ML-Based Structured Light Patch Recognition for Depth Accuracy
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Solution Overview
Problem
Depth camera systems using structured light patterns face challenges in accurately recognizing and triangulating due to imperfections, noise, and distortions, leading to errors in estimating depth and generating accurate 3D images.
Innovation Solution
The implementation of machine learning techniques for recognizing patches within structured light patterns, which involves projecting an ML-based pattern, segmenting and calculating descriptor vectors, and using a classifier to determine patch locations, thereby improving recognition performance and depth estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional structured light pattern recognition is used, then the system structure is simple, but the recognition accuracy deteriorates due to noise, distortion, and imperfections
Solution Approach 1:
The patent introduces machine learning models (classifiers, descriptor calculators) as intermediary components between the structured light projection and depth estimation. These intermediaries process the captured pattern, extract features, and compensate for distortions and noise, thereby improving depth estimation accuracy without requiring fundamental changes to the hardware system structure
Solution Approach 2:
The system performs preliminary actions by pre-processing the captured structured light pattern through machine learning-based feature extraction and patch recognition before depth calculation. The ML models are trained in advance to recognize patterns under various conditions, enabling robust depth estimation even when the captured pattern differs from the projected pattern due to noise or distortion
2Measurement precision
If machine learning techniques are implemented for patch recognition, then recognition accuracy improves, but device complexity increases
Solution Approach 1:
The patent segments the structured light pattern into multiple patches and processes each patch independently through the machine learning pipeline. This segmentation allows the complex recognition task to be divided into manageable units, improving overall recognition accuracy while enabling parallel processing to mitigate the computational burden
Solution Approach 2:
The patent replaces traditional mechanical or algorithmic pattern matching methods with machine learning-based recognition. The ML models learn to identify patches and their locations directly from image data, substituting complex hand-crafted feature extraction and matching algorithms with trained neural networks or classifiers that achieve superior accuracy
3Reliability
If the SL pattern is captured with imperfections, then the captured pattern differs from the projected pattern, but using correction methods increases processing complexity
Solution Approach 1:
The system employs feedback mechanisms where the captured pattern is compared against the projected pattern, and machine learning models learn from the differences and imperfections observed. The ML training process uses feedback from training data that includes various distortion conditions, enabling the system to automatically compensate for common imperfections without requiring complex real-time correction algorithms
Data Source
AI summary
Generally, this disclosure provides systems, devices, methods and computer readable media for a depth camera with ML techniques for recognition of patches within an SL pattern. The system may include a projection module to project an ML-based SL pattern onto a scene; a camera to receive an image of the SL pattern reflected from the scene; a patch recognition and location module to generate a descriptor vector for a patch segmented from the received image and to query an ML system with the descriptor vector, the ML system configured to provide a patch label associated with the descriptor vector, the patch label comprising a location of the patch relative to the projected SL pattern; and a depth estimation module to triangulate a distance between the camera and a region of the scene associated with the patch based on the location of the patch relative to the projected SL pattern.