Point cloud is a widely used 3D data form, which can be produced by depth sensors, such
as Light Detection and Ranging (LIDAR) and RGB-D cameras. Being unordered and …

Interactive 3D simulations have enabled break-throughs in robotics and computer vision, but
simulating the broad diversity of environments needed for deep learning requires large …

Many and varied methods currently exist for featurization, which is the process of map**
persistence diagrams to Euclidean space, with the goal of maximally preserving structure …

Attempts to incorporate topological information in supervised learning tasks have resulted in
the creation of several techniques for vectorizing persistent homology barcodes. In this …

Empirically validating new 3D-printing related algorithms and implementations requires
testing data representative of inputs encountered\emph {in the wild}. An ideal benchmarking …

Topological data analysis offers a rich source of valuable information to study vision
problems. Yet, so far we lack a theoretically sound connection to popular kernel-based …

Point cloud upsampling is vital for the quality of the mesh in three-dimensional
reconstruction. Recent research on point cloud upsampling has achieved great success due …

Abstract Objects Matching is a ubiquitous problem in computer science with particular
relevance for many applications; property transfer between 3D models and statistical study …

Complex geometric variations of 3D models usually pose great challenges in 3D shape
matching and retrieval. In this paper, we propose a novel 3D shape feature learning method …

Complex geometric structural variations of 3D models usually pose great challenges in 3D
shape matching and retrieval. In this paper, we propose a high-level shape feature learning …