.10 150	63.89 161	45.02 205	46.59 142	36.79 165	28.36 205	40.77 149	45.84 180	74.97 97	76.58 76	86.87 55	73.60 186

N_pmnet		47.67 209	47.00 213	48.45 203	54.72 211	62.78 212	46.95 208	51.25 182	36.01 198	26.09 203	26.59 210	25.93 220	35.50 201	55.67 214	59.01 211	76.22 206	63.04 214

test2356		46.29 212	47.37 211	45.03 213	54.38 212	57.99 219	42.03 216	50.32 184	30.78 213	16.65 216	27.40 208	23.70 222	29.86 204	61.20 200	64.31 199	76.93 204	66.22 207

Anonymous20231211		43.05 216	39.25 221	47.48 206	54.29 213	57.29 220	41.10 218	49.06 191	17.67 233	44.00 113	10.63 232	16.79 231	25.15 216	52.14 220	56.70 218	71.30 218	63.51 213

v52		54.79 189	55.15 187	54.36 190	54.07 214	72.13 186	59.84 182	49.39 188	34.50 203	35.08 177	31.63 198	35.74 189	47.21 174	63.90 194	67.92 181	80.59 186	80.23 160

V4		54.78 190	55.14 188	54.37 189	54.07 214	72.13 186	59.83 183	49.39 188	34.46 205	35.11 176	31.64 197	35.72 190	47.22 173	63.90 194	67.92 181	80.59 186	80.23 160

anonymousdsp		54.99 186	57.24 182	52.36 193	53.82 216	71.75 189	51.49 198	48.14 194	33.74 206	33.66 182	38.34 163	36.13 185	47.54 170	64.53 191	70.60 171	79.53 194	85.59 123

testus		42.30 217	43.69 215	40.67 218	53.21 217	53.50 223	31.81 227	49.96 185	27.06 219	11.55 229	25.67 211	19.00 228	25.20 215	55.34 215	62.59 205	72.31 216	62.69 217

new-patchmatchnet		42.21 218	42.97 217	41.33 216	53.05 218	59.89 215	39.38 220	49.61 186	28.26 218	12.10 228	22.17 218	21.54 224	19.22 224	50.96 223	56.04 221	74.61 212	61.92 218

FMVSNet5		58.86 169	60.24 163	57.25 175	52.66 219	66.25 202	63.77 162	52.86 177	57.85 89	37.92 160	36.12 181	52.22 106	51.37 156	70.88 157	71.43 161	84.92 120	66.91 206

ambc			42.30 218		50.36 220	49.51 227	35.47 224		32.04 211	23.53 205	17.36 225	8.95 237	29.06 207	64.88 188	56.26 219	61.29 226	67.12 205

1111		38.93 222	38.98 222	38.86 221	50.10 221	50.42 225	29.52 228	38.00 226	22.67 226	17.99 213	17.40 223	26.26 217	28.72 210	54.86 216	58.20 214	68.82 222	43.08 229

.test1245		25.86 229	24.56 231	27.39 230	50.10 221	50.42 225	29.52 228	38.00 226	22.67 226	17.99 213	17.40 223	26.26 217	28.72 210	54.86 216	0.05 236	0.01 240	0.24 238

EU-MVSNet		44.84 213	47.85 210	41.32 217	49.26 223	56.59 221	43.07 215	47.64 198	33.03 207	13.82 223	36.78 177	30.99 208	24.37 217	53.80 219	55.57 222	69.78 219	68.21 202

testmv		37.40 223	37.95 223	36.76 223	48.97 224	49.33 228	28.65 231	46.74 200	18.34 231	7.68 233	16.80 228	14.47 233	19.18 225	51.72 221	56.93 216	69.36 220	58.09 222

test1235678		37.40 223	37.94 224	36.76 223	48.97 224	49.30 229	28.65 231	46.73 201	18.33 232	7.68 233	16.79 229	14.46 234	19.18 225	51.72 221	56.92 217	69.36 220	58.07 223

RPSCF		55.07 185	58.06 179	51.57 194	48.87 226	58.95 216	53.68 196	41.26 220	62.42 75	45.88 100	54.38 70	54.26 99	53.75 151	57.15 208	53.53 224	66.01 223	65.75 209

PMVS	27.44 18	32.08 226	29.07 228	35.60 225	48.33 227	24.79 236	26.97 233	41.34 218	20.45 229	22.50 207	17.11 227	18.64 229	20.44 222	41.99 229	38.06 230	54.02 232	42.44 230
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PM-MVS		50.11 202	50.38 205	49.80 199	47.23 228	62.08 214	50.91 200	44.84 207	41.90 174	36.10 170	35.22 186	26.05 219	46.83 175	57.64 206	55.42 223	72.90 214	74.32 182

pmmvs3		41.86 219	42.29 219	41.36 215	39.80 229	52.66 224	38.93 222	35.85 231	23.40 225	20.22 211	19.30 221	20.84 226	40.56 194	55.98 213	58.79 212	72.80 215	65.03 210

test12356		29.92 227	31.49 227	28.08 227	38.46 230	37.74 234	21.36 234	40.17 222	16.83 234	5.61 237	15.66 231	11.48 235	6.60 235	42.01 228	51.23 225	56.29 230	45.52 227

