论文标题
Polygen：3D网格的自回旋生成模型
PolyGen: An Autoregressive Generative Model of 3D Meshes
论文作者
论文摘要
多边形网格是3D几何形状的有效表示，并且在计算机图形，机器人和游戏开发中至关重要。现有基于学习的方法避免了使用3D网格的挑战，而是使用与神经体系结构和培训方法更兼容的替代对象表示。我们提出了一种直接建模网格的方法，使用基于变压器的体系结构依次预测网格顶点并面对。我们的模型可以在一系列输入（包括对象类，体素和图像）上进行条件，并且由于模型是概率的，因此可以产生在模棱两可的情况下捕获不确定性的样品。我们表明该模型能够生产高质量的可用网格，并为网格模型任务建立对数类似的基准。我们还针对替代方法评估了表面重建指标上的条件模型，尽管没有直接在此任务上进行培训，但仍表现出竞争性能。
Polygon meshes are an efficient representation of 3D geometry, and are of central importance in computer graphics, robotics and games development. Existing learning-based approaches have avoided the challenges of working with 3D meshes, instead using alternative object representations that are more compatible with neural architectures and training approaches. We present an approach which models the mesh directly, predicting mesh vertices and faces sequentially using a Transformer-based architecture. Our model can condition on a range of inputs, including object classes, voxels, and images, and because the model is probabilistic it can produce samples that capture uncertainty in ambiguous scenarios. We show that the model is capable of producing high-quality, usable meshes, and establish log-likelihood benchmarks for the mesh-modelling task. We also evaluate the conditional models on surface reconstruction metrics against alternative methods, and demonstrate competitive performance despite not training directly on this task.
