Gaussian Mixture Model Hierarchy for Point Cloud Compression
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Solution Overview
Problem
Conventional point cloud processing techniques face challenges such as high computational power and memory requirements due to large data volumes, uneven sampling density, and unstructured data organization, making it difficult to efficiently compress and extract surface information from point cloud data for real-time applications.
Innovation Solution
A method involving the generation of a Gaussian Mixture Model (GMM) hierarchy, where point cloud data is represented using a tree structure with probabilistic occupancy maps, and the Expectation-Maximization algorithm is used to adjust parameters, allowing for efficient compression and extraction of iso-surfaces using a modified marching cubes algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional point cloud processing techniques are used, then processing can be performed on raw data points, but computational power and memory requirements become excessively high
Solution Approach 1:
The patent segments the point cloud data into multiple octants using an octree data structure. Each octant is processed independently, dividing the large-scale processing problem into smaller, more manageable sub-problems. This segmentation allows parallel processing and reduces the computational burden on any single processing unit while maintaining overall processing accuracy.
Solution Approach 2:
The patent transforms the processing approach from operating on individual 3D points to operating on volumetric regions (octants) defined by spatial boundaries. This dimensional transformation allows the system to work with aggregated spatial data representations rather than individual points, significantly reducing computational requirements while preserving essential geometric information.
2Quantity of substance
If spatial subdivision hierarchy (octree) is used to compress point cloud data, then data volume is reduced, but discretization artifacts are produced
Solution Approach 1:
The patent applies partial action by selectively refining only those octants that contain geometric features above a certain threshold. Instead of uniformly processing all octants at maximum detail, the system applies varying levels of refinement based on local geometric complexity, reducing data volume while preserving important geometric features and minimizing discretization artifacts in critical areas.
Solution Approach 2:
The patent dynamically adjusts refinement parameters and occupancy thresholds based on local geometric characteristics. By changing parameters such as occupancy thresholds, refinement levels, and sampling densities adaptively across different regions of the point cloud, the system optimizes the balance between compression ratio and geometric accuracy, reducing artifacts in regions where they would be most noticeable.
3Measurement precision
If more points are stored in the point cloud, then higher fidelity representation is achieved, but memory requirements increase
Solution Approach 1:
The patent applies local quality by assigning different levels of point density and representation fidelity to different regions of the point cloud based on their geometric importance. Regions with complex geometry or high visual significance receive higher fidelity representation with more points, while simpler regions use coarser representations. This localized quality adjustment maintains overall representation fidelity while significantly reducing total memory requirements.
Data Source
AI summary
A method, computer readable medium, and system are disclosed for generating a Gaussian mixture model hierarchy. The method includes the steps of receiving point cloud data defining a plurality of points; defining a Gaussian Mixture Model (GMM) hierarchy that includes a number of mixels, each mixel encoding parameters for a probabilistic occupancy map; and adjusting the parameters for one or more probabilistic occupancy maps based on the point cloud data utilizing a number of iterations of an Expectation-Maximum (EM) algorithm.