PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space: Difference between revisions
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[[File:point_cloud.png | 400px|thumb|center|Examples of point clouds | [[File:point_cloud.png | 400px|thumb|center|Examples of point clouds and their associated task. Classification (left), part segmentation (centre), scene segmentation (right) ]] | ||
= Review of PointNet = | = Review of PointNet = |
Revision as of 20:08, 16 March 2018
Introduction
This paper builds off of ideas from PointNet (Qi et al., 2017). PointNet is a deep neural network architecture that takes a point cloud as an input and has the ability to perform classification or point semantic segmentation. PointNet differs from other point cloud processing networks by directly taking the unordered points from the point cloud as input, instead of re-representing the point cloud by voxelization or 2D images.
Review of PointNet
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PointNet++
Problem Statement
Method
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Sampling Layer
Grouping Layer
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PointNet Layer
Robust Feature Learning under Non-Uniform Sampling Density
Experiments
Sources
1. Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space, 2017
2. Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation, 2017