Robust Point Cloud Processing through Positional Embedding

• URL: http://arxiv.org/abs/2309.00339v1
• Date: Fri, 1 Sep 2023 08:47:52 GMT
• Title: Robust Point Cloud Processing through Positional Embedding
• Authors: Jianqiao Zheng, Xueqian Li, Sameera Ramasinghe, Simon Lucey
• Abstract summary: Methods like PointNet, or the more recent point cloud transformer -- and its variants -- all employ learned per-point embeddings. We explore the role of an analytical per-point embedding based on the criterion of bandwidth. We present compelling results across downstream tasks such as point cloud classification and registration with several categories of OOD noise.
• Score: 46.561004400860945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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