This article addresses the problem of distilling knowledge from a large teacher model to a
slim student network for LiDAR semantic segmentation. Directly employing previous …

Real-time and high-performance 3D object detection is of critical importance for autonomous
driving. Recent top-performing 3D object detectors mainly rely on point-based or 3D voxel …

Vehicle-to-everything (V2X) autonomous driving opens up a promising direction for
developing a new generation of intelligent transportation systems. Collaborative perception …

In recent years, vision-centric Bird's Eye View (BEV) perception has garnered significant
interest from both industry and academia due to its inherent advantages, such as providing …

Training deep models for semantic scene completion is challenging due to the sparse and
incomplete input, a large quantity of objects of diverse scales as well as the inherent label …

There have been two streams in the 3D detection from point clouds: single-stage methods
and two-stage methods. While the former is more computationally efficient, the latter usually …

Casting semantic segmentation of outdoor LiDAR point clouds as a 2D problem, eg, via
range projection, is an effective and popular approach. These projection-based methods …

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

Automatic and accurate instance segmentation of street trees from point clouds is a
fundamental task in urban green space research. Previous studies have achieved …

Abstract Point-, voxel-, and range-views are three representative forms of point clouds. All of
them have accurate 3D measurements but lack color and texture information. RGB images …