UDFStudio: A Unified Framework of Datasets, Benchmarks and Generative Models for Unsigned Distance Functions

Unsigned distance functions (UDFs) have emerged as powerful representation for modeling and reconstructing geometries with open surfaces. However, the development of 3D generative models for UDFs remains largely unexplored, limiting current methods from generating diverse open-surface 3D content. Moreover, mainstream 3D datasets predominantly consist of watertight meshes, revealing a critical challenge: the absence of standardized datasets and benchmarks specifically tailored for open-surface generation and reconstruction. In this paper, we begin by introducing UDiFF, a novel diffusion-based 3D generative model specifically designed for UDFs. UDiFF supports both conditional and unconditional generation of textured 3D shapes with open surfaces. At its core, UDiFF generates UDFs in the spatial-frequency domain using a learnable wavelet transform. Instead of relying on manually selected wavelet transforms, which are labor-intensive and prone to information loss, we introduce a data-driven approach that learns the optimal wavelet transformation from UDFs datasets. Beyond UDiFF, we present the UWings dataset, comprising 1,509 high-quality 3D open surface models of winged creatures. Using UWings, we establish comprehensive benchmarks for evaluating both generative and reconstruction methods based on UDFs.