# Weather-Conditioned Branch Routing for Robust LiDAR-Radar 3D Object Detection Source: [Weather-Conditioned Branch Routing for Robust LiDAR-Radar 3D Object Detection]() Collection index: [llms.txt]() Data updated: 2026-04-09T05:00:04.534Z Authors: Hongsheng Li, Lingfeng Zhang, Zexian Yang, Liang Li, Rong Yin, Xiaoshuai Hao, Wenbo Ding Published: 2026-04-07T03:56:27.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [3D Object Detection on K-Radar (test)](): 3D Object Detection; K-Radar (test); AP BEV (Normal): 82.1. ## Original sources and implementation [Original paper]() [PDF]()