LBT: Enhancing LiDAR-Based 3D Object Detection with a Lightweight Bird’s-Eye-View Transformer

Abstract

Bird’s-eye-view (BEV) representations have become a widely adopted paradigm for light detection and ranging (LiDAR)-based 3D object detection due to their efficiency and structured spatial layout. However, most existing BEV-based detectors rely on convolutional neural networks (CNNs) for BEV feature extraction and fusion, which primarily capture local spatial contexts and may be limited in exploiting global contextual information in complex scenes. To address this limitation, a method is proposed in which a lightweight BEV transformer (LBT) is integrated into the BEV feature learning process to enhance global context modeling capability. The proposed LBT follows a plug-and-play design and can be easily integrated into existing BEV-based detectors. The proposed method is implemented on the CenterPoint framework and is evaluated on a standard LiDAR-based 3D object detection benchmark. Experimental results demonstrate performance improvements over the CNN-only baseline, indicating that incorporating lightweight global context modeling in the BEV space is an effective and practical way to enhance LiDAR-based 3D object detection.

Keywords

3D object detection / light detection and ranging (LiDAR) / swin transformer