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.

CNVR-MVS		97.60 3	98.08 3	97.03 3	99.14 1	99.55 1	98.67 3	95.32 3	97.91 7	92.55 8	97.11 6	97.23 10	97.49 1	98.16 2	97.05 4	99.04 12	99.55 15

v1.0		89.93 54	83.92 115	96.94 4	99.03 2	99.49 3	99.00 2	95.35 2	97.97 6	92.21 11	97.50 4	99.73 1	96.95 3	97.13 10	95.61 22	99.11 6	0.00 242

NCCC		97.01 5	97.74 5	96.16 8	99.02 3	99.35 5	98.63 4	95.04 5	97.84 10	88.95 22	96.83 11	97.02 13	96.39 11	97.44 6	96.51 8	98.90 21	99.16 36

ESAPD		97.69 1	98.16 2	97.14 1	99.01 4	99.52 2	99.12 1	95.38 1	98.00 5	93.31 4	97.71 2	99.61 2	96.94 4	96.99 13	95.45 24	99.09 10	99.81 5

SMA-MVS		96.96 6	97.65 8	96.15 9	98.98 5	99.31 9	97.91 13	94.68 11	97.52 14	90.59 16	94.54 25	99.20 3	96.54 8	97.29 8	96.48 9	98.22 51	99.19 30

MCST-MVS		96.93 7	98.07 4	95.61 16	98.98 5	99.44 4	98.04 8	95.04 5	98.10 3	86.55 29	97.65 3	97.56 8	95.60 20	97.67 5	96.45 11	99.43 1	99.61 14

HPM-MVS++		96.91 8	97.70 6	96.00 11	98.97 7	99.16 13	97.82 16	94.81 8	98.04 4	89.61 19	96.56 13	98.60 5	96.39 11	97.09 11	95.22 29	98.39 43	99.22 29

APDe-MVS		97.31 4	97.51 9	97.08 2	98.95 8	99.29 10	98.58 5	95.11 4	97.69 13	94.16 1	96.91 9	96.81 14	96.57 6	96.71 17	95.39 27	99.08 11	99.79 6

ACMMP_Plus		95.81 15	96.50 18	95.01 20	98.79 9	99.17 12	97.52 22	94.20 15	96.19 26	85.71 33	93.80 28	96.20 16	95.89 16	96.62 19	94.98 35	97.93 78	98.52 66

APD-MVS		96.79 10	96.99 14	96.56 6	98.76 10	98.87 22	98.42 6	94.93 7	97.70 12	91.83 12	95.52 17	95.94 18	96.63 5	95.94 26	95.47 23	98.80 25	99.47 17
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HSP-MVS		97.61 2	98.30 1	96.81 5	98.66 11	99.35 5	98.00 9	94.75 9	98.45 2	92.78 6	97.99 1	98.58 6	97.41 2	98.24 1	96.48 9	99.27 4	98.99 44

MSLP-MVS++		95.49 19	94.84 29	96.25 7	98.64 12	98.63 30	98.35 7	92.37 29	95.04 42	92.62 7	87.12 40	93.79 27	96.55 7	93.53 57	96.78 5	98.98 16	98.99 44

zzz-MVS		95.87 14	95.63 25	96.15 9	98.60 13	98.83 24	97.89 14	93.65 18	96.24 25	93.08 5	91.13 32	95.46 23	95.72 19	95.64 27	93.67 56	97.97 75	98.46 69

HFP-MVS		96.09 13	96.41 19	95.72 15	98.58 14	98.84 23	97.95 11	93.08 23	96.96 18	90.24 17	96.60 12	94.40 26	96.52 9	95.13 35	94.33 43	97.93 78	98.59 63

DeepC-MVS_fast	91.53 1	95.57 18	95.67 23	95.45 17	98.57 15	99.00 18	97.76 17	94.41 13	97.06 16	86.84 28	86.39 43	92.27 38	96.38 13	97.89 4	98.06 2	98.73 31	99.01 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PGM-MVS		94.64 24	95.49 26	93.66 27	98.55 16	98.51 39	97.63 21	87.77 43	94.45 46	84.92 37	97.23 5	91.90 40	95.22 22	94.56 48	93.80 52	97.87 85	97.97 85

ACMMPR		95.59 17	95.89 21	95.25 18	98.41 17	98.74 26	97.69 20	92.73 27	96.88 19	88.95 22	95.33 19	92.91 33	95.79 17	94.73 46	94.33 43	97.92 81	98.32 75

SteuartSystems-ACMMP		96.20 12	97.22 11	95.01 20	98.40 18	99.11 14	97.93 12	93.62 19	96.28 24	87.45 25	97.05 8	96.00 17	94.23 26	96.83 16	95.97 17	98.40 42	99.27 25
Skip Steuart: Steuart Systems R&D Blog.
X-MVS		94.70 23	95.71 22	93.52 29	98.38 19	98.56 33	96.99 26	92.62 28	95.58 33	81.00 55	94.57 24	93.49 29	94.16 29	94.82 42	94.29 45	97.99 74	98.68 57

MP-MVS		95.24 21	95.96 20	94.40 23	98.32 20	98.38 45	97.12 25	92.87 24	95.17 40	85.50 34	95.68 15	94.91 24	94.58 24	95.11 36	93.76 53	98.05 65	98.68 57
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CSCG		93.16 34	92.65 40	93.76 26	98.32 20	99.09 16	96.12 33	89.91 35	93.15 55	89.64 18	83.62 50	88.91 48	92.40 43	91.09 100	93.70 54	96.14 171	98.99 44

AdaColmap		94.28 27	92.94 38	95.84 13	98.32 20	98.33 47	96.06 34	94.62 12	96.29 23	91.22 14	89.89 36	85.50 64	96.38 13	91.85 90	90.89 77	98.44 38	97.81 89

CP-MVS		95.43 20	95.67 23	95.14 19	98.24 23	98.60 31	97.45 23	92.80 25	95.98 29	89.21 21	95.22 20	93.60 28	95.43 21	94.37 50	93.22 61	97.68 89	98.72 55

3Dnovator	85.78 8	92.53 39	91.96 43	93.20 32	97.99 24	98.47 42	95.78 36	85.94 53	93.07 57	86.40 30	73.43 92	89.00 47	94.08 30	94.74 45	96.44 12	99.01 15	98.57 64

mPP-MVS					97.95 25							92.24 39

QAPM		91.68 44	91.97 42	91.34 43	97.86 26	98.72 27	95.60 38	85.72 54	90.86 70	77.14 75	76.06 75	90.35 43	92.69 39	94.10 53	94.60 40	99.04 12	99.09 37

train_agg		95.72 16	97.37 10	93.80 25	97.82 27	98.92 20	97.84 15	93.50 20	96.86 20	81.35 49	97.10 7	97.71 7	94.19 27	96.02 24	95.37 28	98.07 62	99.64 12

PLC	89.12 3	92.67 38	90.84 49	94.81 22	97.69 28	96.10 83	95.42 39	91.70 31	95.82 32	92.52 9	81.24 54	86.01 59	94.36 25	92.44 84	90.27 86	97.19 113	93.99 155
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
OpenMVS	83.41 11	89.84 55	88.89 71	90.95 48	97.63 29	98.51 39	94.64 45	85.47 58	88.14 83	78.39 69	65.06 126	85.42 65	91.04 55	93.06 67	93.70 54	98.53 36	98.37 72

CDPH-MVS		93.22 33	95.08 28	91.04 46	97.57 30	98.49 41	96.74 28	89.35 36	95.19 39	73.57 87	90.26 34	91.59 41	90.68 65	95.09 38	96.15 14	98.31 48	98.81 52

MAR-MVS		90.44 51	91.17 48	89.59 56	97.48 31	97.92 54	90.96 81	79.80 96	95.07 41	77.03 77	80.83 55	79.10 81	94.68 23	93.16 64	94.46 42	97.59 95	97.63 91
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
3Dnovator+	86.26 7	92.90 36	92.45 41	93.42 30	97.25 32	98.45 44	95.82 35	85.71 55	93.83 49	89.55 20	72.31 99	92.28 37	94.01 32	95.10 37	95.92 19	98.17 54	99.23 28

