pointnet2 - PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

  •        178

Created by Charles R. Qi, Li (Eric) Yi, Hao Su, Leonidas J. Guibas from Stanford University. This work is based on our NIPS'17 paper. You can find arXiv version of the paper here or check project webpage for a quick overview. PointNet++ is a follow-up project that builds on and extends PointNet. It is version 2.0 of the PointNet architecture.




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Created by Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas from Stanford University. This work is based on our arXiv tech report, which is going to appear in CVPR 2017. We proposed a novel deep net architecture for point clouds (as unordered point sets). You can also check our project webpage for a deeper introduction.

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Created by Charles R. Qi, Wei Liu, Chenxia Wu, Hao Su and Leonidas J. Guibas from Stanford University and Nuro Inc. This repository is code release for our CVPR 2018 paper (arXiv report here). In this work, we study 3D object detection from RGB-D data. We propose a novel detection pipeline that combines both mature 2D object detectors and the state-of-the-art 3D deep learning techniques. In our pipeline, we firstly build object proposals with a 2D detector running on RGB images, where each 2D bounding box defines a 3D frustum region. Then based on 3D point clouds in those frustum regions, we achieve 3D instance segmentation and amodal 3D bounding box estimation, using PointNet/PointNet++ networks (see references at bottom).

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3D-Machine-Learning - A resource repository for 3D machine learning


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