Abstract
While 3D point clouds are widely utilized across various vision applications, their irregular and sparse nature make them challenging to handle. In response, numerous encoding approaches have been proposed to capture the rich semantic information of point clouds. Yet, a critical limitation persists: a lack of consideration for colored point clouds which are more capable 3D representations as they contain diverse attributes: color and geometry. While existing methods handle these attributes separately on a per-point basis, this leads to a limited receptive field and restricted ability to capture relationships across multiple points. To address this, we pioneer a point cloud encoding methodology that leverages 3D Fourier decomposition to disentangle color and geometric features while extending the receptive field through spectral-domain operations. Our analysis confirms that this encoding approach effectively separates feature components, where the amplitude uniquely captures color attributes and the phase encodes geometric structure, thereby enabling independent learning and utilization of both attributes. Furthermore, the spectral-domain properties of these components naturally aggregate local features while considering multiple points' information. We validate our point cloud encoding approach on point cloud classification and style transfer tasks, achieving state-of-the-art results on the DensePoint dataset with improvements via a proposed amplitude-based data augmentation strategy.
Abstract (translated)
尽管3D点云在各种视觉应用中被广泛使用，但它们的不规则性和稀疏性使其难以处理。为了捕捉点云丰富的语义信息，已经提出了许多编码方法。然而，一个关键限制仍然存在：缺乏对彩色点云的考虑，而彩色点云作为更强大的3D表示形式，包含颜色和几何属性等多种属性。现有的方法在每个点的基础上分别处理这些属性，这导致了有限的感受野以及捕获多个点之间关系的能力受限。 为了解决这个问题，我们首创了一种基于3D傅里叶分解的点云编码方法，这种方法能够分离颜色和几何特征，并通过频域操作扩展感受野。我们的分析表明，这种编码方式有效地分离了特征组件：幅度独特地捕捉颜色属性，而相位则编码了几何结构，从而实现了对两种属性的独立学习和使用。 此外，这些成分在频域上的特性自然聚合局部特征并考虑多个点的信息。我们在点云分类和风格转换任务上验证了我们的点云编码方法，并通过提出的一种基于幅度的数据增强策略，在DensePoint数据集上取得了最先进的结果。
URL
https://arxiv.org/abs/2503.10055
