基于图的动态点云预测网络

P. Gomes
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引用次数: 3

摘要

动态点云使虚拟现实应用得以兴起。然而,由于点云体积庞大,需要有效的压缩方法。虽然有一些文章通过探索连续帧之间的时间冗余来解决动态点云的压缩问题,但很少有文章将点云预测作为有效压缩的工具进行探索。在这篇博士论文中,我们提出了一个端到端学习网络来预测点云序列中的未来帧。为了解决点云处理中存在的挑战,即缺乏结构,我们提出了一种基于图的方法来学习点云的拓扑信息作为几何特征。早期的结果表明,我们的方法能够做出准确的预测,并可以应用于压缩算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Graph-based Network for Dynamic Point Cloud Prediction
Dynamic point clouds have enabled the rise of virtual reality applications. However, due to their voluminous size, point clouds require efficient compression methods. While a few articles have addressed the compression of dynamic point clouds by exploring temporal redundancies between sequential frames, very few have explored point cloud prediction as a tool for efficient compression. In this PhD thesis, we propose an end-to-end learning network to predict future frames in a point cloud sequence. To address the challenges present in point cloud processing, namely the lack of structure we propose a graph-based approach to learn topological information of point clouds as geometric features. Early results demonstrate that our method is able to make accurate predictions and can be applied in a compression algorithm.
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