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NeuralSampler: Euclidean Point Cloud Auto-Encoder and Sampler by Remelli, Edoardo; Baque, Pierre; Fua, Pascal is a scholarly article available to read on EtoBox.

What is NeuralSampler: Euclidean Point Cloud Auto-Encoder and Sampler about?

Most algorithms that rely on deep learning-based approaches to generate 3D point sets can only produce clouds containing fixed number of points. Furthermore, they typically require large networks parameterized by many weights, which makes them hard to train. In this paper, we propose an auto-encoder architecture that can both encode and decode clouds of arbitrary size and demonstrate its effectiveness at upsampling sparse point clouds. Interestingly, we can do so using less than half as many parameters as state-of-the-art architectures while still delivering better performance. We will make our code base fully available.

Author

Remelli, Edoardo; Baque, Pierre; Fua, Pascal

Published

2019

Language

EN