Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending
and drawing extensive attention both from industry and academia. Conventional …

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 …

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 …

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving
experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The …

LiDAR-based 3D detection in point cloud is essential in the perception system of
autonomous driving. In this paper, we present LiDAR R-CNN, a second stage detector that …

We present a unified, efficient and effective framework for point-cloud based 3D object
detection. Our two-stage approach utilizes both voxel representation and raw point cloud …

LiDAR is a commonly used sensor for autonomous driving to make accurate, robust, and fast
decision-making when driving. The sensor is used in the perception system, especially …

As the foundation of intelligent systems, machine vision perceives the surrounding
environment and provides a basis for decision-making. Object detection is the core task in …

Ground segmentation is crucial for terrestrial mobile platforms to perform navigation or
neighboring object recognition. Unfortunately, the ground is not flat, as it features steep …

An accurate and robust perception system is key to understanding the driving environment
of autonomous driving and robots. Autonomous driving needs 3-D information about objects …