3D物体検出における距離範囲の実証的分析
An Empirical Analysis of Range for 3D Object Detection
LiDARによる3D検出は近距離に偏っているため、Argoverse 2.0を用いて遠距離検出を分析し、近距離と遠距離で適したボクセルサイズが異なることを示した上で、距離別の専門家モデルを効率的にアンサンブルする手法を提案した。
著者: Neehar Peri, Mengtian Li, Benjamin Wilson, Yu-Xiong Wang, James Hays, Deva Ramanan
分類: cs.CV, cs.RO
原文アブストラクト
LiDAR-based 3D detection plays a vital role in autonomous navigation. Surprisingly, although autonomous vehicles (AVs) must detect both near-field objects (for collision avoidance) and far-field objects (for longer-term planning), contemporary benchmarks focus only on near-field 3D detection. However, AVs must detect far-field objects for safe navigation. In this paper, we present an empirical analysis of far-field 3D detection using the long-range detection dataset Argoverse 2.0 to better understand the problem, and share the following insight: near-field LiDAR measurements are dense and optimally encoded by small voxels, while far-field measurements are sparse and are better encoded with large voxels. We exploit this observation to build a collection of range experts tuned for near-vs-far field detection, and propose simple techniques to efficiently ensemble models for long-range detection that improve efficiency by 33% and boost accuracy by 3.2% CDS.
