3D Information Generation From Stereo Images Using Object Classification
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Solution Overview
Problem
Existing methods for generating 3D information from stereo image pairs often struggle to accurately classify and prioritize objects, leading to imperfect and incomplete 3D data, which can be problematic for navigation and object recognition, as irrelevant objects like balloons or clouds may be treated similarly to critical objects like lamp posts.
Innovation Solution
A method and system that identify and classify objects within stereo image pairs, allowing for the generation of 3D information that prioritizes relevant objects by excluding or adapting the representation of irrelevant objects, using techniques such as pixel value allocation and shape inference to enhance the accuracy of 3D data for navigation and object recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If 3D information is generated from all objects in a stereo image pair, then completeness of 3D data is improved, but relevance and accuracy for navigation deteriorates due to inclusion of irrelevant objects
Solution Approach 1:
The patent segments objects in the stereo image pair by classifying them into relevant and irrelevant categories based on object recognition. This segmentation allows the system to process only relevant objects (e.g., lamp posts, road signs) for navigation-critical 3D information while excluding irrelevant objects (e.g., balloons, clouds), thereby resolving the contradiction between data completeness and navigation relevance.
Solution Approach 2:
The patent extracts and removes irrelevant objects from the 3D information generation process. By identifying and excluding objects that do not contribute to navigation (such as balloons and clouds), the system generates 3D data that is more relevant and reliable for navigation purposes without processing unnecessary visual information.
2Reliability
If object classification is performed in 2D images to improve 3D information relevance, then navigation accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent performs object classification and identification in the 2D stereo images before generating 3D information. This preliminary action of classifying objects as relevant or irrelevant early in the processing pipeline enables the system to focus computational resources on navigation-critical objects, improving navigation accuracy while managing processing complexity through efficient preprocessing.
3Reliability
If irrelevant objects are excluded from 3D information generation, then data relevance is improved, but information loss occurs
Solution Approach 1:
The patent applies local quality by treating different regions of the visual data differently. Relevant objects (navigation-critical elements) are processed with high fidelity and included in 3D information generation, while irrelevant objects are excluded. This localized differentiation ensures that important information is preserved while unnecessary information is filtered out, resolving the contradiction between relevance and information loss.
Data Source
AI summary
Three-dimensional (3D) information is generated from two or more two-dimensional (2D) images. In the 2D images, one or more objects are identified, and either a corresponding portion of the 3D information representing the same object is identified and/or where the 3D information is corrected correspondingly. The correction may be the leaving out of 3D information representing the object or the correction of the 3D information based on knowledge of a shape of the object.