In this paper, we focus on latent modification and generation of 3D point cloud object models with respect to their semantic parts. Different to the existing methods which use separate networks for part generation and assembly, we propose a single end-to-end Autoencoder model that can handle generation and modification of both semantic parts, and global shapes. The proposed method supports part exchange between 3D point cloud models and composition by different parts to form new models by directly editing latent representations. This holistic approach does not need part-based training to learn part representations and does not introduce any extra loss besides the standard reconstruction loss. The experiments demonstrate the robustness of the proposed method with different object categories and varying number of points. The method can generate new models by integration of generative models such as GANs and VAEs and can work with unannotated point clouds by integration of a segmentation module.

在本文中，我们专注于潜在修改和3D点云对象模型的语义部分的生成。与使用单独的网络进行零件生成和组装的现有方法不同，我们提出了一个单一的端到端自动编码器模型，该模型可以处理语义零件和全局形状的生成和修改。所提出的方法支持3D点云模型之间的零件交换以及由不同零件组成的零件，可以通过直接编辑潜在表示来形成新模型。这种整体方法不需要进行基于零件的培训来学习零件表示，并且除了标准的重建损失外，也不会引入任何额外的损失。实验证明了该方法在不同物体类别和不同点数下的鲁棒性。