论文标题
PT2PC：学会从部分树条件中生成3D点云形状
PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions
论文作者
论文摘要
3D生成形状建模是计算机视觉和交互式计算机图形技术的一个基本研究领域，具有许多真实的应用。本文研究了从符号部分树表示产生3D形状点云几何形状的新问题。为了以端到端的方式学习这样的条件形状生成过程，我们提出了有条件的gan“零件树” - 到 - “点云”模型（pt2pc），该模型（pt2pc）散布了结构和几何因素。提出的模型将零件树的条件纳入体系结构设计中，通过沿零件树层次结构传递消息和自下而上。实验结果和用户研究表明，鉴于零件树的条件，我们方法在产生感知合理和不同的3D点云方面的优势。我们还提出了一种新的结构量度，用于评估生成的形状点云是否满足零件树的条件。
3D generative shape modeling is a fundamental research area in computer vision and interactive computer graphics, with many real-world applications. This paper investigates the novel problem of generating 3D shape point cloud geometry from a symbolic part tree representation. In order to learn such a conditional shape generation procedure in an end-to-end fashion, we propose a conditional GAN "part tree"-to-"point cloud" model (PT2PC) that disentangles the structural and geometric factors. The proposed model incorporates the part tree condition into the architecture design by passing messages top-down and bottom-up along the part tree hierarchy. Experimental results and user study demonstrate the strengths of our method in generating perceptually plausible and diverse 3D point clouds, given the part tree condition. We also propose a novel structural measure for evaluating if the generated shape point clouds satisfy the part tree conditions.
