Point Cloud Decimation Engine Using Grid Contribution Factors
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Solution Overview
Problem
Existing methods for visualizing large point cloud data are inefficient, requiring extensive pre-processing and slow point extraction due to large index and data files, which hinder real-time visualization and interaction.
Innovation Solution
A grid is mapped over the point cloud, with points sorted within each cell and contribution factors calculated, allowing for rapid indexing and real-time visualization by accumulating factors for visible cells and reading a subset of points in a single operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a hierarchical spatial index is created to organize point cloud data, then the data can be structured for visualization, but pre-processing time becomes extensive (several hours or more)
Solution Approach 1:
The point cloud data is divided into multiple cells forming a grid structure, where each cell independently stores points within its spatial bounds. This segmentation allows parallel processing and eliminates the need for extensive hierarchical traversal, reducing pre-processing time while maintaining organization capability.
Solution Approach 2:
Points are pre-sorted within each cell according to their spatial coordinates during the initial grid creation. This preliminary sorting enables efficient point extraction during visualization without requiring additional sorting operations, significantly reducing pre-processing time while maintaining data accessibility.
2Manufacturing precision
If multiple resolutions of point data are stored to enable high-quality visualization, then visualization quality improves, but memory requirements increase significantly
Solution Approach 1:
The system maintains high-resolution point data only in cells that are currently visible or near the viewer, while using lower-resolution or aggregated representations for distant cells. This local quality approach allows high visualization quality for the region of interest while minimizing overall memory consumption by not uniformly storing high-resolution data throughout the entire point cloud.
3Speed
If a comprehensive spatial index is created to enable rapid point extraction, then point extraction speed improves, but the size of index and data files increases significantly
Solution Approach 1:
The spatial index is segmented into individual cell structures, each containing only the points and spatial information relevant to that specific cell. This segmentation eliminates the need for large comprehensive index files, as the index size scales with the number of cells rather than the total number of points, enabling rapid point extraction with minimal file size overhead.
Data Source
AI summary
A method, apparatus, article of manufacture, and computer readable storage medium provide the ability to visualize a point cloud dataset. A grid, having cells, is mapped over the point cloud dataset. The points are sorted and a contribution factor is computed for each cell. Each cell and its contribution factor is stored. A viewing area and total number of viewing points is determined. The cells are intersected with the viewing area to determine visible cells. The contribution factors for the visible cells are accumulated and divided into the total viewing points to determine a point extraction value. A number of points to read (and display) from each visible cell is determined by multiplying the contribution factor of each visible cell by the point extraction value.