论文标题
联合空间 - 周期性优化，用于立体3D对象跟踪
Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking
论文作者
论文摘要
从顺序图像中直接学习多个3D对象运动很困难，而几何束调整则缺乏定位不可见对象质心的能力。为了从深度神经网络的强大对象理解技能中受益，同时解决轨迹估计的精确几何建模，我们提出了一种基于联合时空优化的联合定向3D对象跟踪方法。从网络中，我们在相邻图像上检测到相应的2D边界框，并回归初始的3D边界框。然后使用基于区域的网络预测与对象质心相关联的密集对象提示（本地深度和本地坐标）。考虑到即时定位的精度和运动一致性，我们的优化将对象质心和观察到的线索之间的关系建模为关节时空误差函数。将总结所有历史提示，以通过每框架边缘化策略在没有重复计算的情况下为当前的估计做出贡献。 KITTI跟踪数据集上的定量评估显示，我们的方法的表现优于先前的基于图像的3D跟踪方法，这是显着的边距。我们还报告了多个类别和较大数据集（Kitti Raw和Argoverse跟踪）的广泛结果，以进行未来的基准测试。
Directly learning multiple 3D objects motion from sequential images is difficult, while the geometric bundle adjustment lacks the ability to localize the invisible object centroid. To benefit from both the powerful object understanding skill from deep neural network meanwhile tackle precise geometry modeling for consistent trajectory estimation, we propose a joint spatial-temporal optimization-based stereo 3D object tracking method. From the network, we detect corresponding 2D bounding boxes on adjacent images and regress an initial 3D bounding box. Dense object cues (local depth and local coordinates) that associating to the object centroid are then predicted using a region-based network. Considering both the instant localization accuracy and motion consistency, our optimization models the relations between the object centroid and observed cues into a joint spatial-temporal error function. All historic cues will be summarized to contribute to the current estimation by a per-frame marginalization strategy without repeated computation. Quantitative evaluation on the KITTI tracking dataset shows our approach outperforms previous image-based 3D tracking methods by significant margins. We also report extensive results on multiple categories and larger datasets (KITTI raw and Argoverse Tracking) for future benchmarking.
