基于可学习热扩散的点云重采样
Point Cloud Resampling with Learnable Heat Diffusion
AI总结：
本文提出一种可学习热扩散框架用于点云重采样，通过学习自适应热扩散调度和局部滤波尺度构建自适应条件先验，在点云去噪和上采样任务中取得最先进性能。
摘要：
生成式扩散模型在点云重采样中已展现出经验上的成功，通过将噪声逐步细化为结构，从稀疏或含噪的3D点云生成更密集且更均匀的点分布。然而，现有扩散模型采用人工预定义的方案，由于几何退化的刚性和破坏性，往往无法恢复底层点云结构。为解决这一问题，我们提出了一种新颖的可学习热扩散框架用于点云重采样，该框架通过学习时变热核的自适应热扩散调度和局部滤波尺度，直接参数化前向过程的边际分布，从而为逆向过程生成自适应条件先验。与以往具有固定先验的扩散模型不同，该自适应条件先验通过最小化精炼的变分下界，选择性地保留点云的几何特征，引导点在逆向过程中向底层曲面演化。大量实验结果表明，所提出的点云重采样方法在包括点云去噪和上采样在内的代表性重建任务中达到了最先进的性能。
英文摘要：
Generative diffusion models have shown empirical successes in point cloud resampling, generating a denser and more uniform distribution of points from sparse or noisy 3D point clouds by progressively refining noise into structure. However, existing diffusion models employ manually predefined schemes, which often fail to recover the underlying point cloud structure due to the rigid and disruptive nature of the geometric degradation. To address this issue, we propose a novel learnable heat diffusion framework for point cloud resampling, which directly parameterizes the marginal distribution for the forward process by learning the adaptive heat diffusion schedules and local filtering scales of the time-varying heat kernel, and consequently, generates an adaptive conditional prior for the reverse process. Unlike previous diffusion models with a fixed prior, the adaptive conditional prior selectively preserves geometric features of the point cloud by minimizing a refined variational lower bound, guiding the points to evolve towards the underlying surface during the reverse process. Extensive experimental results demonstrate that the proposed point cloud resampling achieves state-of-the-art performance in representative reconstruction tasks including point cloud denoising and upsampling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
