AI RESEARCH
Selection, Not Fusion: Radar-Modulated State Space Models for Radar-Camera Depth Estimation
arXiv CS.CV
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ArXi:2605.11840v1 Announce Type: new Radar-camera depth estimation must turn an ultra-sparse, all-weather, metric radar signal into a dense per-pixel depth map. Existing methods -- concatenation, confidence-aware gating, sparse supervision, graph-based extraction -- combine radar and image features outside the backbone's sequence operator, and even cross-modal Mamba variants leave the selection mechanism itself unimodal. We argue that the selection mechanism is the right place for radar to enter. We.