Tracking an object's 6D pose, while either the object itself or the observing
camera is moving, is important for many robotics and augmented reality
applications. While exploiting temporal priors eases this problem,
object-specific knowledge is required to recover when tracking is lost. Under
the tight time constraints of the tracking task, RGB(D)-based methods are often
conceptionally complex or rely on heuristic motion models. In comparison, we
propose to simplify object tracking to a reinforced point cloud (depth only)
alignment task. This allows us to train a streamlined approach from scratch
with limited amounts of sparse 3D point clouds, compared to the large datasets
of diverse RGBD sequences required in previous works. We incorporate temporal
frame-to-frame registration with object-based recovery by frame-to-model
refinement using a reinforcement learning (RL) agent that jointly solves for
both objectives. We also show that the RL agent's uncertainty and a
rendering-based mask propagation are effective reinitialization triggers.

追踪物体的六维姿态，在物体本身或观察相机移动时，对许多机器人学和增强现实应用非常重要。在跟踪任务的严格时间限制下，我们提出了将物体追踪简化为加强点云（仅深度）对齐任务的方法。