Selection, Not Fusion: Radar-Modulated State Space Models for Radar-Camera Depth Estimation
The paper introduces Radar-Modulated Selection (RMS), which integrates sparse radar data directly into Mamba's selective scan mechanism for depth estimation. RMS injects radar information into the model's step size and readout while keeping the core image processing unimpaired. This approach outperforms previous methods by focusing on selecting relevant radar data within the scan, proving that selection is a better place for multimodal fusion than fusing features outside the sequence operator.