Point Cloud Object Identification Beyond Range Thresholds
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Solution Overview
Problem
Training deep neural networks (DNNs) to identify objects at varying ranges requires extensive manual annotation of visual or range images, which is time-consuming and resource-intensive, especially for objects beyond a resolution threshold.
Innovation Solution
Generating synthetic point clouds with inserted objects translated to or beyond a range threshold, providing ground truth data without manual annotation, and using these to train DNNs, thereby reducing the time and computational resources needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation of visual or range images is used to train DNNs, then training data accuracy is improved, but time consumption and computational resources increase significantly
Solution Approach 1:
The patent generates synthetic point cloud data that copies the structure and characteristics of real annotated data. Synthetic objects are inserted into point clouds and translated to beyond the range threshold, creating training data that mirrors real-world scenarios without requiring manual annotation. This copying approach preserves the accuracy benefits of annotated data while eliminating the time-consuming manual process.
Solution Approach 2:
The patent uses synthetic data generation as a disposable alternative to manual annotation. Instead of investing significant time and resources into manual annotation processes, the system generates training data through automated synthetic object insertion and translation, treating the data generation as a consumable process that produces sufficient training material without human intervention.
2Measurement precision
If manual annotation of visual or range images is used to train DNNs, then training data accuracy is improved, but computational resources increase significantly
Solution Approach 1:
The patent creates synthetic point cloud datasets that copy the essential features and structures of manually annotated real-world data. By generating training data through automated synthesis rather than manual annotation, the system reduces computational resource consumption while maintaining data accuracy for training DNNs to identify objects at varying ranges.
Solution Approach 2:
The patent replaces the mechanical process of manual annotation with automated synthetic data generation mechanisms. Instead of human annotators processing visual or range images, the system uses computational algorithms to insert and translate synthetic objects into point clouds, substituting manual computational effort with automated processes that consume significantly fewer resources.
3Adaptability or versatility
If objects are translated to beyond the range threshold in synthetic point clouds, then training capability for distant objects is improved, but data resolution requirements increase
Solution Approach 1:
The patent applies different quality characteristics to different parts of the synthetic point cloud data. When objects are translated to beyond the range threshold, the system maintains appropriate local quality by adjusting point cloud density and object representation in the distant regions, ensuring that the training data provides sufficient detail for accurate object identification at remote distances without requiring uniform high resolution throughout the entire dataset.
Data Source
AI summary
Upon obtaining a time series of point clouds, point cloud data associated with an object is inserted in the respective point clouds. In the respective point clouds, the point cloud data is translated such that respective ranges in the point cloud data are increased based on a range threshold. Based on inputting the translated point cloud data to a machine learning program, the object is identified at or beyond the range threshold via output from the machine learning program.