  Tracking and reconstructing 3D objects from cluttered scenes are the key
components for computer vision, robotics and autonomous driving systems. While
recent progress in implicit function has shown encouraging results on
high-quality 3D shape reconstruction, it is still very challenging to
generalize to cluttered and partially observable LiDAR data. In this paper, we
propose to leverage the continuity in video data. We introduce a novel and
unified framework which utilizes a neural implicit function to simultaneously
track and reconstruct 3D objects in the wild. Our approach adapts the DeepSDF
model (i.e., an instantiation of the implicit function) in the video online,
iteratively improving the shape reconstruction while in return improving the
tracking, and vice versa. We experiment with both Waymo and KITTI datasets and
show significant improvements over state-of-the-art methods for both tracking
and shape reconstruction tasks. Our project page is at
https://jianglongye.com/implicit-tracking .
