SARFusion：面向鲁棒相机-激光雷达3D目标检测的场景感知路由融合

中文摘要 AI 辅助

相机-激光雷达融合已成为自动驾驶中3D目标检测的主流范式。然而，现有的融合检测器通常通过从紧密耦合的多模态表示中解码目标查询来建立强模态间依赖。在损坏的驾驶条件下，这种依赖使检测器容易受到不可靠模态的影响，其中退化的观测可能干扰可靠的模态特定证据，导致次优预测。此外，模态可靠性可能随全局驾驶场景和单个目标查询而变化，需要在更细粒度上做出自适应融合决策。为弥合这一差距，我们将鲁棒相机-激光雷达融合重新表述为场景感知分支路由问题，并提出了SARFusion，一种鲁棒的3D目标检测器。SARFusion不是从单一融合表示生成检测结果，而是将目标查询解码解耦为三个并行推理分支：相机分支、激光雷达分支和相机-激光雷达融合分支。在从全局驾驶上下文估计的场景可靠性先验的引导下，SARFusion进一步结合目标级证据，将每个查询路由到最合适的分支。这种逐查询路由策略减轻了有害的跨模态干扰，同时在互补线索可信时保留了多模态融合的优势。在nuScenes测试集上，SARFusion实现了72.5 mAP和74.4 NDS的强劲性能。广泛的分析证明了其在具有挑战性条件下的鲁棒性，包括传感器损坏和环境变化。

英文摘要

Camera-LiDAR fusion has become a prevailing paradigm for 3D object detection in autonomous driving. However, existing fusion detectors often establish strong inter-modality dependencies by decoding object queries from tightly coupled multimodal representations. Under corrupted driving conditions, such dependencies make the detector vulnerable to unreliable modalities, where degraded observations may interfere with reliable modality-specific evidence and lead to suboptimal predictions. Moreover, modality reliability can vary across both global driving scenes and individual object queries, requiring adaptive fusion decisions at a finer granularity. To bridge this gap, we reformulate robust camera-LiDAR fusion as a scene-aware branch routing problem and propose SARFusion, a robust 3D object detector. Instead of producing detections from a single fused representation, SARFusion decouples object-query decoding into three parallel reasoning branches: a camera branch, a LiDAR branch, and a camera-LiDAR fusion branch. Guided by a Scene Reliability Prior estimated from the global driving context, SARFusion further incorporates object-level evidence to route each query to the most suitable branch. This query-wise routing strategy alleviates harmful cross-modal interference while preserving the benefits of multimodal fusion when complementary cues are trustworthy. On the nuScenes test set, SARFusion achieves strong performance with 72.5 mAP and 74.4 NDS. Extensive analyses demonstrate its robustness under challenging conditions, including sensor corruptions and environmental changes.

发表机构

• Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
• School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
• Wuhan College（武汉学院）

机构由 AI 辅助整理，请以论文原文为准。