Abstract. Semantic segmentation of three-dimensional (3D) scenes is a challenging task in 3D scene understanding. Recently, deep learning-based segmentation approaches have made significant progress. A multiscale spatial context feature learning is used in an end-to-end approach for 3D point cloud semantic segmentation. Furthermore, a local feature fusion learning block is then introduced to the hidden layers in the network to improve its feature learning capability. In addition, features learned in several different layers are fused for further improvement. Based on these strategies, end-to-end architectures are finally designed for 3D point cloud semantic segmentation. Several experiments conducted on three publicly available datasets have clearly shown the effectiveness of the proposed network.

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学习三维点云语义分割的多尺度空间上下文

摘要。三维 (3D) 场景的语义分割是 3D 场景理解中的一项具有挑战性的任务。最近，基于深度学习的分割方法取得了重大进展。多尺度空间上下文特征学习用于 3D 点云语义分割的端到端方法。此外，然后将局部特征融合学习块引入网络中的隐藏层以提高其特征学习能力。此外，融合了在几个不同层中学习的特征以进一步改进。基于这些策略，最终为 3D 点云语义分割设计了端到端架构。在三个公开可用的数据集上进行的几项实验清楚地表明了所提出的网络的有效性。