Canonical and Compact Point Cloud Representation for Shape Classification
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...
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Zusammenfassung: | 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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DOI: | 10.48550/arxiv.1809.04820 |