3DMambaIPF: A State Space Model for Iterative Point Cloud Filtering via Differentiable Rendering

2024-04-08

arXiv.org

AI Summary

To address severe geometric distortion, insufficient long-range dependency modeling, and the failure of existing learning-based methods on ultra-large point clouds (e.g., 500K points) in large-scale point cloud denoising, this paper proposes the first iterative point cloud filtering framework based on the Mamba state space model. Methodologically, we innovatively introduce the selective state space model into point cloud processing and integrate a differentiable rendering loss to explicitly enforce surface geometric consistency, enabling end-to-end, geometry-aware denoising optimization. Key contributions include: (1) the first application of Mamba to point cloud denoising, significantly enhancing long-sequence modeling capability; and (2) a novel differentiable rendering-guided surface fidelity mechanism that effectively suppresses outliers and improves geometric realism. Experiments demonstrate state-of-the-art performance on small-scale datasets (≤50K points) and maintain high efficiency and robustness on ultra-large scenes (500K points), where most existing methods fail.

Technology Category

• Machine Learning: Learning with Manifolds
• Computer Vision: Diffusion Models for Vision
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Point Cloud Denoising
• Large-scale Data
• 3D Image Understanding

Innovation

• 3DMambaIPF
• Large-scale Point Cloud Denoising
• Mamba Architecture and Differentiable Rendering Loss