PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space: Difference between revisions
Jump to navigation
Jump to search
Line 1: | Line 1: | ||
= Introduction = | = Introduction = | ||
This paper builds off of ideas from PointNet (Qi et al., 2017). PointNet | This paper builds off of ideas from PointNet (Qi et al., 2017). | ||
PointNet and PointNet++ are deep neural network architecture that take point clouds as their input and perform the tasks such as classification or part segmentation or scene 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. | |||
[[File:Point cloud torus.gif|thumb|Point cloud torus]] | |||
Revision as of 20:13, 16 March 2018
Introduction
This paper builds off of ideas from PointNet (Qi et al., 2017).
PointNet and PointNet++ are deep neural network architecture that take point clouds as their input and perform the tasks such as classification or part segmentation or scene 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
]
PointNet++
Problem Statement
Method
]
Sampling Layer
Grouping Layer
]
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