Single-Stage 6-DoF Object Pose Estimation From RGB Keypoints
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Solution Overview
Problem
Existing methods for determining the pose and relative dimensions of an object from an image, especially without depth information, are inefficient and inaccurate, particularly in environments requiring precise robotic grasping and object recognition.
Innovation Solution
A single-stage keypoint-based neural network system that processes RGB images to estimate six degrees of freedom (6-DoF) pose and relative dimensions of objects using a combined representation of displacements and heatmaps, with a convolutional gated recurrent unit (convGRU) for sequential feature association, enabling robust detection and regression of object locations and dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to determine pose and relative dimensions from images without depth information, then the system can operate with simple imaging equipment, but the accuracy and efficiency of object pose estimation deteriorates
Solution Approach 1:
The system segments the object detection task into multiple components: 2D bounding box detection, keypoint detection, and relative dimension estimation. This segmentation allows each component to be optimized independently, improving overall accuracy while maintaining computational efficiency through specialized processing for each sub-task.
Solution Approach 2:
The system transitions from 2D image space to 3D pose estimation by detecting keypoints and inferring three-dimensional positions and orientations. This dimensional transformation enables accurate pose estimation from 2D images without requiring depth sensors, resolving the contradiction between using simple equipment and achieving high measurement precision.
2Measurement precision
If multi-stage methods are used to improve accuracy of pose estimation, then measurement precision improves, but device complexity and processing time increase
Solution Approach 1:
The system merges multiple detection functions into a single unified neural network that simultaneously performs 2D bounding box detection, keypoint detection, and relative dimension estimation. This consolidation maintains high accuracy by integrating multiple tasks while reducing device complexity compared to separate multi-stage systems.
Solution Approach 2:
The neural network is designed with multi-functionality to handle multiple detection tasks within a single model architecture. This universal approach achieves high measurement precision across different object categories and poses without requiring separate specialized systems for each function.
3Reliability
If traditional methods are used for object detection without depth information, then the system remains simple to implement, but the reliability of robotic grasping and object recognition deteriorates
Solution Approach 1:
The system introduces keypoint detection as an intermediary step between 2D image input and 3D pose estimation. These keypoints serve as intermediate representations that bridge the gap between 2D images and 3D object understanding, significantly improving the reliability of robotic grasping without requiring complex depth sensing equipment.
Data Source
AI summary
Apparatuses, systems, and techniques to determine a pose and relative dimensions of an object from an image. In at least one embodiment, a pose and relative dimensions of an object are determined from an image based at least in part on, for example, features of the image.