Cluster-Based Autoencoders for Volumetric Point Clouds
Computer Vision and Pattern Recognition
2022-11-03 v1
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.
Cite
@article{arxiv.2211.01009,
title = {Cluster-Based Autoencoders for Volumetric Point Clouds},
author = {Stephan Antholzer and Martin Berger and Tobias Hell},
journal= {arXiv preprint arXiv:2211.01009},
year = {2022}
}