TopoRadar: Learning Persistent Topological Structure for Multi-View Radar Semantic Segmentation

Radar semantic segmentation requires recovering foreground structure from sparse and fragmented observations. Range–angle, range–Doppler, and angle–Doppler projections provide complementary spatial and motion information, yet their different projection geometries can separate or merge object responses. Pixel-wise supervision does not explicitly constrain the resulting connectivity errors. We formulate multi-view radar semantic segmentation as a topological structural recovery problem, seeking to recover annotation-consistent foreground structure while respecting differences between views. We introduce TopoRadar, which couples persistent feature conditioning with component-level structural supervision. Its DuoPerLay module computes sublevel and superlevel cubical persistence from learned multi-view features, characterising connectivity and holes across filtration thresholds to condition the segmentation representation. A structural consistency loss uses optimal transport to align persistent component summaries between predictions and annotations within each output view, providing component-level supervision for foreground structure. On CARRADA, TopoRadar achieves 64.87% range–Doppler mIoU, outperforming the published TransRadar and recent Hyper- Radar results by 0.97 and 1.07 percentage points, respectively; in the range–angle view, it exceeds HyperRadar while remaining below TransRadar and MARSS. Relative to a TransRadar backbone re-trained under our protocol, TopoRadar reduces connected-component error by approximately 30% and spurious-component counts by approximately 35% across both views. A single-view adaptation achieves 83.17% mIoU on RADIal, improving TransRadar by 2.07 percentage points.