Bird's eye view (BEV) representation has emerged as a dominant solution for describing 3D
space in autonomous driving scenarios. However objects in the BEV representation typically …

Window-based transformers excel in large-scale point cloud understanding by capturing
context-aware representations with affordable attention computation in a more localized …

Recently, detection transformers (DETRs) have gradually taken a dominant position in 2D
detection thanks to their elegant framework. However, DETR-based detectors for 3D point …

Recent temporal LiDAR-based 3D object detectors achieve promising performance based
on the two-stage proposal-based approach. They generate 3D box candidates from the first …

The Detection Transformer (DETR) has revolutionized the design of CNN-based object
detection systems, showcasing impressive performance. However, its potential in the …

Abstract 3D object detection is an indispensable component for scene understanding.
However, the annotation of large-scale 3D datasets requires significant human effort. To …

Semi-supervised 3D object detection is a common strategy employed to circumvent the
challenge of manually labeling large-scale autonomous driving perception datasets. Pseudo …

Accurate and robust LiDAR 3D object detection is essential for comprehensive scene
understanding in autonomous driving. Despite its importance, LiDAR detection performance …

Accurate and effective 3D object detection is critical for ensuring the driving safety of
autonomous vehicles. Recently, state-of-the-art two-stage 3D object detectors have …

The LiDAR-based 3D object detector that strikes a balance between accuracy and speed is
crucial for achieving real-time perception in autonomous driving and robotic navigation …