arXiv AI July 20, 2026 By Stefano Silvestrini, Michele Ceresoli

On the Geometry of Learned Representations in Event-Based Multi-Modal Egomotion Estimation

arXiv:2607. 15794v1 Announce Type: cross Abstract: Classical approaches to event-based egomotion estimation, including those adopted by the top-performing teams of the ELOPE challenge, rely on geometric optimization frameworks such as contrast maximization, homography estimation, or dense optical flow combined with analytic motion inversion.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention

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MIME: Multimodal Interactive Motion Encoder