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.
翻译:在本文中, 我们专注于 3D 形状抽象和语义分析的两件任务。 这与目前的方法不同, 目前的方法只侧重于 3D 形状抽象或语义分析。 此外, 先前的方法很难产生实例级语义分析结果, 限制了它们的应用。 我们提出了一个用于联合估计 3D 形状抽象和语义分析的新方法。 我们的方法首先为 3D 形状生成若干 3D 语义候选区域; 然后我们使用这些候选人直接预测语义类别, 并同时利用深层的卷变神经网络改进候选区域的参数。 最后, 我们设计了一种算法, 以整合预测的结果, 并获得最终的语义抽象, 事实证明这比标准的非最大抑制效果要好。 实验结果表明, 我们的方法可以产生最新的结果 。 此外, 我们还发现, 我们的结果可以很容易应用到 实例级语义部分和形状匹配 。