Extracting robust and general 3D local features is key to downstream tasks such as point
cloud registration and reconstruction. Existing learning-based local descriptors are either …

Change detection is an important step for the characterization of object dynamics at the
earth's surface. In multi-temporal point clouds, the main challenge is to detect true changes …

Abstract 6D object pose estimation networks are limited in their capability to scale to large
numbers of object instances due to the close-set assumption and their reliance on high …

Over the past years, 3D point cloud registration has attracted unprecedented attention.
Researchers develop various approaches to tackle the challenging task, such as …

An effective 3D descriptor should be invariant to different geometric transformations, such as
scale and rotation, robust to occlusions and clutter, and capable of generalising to different …

Registration of distant outdoor LiDAR point clouds is crucial to extending the 3D vision of
collaborative autonomous vehicles, and yet is challenging due to small overlap** area …

Abstract 3D point cloud semantic segmentation is fundamental for autonomous driving. Most
approaches in the literature neglect an important aspect, ie, how to deal with domain shift …

We propose a method for generalizing deep learning for 3D point cloud registration on new,
totally different datasets. It is based on two components, MS-SVConv and UDGE. Using Multi …

While the Transformer architecture has become ubiquitous in the machine learning field, its
adaptation to 3D shape recognition is non-trivial. Due to its quadratic computational …

We propose a novel fully automated deformation analysis pipeline capable of estimating
real 3D displacement vectors from point cloud data. Different from the traditional methods …