论文标题
学会从一个示例中生成3D形状
Learning to Generate 3D Shapes from a Single Example
论文作者
论文摘要
通常在特定对象类别的大型3D数据集上对3D形状的现有生成模型进行培训。在本文中，我们研究了仅从单个参考3D形状中学习的深层生成模型。具体而言，我们提出了一个基于GAN的多尺度模型，旨在捕获一系列空间尺度的输入形状的几何特征。为了避免在3D体积上操作引起的大量内存和计算成本，我们在三平面混合表示上构建了我们的发电机，这仅需要2D卷积。我们在参考形状的体素金字塔上训练生成模型，而无需任何外部监督或手动注释。一旦受过训练，我们的模型就可以产生可能具有不同尺寸和宽高比的高质量的3D形状。所得的形状跨不同尺度的变化，同时保留了参考形状的整体结构。通过广泛的评估，无论是定性还是定量，我们都证明了我们的模型可以生成各种类型的3D形状。
Existing generative models for 3D shapes are typically trained on a large 3D dataset, often of a specific object category. In this paper, we investigate the deep generative model that learns from only a single reference 3D shape. Specifically, we present a multi-scale GAN-based model designed to capture the input shape's geometric features across a range of spatial scales. To avoid large memory and computational cost induced by operating on the 3D volume, we build our generator atop the tri-plane hybrid representation, which requires only 2D convolutions. We train our generative model on a voxel pyramid of the reference shape, without the need of any external supervision or manual annotation. Once trained, our model can generate diverse and high-quality 3D shapes possibly of different sizes and aspect ratios. The resulting shapes present variations across different scales, and at the same time retain the global structure of the reference shape. Through extensive evaluation, both qualitative and quantitative, we demonstrate that our model can generate 3D shapes of various types.
