Monocular Depth Estimation Using Synthetic Data Domain Adaptation
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Solution Overview
Problem
Monocular depth estimation in computer vision is challenging due to the lack of well-prepared training data, with existing methods struggling to achieve high accuracy when using either real-world data lacking ground truth or synthetic data that differs from real-world scenarios.
Innovation Solution
A combination of synthetic and real data is used for training depth estimation models, employing adversarial loss and domain adaptation techniques to account for domain differences, leveraging cycle generative adversarial networks and dynamic sample weighting to improve model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If synthetic data is used for training depth estimation models, then training can be performed without large curated real-world datasets, but the model accuracy deteriorates due to domain differences between synthetic and real-world scenarios
Solution Approach 1:
A domain adaptation layer is introduced as an intermediary component between the synthetic data training process and the real-world deployment. This layer learns to transform features from the synthetic domain to match the real-world domain distribution, effectively mediating the domain gap. The domain adaptation layer is trained using adversarial loss to minimize distribution differences while preserving depth estimation accuracy.
Solution Approach 2:
The patent dynamically adjusts training parameters including domain adaptation weights and adversarial loss coefficients during the training process. By changing these parameters adaptively, the system optimizes the balance between learning from synthetic data and adapting to real-world conditions, thereby maintaining high accuracy despite using synthetic training data.
2Measurement precision
If real-world data is used for training depth estimation models, then model accuracy improves, but the availability of well-prepared training data deteriorates due to lack of ground truth labels
Solution Approach 1:
The patent uses synthetic data as a copy or approximation of real-world data. By generating realistic synthetic images with known ground truth depth labels, the system creates surrogate training data that mimics real-world scenarios without requiring actual real-world annotations. This copying approach enables supervised training while avoiding the difficulty of obtaining real-world ground truth.
Solution Approach 2:
A domain adaptation mechanism serves as an intermediary that bridges the gap between synthetic training data and real-world application. This intermediary component learns the transformation necessary to make synthetic data representations compatible with real-world depth estimation tasks, effectively solving the data availability problem while maintaining accuracy.
3Reliability
If domain adaptation techniques are applied to bridge synthetic and real-world data gaps, then model reliability improves, but computational complexity increases
Solution Approach 1:
The domain adaptation process is segmented into distinct trainable components including a domain adaptation layer, discriminator networks, and loss function modules. This segmentation allows for modular training where each component can be optimized independently, reducing overall computational complexity while maintaining reliability. The segmented architecture enables efficient resource utilization during both training and inference phases.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. Data from a source domain and data from a target domain is accessed. A set of machine learning models is trained, based on the data from the source domain and the data from the target domain, to generate depth outputs based on input images. Training the set of machine learning models includes: generating a discriminator output based at least in part on an input image frame from either the source domain or the target domain, generating an adversarial loss based on the discriminator output, and refining one or more machine learning models of the set of machine learning models based on the adversarial loss.