APU-SMOG upsamples a sparse input point cloud with any desired scaling factor.
Abstract
Generating dense point clouds from sparse raw data
benefits downstream 3D understanding tasks, but existing models are limited to a fixed upsampling ratio or
to a short range of integer values. In this paper, we
present APU-SMOG, a Transformer-based model for Arbitrary Point cloud Upsampling (APU). The sparse input is
firstly mapped to a Spherical Mixture of Gaussians (SMOG)
distribution, from which an arbitrary number of points can
be sampled. Then, these samples are fed as queries to the
Transformer decoder, which maps them back to the target
surface. Extensive qualitative and quantitative evaluations
show that APU-SMOG outperforms state-of-the-art fixed-ratio methods, while effectively enabling upsampling with
any scaling factor, including non-integer values, with a single trained model.
Video
BibTeX
@article{delleva2022arbitrary,
author = {Dell'Eva, Anthony and Orsingher, Marco and Bertozzi, Massimo},
title = {Arbitrary Point Cloud Upsampling with Spherical Mixture of Gaussians},
journal = {3DV},
year = {2022},
}