Multimodal Sensor Data Insertion for Realistic 3D Augmentation
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Solution Overview
Problem
Existing methods for generating synthetic sensor data lack the ability to create realistic and controlled sensor data, particularly for sensor modalities like radar, which are difficult to simulate accurately, leading to inconsistencies that affect the performance of machine learning models in safety-critical applications such as autonomous vehicles.
Innovation Solution
A method involving a generative model that trains on multimodal sensor data to insert objects with controlled 3D geometric constraints, using a combination of image and point cloud data to enhance realism and accuracy in sensor data augmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If classical physics-based sensor models are used to generate synthetic sensor data, then the generation process is controllable and can be implemented with existing tools, but the realism and accuracy of the generated data is insufficient, particularly for radar modalities
Solution Approach 1:
The patent uses diffusion models trained on real sensor data pairs to learn the statistical properties and realistic characteristics of actual sensor data. The model copies the underlying data distribution and relationships from real training samples, enabling generation of highly realistic synthetic data that preserves authentic sensor characteristics without requiring complex physics-based simulations
Solution Approach 2:
The patent replaces classical physics-based sensor models with machine learning-based diffusion models. This substitution transitions from deterministic physics equations to probabilistic learned models, achieving superior realism for complex modalities like radar while maintaining controllability through the learned data distributions
2Reliability
If more real sensor data is collected for training machine learning models, then the model performance and safety can be improved, but the cost and time required for data collection and annotation increase significantly
Solution Approach 1:
The patent performs preliminary action by training diffusion models on real sensor data in advance. Once trained, these models can generate unlimited amounts of realistic synthetic training data without requiring additional real data collection or annotation efforts, thus preserving model performance while eliminating time losses
Solution Approach 2:
The trained diffusion models copy the statistical properties and characteristics of real sensor data, creating synthetic copies that are indistinguishable from real data for training purposes. This copying mechanism provides unlimited training data without requiring repeated real-world data collection campaigns
3Productivity
If synthetic sensor data is used to train machine learning models, then the productivity and scalability of model training is improved, but the discrepancies between real and synthetic data reduce training accuracy
Solution Approach 1:
The diffusion models are trained to copy the authentic statistical properties, noise characteristics, and data distributions of real sensor data. This ensures that synthetic data generated by the models maintains high fidelity to real data, eliminating training accuracy degradation while preserving the productivity benefits of synthetic data generation
Solution Approach 2:
The patent replaces traditional synthetic data generation methods with diffusion-based generative models that learn realistic data distributions. This substitution maintains training accuracy by generating data that preserves authentic sensor characteristics, while simultaneously enabling scalable and efficient model training through unlimited synthetic data generation
4Adaptability or versatility
If controlled object insertion is implemented in synthetic sensor data, then the ability to test specific scenarios is improved, but the complexity of the data generation process increases
Solution Approach 1:
The diffusion models learn to copy realistic object appearances and sensor characteristics from training data. When performing controlled object insertion, the models generate synthetic objects that authentically match the sensor modality and environmental conditions, providing versatile scenario testing without requiring complex manual data manipulation
Solution Approach 2:
The diffusion models are pre-trained on diverse sensor data including various objects and scenarios. This preliminary training enables the models to perform controlled object insertion by simply providing target objects and locations, automatically generating realistic integrations without requiring complex post-processing or manual adjustments
Data Source
AI summary
The present disclosure relates to techniques for training a generative model to insert an object in spatial sensor data. A first training sample of spatial sensor data of a first sensor modality, and a second training sample of spatial sensor data of a second sensor modality are received, the first training sample and the second training sample capture a common object. A first portion of sensor data corresponding to the object is removed from the first training sample, resulting a cropped training sample. A second portion of spatial sensor data corresponding to the common object is extracted from the second training sample. The generative model is trained to reconstruct the first training sample from the cropped training sample by: providing to the generative model: the cropped training sample as a target input, and the second portion of spatial sensor data as a reference input, resulting in a generated output sample of spatial sensor data, and tuning parameters of the generative model to reduce a reconstruction error between the first training sample and the generated output sample. This results in a trained generative model configured to insert at inference, in a first set of spatial sensor data of the first modality received as a target input, an object indicated in a second set of spatial sensor data of the second modality received as a reference input.