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The success of deep learning in visual SLAM is primarily due to learned 2D data association and uncertainty, not the recurrent architectures that often accompany them.
Suppressing motion-related noise in event camera data can boost Vision Transformer inference by 83% and improve visual odometry accuracy by 13%.