论文标题
用于点云语义分割的级联的非本地神经网络
Cascaded Non-local Neural Network for Point Cloud Semantic Segmentation
论文作者
论文摘要
在本文中，我们提出了一个级联的非本地神经网络，用于点云分割。所提出的网络旨在构建点云的远程依赖性，以进行准确的细分。具体而言，我们开发了一个新型的级联非本地模块，该模块由邻里级别，超级点级和全球级别的非本地块组成。首先，在邻里级别的块中，我们通过将不同的权重分配给相邻点来提取点云的质心点的局部特征。然后，使用非本地操作来编码质心点的局部特征。最后，全局级块在编码器框架框架中汇总了SuperPoints的非本地特征，以进行语义分割。从级联的结构中受益，可以传播具有相同标签的不同社区的几何结构信息。另外，级联的结构可以很大程度上降低点云上原始非本地操作的计算成本。在室内和室外数据集上进行的实验表明，我们的方法可实现最先进的性能，并有效地减少了时间消耗和记忆占用。
In this paper, we propose a cascaded non-local neural network for point cloud segmentation. The proposed network aims to build the long-range dependencies of point clouds for the accurate segmentation. Specifically, we develop a novel cascaded non-local module, which consists of the neighborhood-level, superpoint-level and global-level non-local blocks. First, in the neighborhood-level block, we extract the local features of the centroid points of point clouds by assigning different weights to the neighboring points. The extracted local features of the centroid points are then used to encode the superpoint-level block with the non-local operation. Finally, the global-level block aggregates the non-local features of the superpoints for semantic segmentation in an encoder-decoder framework. Benefiting from the cascaded structure, geometric structure information of different neighborhoods with the same label can be propagated. In addition, the cascaded structure can largely reduce the computational cost of the original non-local operation on point clouds. Experiments on different indoor and outdoor datasets show that our method achieves state-of-the-art performance and effectively reduces the time consumption and memory occupation.
