We introduce a new general-purpose approach to deep learning on three-dimensional
surfaces based on the insight that a simple diffusion layer is highly effective for spatial …

In this paper, we propose a novel local descriptor-based framework, called You Only
Hypothesize Once (YOHO), for the registration of two unaligned point clouds. In contrast to …

Equivariance has been a long-standing concern in various fields ranging from computer
vision to physical modeling. Most previous methods struggle with generality, simplicity, and …

Abstract 3D point clouds deep learning is a promising field of research that allows a neural
network to learn features of point clouds directly, making it a robust tool for solving 3D scene …

We present SyNoRiM, a novel way to jointly register multiple non-rigid shapes by
synchronizing the maps that relate learned functions defined on the point clouds. Even …

In this work, we introduce Deep Bingham Networks (DBN), a generic framework that can
naturally handle pose-related uncertainties and ambiguities arising in almost all real life …

Regressing rotations on SO (3) manifold using deep neural networks is an important yet
unsolved problem. The gap between the Euclidean network output space and the non …

Transformation equivariance has been widely investigated in 3D point cloud representation
learning for more informative descriptors, which formulates the change of the representation …

Capsule networks were proposed as an alternative approach to Convolutional Neural
Networks (CNNs) for learning object-centric representations, which can be leveraged for …

Even though considerable progress has been made in deep learning-based 3D point cloud
processing, how to obtain accurate correspondences for robust registration remains a major …