论文标题
用于体积点云的基于群集的自动编码器
Cluster-Based Autoencoders for Volumetric Point Clouds
论文作者
论文摘要
自动编码器允许重建一组参数的给定输入。但是，由于计算成本，输入大小通常受到限制。因此，我们为体积点云提出了一种聚类和重新组装方法，以便允许高分辨率数据作为输入。我们此外，基于众所周知的折叠网云，提出了一个自动编码器，并讨论如何利用我们的方法在高分辨率点云之间以及将体积设计/样式转移到点云上，同时保持其形状。
Autoencoders allow to reconstruct a given input from a small set of parameters. However, the input size is often limited due to computational costs. We therefore propose a clustering and reassembling method for volumetric point clouds, in order to allow high resolution data as input. We furthermore present an autoencoder based on the well-known FoldingNet for volumetric point clouds and discuss how our approach can be utilized for blending between high resolution point clouds as well as for transferring a volumetric design/style onto a pointcloud while maintaining its shape.
