论文标题
点设置自我安装
Point Set Self-Embedding
论文作者
论文摘要
这项工作提出了一种创新的方法，用于点设置自我安装，该方法以视觉但不可察觉的形式编码了将密集点的结构信息编码为其稀疏版本的结构信息。自我安装的点集可以用作普通的下采样，并在移动设备上有效地可视化。特别是，我们可以利用自我安装的信息充分恢复原始点，以详细分析远程服务器。此任务具有挑战性，因为自我安装点集和恢复点集都应该类似于原始点。为了实现一个可学习的自我安装方案，我们设计了一个具有两个共同训练的网络的新框架：一个用于将设置设置的输入点编码到其自我安装的稀疏点集中，另一个用于利用嵌入式信息来颠倒原始点集。此外，我们在两个网络中开发了一对上剃须和下垫路单元，并制定损失项，以鼓励结果中的形状相似性和点分布。广泛的定性和定量结果证明了我们方法对合成和实扫描数据集的有效性。
This work presents an innovative method for point set self-embedding, that encodes the structural information of a dense point set into its sparser version in a visual but imperceptible form. The self-embedded point set can function as the ordinary downsampled one and be visualized efficiently on mobile devices. Particularly, we can leverage the self-embedded information to fully restore the original point set for detailed analysis on remote servers. This task is challenging since both the self-embedded point set and the restored point set should resemble the original one. To achieve a learnable self-embedding scheme, we design a novel framework with two jointly-trained networks: one to encode the input point set into its self-embedded sparse point set and the other to leverage the embedded information for inverting the original point set back. Further, we develop a pair of up-shuffle and down-shuffle units in the two networks, and formulate loss terms to encourage the shape similarity and point distribution in the results. Extensive qualitative and quantitative results demonstrate the effectiveness of our method on both synthetic and real-scanned datasets.
