An Empirical Analysis of Range for 3D Object Detection

Understand

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.