  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.
