Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

In recent years, point cloud representation has become one of the research hotspots in the
field of computer vision, and has been widely used in many fields, such as autonomous …

Invariance and equivariance to the rotation group have been widely discussed in the 3D
deep learning community for pointclouds. Yet most proposed methods either use complex …

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 …

Symmetry-based neural networks often constrain the architecture in order to achieve
invariance or equivariance to a group of transformations. In this paper, we propose an …

We present RoReg, a novel point cloud registration framework that fully exploits oriented
descriptors and estimated local rotations in the whole registration pipeline. Previous …

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 …

In this work we target a learnable output representation that allows continuous, high
resolution outputs of arbitrary shape. Recent works represent 3D surfaces implicitly with a …

We present a novel learning-based approach for computing correspondences between non-
rigid 3D shapes. Unlike previous methods that either require extensive training data or …

Graph neural networks (GNNs) have been used previously for identifying new crystalline
materials. However, geometric structure is not usually taken into consideration, or only …