用双曲空间提升单图点云重建，更准更高效
Hyperbolic-constraint Point Cloud Reconstruction from Single RGB-D Images
- 引入双曲空间建模点云层级结构，降低表示失真
- 提出双曲Chamfer距离与正则化三元组损失，提升完整与部分点云关联性
- 适配边界条件增强3D结构理解，适合几何复杂场景重建
从单张RGB-D图像重建三维点云是计算机视觉的重要目标。现有单视图重建方法通常依赖昂贵的CAD模型和复杂的几何先验，有效利用数据先验仍具挑战。本文将双曲空间引入点云重建，使模型能以低失真方式表征点云中的复杂层级结构。我们提出双曲Chamfer距离与正则化三元组损失，强化部分与完整点云间的关系建模；设计自适应边界条件，提升对3D结构的理解与重建能力。实验表明，本方法显著优于多数现有模型，消融实验证明各组件的有效性，特征提取能力大幅提升，在多项3D重建任务中表现优异。
原文摘要 · Abstract (English)
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.
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