Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

Autonomous vehicles are becoming central for the future of mobility, supported by advances
in deep learning techniques. The performance of aself-driving system is highly dependent …

Recent advances on 3D object detection heavily rely on how the 3D data are represented,
ie, voxel-based or point-based representation. Many existing high performance 3D detectors …

We present a novel and high-performance 3D object detection framework, named
PointVoxel-RCNN (PV-RCNN), for accurate 3D object detection from point clouds. Our …

Abstract We present Voxel Transformer (VoTr), a novel and effective voxel-based
Transformer backbone for 3D object detection from point clouds. Conventional 3D …

Prevalence of voxel-based 3D single-stage detectors contrast with underexplored point-
based methods. In this paper, we present a lightweight point-based 3D single stage object …

Existing single-stage detectors for locating objects in point clouds often treat object
localization and category classification as separate tasks, so the localization accuracy and …

In this paper, we propose a new deep architecture for fusing camera and LiDAR sensors for
3D object detection. Because the camera and LiDAR sensor signals have different …

We present a flexible and high-performance framework, named Pyramid R-CNN, for two-
stage 3D object detection from point clouds. Current approaches generally rely on the points …

Perception plays a pivotal role in autonomous driving systems, which utilizes onboard
sensors like cameras and LiDARs (Light Detection and Ranging) to assess surroundings …