In this paper, we focus on the two tasks of 3D shape abstraction and semantic analysis. This is in contrast to current methods, which focus solely on either 3D shape abstraction or semantic analysis. In addition, previous methods have had difficulty producing instance-level semantic results, which has limited their application. We present a novel method for the joint estimation of a 3D shape abstraction and semantic analysis. Our approach first generates a number of 3D semantic candidate regions for a 3D shape; we then employ these candidates to directly predict the semantic categories and refine the parameters of the candidate regions simultaneously using a deep convolutional neural network. Finally, we design an algorithm to fuse the predicted results and obtain the final semantic abstraction, which is shown to be an improvement over a standard non maximum suppression. Experimental results demonstrate that our approach can produce state-of-the-art results. Moreover, we also find that our results can be easily applied to instance-level semantic part segmentation and shape matching.

在本文中，我们专注于3D形状抽象和语义分析的两个任务。这与当前的方法相反，后者仅专注于3D形状抽象或语义分析。另外，先前的方法产生了实例级语义困难的结果，这限制了它们的应用。我们提出了一种用于3D形状抽象和语义分析的联合估计的新颖方法。我们的方法首先为3D形状生成许多3D语义候选区域；然后，我们使用一个深度卷积神经网络，利用这些候选对象直接预测语义类别并同时优化候选区域的参数。最后，我们设计一种算法来融合预测结果并获得最终的语义抽象，它被证明是对标准非最大抑制的改进。实验结果表明，我们的方法可以产生最先进的结果。此外，我们还发现我们的结果可以轻松地应用于实例级语义部分的分割和形状匹配。