False negatives (FN) in 3D object detection, {\em e.g.}, missing predictions
of pedestrians, vehicles, or other obstacles, can lead to potentially dangerous
situations in autonomous driving. While being fatal, this issue is understudied
in many current 3D detection methods. In this work, we propose Hard Instance
Probing (HIP), a general pipeline that identifies \textit{FN} in a multi-stage
manner and guides the models to focus on excavating difficult instances. For 3D
object detection, we instantiate this method as FocalFormer3D, a simple yet
effective detector that excels at excavating difficult objects and improving
prediction recall. FocalFormer3D features a multi-stage query generation to
discover hard objects and a box-level transformer decoder to efficiently
distinguish objects from massive object candidates. Experimental results on the
nuScenes and Waymo datasets validate the superior performance of FocalFormer3D.
The advantage leads to strong performance on both detection and tracking, in
both LiDAR and multi-modal settings. Notably, FocalFormer3D achieves a 70.5 mAP
and 73.9 NDS on nuScenes detection benchmark, while the nuScenes tracking
benchmark shows 72.1 AMOTA, both ranking 1st place on the nuScenes LiDAR
leaderboard. Our code is available at
https://github.com/NVlabs/FocalFormer3D.

通过使用多阶段的 Hard Instance Probing（HIP）方法和 FocalFormer3D 模型，本研究探索并改进了三维物体检测中的误检问题，提高了难度较大物体的识别和检测效果。