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Current event-based SLAM algorithms falter when faced with the full complexity of high-speed, 6-DoF maneuvers, highlighting a gap between current capabilities and the promise of event cameras.
Robot control gets a whole lot faster: ProbeFlow slashes action decoding latency by 14.8x in Vision-Language-Action models, all without retraining.
LLM benchmarks for complex tasks often produce scores that are meaningless and misleading, masking distinct failure modes and hindering progress.
Event cameras can now see in the dark: eAP, a new large-scale dataset, enables robust 3D object detection and time-to-contact estimation even under challenging illumination.
By jointly learning edge structures and event-depth flow, LEAR unlocks more robust event camera localization from LiDAR, even when direct correspondence is unreliable.