TSAR-MVS + ACMM		94.99 22	97.02 13	92.61 36	97.19 33	98.71 28	97.74 19	93.21 22	96.97 17	79.27 62	94.09 26	97.14 11	90.84 62	96.64 18	95.94 18	97.42 104	99.67 11

abl_6				93.25 31	97.12 34	98.71 28	94.40 47	87.81 42	97.86 9	87.19 27	91.07 33	95.80 19	94.18 28			98.78 27	99.36 19

PHI-MVS		94.49 26	96.72 16	91.88 39	97.06 35	98.88 21	94.99 43	89.13 37	96.15 27	79.70 59	96.91 9	95.78 20	91.87 47	94.65 47	95.68 20	98.53 36	98.98 47

ACMMP		93.32 31	93.59 36	93.00 34	97.03 36	98.24 48	95.27 41	91.66 33	95.20 38	83.25 42	95.39 18	85.52 62	92.80 38	92.60 80	90.21 89	98.01 70	97.99 83
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
EPNet		93.69 30	95.34 27	91.76 40	96.98 37	98.47 42	95.40 40	86.79 46	95.47 34	82.84 43	95.66 16	89.17 45	90.47 69	95.25 34	94.69 39	98.10 58	98.68 57
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	91.00 2	94.15 28	96.87 15	90.97 47	96.82 38	99.33 8	89.40 94	92.76 26	98.76 1	82.36 45	88.74 37	95.49 22	90.58 68	98.13 3	97.80 3	93.88 200	99.88 3

CNLPA		91.53 45	89.74 59	93.63 28	96.75 39	97.63 57	91.16 78	91.70 31	96.38 22	90.82 15	69.66 111	85.52 62	93.76 33	90.44 109	91.14 76	97.55 96	97.40 100

CPTT-MVS		94.11 29	93.99 33	94.25 24	96.58 40	97.66 55	97.31 24	91.94 30	94.84 43	88.72 24	92.51 29	93.04 32	95.78 18	91.51 93	89.97 93	95.15 188	98.37 72

OMC-MVS		92.05 41	91.88 44	92.25 37	96.51 41	97.94 53	93.18 55	88.97 39	96.53 21	84.47 39	80.79 59	87.85 50	93.25 37	92.48 82	91.81 68	97.12 114	95.73 132

MVS_111021_LR		93.05 35	94.53 31	91.32 44	96.43 42	98.38 45	92.81 58	87.20 45	95.94 31	81.45 47	94.75 23	86.08 58	92.12 46	94.83 41	93.34 59	97.89 84	98.42 70

SD-MVS		96.87 9	97.69 7	95.92 12	96.38 43	99.25 11	97.76 17	94.75 9	97.72 11	92.46 10	95.94 14	99.09 4	96.48 10	96.01 25	96.08 16	97.68 89	99.73 9

MVS_111021_HR		92.73 37	94.83 30	90.28 52	96.27 44	99.10 15	92.77 59	86.15 52	93.41 51	77.11 76	93.82 27	87.39 51	90.61 66	95.60 28	95.15 31	98.79 26	99.32 20

DeepC-MVS	88.77 4	92.39 40	91.74 45	93.14 33	96.21 45	98.55 36	96.30 31	93.84 17	93.06 58	81.09 53	74.69 87	85.20 67	93.48 35	95.41 31	96.13 15	97.92 81	99.18 31
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TAPA-MVS	87.40 6	90.98 47	90.71 50	91.30 45	96.14 46	97.66 55	94.80 44	89.00 38	94.74 45	77.42 74	80.22 60	86.70 54	92.27 44	91.65 92	90.17 91	98.15 57	93.83 160
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TSAR-MVS + MP.		96.50 11	97.08 12	95.82 14	96.12 47	98.97 19	98.00 9	94.13 16	97.89 8	91.49 13	95.11 22	97.52 9	96.26 15	96.27 23	94.07 49	98.91 20	99.74 8

DELS-MVS		91.09 46	90.56 55	91.71 41	95.82 48	98.59 32	95.74 37	86.68 48	85.86 96	85.12 36	72.71 95	81.36 73	88.06 89	97.31 7	98.27 1	98.86 23	99.82 4
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
MSDG		85.81 89	82.29 132	89.93 55	95.52 49	92.61 122	91.51 66	91.46 34	85.12 103	78.56 66	63.25 134	69.01 117	85.31 103	88.45 126	88.23 126	97.21 112	89.33 197

CANet		93.23 32	93.72 35	92.65 35	95.48 50	99.09 16	96.55 30	86.74 47	95.28 37	85.22 35	77.30 70	91.25 42	92.60 41	97.06 12	96.63 6	99.31 2	99.45 18

EPNet_dtu		84.87 101	89.01 69	80.05 127	95.25 51	92.88 120	88.84 99	84.11 61	91.69 65	49.28 200	85.69 46	78.95 83	65.39 203	92.22 89	91.66 69	97.43 103	89.95 194
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		87.19 72	85.48 96	89.18 60	94.96 52	95.47 95	92.02 63	93.36 21	88.69 79	67.01 111	70.56 107	72.10 102	92.47 42	89.96 118	89.93 95	95.25 185	91.68 184

PVSNet_BlendedMVS		90.74 48	90.66 51	90.82 49	94.75 53	98.54 37	91.30 76	86.53 49	95.43 35	85.75 31	78.66 65	70.67 113	87.60 90	96.37 21	95.08 33	98.98 16	99.90 1

PVSNet_Blended		90.74 48	90.66 51	90.82 49	94.75 53	98.54 37	91.30 76	86.53 49	95.43 35	85.75 31	78.66 65	70.67 113	87.60 90	96.37 21	95.08 33	98.98 16	99.90 1

PCF-MVS	88.14 5	90.42 52	89.56 65	91.41 42	94.44 55	98.18 50	94.35 48	94.33 14	84.55 109	76.61 80	75.84 78	88.47 49	91.29 50	90.37 111	90.66 83	97.46 98	98.88 51
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PatchMatch-RL		86.75 79	85.43 97	88.29 64	94.06 56	96.37 81	86.82 120	82.94 74	88.94 77	79.59 60	79.83 63	59.17 153	89.46 79	91.12 99	88.81 118	96.88 120	93.78 162

CHOSEN 1792x2688		84.59 106	84.30 111	84.93 96	93.71 57	98.23 49	89.91 88	77.96 113	84.81 105	65.93 115	45.19 211	71.76 109	83.13 117	95.46 30	95.13 32	98.94 19	99.53 16

MVS_0304		91.90 43	92.93 39	90.69 51	93.66 58	98.78 25	96.73 29	85.43 59	93.13 56	78.11 71	77.02 73	89.09 46	91.10 53	96.98 14	96.54 7	99.11 6	98.96 48

OPM-MVS		85.69 91	82.79 125	89.06 61	93.42 59	94.21 110	94.21 50	87.61 44	72.68 153	70.79 99	71.09 101	67.27 125	90.74 64	91.29 97	89.05 113	97.61 94	93.94 157

HyFIR lowres test		83.43 114	82.94 124	84.01 101	93.41 60	97.10 62	87.21 116	74.04 144	80.15 134	64.98 118	41.09 219	76.61 88	86.51 98	93.31 60	93.01 63	97.91 83	99.30 23

TSAR-MVS + COLMAP		89.59 57	89.64 62	89.53 58	93.32 61	96.51 73	95.03 42	88.53 40	95.98 29	69.10 106	91.81 30	64.53 137	93.40 36	93.53 57	91.35 74	97.77 86	93.75 165

CHOSEN 280x420		90.61 50	94.27 32	86.35 84	93.12 62	98.16 52	89.99 87	69.62 178	92.48 62	76.89 79	87.28 39	96.72 15	90.31 71	94.81 43	92.33 65	98.17 54	98.08 80

