Time varying sequences of 3D point clouds, or 4D point clouds, are now being acquired at an increasing pace in several applications (e.g., LiDAR in autonomous or assisted driving). In many cases, such volume of data is transmitted, thus requiring that proper compression tools are applied to either reduce the resolution or the bandwidth. In this paper, we propose a new solution for upscaling and restoration of time-varying 3D video point clouds after they have been heavily compressed. In consideration of recent growing relevance of 3D applications, %We focused on a model allowing user-side upscaling and artifact removal for 3D video point clouds, a real-time stream of which would require . Our model consists of a specifically designed Graph Convolutional Network (GCN) that combines Dynamic Edge Convolution and Graph Attention Networks for feature aggregation in a Generative Adversarial setting. By taking inspiration PointNet++, We present a different way to sample dense point clouds with the intent to make these modules work in synergy to provide each node enough features about its neighbourhood in order to later on generate new vertices. Compared to other solutions in the literature that address the same task, our proposed model is capable of obtaining comparable results in terms of quality of the reconstruction, while using a substantially lower number of parameters (about 300KB), making our solution deployable in edge computing devices such as LiDAR.

翻译：随时间变化的三维点云序列（即4D点云）正日益广泛应用于多种场景（例如自动驾驶或辅助驾驶中的LiDAR）。在此类数据量传输过程中，通常需要应用适当的压缩工具来降低分辨率或带宽。本文提出了一种新型解决方案，用于对经过重度压缩的时变三维视频点云进行上采样与重建修复。鉴于三维应用近年来的重要性日益增长，我们设计了一种专门用于用户侧上采样与伪影消除的模型，该模型由特定设计的图卷积网络（GCN）构成，在生成对抗框架下结合动态边缘卷积与图注意力网络进行特征聚合。受PointNet++启发，我们提出了一种对密集点云进行采样的新方法，旨在使这些模块协同工作，为每个节点提供足够的邻域特征信息，以便后续生成新顶点。与现有同类解决方案相比，本模型在实现相当重建质量的同时，参数数量显著降低（约300KB），可部署于LiDAR等边缘计算设备。