With the significant advancement of sensor and communication technology and the reliable
application of obstacle detection techniques and algorithms, automated driving is becoming …

Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

Multi-sensor fusion is essential for an accurate and reliable autonomous driving system.
Recent approaches are based on point-level fusion: augmenting the LiDAR point cloud with …

LiDAR and camera are two important sensors for 3D object detection in autonomous driving.
Despite the increasing popularity of sensor fusion in this field, the robustness against inferior …

Fusing the camera and LiDAR information has become a de-facto standard for 3D object
detection tasks. Current methods rely on point clouds from the LiDAR sensor as queries to …

Lidars and cameras are critical sensors that provide complementary information for 3D
detection in autonomous driving. While prevalent multi-modal methods simply decorate raw …

We study the problem of efficient object detection of 3D LiDAR point clouds. To reduce the
memory and computational cost, existing point-based pipelines usually adopt task-agnostic …

How should we integrate representations from complementary sensors for autonomous
driving? Geometry-based fusion has shown promise for perception (eg, object detection …

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

Sensor fusion is an essential topic in many perception systems, such as autonomous driving
and robotics. Existing multi-modal 3D detection models usually involve customized designs …