- The paper introduces P2P-Bridge, which uses diffusion Schrödinger bridges to optimally transform noisy 3D point clouds into clean ones.
- It employs a hybrid network architecture integrating PVCNN, global attention, and DINOv2 features to capture both local and global context.
- Empirical results on datasets like PU-Net and ScanNet++ show significant improvements in Chamfer Distance and Point-to-Mesh accuracy over state-of-the-art methods.
P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising
The paper "P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising" introduces a novel approach for addressing the problem of denoising point clouds using diffusion Schrödinger bridges. This method, referred to as P2P-Bridge, aims to enhance the quality of 3D point clouds by learning an optimal transport plan between noisy and clean point clouds, thereby making significant strides in improving the performance over existing point cloud denoising methods.
Technical Contribution and Methodology
The primary novelty of this paper is the application of diffusion Schrödinger bridges to the domain of point cloud denoising. Unlike traditional methods that either rely on point-wise displacements from point features or learned noise distributions, P2P-Bridge leverages an optimal transport approach to learn the transformation from noisy to clean point clouds.
Diffusion Models and Schrödinger Bridges
The paper formulates the denoising task as a Schrödinger bridge problem, constructing a diffusion process that interpolates between paired noisy and clean point clouds. The optimal transport path is derived using an analytic form of the Schrödinger bridge, specifically designed for point clouds, and is guided by a shortest-path interpolation mechanism. This mechanism helps to alleviate issues arising from the unordered nature of point cloud data.
Network Architecture
P2P-Bridge employs a hybrid network architecture inspired by PointVoxel-CNN (PVCNN). The network is augmented with multi-headed global attention and a feature embedding module, incorporating both point coordinates and additional features such as color information and point-wise DINOv2 features. This mixed architecture aims to leverage both local and global contextual information, thereby enhancing the model’s ability to generalize to different types of noise and varying data scales.
Implementation Details and Robustness
The learning framework is underpinned by a tractable form of the diffusion Schrödinger bridge, trained to predict the noise in point clouds at each timestep using a noise-prediction loss. During inference, a modified DDPM (Denoising Diffusion Probabilistic Model) sampling technique is employed, which iterates through the learned path to derive the clean point clouds from noisy inputs in multiple steps.
Experimental Results and Analysis
The P2P-Bridge framework has been rigorously evaluated on various datasets, including PU-Net, ScanNet++, and ARKitScenes, demonstrating its robustness across both synthetic and real-world noisy data scenarios. The results illustrate that P2P-Bridge outperforms several state-of-the-art methods across multiple metrics such as Chamfer Distance (CD) and Point-to-Mesh (P2M) distance.
Object-Level Denoising
For object-level denoising, P2P-Bridge is shown to provide significant improvements in noise reduction on both PU-Net and PC-Net datasets, surpassing traditional and deep-learning-based methods particularly in higher noise settings. This robustness is evidenced through lower CD and P2M values, indicating more precise alignment with the underlying clean point cloud structures.
Scene-Level Denoising
When extended to large-scale indoor scenes, the method demonstrates its capability to deal effectively with complex noise patterns, including outlier clusters and geometric distortions. Especially noteworthy is the inclusion of real-world noisy data from Apple LiDAR sensors, which underscores the practical applicability of P2P-Bridge. Comparative studies against other methods reveal the superiority of P2P-Bridge, particularly when additional RGB and high-level DINOv2 features are incorporated.
Theoretical Implications
The application of diffusion Schrödinger bridges to point cloud processing opens up new avenues for optimal transport and diffusion models in non-Euclidean data domains. By formulating the problem in terms of an optimal transport framework, this approach provides a theoretically sound method for addressing data alignment issues in unordered sets, which is a common challenge in point cloud processing.
Future Directions
Future developments following this work could involve extending the P2P-Bridge framework to other 3D data processing tasks such as point cloud completion, segmentation, and further integration of multi-modal data sources. Additionally, exploring the balance between algorithmic complexity and computational efficiency in large-scale deployments can provide broader real-world applicability.
In conclusion, the P2P-Bridge framework presents a robust and efficient approach for 3D point cloud denoising, leveraging the strengths of diffusion models and optimal transport theory. The detailed analysis and comprehensive experiments conducted underscore its potential in significantly enhancing the quality of point cloud data, thereby supporting a wide range of downstream applications in 3D vision, robotics, AR/VR, and beyond. The availability of code and pretrained models further facilitates adoption and experimentation within the research community.