LiDAR segmentation is crucial for autonomous driving perception. Recent trends favor point-
or voxel-based methods as they often yield better performance than the traditional range …

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 …

State-of-the-art methods for large-scale driving-scene LiDAR segmentation often project the
point clouds to 2D space and then process them via 2D convolution. Although this …

Point clouds can be represented in many forms (views), typically, point-based sets, voxel-
based cells or range-based images (ie, panoramic view). The point-based view is …

Autonomous robotic systems and self driving cars rely on accurate perception of their
surroundings as the safety of the passengers and pedestrians is the top priority. Semantic …

Semantic segmentation of LiDAR point clouds is an important task in autonomous driving.
However, training deep models via conventional supervised methods requires large …

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 …

Densely annotating LiDAR point clouds remains too expensive and time-consuming to keep
up with the ever growing volume of data. While current literature focuses on fully-supervised …

State-of-the-art methods for driving-scene LiDAR-based perception (including point cloud
semantic segmentation, panoptic segmentation and 3D detection, etc.) often project the …

Panoptic segmentation presents a new challenge in exploiting the merits of both detection
and segmentation, with the aim of unifying instance segmentation and semantic …