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：点云是丰富的几何数据结构,其三维结构为了解3D空间的演示学习和基因模型提供了极好的领域。在这项工作中,我们的目标是通过实验变压器编码器、潜空流动模型和自动递减解析器来改进点云潜空变异模型的性能。我们分析并比较这些模型在不同对象类型的生成和重建性能。</s>