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Using this dataset, we train a multi-head perception module with staged learning and reprojection consistency, and fuse its outputs with class-conditione","title":"FalconTrack: Photorealistic Auto-Labeled Perception and Physics-Aware Vision-Based Aerial Tracking","url":"https://arxiv.org/abs/2606.29783","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.29783v1 Announce Type: cross \nAbstract: Vision-based aerial tracking is critical in GPS-denied environments. Reliable perception for tracking depends on large-scale labeled data, yet most photorealistic datasets rely on heavy manual annotation and are time-consuming to produce. We present FalconTrack, a unified perception-and-tracking framework that (i) leverages a photorealistic editable simulator for automated label generation and (ii) combines multi-head perception with physics-aware tracking for zero-shot sim-to-real transfer. 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