ADBM:对抗性扩散桥模型,用于拒绝3D点云数据.
1Division of Electronic Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Republic of Korea.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
我们介绍了对抗性扩散桥梁模型 (ADBM) 用于3D点云排斥. 这种方法使用与扩散模型的对抗训练来改善几何细节的恢复,即使有很大的噪音.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 3D数据处理 3D数据处理
背景情况:
- 扩散模型擅长学习复杂的数据分布,但在杂的点云中难以恢复精细的几何细节.
- 现有的3D点云消噪方法在严重的噪音条件下往往无法保存复杂的细节.
研究的目的:
- 提出一种新的方法,用于强大的3D点云去,从而提高细形几何细节的恢复.
- 将对抗式学习与扩散桥梁模型集成在一起,以提高无光点云的保真度和清晰度.
主要方法:
- 开发了对抗性扩散桥梁模型 (ADBM),将扩散桥梁模型与对抗性训练相结合.
- 整合了一个轻量级的歧视器,用于对抗性监督,以指导拒绝的过程.
- 使用基于施罗丁格桥的破坏性扩散目标训练了破坏者.
主要成果:
- 与现有方法相比,ADBM在重建精细的几何细节方面表现出卓越的性能.
- 敌对监督显著提高了无效的3D点云的清晰度和忠实性.
- 在PU-Net和PC-Net数据集上的实验显示使用Chamfer距离和Point-to-Mesh指标的结果得到了改善.
结论:
- 敌对监督是有效的提高局部细节重建的3D点云 denoising.
- 对抗性扩散桥模型 (ADBM) 为强大的点云恢复提供了一个有希望的方向.
- 提出的方法有效地解决了标准扩散模型在处理严重噪音和保持几何准确性方面的局限性.
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