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 7	76.51 3	86.85 15	72.91 18	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		85.24 7	88.27 5	81.72 10	91.74 7	90.71 15	86.71 6	73.16 14	90.56 7	74.33 15	83.07 14	85.88 2	77.16 14	86.28 7	85.58 6	87.23 47	95.77 11

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 68	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 85	67.86 50	77.47 12	82.89 26	84.24 16	88.61 18	89.99 71

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 32	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	83.75 151	96.77 8

OpenMVS	67.62 8	74.92 52	73.91 59	76.09 39	90.10 15	90.38 19	78.01 52	66.35 47	66.09 67	62.80 43	46.33 110	64.55 59	71.77 40	79.92 55	80.88 51	87.52 40	89.20 78

MAR-MVS		77.19 44	78.37 42	75.81 41	89.87 16	90.58 17	79.33 49	65.56 53	77.62 45	58.33 63	59.24 60	67.98 48	74.83 23	82.37 34	83.12 26	86.95 52	87.67 106
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 45	85.96 17	65.43 38	88.98 7	82.29 9	67.57 70	82.06 38	81.33 43	83.93 149	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 33	83.10 25	82.64 29	87.21 50	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 10

APD-MVS		84.83 8	87.00 8	82.30 8	89.61 20	89.21 30	86.51 8	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 53	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 17	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 5	88.32 25	92.13 47

AdaColmap		76.23 48	73.55 62	79.35 20	89.38 22	85.00 65	79.99 46	73.04 15	76.60 47	71.17 22	55.18 66	57.99 84	77.87 10	76.82 77	76.82 76	84.67 134	86.45 114

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 78	70.68 42	76.27 19	82.27 36	82.04 32	85.92 80	90.77 62

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 63	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	85.48 106	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 16	77.13 22	71.60 42	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 49	83.02 24	69.18 32	83.91 21	68.35 32	82.89 15	73.64 29	72.16 38	80.78 50	81.13 47	86.10 68	91.43 53

MSLP-MVS++		78.57 33	77.33 45	80.02 17	88.39 29	84.79 66	84.62 17	66.17 49	75.96 48	78.40 5	61.59 52	71.47 39	73.54 31	78.43 65	78.88 61	88.97 12	90.18 70

PGM-MVS		79.42 30	81.84 29	76.60 35	88.38 30	86.69 51	82.97 26	65.75 51	80.39 35	64.94 39	81.95 18	72.11 37	71.41 43	80.45 51	80.55 53	86.18 65	90.76 63

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 44	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 53	72.75 34	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 71	80.03 54	80.52 54	85.66 103	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 13	82.78 25	71.01 24	62.86 48	69.93 45	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 60	82.00 29	68.44 37	81.82 29	67.39 34	77.43 25	73.68 28	71.62 41	79.56 57	79.58 55	85.73 96	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 60	72.08 39	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 15	84.68 15	84.77 15	89.50 8	95.50 15

CHOSEN 1792x2688		72.55 60	71.98 67	73.22 55	86.57 41	92.41 5	75.63 64	66.77 44	62.08 77	52.32 78	30.27 203	50.74 114	66.14 74	86.22 8	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 81	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 61	81.42 34	67.38 42	79.38 39	62.61 44	78.53 22	65.79 55	68.80 62	78.56 64	78.50 65	85.75 92	90.80 61
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 58	70.81 45	82.46 31	81.78 36	86.30 63	93.18 38

OPM-MVS		72.74 59	70.93 75	74.85 48	85.30 45	84.34 69	82.82 27	69.79 27	49.96 119	55.39 74	54.09 73	60.14 75	70.04 51	80.38 53	79.43 56	85.74 95	88.20 102

MS-PatchMatch		70.34 72	69.00 84	71.91 62	85.20 46	85.35 62	77.84 55	61.77 89	58.01 88	55.40 73	41.26 134	58.34 81	61.69 94	81.70 42	78.29 66	89.56 7	80.02 165

MVS_0304		79.43 28	82.20 26	76.20 37	84.22 47	91.79 11	81.82 32	63.81 64	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 61	57.52 66	60.59 57	69.18 46	70.65 46	77.11 74	77.65 72	84.75 132	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 68	68.49 88	72.67 57	82.85 49	77.76 142	77.70 56	64.76 58	64.61 73	60.74 56	49.29 87	53.97 103	65.86 75	74.97 98	75.57 92	84.13 147	83.29 140
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVS					82.43 50	86.27 56	75.70 62			61.07 52		72.27 33				85.67 100

X-MVStestdata					82.43 50	86.27 56	75.70 62			61.07 52		72.27 33				85.67 100

PVSNet_BlendedMVS		76.84 46	78.47 39	74.95 46	82.37 52	89.90 25	75.45 68	65.45 54	74.99 49	70.66 26	63.07 46	58.27 82	67.60 68	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 68	65.45 54	74.99 49	70.66 26	63.07 46	58.27 82	67.60 68	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 76	73.57 30	81.75 41	81.27 44	88.41 24	90.39 67
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	80.26 117	79.58 47	63.04 72	70.09 56	51.98 79	65.06 43	55.62 96	62.49 91	75.97 89	76.32 83	84.80 131	88.93 82

MSDG		65.57 101	61.57 151	70.24 68	82.02 56	76.47 155	74.46 80	68.73 36	56.52 93	50.33 88	38.47 161	41.10 143	62.42 92	72.12 149	72.94 147	83.47 154	73.37 190

IB-MVS	64.48 11	69.02 77	68.97 85	69.09 76	81.75 57	89.01 31	64.50 155	64.91 57	56.65 92	62.59 45	47.89 94	45.23 126	51.99 156	69.18 177	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	60.74 101	80.05 36	66.72 35	68.43 36	65.09 56	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 80	80.96 40	64.35 59	77.95 43	61.39 49	69.20 35	70.94 41	69.38 58	73.89 113	73.32 139	83.14 164	92.06 49

DWT-MVSNet_training		72.81 58	73.98 58	71.45 63	81.26 60	86.37 55	72.08 86	59.82 109	69.13 62	58.15 64	54.71 67	61.33 73	67.81 67	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	60.74 101	77.66 44	59.79 59	62.47 49	66.82 52	77.17 13	83.43 22	83.84 20	88.53 21	93.02 39

EPNet_dtu		66.17 96	70.13 79	61.54 149	81.04 62	77.39 147	68.87 133	62.50 81	69.78 57	33.51 183	63.77 45	56.22 90	37.65 201	72.20 147	72.18 155	85.69 99	79.38 167
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 60	80.96 63	80.87 102	77.93 54	64.13 61	69.29 59	60.79 55	64.04 44	53.54 105	63.91 83	73.74 117	75.27 94	84.45 139	88.98 81
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HyFIR lowres test		68.39 81	68.28 90	68.52 79	80.85 64	88.11 41	71.08 114	58.09 118	54.87 106	47.80 95	27.55 209	55.80 93	64.97 78	79.11 59	79.14 58	88.31 26	93.35 34

