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Training Deep Neural Networks to Detect Repeatable 2D Features Using
  Large Amounts of 3D World Capture Data

Training Deep Neural Networks to Detect Repeatable 2D Features Using Large Amounts of 3D World Capture Data

9 December 2019
Alexander Mai
Joseph Menke
An Yang
    3DV3DPC
ArXiv (abs)PDFHTML

Papers citing "Training Deep Neural Networks to Detect Repeatable 2D Features Using Large Amounts of 3D World Capture Data"

5 / 5 papers shown
Title
Matterport3D: Learning from RGB-D Data in Indoor Environments
Matterport3D: Learning from RGB-D Data in Indoor Environments
Angel X. Chang
Angela Dai
Thomas Funkhouser
Maciej Halber
Matthias Nießner
Manolis Savva
Shuran Song
Andy Zeng
Yinda Zhang
3DV3DPC
208
1,917
0
18 Sep 2017
HPatches: A benchmark and evaluation of handcrafted and learned local
  descriptors
HPatches: A benchmark and evaluation of handcrafted and learned local descriptors
Vassileios Balntas
Karel Lenc
Andrea Vedaldi
K. Mikolajczyk
106
722
0
19 Apr 2017
ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
Angela Dai
Angel X. Chang
Manolis Savva
Maciej Halber
Thomas Funkhouser
Matthias Nießner
3DPC3DV
502
4,084
0
14 Feb 2017
Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT
Philipp Fischer
Alexey Dosovitskiy
Thomas Brox
87
275
0
22 May 2014
Faster and better: a machine learning approach to corner detection
Faster and better: a machine learning approach to corner detection
E. Rosten
R. Porter
Tom Drummond
93
1,920
0
14 Oct 2008
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