IROAM：从自动驾驶车辆数据域中改进路侧单目3D目标检测学习
IROAM: Improving Roadside Monocular 3D Object Detection Learning from Autonomous Vehicle Data Domain
AI总结：
针对车载与路侧摄像头视点域差异导致单目3D检测不适用的问题，提出语义-几何解耦对比学习框架IROAM，通过域内查询交互与跨域查询增强模块学习跨域信息，有效提升了路侧检测器性能。
摘要：
在自动驾驶中，自车的感知能力可以通过路侧传感器得到提升，因为路侧传感器能够提供环境的全局视角。然而，由于视点域差异，为车载摄像头设计的现有单目检测方法并不适用于路侧摄像头。为了弥合这一差距并改进路侧单目3D目标检测（ROAdside Monocular 3D object detection），我们提出了IROAM，这是一个语义-几何解耦的对比学习框架，它同时将车侧和路侧数据作为输入。IROAM包含两个重要模块。域内查询交互模块利用transformer学习每个域的内容和深度信息，并输出目标查询。为了从两个域中学习更好的特征表示，跨域查询增强模块将查询解耦为语义和几何部分，且仅将前者用于对比学习。实验证明了IROAM在提升路侧检测器性能方面的有效性。结果验证了IROAM具备学习跨域信息的能力。
英文摘要：
In autonomous driving, The perception capabilities of the ego-vehicle can be improved with roadside sensors, which can provide a holistic view of the environment. However, existing monocular detection methods designed for vehicle cameras are not suitable for roadside cameras due to viewpoint domain gaps. To bridge this gap and Improve ROAdside Monocular 3D object detection, we propose IROAM, a semantic-geometry decoupled contrastive learning framework, which takes vehicle-side and roadside data as input simultaneously. IROAM has two significant modules. In-Domain Query Interaction module utilizes a transformer to learn content and depth information for each domain and outputs object queries. Cross-Domain Query Enhancement To learn better feature representations from two domains, Cross-Domain Query Enhancement decouples queries into semantic and geometry parts and only the former is used for contrastive learning. Experiments demonstrate the effectiveness of IROAM in improving roadside detector's performance. The results validate that IROAM has the capabilities to learn cross-domain information.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
