ADBM: Adversarial Diffusion Bridge Model for Denoising of 3D Point Cloud Data.

Area of Science:

• Computer Vision
• Machine Learning
• 3D Data Processing

Background:

• Diffusion models excel at learning complex data distributions but struggle with fine geometric detail recovery in noisy point clouds.
• Existing methods for 3D point cloud denoising often fail to preserve intricate details under severe noise conditions.

Purpose of the Study:

• To propose a novel approach for robust 3D point cloud denoising that enhances the recovery of fine geometric details.
• To integrate adversarial learning with a diffusion bridge model to improve the fidelity and sharpness of denoised point clouds.

Main Methods:

• Developed the Adversarial Diffusion Bridge Model (ADBM), combining a diffusion bridge model with adversarial training.
• Incorporated a lightweight discriminator for adversarial supervision to guide the denoising process.
• Trained the denoiser using a denoising diffusion objective based on a Schrödinger Bridge.

Main Results:

• ADBM demonstrated superior performance in reconstructing fine geometric details compared to existing methods.
• Adversarial supervision significantly enhanced the sharpness and faithfulness of the denoised 3D point clouds.
• Experiments on PU-Net and PC-Net datasets showed improved results using Chamfer distance and Point-to-Mesh metrics.

Conclusions:

• Adversarial supervision is effective in enhancing local detail reconstruction for 3D point cloud denoising.
• The Adversarial Diffusion Bridge Model (ADBM) offers a promising direction for robust point cloud restoration.
• The proposed method effectively addresses the limitations of standard diffusion models in handling severe noise and preserving geometric fidelity.