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 …

LiDAR-based 3D point cloud recognition has benefited various applications. Without
specially considering the LiDAR point distribution, most current methods suffer from …

As camera and LiDAR sensors capture complementary information in autonomous driving,
great efforts have been made to conduct semantic segmentation through multi-modality data …

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 …

Deep learning (DL) has become one of the mainstream and effective methods for point
cloud analysis tasks such as detection, segmentation and classification. To reduce …

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 …

In this paper, we introduce SalsaNext for the uncertainty-aware semantic segmentation of a
full 3D LiDAR point cloud in real-time. SalsaNext is the next version of SalsaNet 1 which has …

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

LiDAR point cloud analysis is a core task for 3D computer vision, especially for autonomous
driving. However, due to the severe sparsity and noise interference in the single sweep …

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 …