Understanding Temporal Semantic Stability in Open-Vocabulary UAV Perception
Overview
This research investigates temporal semantic stability in UAV perception by associating frame-wise predictions with persistent world-space locations through metric 3D fusion. The study introduces a voxel-level evaluation framework that characterizes semantic agreement, belief drift, and observation persistence, revealing that high aggregate world-space agreement can overstate temporal stability when many locations have limited repeated-observation support.
From arXiv Robotics — research abstracts
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- arXiv Robotics — research abstractsSignalUnderstanding Temporal Semantic Stability in Open-Vocabulary UAV Perception through Metric 3D Fusion
This research investigates temporal semantic stability in UAV perception by associating frame-wise predictions with persistent world-space locations through metric 3D fusion. The study introduces a voxel-level evaluation framework that characterizes semantic agreement, belief drift, and observation persistence, revealing that high aggregate world-space agreement can overstate temporal stability when many locations have limited repeated-observation support.
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