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arXiv · 2605.07367

Weather-Robust Scene Semantics with Vision-Aligned 4D Radar

Kali Hamilton·Christoffer Heckman
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Abstract

Cameras and LiDAR degrade in rain, fog, and snow, while millimeter-wave radar remains largely unaffected. We align a radar encoder to frozen SigLIP vision embeddings and decode structured scene captions through a frozen vision-language model (VLM) with approximately 7M trainable parameters. On K-RADAR with held-out fog, light snow, and heavy snow sequences, all radar configurations outperform a camera baseline that collapses to over 90% hallucination. We identify a token-norm mismatch as the dominant failure mode when bridging radar to a frozen VLM and show that projector-output LayerNorm resolves it. Analysis of encoder complexity, caption format, and pooling strategy reveals tradeoffs that inform future radar-VLM pipeline design.

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Kali Hamilton, Christoffer Heckman. 2026-05-08. Weather-Robust Scene Semantics with Vision-Aligned 4D Radar. https://arxiv.org/abs/2605.07367

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