3D Point Cloud Object Detector Training for Cross-Domain Adaptation
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Solution Overview
Problem
Existing object detection models struggle with scalability and performance when adapted across multiple domains, particularly in 3D LiDAR-based object detection, requiring extensive labeled data and failing to maintain performance across source and target domains.
Innovation Solution
A training pipeline for a cross-domain object detector that utilizes a source and target domain dataset to generate a cross-domain dataset, reducing the need for labeled data and enhancing accuracy by transferring feature representations across domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a cross-domain object detection model is trained using traditional methods with extensive labeled data from multiple domains, then the model can achieve basic cross-domain detection capability, but the training cost becomes prohibitively expensive and the process lacks scalability
Solution Approach 1:
The patent introduces pseudo-labeled data as an intermediary between source domain labeled data and target domain data. The teacher model generates pseudo-labels for unlabeled target domain data, which then serve as training signals for the student model, eliminating the need for expensive human annotation while enabling cross-domain adaptation
Solution Approach 2:
The patent creates a copy of the training process by training multiple student models with different configurations (different numbers of pseudo-labeled data, different teacher models) and ensembles their predictions. This copying approach allows the system to achieve robust cross-domain performance without requiring extensive manual labeling for each domain
2Measurement precision
If a model is adapted to new target domain data to improve performance in the target domain, then target domain detection accuracy improves, but performance on source domain data deteriorates due to bias towards target domain
Solution Approach 1:
The patent merges source domain labeled data and target domain pseudo-labeled data into a unified training set. The student model is trained on this combined dataset, allowing it to learn from both domains simultaneously and maintain performance across both source and target domains through multi-domain joint optimization
Solution Approach 2:
The patent implements dynamic model ensembling where multiple student models with different training configurations are trained and their predictions are dynamically combined. This dynamic approach allows the system to adapt to different domains while maintaining overall performance by leveraging the strengths of different model configurations
3Manufacturing precision
If all training data is labeled to train a generic object detector from multiple domains, then the model achieves comprehensive training coverage, but the labeling process becomes time-consuming and inefficient
Solution Approach 1:
The patent implements self-service labeling where the teacher model automatically generates pseudo-labels for target domain data without human intervention. The system uses its own learned knowledge from source domains to label target domain data, eliminating the need for expensive and time-consuming manual annotation while maintaining reasonable data quality
Solution Approach 2:
The patent performs preliminary training on source domain data to build a teacher model that can generate pseudo-labels before actual target domain training begins. This preliminary action prepares the system to efficiently process target domain data without requiring manual labeling, saving significant time in the overall training pipeline
Data Source
AI summary
A method and server for training an Object Detector (OD) to detect objects in 3D point clouds are provided. The method comprises: during a first stage of a training pipeline: training the OD using a source domain dataset to detect the objects in a source domain, thereby generating a first trained OD; during a second stage of the training pipeline: training the first trained OD using a target domain dataset to detect the objects in a target domain, thereby generating a second trained OD; and during a third stage of the training pipeline: generating, based on the source domain dataset and the target domain dataset, a cross-domain dataset; and training the second trained OD using the cross-domain dataset to detect objects in both the source domain and the target domain, thereby generating a cross-domain OD.