Refining Image Keypoints via Feature Embeddings
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Solution Overview
Problem
Existing computer-vision techniques for generating keypoints and descriptors are computationally intensive, poorly suited for real-time operation, and perform poorly in low-light or harsh conditions, especially when dealing with large viewpoint changes.
Innovation Solution
The system refines image keypoints by encoding them into keypoint embeddings, encoding descriptors into descriptor embeddings, combining these embeddings to generate feature embeddings, and then using these embeddings to refine the keypoints and descriptors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional keypoint and descriptor generation techniques are used, then computational complexity is high, but real-time performance is poor
Solution Approach 1:
The system segments the feature extraction process into separate encoding networks for keypoints and descriptors, allowing parallel processing and optimization of each component independently, thereby reducing overall computational complexity while maintaining real-time performance
Solution Approach 2:
The system changes parameters by using learned embedding dimensions and network architectures that balance computational efficiency with feature quality, enabling real-time operation without significant loss in accuracy
2Measurement precision
If traditional keypoint and descriptor techniques are used, then computational resources are consumed, but accuracy in challenging conditions is poor
Solution Approach 1:
The system introduces embedding vectors as intermediary representations between raw image data and final keypoint/descriptor outputs, allowing for more efficient computation while preserving or enhancing feature quality in challenging conditions
Solution Approach 2:
The system performs preliminary encoding of keypoints and descriptors into compact embedding representations before final processing, reducing the computational burden of subsequent operations while maintaining accuracy in low-light and harsh conditions
3Reliability
If traditional feature extraction is used, then processing speed is limited, but robustness to viewpoint changes is poor
Solution Approach 1:
The system transforms keypoint and descriptor data into embedding space with different dimensional characteristics, enabling more robust representation of viewpoint variations while maintaining processing efficiency through optimized network architectures
Data Source
AI summary
Systems and techniques are described herein for refining image keypoints. For instance, a method for refining image keypoints is provided. The method may include encoding keypoints to generate keypoint embeddings, wherein each keypoint of the keypoints comprises a respective image coordinate; encoding descriptors to generate descriptor embeddings, wherein each descriptor of the descriptors comprises a respective vector of values based on pixels within a threshold distance from a respective image coordinate of a respective keypoint corresponding to the descriptor; combining the keypoint embeddings and the descriptor embeddings to generate feature embeddings; refining the keypoints based on the feature embeddings; and refining the descriptors based on the feature embeddings.