  Object pose estimation constitutes a critical area within the domain of 3D
vision. While contemporary state-of-the-art methods that leverage real-world
pose annotations have demonstrated commendable performance, the procurement of
such real training data incurs substantial costs. This paper focuses on a
specific setting wherein only 3D CAD models are utilized as a priori knowledge,
devoid of any background or clutter information. We introduce a novel method,
CPPF++, designed for sim-to-real pose estimation. This method builds upon the
foundational point-pair voting scheme of CPPF, reformulating it through a
probabilistic view. To address the challenge posed by vote collision, we
propose a novel approach that involves modeling the voting uncertainty by
estimating the probabilistic distribution of each point pair within the
canonical space. Furthermore, we augment the contextual information provided by
each voting unit through the introduction of N-point tuples. To enhance the
robustness and accuracy of the model, we incorporate several innovative
modules, including noisy pair filtering, online alignment optimization, and a
tuple feature ensemble. Alongside these methodological advancements, we
introduce a new category-level pose estimation dataset, named DiversePose 300.
Empirical evidence demonstrates that our method significantly surpasses
previous sim-to-real approaches and achieves comparable or superior performance
on novel datasets. Our code is available on https://github.com/qq456cvb/CPPF2.
