UVSegNet: Semantic Boundary-Aware Neural UV Parameterization for Man-Made Objects

UV parameterization is a fundamental step in building textured 3D models, but minimizing texture distortion and ensuring seams are placed along meaningful boundaries remains a challenge. This paper proposes UVSegNet, a novel semantic boundary-aware UV parameterization framework that combines part-level segmentation with geometry-aware parameterization. To address the common seam placement issues in parameterization, we introduce a boundary-aware guided UV mapping module that jointly optimizes geometric accuracy and seam layout. Furthermore, to better handle the cylindrical structures common in man-made objects, we introduce a cylindrical supervision strategy to reduce misalignment and unfolding distortion. Experiments on representative object categories show that UVSegNet outperforms other excellent baseline models in both texture quality and seam quality. Compared to Nuvo, UVSegNet improves the angular distortion (conformality) metric by 24.1% and seam compactness by 60.5% by generating a more compact seam layout. Experimental results demonstrate that UVSegNet outperforms baseline methods in both mapping quality and seam quality, thanks to the complementary mechanism of boundary constraints and geometry-driven modeling.