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This point-centric formulation enables flexible, data-dependent receptive fields and long-range spatial interactions that are difficult to capture with local and dense vox","title":"Latent Gaussian Splatting for 4D Panoptic Occupancy Tracking","url":"https://arxiv.org/abs/2602.23172","vendor":"arxiv_cs_ai"},"summary":"arXiv:2602.23172v2 Announce Type: replace-cross \nAbstract: Capturing 4D spatiotemporal scene structure is crucial for the safe and reliable operation of robots in dynamic environments. However, existing approaches typically address only part of the problem: they either provide coarse geometric tracking via bounding boxes or detailed 3D occupancy estimates that lack explicit temporal association and instance-level reasoning. In this work, we present Latent Gaussian Splatting (LaGS) for 4D Panoptic Occupancy Tracking (4D-POT). We revisit the underlying representation and model 3D features as a sparse set of feature-bearing Gaussians. 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