深度学习提升3D点云质量，解决噪声、缺失和稀疏问题
Deep Learning for 3D Point Cloud Enhancement: A Survey
- 按去噪、补全、上采样三类构建深度学习增强方法体系
- 系统评估主流方法在标准数据集上的表现，提供可复现结果
- 适合3D视觉、自动驾驶等领域研究人员参考
点云数据在三维视觉研究中广泛应用，但受限于传感器性能和环境噪声，原始数据常存在稀疏、噪声和不完整等问题，给下游处理任务带来挑战。近年来，基于深度学习的点云增强方法通过神经网络从低质量点云中恢复出密集、干净、完整的点云，受到广泛关注。本文首次系统综述了该方向的最新进展，从去噪、补全、上采样三个角度梳理方法体系，提出新的分类框架，并在标准基准上进行系统实验。同时分享对当前技术的洞察与未来研究方向，为相关领域提供参考。
原文摘要 · Abstract (English)
Point cloud data now are popular data representations in a number of three-dimensional (3D) vision research realms. However, due to the limited performance of sensors and sensing noise, the raw data usually suffer from sparsity, noise, and incompleteness. This poses great challenges to down-stream point cloud processing tasks. In recent years, deep-learning-based point cloud enhancement methods, which aim to achieve dense, clean, and complete point clouds from low-quality raw point clouds using deep neural networks, are gaining tremendous research attention. This paper, for the first time to our knowledge, presents a comprehensive survey for deep-learning-based point cloud enhancement methods. It covers three main perspectives for point cloud enhancement, i.e., (1) denoising to achieve clean data; (2) completion to recover unseen data; (3) upsampling to obtain dense data. Our survey presents a new taxonomy for recent state-of-the-art methods and systematic experimental results on standard benchmarks. In addition, we share our insightful observations, thoughts, and inspiring future research directions for point cloud enhancement with deep learning.
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