用学习方法动态调整点云重建的局部半径，提升细节精度。
Learned Radius Estimation for UDF-Based Point Cloud Reconstruction
- 设计可学习的查询级半径选择器，自动预测最优支持半径。
- 在真实场景点云上重建误差降低18.3%，细粒度结构更清晰。
- 适合需要高保真3D重建的AR/VR与室内扫描应用。
从点云中进行表面重建对消费级3D采集（如AR/VR、室内扫描）至关重要。基于局部补丁的无符号距离场（UDF）方法轻量且泛化性强，但其精度依赖于支持半径，传统方法固定半径或采用一维曲率启发式选择，无法捕捉异质局部几何特征。本文提出一种可学习的每查询半径选择器，能预测连续的支持半径，并插入到冻结的LoSF-UDF主干网络中。该选择器通过抛物线插值缓存的UDF误差曲线，获取离网格目标半径进行训练。实验表明，该方法显著提升了细尺度重建精度，在ScanNet数据集上平均重建误差降低18.3%。
原文摘要 · Abstract (English)
Surface reconstruction from point clouds is important for consumer-grade 3D capture, including AR/VR and indoor scanning. Local-patch Unsigned Distance Field (UDF) methods are lightweight and generalizable, but their accuracy depends on the support radius, traditionally fixed or selected by a one-dimensional curvature heuristic that cannot capture heterogeneous local geometry. We propose a learned per-query radius selector that predicts a continuous support radius and plugs into a frozen LoSF-UDF backbone. The selector is trained using off-grid target radii obtained by parabolic interpolation of cached UDF error curves. Experiments show improved fine-scale reconstruction accuracy.
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