Depth Refinement Using Machine Learning Confidence Scores
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Solution Overview
Problem
Existing methods for computing depth in images, such as stereo depth estimation, face challenges in measuring objects lacking trackable features like plain walls, small or thin objects, hair, black objects, and those with complex patterns, and are further hindered by imaging artifacts like glare and overexposure.
Innovation Solution
A machine-learning model is trained to refine depth measurements using supervised or unsupervised learning, processing images and confidence scores to generate more accurate and reliable depth maps, particularly for objects with failed depth measurements, and can be updated using pose shifts and occlusion detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stereo depth estimation techniques are used, then depth measurements can be obtained, but measurement precision deteriorates for objects lacking trackable features
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the stereo depth estimation technique and the final depth map. The ML model processes the initial depth measurements and confidence scores to produce refined depth values, effectively mediating the transformation from unreliable measurements to accurate results. This intermediary component enables the system to overcome the limitations of traditional stereo estimation for objects without trackable features.
Solution Approach 2:
The system implements feedback by using confidence scores associated with each depth measurement. These confidence scores provide information about the reliability of individual measurements, which is then fed back into the machine learning model. The ML model uses this feedback to selectively refine depth values, focusing computational resources on measurements with lower confidence scores and thereby improving overall measurement precision.
2Measurement precision
If traditional depth estimation methods are applied, then processing speed is maintained, but measurement precision deteriorates due to imaging artifacts
Solution Approach 1:
The patent replaces traditional mechanical/optical depth estimation methods with a machine learning-based system. Instead of relying solely on geometric constraints and feature matching (mechanical approaches), the system uses trained neural networks to predict depth values. This substitution enables the system to handle imaging artifacts like glare and overexposure that traditional methods struggle with, significantly improving measurement precision.
Solution Approach 2:
The system changes the parameters being processed by introducing confidence scores alongside depth measurements. Rather than processing only raw depth values, the system transforms the input space to include reliability metrics. This parameter change allows the machine learning model to make more informed decisions about which measurements to refine, improving accuracy without proportionally increasing complexity.
3Measurement precision
If machine learning models are used to refine depth measurements, then measurement precision improves, but use of energy increases
Solution Approach 1:
The system applies partial action by selectively refining only those depth measurements that have lower confidence scores. Rather than processing every single depth value through the computationally intensive machine learning model, the system identifies and focuses computational resources on measurements that most need refinement. This approach maintains measurement precision for critical areas while reducing overall energy consumption.
Solution Approach 2:
The system performs preliminary action by pre-computing confidence scores for all depth measurements before applying the machine learning refinement. This preliminary step allows the system to identify which measurements require refinement in advance, enabling more efficient resource allocation. The pre-computed confidence information guides subsequent processing, reducing unnecessary computational energy expenditure.
4Measurement precision
If depth refinement is applied to all objects, then measurement precision improves, but productivity decreases
Solution Approach 1:
The system applies local quality by providing different levels of refinement to different regions of the depth map. Objects or regions with high confidence scores receive minimal or no refinement, while regions with low confidence scores undergo full machine learning processing. This localized approach ensures measurement precision is improved where needed while maintaining high processing throughput for regions that already have reliable measurements.
Solution Approach 2:
The system implements partial action by applying depth refinement selectively rather than universally. By identifying and focusing computational resources on specific objects or regions that require refinement (those with low confidence scores), the system maintains high productivity while still improving measurement precision for the most challenging cases. This selective approach avoids the computational overhead of processing every single depth value.
Data Source
AI summary
In one embodiment, a system may capture one or more images of an object using one or more cameras. The system may detect features of the object depicted in the one or more images. The system may compute, based on the detected features in the one or more images, depth measurements for the object. The system may generate confidence scores associated with the depth measurements. The system may refine the computed depth measurements of the object by processing the depth measurements, the associated confidence scores, and at least one of the one or more images using a machine-learning model trained to refine depth measurements. The trained machine-learning model is configured to process one or more input images to refine the processed depth measurements in the input images.