PosDiffNet: Positional Neural Diffusion for Point Cloud Registration in a Large Field of View with Perturbations

• URL: http://arxiv.org/abs/2401.03167v1
• Date: Sat, 6 Jan 2024 08:58:15 GMT
• Title: PosDiffNet: Positional Neural Diffusion for Point Cloud Registration in a Large Field of View with Perturbations
• Authors: Rui She, Sijie Wang, Qiyu Kang, Kai Zhao, Yang Song, Wee Peng Tay, Tianyu Geng, Xingchao Jian
• Abstract summary: PosDiffNet is a model for point cloud registration in 3D computer vision. We leverage a graph neural partial differential equation (PDE) based on Beltrami flow to obtain high-dimensional features. We employ the multi-level correspondence derived from the high feature similarity scores to facilitate alignment between point clouds. 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.
• Score: 27.45001809414096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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