Self-supervised representation learning has shown significant improvement in Natural Language Processing and 2D Computer Vision. However, existing methods face difficulties in representing 3D data because of its unordered and uneven density. Through an in-depth analysis of mainstream contrastive and generative approaches, we find that contrastive models tend to suffer from overfitting, while 3D Mask Autoencoders struggle to handle unordered point clouds. This motivates us to learn 3D representations by sharing the merits of diffusion and contrast models, which is non-trivial due to the pattern difference between the two paradigms. In this paper, we propose \textit{PointDico}, a novel model that seamlessly integrates these methods. \textit{PointDico} learns from both denoising generative modeling and cross-modal contrastive learning through knowledge distillation, where the diffusion model serves as a guide for the contrastive model. We introduce a hierarchical pyramid conditional generator for multi-scale geometric feature extraction and employ a dual-channel design to effectively integrate local and global contextual information. \textit{PointDico} achieves a new state-of-the-art in 3D representation learning, \textit{e.g.}, \textbf{94.32\%} accuracy on ScanObjectNN, \textbf{86.5\%} Inst. mIoU on ShapeNetPart.

翻译：自监督表示学习在自然语言处理和二维计算机视觉领域已展现出显著进展。然而，现有方法在表示三维数据时面临挑战，主要源于其无序性和不均匀密度特性。通过对主流对比式与生成式方法的深入分析，我们发现对比模型易出现过拟合问题，而三维掩码自编码器难以有效处理无序点云数据。这促使我们探索融合扩散模型与对比模型优势的三维表示学习方法，但由于两种范式存在模式差异，该任务具有显著挑战性。本文提出\\textit{PointDico}——一种创新模型，能够无缝整合这两种方法。\\textit{PointDico}通过知识蒸馏同时学习去噪生成建模与跨模态对比学习，其中扩散模型作为对比模型的引导者。我们设计了分层金字塔条件生成器以实现多尺度几何特征提取，并采用双通道架构有效融合局部与全局上下文信息。\\textit{PointDico}在三维表示学习中实现了新的最优性能，例如在ScanObjectNN数据集上达到\\textbf{94.32\\%}准确率，在ShapeNetPart数据集上获得\\textbf{86.5\\%}实例级mIoU。