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

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 …

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 …

Many point-based semantic segmentation methods have been designed for indoor
scenarios, but they struggle if they are applied to point clouds that are captured by a light …

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 …

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

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

The success of transformer networks in the natural language processing and 2D vision
domains has encouraged the adaptation of transformers for 3D computer vision tasks …

Semantic segmentation of point clouds is a key component of scene understanding for
robotics and autonomous driving. In this paper, we introduce TORNADO-Net-a neural …