确定性点云扩散用于去除分离
IEEE transactions on visualization and computer graphics
|October 16, 2025
概括
这项研究引入了一个新的确定性扩散框架,用于点云消音. 该方法通过学习定向残留物有效地消除噪声,从而在3D表面回收方面取得了最先进的结果.
科学领域:
- 计算机视觉 计算机视觉
- 3D几何处理处理 3D几何处理
- 机器学习 机器学习
背景情况:
- 扩散模型擅长通过改进噪音数据来恢复图像.
- 将扩散模型应用于3D点云去噪声面临由于结构化移位和噪声复杂性的挑战.
- 现有的方法在点云消噪中与几何关系和表面恢复作斗争.
研究的目的:
- 开发一个确定性的无噪声扩散框架,用于点云消噪.
- 解决传统高斯噪声扩散在3D领域的局限性.
- 为了实现可靠的表面回收,并减轻常见的染工件.
主要方法:
- 对于点云消噪,建议采用两相残留扩散工艺.
- 前进阶段将定向残留物注入到干净的表面,创建一个降解轨迹.
- 反向阶段使用基于U-Net的网络来估计和去除残留物,恢复表面.
主要成果:
- 拟议的方法在合成和现实世界数据集上实现了最先进的性能.
- 定量指标和视觉质量显示出优越的无色化能力.
- 该框架成功地减轻了过度平滑和不足平滑的工件.
结论:
- 确定性的无噪声扩散框架为点云消噪提供了一个强大的解决方案.
- 两相残留扩散过程有效地处理结构化位移和几何推理.
- 这种方法使得可靠的3D表面恢复具有高保真度.
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