3D Point Cloud Generation With Unified Object And Category Labels
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Solution Overview
Problem
The processing cost of generating three-dimensional point cloud data for object recognition is high due to the need for class classification-specific learning data sets, and the cost increases when recognition labels differ by task.
Innovation Solution
An information processing apparatus that includes a recognition unit for image processing, a classification unit for task-specific classification, and a three-dimensional point cloud generation unit to generate classification results for each point, reducing the need for task-specific learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If three-dimensional point cloud data is generated with class classification for object recognition, then object recognition capability is improved, but processing cost increases due to the need for classification-specific learning data sets
Solution Approach 1:
The patent applies universality by creating a single learning data set with unified class classification that can be used across multiple tasks and applications. Instead of preparing separate learning data sets for each task, the system uses one universal data set that enables various object recognition tasks (obstacle detection, background object recognition, etc.) to be performed with the same classified three-dimensional point cloud data, thereby reducing processing costs while maintaining recognition capability.
2Measurement precision
If separate learning models are prepared for each task with different recognition labels, then task-specific recognition accuracy is improved, but processing cost increases
Solution Approach 1:
The patent implements universality by developing a single learning model that processes unified class classification results for multiple tasks. The learning model is trained on a universal learning data set with standardized class labels, enabling it to perform various recognition tasks (automatic driving, AR, SLAM, etc.) without requiring separate task-specific models, thus reducing processing costs while maintaining task-specific recognition accuracy through flexible application of the same model.
3Adaptability or versatility
If three-dimensional point cloud data undergoes class classification defined in advance, then object categorization capability is improved, but processing cost of generating learning data set increases
Solution Approach 1:
The patent applies preliminary action by pre-defining a unified class classification system with standardized categories and labels that can be applied to various tasks. The learning data set is generated in advance with this predefined classification structure, enabling flexible adaptation to different tasks without requiring reclassification or additional processing, thereby reducing generation costs while maintaining categorization capability.
Solution Approach 2:
The patent creates a universal class classification framework with standardized categories that serve multiple purposes across different applications. The same classification system and learning data set can be used for various tasks (obstacle detection, scene understanding, navigation, etc.), eliminating the need to create separate classification systems for each task and thereby reducing processing costs while maintaining versatile categorization capability.
Data Source
AI summary
A system for generating three-dimensional (3D) point cloud data comprising: at least one first processor configured to generate, based on two-dimensional (2D) image data, the 3D point cloud data, wherein each point of the 3D point cloud data comprises: position information, the position information comprising at least three coordinates indicating a position of the point; object information labeling the point as a first object selected from a plurality of objects; and category information labeling the point as belonging to a first category of a plurality of categories, wherein each of the plurality of categories comprises at least one respective object of the plurality of objects.