Voxel Localization Using Heart and Border Voxels
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Solution Overview
Problem
Existing methods for determining the localization of digitally-modeled objects within digitally-modeled spaces using voxel-based representations are prone to errors near boundaries, particularly in large systems like ships and buildings, due to approximate geometries, leading to incorrect volumetric queries and potential misses or false positives.
Innovation Solution
The method partitions voxel representations of objects and spaces into 'heart' and 'border' voxels, evaluating six Boolean functions to accurately determine the localization of objects with respect to spaces, using three-valued logic functions to classify objects into specific categories based on their spatial relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If voxel-based representations are used for volumetric queries, then processing speed and memory efficiency are improved, but measurement precision deteriorates due to approximate geometries causing errors near boundaries
Solution Approach 1:
The patent segments the voxel representation into two distinct sets: heart voxels (fully inside the space) and border voxels (intersecting the boundary). This segmentation allows different assessment rules to be applied to different regions, enabling accurate localization while maintaining voxel-based processing efficiency. Objects are classified based on their relationship with these segmented voxel sets, resolving the contradiction between speed and precision.
2Measurement precision
If higher spatial resolution voxel representations are used, then measurement precision is improved, but device complexity and computational resources increase significantly
Solution Approach 1:
The patent applies local quality by treating heart voxels and border voxels differently in the assessment process. Heart voxels receive simple inclusion checks, while border voxels receive more sophisticated intersection assessments. This localized differentiation maintains high precision at boundaries without requiring uniform high-resolution voxelization throughout the entire space, thus avoiding excessive computational complexity.
3Ease of operation
If approximate voxel geometries are used, then ease of operation is improved for volumetric queries, but reliability deteriorates due to spurious intersections and missed detections
Solution Approach 1:
The patent implements a feedback mechanism through six Boolean functions that systematically evaluate the relationship between objects and the segmented voxel sets. These functions provide feedback on object localization status, enabling accurate classification despite using approximate voxel geometries. The feedback loop ensures that spurious intersections are detected and handled correctly, maintaining reliability while preserving the simplicity of voxel-based operations.
Data Source
Figure 1A~2A
Figure 2B~3B
Figure 3C
AI summary
A computer-implemented method of determining a localization of a digitally-modeled object with respect to a digitally-modeled space and performing volumetric queries, comprising the steps of: a) retrieving or creating voxel representations of the object (OV) and of the space; b) partitioning these representations of the digitally-modeled object into sets of heart (OH) and border (OB) voxels; c) determining intersections between sets of voxels of the object and of the space; d) based on said intersections, determining the localization of the digitally-modeled object with respect to the digitally-modeled space.