Self-Tuned Two-Stage Point Cloud Reconstruction Framework Combining TPDn and PU-Net.

Area of Science:

• Computer Vision
• Geometric Modeling
• 3D Reconstruction

Background:

• Point cloud data is crucial for 3D applications but often suffers from noise and low resolution.
• Existing reconstruction methods require manual parameter tuning, limiting efficiency and scalability.
• Robust and automated techniques are needed for accurate point cloud processing.

Purpose of the Study:

• To present a self-tuned, two-stage framework for automated point cloud reconstruction.
• To improve geometric accuracy, structural consistency, and robustness to noise.
• To offer an efficient and scalable solution for processing large-scale point clouds.

Main Methods:

• A parameter-free denoising module (TPDn) utilizes polynomial model fitting for automatic noise and outlier removal.
• A point cloud upsampling network (PU-Net) recovers fine-grained geometry from the denoised data.
• The framework integrates denoising and upsampling in a synergistic two-stage process.

Main Results:

• The TPDn module effectively removes noise without manual threshold selection.
• The integrated framework demonstrates enhanced structural consistency and robustness across various noise levels.
• Experiments show improved geometric accuracy and uniformity on synthetic and real-world data.
• The method maintains low computational cost, proving efficient and scalable.

Conclusions:

• The proposed self-tuned framework provides an effective and automated solution for point cloud reconstruction.
• The synergy between parameter-free denoising and advanced upsampling significantly enhances reconstruction quality.
• The framework's simplicity, efficiency, and scalability make it suitable for diverse 3D applications.