针对恶劣天气的分支路由用于鲁棒的激光雷达-雷达3D目标检测
Weather-Conditioned Branch Routing for Robust LiDAR-Radar 3D Object Detection
- Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
- College of Computer and Data Science, Fuzhou University（福州大学计算机与数据科学学院）
- Institute of Information Engineering, CAS（中国科学院信息工程研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种基于天气条件的分支路由方法，通过动态调整模态偏好提升恶劣天气下3D目标检测的鲁棒性，实验表明其在K-Radar基准上达到最佳性能。
AI中文摘要：
在恶劣天气下，不同传感器的可靠性差异使得3D目标检测极具挑战性。尽管现有激光雷达-4D雷达融合方法提升了鲁棒性，但其主要依赖固定或弱适应性管道，无法动态调整模态偏好。为此，我们将多模态感知重新表述为天气条件下的分支路由问题。我们的框架维护三个并行的3D特征流：纯激光雷达分支、纯4D雷达分支以及条件门控融合分支。通过从视觉和语义提示中提取的条件标记，轻量级路由器动态预测样本特定的权重以软聚合这些表示。此外，为防止分支崩溃，我们引入了天气监督学习策略，结合辅助分类和多样性正则化以强制不同的、依赖条件的路由行为。在K-Radar基准上的大量实验表明，我们的方法实现了最先进的性能。此外，它提供了明确且高度可解释的模态偏好见解，清晰揭示了如何在多样化的恶劣天气场景中，鲁棒地在激光雷达和4D雷达之间切换依赖。源代码将被发布。
英文摘要：
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.
