Online Adaptation for Implicit Object Tracking and Shape Reconstruction in the Wild

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 (e.g., DeepSDF) 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 DeepSDF model to simultaneously track and reconstruct 3D objects in the wild. We online adapt the DeepSDF model in the video, 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.