3D scene parsing has always been a hot topic and point clouds are efficient data format to represent scenes. The semantic segmentation of point clouds is critical to the 3D scene, which is a challenging problem due to the unordered structure of point clouds. The max-pooling operation is typically used to obtain the order invariant features, while the point-wise features are destroyed after the max-pooling operation. In this paper, we propose a feature fusion network that fuses point-wise features and local features by attention mechanism to compensate for the loss caused by max-pooling operation. By incorporating point-wise features into local features, the point-wise variation is preserved to obtain a refined segmentation accuracy, and the attention mechanism is used to measure the importance of the point-wise features and local features for each 3D point. Extensive experiments show that our method achieves better performances than other prestigious methods.

3D场景解析一直是热门话题，而点云是表示场景的高效数据格式。点云的语义分割对于3D场景至关重要，由于点云的无序结构，这是一个具有挑战性的问题。max-pooling操作通常用于获取阶数不变特征，而max-pooling操作后将破坏逐点特征。在本文中，我们提出了一种特征融合网络，该网络通过注意机制将点状特征和局部特征融合在一起，以补偿最大池操作所造成的损失。通过将逐点特征合并到局部特征中，可以保留逐点变化以获得精确的分割精度，注意机制用于衡量每个3D点的逐点特征和局部特征的重要性。大量的实验表明，我们的方法比其他著名方法具有更好的性能。