Weather-Conditioned Branch Routing for Robust LiDAR-Radar 3D Object Detection

• URL: http://arxiv.org/abs/2604.05405v1
• Date: Tue, 07 Apr 2026 03:56:27 GMT
• Title: Weather-Conditioned Branch Routing for Robust LiDAR-Radar 3D Object Detection
• Abstract summary: We reformulate multi-modal perception as a weather-conditioned branch routing problem.<n>Our framework explicitly maintains three parallel 3D feature streams: a pure LiDAR branch, a pure 4D radar branch, and a condition-gated fusion branch.<n>Experiments on the K-Radar benchmark demonstrate that our method achieves state-of-the-art performance.
• Score: 43.35380334829282
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robust 3D object detection in adverse weather is highly challenging due to the varying reliability of different sensors. While existing LiDAR-4D radar fusion methods improve robustness, they predominantly rely on fixed or weakly adaptive pipelines, failing to dy-namically adjust modality preferences as environmental conditions change. To bridge this gap, we reformulate multi-modal perception as a weather-conditioned branch routing problem. Instead of computing a single fused output, our framework explicitly maintains three parallel 3D feature streams: a pure LiDAR branch, a pure 4D radar branch, and a condition-gated fusion branch. Guided by a condition token extracted from visual and semantic prompts, a lightweight router dynamically predicts sample-specific weights to softly aggregate these representations. Furthermore, to prevent branch collapse, we introduce a weather-supervised learning strategy with auxiliary classification and diversity regularization to enforce distinct, condition-dependent routing behaviors. Extensive experiments on the K-Radar benchmark demonstrate that our method achieves state-of-the-art performance. Furthermore, it provides explicit and highly interpretable insights into modality preferences, transparently revealing how adaptive routing robustly shifts reliance between LiDAR and 4D radar across diverse adverse-weather scenarios. The source code with be released.

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