An Empirical Analysis of Range for 3D Object Detection
Neehar Peri Mengtian Li Benjamin Wilson Yu-Xiong Wang James Hays Deva Ramanan
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.