Pose Estimation With Edge-Guided Synthetic Keypoint Training
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Solution Overview
Problem
Current state-of-the-art pose estimation methods in computer vision are time-consuming and require extensive labeled training data, which is difficult to obtain, especially for a wide range of objects, making them inefficient and impractical for scalable applications.
Innovation Solution
A computer-implemented pose estimation method using a previously trained artificial neural keypoint detection network that utilizes synthetic models for training, determining edge data and keypoints from a visual representation, and estimating poses based on these keypoints, bridging the domain gap with a structured training data preparation scheme.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning methods with comprehensive databases are used for pose estimation, then detection accuracy is improved, but processing time increases and data availability becomes problematic
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on synthetic data before deployment. The keypoint detection network is trained offline on generated synthetic images with known poses, so that during actual pose estimation, the pre-trained network can quickly process real images without requiring extensive real-world training data collection and processing time
Solution Approach 2:
The patent uses copying by creating synthetic copies of real objects through 3D models and rendering them as training images. Instead of collecting and processing numerous real-world images with manual annotations, the system generates synthetic training data that replicates the visual appearance and geometric properties of real objects, enabling efficient training without time-consuming data collection
2Measurement precision
If comprehensive labeled training data is collected for diverse objects, then detection accuracy is improved, but data collection and annotation effort increases
Solution Approach 1:
The system creates synthetic copies of objects using 3D models and rendering software. Instead of manually collecting and annotating real images, the patent generates training data by rendering 3D models from various viewpoints, lighting conditions, and backgrounds, automatically producing labeled training images without manual annotation effort
Solution Approach 2:
The system applies self-service by automatically generating training data and annotations through the rendering pipeline. The synthetic data generation process automatically produces images with known ground truth poses, keypoint locations, and object boundaries without requiring human annotators, making the data preparation process self-sufficient
3Productivity
If synthetic data is used for training, then data collection effort is reduced and training efficiency is improved, but domain gap between synthetic and real data may affect accuracy
Solution Approach 1:
The patent applies parameter changes by systematically varying rendering parameters such as lighting conditions, camera angles, background environments, and object poses during synthetic data generation. This creates diverse training samples that cover a wide range of possible real-world scenarios, reducing the domain gap between synthetic training data and real test images
Solution Approach 2:
The system applies local quality by ensuring that synthetic training images locally resemble real images in terms of lighting, shadows, textures, and geometric properties. By carefully controlling rendering parameters to match real-world conditions, the synthetic data maintains local visual characteristics that help the network generalize to real images
Data Source
AI summary
Various embodiments of the teachings herein include a computer implemented pose estimation method for providing poses of objects of interest in a scene. The scene comprises a visual representation of the objects of interest in an environment. The method comprising: conducting for each one of the objects of interest a pose estimation; determining edge data of the object of interest from the visual representation representing the edges of the respective object of interest; determining keypoints of the respective object of interest by a previously trained artificial neural keypoint detection network, wherein the artificial neural keypoint detection network utilizes the determined edge data of the respective object of interest Oi as input and provides the keypoints of the respective object of interest as output; and estimating the pose of the respective object of interest based on the respective object's keypoints provided by the artificial neural keypoint detection network.