Neural Network Pose Estimation via Vector Conversion
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Solution Overview
Problem
Current methods for multi-dimensional object pose estimation in neural networks are either computationally expensive due to reliance on rotation refinement techniques or provide weak predictions, making them unsuitable for real-world applications that require efficient and accurate pose estimation.
Innovation Solution
An end-to-end approach for six-dimensional object pose estimation using machine learning models that predict parameterized rotation and translation vectors, converting them into non-parameterized vectors to update image data, thereby reducing computational complexity and improving accuracy without the need for additional refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rotation refinement techniques are used to improve accuracy, then measurement precision improves, but device complexity and computational cost increase
Solution Approach 1:
The patent applies preliminary action by performing pose estimation for all objects in the image simultaneously during the initial network forward pass, rather than sequentially refining each object's pose. The network architecture predicts poses for multiple objects in parallel, eliminating the need for subsequent iterative refinement steps that would increase computational complexity.
Solution Approach 2:
The patent merges the pose estimation processes for multiple objects into a single unified network execution. Instead of separate refinement networks for each object, the system combines all pose estimation tasks into one end-to-end process that handles multiple objects simultaneously, reducing overall computational burden while maintaining accuracy.
2Measurement precision
If multiple machine learning models are employed to improve accuracy, then measurement precision improves, but productivity decreases
Solution Approach 1:
The patent combines multiple pose estimation functions into a single machine learning model that processes all objects in an image simultaneously. This unified approach eliminates the need to sequentially execute multiple separate models, thereby maintaining high accuracy while significantly improving processing speed and productivity for real-world applications.
3Ease of operation
If traditional two-stage approaches with PnP are used, then ease of operation improves, but measurement precision worsens
Solution Approach 1:
The patent changes the parameter representation from traditional keypoint coordinates to direct pose parameters (rotation and translation vectors). This parameter transformation allows the network to directly predict accurate pose values without relying on the PnP algorithm, thereby improving measurement precision while maintaining implementation simplicity through a streamlined end-to-end approach.
Data Source
AI summary
Disclosed herein are systems and methods that provide an end-to-end approach for performing multi-dimensional object pose estimation in the context of machine learning models. In an implementation, processing circuitry of a suitable computer inputs image data to a machine learning model that predicts a parameterized rotation vector and a parameterized translation vector for an object in the image. Next, the processing circuitry converts the parameterized rotation vector and the parameterized translation vector into a non-parameterized rotation vector and a non-parameterized translation vector respectively. Finally, the processing circuitry updates the image data based on the non-parameterized rotation vector and the non-parameterized translation vector.