Active Learning Point Cloud Selection for Diverse Training
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Solution Overview
Problem
Existing active learning systems face inefficiencies in training neural networks for generating point clouds due to low diversity in data, resulting from overlapping or too distant point clouds, which can lead to poorly trained models.
Innovation Solution
An active learning system that selectively uses adjacent frames for training based on uncertainty estimations of point cloud points and overlap ratios, ensuring sufficient confidence and overlap for effective self-supervised learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If point clouds are generated iteratively using self-supervised learning, then training without labeled data is achieved, but data diversity decreases due to overlapping or too distant point clouds
Solution Approach 1:
The system dynamically adjusts the selection criteria for training frames based on real-time analysis of point cloud overlap ratios and uncertainty estimates. By continuously adapting which frames are selected for training based on current system state, the method maintains data diversity while using self-supervised learning without labeled data.
Solution Approach 2:
The patent changes the parameter of frame selection criteria by introducing overlap ratio thresholds and uncertainty probability thresholds. This allows the system to selectively train on frames that provide sufficient new information while avoiding redundant or insufficient data, thus maintaining diversity in the training set.
2Stability of the object's composition
If point clouds significantly overlap, then continuous mapping is achieved, but additional processing is required for alignment and data diversity is reduced
Solution Approach 1:
The system extracts and removes overlapping frames from the training set based on calculated overlap ratios. By taking out frames that would cause redundant processing and reduce diversity, the method maintains continuous mapping capability while avoiding the complexity of aligning highly overlapping data points.
Solution Approach 2:
The system performs preliminary calculation of overlap ratios between consecutive frames before selecting them for training. This preliminary action filters out frames that would require complex alignment processing, thereby maintaining continuous mapping while reducing downstream processing complexity.
3Device complexity
If point clouds are too far apart, then processing simplicity is maintained, but no information is shared and training effectiveness decreases
Solution Approach 1:
The system uses feedback from uncertainty estimates of depth values to determine whether to include a frame in training. Frames with high uncertainty (indicating insufficient overlap) are excluded from training, ensuring that only frames providing reliable information are used, thus maintaining training effectiveness without complex processing.
Solution Approach 2:
The system applies partial action by selectively training on only those frames that meet the overlap and uncertainty criteria, rather than processing all available frames. This maintains processing simplicity by avoiding unnecessary computation on ineffective frames while ensuring training effectiveness through selective processing.
Data Source
AI summary
Disclosed are systems and methods for training an active learning system. In one example, a method for training an active learning system includes the steps of generating a point cloud based on a first image captured at a first pose, projecting the point cloud into a second image captured at a second pose to determine an overlap ratio between the point cloud and the second image, and using the second image to train the active learning system based on probabilities associated with the points of the point cloud projected into the second image and the overlap ratio.