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 review comprehensively investigates the current state and emerging trends of
autonomous vehicle terrain detection and segmentation. By systematically reviewing …

Efficient data utilization is crucial for advancing 3D scene understanding in autonomous
driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully …

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 …

Semantic segmentation of point clouds in autonomous driving datasets requires techniques
that can process large numbers of points efficiently. Sparse 3D convolutions have become …

3D segmentation is a fundamental and challenging problem in computer vision with
applications in autonomous driving and robotics. It has received significant attention from the …

Autonomous driving demands high-quality LiDAR data, yet the cost of physical LiDAR
sensors presents a significant scaling-up challenge. While recent efforts have explored deep …

The development of self-driving or autonomous vehicles has led to significant
advancements in 3D object detection technologies, which are critical for the safety and …

Semantic segmentation is a crucial component of autonomous driving. However, the
segmentation performance in unstructured roads is challenging owing to the following …

A unified and versatile LiDAR segmentation model with strong robustness and
generalizability is desirable for safe autonomous driving perception. This work presents …