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arXiv · 2211.01009

Cluster-Based Autoencoders for Volumetric Point Clouds

Stephan Antholzer·Martin Berger·Tobias Hell
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Abstract

Autoencoders allow to reconstruct a given input from a small set of parameters. However, the input size is often limited due to computational costs. We therefore propose a clustering and reassembling method for volumetric point clouds, in order to allow high resolution data as input. We furthermore present an autoencoder based on the well-known FoldingNet for volumetric point clouds and discuss how our approach can be utilized for blending between high resolution point clouds as well as for transferring a volumetric design/style onto a pointcloud while maintaining its shape.

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Stephan Antholzer, Martin Berger, Tobias Hell. 2022-11-02. Cluster-Based Autoencoders for Volumetric Point Clouds. https://arxiv.org/abs/2211.01009

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