用扩散模型统一压缩点云的几何与颜色，无需特定数据训练
Unified Geometry and Color Compression Framework for Point Clouds via Generative Diffusion Priors
- 通过提示调优适配预训练扩散模型，统一处理点云几何与颜色
- 在物体和室内场景上，压缩率与重建质量均优于现有方法
- 无需针对特定数据集训练，适合跨分布点云通用压缩
随着3D应用增长和传感器采集点云数据激增，高效压缩算法需求上升。现有基于学习的压缩方法通常分开处理几何与颜色属性，难以直接应用于带色点云。此外，训练数据集容量有限，限制了其在不同分布点云上的泛化能力。本文提出一种测试时统一的几何与颜色压缩框架。不依赖特定数据集训练压缩模型，而是通过提示调优适配预训练生成式扩散模型，将原始彩色点云压缩为稀疏集合（称作'种子'），再通过多步去噪与独立采样过程实现解压。在物体与室内场景上的实验表明，该方法在几何与颜色压缩性能上均优于现有基线。
原文摘要 · Abstract (English)
With the growth of 3D applications and the rapid increase in sensor-collected 3D point cloud data, there is a rising demand for efficient compression algorithms. Most existing learning-based compression methods handle geometry and color attributes separately, treating them as distinct tasks, making these methods challenging to apply directly to point clouds with colors. Besides, the limited capacities of training datasets also limit their generalizability across points with different distributions. In this work, we introduce a test-time unified geometry and color compression framework of 3D point clouds. Instead of training a compression model based on specific datasets, we adapt a pre-trained generative diffusion model to compress original colored point clouds into sparse sets, termed 'seeds', using prompt tuning. Decompression is then achieved through multiple denoising steps with separate sampling processes. Experiments on objects and indoor scenes demonstrate that our method has superior performances compared to existing baselines for the compression of geometry and color.
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