  In this work, we tackle the problem of category-level online pose tracking of
objects from point cloud sequences. For the first time, we propose a unified
framework that can handle 9DoF pose tracking for novel rigid object instances
as well as per-part pose tracking for articulated objects from known
categories. Here the 9DoF pose, comprising 6D pose and 3D size, is equivalent
to a 3D amodal bounding box representation with free 6D pose. Given the depth
point cloud at the current frame and the estimated pose from the last frame,
our novel end-to-end pipeline learns to accurately update the pose. Our
pipeline is composed of three modules: 1) a pose canonicalization module that
normalizes the pose of the input depth point cloud; 2) RotationNet, a module
that directly regresses small interframe delta rotations; and 3) CoordinateNet,
a module that predicts the normalized coordinates and segmentation, enabling
analytical computation of the 3D size and translation. Leveraging the small
pose regime in the pose-canonicalized point clouds, our method integrates the
best of both worlds by combining dense coordinate prediction and direct
rotation regression, thus yielding an end-to-end differentiable pipeline
optimized for 9DoF pose accuracy (without using non-differentiable RANSAC). Our
extensive experiments demonstrate that our method achieves new state-of-the-art
performance on category-level rigid object pose (NOCS-REAL275) and articulated
object pose benchmarks (SAPIEN, BMVC) at the fastest FPS ~12.
