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This study introduces a Real-to-Synthetic Feature Transform (REST) to improve camera localization accuracy. REST enhances feature matching between real and synthetic images, boosting robustness against illumination variations.

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Area of Science:

Computer Vision
Robotics
Machine Learning

Background:

Accurate camera localization is crucial for tracking systems but is sensitive to illumination.
Training localization algorithms with diverse lighting conditions improves robustness but is time-intensive.
Synthetic images offer controlled illumination but suffer from a real-to-synthetic domain gap.

Purpose of the Study:

To introduce a novel method for robust camera localization.
To bridge the domain gap between real and synthetic images for improved feature matching.
To enhance the accuracy and reliability of camera localization algorithms under varying illumination.

Main Methods:

Development of a Real-to-Synthetic Feature Transform (REST) using a fully connected neural network.
Transforming real image features into their synthetic counterparts.
Matching transformed real features against a synthetic image database for localization.

Main Results:

REST effectively reduces the impact of the real-to-synthetic domain gap.
Experimental results demonstrate a significant improvement in matching accuracy.
REST achieved approximately 28% improvement in matching accuracy compared to a baseline method.

Conclusions:

The proposed Real-to-Synthetic Feature Transform (REST) enhances camera localization robustness.
REST enables more accurate feature matching by aligning real and synthetic image features.
This approach offers a time-efficient solution for improving localization algorithms' performance under diverse lighting.