论文标题
3D形状分割，几何深度学习
3D Shape Segmentation with Geometric Deep Learning
论文作者
论文摘要
由于记忆要求很大，因此具有高密度顶点的3D形状的语义分割可能是不切实际的。为了使这个问题在计算上可以处理，我们提出了一种基于神经网络的方法，该方法产生3D增强了3D形状的视图，以将整个分割作为子细分问题解决。 3D增强视图是通过将3D形状的顶点和正态从形状周围的不同视点带到2D常规网格上获得的。然后，这些3D视图由卷积神经网络处理，以在每个顶点的语义类集合中产生概率分布函数（PDF）。然后将这些PDF重新投影在原始的3D形状上，并通过有条件的随机字段使用上下文信息进行后处理。我们使用公开可用数据集的3D形状和使用摄影测量技术重建的真实对象来验证我们的方法。我们将方法与最先进的替代方案进行了比较。
The semantic segmentation of 3D shapes with a high-density of vertices could be impractical due to large memory requirements. To make this problem computationally tractable, we propose a neural-network based approach that produces 3D augmented views of the 3D shape to solve the whole segmentation as sub-segmentation problems. 3D augmented views are obtained by projecting vertices and normals of a 3D shape onto 2D regular grids taken from different viewpoints around the shape. These 3D views are then processed by a Convolutional Neural Network to produce a probability distribution function (pdf) over the set of the semantic classes for each vertex. These pdfs are then re-projected on the original 3D shape and postprocessed using contextual information through Conditional Random Fields. We validate our approach using 3D shapes of publicly available datasets and of real objects that are reconstructed using photogrammetry techniques. We compare our approach against state-of-the-art alternatives.
