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You will research and develop advanced deep learning methods to improve keypoint matching accuracy for autonomous driving applications.

You will design a unified neural network architecture that jointly addresses correspondence refinement, outlier rejection, and uncertainty estimation, aiming to replace complex conventional post-processing chains.

You will investigate novel approaches to achieve sub-pixel precision and handle ambiguous image textures within the feature matching pipeline.

You will implement and train these models, focusing on efficiency and the potential for distilling the architecture into a fast, real-time capable solution.

You will benchmark your approach against current state-of-the-art matchers (e.g., in Visual Odometry scenarios) to demonstrate improvements in robustness and accuracy.