As details are missing in most representations of structures, the lack of controllability to more information is one of the major weaknesses in structure-based controllable point cloud generation. It is observable that definitions of details and structures are subjective. Details can be treated as structures on small scales. To represent structures in different scales at the same time, we present a graph-based representation of structures called the Multiscale Structure Graph (MSG). Given structures in multiple scales, similar patterns of local structures can be found at different scales, positions, and angles. The knowledge learned from a regional structure pattern shall be transferred to other similar patterns. An encoding and generation mechanism, namely the Multiscale Structure-based Point Cloud Generator (MSPCG) is proposed, which can simultaneously learn point cloud generation from local patterns with miscellaneous spatial properties. The proposed method supports multiscale editions on point clouds by editing the MSG. By generating point clouds from local structures and learning simultaneously in multiple scales, our MSPCG has better generalization ability and scalability. Trained on the ShapeNet, our MSPCG can generate point clouds from a given structure for unseen categories and indoor scenes. The experimental results show that our method significantly outperforms baseline methods.

翻译：由于大多数结构表示中缺乏细节信息，对更多信息的可控性不足是基于结构的可控点云生成的主要短板之一。观察发现，细节和结构的定义具有主观性：细节可被视为小尺度上的结构。为同时表示不同尺度的结构，我们提出一种基于图的结构表示方法——多尺度结构图（MSG）。给定多尺度结构后，可在不同尺度、位置和角度上发现局部结构的相似模式。从局部结构模式习得的知识可迁移至其他相似模式。我们提出一种编码与生成机制——基于多尺度结构的点云生成器（MSPCG），该机制能同时从具有不同空间属性的局部模式中学习点云生成。所提方法通过编辑MSG支持点云的多尺度编辑。由于从局部结构生成点云并同时在多尺度上学习，我们的MSPCG具有更强的泛化能力和可扩展性。在ShapeNet上训练后，MSPCG能根据给定结构为未见类别和室内场景生成点云。实验结果表明，我们的方法显著优于基线方法。