@inproceedings{1604ffcfa8db43569111be5364f0a502,
title = "An Empirical Analysis of Range for 3D Object Detection",
abstract = "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.",
keywords = "3D Detection, Autonomous Vehicles, LiDAR, Long Range Detection",
author = "Neehar Peri and Mengtian Li and Benjamin Wilson and Wang, {Yu Xiong} and James Hays and Deva Ramanan",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 ; Conference date: 02-10-2023 Through 06-10-2023",
year = "2023",
doi = "10.1109/ICCVW60793.2023.00440",
language = "English (US)",
series = "Proceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4076--4085",
booktitle = "Proceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023",
address = "United States",
}