Distortion quantification of point clouds plays a stealth, yet vital role in a wide range of human and machine perception tasks. For human perception tasks, a distortion quantification can substitute subjective experiments to guide 3D visualization; while for machine perception tasks, a distortion quantification can work as a loss function to guide the training of deep neural networks for unsupervised learning tasks. To handle a variety of demands in many applications, a distortion quantification needs to be distortion discriminable, differentiable, and have a low computational complexity. Currently, however, there is a lack of a general distortion quantification that can satisfy all three conditions. To fill this gap, this work proposes multiscale potential energy discrepancy (MPED), a distortion quantification to measure point cloud geometry and color difference. By evaluating at various neighborhood sizes, the proposed MPED achieves global-local tradeoffs, capturing distortion in a multiscale fashion. Extensive experimental studies validate MPED's superiority for both human and machine perception tasks.

中文翻译：

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基于人和机器感知的势能的点云失真量化

点云的失真量化在各种人机感知任务中起着隐性但至关重要的作用。对于人类感知任务，失真量化可以替代主观实验来指导3D可视化；而对于机器感知任务，失真量化可以作为损失函数来指导无监督学习任务的深度神经网络训练。为了满足许多应用中的各种需求，失真量化需要可辨别，可区分且具有低计算复杂度。但是，目前缺乏可以满足所有三个条件的一般失真量化。为了填补这一空白，这项工作提出了多尺度势能差异（MPED），失真量化以测量点云的几何形状和色差。通过在各种邻域大小上进行评估，建议的MPED可以实现全局-局部权衡，以多尺度方式捕获失真。广泛的实验研究证实了MPED在人和机器感知任务上的优越性。