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arXiv · 2204.10750

PU-EVA: An Edge Vector based Approximation Solution for Flexible-scale Point Cloud Upsampling

Luqing Luo·Lulu Tang·Wanyi Zhou·Shizheng Wang·Zhi-Xin Yang
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Abstract

High-quality point clouds have practical significance for point-based rendering, semantic understanding, and surface reconstruction. Upsampling sparse, noisy and nonuniform point clouds for a denser and more regular approximation of target objects is a desirable but challenging task. Most existing methods duplicate point features for upsampling, constraining the upsampling scales at a fixed rate. In this work, the flexible upsampling rates are achieved via edge vector based affine combinations, and a novel design of Edge Vector based Approximation for Flexible-scale Point clouds Upsampling (PU-EVA) is proposed. The edge vector based approximation encodes the neighboring connectivity via affine combinations based on edge vectors, and restricts the approximation error within the second-order term of Taylor's Expansion. The EVA upsampling decouples the upsampling scales with network architecture, achieving the flexible upsampling rates in one-time training. Qualitative and quantitative evaluations demonstrate that the proposed PU-EVA outperforms the state-of-the-art in terms of proximity-to-surface, distribution uniformity, and geometric details preservation.

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Luqing Luo, Lulu Tang, Wanyi Zhou, Shizheng Wang, Zhi-Xin Yang. 2022-04-22. PU-EVA: An Edge Vector based Approximation Solution for Flexible-scale Point Cloud Upsampling. https://arxiv.org/abs/2204.10750

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