Point Set Self-Embedding

TL;DR

This paper introduces a novel self-embedding framework for point sets that encodes structural information into sparser versions, enabling efficient visualization and accurate restoration of the original data for detailed analysis.

Contribution

We propose a learnable, dual-network framework with novel shuffle units for self-embedding and restoring point sets, advancing the state-of-the-art in point cloud processing.

Findings

Effective on synthetic datasets

Accurate restoration of original point sets

Efficient visualization on mobile devices

Abstract

This work presents an innovative method for point set self-embedding, that encodes the structural information of a dense point set into its sparser version in a visual but imperceptible form. The self-embedded point set can function as the ordinary downsampled one and be visualized efficiently on mobile devices. Particularly, we can leverage the self-embedded information to fully restore the original point set for detailed analysis on remote servers. This task is challenging since both the self-embedded point set and the restored point set should resemble the original one. To achieve a learnable self-embedding scheme, we design a novel framework with two jointly-trained networks: one to encode the input point set into its self-embedded sparse point set and the other to leverage the embedded information for inverting the original point set back. Further, we develop a pair of up-shuffle and down-shuffle units in the two networks, and formulate loss terms to encourage the shape similarity and point distribution in the results. Extensive qualitative and quantitative results demonstrate the effectiveness of our method on both synthetic and real-scanned datasets.