Hyperbolic-Constraint Point Cloud Reconstruction from Single RGB-D Images
Wenrui Li, Zhe Yang, Wei Han, Hengyu Man, Xingtao Wang, Xiaopeng Fan
Abstract
Reconstructing desired objects and scenes has long been a primary goal in 3D computer vision. Single-view point cloud reconstruction has become a popular technique due to its low cost and accurate results. However, single-view reconstruction methods often rely on expensive CAD models and complex geometric priors. Effectively utilizing prior knowledge about the data remains a challenge. In this paper, we introduce hyperbolic space to 3D point cloud reconstruction, enabling the model to represent and understand complex hierarchical structures in point clouds with low distortion. We build upon previous methods by proposing a hyperbolic Chamfer distance and a regularized triplet loss to enhance the relationship between partial and complete point clouds. Additionally, we design adaptive boundary conditions to improve the model's understanding and reconstruction of 3D structures. Our model outperforms most existing models, and ablation studies demonstrate the significance of our model and its components. Experimental results show that our method significantly improves feature extraction capabilities. Our model achieves outstanding performance in 3D reconstruction tasks.
BibTeX
@article{Li_Yang_Han_Man_Wang_Fan_2025, title={Hyperbolic-Constraint Point Cloud Reconstruction from Single RGB-D Images}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/32525}, DOI={10.1609/aaai.v39i5.32525}, abstractNote={Reconstructing desired objects and scenes has long been a primary goal in 3D computer vision. Single-view point cloud reconstruction has become a popular technique due to its low cost and accurate results. However, single-view reconstruction methods often rely on expensive CAD models and complex geometric priors. Effectively utilizing prior knowledge about the data remains a challenge. In this paper, we introduce hyperbolic space to 3D point cloud reconstruction, enabling the model to represent and understand complex hierarchical structures in point clouds with low distortion. We build upon previous methods by proposing a hyperbolic Chamfer distance and a regularized triplet loss to enhance the relationship between partial and complete point clouds. Additionally, we design adaptive boundary conditions to improve the model’s understanding and reconstruction of 3D structures. Our model outperforms most existing models, and ablation studies demonstrate the significance of our model and its components. Experimental results show that our method significantly improves feature extraction capabilities. Our model achieves outstanding performance in 3D reconstruction tasks.}, number={5}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Li, Wenrui and Yang, Zhe and Han, Wei and Man, Hengyu and Wang, Xingtao and Fan, Xiaopeng}, year={2025}, month={Apr.}, pages={4959-4967} }