Intrinsic and Isotropic Resampling for 3D Point Clouds.

Area of Science:

• Computer Vision
• Geometric Processing
• 3D Data Analysis

Background:

• 3D point cloud research is growing, but faces challenges like missing local adjacency, uneven density, and uncontrolled point counts.
• These issues hinder the effective utilization of 3D point cloud data in various applications.

Purpose of the Study:

• To propose a novel two-step intrinsic and isotropic (I&I) resampling framework.
• To address key difficulties in 3D point cloud utilization: local adjacency, point density, and point number control.

Main Methods:

• An efficient intrinsic control step using geodesic measurements for improved local region detection.
• A geometrically-optimized resampling step to achieve isotropic or adaptively-isotropic point clouds.
• Adjustable point density for global uniformity or local uniformity while preserving geometric features.

Main Results:

• The I&I framework effectively improves local region detection and avoids redundant calculations.
• Achieved uniform (isotropic) or adaptively uniform (adaptively-isotropic) point cloud density.
• Demonstrated outstanding performance in point cloud simplification, mesh reconstruction, and shape registration.

Conclusions:

• The proposed I&I resampling framework successfully tackles major 3D point cloud utilization challenges.
• The method offers flexible control over point density and number, enhancing data quality.
• The framework shows significant improvements across diverse 3D data processing applications.