LiDAR-based 3D object detection, semantic segmentation, and panoptic segmentation are
usually implemented in specialized networks with distinctive architectures that are difficult to …

Recent Transformer-based 3D object detectors learn point cloud features either from point-
or voxel-based representations. However, the former requires time-consuming sampling …

Sparse convolution plays a pivotal role in emerging workloads, including point cloud
processing in AR/VR, autonomous driving, and graph understanding in recommendation …

This paper aims for high-performance offline LiDAR-based 3D object detection. We first
observe that experienced human annotators annotate objects from a track-centric …

Point cloud computation has become an increasingly more important workload thanks to its
applications in autonomous driving. Unlike dense 2D computation, point cloud convolution …

With the rapid advances in autonomous driving, it becomes critical to equip its sensing
system with more holistic 3D perception. However, widely explored tasks like 3D detection …

The performance of perception systems developed for autonomous driving vehicles has
seen significant improvements over the last few years. This improvement was associated …

There is an increasing trend towards multi-modal sensor fusion to improve the perception
capability of autonomous vehicles, such as the fusion of light detection and ranging (LiDAR) …

Abstract 3D object detection is an important perception module in autonomous driving
systems. It recognizes sensor observations and predicts locations, sizes and orientations of …

3D point clouds play a pivotal role in outdoor scene perception, especially in the context of
autonomous driving. Recent advancements in 3D LiDAR segmentation often focus intensely …