Noise is an inevitable aspect of point cloud acquisition, necessitating filtering as a fundamental task within the realm of 3D vision. Existing learning-based filtering methods have shown promising capabilities on small-scale synthetic or real-world datasets. Nonetheless, the effectiveness of these methods is constrained when dealing with a substantial quantity of point clouds. This limitation primarily stems from their limited denoising capabilities for large-scale point clouds and their inclination to generate noisy outliers after denoising. The recent introduction of State Space Models (SSMs) for long sequence modeling in Natural Language Processing (NLP) presents a promising solution for handling large-scale data. Encouraged by iterative point cloud filtering methods, we introduce 3DMambaIPF, firstly incorporating Mamba (Selective SSM) architecture to sequentially handle extensive point clouds from large scenes, capitalizing on its strengths in selective input processing and long sequence modeling capabilities. Additionally, we integrate a robust and fast differentiable rendering loss to constrain the noisy points around the surface. In contrast to previous methodologies, this differentiable rendering loss enhances the visual realism of denoised geometric structures and aligns point cloud boundaries more closely with those observed in real-world objects. Extensive evaluation on datasets comprising small-scale synthetic and real-world models (typically with up to 50K points) demonstrate that our method achieves state-of-the-art results. Moreover, we showcase the superior scalability and efficiency of our method on large-scale models with about 500K points, where the majority of the existing learning-based denoising methods are unable to handle.

翻译：噪声是点云采集过程中不可避免的问题，因此滤波成为三维视觉领域的基础任务。现有基于学习的滤波方法在合成或真实小规模数据集上已展现出良好效果，但在处理大规模点云时效能受限。这一局限主要源于其在大规模点云去噪能力上的不足，以及去噪后易产生噪声离群点的倾向。自然语言处理（NLP）中近期引入的状态空间模型（SSMs）为长序列建模提供了新思路，为处理大规模数据带来了可行方案。受迭代点云滤波方法启发，我们提出3DMambaIPF，首次将Mamba（选择性SSM）架构融入其中，利用其在选择性输入处理和长序列建模方面的优势，顺序处理大型场景中的海量点云。此外，我们整合了稳健且快速的可微渲染损失函数，以约束曲面附近的噪声点。与先前方法不同，该可微渲染损失增强了去噪几何结构的视觉真实感，并使点云边界更贴近真实世界物体的观测边界。在包含小规模合成及真实模型（通常含多达5万点）的数据集上的全面评估表明，我们的方法达到了最先进水平。此外，我们展示了该方法在约50万点的大规模模型上的卓越可扩展性和效率，而现有大多数基于学习的去噪方法无法处理此类模型。