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With the development of Unmanned Surface Vehicles (USVs), the perception of inland waterways has become significant to autonomous navigation. RGB cameras can capture images with rich semantic features, but they would fail in adverse weather and at night. As a perception sensor that has initially emerged in recent years, 4D millimeter-wave radar (4D mmWave radar) can work in all weather and has more abundant point-cloud features than ordinary radar, but it also suffers from water-surface clutter seriously. Furthermore, the shape and outline of dense point cloud captured by 4D mmWave radar are irregular. CNN-based neural networks treat features as 2D rectangle grids, which excessively favor image modality and are unfriendly to radar modality. Therefore, we transform both features of image and radar into non-Euclidean space as graph structures. In this paper, we focus on robust panoptic perception in …