Fourier Decomposition for Explicit Representation of 3D Point Cloud Attributes
This work addresses the problem of limited receptive fields in colored point cloud processing for vision applications, offering a novel encoding approach that improves performance in tasks like classification and style transfer.
The paper tackles the challenge of encoding colored 3D point clouds by proposing a method that uses 3D Fourier decomposition to disentangle color and geometric features, achieving state-of-the-art results on the DensePoint dataset for classification and style transfer tasks.
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.