Point cloud upsampling (PCU) enriches the representation of raw point clouds,
significantly improving the performance in downstream tasks such as
classification and reconstruction. Most of the existing point cloud upsampling
methods focus on sparse point cloud feature extraction and upsampling module
design. In a different way, we dive deeper into directly modelling the gradient
of data distribution from dense point clouds. In this paper, we proposed a
conditional denoising diffusion probability model (DDPM) for point cloud
upsampling, called PUDM. Specifically, PUDM treats the sparse point cloud as a
condition, and iteratively learns the transformation relationship between the
dense point cloud and the noise. Simultaneously, PUDM aligns with a dual
mapping paradigm to further improve the discernment of point features. In this
context, PUDM enables learning complex geometry details in the ground truth
through the dominant features, while avoiding an additional upsampling module
design. Furthermore, to generate high-quality arbitrary-scale point clouds
during inference, PUDM exploits the prior knowledge of the scale between sparse
point clouds and dense point clouds during training by parameterizing a rate
factor. Moreover, PUDM exhibits strong noise robustness in experimental
results. In the quantitative and qualitative evaluations on PU1K and PUGAN,
PUDM significantly outperformed existing methods in terms of Chamfer Distance
(CD) and Hausdorff Distance (HD), achieving state of the art (SOTA)
performance.

本研究提出了一种基于条件去噪扩散概率模型（DDPM）的点云上采样方法，称为 PUDM。PUDM 通过对稀疏点云进行条件建模，学习了稠密点云和噪声之间的转换关系，并利用双映射范式提高点特征的辨别力。此外，PUDM 还通过参数化速率因子，在训练过程中利用稀疏点云和稠密点云之间的比例关系生成高质量的任意尺度点云，实验结果表明其具有较强的噪声鲁棒性，相比现有方法，在 Chamfer 距离和 Hausdorff 距离上取得了最先进的性能。