Codeword Boundary Detection for Structured Light Depth Maps
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Solution Overview
Problem
Generating depth maps using structured light systems is computationally expensive, hindering the commercialization of these systems due to the high computation time required.
Innovation Solution
The system employs a structured light active sensing method that projects and receives spatial codes (codewords) to generate depth maps, utilizing a transmitter and receiver configuration with a code mask to identify codeword boundaries and calculate disparities, thereby reducing processing complexity through efficient codeword boundary detection and disparity mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional structured light depth map generation methods are used, then depth map accuracy is maintained, but computation time becomes excessively long
Solution Approach 1:
The patent segments the depth map generation process into distinct phases: codeword boundary detection, codeword identification, and disparity calculation. By dividing the received image into potential codeword regions first and then processing each segment independently, the system reduces overall computation time while maintaining depth map accuracy through systematic processing of each segmented region.
Solution Approach 2:
The patent performs preliminary codeword boundary detection and candidate codeword identification before final disparity calculation. By pre-identifying potential codeword locations and boundaries in the received image, the system prepares data structures and reduces the search space for subsequent depth calculation, significantly reducing computation time while preserving measurement precision.
2Measurement precision
If comprehensive image processing is performed to ensure accurate depth extraction, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent divides the complex image processing task into segmented steps: boundary detection, codeword candidate identification, and disparity computation. Each segment handles a specific aspect of the problem with dedicated algorithms, reducing overall processing complexity while maintaining accuracy through systematic multi-stage processing.
Solution Approach 2:
The patent introduces intermediate data structures and processing stages, such as codeword candidate regions and boundary detection maps, that serve as mediators between the raw received image and the final depth map. These intermediaries organize complex processing into manageable steps, reducing device complexity while preserving measurement precision.
Data Source
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AI summary
A structured light active sensing systems may be configured to transmit and received codewords to generate a depth map by analyzing disparities between the locations of the transmitted and received codewords. To determine the locations of received codewords, an image of the projected codewords is identified, from which one or more codeword boundaries are detected. The codeword boundaries may be detected based upon a particular codeword bit of each codeword. Each detected codeword boundary may be constrained from overlapping with other detected codeword boundaries, such that no pixel of the received image is associated with more than one codeword boundary.