Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving 
Scenes

Wu, Yu-Huan; Zhang, Da; Zhang, Le; Zhan, Xin; Dai, Dengxin; Liu, Yun; Cheng, Ming-Ming

アイテム詳細

登録内容を編集ファイル形式で保存

一時保存へ追加

タグ情報を表示リリース履歴を表示詳細要約

公開

成果報告書

Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes

MPS-Authors

/persons/resource/persons261420

Dai, Dengxin
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

External Resource

There are no locators available

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

フルテキスト (公開)

arXiv:2208.08621.pdf
(プレプリント), 761KB

付随資料 (公開)

There is no public supplementary material available

引用

Wu, Y.-H., Zhang, D., Zhang, L., Zhan, X., Dai, D., Liu, Y., & Cheng, M.-M. (2022). Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes. Retrieved from https://arxiv.org/abs/2208.08621.

引用: https://hdl.handle.net/21.11116/0000-000C-1BA0-1

要旨

Current efficient LiDAR-based detection frameworks are lacking in exploiting
object relations, which naturally present in both spatial and temporal manners.
To this end, we introduce a simple, efficient, and effective two-stage
detector, termed as Ret3D. At the core of Ret3D is the utilization of novel
intra-frame and inter-frame relation modules to capture the spatial and
temporal relations accordingly. More Specifically, intra-frame relation module
(IntraRM) encapsulates the intra-frame objects into a sparse graph and thus
allows us to refine the object features through efficient message passing. On
the other hand, inter-frame relation module (InterRM) densely connects each
object in its corresponding tracked sequences dynamically, and leverages such
temporal information to further enhance its representations efficiently through
a lightweight transformer network. We instantiate our novel designs of IntraRM
and InterRM with general center-based or anchor-based detectors and evaluate
them on Waymo Open Dataset (WOD). With negligible extra overhead, Ret3D
achieves the state-of-the-art performance, being 5.5% and 3.2% higher than the
recent competitor in terms of the LEVEL 1 and LEVEL 2 mAPH metrics on vehicle
detection, respectively.