AdaptiveShape：利用几何感知锚点分布解决3D目标检测的形状可变性
AdaptiveShape: Solving Shape Variability for 3D Object Detection with Geometry Aware Anchor Distributions
摘要：
基于点云和图像的3D目标检测在自动驾驶等感知任务中发挥着重要作用。目前的方法在标准形状的车辆的检测和姿态估计上表现出很好的性能，但在更复杂的形状例如半挂卡车组合上缺乏落后。准确地确定这些特殊车辆的形状和运动在堆场操作和操纵以及工业自动化应用中至关重要。这项工作介绍了几种新的方法来改善和衡量这样的类的性能。最先进的方法是基于地面实况目标的预定义锚网格或热图。然而，底层表示不考虑不同大小的对象的形状。我们的主要贡献，AdaptiveShape，使用形状感知锚点分布和热图来提高检测能力。对于大型车辆，与当前的形状不可知方法相比，我们实现了+10.9%AP。此外，我们介绍了一种新的快速激光雷达相机融合。它基于2D限界框相机检测，这在许多处理管道中可用。这种融合方法不依赖于完全校准或时间同步的系统，因此适用于广泛的机器人应用。我们扩展了一个标准的点柱网络来解释时间数据，并改进了复杂对象运动的学习。此外，我们扩展了地面实况增强，使用分组对象对，与传统增强相比，进一步将卡车AP提高了+2.2%。
英文摘要：
3D object detection with point clouds and images plays an important role in perception tasks such as autonomous driving. Current methods show great performance on detection and pose estimation of standard-shaped vehicles but lack behind on more complex shapes as e.g. semi-trailer truck combinations. Determining the shape and motion of those special vehicles accurately is crucial in yard operation and maneuvering and industrial automation applications. This work introduces several new methods to improve and measure the performance for such classes. State-of-the-art methods are based on predefined anchor grids or heatmaps for ground truth targets. However, the underlying representations do not take the shape of different sized objects into account. Our main contribution, AdaptiveShape, uses shape aware anchor distributions and heatmaps to improve the detection capabilities. For large vehicles we achieve +10.9% AP in comparison to current shape agnostic methods. Furthermore we introduce a new fast LiDAR-camera fusion. It is based on 2D bounding box camera detections which are available in many processing pipelines. This fusion method does not rely on perfectly calibrated or temporally synchronized systems and is therefore applicable to a broad range of robotic applications. We extend a standard point pillar network to account for temporal data and improve learning of complex object movements. In addition we extended a ground truth augmentation to use grouped object pairs to further improve truck AP by +2.2% compared to conventional augmentation.
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