3D Pose Estimation With Simulated Training and Occlusion Handling
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Solution Overview
Problem
Acquiring and labeling real-world data for 3D pose estimation in robotics is challenging, time-consuming, and prone to error, especially for specialized scenarios, leading to slow adoption of models and inaccurate results due to computational overhead and occlusions.
Innovation Solution
Training autoencoders using simulation data to predict 3D pose labels, which includes an object detector and an autoencoder that predicts segmentation images invariant to occlusions, and adapting models with Generative Adversarial Networks to differentiate between simulated and real-world data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional systems use a trained augmented autoencoder with codebook generation and lookup for 3D pose estimation, then labeled training data requirements are reduced, but computational overhead increases and accuracy decreases in specialized scenarios
Solution Approach 1:
The patent extracts and removes the codebook generation and lookup mechanism from the autoencoder system. Instead of using a separate codebook for decoding, the system directly processes images through the autoencoder to obtain 3D pose estimates, eliminating the computational overhead associated with codebook maintenance and comparison while preserving the core functionality of unsupervised learning from unlabeled data
Solution Approach 2:
The patent enhances the autoencoder to handle multiple functions within a single unified architecture. The model simultaneously performs 3D pose estimation, occlusion handling, and adaptation to specialized scenarios without requiring separate codebooks or multiple specialized models, thereby reducing computational overhead while maintaining versatility across different applications
2Ease of operation
If conventional systems use bounding box comparison to determine object center for 3D pose, then the process is simple, but accuracy decreases when occlusions are present
Solution Approach 1:
The patent applies preliminary actions by training the autoencoder to predict segmentation masks and handle occlusions before determining the object center. The model first processes the input image through the autoencoder to obtain a refined representation that accounts for occlusions, then uses this enhanced representation to accurately determine the object center, rather than directly comparing bounding boxes which would be inaccurate under occlusion
Solution Approach 2:
The patent introduces an intermediary mechanism in the form of the autoencoder's latent space and segmentation predictions. Instead of directly comparing bounding boxes, the system uses the autoencoder's learned representations and segmentation masks as intermediaries to infer the object center, thereby achieving accurate center determination even when occlusions are present
3Reliability
If augmented autoencoders are trained with various noise augmentations to enhance robustness, then noise robustness improves, but the number of parameters required increases significantly
Solution Approach 1:
The patent applies partial action by selectively applying noise robustness techniques only where needed. Instead of training with all possible noise augmentations across the entire system, the approach focuses on training the autoencoder with representative noise patterns and occlusions, achieving sufficient robustness without the excessive parameter inflation that would result from comprehensive noise augmentation training
Data Source
AI summary
An autoencoder may be trained to predict 3D pose labels using simulation data extracted from a simulated environment, which may be configured to represent an environment in which the 3D pose estimator is to be deployed. Assets may be used to mimic the deployment environment such as 3D models or textures and parameters used to define deployment scenarios and/or conditions that the 3D pose estimator will operate under in the environment. The autoencoder may be trained to predict a segmentation image from an input image that is invariant to occlusions. Further, the autoencoder may be trained to exclude areas of the input image from the object that correspond to one or more appendages of the object. The 3D pose may be adapted to unlabeled real-world data using a GAN, which predicts whether output of the 3D pose estimator was generated from real-world data or simulated data.