End-to-end trained per-point embeddings are an essential ingredient of any state-of-the-art 3D point cloud processing such as detection or alignment. Methods like PointNet, or the more recent point cloud transformer -- and its variants -- all employ learned per-point embeddings. Despite impressive performance, such approaches are sensitive to out-of-distribution (OOD) noise and outliers. In this paper, we explore the role of an analytical per-point embedding based on the criterion of bandwidth. The concept of bandwidth enables us to draw connections with an alternate per-point embedding -- positional embedding, particularly random Fourier features. We present compelling robust results across downstream tasks such as point cloud classification and registration with several categories of OOD noise.
@article{arxiv.2309.00339,
title = {Robust Point Cloud Processing through Positional Embedding},
author = {Jianqiao Zheng and Xueqian Li and Sameera Ramasinghe and Simon Lucey},
journal= {arXiv preprint arXiv:2309.00339},
year = {2023}
}