Camera-based 3D object detection and tracking are central to autonomous driving, yet precise 3D object localization remains fundamentally constrained by depth ambiguity when no expensive, depth-rich online LiDAR is available at inference. In many deployments, however, vehicles repeatedly traverse the same environments, making static point cloud maps from prior traversals a practical source of geometric priors. We propose DualViewMapDet, a camera-only inference framework that retrieves such map priors online and leverages them to mitigate the absence of a LiDAR sensor during deployment. The key idea is a dual-space camera-map fusion strategy that avoids one-sided view conversion. Specifically, we (i) project the map into perspective view (PV) and encode multi-channel geometric cues to enrich image features and support BEV lifting, and (ii) encode the map directly in bird's-eye view (BEV) with a sparse voxel backbone and fuse it with lifted camera features in a shared metric space. Extensive evaluations on nuScenes and Argoverse 2 demonstrate consistent improvements over strong camera-only baselines, with particularly strong gains in object localization. Ablations further validate the contributions of PV/BEV fusion and prior-map coverage. We make the code and pre-trained models available at https://dualviewmapdet.cs.uni-freiburg.de .

翻译：基于相机的3D目标检测与跟踪是自动驾驶的核心任务，然而，当推理时无法使用昂贵且富含深度信息的在线激光雷达时，精确的3D目标定位从根本上受到深度模糊性的限制。然而，在许多部署场景中，车辆会反复遍历相同的环境，这使得先前遍历中生成的静态点云地图成为几何先验的实用来源。我们提出DualViewMapDet，一种纯相机推理框架，该框架在线检索此类地图先验，并利用其弥补部署期间激光雷达缺失的问题。核心思路是一种双视图相机-地图融合策略，避免了单侧视图转换。具体而言，我们(i)将地图投影到透视视图(PV)中，编码多通道几何线索以丰富图像特征并支持BEV提升，以及(ii)利用稀疏体素骨干直接在鸟瞰图(BEV)中编码地图，并在共享度量空间中将其与提升后的相机特征融合。在nuScenes和Argoverse 2上的广泛评估表明，与强大的纯相机基线相比，该方法取得了一致的改进，尤其在目标定位方面表现突出。消融实验进一步验证了PV/BEV融合以及先验地图覆盖范围的贡献。我们在https://dualviewmapdet.cs.uni-freiburg.de公开了代码和预训练模型。