Camera Depth Estimation With Reliability Scoring for Autofocus
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Solution Overview
Problem
Existing imaging control technologies, such as autofocus, suffer from inaccurate estimation results when the learning data used does not match the imaging conditions, leading to deteriorated accuracy.
Innovation Solution
A control device and method that utilizes a Depth estimation model to generate distance information and a reliability estimation model to assess the reliability of this information, determining whether to use it for imaging control based on the reliability score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If learning is performed using a large amount of learning data representing a situation close to the imaging situation, then the estimation result accuracy is improved, but the device complexity and learning time increase
Solution Approach 1:
The patent divides the single estimation model into two separate models: a Depth estimation model and a reliability estimation model. This segmentation allows each model to have specialized functions, with the Depth model focusing on accuracy and the reliability model assessing confidence levels, thereby improving overall estimation accuracy without proportionally increasing system complexity
Solution Approach 2:
The reliability estimation model acts as an intermediary that evaluates the quality of intermediate data from the Depth estimation model. By introducing this intermediary assessment layer, the system can determine whether to trust the Depth estimation results without requiring extensive learning data for all possible imaging situations
2Loss of time
If learning is performed using learning data that does not match the imaging situation, then the learning time is reduced, but the estimation result accuracy deteriorates
Solution Approach 1:
The reliability estimation model provides feedback about the quality and trustworthiness of Depth estimation results. This feedback mechanism allows the system to identify when estimation results may be unreliable due to mismatched learning data, enabling corrective actions without requiring relearning for every new imaging situation
Solution Approach 2:
The reliability estimation model is trained in advance to recognize patterns of unreliable Depth estimations. By performing this preliminary training with available data, the system prepares an assessment tool that can evaluate future estimation quality without requiring extensive situation-specific learning data
3Speed
If the Depth information is used without reliability assessment, then the processing speed is improved, but the imaging control accuracy deteriorates
Solution Approach 1:
The system performs a partial assessment by using the reliability estimation model to evaluate only the critical Depth estimation results that will be used for imaging control. Rather than assessing all possible outputs, the system selectively applies reliability evaluation to maintain processing speed while ensuring accuracy for control-critical decisions
Data Source
AI summary
The present technology relates to a control device, a control method, an information processing device, a generation method, and a program capable of determining whether or not Depth information of an estimation result is reliable information.A control device according to one aspect of the present technology generates Depth information indicating a distance to each position of a subject appearing in a captured image on the basis of output of a first estimation model when the captured image is input, and generates reliability information indicating reliability of the Depth information on the basis of output of a second estimation model when intermediate data generated in the first estimation model is input at the time of estimating the Depth information. The present technology can be applied to a device including a camera.