3D Pose Estimation Using Synthetic Training for Occlusion Robustness
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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 computational overhead and inaccurate results due to occlusions and noise.
Innovation Solution
Training Machine Learning Models, such as Convolutional Neural Networks, using simulated data from a simulated environment to predict 3D pose labels, which excludes the need for real-world data and enhances robustness to occlusions and noise by generating synthetic training data on-the-fly, adapting to deployment scenarios and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional systems use trained augmented autoencoders with codebook generation and lookup for 3D pose estimation, then labeled training data requirements are reduced, but computational overhead increases and accuracy decreases in occluded scenarios
Solution Approach 1:
The patent uses synthetic 3D rendered images as copies of real-world objects to train the neural network, eliminating the need for costly real-world labeled data collection. The synthetic images are generated from 3D models with known ground truth poses, providing unlimited training data without physical object manipulation.
Solution Approach 2:
The patent introduces a segmentation network as an intermediary component that processes the input image to generate object masks before feeding to the pose estimation network. This intermediary step isolates the object of interest from occlusions and background, improving pose estimation accuracy in challenging scenarios.
2Reliability
If augmented autoencoders are trained with various noise augmentations (occlusions, lighting, reflections) to enhance robustness, then robustness to noise improves, but the number of parameters required increases significantly
Solution Approach 1:
The patent applies data augmentation techniques including random occlusions, lighting variations, and geometric transformations during the training phase. By pre-exposing the network to noisy and varied synthetic images during training, the model learns robust features without requiring complex architectures with numerous parameters.
Solution Approach 2:
The patent employs data augmentation by dynamically changing image parameters such as brightness, contrast, rotation, and occlusion patterns during training. This allows the model to learn invariance to these transformations without increasing model capacity, maintaining parameter efficiency while improving robustness.
3Measurement precision
If real-world data is collected for every specialized robotics scenario to improve accuracy, then estimation accuracy improves, but time consumption and error proneness increase prohibitively
Solution Approach 1:
The patent generates synthetic training data by rendering 3D models of objects in various poses and environments, creating copies that simulate real-world scenarios without physical data collection. This approach provides unlimited diverse training data instantaneously, eliminating time-consuming field data collection and manual labeling processes.
Solution Approach 2:
The patent pre-processes and prepares synthetic training data in advance by generating diverse 3D rendered images with known ground truth poses, lighting conditions, and occlusion patterns. This preliminary data preparation creates a ready-to-use training dataset that covers multiple specialized scenarios without requiring subsequent real-world data collection for each application.
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.