Core Concepts
3DMambaIPF introduces a novel iterative point cloud filtering model that leverages the Mamba module for efficient long-sequence modeling and integrates a differentiable rendering loss to enhance the visual realism of denoised geometric structures, enabling superior performance on both small-scale and large-scale point cloud datasets.
Abstract
The paper presents 3DMambaIPF, a novel iterative point cloud filtering model that addresses the limitations of existing methods in handling large-scale and high-noise point clouds.
Key highlights:
3DMambaIPF incorporates the Mamba module, a selective state space model architecture, to enable efficient and scalable processing of long sequences of point cloud data.
The model integrates a differentiable rendering loss, which aligns the denoised point cloud boundaries more closely with the ground truth, resulting in visually realistic geometric structures.
Extensive evaluations on small-scale synthetic and real-world datasets, as well as large-scale synthetic datasets, demonstrate that 3DMambaIPF outperforms state-of-the-art methods in terms of both quantitative metrics and visual quality.
Ablation studies are conducted to analyze the impact of various components, such as the loss function, number of rendered views, and Mamba layers, on the denoising performance.
The paper showcases the effectiveness of 3DMambaIPF in addressing the challenges of point cloud denoising, particularly in large-scale and high-noise environments, by leveraging the strengths of the Mamba module and differentiable rendering techniques.
Stats
The paper reports the following key metrics:
Chamfer Distance (CD) and Point-to-Mesh (P2M) Distance on the PU-Net dataset with varying noise levels and point cloud resolutions.
Visualization comparisons on the PU-Net dataset, large-scale synthetic models from the Stanford 3D Scanning Repository, and the Paris-rue-Madame real-world dataset.