Structured Light Codeword Correction for Depth Map Gaps
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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 and apparatus for error correction in structured light systems, which involves receiving a structured light image, detecting invalid codewords, generating candidate codewords, selecting the most similar codeword based on a local neighborhood, and using this 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, but interference such as speckle causes errors and gaps in the depth map
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 them from creating gaps in the final depth map. This preliminary validation step ensures that only reliable depth measurements are included in the output.
Solution Approach 2:
The system employs feedback mechanisms where the received spatial codes are continuously validated against the transmitted code patterns. When errors are detected through codeword validation, the system uses feedback loops to identify and correct these errors by comparing with neighboring valid codewords and interpolating missing depth values, thereby maintaining depth map reliability.
2Measurement precision
If error correction is performed by generating and evaluating candidate codewords, then accuracy of depth information improves, but computational complexity increases
Solution Approach 1:
Instead of exhaustively searching all possible codeword combinations, the system applies partial action by only generating candidate codewords that differ by a single bit from the invalid codeword. This dramatically reduces the search space from 2^N possible codewords to just N candidates, making the error correction computationally feasible while still achieving high accuracy in depth information recovery.
Solution Approach 2:
The error correction process applies local quality by focusing computational resources only on regions of the depth map where invalid codewords are detected. The system identifies specific erroneous codewords and corrects them individually using local neighborhood information, rather than processing the entire depth map uniformly. This localized approach minimizes overall computational complexity while maintaining accuracy where it matters most.
Data Source
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.