Adapter Network for Monocular 3D Object Detection Domain Gap
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Solution Overview
Problem
Monocular 3-D object detection models trained on synthetic data struggle when adapted to real-world datasets due to the 'domain gap,' where they perform poorly despite being precise on synthetic data, mainly due to the scarcity of annotated real-world data and the expense of 6D annotation processes.
Innovation Solution
An adapter network is trained to adapt a model pre-trained on synthetic images to real-world data by comparing rendered maps generated using a differentiable renderer to output maps, allowing the model to adjust for image deformations and improve transferability to multiple real-world datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If deep neural networks are trained on synthetic data, then manufacturing precision of 3-D bounding boxes is improved, but adaptability to real-world datasets deteriorates
Solution Approach 1:
An adapter network is introduced as an intermediary component between the synthetic data-trained neural network and real-world data. The adapter network receives features from the pre-trained network and learns to transform them to match real-world distributions, effectively mediating the domain gap while preserving the precision learned from synthetic data
Solution Approach 2:
The adapter network learns to transform features by adjusting parameters through domain adaptation training. By comparing rendered maps from the pre-trained network with actual real-world images, the adapter network modifies feature representations to bridge the domain gap, changing the parameter space to accommodate real-world variations
2Adaptability or versatility
If deep neural networks are trained on real-world annotated data, then adaptability to real-world datasets is improved, but loss of time and resources increases due to expensive 6D annotation processes
Solution Approach 1:
Instead of manually annotating real-world data, the system creates synthetic copies of real-world scenes through rendering. The differentiable renderer generates synthetic images that mimic real-world appearances, providing ground truth labels automatically without manual annotation while preserving the statistical properties of real data
Solution Approach 2:
The system performs preliminary rendering of synthetic data with perfect ground truth labels before actual deployment. By pre-computing rendered maps and surface normals for synthetic scenes, the system eliminates the need for time-consuming manual 6D annotation while having adaptation data ready in advance
Data Source
AI summary
Systems and methods for training an adapter network that adapts a model pre-trained on synthetic images to real-world data are disclosed herein. A system may include a processor and a memory in communication with the processor and having machine-readable that cause the processor to output, using a neural network, a predicted scene that includes a three-dimensional bounding box having pose information of an object, generate a rendered map of the object that includes a rendered shape of the object and a rendered surface normal of the object, and train the adapter network, which adapts the predicted scene to adjust for a deformation of the input image by comparing the rendered map to the output map acting as a ground truth.