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Oct 2026
Roadside 4D occupancy forecasting predicts future semantic occupancy in a fixed infrastructure coordinate system. Roadside scenes are globally structured yet locally evolving, requiring comprehensive scene-level context while precisely modeling sparse localized motion. However, existing forecasting paradigms struggle t...
Song Wang, Xiao-Kai Bai, Lei Yang et al.
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Preprint
Sep 2026
Fixed roadside cameras repeatedly observe a stable scene overlaid by sparse moving traffic. Temporal memory can recover weak observations, but reusing moving evidence at stale locations can corrupt occupancy predictions. Motion compensation addresses displacement, while reliance on the resulting history remains a separ...
Xiao-Kai Bai, Lei Yang, Song Wang et al.
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Preprint
Sep 2026
4D radar complements dense image semantics with long-range geometry and radial motion, but existing radar--camera detectors largely solve \emph{where} to align the modalities while leaving \emph{whether} a piece of evidence supports an evolving object hypothesis implicit. An image token may describe an occluder, a near...
Xiao-Kai Bai, Zhen-Yu Fan, Lian-Qing Zheng et al.
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Jul 2026
A 4D radar-camera framework for 360 full-scene perception, which models semantic occupancy as a persistent scene state rather than a terminal output, and follows a cross-modal state reasoning paradigm.
Xiao-Kai Bai, Lian-Qing Zheng, Run-Wei Guan et al.
· arXiv.org · 3 citations
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