LS3D		64.54 110	62.14 144	67.34 87	80.85 64	75.79 161	69.99 124	65.87 50	60.77 80	44.35 112	42.43 128	45.95 124	65.01 77	69.88 172	68.69 182	77.97 203	71.43 200

CNLPA		71.37 67	70.27 78	72.66 58	80.79 66	81.33 96	71.07 115	65.75 51	82.36 26	64.80 40	42.46 127	56.49 89	72.70 35	73.00 125	70.52 174	80.84 185	85.76 123

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 16	85.23 11	83.68 5	76.64 16	80.43 52	81.47 41	88.15 31	95.66 13

PLC	64.00 12	68.54 79	66.66 99	70.74 66	80.28 68	74.88 166	72.64 84	63.70 66	69.26 60	55.71 71	47.24 102	55.31 98	70.42 47	72.05 151	70.67 172	81.66 179	77.19 174
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 61	79.56 69	80.98 100	75.35 70	63.21 68	84.48 20	61.83 47	61.54 53	66.89 51	69.41 57	76.60 79	74.07 128	82.34 174	86.15 118

CostFormer		72.18 62	73.90 60	70.18 69	79.47 70	86.19 59	76.94 60	48.62 193	66.07 68	60.40 57	54.14 72	65.82 54	67.98 65	75.84 90	76.41 82	87.67 38	92.83 42

MVS_111021_LR		74.26 53	75.95 54	72.27 59	79.43 71	85.04 64	72.71 83	65.27 56	70.92 55	63.58 42	69.32 34	60.31 74	69.43 56	77.01 75	77.15 73	83.22 159	91.93 51

MVS_Test		75.22 51	76.69 51	73.51 52	79.30 72	88.82 32	80.06 45	58.74 112	69.77 58	57.50 67	59.78 59	61.35 71	75.31 20	82.07 37	83.60 24	90.13 5	91.41 54

PVSNet_Blended_VisFu		71.76 65	73.54 63	69.69 70	79.01 73	87.16 48	72.05 87	61.80 88	56.46 94	59.66 60	53.88 74	62.48 63	59.08 133	81.17 46	78.90 59	86.53 60	94.74 22

ACMH	59.42 14	61.59 155	59.22 177	64.36 112	78.92 74	78.26 135	67.65 138	67.48 41	39.81 184	30.98 191	38.25 164	34.59 197	61.37 102	70.55 164	73.47 135	79.74 194	79.59 166
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 75	79.94 118	64.72 154	66.38 46	54.96 103	54.51 76	56.75 63	47.91 120	66.91 72	75.57 94	75.75 88	85.92 80	87.12 109

tpmp4_e23		69.38 73	69.47 81	69.28 73	78.20 76	82.35 82	75.92 61	49.20 192	64.15 74	59.96 58	47.93 92	55.77 94	68.06 64	73.05 124	74.53 107	84.34 141	88.50 100

Effi-MVS+		70.42 68	71.23 73	69.47 71	78.04 77	85.24 63	75.57 66	58.88 111	59.56 83	48.47 92	52.73 77	54.94 99	69.69 53	78.34 67	77.06 74	86.18 65	90.73 64

Anonymous202405211			66.35 103		78.00 78	84.41 68	74.85 72	63.18 69	51.00 115		31.37 200	53.73 104	69.67 54	76.28 81	76.84 75	83.21 161	90.85 60

conf0.002		67.12 92	67.13 97	67.11 88	77.95 79	82.11 83	71.71 96	63.06 70	49.16 124	43.43 117	47.76 96	48.79 117	61.42 96	76.61 78	76.55 80	85.07 116	88.92 84

conf0.01		66.60 93	66.18 105	67.09 89	77.90 80	82.02 84	71.71 96	63.05 71	49.16 124	43.41 119	46.23 111	45.78 125	61.42 96	76.55 80	74.63 101	85.04 117	88.87 86

tfpn111		66.52 94	66.12 106	66.98 91	77.70 81	81.58 90	71.71 96	62.94 76	49.16 124	43.28 122	51.38 80	41.34 136	61.42 96	76.24 83	74.63 101	84.84 125	88.52 96

conf200view11		65.89 99	64.96 112	66.98 91	77.70 81	81.58 90	71.71 96	62.94 76	49.16 124	43.28 122	43.24 117	41.34 136	61.42 96	76.24 83	74.63 101	84.84 125	88.52 96

thres100view900		67.14 91	66.09 107	68.38 81	77.70 81	83.84 73	74.52 77	66.33 48	49.16 124	43.40 120	43.24 117	41.34 136	62.59 90	79.31 58	75.92 87	85.73 96	89.81 72

tfpn200view9		65.90 98	64.96 112	67.00 90	77.70 81	81.58 90	71.71 96	62.94 76	49.16 124	43.40 120	43.24 117	41.34 136	61.42 96	76.24 83	74.63 101	84.84 125	88.52 96

Anonymous20240521		69.13 76	69.07 83	69.21 74	77.65 85	77.52 144	74.68 73	57.85 121	54.92 104	55.34 75	55.74 65	55.56 97	66.35 73	75.05 96	76.56 79	83.35 156	88.13 103

Anonymous20231211		68.44 80	66.37 102	70.86 65	77.58 86	83.49 74	75.15 71	61.89 86	52.54 112	58.50 62	28.89 205	56.78 87	69.29 59	74.96 100	76.61 77	82.73 167	91.36 55

UA-Net		64.62 107	68.23 91	60.42 154	77.53 87	81.38 95	60.08 183	57.47 130	47.01 137	44.75 110	60.68 56	71.32 40	41.84 192	73.27 119	72.25 154	80.83 186	71.68 198

thres200		65.58 100	64.74 115	66.56 93	77.52 88	81.61 88	73.44 82	62.95 74	46.23 145	42.45 137	42.76 121	41.18 141	58.12 137	76.24 83	75.59 91	84.89 122	89.58 73

ACMH+	60.36 13	61.16 156	58.38 179	64.42 111	77.37 89	74.35 171	68.45 134	62.81 80	45.86 147	38.48 156	35.71 183	37.35 179	59.81 122	67.24 183	69.80 178	79.58 195	78.32 172

