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Iterative consolidation of unorganized point clouds
IEEE Computer Graphics and Applications
|May 9, 2014
Summary
This study introduces a novel framework to clean and organize noisy 3D point clouds. The method effectively removes outliers and fills gaps, improving data quality for 3D shape acquisition.
Area of Science:
- Computer Vision
- 3D Data Processing
- Geometric Modeling
Background:
- Unorganized point clouds from 3D acquisition devices often contain noise, outliers, and uneven point distribution.
- These imperfections hinder accurate 3D shape analysis and reconstruction.
Purpose of the Study:
- To develop a robust framework for consolidating and refining unorganized 3D point clouds.
- To address challenges of noise, outliers, and nonuniformity in 3D scan data.
Main Methods:
- An iterative procedure combining interlaced downsampling and upsampling.
- Outlier removal using selection operations that preserve geometric details.
- Point uniformity enhancement through particle movement and sample refinement.
- Surface extrapolation for filling missing data regions.
- Adaptive sampling strategy to accelerate iterative processes.
Main Results:
- The framework successfully consolidates noisy and unorganized point clouds.
- Demonstrated effectiveness in removing outliers while retaining fine geometric features.
- Achieved improved point uniformity and filled incomplete surface regions.
- The adaptive sampling strategy significantly sped up the processing iterations.
Conclusions:
- The proposed framework offers an effective solution for cleaning and organizing 3D point cloud data.
- It enhances data quality for downstream 3D shape analysis and modeling applications.
