Structured Light Coding Distance Topologies for 3D Reconstruction
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Solution Overview
Problem
Current 3D reconstruction methods using structured light patterns face errors due to misrecognition of projected patterns, particularly because they lack effective error-correcting capabilities in local neighborhoods, which is critical given the static codeword order and specific a-priori conditions in structured light systems.
Innovation Solution
The use of error-correcting codes with a specific spatial topology in the projected patterns, where each symbol is more dissimilar to its proximal neighbors than to its distal neighbors, enhancing error-correcting capabilities by arranging symbols such that the Hamming distance between proximal symbol pairs exceeds that of distal pairs, ensuring accurate 3D location computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional structured light patterns are used for 3D reconstruction, then the system structure is simple and easy to implement, but the error rate increases due to misrecognition of projected patterns
Solution Approach 1:
The patent applies local quality by making different parts of the pattern have different properties: proximal symbol pairs are designed with high Hamming distance to resist misrecognition errors, while distal symbol pairs can have lower Hamming distance. This localized differentiation of symbol properties allows error correction where it is most needed (in local neighborhoods) without requiring the entire pattern to be overly complex.
Solution Approach 2:
The patent implements preliminary action by pre-arranging symbols in the projected pattern according to specific topological rules before projection. The symbols are organized such that proximal pairs have high Hamming distance, creating built-in error-correcting capability in advance. This preliminary structuring allows the system to correct errors during recognition without requiring complex real-time processing.
2Measurement precision
If symbols are arranged with high Hamming distance between proximal pairs to correct errors, then the error-correcting capability improves, but the pattern complexity increases
Solution Approach 1:
The patent makes different spatial regions of the pattern have different symbol properties. Specifically, proximal symbol pairs (neighbors) are assigned high Hamming distance for error correction, while distal symbol pairs can have lower Hamming distance. This localized approach concentrates complexity only where needed for error correction rather than uniformly across the entire pattern.
Solution Approach 2:
The patent changes the Hamming distance parameter of symbols based on their spatial relationship. By dynamically adjusting the Hamming distance parameter according to whether symbols are proximal or distal, the system achieves adaptive error correction capability. This parameter change allows precise control over where error correction is applied, improving measurement precision without uniformly increasing pattern complexity.
3Reliability
If conventional pattern encoding is used, then the system is easier to operate, but the system lacks error-correcting capabilities in local neighborhoods
Solution Approach 1:
The patent segments the pattern decoding process by treating proximal and distal symbol pairs differently. The decoding algorithm can focus computational effort on proximal pairs that require error correction, while using simpler methods for distal pairs. This segmentation of the decoding task based on spatial relationships makes the overall process more manageable and easier to implement with appropriate error correction.
Solution Approach 2:
The patent performs preliminary organization of symbols according to topological rules before the decoding operation. By pre-arranging symbols with specific Hamming distance properties based on their spatial relationships, the system simplifies the actual decoding operation. The preliminary structuring creates a framework that guides the decoding process, making it easier to implement error correction without excessive complexity.
Data Source
AI summary
Methods, systems, and devices for 3D measurement and/or pattern generation are provided in accordance with various embodiments. Some embodiments include a method of pattern projection that may include projecting one or more patterns. Each pattern from the one or more patterns may include an arrangement of three or more symbols that are arranged such that for each symbol in the arrangement, a degree of similarity between said symbol and a most proximal of the remaining symbols in the arrangement is less than a degree of similarity between said symbol and a most distal of the remaining symbols in the arrangement. Some embodiments further include: illuminating an object using the one or more projected patterns; collecting one or more images of the illuminated object; and/or computing one or more 3D locations of the illuminated object based on the one or more projected patterns and the one or more collected images.