TAPA-MVS	67.10 9	71.45 66	73.47 64	69.10 75	77.04 90	80.78 103	73.81 81	62.10 82	80.80 33	51.28 82	60.91 55	63.80 62	67.98 65	74.59 102	72.42 153	82.37 173	80.97 160
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS_MVSNet		67.29 89	71.98 67	61.82 147	76.92 91	84.32 71	65.90 152	58.22 116	55.75 99	39.22 150	54.51 70	62.47 64	45.99 181	78.83 62	78.52 64	84.70 133	89.47 75

tfpn_ndepth		62.95 135	63.75 120	62.02 145	76.89 92	79.48 125	64.09 158	60.98 96	49.48 121	38.73 154	49.92 86	44.79 127	47.37 173	71.91 152	71.66 158	84.07 148	79.00 170

CANet_DTU		72.84 57	76.63 52	68.43 80	76.81 93	86.62 52	75.54 67	54.71 161	72.06 53	43.54 115	67.11 38	58.46 79	72.40 36	81.13 48	80.82 52	87.57 39	90.21 69

tpm cat1		67.47 87	67.05 98	67.98 82	76.63 94	81.51 94	74.49 79	47.65 198	61.18 79	61.12 51	42.51 126	53.02 108	64.74 81	70.11 169	71.50 160	83.22 159	89.49 74

DI_MVS_plusplus_trai		73.94 55	74.85 57	72.88 56	76.57 95	86.80 50	80.41 43	61.47 91	62.35 76	59.44 61	47.91 93	68.12 47	72.24 37	82.84 28	81.50 40	87.15 51	94.42 25

thres400		65.18 105	64.44 117	66.04 94	76.40 96	82.63 78	71.52 106	64.27 60	44.93 153	40.69 145	41.86 131	40.79 150	58.12 137	77.67 68	74.64 100	85.26 109	88.56 95

diffmvs		72.46 61	73.75 61	70.95 64	76.33 97	87.21 47	77.96 53	58.43 115	66.25 64	55.75 70	59.11 61	56.77 88	70.42 47	77.35 73	78.90 59	86.80 55	90.64 65

tpmrst		67.15 90	68.12 92	66.03 95	76.21 98	80.98 100	71.27 108	45.05 205	60.69 81	50.63 86	46.95 107	54.15 102	65.30 76	71.80 154	71.77 157	87.72 36	90.48 66

gg-mvs-nofinetune		62.34 138	66.19 104	57.86 172	76.15 99	88.61 34	71.18 111	41.24 222	25.74 224	13.16 226	22.91 219	63.97 61	54.52 151	85.06 13	85.25 10	90.92 3	91.78 52

thresconf0.02		63.92 117	65.18 111	62.46 140	75.91 100	80.65 111	67.51 141	63.86 63	45.00 152	33.32 184	51.38 80	51.68 110	48.34 168	75.49 95	75.13 95	85.84 91	76.91 176

EPMVS		66.21 95	67.49 95	64.73 104	75.81 101	84.20 72	68.94 132	44.37 209	61.55 78	48.07 94	49.21 89	54.87 100	62.88 88	71.82 153	71.40 164	88.28 27	79.37 168

EPP-MVSNet		67.58 85	71.10 74	63.48 126	75.71 102	83.35 75	66.85 144	57.83 122	53.02 111	41.15 142	55.82 64	67.89 49	56.01 145	74.40 104	72.92 148	83.33 157	90.30 68

view600		63.91 118	63.27 126	64.66 106	75.57 103	81.73 86	69.71 127	63.04 72	43.97 156	39.18 151	41.09 135	40.24 158	55.38 147	76.28 81	72.04 156	85.08 115	87.52 107

thres600view7		63.77 119	63.14 128	64.51 108	75.49 104	81.61 88	69.59 128	62.95 74	43.96 157	38.90 153	41.09 135	40.24 158	55.25 149	76.24 83	71.54 159	84.89 122	87.30 108

dps		64.08 113	63.22 127	65.08 98	75.27 105	79.65 122	66.68 146	46.63 203	56.94 90	55.67 72	43.96 113	43.63 132	64.00 82	69.50 176	69.82 177	82.25 175	79.02 169

MVSTER		76.92 45	79.92 34	73.42 54	74.98 106	82.97 76	78.15 51	63.41 67	78.02 41	64.41 41	67.54 37	72.80 31	71.05 44	83.29 24	83.73 21	88.53 21	91.12 57

TSAR-MVS + COLMAP		73.09 56	76.86 49	68.71 77	74.97 107	82.49 81	74.51 78	61.83 87	83.16 23	49.31 91	82.22 17	51.62 111	68.94 61	78.76 63	75.52 93	82.67 169	84.23 132

view800		63.02 131	62.69 139	63.39 128	74.79 108	80.76 107	67.83 137	61.93 85	43.16 167	37.78 162	40.43 140	39.73 165	53.16 154	75.01 97	73.32 139	84.87 124	86.43 115

tpm		64.85 106	66.02 108	63.48 126	74.52 109	78.38 134	70.98 116	44.99 207	51.61 114	43.28 122	47.66 97	53.18 106	60.57 108	70.58 163	71.30 169	86.54 59	89.45 76

tfpn		62.54 137	62.79 135	62.25 144	74.16 110	79.86 120	66.07 151	60.97 97	42.43 172	36.41 166	39.88 144	43.76 131	51.25 161	73.85 114	74.17 124	84.67 134	85.57 126

Vis-MVSNet (Re-imp)		62.25 141	68.74 86	54.68 187	73.70 111	78.74 130	56.51 194	57.49 129	55.22 101	26.86 201	54.56 69	61.35 71	31.06 204	73.10 121	74.90 97	82.49 171	83.31 139

Fast-Effi-MVS+		67.59 84	67.56 94	67.62 85	73.67 112	81.14 99	71.12 112	54.79 160	58.88 84	50.61 87	46.70 108	47.05 121	69.12 60	76.06 88	76.44 81	86.43 61	86.65 112

IterMVS-LS		66.08 97	66.56 101	65.51 96	73.67 112	74.88 166	70.89 118	53.55 168	50.42 117	48.32 93	50.59 83	55.66 95	61.83 93	73.93 112	74.42 114	84.82 130	86.01 120
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		65.43 103	67.71 93	62.78 136	73.49 114	82.83 77	66.42 149	45.40 204	60.40 82	45.27 104	49.22 88	57.60 85	60.01 117	70.61 161	71.38 167	86.08 70	81.91 155
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
COLMAP_ROB	51.17 15	55.13 185	52.90 199	57.73 173	73.47 115	67.21 202	62.13 172	55.82 146	47.83 135	34.39 179	31.60 199	34.24 198	44.90 186	63.88 198	62.52 208	75.67 209	63.02 216
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
conf0.05thres1000		60.33 163	59.42 174	61.40 150	73.15 116	78.25 136	65.29 153	60.30 105	36.61 196	35.75 172	33.25 190	39.23 169	50.35 164	72.18 148	72.67 151	83.57 153	83.74 134

