论文标题
仿真到现实域的适应离线3D对象注释与相关对齐的点云
Simulation-to-Reality domain adaptation for offline 3D object annotation on pointclouds with correlation alignment
论文作者
论文摘要
在LiDar PointClouds中使用3D边界框的注释对象是自主驾驶感知系统中昂贵的人类驱动过程。在本文中，我们提出了一种使用模拟数据，通过部署车辆收集的半自动注释现实的点云的方法。我们将3D对象探测器模型训练来自卡拉的标记模拟数据，共同与目标车辆的现实世界尖端训练。有监督的对象检测损失以珊瑚损失项增强，以减少标记的模拟和未标记的真实点云特征表示之间的距离。这里的目标是学习模拟（标签）和现实世界（未标记）目标域的不变的表示形式。我们还提供了有关点云的域适应方法的更新调查。
Annotating objects with 3D bounding boxes in LiDAR pointclouds is a costly human driven process in an autonomous driving perception system. In this paper, we present a method to semi-automatically annotate real-world pointclouds collected by deployment vehicles using simulated data. We train a 3D object detector model on labeled simulated data from CARLA jointly with real world pointclouds from our target vehicle. The supervised object detection loss is augmented with a CORAL loss term to reduce the distance between labeled simulated and unlabeled real pointcloud feature representations. The goal here is to learn representations that are invariant to simulated (labeled) and real-world (unlabeled) target domains. We also provide an updated survey on domain adaptation methods for pointclouds.
