<scp>ACL</scp> ‐ <scp>UDF</scp> : Asymptotically Consistent Learning of Unsigned Distance Fields for Surface Reconstruction

ABSTRACT Surface reconstruction from point clouds with open boundaries and incomplete geometry remains challenging for existing implicit learning methods, as signed distance field and occupancy‐based approaches inherently rely on closed‐surface assumptions and often produce false closures or boundary distortions, while existing unsigned distance field (UDF) methods suffer from unstable optimization, discontinuities near boundaries, and difficulties in reliable surface extraction. To address these issues, this paper proposes an asymptotically consistent surface reconstruction framework based on UDFs for non‐watertight point clouds. The method adopts a fully unsupervised learning strategy, in which a continuous UDF is optimized through a gradient‐guided point projection mechanism combined with geometric and gradient consistency constraints, enabling stable distance estimation without requiring ground‐truth distances or normals. A progressive learning strategy based on high‐confidence projected points is further introduced to alleviate early‐stage instability and improve robustness under sparse sampling and complex topology. In addition, an intersection‐detection‐based isosurface extraction scheme is designed to infer pseudo‐sign information from local gradient configurations, allowing reliable mesh generation using the Marching Cubes algorithm. Extensive experiments demonstrate that the proposed method achieves superior reconstruction accuracy and robustness compared with state‐of‐the‐art methods, particularly in preserving open boundaries, thin‐walled structures, and fractured regions without erroneous infilling.