Computer Vision News - June 2023

6 CVPR Best Paper Award Candidate approach so that the network implicitly learns the association between a grayscale patch and event sequences. “ Another big challenge was the overfitting to training data,” Nico tells us. “We needed to come up with different augmentation strategies to augment the synthetic data. We proposed a novel supervision method that can be used directly on real data and relies only on the camera poses . With that, we can fine-tune our network on the real data. ” Carter adds: “ Our model predicts the overall performance of feature tracking. “ What is new about this paper is that we have an end-to-end solution to feature tracking that combines both frame-based and event-based domains ,” Mathias explains. “One of the big challenges, if you use both modalities, is how can you associate information from one modality to the other while not losing the advantages of each modality? ” Existing event-based trackers use model assumptions to solve the problem. In this work, the team uses a novel data-driven