title: FocalFormer3D : Focusing on Hard Instance for 3D Object Detection
summary: 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.
paper_authors._id: 64d45ae7833c0b78bc618caa
paper_authors.name: Yilun Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-08-11T02:11:23.311Z
paper_authors._id: 642a9380ecec03b446462e51
paper_authors.avatarUrl: /avatars/5e893bf493ad66205e11aac3732b091b.svg
paper_authors.fullname: chenyilun95
paper_authors.type: user
paper_authors.user: chenyilun95
paper_authors._id: 64d45ae7833c0b78bc618cab
paper_authors.name: Zhiding Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-29T07:25:27.765Z
paper_authors._id: 66c8037c737ba92ae3fe0322
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66c8037c737ba92ae3fe0322/WR_Yh5DWOVVh7IFlF24NM.jpeg
paper_authors.fullname: Zhiding Yu
paper_authors.type: user
paper_authors.user: Zhiding
paper_authors._id: 64d45ae7833c0b78bc618cac
paper_authors.name: Yukang Chen
paper_authors._id: 64d45ae7833c0b78bc618cad
paper_authors.name: Shiyi Lan
paper_authors._id: 64d45ae7833c0b78bc618cae
paper_authors.name: Animashree Anandkumar
paper_authors._id: 64d45ae7833c0b78bc618caf
paper_authors.name: Jiaya Jia
paper_authors._id: 64d45ae7833c0b78bc618cb0
paper_authors.name: Jose Alvarez
paper_title: FocalFormer3D : Focusing on Hard Instance for 3D Object Detection
paper_summary: 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.
paper_ai_keywords: Hard Instance Probing FocalFormer3D multi-stage query generation box-level transformer decoder multi-modal settings mAP NDS AMOTA LiDAR nuScenes Waymo