As the interest in deep learning tools continues to rise, new multimedia research fields begin to discover its potential. Both image and point cloud coding are good examples of technologies, where deep learning-based solutions have recently displayed very competitive performance. In this context, this article brings two novel contributions to the point cloud geometry coding state-of-the-art; first, a novel neighborhood adaptive distortion metric to be used in the training loss function, which allows significantly improving the rate-distortion performance with commonly used objective quality metrics; second, an explicit quantization approach at the training and coding times to generate varying rate/quality with a single trained deep learning coding model, effectively reducing the training complexity and storage requirements. The result is an improved deep learning-based point cloud geometry coding solution, which is both more compression efficient and less demanding in training complexity and storage.

中文翻译：

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具有隐式和显式量化的基于深度学习的点云编码的邻域自适应损失函数

随着对深度学习工具的兴趣不断上升，新的多媒体研究领域开始发现其潜力。图像和点云编码都是很好的技术示例，其中基于深度学习的解决方案最近表现出非常有竞争力的性能。在这种情况下，本文为点云几何编码的最新技术带来了两个新的贡献；首先，在训练损失函数中使用一种新的邻域自适应失真度量，它可以通过常用的客观质量度量显着提高率失真性能；其次，在训练和编码时间采用显式量化方法，通过单一训练的深度学习编码模型生成不同的速率/质量，有效降低训练复杂度和存储要求。