  Shape and pose estimation is a critical perception problem for a self-driving
car to fully understand its surrounding environment. One fundamental challenge
in solving this problem is the incomplete sensor signal (e.g., LiDAR scans),
especially for faraway or occluded objects. In this paper, we propose a novel
algorithm to address this challenge, which explicitly leverages the sensor
signal captured over consecutive time: the consecutive signals can provide more
information about an object, including different viewpoints and its motion. By
encoding the consecutive signals via a recurrent neural network, not only our
algorithm improves the shape and pose estimates, but also produces a labeling
tool that can benefit other tasks in autonomous driving research. Specifically,
building upon our algorithm, we propose a novel pipeline to automatically
annotate high-quality labels for amodal segmentation on images, which are hard
and laborious to annotate manually. Our code and data will be made publicly
available.
