Among 2D convolutional networks on point clouds, point-based approaches consume point clouds of fixed size directly. By analysis of PointNet, a pioneer in introducing deep learning into point sets, we reveal that current point-based methods are essentially spatial relationship processing networks. In this paper, we take a different approach. Our architecture, named PE-Net, learns the representation of point clouds in high-dimensional space, and encodes the unordered input points to feature vectors, which standard 2D CNNs can be applied to. The recommended network can adapt to changes in the number of input points which is the limit of current methods. Experiments show that in the tasks of classification and part segmentation, PE-Net achieves the state-of-the-art performance in multiple challenging datasets, such as ModelNet and ShapeNetPart.
翻译:在点云上的2D连锁网络中,点基方法直接消耗固定大小的点云。通过分析点网,我们发现,目前点网方法基本上是空间关系处理网络。在本文中,我们采取了不同的方法。我们的架构名为PE-Net,在高维空间中学习点云的表示方式,并将未经排序的输入点编码为特性矢量,标准2DCNN可以应用。推荐的网络可以适应作为当前方法极限的输入点数的变化。实验显示,在分类和部分分割任务中,PE-Net在多套具有挑战性的数据集(如模型Net和ShapeNetPart)中达到了最先进的性能。