根据曲率自适应调整局部区域，提升点云重建精度与效率
Accurate and Efficient Surface Reconstruction from Point Clouds via Geometry-Aware Local Adaptation
- 依据输入点云曲率动态调节局部区域的间距和大小
- 相比固定区域方法，重建精度显著提升且计算更高效
- 适合需要高精度重建的基础设施检测等场景
点云表面重建在深度学习进步推动下精度不断提升，已应用于基础设施检测等场景。近期研究采用从局部小区域而非整个点云进行重建的方法，因其具备较强的泛化能力而受到关注。然而，以往工作通常以均匀方式放置局部区域并保持大小固定，难以适应几何复杂度的变化。本研究提出一种新方法，通过根据输入点云的曲率自适应调节局部区域的间距和大小，从而在提升重建精度的同时增强计算效率。
原文摘要 · Abstract (English)
Point cloud surface reconstruction has improved in accuracy with advances in deep learning, enabling applications such as infrastructure inspection. Recent approaches that reconstruct from small local regions rather than entire point clouds have attracted attention for their strong generalization capability. However, prior work typically places local regions uniformly and keeps their size fixed, limiting adaptability to variations in geometric complexity. In this study, we propose a method that improves reconstruction accuracy and efficiency by adaptively modulating the spacing and size of local regions based on the curvature of the input point cloud.
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