HQP-MVS		89.57 58	90.57 54	88.41 63	92.77 63	94.71 102	94.24 49	87.97 41	93.44 50	68.18 109	91.75 31	71.54 110	89.90 73	92.31 87	91.43 72	97.39 105	98.80 53

COLMAP_ROB	75.69 15	79.47 137	76.90 155	82.46 114	92.20 64	90.53 138	85.30 130	83.69 62	78.27 141	61.47 127	58.26 148	62.75 142	78.28 143	82.41 191	82.13 203	93.83 205	83.98 213
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XVS					92.16 65	98.56 33	91.04 79			81.00 55		93.49 29				98.00 71

X-MVStestdata					92.16 65	98.56 33	91.04 79			81.00 55		93.49 29				98.00 71

PVSNet_Blended_VisFu		87.44 69	88.72 72	85.95 89	92.02 67	97.26 59	86.88 119	82.66 77	83.86 119	79.16 63	66.96 120	84.91 68	77.26 153	94.97 39	93.48 57	97.73 87	99.64 12

ACMM	84.23 10	86.40 82	84.64 104	88.46 62	91.90 68	91.93 133	88.11 105	85.59 57	88.61 80	79.13 64	75.31 83	66.25 130	89.86 76	89.88 119	87.64 133	96.16 170	92.86 178
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	79.58 12	83.83 111	84.81 101	82.68 109	91.85 69	97.35 58	75.75 201	82.57 78	86.55 93	84.01 41	70.90 102	65.43 133	63.18 209	84.19 162	89.92 97	98.74 30	99.31 22
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
LGP-MVS_train		86.95 77	87.65 78	86.12 88	91.77 70	93.84 113	93.04 56	82.77 75	88.04 84	65.33 117	87.69 38	67.09 126	86.79 95	90.20 114	88.99 114	97.05 116	97.71 90

MS-PatchMatch		82.16 125	82.18 133	82.12 116	91.65 71	93.50 116	89.51 91	71.95 158	81.48 126	64.45 121	59.58 145	77.54 86	77.23 154	89.88 119	85.62 153	97.94 77	87.68 201

CANet_DTU		87.91 63	91.57 46	83.64 105	90.96 72	97.12 61	91.90 64	75.97 129	92.83 60	53.16 183	86.02 45	79.02 82	90.80 63	95.40 32	94.15 48	99.03 14	96.47 128

UGNet		87.04 76	89.59 64	84.07 100	90.94 73	95.95 86	86.02 125	81.65 84	85.94 95	78.54 68	78.00 68	85.40 66	69.62 191	91.83 91	91.53 70	97.63 93	98.51 67
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
ACMP	85.16 9	87.15 74	87.04 84	87.27 76	90.80 74	94.45 106	89.41 93	83.09 73	89.15 76	76.98 78	86.35 44	65.80 131	86.94 93	88.45 126	87.52 135	96.42 161	97.56 96
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
conf0.002		87.85 64	87.85 77	87.84 68	90.63 75	96.81 68	91.35 70	83.36 65	84.16 113	72.61 89	78.06 67	71.90 107	90.91 56	93.29 61	91.47 71	98.20 52	99.28 24

conf0.01		87.22 71	86.71 88	87.81 69	90.61 76	96.75 70	91.35 70	83.33 66	84.16 113	72.45 90	75.61 79	68.65 119	90.91 56	93.23 62	89.34 101	98.17 54	99.27 25

tfpn111		87.30 70	87.03 85	87.61 71	90.54 77	96.39 75	91.35 70	83.15 67	84.16 113	71.65 92	86.75 41	60.49 143	90.91 56	92.89 71	89.34 101	98.05 65	99.17 32

conf200view11		86.07 85	84.76 102	87.61 71	90.54 77	96.39 75	91.35 70	83.15 67	84.16 113	71.65 92	70.86 103	60.49 143	90.91 56	92.89 71	89.34 101	98.05 65	99.17 32

thres100view900		86.48 81	85.08 100	88.12 67	90.54 77	96.90 67	92.39 60	84.82 60	84.16 113	71.65 92	70.86 103	60.49 143	91.23 52	93.65 55	90.19 90	98.10 58	99.32 20

tfpn200view9		86.07 85	84.76 102	87.61 71	90.54 77	96.39 75	91.35 70	83.15 67	84.16 113	71.65 92	70.86 103	60.49 143	90.91 56	92.89 71	89.34 101	98.05 65	99.17 32

thres200		85.80 90	84.38 109	87.46 74	90.51 81	96.39 75	91.64 65	83.15 67	81.59 125	71.54 96	70.24 108	60.41 147	89.88 74	92.89 71	89.85 98	98.06 63	99.26 27

canonicalmvs		89.62 56	89.87 58	89.33 59	90.47 82	97.02 65	93.46 53	79.67 99	92.45 63	81.05 54	82.84 51	73.00 97	93.71 34	90.38 110	94.85 36	97.65 92	98.54 65

thres400		85.59 92	84.08 112	87.36 75	90.45 83	96.60 72	90.95 82	83.67 63	80.99 128	71.17 98	69.08 113	60.25 148	89.88 74	93.14 65	89.34 101	98.02 69	99.17 32

CMPMVS	54.54 17	71.74 200	67.94 214	76.16 172	90.41 84	93.25 117	78.32 193	75.60 137	59.81 215	53.95 178	44.64 214	51.22 168	70.70 186	74.59 221	75.88 221	88.01 223	76.23 223
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tfpn_ndepth		86.61 80	87.92 76	85.08 95	90.39 85	95.45 96	88.21 103	82.30 79	90.79 71	71.22 97	82.59 53	72.09 104	80.42 131	91.37 95	88.61 122	97.93 78	94.56 147

casdiffmvs1		90.10 53	90.55 56	89.57 57	90.39 85	97.06 63	93.29 54	78.71 108	93.34 52	84.02 40	82.80 52	75.54 93	92.62 40	91.33 96	95.44 25	98.41 41	98.38 71

view600		85.15 95	83.59 119	86.96 79	90.38 87	96.39 75	90.33 84	83.15 67	80.46 129	70.61 101	67.96 116	60.04 149	89.22 80	92.89 71	88.30 124	98.10 58	99.08 38

UA-Net		84.69 104	87.64 79	81.25 122	90.38 87	95.67 89	87.33 115	79.41 101	72.07 156	66.48 114	75.09 84	92.48 35	66.88 200	94.03 54	94.25 46	97.01 119	89.88 195

thres600view7		85.14 96	83.58 120	86.96 79	90.37 89	96.39 75	90.33 84	83.15 67	80.46 129	70.60 102	67.96 116	60.04 149	89.22 80	92.89 71	88.28 125	98.06 63	99.08 38

DWT-MVSNet_training		87.65 67	88.45 73	86.71 81	90.32 90	95.64 91	87.91 107	75.69 133	93.27 54	81.43 48	74.99 85	76.48 89	86.92 94	87.74 132	92.29 66	98.00 71	98.74 54

view800		84.86 102	83.35 122	86.63 82	90.31 91	96.17 82	89.86 89	82.67 76	79.95 135	70.04 103	67.25 119	59.75 151	88.72 83	92.64 79	88.72 120	98.19 53	98.95 49

TSAR-MVS + GP.		94.59 25	96.60 17	92.25 37	90.25 92	98.17 51	96.22 32	86.53 49	97.49 15	87.26 26	95.21 21	97.06 12	94.07 31	94.34 52	94.20 47	99.18 5	99.71 10

tfpn		85.32 94	84.47 107	86.31 87	90.24 93	95.99 85	89.39 95	82.28 80	79.44 136	69.50 104	66.59 122	67.71 122	88.20 88	92.47 83	90.22 88	98.26 49	98.89 50

thresconf0.02		86.84 78	89.56 65	83.67 104	90.08 94	95.66 90	89.03 96	83.62 64	87.45 87	62.19 126	86.75 41	80.81 74	78.48 141	92.24 88	91.27 75	98.60 33	92.72 180

