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TENet: Attention-Frequency Edge-Enhanced 3D Texture Enhancement Network.

Ying Wang1,2, Tao Fu1,2, Yu Zhou3

  • 1School of Aerospace, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China.

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|February 13, 2025
PubMed
Summary

Researchers developed a new 3D texture enhancement network (TENet) to improve the quality of 3D models derived from photogrammetry. This method sharpens details and restores edges, significantly enhancing surface texture quality and reconstruction accuracy.

Keywords:
3D surface texture enhancementfrequency domain enhancementoblique photogrammetryself-attention mechanism

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Area of Science:

  • Computer Vision
  • 3D Reconstruction
  • Photogrammetry

Background:

  • Oblique photogrammetry imagery often results in 3D reconstructions with poor surface texture quality due to uneven resolution and blurred details, especially for building facades.
  • Existing methods struggle to effectively restore fine-grained texture and critical edge features in 3D models.

Purpose of the Study:

  • To introduce a novel Attention-Frequency Edge-Enhanced 3D Texture Enhancement Network (TENet) and a comprehensive pipeline for improving 3D texture quality.
  • To enhance the sharpness, edge accuracy, and overall surface texture of 3D models generated from photogrammetry data.

Main Methods:

  • Developed TENet, incorporating attention mechanisms and frequency-domain techniques for texture enhancement.
  • Introduced a Region-Resolution Adaptive Enhancement Module (RAEM) and a Frequency-Domain Edge Enhancement Mechanism (FDEEM) within the pipeline.
  • Applied 2D texture super-resolution techniques adapted for 3D models.

Main Results:

  • TENet significantly outperformed existing methods in improving 3D texture quality and reconstruction performance.
  • Ablation studies confirmed the effectiveness of individual components (RAEM, FDEEM) in enhancing 3D texture reconstruction.
  • Validated for real-world applications, TENet effectively reduced edge artifacts and restored clear, accurate textures in 3D surface models.

Conclusions:

  • The proposed TENet and enhancement pipeline offer a robust solution for improving 3D texture quality in photogrammetry.
  • The method demonstrates significant improvements in detail restoration and artifact reduction for 3D models, particularly building facades.
  • TENet provides a valuable tool for applications requiring high-fidelity 3D surface representations.