Adaptive Point Cloud Augmentation for ML Training
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Solution Overview
Problem
Existing data augmentation techniques for point cloud datasets in machine learning models for tasks like segmentation and object detection are inadequate, as they fail to consider scene understanding and object occurrences, leading to limited diversity and prevalence of object class combinations, which results in models struggling to learn distinguishing features for classes that share common features.
Innovation Solution
The method involves generating augmented point cloud frames using a primary policy based on confusion analysis and quantitative metrics like confusion matrices, applying object instance transformation techniques such as ground truth augmentation, random flip, and frustum dropout to inject specific object instances into targeted frames, addressing model inaccuracies and improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data augmentation techniques are used for point cloud datasets, then the training data volume increases, but the diversity and prevalence of object class combinations remains limited
Solution Approach 1:
The patent implements dynamic data augmentation by using a confusion matrix to identify deficient object classes and selectively injecting only those classes into training frames. This dynamic approach adapts the augmentation process based on model performance metrics, ensuring that the most needed object class combinations are prioritized rather than applying uniform random augmentation to all classes.
Solution Approach 2:
The patent changes the parameters of data augmentation by introducing confusion-based selection criteria. Instead of random or uniform augmentation, the system modifies the augmentation parameters to focus on specific object classes that exhibit confusion in model predictions, thereby improving the diversity of meaningful object class combinations in the training data.
2Reliability
If more labeled point cloud frames are collected to improve model accuracy, then the prediction performance improves, but the time and cost of labeling increases significantly
Solution Approach 1:
The patent uses copying by generating synthetic training examples through data augmentation. Instead of collecting and labeling new real-world point cloud frames, the system creates copies and variations of existing labeled frames by injecting additional object instances, thereby expanding the training dataset without incurring additional labeling costs or time.
Solution Approach 2:
The patent performs preliminary action by pre-identifying deficient object classes using confusion matrix analysis before the actual training process. This allows the system to prepare targeted augmentation strategies in advance, focusing computational resources on the most critical gaps in the training data rather than uniformly processing all possible object classes.
3Quantity of substance
If uniform data augmentation is applied to all object classes, then the data volume increases, but the model cannot effectively learn distinguishing features of similar classes
Solution Approach 1:
The patent applies local quality by treating different object classes differently based on their specific needs identified through confusion matrix analysis. Instead of uniform augmentation, the system selectively applies data injection to specific deficient object classes and their related frames, ensuring that each class receives appropriate attention based on its confusion patterns and importance for model performance.
Data Source
AI summary
Devices, systems, methods, and media are described for adaptive scene augmentation of a point cloud frame for inclusion in a labeled point cloud dataset used for training a machine learned model for a prediction task for point cloud frames, such as object detection or segmentation. A formal method is described for generating new point cloud frames based on pre-existing annotated large-scale labeled point cloud frames included in a point cloud dataset to generate new, augmented point cloud frames. A policy is generated for large-scale data augmentation using detailed quantitative metrics such as confusion matrices. The policy is a detailed and stepwise set of rules, procedures, and/or conditions that may be used to generate augmented data specifically targeted to mitigate the existing inaccuracies in the trained model. The augmented point cloud frames may then be used to further train the model to improve the prediction accuracy of the model.