论文标题
解释动态点云处理中的层次特征
Explaining Hierarchical Features in Dynamic Point Cloud Processing
论文作者
论文摘要
本文旨在为动态云处理的深度学习带来一些灯光和理解。具体来说，我们专注于层次特征学习方面，其最终目标是了解在过程的不同阶段学习哪些特征及其含义。最后，我们清楚地了解网络的层次组成部分如何影响学习的功能及其对成功学习模型的重要性。这项研究是针对点云预测任务进行的，可用于预测编码应用程序。
This paper aims at bringing some light and understanding to the field of deep learning for dynamic point cloud processing. Specifically, we focus on the hierarchical features learning aspect, with the ultimate goal of understanding which features are learned at the different stages of the process and what their meaning is. Last, we bring clarity on how hierarchical components of the network affect the learned features and their importance for a successful learning model. This study is conducted for point cloud prediction tasks, useful for predicting coding applications.
