  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.
Meanwhile, Far3D exhibits superior performance compared to previous methods on
the nuScenes dataset. The code is available at
https://github.com/megvii-research/Far3D.
