3D点群のための深層学習：サーベイ
Deep Learning for 3D Point Clouds: A Survey
3D点群に対する深層学習手法の近年の進展を、形状分類・物体検出/追跡・セグメンテーションの3タスクにわたって包括的にレビューし、公開データセットでの比較結果と今後の研究方向を示したサーベイ論文。
著者: Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, Mohammed Bennamoun
分類: cs.CV, cs.LG, cs.RO, eess.IV
原文アブストラクト
Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks. Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area. To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions.
