arXiv Computer Vision
Selection, Not Fusion: Radar-Modulated State Space Models for Radar-Camera Depth Estimation
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DeeperRadar: End-to-End MIMO Radar Design and Multi-Modal Fusion for Autonomous Vehicle Perception
arXiv:2607. 17351v1 Announce Type: new Abstract: DeeperRadar is a radar-centric, sensor-stack-conditioned framework that co-designs radar sensing and multi-modal 3D detection for autonomous mobility by learning a sparse acquisition pattern end-to-end with the fusion model.
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arXiv Computer Vision
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SARES-DEIM: Sparse Mixture-of-Experts Meets DETR for Clutter-Aware SAR Ship Detection
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arXiv Computer Vision
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SGDet3D++: Geometry-Grounded Semantics for 4D Radar and Camera 3D Object Detection
SGDet3D++ introduces a geometry‑grounded approach to 4D radar‑camera 3D object detection by explicitly conditioning evidence on evolving object hypotheses. It employs Anchor‑Grounded Semantic Retrieval, Geometry‑Consistent Anchor Refinement, and Doppler‑Verified Correspondence to filter and align semantic, geometric, and temporal cues before updating queries. The method achieves significant performance gains on OmniHD‑Scenes, ManTruckScenes, and TJ4DRadSet, with detailed ablations showing improvements in occlusion handling, target‑return purity, and motion consistency.
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SGDet3D++: Geometry-Grounded Semantics for 4D Radar and Camera 3D Object Detection
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{wheth...