IS_MVSNet		87.83 65	90.66 51	84.53 98	90.08 94	96.79 69	88.16 104	79.89 95	85.44 99	72.20 91	75.50 82	87.14 52	80.21 132	95.53 29	95.22 29	96.65 136	99.02 42

PMMVS		88.56 62	91.22 47	85.47 93	90.04 96	95.60 93	86.62 121	78.49 109	93.86 48	70.62 100	90.00 35	80.08 80	91.64 48	92.36 85	89.80 99	95.40 183	96.84 110

tfpn1000		84.98 98	86.47 89	83.24 106	89.93 97	94.98 97	86.58 122	81.22 90	88.54 81	67.35 110	79.39 64	70.93 112	76.07 173	90.70 103	87.37 137	98.32 47	93.37 170

casdiffmvs		89.05 59	89.70 61	88.29 64	89.85 98	97.01 66	91.32 75	78.22 112	92.41 64	80.86 58	79.85 62	70.96 111	90.90 61	94.89 40	94.81 37	98.61 32	98.00 82

Vis-MVSNet (Re-imp)		85.89 88	89.62 63	81.55 120	89.85 98	96.08 84	87.55 111	79.80 96	84.80 106	66.55 113	73.70 91	86.71 53	68.25 198	94.40 49	94.53 41	97.32 108	97.09 106

MVS_Test		89.02 60	90.20 57	87.64 70	89.83 100	97.05 64	92.30 61	77.59 117	92.89 59	75.01 83	77.36 69	76.10 90	92.27 44	95.30 33	95.42 26	98.83 24	97.30 103

MVSTER		91.91 42	93.43 37	90.14 53	89.81 101	92.32 128	94.53 46	81.32 89	96.00 28	84.77 38	85.41 48	92.39 36	91.32 49	96.41 20	94.01 50	99.11 6	97.45 99

EPMVS		83.71 113	86.76 87	80.16 126	89.72 102	95.64 91	84.68 132	59.73 215	89.61 75	62.67 124	72.65 97	81.80 72	86.22 99	86.23 144	88.03 131	97.96 76	93.35 171

tfpnview11		83.86 110	85.36 98	82.10 117	89.66 103	94.55 103	87.73 108	81.81 83	85.72 98	58.99 133	80.80 56	66.64 127	76.13 172	90.79 101	88.15 127	98.26 49	90.90 188

CLD-MVS		88.99 61	88.07 74	90.07 54	89.61 104	94.94 99	93.82 52	85.70 56	92.73 61	82.73 44	79.97 61	69.59 116	90.44 70	90.32 112	89.93 95	98.10 58	99.04 41
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ACMH	78.51 14	79.27 140	78.08 146	80.65 125	89.52 105	90.40 139	80.45 184	79.77 98	69.54 173	54.85 165	64.83 128	56.16 160	83.94 112	84.58 160	86.01 149	95.41 182	95.03 143
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tfpn_n400		83.32 115	84.61 105	81.81 118	89.50 106	94.81 100	87.41 113	81.65 84	80.24 132	58.99 133	80.80 56	66.64 127	75.84 174	90.09 115	89.33 108	97.46 98	90.37 190

tfpnconf		83.32 115	84.61 105	81.81 118	89.50 106	94.81 100	87.41 113	81.65 84	80.24 132	58.99 133	80.80 56	66.64 127	75.84 174	90.09 115	89.33 108	97.46 98	90.37 190

conf0.05thres1000		81.86 126	79.55 142	84.56 97	89.39 108	94.15 111	87.57 110	81.36 88	69.95 169	65.78 116	56.38 154	59.38 152	86.04 100	90.58 105	88.49 123	97.22 111	97.97 85

FC-MVSNet-train		84.88 100	84.08 112	85.82 92	89.21 109	91.74 134	85.87 126	81.20 91	81.71 124	74.66 86	73.38 93	64.99 135	86.60 97	90.75 102	88.08 128	97.36 106	97.90 87

EPP-MVSNet		87.72 66	89.74 59	85.37 94	89.11 110	95.57 94	86.31 123	79.44 100	85.83 97	75.73 82	77.23 71	90.05 44	84.78 106	91.22 98	90.25 87	96.83 121	98.04 81

diffmvs		87.10 75	87.57 80	86.56 83	89.03 111	96.67 71	90.78 83	77.73 115	91.46 69	74.68 85	76.52 74	68.42 120	89.61 78	90.24 113	92.77 64	97.52 97	96.92 107

tpmrst		81.71 127	83.87 117	79.20 135	89.01 112	93.67 114	84.22 134	60.14 213	87.45 87	59.49 132	64.97 127	71.86 108	85.30 104	84.72 158	86.30 144	97.04 117	98.09 79

CostFormer		85.47 93	86.98 86	83.71 103	88.70 113	94.02 112	88.07 106	62.72 208	89.78 74	78.68 65	72.69 96	78.37 84	87.35 92	85.96 148	89.32 110	96.73 127	98.72 55

DI_MVS_plusplus_trai		87.63 68	87.13 83	88.22 66	88.61 114	95.92 87	94.09 51	81.41 87	87.00 91	78.38 70	59.70 143	80.52 78	89.08 82	94.37 50	93.34 59	97.73 87	99.05 40

ACMH+	79.09 13	79.12 142	77.22 153	81.35 121	88.50 115	90.36 140	82.14 176	79.38 103	72.78 152	58.59 136	62.31 138	56.44 159	84.10 110	82.03 195	84.05 186	95.40 183	92.55 181

tpmp4_e23		83.72 112	84.45 108	82.86 107	88.25 116	92.54 124	88.95 97	63.01 206	88.20 82	74.83 84	68.07 115	71.99 106	86.65 96	84.11 164	88.74 119	95.47 181	97.51 98

RPSCF		82.91 119	81.86 134	84.13 99	88.25 116	88.32 174	87.67 109	80.86 92	84.78 107	76.57 81	85.56 47	76.00 91	84.61 107	78.20 213	76.52 220	86.81 229	83.63 214

tpm cat1		82.39 124	82.32 131	82.47 113	88.13 118	92.42 127	87.43 112	62.79 207	85.30 100	78.05 72	60.14 141	72.10 102	83.20 116	82.26 193	85.67 152	95.23 186	98.35 74

Anonymous202405211			81.72 136		88.09 119	94.27 108	89.62 90	82.14 81	82.27 122		48.83 198	72.58 99	91.08 54	87.40 134	88.70 121	94.90 191	97.99 83

Effi-MVS+		84.80 103	85.71 95	83.73 102	87.94 120	95.76 88	90.08 86	73.45 147	85.12 103	62.66 125	72.39 98	64.97 136	90.59 67	92.95 70	90.69 82	97.67 91	98.12 77

Anonymous20240521		85.90 87	85.88 94	85.93 90	87.86 121	88.37 173	89.45 92	77.46 118	87.33 89	77.51 73	76.06 75	75.76 92	88.48 87	87.40 134	88.89 117	94.80 193	97.37 101

ADS-MVSNet		80.25 134	82.96 123	77.08 160	87.86 121	92.60 123	81.82 179	56.19 225	86.95 92	56.16 154	68.19 114	72.42 100	83.70 114	82.05 194	85.45 158	96.75 125	93.08 176

dps		82.63 121	82.64 128	82.62 111	87.81 123	92.81 121	84.39 133	61.96 209	86.43 94	81.63 46	69.72 110	67.60 124	84.42 108	82.51 190	83.90 188	95.52 179	95.50 138

Vis-MVSNet		82.88 120	86.04 91	79.20 135	87.77 124	96.42 74	86.10 124	76.70 120	74.82 148	61.38 128	70.70 106	77.91 85	64.83 204	93.22 63	93.19 62	98.43 39	96.01 130
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TDRefinement		75.54 169	73.22 186	78.25 145	87.65 125	89.65 148	85.81 127	79.28 104	71.14 160	56.06 157	52.17 166	51.96 167	68.74 197	81.60 196	80.58 209	91.94 216	85.45 207

