PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors
- 6 October 2018
- book chapter
- Published by Springer Nature
- p. 620-638
Abstract
No abstract availableKeywords
All Related Versions
This publication has 39 references indexed in Scilit:
- Noise-Resistant Deep Learning for Object Classification in Three-Dimensional Point Clouds Using a Point Pair DescriptorIEEE Robotics and Automation Letters, 2018
- Learning Local Shape Descriptors from Part Correspondences with Multiview Convolutional NetworksACM Transactions on Graphics, 2017
- CAD Priors for Accurate and Flexible Instance Reconstruction2017
- Learning Compact Geometric Features2017
- Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models2017
- SEMANTIC3D.NET: A NEW LARGE-SCALE POINT CLOUD CLASSIFICATION BENCHMARKISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017
- Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation2016
- Unsupervised feature learning for 3D scene labeling2014
- Using spin images for efficient object recognition in cluttered 3D scenesIEEE Transactions on Pattern Analysis and Machine Intelligence, 1999
- Object recognition from local scale-invariant features1999