Effi-MVS+-dtu		64.58 108	64.08 118	65.16 97	73.04 117	75.17 165	70.68 120	56.23 143	54.12 109	44.71 111	47.42 98	51.10 112	63.82 84	68.08 181	66.32 194	82.47 172	86.38 116

tfpnview11		58.92 169	59.60 172	58.13 167	72.99 118	77.11 151	60.48 178	60.37 103	42.10 175	29.10 195	43.45 114	40.72 153	41.67 193	70.53 165	70.43 175	84.17 146	72.85 192

tfpn_n400		58.64 173	59.27 175	57.89 170	72.83 119	77.26 149	60.35 179	60.29 106	39.77 186	29.10 195	43.45 114	40.72 153	41.61 194	70.06 170	71.39 165	83.17 162	72.26 195

tfpnconf		58.64 173	59.27 175	57.89 170	72.83 119	77.26 149	60.35 179	60.29 106	39.77 186	29.10 195	43.45 114	40.72 153	41.61 194	70.06 170	71.39 165	83.17 162	72.26 195

tfpn1000		58.35 177	59.96 169	56.47 180	72.78 121	77.51 145	56.66 193	59.16 110	43.74 158	29.76 194	42.79 120	42.49 133	37.04 202	68.92 178	68.98 180	83.45 155	75.25 180

EG-PatchMatch MVS		58.73 172	58.03 182	59.55 159	72.32 122	80.49 113	63.44 167	55.55 150	32.49 211	38.31 157	28.87 206	37.22 180	42.84 190	74.30 110	75.70 89	84.84 125	77.14 175

TransMVSNet (Re)		57.83 178	56.90 185	58.91 164	72.26 123	74.69 169	63.57 166	61.42 92	32.30 212	32.65 186	33.97 189	35.96 189	39.17 199	73.84 116	72.84 149	84.37 140	74.69 183

CMPMVS	43.63 17	57.67 180	55.43 188	60.28 155	72.01 124	79.00 128	62.77 171	53.23 173	41.77 177	45.42 103	30.74 202	39.03 170	53.01 155	64.81 191	64.65 200	75.26 211	68.03 206
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
NR-MVSNet		61.08 158	62.09 145	59.90 156	71.96 125	75.87 159	63.60 165	61.96 83	49.31 122	27.95 198	42.76 121	33.85 201	48.82 167	74.35 107	74.05 129	85.13 111	84.45 129

PMMVS		70.37 71	75.06 56	64.90 99	71.46 126	81.88 85	64.10 157	55.64 149	71.31 54	46.69 98	70.69 33	58.56 77	69.53 55	79.03 60	75.63 90	81.96 177	88.32 101

test-LLR		68.23 82	71.61 71	64.28 117	71.37 127	81.32 97	63.98 161	61.03 94	58.62 85	42.96 127	52.74 75	61.65 69	57.74 139	75.64 92	78.09 70	88.61 18	93.21 35

test0.0.03 1		57.35 181	59.89 170	54.38 189	71.37 127	73.45 174	52.71 199	61.03 94	46.11 146	26.33 202	41.73 132	44.08 129	29.72 207	71.43 157	70.90 170	85.10 112	71.56 199

tfpnnormal		58.97 168	56.48 187	61.89 146	71.27 129	76.21 158	66.65 147	61.76 90	32.90 210	36.41 166	27.83 208	29.14 213	50.64 163	73.06 122	73.05 146	84.58 137	83.15 145

Fast-Effi-MVS+-dtu		63.05 130	64.72 116	61.11 151	71.21 130	76.81 154	70.72 119	43.13 213	52.51 113	35.34 175	46.55 109	46.36 122	61.40 101	71.57 156	71.44 162	84.84 125	87.79 105

MDTV_nov1_ep13		65.21 104	67.28 96	62.79 135	70.91 131	81.72 87	69.28 131	49.50 189	58.08 87	43.94 114	50.50 84	56.02 91	58.86 134	70.72 160	73.37 137	84.24 143	80.52 161

FMVSNet3		70.41 70	71.89 69	68.68 78	70.89 132	79.42 126	75.63 64	60.97 97	65.32 69	51.06 83	47.37 99	62.05 65	64.90 79	82.49 30	82.27 31	88.64 17	84.34 131

Vis-MVSNet		65.53 102	69.83 80	60.52 153	70.80 133	84.59 67	66.37 150	55.47 152	48.40 132	40.62 146	57.67 62	58.43 80	45.37 185	77.49 69	76.24 84	84.47 138	85.99 121
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CDS-MVSNet		64.22 111	65.89 109	62.28 143	70.05 134	80.59 112	69.91 126	57.98 119	43.53 163	46.58 99	48.22 91	50.76 113	46.45 178	75.68 91	76.08 85	82.70 168	86.34 117
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UGNet		67.57 86	71.69 70	62.76 137	69.88 135	82.58 79	66.43 148	58.64 113	54.71 107	51.87 80	61.74 51	62.01 68	45.46 184	74.78 101	74.99 96	84.24 143	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		64.55 109	65.76 110	63.12 132	69.68 136	81.56 93	69.59 128	58.16 117	45.23 151	35.58 174	47.01 106	41.82 135	59.41 128	79.62 56	78.54 63	86.32 62	86.56 113

GBi-Net		69.21 74	70.40 76	67.81 83	69.49 137	78.65 131	74.54 74	60.97 97	65.32 69	51.06 83	47.37 99	62.05 65	63.43 85	77.49 69	78.22 67	87.37 41	83.73 135

test1		69.21 74	70.40 76	67.81 83	69.49 137	78.65 131	74.54 74	60.97 97	65.32 69	51.06 83	47.37 99	62.05 65	63.43 85	77.49 69	78.22 67	87.37 41	83.73 135

FMVSNet2		68.06 83	68.57 87	67.45 86	69.49 137	78.65 131	74.54 74	60.23 108	56.29 95	49.64 90	42.13 130	57.08 86	63.43 85	81.15 47	80.99 48	87.37 41	83.73 135

UniMVSNet_NR-MVSNet		62.30 140	63.51 122	60.89 152	69.48 140	77.83 140	64.07 159	63.94 62	50.03 118	31.17 189	44.82 112	41.12 142	51.37 158	71.02 158	74.81 99	85.30 108	84.95 127

gm-plane-assit		54.99 187	57.99 183	51.49 197	69.27 141	54.42 223	32.32 227	42.59 214	21.18 230	13.71 224	23.61 215	43.84 130	60.21 116	87.09 4	86.55 4	90.81 4	89.28 77

