SuperPC：一种用于点云补全、上采样、去噪和着色的单一扩散模型
SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization
AI总结：
针对点云补全、上采样、去噪和着色四类缺陷常共存且相互关联的问题，本文提出首个统一扩散模型SuperPC，通过三级条件框架和空间混合融合策略实现四任务并行处理，性能超越各专用模型及其组合。
摘要：
点云（PC）处理任务——如补全、上采样、去噪和着色——在自动驾驶和三维重建等应用中至关重要。尽管已有显著进展，先前的方法通常独立处理每个任务，使用分别针对单个问题的独立模型。然而，这种孤立的方法未能考虑到不完整、低分辨率、噪声和缺乏颜色等缺陷经常同时存在，且每种缺陷相互影响并相互关联。简单地顺序应用这些模型可能导致每个模型的误差累积，并增加计算成本。为了解决这些挑战，我们提出了SuperPC，这是第一个能够同时处理所有四个任务的统一扩散模型。我们的方法采用三级条件扩散框架，并通过一种新颖的空间混合融合策略加以增强，以利用这四种缺陷之间的相关性进行同时、高效的处理。我们证明SuperPC在所有四个单独任务上均优于最先进的专用模型及其组合。
英文摘要：
Point cloud (PC) processing tasks-such as completion, upsampling, denoising, and colorization-are crucial in applications like autonomous driving and 3D reconstruction. Despite substantial advancements, prior approaches often address each of these tasks independently, with separate models focused on individual issues. However, this isolated approach fails to account for the fact that defects like incompleteness, low resolution, noise, and lack of color frequently coexist, with each defect influencing and correlating with the others. Simply applying these models sequentially can lead to error accumulation from each model, along with increased computational costs. To address these challenges, we introduce SuperPC, the first unified diffusion model capable of concurrently handling all four tasks. Our approach employs a three-level-conditioned diffusion framework, enhanced by a novel spatial-mix-fusion strategy, to leverage the correlations among these four defects for simultaneous, efficient processing. We show that SuperPC outperforms the state-of-the-art specialized models as well as their combination on all four individual tasks.
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。
