Ethan Elms

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About

Ethan Elms is a researcher at the forefront of neuromorphic vision and its application to space-based robotics. His primary research areas include event-based sensing, 3D perception, and satellite pose estimation—fields that leverage the high temporal resolution of event cameras to overcome the limitations of traditional frame-based imaging. Elms’s most notable contribution is his pioneering work on "Event-based Structure-from-Orbit," which demonstrates how event sensors can be used to reconstruct 3D structures of objects undergoing rapid circular or spinning motion without motion blur—a critical capability for autonomous spacecraft navigation and debris tracking. This paper has already garnered 10 citations since its 2024 publication, signaling strong early impact. Additionally, Elms co-created the SEENIC dataset, a specialized resource for spacecraft pose estimation using neuromorphic vision. This dataset, which provides synchronized event streams and ground truth camera poses across 20 scenes, has been instrumental in bridging the domain gap between simulated and real-world space environments. With 2 citations each for its two 2022 entries, the SEENIC dataset is a foundational tool for researchers working on satellite servicing and on-orbit autonomy. Elms’s work is shaping the future of vision-based navigation in the challenging conditions of space.