PatchMatch-RL		62.22 144	60.69 158	64.01 118	68.74 142	75.75 162	59.27 186	60.35 104	56.09 96	53.80 77	47.06 105	36.45 184	64.80 80	68.22 180	67.22 189	77.10 205	74.02 185

CR-MVSNet		62.31 139	64.75 114	59.47 160	68.63 143	71.29 192	67.53 139	43.18 211	55.83 97	41.40 139	41.04 137	55.85 92	57.29 142	72.76 137	73.27 142	78.77 200	83.23 143

v18		63.31 127	62.02 146	64.81 103	68.48 144	73.38 175	72.14 85	54.28 163	48.99 131	47.21 96	39.56 146	41.20 140	60.80 105	72.89 129	74.46 113	85.96 79	83.64 138

v16		63.12 129	61.78 148	64.68 105	68.45 145	73.29 176	71.86 89	54.12 164	48.36 133	47.00 97	39.30 151	41.01 144	60.67 106	72.83 135	74.40 115	86.01 74	83.24 142

v17		62.99 134	61.70 149	64.51 108	68.40 146	73.28 177	71.80 94	54.11 165	47.87 134	46.14 100	39.29 152	41.01 144	60.60 107	72.81 136	74.39 120	85.99 77	83.25 141

TranMVSNet+NR-MVSNet		60.38 162	61.30 153	59.30 161	68.34 147	75.57 164	63.38 168	63.78 65	46.74 139	27.73 199	42.56 125	36.84 182	47.66 171	70.36 167	74.59 105	84.91 121	82.46 149

v8		63.44 126	62.58 140	64.43 110	68.28 148	78.07 137	71.82 93	54.85 158	46.70 141	45.20 105	39.40 147	40.91 146	60.54 111	72.85 134	74.39 120	85.92 80	85.76 123

v6		64.09 112	63.40 123	64.90 99	68.28 148	80.78 103	71.85 90	57.64 126	46.73 140	45.18 106	39.40 147	40.89 147	60.54 111	72.86 130	74.40 115	85.92 80	88.72 91

v1neww		64.08 113	63.38 124	64.89 101	68.27 150	80.77 105	71.84 91	57.65 124	46.66 142	45.10 107	39.40 147	40.86 148	60.57 108	72.86 130	74.40 115	85.92 80	88.71 92

v7new		64.08 113	63.38 124	64.89 101	68.27 150	80.77 105	71.84 91	57.65 124	46.66 142	45.10 107	39.40 147	40.86 148	60.57 108	72.86 130	74.40 115	85.92 80	88.71 92

v1141		63.48 123	62.75 138	64.32 113	68.13 152	80.69 109	71.69 103	57.43 131	43.66 162	42.83 134	39.02 154	39.74 164	59.95 118	72.94 126	74.49 110	85.86 88	88.75 89

divwei89l23v2f112		63.48 123	62.76 137	64.32 113	68.13 152	80.68 110	71.71 96	57.43 131	43.69 160	42.84 132	39.01 155	39.75 163	59.94 119	72.93 127	74.49 110	85.86 88	88.75 89

v1		63.49 122	62.77 136	64.32 113	68.13 152	80.70 108	71.70 102	57.43 131	43.69 160	42.89 131	39.03 153	39.77 162	59.93 120	72.93 127	74.48 112	85.86 88	88.77 87

v15		62.07 145	60.70 157	63.67 123	68.09 155	73.00 178	71.27 108	53.41 169	43.70 159	43.43 117	38.77 157	39.83 160	59.87 121	72.74 139	74.25 122	85.98 78	82.61 147

V14		61.96 148	60.56 159	63.59 124	68.06 156	72.93 181	71.10 113	53.33 171	43.47 164	43.28 122	38.59 158	39.78 161	59.76 123	72.65 141	74.19 123	86.01 74	82.32 152

V9		61.85 150	60.42 162	63.51 125	68.02 157	72.85 182	70.91 117	53.24 172	43.25 166	43.27 126	38.41 162	39.73 165	59.60 125	72.55 143	74.13 126	86.04 72	82.04 154

v2v482		63.68 120	62.85 133	64.65 107	68.01 158	80.46 114	71.90 88	57.60 127	44.26 154	42.82 135	39.80 145	38.62 174	61.56 95	73.06 122	74.86 98	86.03 73	88.90 85

v12		61.70 152	60.27 164	63.38 129	68.00 159	72.76 183	70.63 121	53.14 174	43.01 168	42.95 130	38.25 164	39.64 167	59.48 127	72.47 145	74.05 129	86.06 71	81.71 157

pm-mvs1		59.21 167	59.58 173	58.77 165	67.97 160	77.07 153	64.12 156	57.20 135	34.73 204	36.86 164	35.34 185	40.54 157	43.34 189	74.32 109	73.30 141	83.13 165	81.77 156

v13		61.60 154	60.13 167	63.31 130	67.95 161	72.67 185	70.51 122	53.05 175	42.80 169	42.96 127	38.10 169	39.57 168	59.31 130	72.36 146	73.98 131	86.10 68	81.40 159

v7		63.61 121	63.02 130	64.29 116	67.88 162	80.32 115	71.60 104	56.63 139	45.37 149	42.84 132	38.54 159	38.91 172	61.05 103	74.39 105	74.52 108	85.75 92	89.10 80

v10		63.00 132	62.22 143	63.90 121	67.88 162	77.78 141	71.59 105	54.34 162	45.37 149	42.76 136	38.53 160	38.93 171	61.05 103	74.39 105	74.52 108	85.75 92	86.04 119

v11		61.74 151	60.47 161	63.22 131	67.83 164	72.72 184	70.31 123	52.95 178	42.75 170	41.89 138	38.16 167	38.49 175	60.40 115	74.35 107	74.40 115	85.92 80	82.39 151

v1144		63.00 132	62.39 142	63.70 122	67.72 165	80.27 116	71.23 110	56.40 140	42.51 171	40.81 144	38.12 168	37.73 176	60.42 114	74.46 103	74.55 106	85.64 104	89.12 79

UniMVSNet (Re)		60.62 160	62.93 132	57.92 169	67.64 166	77.90 139	61.75 174	61.24 93	49.83 120	29.80 193	42.57 124	40.62 156	43.36 188	70.49 166	73.27 142	83.76 150	85.81 122

RPMNet		58.63 175	62.80 134	53.76 193	67.59 167	71.29 192	54.60 197	38.13 226	55.83 97	35.70 173	41.58 133	53.04 107	47.89 170	66.10 185	67.38 187	78.65 202	84.40 130

v148		62.00 147	61.19 154	62.96 133	67.46 168	79.49 124	67.87 136	57.66 123	42.30 173	45.02 109	38.20 166	38.89 173	54.77 150	69.83 173	72.60 152	84.96 118	87.01 110

