Point clouds are rich geometric data structures, where their three dimensional structure offers an excellent domain for understanding the representation learning and generative modeling in 3D space. In this work, we aim to improve the performance of point cloud latent-space generative models by experimenting with transformer encoders, latent-space flow models, and autoregressive decoders. We analyze and compare both generation and reconstruction performance of these models on various object types.

翻译：点云是一种丰富的几何数据结构，其三维结构为理解三维空间的表示学习与生成建模提供了极佳的领域。本研究旨在通过实验Transformer编码器、潜空间流模型及自回归解码器，提升点云潜空间生成模型的性能。我们分析并比较了这些模型在不同物体类型上的生成与重建性能。