PatchmatchNet		83.28 117	87.57 80	78.29 142	87.46 126	94.95 98	83.36 141	59.43 218	90.20 73	58.10 139	74.29 89	86.20 57	84.13 109	85.27 154	87.39 136	97.25 110	94.67 146
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Anonymous20231211		84.23 107	81.71 137	87.17 77	87.38 127	93.59 115	88.95 97	82.14 81	83.82 120	78.56 66	48.09 202	73.89 96	91.25 51	86.38 142	88.06 130	94.74 196	98.14 76

IterMVS-LS		82.62 122	82.75 127	82.48 112	87.09 128	87.48 187	87.19 117	72.85 150	79.09 137	66.63 112	65.22 124	72.14 101	84.06 111	88.33 129	91.39 73	97.03 118	95.60 137
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
RPMNet		81.47 128	86.24 90	75.90 180	86.72 129	92.12 130	82.82 162	55.76 226	85.21 101	53.73 179	63.45 133	83.16 71	80.13 133	92.34 86	89.52 100	96.23 168	97.90 87

CR-MVSNet		81.44 129	85.29 99	76.94 164	86.53 130	92.12 130	83.86 135	58.37 220	85.21 101	56.28 151	59.60 144	80.39 79	80.50 129	92.77 77	89.32 110	96.12 172	97.59 94

USDC		80.10 136	79.33 143	81.00 124	86.36 131	91.71 135	88.74 100	75.77 131	81.90 123	54.90 164	67.67 118	52.05 166	83.94 112	88.44 128	86.25 145	96.31 164	87.28 205

MDTV_nov1_ep13		84.17 108	88.03 75	79.66 130	86.00 132	94.41 107	85.05 131	66.01 200	90.36 72	64.34 122	77.13 72	84.56 69	82.71 121	87.12 138	88.92 115	93.84 203	93.69 166

gg-mvs-nofinetune		77.08 151	79.79 141	73.92 188	85.95 133	97.23 60	92.18 62	52.65 231	46.19 231	27.79 236	38.27 223	85.63 61	85.67 101	96.95 15	95.62 21	99.30 3	98.67 60

tpm		78.87 144	81.33 139	76.00 177	85.57 134	90.19 143	82.81 163	59.66 216	78.35 140	51.40 190	66.30 123	67.92 121	80.94 127	83.28 180	85.73 150	95.65 178	97.56 96

PatchT		79.28 139	83.88 116	73.93 187	85.54 135	90.95 137	66.14 220	56.53 224	83.21 121	56.28 151	56.50 153	76.80 87	80.50 129	92.77 77	89.32 110	98.57 35	97.59 94

Fast-Effi-MVS+		82.61 123	82.51 130	82.72 108	85.49 136	93.06 118	87.17 118	71.39 164	84.18 112	64.59 120	63.03 135	58.89 154	90.22 72	91.39 94	90.83 78	97.44 101	96.21 129

test-LLR		85.11 97	89.49 67	80.00 128	85.32 137	94.49 104	82.27 171	74.18 142	87.83 85	56.70 146	75.55 80	86.26 55	82.75 119	93.06 67	90.60 84	98.77 28	98.65 61

test0.0.03 1		80.99 131	84.37 110	77.05 161	85.32 137	89.79 146	78.43 192	74.18 142	84.78 107	57.98 142	76.06 75	72.88 98	69.14 195	88.02 130	87.70 132	97.27 109	91.37 185

CVMVSNet		76.86 153	79.09 144	74.26 185	85.29 139	89.44 155	79.91 187	78.47 110	68.94 177	44.45 216	62.35 137	69.70 115	64.50 206	85.82 149	87.03 140	92.94 211	90.33 192

TinyColmap		75.75 165	73.19 188	78.74 139	84.82 140	87.69 182	81.59 180	74.62 140	71.81 157	54.01 177	55.79 156	44.42 217	82.89 118	84.61 159	83.76 190	94.50 197	84.22 212

Effi-MVS+-dtu		81.18 130	82.77 126	79.33 133	84.70 141	92.54 124	85.81 127	71.55 162	78.84 138	57.06 144	71.98 100	63.77 139	85.09 105	88.94 123	87.62 134	91.79 218	95.68 133

CDS-MVSNet		83.13 118	83.73 118	82.43 115	84.52 142	92.92 119	88.26 102	77.67 116	72.08 155	69.08 107	66.96 120	74.66 94	78.61 138	90.70 103	91.96 67	96.46 160	96.86 109
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS		78.85 146	81.36 138	75.93 178	84.27 143	85.74 196	83.83 137	66.35 199	76.82 142	50.48 193	63.48 132	68.82 118	73.99 178	89.68 121	89.34 101	96.63 140	95.67 134
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+-dtu		80.57 132	83.44 121	77.22 157	83.98 144	91.52 136	85.78 129	64.54 204	80.38 131	50.28 196	74.06 90	62.89 141	82.00 123	89.10 122	88.91 116	96.75 125	97.21 105

GA-MVS		78.86 145	80.42 140	77.05 161	83.27 145	92.17 129	83.24 144	75.73 132	73.75 149	46.27 211	62.43 136	57.12 156	76.94 161	93.14 65	89.34 101	96.83 121	95.00 144

FMVSNet3		87.19 72	87.32 82	87.04 78	82.82 146	90.21 142	92.88 57	76.53 121	91.69 65	81.31 50	64.81 129	80.64 75	89.79 77	94.80 44	94.76 38	98.88 22	94.32 150

LP		68.35 212	68.20 213	68.53 211	82.61 147	82.93 204	69.42 214	53.36 230	71.06 161	45.32 214	41.19 218	49.10 192	67.20 199	73.89 222	78.16 216	93.25 208	81.04 218

FC-MVSNet-test		77.95 149	81.85 135	73.39 192	82.31 148	88.99 166	79.33 188	74.24 141	78.75 139	47.40 208	70.22 109	72.09 104	60.78 215	86.66 140	85.62 153	96.30 165	90.61 189

tfpnnormal		75.27 174	72.12 198	78.94 137	82.30 149	88.52 172	82.41 168	79.41 101	58.03 217	55.59 162	43.83 217	44.71 214	77.35 150	87.70 133	85.45 158	96.60 144	96.61 115

TESTMET0.1,1		84.62 105	89.49 67	78.94 137	82.18 150	94.49 104	82.27 171	70.94 167	87.83 85	56.70 146	75.55 80	86.26 55	82.75 119	93.06 67	90.60 84	98.77 28	98.65 61

testpf		71.11 206	76.92 154	64.33 215	81.95 151	78.78 222	61.99 222	43.97 238	84.31 111	46.81 209	61.76 139	63.32 140	62.03 212	77.13 218	80.68 208	89.25 222	92.50 182

test-mter		84.06 109	89.00 70	78.29 142	81.92 152	94.23 109	81.07 182	70.38 171	87.12 90	56.10 156	74.75 86	85.80 60	81.81 124	92.52 81	90.10 92	98.43 39	98.49 68

TAMVS		79.23 141	78.95 145	79.56 131	81.89 153	92.52 126	82.97 156	73.70 146	67.27 190	64.97 119	61.66 140	65.06 134	78.61 138	87.12 138	88.07 129	95.23 186	90.95 187

GBi-Net		86.16 83	86.00 92	86.35 84	81.81 154	89.52 149	91.40 67	76.53 121	91.69 65	81.31 50	64.81 129	80.64 75	88.72 83	90.54 106	90.72 79	98.34 44	94.08 152

test1		86.16 83	86.00 92	86.35 84	81.81 154	89.52 149	91.40 67	76.53 121	91.69 65	81.31 50	64.81 129	80.64 75	88.72 83	90.54 106	90.72 79	98.34 44	94.08 152