IterMVS		61.87 149	63.55 121	59.90 156	67.29 169	72.20 187	67.34 142	48.56 194	47.48 136	37.86 161	47.07 104	48.27 118	54.08 152	72.12 149	73.71 132	84.30 142	83.99 133
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1192		62.25 141	61.64 150	62.96 133	66.88 170	79.72 121	69.96 125	55.77 147	41.58 178	39.42 148	37.05 174	35.96 189	60.50 113	74.30 110	74.09 127	85.24 110	88.76 88

DU-MVS		60.87 159	61.82 147	59.76 158	66.69 171	75.87 159	64.07 159	61.96 83	49.31 122	31.17 189	42.76 121	36.95 181	51.37 158	69.67 174	73.20 145	83.30 158	84.95 127

Baseline_NR-MVSNet		59.47 166	60.28 163	58.54 166	66.69 171	73.90 172	61.63 175	62.90 79	49.15 130	26.87 200	35.18 187	37.62 177	48.20 169	69.67 174	73.61 133	84.92 119	82.82 146

v144192		62.05 146	61.46 152	62.73 139	66.59 173	79.87 119	69.30 130	55.88 145	41.50 179	39.41 149	37.23 172	36.45 184	59.62 124	72.69 140	73.51 134	85.61 105	88.93 82

v1921920		61.66 153	61.10 155	62.31 142	66.32 174	79.57 123	68.41 135	55.49 151	41.03 180	38.69 155	36.64 180	35.27 195	59.60 125	73.23 120	73.41 136	85.37 107	88.51 99

TESTMET0.1,1		67.38 88	71.61 71	62.45 141	66.05 175	81.32 97	63.98 161	55.36 153	58.62 85	42.96 127	52.74 75	61.65 69	57.74 139	75.64 92	78.09 70	88.61 18	93.21 35

pmmvs4		63.14 128	62.46 141	63.94 120	66.03 176	76.40 156	66.82 145	57.60 127	56.74 91	50.26 89	40.81 139	37.51 178	59.26 131	71.75 155	71.48 161	83.68 152	82.53 148

PatchT		60.46 161	63.85 119	56.51 179	65.95 177	75.68 163	47.34 208	41.39 218	53.89 110	41.40 139	37.84 170	50.30 115	57.29 142	72.76 137	73.27 142	85.67 100	83.23 143

v1240		61.09 157	60.55 160	61.72 148	65.92 178	79.28 127	67.16 143	54.91 157	39.79 185	38.10 158	36.08 182	34.64 196	59.15 132	72.86 130	73.36 138	85.10 112	87.84 104

ADS-MVSNet		58.40 176	59.16 178	57.52 174	65.80 179	74.57 170	60.26 181	40.17 223	50.51 116	38.01 159	40.11 143	44.72 128	59.36 129	64.91 189	66.55 192	81.53 180	72.72 194

testpf		43.39 216	47.17 214	38.98 219	65.58 180	47.38 231	36.09 224	31.67 233	36.97 193	19.47 212	33.01 192	35.62 194	23.61 219	50.86 225	56.08 221	57.48 230	70.27 203

FMVSNet1		63.48 123	63.07 129	63.97 119	65.31 181	76.37 157	71.77 95	57.90 120	43.32 165	45.66 102	35.06 188	49.43 116	58.57 135	77.49 69	78.22 67	84.59 136	81.60 158

testgi		48.51 208	50.53 206	46.16 210	64.78 182	67.15 203	41.54 219	54.81 159	29.12 218	17.03 215	32.07 196	31.98 205	20.15 224	65.26 188	67.00 191	78.67 201	61.10 221

LTVRE_ROB	47.26 16	49.41 206	49.91 209	48.82 202	64.76 183	69.79 195	49.05 203	47.12 200	20.36 232	16.52 218	36.65 179	26.96 216	50.76 162	60.47 203	63.16 205	64.73 225	72.00 197
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		52.23 198	52.80 200	51.56 196	64.70 184	69.41 196	51.01 201	58.60 114	36.63 195	22.44 208	21.80 221	31.42 208	30.52 205	66.79 184	67.83 186	82.10 176	75.73 178

USDC		59.69 165	60.03 168	59.28 162	64.04 185	71.84 190	63.15 170	55.36 153	54.90 105	35.02 178	48.34 90	29.79 212	58.16 136	70.60 162	71.33 168	79.99 192	73.42 189

WR-MVS		51.02 200	54.56 193	46.90 208	63.84 186	69.23 197	44.78 215	56.38 141	38.19 191	14.19 222	37.38 171	36.82 183	22.39 220	60.14 204	66.20 196	79.81 193	73.95 187

our_test_3					63.32 187	71.07 194	55.90 195

test20.03		47.23 212	48.69 211	45.53 212	63.28 188	64.39 209	41.01 220	56.93 138	29.16 217	15.21 221	23.90 214	30.76 211	17.51 229	64.63 192	65.26 197	79.21 199	62.71 217

pmmvs6		54.20 194	53.54 196	54.97 185	63.22 189	72.98 179	60.17 182	52.32 181	26.77 223	34.30 180	23.29 218	36.23 186	40.33 197	68.77 179	68.76 181	79.47 197	78.00 173

v7n		57.04 182	56.64 186	57.52 174	62.85 190	74.75 168	61.76 173	51.80 182	35.58 203	36.02 171	32.33 194	33.61 202	50.16 165	67.73 182	70.34 176	82.51 170	82.12 153

pmmvs5		59.72 164	60.24 165	59.11 163	62.77 191	77.33 148	63.17 169	54.00 166	40.21 183	37.23 163	40.41 141	35.99 188	51.75 157	72.55 143	72.74 150	85.72 98	82.45 150

CVMVSNet		54.92 189	58.16 180	51.13 198	62.61 192	68.44 199	55.45 196	52.38 180	42.28 174	21.45 209	47.10 103	46.10 123	37.96 200	64.42 194	63.81 202	76.92 207	75.01 182

TAMVS		58.86 170	60.91 156	56.47 180	62.38 193	77.57 143	58.97 188	52.98 176	38.76 190	36.17 169	42.26 129	47.94 119	46.45 178	70.23 168	70.79 171	81.86 178	78.82 171

DTE-MVSNet		49.82 204	51.92 204	47.37 207	61.75 194	64.38 210	45.89 214	57.33 134	36.11 199	12.79 227	36.87 176	31.93 207	25.73 216	58.01 206	65.22 198	80.75 187	70.93 202

PEN-MVS		51.04 199	52.94 198	48.82 202	61.45 195	66.00 205	48.68 205	57.20 135	36.87 194	15.36 220	36.98 175	32.72 204	28.77 211	57.63 209	66.37 193	81.44 182	74.00 186

v748		55.19 184	54.63 191	55.85 182	61.44 196	72.97 180	58.72 189	51.62 183	34.48 206	36.39 168	32.09 195	33.05 203	45.48 183	61.85 201	67.87 185	81.45 181	80.08 164

