This paper investigates an open research task of reconstructing and generating 3D point clouds. Most existing works of 3D generative models directly take the Gaussian prior as input for the decoder to generate 3D point clouds, which fail to learn disentangled latent codes, leading noisy interpolated results. Most of the GAN-based models fail to discriminate the local geometries, resulting in the point clouds generated not evenly distributed at the object surface, hence degrading the point cloud generation quality. Moreover, prevailing methods adopt computation-intensive frameworks, such as flow-based models and Markov chains, which take plenty of time and resources in the training phase. To resolve these limitations, this paper proposes a unified style-aware network architecture combining both point-wise distance loss and adversarial loss, StarNet which is able to reconstruct and generate high-fidelity and even 3D point clouds using a mapping network that can effectively disentangle the Gaussian prior from input's high-level attributes in the mapped latent space to generate realistic interpolated objects. Experimental results demonstrate that our framework achieves comparable state-of-the-art performance on various metrics in the point cloud reconstruction and generation tasks, but is more lightweight in model size, requires much fewer parameters and less time for model training.

翻译：本文探讨了三维点云重建与生成这一开放研究任务。现有的大多数三维生成模型直接将高斯先验作为解码器的输入来生成三维点云，未能学习到解耦的潜在编码，导致插值结果存在噪声。多数基于GAN的模型无法区分局部几何结构，使得生成的点云在物体表面分布不均匀，从而降低了点云生成质量。此外，主流方法采用计算密集型的框架（如基于流的模型和马尔可夫链），在训练阶段消耗大量时间和资源。为解决这些局限，本文提出了一种统一的风格感知网络架构StarNet，该架构结合了点距离损失和对抗损失，通过一个映射网络在映射后的潜在空间中有效解耦高斯先验与输入的高级属性，从而生成高保真且均匀的三维点云及真实的插值对象。实验结果表明，我们的框架在点云重建与生成任务的多项指标上达到了与最先进方法相当的性能，且模型更加轻量化，所需参数更少，模型训练时间更短。