対話的シナリオにおける逐次点群予測のサーベイ
Sequential Point Cloud Prediction in Interactive Scenarios: A Survey
自動運転向けに点群系列を予測する研究を整理したサーベイ。時空間特徴抽出やシーンフロー推定・位置予測の手法と課題を概観する。
著者: Haowen Wang, Zirui Li, Jianwei Gong
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Point cloud has been widely used in the field of autonomous driving since it can provide a more comprehensive three-dimensional representation of the environment than 2D images. Point-wise prediction based on point cloud sequence (PCS) is an essential part of environment understanding, which can assist in the decision-making and motion-planning of autonomous vehicles. However, PCS prediction has not been deeply researched in the literature. This paper proposes a brief review of the sequential point cloud prediction methods, focusing on interactive scenarios. Firstly, we define the PCS prediction problem and introduce commonly-used frameworks. Secondly, by reviewing non-predictive problems, we analyze and summarize the spatio-temporal feature extraction methods based on PCS. On this basis, we review two types of PCS prediction tasks, scene flow estimation (SFE) and point cloud location prediction (PCLP), highlighting their connections and differences. Finally, we discuss some opening issues and point out some potential research directions.
