论文标题
JSenet：3D点云的联合语义分割和边缘检测网络
JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds
论文作者
论文摘要
语义分割和语义边缘检测可以看作是两个双重问题，这些问题与计算机视觉中的密切关系。尽管基于学习的3D语义分割方法的快速发展，但对3D语义边缘检测器的学习几乎没有引起关注，甚至更少，对于这两个任务的联合学习方法甚至更少。在本文中，我们首次解决了3D语义边缘检测任务，并提出了一个新的两流完全跨跨跨横向的网络，该网络共同执行这两个任务。特别是，我们设计了一个联合改进模块，该模块明确地将区域信息和边缘信息挂载，以改善这两个任务的性能。此外，我们提出了一种新颖的损失功能，该功能鼓励网络以更好的边界产生语义分割结果。对S3DIS和SCANNET数据集的广泛评估表明，我们的方法比语义分割的最先进方法在PAR或更好的性能方面取得了更好的表现，并且要优于语义边缘检测的基线方法。代码发布：https：//github.com/hzykent/jsenet
Semantic segmentation and semantic edge detection can be seen as two dual problems with close relationships in computer vision. Despite the fast evolution of learning-based 3D semantic segmentation methods, little attention has been drawn to the learning of 3D semantic edge detectors, even less to a joint learning method for the two tasks. In this paper, we tackle the 3D semantic edge detection task for the first time and present a new two-stream fully-convolutional network that jointly performs the two tasks. In particular, we design a joint refinement module that explicitly wires region information and edge information to improve the performances of both tasks. Further, we propose a novel loss function that encourages the network to produce semantic segmentation results with better boundaries. Extensive evaluations on S3DIS and ScanNet datasets show that our method achieves on par or better performance than the state-of-the-art methods for semantic segmentation and outperforms the baseline methods for semantic edge detection. Code release: https://github.com/hzykent/JSENet
