Structured Light Codeword Correction for Complete Depth Maps
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Solution Overview
Problem
Structured light active sensing systems face errors in depth maps due to interference, such as speckle, which cause gaps or holes in the received spatial codes, leading to incorrect or missing depth values.
Innovation Solution
A method for error correction in structured light systems involves detecting invalid codewords, generating candidate codewords by perturbing the invalid ones, and selecting the most similar candidate based on a local neighborhood similarity measure to replace the invalid codeword, thereby generating a corrected depth map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If structured light patterns are transmitted to generate depth maps, then depth information can be obtained for scene representation, but interference such as speckle causes errors in received spatial codes resulting in gaps or holes in depth maps
Solution Approach 1:
The system performs preliminary error detection by comparing received codewords against a codebook of valid codewords before depth map generation. Invalid codewords are identified and flagged for correction, preventing gaps in the depth map before they occur. This preliminary validation step ensures that only valid codewords contribute to depth information, maintaining both accuracy and completeness.
Solution Approach 2:
The system implements feedback by comparing received spatial codes against expected valid codewords from a codebook. When invalid codewords are detected, the system uses feedback from neighboring valid codewords and depth continuity constraints to correct the errors. This feedback mechanism continuously refines the depth map by identifying and correcting erroneous depth values based on spatial and temporal consistency.
2Measurement precision
If error correction is performed by generating candidate codewords and selecting based on local neighborhood similarity, then gaps in depth maps are filled and accuracy is improved, but computational complexity increases
Solution Approach 1:
The error correction process applies local quality by focusing computational resources only on regions with invalid codewords rather than processing the entire depth map. When an invalid codeword is detected, the system generates candidate corrections using only local neighborhood information from adjacent valid codewords. This localized approach maintains high accuracy for corrected regions while minimizing overall computational complexity.
Solution Approach 2:
The system changes parameters dynamically based on error detection results. Valid codewords are processed with standard depth calculation parameters, while invalid codewords trigger a correction mode with different parameters including candidate generation thresholds and similarity metrics. This parameter adaptation allows the system to handle errors efficiently without increasing complexity for all processing operations.
Data Source
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AI summary
Systems and methods for error correction in structured light are disclosed. In one aspect, a method includes receiving, via a receiver sensor, a structured light image of at least a portion of a composite code mask encoding a plurality of codewords, the image including an invalid codeword. The method further includes detecting the invalid codeword. The method further includes generating a plurality of candidate codewords based on the invalid codeword. The method further includes selecting one of the plurality of candidate codewords to replace the invalid codeword. The method further includes generating a depth map for an image of the scene based on the selected candidate codeword. The method further includes generating a digital representation of a scene based on the depth map. The method further includes outputting the digital representation of the scene to an output device.