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
this https URL

本文提出了一种新的基于点对特征的类别级别投票方法来实现野外物体的精准、鲁棒和可推广的 9D 姿态估计，并在标准基准测试中证明了该方法与目前现有技术相当。

CPPF: 面向野外环境的稳健类别级别 9D 姿态估计

CPPF: Towards Robust Category-Level 9D Pose Estimation in the Wild

Point Pair Features is a widely used method to detect 3D objects in point
clouds, however they are prone to fail in presence of sensor noise and
background clutter. We introduce novel sampling and voting schemes that
significantly reduces the influence of clutter and sensor noise. Our
experiments show that with our improvements, PPFs become competitive against
state-of-the-art methods as it outperforms them on several objects from
challenging benchmarks, at a low computational cost.

本文介绍了一种改进的点对特征方法，通过引入新的采样技术和投票策略，显著降低了噪声和背景干扰对算法的影响，实验表明，该方法在低计算成本下优于目前同类方法，能够检测出具有挑战性的物体。