论文标题
3D点云理解的深度学习：调查
Deep Learning for 3D Point Cloud Understanding: A Survey
论文作者
论文摘要
实际应用的开发，例如自动驾驶和机器人技术，引起了人们对3D点云理解的越来越多的关注。尽管深度学习在基于图像的任务上取得了巨大的成功，但深度神经网络在处理大量，非结构化和嘈杂的3D点方面面临许多独特的挑战。为了证明3D点云理解的深度学习的最新进展，本文总结了该领域的最新出色研究贡献（分类，细分，检测，跟踪，跟踪，流程估计，注册，增强和完成），以及常用的数据集，度量级和最新的表演。有关此调查的更多信息，请访问：https：//github.com/shi-labs/3d-point-cloud-learning。
The development of practical applications, such as autonomous driving and robotics, has brought increasing attention to 3D point cloud understanding. While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unstructured and noisy 3D points. To demonstrate the latest progress of deep learning for 3D point cloud understanding, this paper summarizes recent remarkable research contributions in this area from several different directions (classification, segmentation, detection, tracking, flow estimation, registration, augmentation and completion), together with commonly used datasets, metrics and state-of-the-art performances. More information regarding this survey can be found at: https://github.com/SHI-Labs/3D-Point-Cloud-Learning.
