Spatio-Probabilistic 3D Modeling for Spatial Uncertainty
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Solution Overview
Problem
Traditional polygon/facet-based 3D modeling and visualization techniques are too rigid to effectively model and visualize uncertain spatial information, as they constrain space into 2D planes and do not account for the inherent uncertainty in measuring and predicting position and motion.
Innovation Solution
A flexible modeling and visualization system using spatio-probabilistic models (SPMs) that capture spatial uncertainty through probabilistic predictions and machine learning, allowing for the creation of 3D spatial models that can reason with spatial uncertainty and combine various types of information for realistic simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional polygon/facet-based 3D modeling techniques are used, then the modeling structure is simple and rigid, but the system cannot effectively model and visualize uncertain spatial information
Solution Approach 1:
The patent transforms static facet-based modeling into dynamic volumetric probabilistic modeling. Instead of fixed 2D facets, the system uses dynamic 3D volumetric pixels (voxels) with probabilistic occupancy values that can adapt and update as new spatial information becomes available, enabling the model to reflect uncertainty and change over time
Solution Approach 2:
The patent introduces probabilistic parameters (occupancy probabilities, spatial distribution parameters) to transform deterministic facet coordinates into probabilistic volumetric representations. This allows the system to model uncertainty by varying parameter values rather than relying on fixed geometric constraints
2Measurement precision
If facet-based modeling constrains space into 2D planes, then the modeling approach is computationally efficient, but it cannot represent the true three-dimensional uncertain nature of spatial objects
Solution Approach 1:
The patent transitions from 2D facet surfaces to 3D volumetric pixels, adding the depth dimension to spatial representation. This dimensional expansion enables accurate 3D positioning and uncertainty modeling while maintaining computational efficiency through discrete voxel-based operations and probabilistic algorithms
3Reliability
If vertices of facets describe only one position, then the geometric representation is precise and simple, but it cannot capture the uncertainty in object position and motion
Solution Approach 1:
The patent creates probabilistic copies of spatial information where each volumetric pixel contains probability distributions rather than single position values. This allows multiple possible positions to be represented simultaneously through probabilistic weighting, capturing uncertainty without requiring complex multi-position data structures
Data Source
AI summary
A system and method receive an object representative of a new element of a scene to be simulated. A probabilistic prediction of coordinates of the new element in the scene is provided. The new element is placed in the scene as a function of rules for combining probabilistic nature objects in the scene. A visual representation of the simulated scene including the new element is also provided for display.