An Efficient Hypergraph Approach to Robust Point Cloud Resampling.

Area of Science:

• Computer Vision
• Signal Processing
• Cyber-Physical Systems

Background:

• Efficient processing and feature extraction of large-scale point clouds are crucial for computer vision and cyber-physical systems.
• Existing methods may struggle with complex relationships within point cloud data.

Purpose of the Study:

• To investigate point cloud resampling using hypergraph signal processing (HGSP).
• To enhance feature extraction and preserve surface outlines of point clouds.
• To develop a low-complexity approach for estimating hypergraph spectrum directly from 3D coordinates.

Main Methods:

• Design of hypergraph spectral filters to capture multilateral interactions among point cloud nodes.
• Leveraging hypergraph stationary processes to estimate the hypergraph spectrum directly from observed 3D coordinates.
• Development of a resampling method that avoids explicit hypergraph construction.

Main Results:

• Validated the high efficacy of hypergraph characterization for point clouds.
• Demonstrated the robustness of hypergraph-based resampling under noisy observations.
• Achieved contour-enhanced feature extraction and preservation of surface outlines.

Conclusions:

• HGSP offers an effective approach for point cloud resampling and feature extraction.
• The proposed low-complexity method efficiently handles large-scale and noisy point cloud data.
• This technique improves the exploration of relationships among points in a point cloud.