One of the main challenges in point cloud compression (PCC) is how to evaluate the perceived distortion so that the codec can be optimized for perceptual quality. Current standard practices in PCC highlight a primary issue: while single-feature metrics are widely used to assess compression distortion, the classic method of searching point-to-point nearest neighbors frequently fails to adequately build precise correspondences between point clouds, resulting in an ineffective capture of human perceptual features. To overcome the related limitations, we propose a novel assessment method called RBFIM, utilizing radial basis function (RBF) interpolation to convert discrete point features into a continuous feature function for the distorted point cloud. By substituting the geometry coordinates of the original point cloud into the feature function, we obtain the bijective sets of point features. This enables an establishment of precise corresponding features between distorted and original point clouds and significantly improves the accuracy of quality assessments. Moreover, this method avoids the complexity caused by bidirectional searches. Extensive experiments on multiple subjective quality datasets of compressed point clouds demonstrate that our RBFIM excels in addressing human perception tasks, thereby providing robust support for PCC optimization efforts.

翻译：点云压缩（PCC）中的一个主要挑战是如何评估感知失真，以便编解码器能够针对感知质量进行优化。当前点云压缩领域的标准实践突显出一个核心问题：虽然单特征指标被广泛用于评估压缩失真，但传统的逐点最近邻搜索方法往往无法在点云之间充分建立精确的对应关系，导致难以有效捕捉人类感知特征。为克服相关局限性，我们提出一种名为RBFIM的新型评估方法，该方法利用径向基函数（RBF）插值将失真点云的离散点特征转换为连续特征函数。通过将原始点云的几何坐标代入该特征函数，我们可获得双射的点特征集合。这能够在失真点云与原始点云之间建立精确的对应特征关系，并显著提升质量评估的准确性。此外，该方法避免了双向搜索带来的复杂度问题。在多个压缩点云主观质量数据集上的大量实验表明，我们的RBFIM在解决人类感知任务方面表现优异，从而为点云压缩优化工作提供了有力支持。