PathNet: Path-Selective Point Cloud Denoising.

Area of Science:

• Computer Vision
• Geometric Deep Learning
• 3D Data Processing

Background:

• Current point cloud denoising (PCD) models often use a single network, failing to account for varying noise levels and geometric structures across points.
• This limitation leads to issues like residual noise, smoothed edges, and shape distortion in denoised point clouds.

Purpose of the Study:

• To introduce PathNet, a novel path-selective PCD paradigm leveraging reinforcement learning (RL).
• To enable dynamic selection of optimal denoising paths for individual points to accurately reconstruct underlying surfaces.

Main Methods:

• Proposed a path-selective PCD framework utilizing RL for dynamic path selection.
• Introduced a noise- and geometry-aware reward function to train the RL routing agent.
• Implemented joint training of the routing agent and denoising network to prevent over- or under-smoothing.

Main Results:

• PathNet demonstrated significant improvements over existing PCD methods.
• Achieved effective removal of varying noise levels while preserving multi-scale surface geometries.
• Showed superior generalization capabilities on real-world scanned data compared to state-of-the-art models.

Conclusions:

• PathNet offers a more effective and adaptive solution for point cloud denoising.
• The path-selective paradigm and tailored reward function enhance noise reduction and geometric fidelity.
• PathNet represents a promising advancement for processing noisy 3D data, particularly for real-world applications.