3D Point Cloud Compression via Spatial Cube Segmentation
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Solution Overview
Problem
Large point clouds generated to map three-dimensional environments are often too large to store or transmit efficiently, requiring a method to compress them into a more sparse representation while maintaining accuracy.
Innovation Solution
The method involves dividing the three-dimensional environment into spatial cubes, converting three-dimensional data points within each cube to a two-dimensional plane, and determining polygon vertices where the plane intersects the cube boundaries, reducing data by representing the environment with fewer vertices instead of data points, and transmitting these vertices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large point clouds are used to map three-dimensional environments, then measurement precision and environmental accuracy are improved, but data quantity and storage requirements worsen
Solution Approach 1:
The patent divides the three-dimensional environment into multiple spatial cubes, with each cube containing a subset of the original point cloud data. This segmentation allows the large dataset to be processed and stored in smaller, manageable units while preserving the complete environmental information across all cubes.
Solution Approach 2:
The patent converts three-dimensional data points into two-dimensional planar representations by determining polygon vertices where cube edges intersect with planes fitted to the point cloud data. This dimensionality reduction significantly compresses the data while maintaining the essential spatial relationships and environmental accuracy.
2Loss of information
If large point clouds are transmitted, then data completeness is improved, but transmission time and resource usage worsen
Solution Approach 1:
The patent transforms the data representation from individual three-dimensional points to polygon vertex coordinates, fundamentally changing the data parameters. This transformation reduces the number of data elements that need to be transmitted while encoding the same environmental information, thereby decreasing transmission time without significant loss of completeness.
3Measurement precision
If three-dimensional data points are stored, then data accuracy is improved, but storage efficiency worsens
Solution Approach 1:
The patent creates a simplified copy of the original point cloud data by generating polygon vertices that represent the spatial boundaries of data distributions within each cube. This copy contains fewer data elements but preserves the essential spatial information, achieving efficient storage while maintaining data accuracy for environmental mapping applications.
Data Source
AI summary
A method for compressing three-dimensional data points is disclosed. The method includes measuring a plurality of three-dimensional data points using one or more sensors communicatively coupled to the computing device, where each three-dimensional data point represents a point in a three-dimensional environment. The method also includes dividing the three-dimensional environment into a plurality of spatial cubes, wherein each three-dimensional data point is mapped to one spatial cube. The method also includes, for each spatial cube, converting the three-dimensional data points in the respective spatial cube to a two-dimensional plane based on a number of three-dimensional data points in the respective spatial cube. The method also includes, for each two-dimensional plane, determining polygon vertices of a planar polygon at points where an edge of an associated spatial cube intersects the respective two-dimensional plane. The method also includes sending the polygon vertices of each planar polygon to a ground station.