Generalized Convolutional Neural Networks for Point Cloud Data

Aleksandr Savchenkov; Andrew Davis; Xuan Zhao

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arXiv · 1707.06719

Generalized Convolutional Neural Networks for Point Cloud Data

Aleksandr Savchenkov·Andrew Davis·Xuan Zhao

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Abstract

The introduction of cheap RGB-D cameras, stereo cameras, and LIDAR devices has given the computer vision community 3D information that conventional RGB cameras cannot provide. This data is often stored as a point cloud. In this paper, we present a novel method to apply the concept of convolutional neural networks to this type of data. By creating a mapping of nearest neighbors in a dataset, and individually applying weights to spatial relationships between points, we achieve an architecture that works directly with point clouds, but closely resembles a convolutional neural net in both design and behavior. Such a method bypasses the need for extensive feature engineering, while proving to be computationally efficient and requiring few parameters.

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BibTeX RIS

Aleksandr Savchenkov, Andrew Davis, Xuan Zhao. 2018-10-19. Generalized Convolutional Neural Networks for Point Cloud Data. https://arxiv.org/abs/1707.06719

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