任意の動体検出：LiDAR点群列からクラス非依存なシーン動態を学習
Any Motion Detector: Learning Class-agnostic Scene Dynamics from a Sequence of LiDAR Point Clouds
LiDAR点群の時系列から自己運動補償を用いてリアルタイムに動体検出と運動パラメータ推定を行う手法を提案し、未知カテゴリにも汎化することを示した。
著者: Artem Filatov, Andrey Rykov, Viacheslav Murashkin
分類: cs.CV, cs.RO
原文アブストラクト
Object detection and motion parameters estimation are crucial tasks for self-driving vehicle safe navigation in a complex urban environment. In this work we propose a novel real-time approach of temporal context aggregation for motion detection and motion parameters estimation based on 3D point cloud sequence. We introduce an ego-motion compensation layer to achieve real-time inference with performance comparable to a naive odometric transform of the original point cloud sequence. Not only is the proposed architecture capable of estimating the motion of common road participants like vehicles or pedestrians but also generalizes to other object categories which are not present in training data. We also conduct an in-deep analysis of different temporal context aggregation strategies such as recurrent cells and 3D convolutions. Finally, we provide comparison results of our state-of-the-art model with existing solutions on KITTI Scene Flow dataset.