AI Depth Estimation from Monochrome Images
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Solution Overview
Problem
Conventional methods face challenges in obtaining and processing three-dimensional image data using general monochrome cameras, particularly in determining the type of objects and converting this data into usable information for various industry fields.
Innovation Solution
An electronic device equipped with a processor and AI learning models processes two-dimensional images to derive depth information, using a first learning model to identify object types and a second model to refine depth data, ultimately generating three-dimensional information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a stereo camera is used to obtain three-dimensional image data, then depth information can be obtained, but the device complexity increases and ordinary people find it difficult to process and use the depth information
Solution Approach 1:
The patent replaces the mechanical stereo camera system with a computational approach using AI learning models. Instead of using hardware complexity to capture depth information, the system uses software-based AI algorithms that process two-dimensional images to generate three-dimensional information, thereby reducing device complexity while maintaining measurement precision.
Solution Approach 2:
The patent introduces AI learning models as an intermediary between the two-dimensional image and the three-dimensional information. This intermediary component automatically processes the image data and generates depth information and object type classification, eliminating the need for complex stereo camera hardware and making the system easier to use.
2Measurement precision
If a stereo camera is used to obtain three-dimensional image data, then depth information can be obtained, but the ease of operation decreases for ordinary people
Solution Approach 1:
The AI learning model performs self-service by automatically processing the two-dimensional image and generating three-dimensional information without requiring user intervention. The system handles the complex processing tasks autonomously, making it easy for ordinary people to use while maintaining accurate depth information extraction.
3Measurement precision
If three-dimensional image data is obtained by a stereo camera, then depth information can be obtained, but the lack of information regarding types of captured objects reduces adaptability
Solution Approach 1:
The AI learning model serves multiple functions simultaneously: it classifies object types and generates depth information from the same two-dimensional image input. This multi-functionality provides both adaptability (object type recognition) and measurement precision (depth information), overcoming the limitation of stereo cameras that only provide depth data without object identification.
4Device complexity
If a monochrome camera is used to capture two-dimensional images, then the device complexity is reduced, but the ability to obtain three-dimensional information directly is lost
Solution Approach 1:
The patent replaces the need for complex stereo camera hardware with a computational system that uses AI learning models to extract three-dimensional information from simple two-dimensional monochrome images. This substitution maintains low device complexity while achieving accurate three-dimensional information extraction through software-based processing.
Data Source
AI summary
Disclosed is an electronic device including a learning model having been learned according to an artificial intelligence algorithm. An electronic device according to the present disclosure may comprise: an input unit; and a processor which, when a two-dimensional image including at least one object is received through the input unit, acquires first depth information relating to at least one object by applying the two-dimensional image to a first learning model, acquires second depth information relating to the at least one object by applying the first depth information and actually measured depth data of the at least one object to a second learning model, and acquires three-dimensional information relating to the two-dimensional image on the basis of the second depth information, wherein the first depth information is implemented to include depth data according to a type of the at least one object.