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

• URL: http://arxiv.org/abs/2204.10750v1
• Date: Fri, 22 Apr 2022 15:14:05 GMT
• Title: PU-EVA: An Edge Vector based Approximation Solution for Flexible-scale Point Cloud Upsampling
• Authors: Luqing Luo, Lulu Tang, Wanyi Zhou, Shizheng Wang, Zhi-Xin Yang
• Abstract summary: Upsampling sparse, noisy and nonuniform point clouds is a challenging task. A novel design of Edge Vector based Approximation for Flexible-scale Point clouds Upsampling (PU-EVA) is proposed. The EVA upsampling decouples the upsampling scales with network architecture, achieving the flexible upsampling rates in one-time training.
• Score: 4.418205951027186
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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