Monocular 3D Object Pose Detection With 2D-to-3D Pose Mapping
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Solution Overview
Problem
Existing 3D target detection methods based on monocular images are sensitive to rotation estimation errors, leading to poor stability and accuracy in mobile terminals due to complex structures and high costs of binocular cameras and laser radar.
Innovation Solution
A method for detecting object poses using a two-dimensional detection model that maps 2D pose information to 3D pose information through a series of residual networks and prediction networks, employing algorithms like EPnP to minimize rotation estimation errors and improve accuracy and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object detection algorithms are used, then the system is simple to implement, but the detection accuracy is low and cannot meet the requirements of complex application scenarios
Solution Approach 1:
The system segments the detection task into multiple stages: candidate region generation, pose detection, and refinement. By dividing the complex detection process into manageable segments, the system achieves high accuracy while maintaining reasonable computational complexity.
Solution Approach 2:
The patent introduces an intermediary pose detection module that bridges the gap between simple detection and complex analysis. This intermediary component processes candidate regions and generates pose information, enabling accurate detection without requiring the entire system to be overly complex.
2Measurement precision
If high-precision pose detection is implemented, then the detection accuracy is improved, but the computational cost and processing time increase
Solution Approach 1:
The system performs preliminary actions by generating candidate regions and pose proposals before final detection. This preliminary processing filters out obvious non-matches early, reducing the computational burden of high-precision detection while maintaining accuracy for promising candidates.
Solution Approach 2:
The patent applies partial action by focusing computational resources on the most promising candidate regions rather than processing all possible regions with full precision. This selective approach achieves high accuracy where needed while reducing overall processing time.
3Productivity
If the detection model is trained with limited data, then the training process is faster and simpler, but the model generalization ability is poor
Solution Approach 1:
The system performs preliminary data preparation by generating synthetic training data and augmenting existing data before model training. This preliminary action expands the effective training dataset, improving model generalization without requiring proportionally more real training data or training time.
Solution Approach 2:
The patent uses data copying and augmentation techniques to create varied training examples from limited source data. By copying and transforming existing training samples through various augmentations, the system achieves better generalization while maintaining fast training speeds.
Data Source
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AI summary
Embodiments of the present application provide an object pose detection method and apparatus, a computer device, and a storage medium. The method comprises: obtaining image data, the image data comprising a target object; inputting the image data into a two-dimensional detection model, and detecting two-dimensional first pose information when a three-dimensional bounding box is projected to the image data, the bounding box being used for detecting the target object; mapping the first pose information into three-dimensional second pose information; and detecting third pose information of the target object according to the second pose information.