FMVSNet2		84.89 99	84.02 114	85.91 91	81.81 154	89.52 149	91.40 67	75.79 130	84.45 110	79.39 61	58.75 146	74.35 95	88.72 83	93.51 59	93.46 58	98.34 44	94.08 152

testgi		73.22 188	75.84 158	70.16 208	81.67 157	85.50 198	71.45 210	70.81 168	69.56 172	44.74 215	74.52 88	49.25 189	58.45 216	84.10 165	83.37 194	93.86 202	84.56 211

TransMVSNet (Re)		72.90 191	70.51 206	75.69 183	80.88 158	85.26 200	79.25 189	78.43 111	56.13 223	52.81 184	46.81 206	48.20 200	66.77 201	85.18 156	83.70 191	95.98 176	88.28 200

LTVRE_ROB	71.82 16	72.62 194	71.77 199	73.62 190	80.74 159	87.59 185	80.42 185	70.37 172	49.73 227	37.12 226	59.76 142	42.52 224	80.92 128	83.20 181	85.61 155	92.13 215	93.95 156
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
MVS-HIRNet		72.32 196	73.45 184	71.00 207	80.58 160	89.97 144	68.51 217	55.28 227	70.89 163	52.27 185	39.09 221	57.11 157	75.02 177	85.76 150	86.33 143	94.36 199	85.00 209

EG-PatchMatch MVS		71.81 199	71.54 201	72.12 201	80.53 161	89.94 145	78.51 191	66.56 198	57.38 219	47.46 207	44.28 216	52.22 164	63.10 210	85.22 155	84.42 184	96.56 150	87.35 204

pmmvs4		79.32 138	77.78 149	81.11 123	80.18 162	88.96 167	83.39 139	76.07 127	81.27 127	69.35 105	58.66 147	51.19 169	82.01 122	87.16 136	84.39 185	95.66 177	92.82 179

NR-MVSNet		77.21 150	76.41 157	78.14 146	80.18 162	89.26 161	83.38 140	79.06 105	76.52 143	56.59 149	54.89 157	45.32 212	72.89 181	85.39 153	86.12 146	96.71 128	97.36 102

pm-mvs1		75.61 167	74.19 175	77.26 155	80.16 164	88.79 169	81.49 181	75.49 138	59.49 216	58.09 140	48.32 200	55.53 161	72.35 182	88.61 125	85.48 157	95.99 175	93.12 175

tmp_tt				57.89 225	79.94 165	59.29 237	52.84 232	36.65 240	94.77 44	68.22 108	72.96 94	65.62 132	33.65 233	66.20 229	58.02 231	76.06 234

UniMVSNet (Re)		78.00 147	77.52 150	78.57 140	79.66 166	90.36 140	82.09 177	77.86 114	76.38 145	60.26 129	54.63 159	52.07 165	75.31 176	84.97 157	86.10 147	96.22 169	98.11 78

UniMVSNet_NR-MVSNet		78.89 143	78.04 148	79.88 129	79.40 167	89.70 147	82.92 158	80.17 93	76.37 146	58.56 137	57.10 151	54.92 162	81.44 125	83.51 168	87.12 139	96.76 124	97.60 92

pmmvs5		75.46 172	75.12 161	75.87 181	79.39 168	89.44 155	78.12 194	72.27 156	65.98 197	51.54 188	55.83 155	46.23 205	76.80 162	88.77 124	85.73 150	97.07 115	93.84 159

v18		75.49 171	74.04 176	77.18 158	79.31 169	82.47 206	83.66 138	68.68 182	71.77 158	57.43 143	50.71 171	51.01 170	77.31 152	83.35 172	85.03 168	96.70 130	93.91 158

v16		75.32 173	73.90 178	76.98 163	79.23 170	82.37 209	83.27 142	68.48 183	71.54 159	57.06 144	50.43 176	50.93 172	77.18 155	83.30 178	84.92 175	96.70 130	93.79 161

v17		75.24 175	73.83 179	76.89 165	79.15 171	82.38 208	83.16 149	68.48 183	70.93 162	56.69 148	50.53 172	50.98 171	77.13 156	83.29 179	84.93 174	96.71 128	93.77 163

v8		75.89 164	74.74 168	77.23 156	79.09 172	88.00 178	83.19 147	71.08 166	70.03 168	56.29 150	50.50 173	50.88 174	77.06 157	83.32 175	84.99 169	96.68 132	95.49 139

v1neww		76.39 156	75.09 163	77.91 149	79.08 173	89.49 152	83.21 145	75.62 134	70.20 165	55.81 160	50.43 176	50.74 176	77.05 158	83.33 173	84.99 169	96.66 133	96.48 126

v7new		76.39 156	75.09 163	77.91 149	79.08 173	89.49 152	83.21 145	75.62 134	70.20 165	55.81 160	50.43 176	50.74 176	77.05 158	83.33 173	84.99 169	96.66 133	96.48 126

v6		76.41 155	75.11 162	77.93 148	79.08 173	89.48 154	83.25 143	75.62 134	70.21 164	55.94 159	50.48 175	50.81 175	77.01 160	83.32 175	84.97 172	96.66 133	96.50 123

gm-plane-assit		71.33 204	75.18 160	66.83 213	79.06 176	75.57 225	48.05 233	60.33 210	48.28 228	34.67 230	44.34 215	67.70 123	79.78 134	97.25 9	96.21 13	99.10 9	96.92 107

v148		74.98 177	73.52 183	76.69 166	78.84 177	89.02 165	78.78 190	76.82 119	67.22 191	59.61 131	49.18 196	47.94 201	70.57 190	80.76 200	83.99 187	95.52 179	96.52 119

v1141		76.03 161	74.64 171	77.66 151	78.78 178	89.32 160	83.14 152	76.22 124	68.27 179	54.56 172	50.06 183	49.84 186	76.78 163	83.40 169	85.07 164	96.50 155	96.51 120

divwei89l23v2f112		76.03 161	74.64 171	77.65 153	78.78 178	89.33 157	83.15 150	76.21 126	68.26 180	54.55 173	50.08 182	49.86 184	76.73 164	83.39 170	85.06 166	96.51 154	96.51 120

v1		76.04 160	74.65 170	77.66 151	78.77 180	89.33 157	83.18 148	76.22 124	68.17 181	54.58 171	50.10 181	49.99 181	76.70 165	83.38 171	85.05 167	96.50 155	96.51 120

v7		76.00 163	75.01 166	77.15 159	78.73 181	88.87 168	83.15 150	72.40 154	69.20 175	53.57 180	49.73 187	49.23 190	78.49 140	86.15 147	85.17 163	96.53 152	96.73 113

v10		75.57 168	74.67 169	76.62 168	78.73 181	87.46 188	83.14 152	69.41 179	69.27 174	53.44 181	49.73 187	49.21 191	78.44 142	86.17 146	85.18 162	96.53 152	95.65 136

v15		74.54 182	73.06 190	76.26 169	78.70 183	82.14 210	82.89 160	68.05 187	68.07 183	54.77 166	49.76 186	49.88 183	76.56 166	83.19 182	84.76 178	96.59 145	93.60 167

V42		76.21 159	75.04 165	77.58 154	78.68 184	89.33 157	82.93 157	74.64 139	69.84 170	56.13 155	50.42 179	50.93 172	76.30 170	83.32 175	84.89 177	96.83 121	96.54 117

DU-MVS		77.98 148	76.71 156	79.46 132	78.68 184	89.26 161	82.92 158	79.06 105	76.52 143	58.56 137	54.89 157	48.35 199	81.44 125	83.16 183	87.21 138	96.08 174	97.60 92

Baseline_NR-MVSNet		76.71 154	74.56 173	79.23 134	78.68 184	84.15 203	82.45 167	78.87 107	75.83 147	60.05 130	47.92 203	50.18 180	79.06 137	83.16 183	83.86 189	96.26 166	96.80 111

V14		74.48 183	73.00 192	76.20 170	78.65 187	82.09 211	82.79 164	67.88 190	68.04 184	54.75 167	49.68 190	49.92 182	76.51 167	83.12 185	84.67 180	96.63 140	93.44 169

