2 January 2019

Signal Processing on Static and Dynamic 3D Meshes: Sparse Representations and Applications

Lalos, A. S., Vlachos, E., Arvanitis, G., Moustakas, K., Berberidis, K.
IEEE Access
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Abstract

Nowadays, real-time 3D scanning and reconstruction becomes a requirement for a variety of interactive applications in various fields, including heritage science, gaming, engineering, landscape topography, and medicine. From the

Type 2
Publication IEEE Access
DOI 10.1109/access.2019.2894533
Date January 2019
Research 6G UAVs

Key Contributions

  • Comprehensive review of fundamental signal processing models (RPCA, CS, MC) for geometry processing.
  • Analysis of scalable architectures and optimization algorithms for sparse representation tasks.
  • Demonstration of sparse modeling impact on 3D processing tasks (noise removal, outlier rejection, data completion).

Performance comparison of different sparse recovery methods on static 3D meshes.
Performance comparison of different sparse recovery methods on static 3D meshes.
This figure shows that model-based methods outperform traditional approaches in terms of reconstruction accuracy for static meshes.

Comparison of denoising results using PCA-based sparse representation versus conventional methods.
Comparison of denoising results using PCA-based sparse representation versus conventional methods.
The results demonstrate that the proposed sparse representation method achieves superior feature preservation and noise reduction compared to standard techniques.

Research 6G UAVs