MDA-MVSNet-bldmvs		44.15 214	42.27 220	46.34 209	38.34 231	62.31 213	46.28 209	55.74 146	29.83 214	20.98 210	27.11 209	16.45 232	41.98 189	41.11 230	57.47 215	74.72 211	61.65 219

no-one		26.96 228	26.51 229	27.49 229	37.87 232	39.14 233	17.12 236	41.31 219	12.02 236	3.68 239	8.04 234	8.42 238	10.67 233	28.11 232	45.96 228	54.27 231	43.89 228

MIMVSNet1		40.84 220	43.46 216	37.79 222	32.14 233	58.92 217	39.24 221	50.83 183	27.00 220	11.29 230	16.76 230	26.53 216	17.75 227	57.14 209	61.12 210	75.46 208	56.78 224

Gipuma		24.91 230	24.61 230	25.26 231	31.47 234	21.59 237	18.06 235	37.53 228	25.43 223	10.03 231	4.18 238	4.25 240	14.85 230	43.20 227	47.03 226	39.62 234	26.55 234
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		15.08 232	11.65 234	19.08 232	28.73 235	12.31 240	6.95 241	36.87 230	10.71 238	3.63 240	5.13 235	2.22 243	13.81 232	11.34 236	18.50 234	24.49 236	21.32 235

EMVS		14.40 233	10.71 235	18.70 233	28.15 236	12.09 241	7.06 240	36.89 229	11.00 237	3.56 241	4.95 236	2.27 242	13.91 231	10.13 237	16.06 235	22.63 237	18.51 236

new_pmnet		33.19 225	35.52 226	30.47 226	27.55 237	45.31 232	29.29 230	30.92 233	29.00 217	9.88 232	18.77 222	17.64 230	26.77 213	44.07 225	45.98 227	58.41 228	47.87 225

PMMVS2		20.45 231	22.31 232	18.27 234	20.52 238	26.73 235	14.85 238	28.43 235	13.69 235	0.79 242	10.35 233	9.10 236	3.83 237	27.64 233	32.87 231	41.17 233	35.81 231

tmp_tt				16.09 235	13.07 239	8.12 242	13.61 239	2.08 237	55.09 103	30.10 192	40.26 141	22.83 223	5.35 236	29.91 231	25.25 233	32.33 235

MVE	15.98 19	14.37 234	16.36 233	12.04 236	7.72 240	20.24 238	5.90 242	29.05 234	8.28 239	3.92 238	4.72 237	2.42 241	9.57 234	18.89 235	31.46 232	16.07 239	28.53 233
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GG-mvs-BLEND		54.54 191	77.58 43	27.67 228	0.03 241	90.09 24	77.20 59	0.02 238	66.83 64	0.05 243	59.90 58	73.33 30	0.04 238	78.40 67	79.30 58	88.65 16	95.20 20

sosnet-low-res		0.00 237	0.00 238	0.00 239	0.00 242	0.00 245	0.00 246	0.00 240	0.00 242	0.00 244	0.00 241	0.00 245	0.00 241	0.00 240	0.00 239	0.00 242	0.00 240

sosnet		0.00 237	0.00 238	0.00 239	0.00 242	0.00 245	0.00 246	0.00 240	0.00 242	0.00 244	0.00 241	0.00 245	0.00 241	0.00 240	0.00 239	0.00 242	0.00 240

testmvs		0.05 235	0.08 236	0.01 237	0.00 242	0.01 243	0.03 244	0.01 239	0.05 240	0.00 244	0.14 240	0.01 244	0.03 240	0.05 238	0.05 236	0.01 240	0.24 238

test123		0.05 235	0.08 236	0.01 237	0.00 242	0.01 243	0.01 245	0.00 240	0.05 240	0.00 244	0.16 239	0.00 245	0.04 238	0.02 239	0.05 236	0.00 242	0.26 237

MTAPA										78.32 6		79.42 18

MTMP										76.04 11		76.65 23

Patchmatch-RL test							2.17 243

NP-MVS									81.60 31

Patchmtry						78.06 137	67.53 137	43.18 210		41.40 139

DeepMVS_CX						19.81 239	17.01 237	10.02 236	23.61 224	5.85 236	17.21 226	8.03 239	21.13 221	22.60 234		21.42 238	30.01 232

High-resolution multi-view benchmark