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Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
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Related Experiment Video

Updated: Mar 29, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Hairun Zhang1, Ying Song1

  • 1School of Artificial Intelligence, College of Computer Science and Technology, Zhejiang Sci-Tech University, Xiasha Campus, Hangzhou 310018, China.

Journal of Imaging
|March 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces UVSegNet, a new framework for UV parameterization that improves 3D model texturing. It minimizes texture distortion and optimizes seam placement for better quality.

Keywords:
UV Unwrappingboundary-sensitive parameterizationneural mappingsemantic-aware UV mapping

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Area of Science:

  • Computer Graphics
  • 3D Modeling
  • Geometric Processing

Background:

  • UV parameterization is crucial for 3D model texturing.
  • Challenges include minimizing texture distortion and placing seams effectively.
  • Existing methods struggle with complex geometries and optimal seam layout.

Purpose of the Study:

  • To develop a novel semantic boundary-aware UV parameterization framework.
  • To improve texture quality and seam placement in 3D models.
  • To address limitations in current UV mapping techniques.

Main Methods:

  • UVSegNet framework combining part-level segmentation and geometry-aware parameterization.
  • Boundary-aware guided UV mapping module for joint optimization of accuracy and seam layout.
  • Cylindrical supervision strategy for handling cylindrical structures and reducing distortion.

Main Results:

  • UVSegNet significantly outperforms baseline models in texture and seam quality.
  • Achieved a 24.1% improvement in angular distortion (conformality) compared to Nuvo.
  • Demonstrated a 60.5% improvement in seam compactness with a more compact seam layout.

Conclusions:

  • UVSegNet offers superior UV parameterization by integrating semantic boundary awareness and geometry-driven modeling.
  • The framework effectively minimizes distortion and enhances seam quality for textured 3D models.
  • The proposed methods provide a robust solution for complex object structures and improve overall mapping quality.