3D Object Orientation Estimation Without Symmetry Labels
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Solution Overview
Problem
Existing technologies face challenges in determining the 3D orientation of objects, particularly those with symmetries, due to ambiguities in symmetry labeling and the need for explicit symmetry loss specification, which complicates the training of neural networks for pose estimation.
Innovation Solution
A novel approach using an adversarial training paradigm with a viewpoint regressor and a 3D renderer, where a discriminator learns to provide a consistent loss function with object symmetries, and a randomized sampling method is employed to overcome non-differentiability, allowing the system to estimate object orientation without explicit symmetry labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If explicit symmetry labeling and symmetry loss specification are used to train neural networks for pose estimation, then the training process becomes more accurate for symmetric objects, but the system complexity and difficulty of implementation increase significantly
Solution Approach 1:
The patent extracts and removes the need for explicit symmetry labeling and symmetry loss specification from the training process. By using a discriminator network that automatically learns symmetry-invariant features, the method eliminates the complex manual symmetry annotation and specialized loss function design, thereby reducing system complexity while maintaining pose estimation accuracy for symmetric objects
Solution Approach 2:
The patent introduces a discriminator network as an intermediary component between the image input and pose estimation output. This discriminator acts as a mediator that automatically handles symmetry considerations through adversarial training, eliminating the need for direct symmetry loss specification and simplifying the overall training process while improving robustness to symmetric object variations
2Measurement precision
If a discriminator is trained to provide consistent loss function with object symmetries, then the orientation estimation accuracy improves, but the training time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by training the discriminator network in advance with adversarial training to learn symmetry-invariant features before using it for pose estimation. This pre-training phase, while computationally intensive, is performed once and then the trained discriminator can be reused for multiple pose estimation tasks, reducing the overall time cost for subsequent applications
Solution Approach 2:
The patent maintains continuity of useful action by using the trained discriminator continuously during the pose estimation process to provide consistent loss function guidance. Once the discriminator is trained, it continuously provides symmetry-aware constraints during inference, ensuring accurate orientation estimation without requiring repeated training iterations and minimizing time loss
3Stability of the object's composition
If randomized sampling method is used to overcome non-differentiability in adversarial training, then the training stability improves, but the computational complexity increases
Solution Approach 1:
The patent replaces the mechanical gradient computation system with a randomized sampling approach. Instead of computing gradients through the non-differentiable renderer using complex adjoint methods or perturbation techniques, the system uses randomized sampling to estimate gradients, substituting a computationally simpler statistical method for a complex mechanical gradient system, thereby improving training stability while managing computational complexity
Data Source
AI summary
Apparatuses, systems, and techniques to determine orientation of an objects in an image. In at least one embodiment, images are processed using a neural network trained to determine orientation of an object.