PosDiffNet: Positional Neural Diffusion for Point Cloud Registration in a Large Field of View with Perturbations
This addresses a challenging problem in 3D computer vision for applications like robotics or autonomous vehicles, but appears incremental as it builds on existing registration and neural ODE/PDE techniques.
The paper tackles point cloud registration in large fields of view with perturbations by proposing PosDiffNet, which uses hierarchical registration and neural PDE/ODE methods, achieving state-of-the-art performance on several 3D datasets.
Point cloud registration is a crucial technique in 3D computer vision with a wide range of applications. However, this task can be challenging, particularly in large fields of view with dynamic objects, environmental noise, or other perturbations. To address this challenge, we propose a model called PosDiffNet. Our approach performs hierarchical registration based on window-level, patch-level, and point-level correspondence. We leverage a graph neural partial differential equation (PDE) based on Beltrami flow to obtain high-dimensional features and position embeddings for point clouds. We incorporate position embeddings into a Transformer module based on a neural ordinary differential equation (ODE) to efficiently represent patches within points. We employ the multi-level correspondence derived from the high feature similarity scores to facilitate alignment between point clouds. Subsequently, we use registration methods such as SVD-based algorithms to predict the transformation using corresponding point pairs. We evaluate PosDiffNet on several 3D point cloud datasets, verifying that it achieves state-of-the-art (SOTA) performance for point cloud registration in large fields of view with perturbations. The implementation code of experiments is available at https://github.com/AI-IT-AVs/PosDiffNet.