AI RESEARCH
Extending Deep Event Visual Odometry with Sparse Point-Cloud Export
arXiv CS.CV
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ArXi:2605.22890v1 Announce Type: cross Event cameras are well suited for visual odometry under high-speed motion and challenging lighting conditions due to their low latency, high temporal resolution, and high dynamic range. Deep Event Visual Odometry (DEVO) nstrated that monocular event-only odometry can achieve strong performance by combining sparse patch tracking, learned patch selection, recurrent correspondence refinement, and differentiable bundle adjustment. In this project, we extend DEVO with a sparse point-cloud export pipeline.