We propose a conditional generative model, named Part Geometry Network (PG-Net), which synthesizes realistic objects and can be used as a robust feature descriptor for object reconstruction and classification. Surface and volumetric representations of objects have complementary properties of three-dimensional objects. Combining these modalities is more informative than using one modality alone. Therefore, PG-Net utilizes complementary properties of surface and volumetric representations by estimating curvature, surface area, and occupancy in voxel grids of objects with a single decoder as a multi-task learning. Objects are combinations of multiple parts, and therefore part geometry (PG) is essential to synthesize each part of the objects. PG-Net employs a part identifier to learn the part geometry. Additionally, we augmented a dataset by interpolating individual functional parts such as wings of an airplane, which helps learning part geometry and finding local/global minima of PG-Net. To demonstrate the capability of learning object representations of PG-Net, we performed object reconstruction and classification tasks on two standard large-scale datasets. PG-Net outperformed the state-of-the-art methods in object synthesis, classification, and reconstruction in a large margin.

我们提出了一个条件生成模型，称为零件几何网络（PG-Net），该模型可以合成现实对象，并且可以用作对象重建和分类的鲁棒特征描述符。对象的表面和体积表示具有三维对象的互补属性。与仅使用一种模式相比，将这些模式组合起来更能提供更多信息。因此，PG-Net通过使用单个解码器作为多任务学习来估计对象的体素网格中的曲率，表面积和占有率，从而利用了表面和体积表示的互补属性。对象是多个零件的组合，因此零件几何图形（PG）对于合成对象的每个零件都是必不可少的。PG-Net使用零件标识符来学习零件几何形状。另外，我们通过内插各个功能零件（例如飞机机翼）来扩充数据集，这有助于学习零件几何形状并查找PG-Net的局部/全局最小值。为了演示学习PG-Net对象表示的能力，我们在两个标准的大型数据集上执行了对象重建和分类任务。PG-Net在对象合成，分类和重建方面远远超过了最新技术。