FocalFormer3D : Focusing on Hard Instance for 3D Object Detection
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 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.
64d45ae7833c0b78bc618caa
Yilun Chen
claimed_verified
2023-08-11T02:11:23.311Z
642a9380ecec03b446462e51
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chenyilun95
user
chenyilun95
64d45ae7833c0b78bc618cab
Zhiding Yu
claimed_verified
2024-08-29T07:25:27.765Z
66c8037c737ba92ae3fe0322
https://cdn-avatars.huggingface.co/v1/production/uploads/66c8037c737ba92ae3fe0322/WR_Yh5DWOVVh7IFlF24NM.jpeg
Zhiding Yu
user
Zhiding
64d45ae7833c0b78bc618cac
Yukang Chen
64d45ae7833c0b78bc618cad
Shiyi Lan
64d45ae7833c0b78bc618cae
Animashree Anandkumar
64d45ae7833c0b78bc618caf
Jiaya Jia
64d45ae7833c0b78bc618cb0
Jose Alvarez
FocalFormer3D : Focusing on Hard Instance for 3D Object Detection
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 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.
A Hard Instance Probing (HIP) pipeline and its instantiation, FocalFormer3D, improve 3D object detection recall and performance through multi-stage query generation and a box-level transformer decoder.
Hard Instance Probing FocalFormer3D 3D object detection false negatives multi-stage query generation box-level transformer decoder nuScenes Waymo mAP NDS AMOTA LiDAR multi-modal settings