V42		62.86 136	62.97 131	62.74 138	60.84 197	78.99 129	71.46 107	57.13 137	46.85 138	44.28 113	38.87 156	40.73 152	57.63 141	72.60 142	74.14 125	85.09 114	88.63 94

MDTV_nov1_ep13_2view		54.47 193	54.61 192	54.30 192	60.50 198	73.82 173	57.92 190	43.38 210	39.43 189	32.51 187	33.23 191	34.05 199	47.26 174	62.36 199	66.21 195	84.24 143	73.19 191

LP		48.21 209	46.65 216	50.03 199	60.39 199	63.86 213	48.73 204	38.71 225	35.60 202	32.99 185	23.31 217	24.95 223	40.07 198	57.73 207	61.56 210	79.29 198	59.51 222

MVS-HIRNet		53.86 195	53.02 197	54.85 186	60.30 200	72.36 186	44.63 216	42.20 216	39.45 188	43.47 116	21.66 222	34.00 200	55.47 146	65.42 187	67.16 190	83.02 166	71.08 201

CHOSEN 280x420		62.23 143	66.57 100	57.17 177	59.88 201	68.92 198	61.20 177	42.28 215	54.17 108	39.57 147	47.78 95	64.97 57	62.68 89	73.85 114	69.52 179	77.43 204	86.75 111

TinyColmap		52.66 197	50.09 208	55.65 183	59.72 202	64.02 212	57.15 192	52.96 177	40.28 182	32.51 187	32.42 193	20.97 227	56.65 144	63.95 195	65.15 199	74.91 212	63.87 214

FC-MVSNet-test		47.24 211	54.37 194	38.93 220	59.49 203	58.25 220	34.48 226	53.36 170	45.66 148	6.66 235	50.62 82	42.02 134	16.62 230	58.39 205	61.21 211	62.99 226	64.40 213

test-mter		64.06 116	69.24 82	58.01 168	59.07 204	77.40 146	59.13 187	48.11 196	55.64 100	39.18 151	51.56 79	58.54 78	55.38 147	73.52 118	76.00 86	87.22 49	92.05 50

WR-MVS_H		49.62 205	52.63 201	46.11 211	58.80 205	67.58 201	46.14 213	54.94 155	36.51 197	13.63 225	36.75 178	35.67 193	22.10 221	56.43 213	62.76 206	81.06 184	72.73 193

CP-MVSNet		50.57 201	52.60 202	48.21 205	58.77 206	65.82 206	48.17 206	56.29 142	37.41 192	16.59 217	37.14 173	31.95 206	29.21 208	56.60 212	63.71 203	80.22 190	75.56 179

PS-CasMVS		50.17 202	52.02 203	48.02 206	58.60 207	65.54 207	48.04 207	56.19 144	36.42 198	16.42 219	35.68 184	31.33 209	28.85 210	56.42 214	63.54 204	80.01 191	75.18 181

SixPastTwentyTwo		49.11 207	49.22 210	48.99 201	58.54 208	64.14 211	47.18 209	47.75 197	31.15 214	24.42 204	41.01 138	26.55 217	44.04 187	54.76 220	58.70 215	71.99 219	68.21 204

TDRefinement		52.70 196	51.02 205	54.66 188	57.41 209	65.06 208	61.47 176	54.94 155	44.03 155	33.93 181	30.13 204	27.57 215	46.17 180	61.86 200	62.48 209	74.01 215	66.06 210

pmmvs-eth3d		55.20 183	53.95 195	56.65 178	57.34 210	67.77 200	57.54 191	53.74 167	40.93 181	41.09 143	31.19 201	29.10 214	49.07 166	65.54 186	67.28 188	81.14 183	75.81 177

FPMVS		39.11 221	36.39 226	42.28 214	55.97 211	45.94 232	46.23 212	41.57 217	35.73 201	22.61 206	23.46 216	19.82 229	28.32 214	43.57 227	40.67 230	58.96 228	45.54 227

MIMVSNet		57.78 179	59.71 171	55.53 184	54.79 212	77.10 152	63.89 163	45.02 206	46.59 144	36.79 165	28.36 207	40.77 151	45.84 182	74.97 98	76.58 78	86.87 54	73.60 188

N_pmnet		47.67 210	47.00 215	48.45 204	54.72 213	62.78 214	46.95 210	51.25 184	36.01 200	26.09 203	26.59 212	25.93 222	35.50 203	55.67 216	59.01 213	76.22 208	63.04 215

test2356		46.29 213	47.37 213	45.03 213	54.38 214	57.99 221	42.03 218	50.32 186	30.78 215	16.65 216	27.40 210	23.70 224	29.86 206	61.20 202	64.31 201	76.93 206	66.22 209

v52		54.79 190	55.15 189	54.36 191	54.07 215	72.13 188	59.84 184	49.39 190	34.50 205	35.08 177	31.63 198	35.74 191	47.21 176	63.90 196	67.92 183	80.59 188	80.23 162

V4		54.78 191	55.14 190	54.37 190	54.07 215	72.13 188	59.83 185	49.39 190	34.46 207	35.11 176	31.64 197	35.72 192	47.22 175	63.90 196	67.92 183	80.59 188	80.23 162

anonymousdsp		54.99 187	57.24 184	52.36 194	53.82 217	71.75 191	51.49 200	48.14 195	33.74 208	33.66 182	38.34 163	36.13 187	47.54 172	64.53 193	70.60 173	79.53 196	85.59 125

testus		42.30 217	43.69 217	40.67 218	53.21 218	53.50 224	31.81 228	49.96 187	27.06 221	11.55 229	25.67 213	19.00 230	25.20 217	55.34 217	62.59 207	72.31 218	62.69 218

new-patchmatchnet		42.21 218	42.97 219	41.33 216	53.05 219	59.89 217	39.38 221	49.61 188	28.26 220	12.10 228	22.17 220	21.54 226	19.22 225	50.96 224	56.04 222	74.61 214	61.92 219

FMVSNet5		58.86 170	60.24 165	57.25 176	52.66 220	66.25 204	63.77 164	52.86 179	57.85 89	37.92 160	36.12 181	52.22 109	51.37 158	70.88 159	71.43 163	84.92 119	66.91 208

ambc			42.30 220		50.36 221	49.51 228	35.47 225		32.04 213	23.53 205	17.36 227	8.95 238	29.06 209	64.88 190	56.26 220	61.29 227	67.12 207

