论文标题
从单个RGB图像中学习3D对象的无监督分层零件分解
Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image
论文作者
论文摘要
人类将3D世界视为一组不同的对象，这些对象的特征是各种低级（几何，反射率）和高级（连通性，邻接，邻接，对称性）的特征。基于卷积神经网络（CNN）的最新方法在3D重建中也表现出令人印象深刻的进展，即使使用单个2D图像作为输入。但是，这些方法中的大多数侧重于恢复对象的局部3D几何形状，而无需考虑其基于部分的分解或零件之间的关系。我们通过提出一种新颖的表述来解决这个具有挑战性的问题，该表述可以共同恢复3D对象的几何形状作为一组原始物以及其潜在的分层结构，而无需零件级别的监督。我们的模型以二进制原始树的形式恢复了各种对象的较高级别的结构分解，在该形式中，简单的部分用更少的原始图表示，更复杂的部分用更多的组件建模。我们在Shapenet和D-Faust数据集上的实验表明，考虑到零件的组织确实有助于3D几何形状的推理。
Humans perceive the 3D world as a set of distinct objects that are characterized by various low-level (geometry, reflectance) and high-level (connectivity, adjacency, symmetry) properties. Recent methods based on convolutional neural networks (CNNs) demonstrated impressive progress in 3D reconstruction, even when using a single 2D image as input. However, the majority of these methods focuses on recovering the local 3D geometry of an object without considering its part-based decomposition or relations between parts. We address this challenging problem by proposing a novel formulation that allows to jointly recover the geometry of a 3D object as a set of primitives as well as their latent hierarchical structure without part-level supervision. Our model recovers the higher level structural decomposition of various objects in the form of a binary tree of primitives, where simple parts are represented with fewer primitives and more complex parts are modeled with more components. Our experiments on the ShapeNet and D-FAUST datasets demonstrate that considering the organization of parts indeed facilitates reasoning about 3D geometry.