V9		74.37 184	72.87 193	76.11 173	78.58 188	82.02 212	82.68 165	67.75 192	67.80 186	54.63 169	49.50 192	49.86 184	76.40 168	83.05 186	84.59 181	96.63 140	93.30 172

our_test_3					78.55 189	84.98 201	70.12 212

FMVSNet1		80.18 135	78.07 147	82.65 110	78.55 189	87.57 186	88.41 101	73.93 145	70.16 167	73.57 87	49.80 185	64.45 138	85.35 102	90.54 106	90.72 79	96.10 173	93.21 174

v11		74.62 180	73.41 185	76.03 175	78.54 191	81.97 213	82.34 169	67.33 196	68.08 182	53.39 182	49.73 187	48.87 193	78.01 148	86.66 140	84.97 172	96.56 150	93.58 168

v12		74.29 185	72.82 194	76.02 176	78.52 192	81.96 214	82.27 171	67.65 193	67.88 185	54.63 169	49.40 193	49.74 188	76.40 168	82.99 187	84.52 182	96.64 138	93.23 173

v2v482		76.25 158	74.78 167	77.96 147	78.50 193	89.14 164	83.05 154	76.02 128	68.78 178	54.11 175	51.36 168	48.59 196	79.49 135	83.53 167	85.60 156	96.59 145	96.49 125

v13		74.20 186	72.72 196	75.92 179	78.49 194	81.90 215	82.28 170	67.55 194	67.64 188	54.29 174	49.25 195	49.75 187	76.30 170	82.92 189	84.47 183	96.63 140	93.08 176

TranMVSNet+NR-MVSNet		77.02 152	75.76 159	78.49 141	78.46 195	88.24 175	83.03 155	79.97 94	73.49 151	54.73 168	54.00 162	48.74 194	78.15 145	82.36 192	86.90 141	96.59 145	96.55 116

SixPastTwentyTwo		72.65 193	73.22 186	71.98 203	78.40 196	87.64 184	70.09 213	70.37 172	66.49 194	47.60 206	65.09 125	45.94 207	73.09 180	78.94 206	78.66 215	92.33 214	89.82 196

v1144		75.54 169	74.55 174	76.69 166	78.33 197	88.77 170	82.89 160	72.76 151	67.18 192	51.73 187	49.34 194	48.37 197	78.10 146	86.22 145	85.24 160	96.35 163	96.74 112

WR-MVS		72.93 190	73.57 181	72.19 200	78.14 198	87.71 181	76.21 199	73.02 149	67.78 187	50.09 197	50.35 180	50.53 178	61.27 214	80.42 203	83.10 197	94.43 198	95.11 142

WR-MVS_H		72.69 192	72.80 195	72.56 197	77.94 199	87.83 180	75.26 205	71.53 163	64.75 203	52.19 186	49.83 184	48.62 195	61.96 213	81.12 198	82.44 200	96.50 155	95.00 144

v1192		74.96 178	73.92 177	76.17 171	77.76 200	88.19 177	82.54 166	71.94 159	66.84 193	50.07 198	48.10 201	46.14 206	78.28 143	86.30 143	85.23 161	96.41 162	96.67 114

v144192		74.76 179	73.64 180	76.06 174	77.58 201	88.23 176	81.87 178	71.63 161	66.03 196	51.08 191	48.63 199	46.77 204	77.59 149	84.53 161	84.76 178	96.64 138	96.54 117

CP-MVSNet		73.19 189	72.37 197	74.15 186	77.54 202	86.77 193	76.34 197	72.05 157	65.66 199	51.47 189	50.49 174	43.66 219	70.90 184	80.93 199	83.40 193	96.59 145	95.66 135

v1921920		74.60 181	73.56 182	75.81 182	77.43 203	87.94 179	82.18 175	71.33 165	66.48 195	49.23 202	47.84 204	45.56 210	78.03 147	85.70 151	84.92 175	96.65 136	96.50 123

PS-CasMVS		72.37 195	71.47 202	73.43 191	77.32 204	86.43 194	75.99 200	71.94 159	63.37 206	49.24 201	49.07 197	42.42 225	69.60 192	80.59 202	83.18 196	96.48 159	95.23 141

v1240		74.04 187	73.04 191	75.20 184	77.19 205	87.69 182	80.93 183	70.72 170	65.08 202	48.47 203	47.31 205	44.71 214	77.33 151	85.50 152	85.07 164	96.59 145	95.94 131

PEN-MVS		72.24 197	71.30 203	73.33 193	77.08 206	85.57 197	76.75 195	72.52 153	63.89 205	48.12 204	50.79 169	43.09 222	69.03 196	78.54 208	83.46 192	96.50 155	93.76 164

anonymousdsp		75.14 176	77.25 152	72.69 195	76.68 207	89.26 161	75.26 205	68.44 185	65.53 200	46.65 210	58.16 149	56.67 158	73.96 179	87.84 131	86.05 148	95.13 189	97.22 104

DTE-MVSNet		71.19 205	70.45 207	72.06 202	76.61 208	84.59 202	75.61 204	72.32 155	63.12 208	45.70 213	50.72 170	43.02 223	65.89 202	77.53 217	82.23 202	96.26 166	91.93 183

pmmvs6		70.29 208	67.90 215	73.07 194	76.17 209	85.31 199	76.29 198	70.75 169	47.39 230	55.33 163	37.15 227	50.49 179	69.55 193	82.96 188	80.85 206	90.34 221	91.18 186

N_pmnet		68.54 211	67.83 216	69.38 210	75.77 210	81.90 215	66.21 219	72.53 152	65.91 198	46.09 212	44.67 213	45.48 211	63.82 208	74.66 220	77.39 218	91.87 217	84.77 210

v748		70.94 207	70.25 208	71.75 205	75.58 211	86.28 195	72.12 208	70.25 175	60.25 214	54.08 176	46.18 208	44.41 218	64.61 205	77.92 215	82.49 199	93.87 201	94.19 151

test20.03		65.17 217	67.41 218	62.55 218	75.35 212	79.31 221	62.22 221	68.83 180	56.50 222	35.35 229	51.97 167	44.70 216	40.01 229	80.69 201	79.25 213	93.55 206	79.47 222

v7n		72.11 198	71.66 200	72.63 196	75.26 213	86.85 189	76.74 196	68.77 181	62.70 209	49.40 199	45.92 210	43.51 220	70.63 189	84.16 163	83.21 195	94.99 190	95.25 140

MDTV_nov1_ep13_2view		71.65 203	73.08 189	69.97 209	75.22 214	86.81 191	73.98 207	59.61 217	69.75 171	48.01 205	54.21 161	53.06 163	69.19 194	78.50 211	80.43 210	93.84 203	88.79 198

Anonymous20231206		68.09 213	68.68 212	67.39 212	75.16 215	82.55 205	69.33 215	70.06 176	63.34 207	42.28 218	37.91 225	43.12 221	52.67 220	83.56 166	82.71 198	94.84 192	87.59 202

FPMVS		56.54 224	52.82 229	60.87 221	74.90 216	67.58 232	67.69 218	65.38 202	57.86 218	41.51 219	37.83 226	34.19 230	41.21 228	55.88 232	53.09 234	74.55 235	63.31 231

EU-MVSNet		68.07 214	70.25 208	65.52 214	74.68 217	81.30 218	68.53 216	70.31 174	62.40 213	37.43 225	54.62 160	48.36 198	51.34 224	78.32 212	79.27 212	90.84 219	87.47 203

V4		71.67 201	71.15 205	72.27 198	73.91 218	86.82 190	75.73 202	68.04 188	62.49 212	50.47 194	46.20 207	47.74 203	70.70 186	78.54 208	81.76 204	94.76 194	94.52 149

v52		71.67 201	71.16 204	72.26 199	73.90 219	86.80 192	75.72 203	68.04 188	62.53 211	50.43 195	46.15 209	47.83 202	70.73 185	78.53 210	81.76 204	94.75 195	94.53 148

