  In this paper, we tackle the problem of category-level 9D pose estimation in
the wild, given a single RGB-D frame. Using supervised data of real-world 9D
poses is tedious and erroneous, and also fails to generalize to unseen
scenarios. Besides, category-level pose estimation requires a method to be able
to generalize to unseen objects at test time, which is also challenging.
Drawing inspirations from traditional point pair features (PPFs), in this
paper, we design a novel Category-level PPF (CPPF) voting method to achieve
accurate, robust and generalizable 9D pose estimation in the wild. To obtain
robust pose estimation, we sample numerous point pairs on an object, and for
each pair our model predicts necessary SE(3)-invariant voting statistics on
object centers, orientations and scales. A novel coarse-to-fine voting
algorithm is proposed to eliminate noisy point pair samples and generate final
predictions from the population. To get rid of false positives in the
orientation voting process, an auxiliary binary disambiguating classification
task is introduced for each sampled point pair. In order to detect objects in
the wild, we carefully design our sim-to-real pipeline by training on synthetic
point clouds only, unless objects have ambiguous poses in geometry. Under this
circumstance, color information is leveraged to disambiguate these poses.
Results on standard benchmarks show that our method is on par with current
state of the arts with real-world training data. Extensive experiments further
show that our method is robust to noise and gives promising results under
extremely challenging scenarios. Our code is available on
https://github.com/qq456cvb/CPPF.
