3 accepted papers
Recent state-of-the-art learning-based approaches to point cloud registration have largely been based on graph neural networks (GNN). However, these prominent GNN backbones suffer from the indistinguishable features problem associated with over-smoothing and structural ambiguity of the high-level fe…
Point cloud registration is a well-known way to align two different point clouds via a rigid transform estimation in robotics and computer vision applications. In particular, deep learning-based methods have recently attempted to extract highly distinguishable point features for each point. However,
Integrating model-based machine learning methods into deep neural architectures allows one to leverage both the expressive power of deep neural nets and the ability of model-based methods to incorporate domain-specific knowledge. In particular, many works have employed the expectation maximization (…