Classical clustering methods do not provide users with direct control of the clustering results,
and the clustering results may not be consistent with the relevant criterion that a user has in …

3D shape analysis has been largely focused on traditional 3D representations of point
clouds and meshes, but the discrete nature of these data makes the analysis susceptible to …

Latent representations are used extensively for downstream tasks, such as visualization,
interpolation or feature extraction of deep learning models. Invariant and equivariant neural …

Abstract Representation learning has become increasingly important, especially as powerful
models have shifted towards learning latent representations before fine-tuning for …

3D shape analysis has been widely explored based on traditional 3D data of point clouds
and meshes, but the discrete nature of these data makes the analysis methods susceptible …

Robust techniques for learning centroids and orientations of objects and shapes in two-
dimensional images, along with other features is crucial for image-and video-processing …