1111		38.93 222	38.98 223	38.86 221	50.10 222	50.42 226	29.52 229	38.00 227	22.67 228	17.99 213	17.40 225	26.26 219	28.72 212	54.86 218	58.20 216	68.82 223	43.08 230

.test1245		25.86 229	24.56 232	27.39 230	50.10 222	50.42 226	29.52 229	38.00 227	22.67 228	17.99 213	17.40 225	26.26 219	28.72 212	54.86 218	0.05 237	0.01 241	0.24 239

EU-MVSNet		44.84 214	47.85 212	41.32 217	49.26 224	56.59 222	43.07 217	47.64 199	33.03 209	13.82 223	36.78 177	30.99 210	24.37 218	53.80 221	55.57 223	69.78 220	68.21 204

testmv		37.40 223	37.95 224	36.76 223	48.97 225	49.33 229	28.65 232	46.74 201	18.34 233	7.68 233	16.80 230	14.47 234	19.18 226	51.72 222	56.93 218	69.36 221	58.09 223

test1235678		37.40 223	37.94 225	36.76 223	48.97 225	49.30 230	28.65 232	46.73 202	18.33 234	7.68 233	16.79 231	14.46 235	19.18 226	51.72 222	56.92 219	69.36 221	58.07 224

RPSCF		55.07 186	58.06 181	51.57 195	48.87 227	58.95 218	53.68 198	41.26 221	62.42 75	45.88 101	54.38 71	54.26 101	53.75 153	57.15 210	53.53 225	66.01 224	65.75 211

PMVS	27.44 18	32.08 226	29.07 229	35.60 225	48.33 228	24.79 237	26.97 234	41.34 219	20.45 231	22.50 207	17.11 229	18.64 231	20.44 223	41.99 230	38.06 231	54.02 233	42.44 231
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PM-MVS		50.11 203	50.38 207	49.80 200	47.23 229	62.08 216	50.91 202	44.84 208	41.90 176	36.10 170	35.22 186	26.05 221	46.83 177	57.64 208	55.42 224	72.90 216	74.32 184

pmmvs3		41.86 219	42.29 221	41.36 215	39.80 230	52.66 225	38.93 223	35.85 232	23.40 227	20.22 211	19.30 223	20.84 228	40.56 196	55.98 215	58.79 214	72.80 217	65.03 212

test12356		29.92 227	31.49 228	28.08 227	38.46 231	37.74 235	21.36 235	40.17 223	16.83 235	5.61 237	15.66 233	11.48 236	6.60 236	42.01 229	51.23 226	56.29 231	45.52 228

MDA-MVSNet-bldmvs		44.15 215	42.27 222	46.34 209	38.34 232	62.31 215	46.28 211	55.74 148	29.83 216	20.98 210	27.11 211	16.45 233	41.98 191	41.11 231	57.47 217	74.72 213	61.65 220

no-one		26.96 228	26.51 230	27.49 229	37.87 233	39.14 234	17.12 237	41.31 220	12.02 237	3.68 239	8.04 235	8.42 239	10.67 234	28.11 233	45.96 229	54.27 232	43.89 229

MIMVSNet1		40.84 220	43.46 218	37.79 222	32.14 234	58.92 219	39.24 222	50.83 185	27.00 222	11.29 230	16.76 232	26.53 218	17.75 228	57.14 211	61.12 212	75.46 210	56.78 225

Gipuma		24.91 230	24.61 231	25.26 231	31.47 235	21.59 238	18.06 236	37.53 229	25.43 225	10.03 231	4.18 239	4.25 241	14.85 231	43.20 228	47.03 227	39.62 235	26.55 235
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		15.08 232	11.65 235	19.08 232	28.73 236	12.31 241	6.95 242	36.87 231	10.71 239	3.63 240	5.13 236	2.22 244	13.81 233	11.34 237	18.50 235	24.49 237	21.32 236

EMVS		14.40 233	10.71 236	18.70 233	28.15 237	12.09 242	7.06 241	36.89 230	11.00 238	3.56 241	4.95 237	2.27 243	13.91 232	10.13 238	16.06 236	22.63 238	18.51 237

new_pmnet		33.19 225	35.52 227	30.47 226	27.55 238	45.31 233	29.29 231	30.92 234	29.00 219	9.88 232	18.77 224	17.64 232	26.77 215	44.07 226	45.98 228	58.41 229	47.87 226

PMMVS2		20.45 231	22.31 233	18.27 234	20.52 239	26.73 236	14.85 239	28.43 236	13.69 236	0.79 242	10.35 234	9.10 237	3.83 238	27.64 234	32.87 232	41.17 234	35.81 232

tmp_tt				16.09 235	13.07 240	8.12 243	13.61 240	2.08 238	55.09 102	30.10 192	40.26 142	22.83 225	5.35 237	29.91 232	25.25 234	32.33 236

MVE	15.98 19	14.37 234	16.36 234	12.04 236	7.72 241	20.24 239	5.90 243	29.05 235	8.28 240	3.92 238	4.72 238	2.42 242	9.57 235	18.89 236	31.46 233	16.07 240	28.53 234
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GG-mvs-BLEND		54.54 192	77.58 43	27.67 228	0.03 242	90.09 24	77.20 59	0.02 239	66.83 63	0.05 243	59.90 58	73.33 30	0.04 239	78.40 66	79.30 57	88.65 16	95.20 20

sosnet-low-res		0.00 237	0.00 239	0.00 239	0.00 243	0.00 246	0.00 247	0.00 241	0.00 243	0.00 244	0.00 242	0.00 246	0.00 242	0.00 241	0.00 240	0.00 243	0.00 241

sosnet		0.00 237	0.00 239	0.00 239	0.00 243	0.00 246	0.00 247	0.00 241	0.00 243	0.00 244	0.00 242	0.00 246	0.00 242	0.00 241	0.00 240	0.00 243	0.00 241

testmvs		0.05 235	0.08 237	0.01 237	0.00 243	0.01 244	0.03 245	0.01 240	0.05 241	0.00 244	0.14 241	0.01 245	0.03 241	0.05 239	0.05 237	0.01 241	0.24 239

test123		0.05 235	0.08 237	0.01 237	0.00 243	0.01 244	0.01 246	0.00 241	0.05 241	0.00 244	0.16 240	0.00 246	0.04 239	0.02 240	0.05 237	0.00 243	0.26 238

MTAPA										78.32 6		79.42 18

MTMP										76.04 11		76.65 23

Patchmatch-RL test							2.17 244

NP-MVS									81.60 31

Patchmtry						78.06 138	67.53 139	43.18 211		41.40 139

DeepMVS_CX						19.81 240	17.01 238	10.02 237	23.61 226	5.85 236	17.21 228	8.03 240	21.13 222	22.60 235		21.42 239	30.01 233

High-resolution multi-view benchmark