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arXiv · 2606.18787

Learned Radius Estimation for UDF-Based Point Cloud Reconstruction

Eito Ogawa·Hiroshi Watanabe
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Abstract

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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Eito Ogawa, Hiroshi Watanabe. 2026-06-17. Learned Radius Estimation for UDF-Based Point Cloud Reconstruction. https://arxiv.org/abs/2606.18787

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