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arXiv · 1809.04820

Canonical and Compact Point Cloud Representation for Shape Classification

Kent Fujiwara·Ikuro Sato·Mitsuru Ambai·Yuichi Yoshida·Yoshiaki Sakakura
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Abstract

We present a novel compact point cloud representation that is inherently invariant to scale, coordinate change and point permutation. The key idea is to parametrize a distance field around an individual shape into a unique, canonical, and compact vector in an unsupervised manner. We firstly project a distance field to a $4$D canonical space using singular value decomposition. We then train a neural network for each instance to non-linearly embed its distance field into network parameters. We employ a bias-free Extreme Learning Machine (ELM) with ReLU activation units, which has scale-factor commutative property between layers. We demonstrate the descriptiveness of the instance-wise, shape-embedded network parameters by using them to classify shapes in $3$D datasets. Our learning-based representation requires minimal augmentation and simple neural networks, where previous approaches demand numerous representations to handle coordinate change and point permutation.

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Kent Fujiwara, Ikuro Sato, Mitsuru Ambai, Yuichi Yoshida, Yoshiaki Sakakura. 2018-09-13. Canonical and Compact Point Cloud Representation for Shape Classification. https://arxiv.org/abs/1809.04820

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