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 category-level 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 NN-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

中文翻译：

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CPPF++：通过投票聚合进行不确定性感知 Sim2Real 物体姿态估计


物体姿态估计构成了 3D 视觉领域的一个关键领域。虽然利用现实世界姿势注释的当代最先进的方法已经表现出值得称赞的性能，但购买这种真实的训练数据会产生巨大的成本。本文重点关注一种特定设置，其中仅使用 3D CAD 模型作为先验知识，没有任何背景或杂乱信息。我们引入了一种新颖的方法 CPPF++，专为模拟到真实的类别级姿态估计而设计。该方法建立在 CPPF 的基本点对投票方案的基础上，通过概率观点重新表述它。为了解决投票冲突带来的挑战，我们提出了一种新颖的方法，该方法涉及通过估计规范空间内每个点对的概率分布来对投票不确定性进行建模。此外，我们通过引入 NN 点元组来增强每个投票单元提供的上下文信息。为了增强模型的鲁棒性和准确性，我们结合了几个创新模块，包括噪声对过滤、在线对齐优化和元组特征集成。除了这些方法上的进步之外，我们还引入了一个新的类别级姿势估计数据集，名为 DiversePose 300。经验证据表明，我们的方法显着超越了以前的模拟到真实方法，并在新颖的数据集上实现了可比或优越的性能。我们的代码可以在 https://github.com/qq456cvb/CPPF2 上找到