Far3D: Expanding the Horizon for Surround-View 3D Object Detection
DOI:
https://doi.org/10.1609/aaai.v38i3.28033Keywords:
CV: Vision for Robotics & Autonomous Driving, CV: Object Detection & CategorizationAbstract
Recently 3D object detection from surround-view images has made notable advancements with its low deployment cost. However, most works have primarily focused on close perception range while leaving long-range detection less explored. Expanding existing methods directly to cover long distances poses challenges such as heavy computation costs and unstable convergence. To address these limitations, this paper proposes a novel sparse query-based framework, dubbed Far3D. By utilizing high-quality 2D object priors, we generate 3D adaptive queries that complement the 3D global queries. To efficiently capture discriminative features across different views and scales for long-range objects, we introduce a perspective-aware aggregation module. Additionally, we propose a range-modulated 3D denoising approach to address query error propagation and mitigate convergence issues in long-range tasks. Significantly, Far3D demonstrates SoTA performance on the challenging Argoverse 2 dataset, covering a wide range of 150 meters, surpassing several LiDAR-based approaches. The code is available at https://github.com/megvii-research/Far3D.Downloads
Published
2024-03-24
How to Cite
Jiang, X., Li, S., Liu, Y., Wang, S., Jia, F., Wang, T., Han, L., & Zhang, X. (2024). Far3D: Expanding the Horizon for Surround-View 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 38(3), 2561-2569. https://doi.org/10.1609/aaai.v38i3.28033
Issue
Section
AAAI Technical Track on Computer Vision II