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.· 0 citations
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.· 0 citations
4D millimeter-wave (mmWave) radar enables all-weather 3D object detection with reliable Doppler sensing. However, its practical application is hindered by inherent sparsity and noise. While multi-frame accumulation densifies point clouds, it inevitably introduces motion-induced spatiotemporal misalignment and geometric...
Xing-Kai Jin, Guang-Xian Xu, Fei Ma et al.· IEEE Robotics and Automation...· 0 citations
Reliable autonomous driving requires full-scene perception that couples foreground objects with dense semantic layout. Recently, 4D millimeter-wave radar has emerged as a robust and affordable sensor, yet its sparse returns make radar-camera fusion necessary for comprehensive scene understanding. Existing radar-camera...
Xiaokai Bai, Lianqing Zheng, Runwei Guan et al.· 0 citations
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