PrimitiveUDF: Primitive-Based Unsigned Distance Fields for Surface Reconstruction from Point Clouds

Point-Based unsigned distance fields (UDFs) learning oftenaggregates Euclidean neighborhoods as unstructured point sets. Eachquery is typically supervised by a single distance value. This offers lit-tle relational guidance for separating nearby surfaces, reducing featurediscriminability for distance regression. We propose PrimitiveUDF, aprimitive-based UDF framework with two coupled components: projection-induced neighborhood reorganization and hybrid primitive construction.The former uses spherical projection and a sphere-to-plane mapping toobtain a stable local chart for assessing surface-relative relations. The lat-ter grows triangle primitives with continuity and non-crossing preferences,and prunes direction-consistent segments for thin or sparse structures.Primitives are then encoded and aggregated by an attention-based re-gressor for query-conditioned UDF prediction. Experimental results onsynthetic and real-world datasets demonstrate around 10% improvementon scene-level benchmarks and consistent cross-scene gains, with balancedrobustness under noise and sparsity conditions. Code: PrimitiveUDF.