FMVSNet5		80.56 133	82.53 129	78.26 144	73.80 220	81.52 217	82.26 174	68.36 186	88.85 78	64.21 123	69.09 112	84.38 70	83.49 115	87.13 137	86.76 142	97.44 101	79.95 220

MIMVSNet		75.71 166	77.26 151	73.90 189	70.93 221	88.71 171	79.98 186	57.67 223	73.58 150	58.08 141	53.93 163	58.56 155	79.41 136	90.04 117	89.97 93	97.34 107	86.04 206

PM-MVS		70.17 209	69.42 211	71.04 206	70.82 222	81.26 219	71.25 211	67.80 191	69.16 176	51.04 192	53.15 165	34.93 229	72.19 183	80.30 204	76.95 219	93.16 210	90.21 193

test2356		66.34 215	69.50 210	62.65 217	70.77 223	74.02 227	61.29 223	64.23 205	67.61 189	33.88 233	56.51 152	44.92 213	53.09 219	80.01 205	82.24 201	92.66 213	81.22 217

pmmvs-eth3d		69.59 210	67.57 217	71.95 204	70.04 224	80.05 220	71.48 209	70.00 177	62.57 210	55.99 158	44.92 212	35.73 228	70.64 188	81.56 197	79.69 211	93.55 206	88.43 199

testus		64.41 218	66.39 219	62.10 219	70.01 225	72.88 228	59.74 228	64.99 203	65.18 201	33.49 234	57.35 150	30.48 235	51.71 223	78.09 214	80.75 207	92.69 212	79.97 219

new-patchmatchnet		60.74 221	59.78 224	61.87 220	69.52 226	76.67 224	57.99 230	65.78 201	52.63 225	38.47 222	38.08 224	32.92 232	48.88 226	68.50 226	69.87 228	90.56 220	79.75 221

testmv		53.23 226	53.37 227	53.06 227	64.78 227	63.76 235	42.27 236	60.18 211	38.40 234	24.60 237	33.04 228	23.85 237	39.28 230	68.05 227	72.53 225	87.23 226	73.98 226

test1235678		53.22 227	53.36 228	53.05 228	64.78 227	63.75 236	42.27 236	60.17 212	38.36 235	24.60 237	33.03 229	23.84 238	39.28 230	68.04 228	72.52 226	87.23 226	73.96 227

new_pmnet		61.60 220	62.68 220	60.35 222	63.02 229	74.93 226	60.97 225	58.86 219	64.21 204	35.38 228	39.51 220	39.89 226	57.37 217	72.78 223	72.56 224	86.49 230	74.85 225

pmmvs3		60.52 222	60.87 223	60.12 223	61.38 230	71.62 229	57.42 231	53.94 229	48.09 229	35.95 227	38.62 222	32.19 234	64.12 207	75.33 219	77.99 217	87.89 225	82.28 215

PMVS	42.57 18	45.71 229	42.61 232	49.32 229	61.35 231	37.82 242	36.96 240	60.10 214	37.20 236	41.50 220	28.53 235	33.11 231	28.82 236	53.45 234	48.70 236	67.22 238	59.42 233
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1111		54.82 225	55.44 226	54.10 226	61.33 232	64.37 233	42.52 234	46.65 236	42.29 232	34.21 231	29.57 232	45.65 208	51.95 221	71.47 224	74.60 222	87.95 224	60.10 232

.test1245		40.04 232	40.41 234	39.60 233	61.33 232	64.37 233	42.52 234	46.65 236	42.29 232	34.21 231	29.57 232	45.65 208	51.95 221	71.47 224	5.65 239	0.92 243	23.86 240

test12356		48.96 228	49.54 230	48.28 230	59.74 234	57.59 238	42.10 238	58.32 222	36.65 237	23.11 239	31.44 230	19.22 239	23.46 237	61.17 231	71.98 227	82.97 231	68.75 228

MDA-MVSNet-bldmvs		62.23 219	61.13 222	63.52 216	58.94 235	82.44 207	60.71 226	73.28 148	57.22 220	38.42 223	49.63 191	27.64 236	62.83 211	54.98 233	74.16 223	86.96 228	81.83 216

ambc			57.08 225		58.68 236	67.71 231	60.07 227		57.13 221	42.79 217	30.00 231	11.64 242	50.18 225	78.89 207	69.14 229	82.64 232	85.02 208

Gipuma		43.95 230	42.62 231	45.50 231	50.79 237	41.20 241	35.55 241	52.51 232	52.95 224	29.09 235	12.92 238	11.48 243	38.15 232	62.01 230	66.62 230	66.89 239	51.17 235
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
no-one		36.24 233	35.28 235	37.36 234	49.42 238	52.08 240	23.67 242	54.16 228	20.93 241	12.98 241	13.94 237	12.99 241	16.68 240	34.98 238	55.52 233	67.24 237	56.51 234

MIMVSNet1		60.51 223	61.43 221	59.44 224	48.75 239	77.21 223	60.98 224	66.84 197	52.09 226	38.74 221	29.29 234	39.40 227	48.08 227	77.60 216	78.87 214	93.22 209	75.56 224

PMMVS2		41.25 231	42.55 233	39.74 232	43.25 240	55.05 239	38.15 239	47.11 235	31.78 238	11.83 242	21.16 236	19.12 240	20.98 239	49.95 236	56.09 232	77.09 233	64.68 230

EMVS		26.96 236	22.96 238	31.63 237	41.91 241	25.73 244	16.30 245	49.10 234	22.38 239	9.03 244	11.22 243	8.12 244	29.93 234	20.16 240	31.04 238	43.49 241	42.04 238

E-PMN		27.87 234	24.36 237	31.97 236	41.27 242	25.56 245	16.62 244	49.16 233	22.00 240	9.90 243	11.75 240	7.86 245	29.57 235	22.22 239	34.70 237	45.27 240	46.41 237

MVE	32.98 19	27.61 235	29.89 236	24.94 238	21.97 243	37.22 243	15.56 246	38.83 239	17.49 242	14.72 240	11.64 242	5.62 246	21.26 238	35.20 237	50.95 235	37.29 242	51.13 236
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GG-mvs-BLEND		65.67 216	93.78 34	32.89 235	0.47 244	99.35 5	96.92 27	0.22 243	93.28 53	0.51 245	84.07 49	92.50 34	0.62 243	93.59 56	93.86 51	98.59 34	99.79 6

testmvs		5.16 237	8.14 239	1.69 239	0.36 245	1.65 246	3.02 247	0.66 241	7.17 243	0.50 246	12.58 239	0.69 247	4.67 241	5.42 241	5.65 239	0.92 243	23.86 240

test123		4.39 238	7.11 240	1.21 240	0.11 246	1.16 247	1.67 248	0.35 242	5.91 244	0.16 247	11.65 241	0.16 248	4.45 242	1.72 242	4.92 241	0.51 245	24.28 239

sosnet-low-res		0.00 239	0.00 241	0.00 241	0.00 247	0.00 248	0.00 249	0.00 244	0.00 245	0.00 248	0.00 244	0.00 249	0.00 244	0.00 243	0.00 242	0.00 246	0.00 242

sosnet		0.00 239	0.00 241	0.00 241	0.00 247	0.00 248	0.00 249	0.00 244	0.00 245	0.00 248	0.00 244	0.00 249	0.00 244	0.00 243	0.00 242	0.00 246	0.00 242

MTAPA										93.37 3		95.71 21

MTMP										93.84 2		94.86 25

Patchmatch-RL test							19.65 243

NP-MVS									94.12 47

Patchmtry						92.08 132	83.86 135	58.37 220		56.28 151

DeepMVS_CX						70.68 230	59.61 229	67.36 195	72.12 154	38.41 224	53.88 164	32.44 233	55.15 218	50.88 235		74.35 236	68.42 229

High-resolution multi-view benchmark