PosDiffNet: Positional Neural Diffusion for Point Cloud Registration in a Large Field of View with Perturbations
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910409208889344 |
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| author | She, Rui Wang, Sijie Kang, Qiyu Zhao, Kai Song, Yang Tay, Wee Peng Geng, Tianyu Jian, Xingchao |
| author_facet | She, Rui Wang, Sijie Kang, Qiyu Zhao, Kai Song, Yang Tay, Wee Peng Geng, Tianyu Jian, Xingchao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03167 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PosDiffNet: Positional Neural Diffusion for Point Cloud Registration in a Large Field of View with Perturbations She, Rui Wang, Sijie Kang, Qiyu Zhao, Kai Song, Yang Tay, Wee Peng Geng, Tianyu Jian, Xingchao Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | PosDiffNet: Positional Neural Diffusion for Point Cloud Registration in a Large Field of View with Perturbations |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2401.03167 |