Dynamic Feature Extraction for 3D Object Pose Estimation
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Solution Overview
Problem
Current 3D object pose estimation technologies struggle with accuracy and speed, particularly in varying scenes, due to the use of fixed feature extraction strategies, which require continuous adjustments by professionals and are time-consuming, posing an entry barrier for industries like metal processing.
Innovation Solution
An object pose estimation system that employs a feature extraction strategy neural network model to dynamically determine the feature extraction strategy based on a scene point cloud, allowing for quick adaptation and optimal feature extraction for accurate pose estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed feature extraction strategy is used for 3D object pose estimation, then the system structure remains simple, but the estimation accuracy and speed cannot be improved when scenes vary
Solution Approach 1:
The patent implements a dynamic feature extraction strategy where the system automatically adapts feature extraction parameters based on scene characteristics. The neural network model dynamically selects and adjusts feature extraction strategies according to the specific scene point cloud being processed, transforming the static feature extraction approach into a dynamic one that responds to scene variations, thereby improving pose estimation accuracy without requiring manual intervention for each scene change
Solution Approach 2:
The patent changes the parameters of the feature extraction strategy based on scene characteristics. The neural network model analyzes the scene point cloud and automatically adjusts feature extraction parameters such as feature types, extraction depth, and processing intensity to match the specific scene conditions, enabling accurate pose estimation across diverse scenes without increasing system structural complexity
2Ease of operation
If a fixed feature extraction strategy is used, then the system is easy to operate, but continuous manual adjustments are required when scenes change
Solution Approach 1:
The patent implements self-service through an automated neural network model that independently determines and adjusts the feature extraction strategy based on scene point cloud analysis. The system performs self-adjustment without requiring professional engineers to manually modify parameters, automatically adapting to different scenes and eliminating the need for continuous manual intervention, thus maintaining ease of operation while eliminating time loss from manual adjustments
Solution Approach 2:
The patent incorporates feedback mechanisms where the neural network model continuously analyzes the scene point cloud and adjusts the feature extraction strategy accordingly. The system receives feedback from scene analysis and automatically modifies its feature extraction approach in real-time, creating a closed-loop control system that eliminates manual adjustments while maintaining optimal performance across varying scenes
3Measurement precision
If professional engineers continuously adjust the feature extraction strategy, then the pose estimation accuracy can be maintained, but the production line cannot be promptly shifted
Solution Approach 1:
The patent replaces the mechanical process of manual parameter adjustment by professional engineers with an intelligent neural network-based automatic adjustment system. The neural network model computationally determines the optimal feature extraction strategy based on scene analysis, substituting human engineering judgment with automated algorithmic decision-making, thereby maintaining pose estimation accuracy while enabling rapid production line switching without human intervention
Solution Approach 2:
The patent performs preliminary analysis of the scene point cloud to automatically determine the appropriate feature extraction strategy before pose estimation begins. The neural network model pre-processes the scene data and configures the optimal feature extraction parameters in advance, eliminating the need for time-consuming manual adjustments when production lines need to be switched, thus maintaining both accuracy and productivity
4Measurement precision
If the same feature extraction strategy is adopted for all scenes, then the system complexity remains low, but the pose estimation accuracy cannot be increased
Solution Approach 1:
The patent transforms the static feature extraction strategy into a dynamic one by implementing a neural network model that automatically adapts feature extraction parameters based on scene characteristics. The system dynamically selects and adjusts feature extraction strategies according to the specific scene point cloud being processed, enabling accurate pose estimation across diverse scenes without requiring complex manual configuration for each scene type
Solution Approach 2:
The patent implements parameter changes in the feature extraction strategy by having the neural network model automatically adjust feature extraction parameters such as feature types, extraction depth, and processing intensity based on scene analysis. This allows the system to optimize pose estimation accuracy for different scene conditions while maintaining a unified system architecture, avoiding the need for multiple fixed strategies
Data Source
AI summary
An object pose estimation system, an execution method thereof and a graphic user interface are provided. The execution method of the object pose estimation system includes the following steps. A feature extraction strategy of a pose estimation unit is determined by a feature extraction strategy neural network model according to a scene point cloud. According to the feature extraction strategy, a model feature is extracted from a 3D model of an object and a scene feature is extracted from the scene point cloud by the pose estimation unit. The model feature is compared with the scene feature by the pose estimation unit to obtain an estimated pose of the object.