3D Transformers have achieved great success in point cloud understanding and
representation. However, there is still considerable scope for further development in …

Point cloud data, representing 3D objects, has become an indispensable format in
numerous applications. However, directly processing this data form, particularly for tasks like …

Although the application of Transformers to 3-D point cloud processing has achieved
significant progress and success, it is still challenging for existing 3-D Transformer methods …

Scene understanding based on LiDAR point cloud is an essential task for autonomous cars
to drive safely, which often employs spherical projection to map 3D point cloud into multi …

Semantic segmentation for 3D LiDAR point clouds is critical for environment perception in
autonomous driving. Recently, several successful methods are proposed based on …

Point cloud segmentation is one of the most important tasks in LiDAR remote sensing with
widespread scientific, industrial, and commercial applications. The research thereof has …

Transformer models have achieved promising performances in point cloud segmentation.
However, most existing attention schemes provide the same feature learning paradigm for …

Semantic segmentation of LiDAR point clouds has implications in self-driving, robots, and
augmented reality, among others. In this paper, we propose a Multi-Scale Attentive …

LiDAR-generated point clouds are crucial for perceiving outdoor environments. The
segmentation of point clouds is also essential for many applications. Previous research has …

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 …