论文标题
MVP-NET：多视图大规模点云的点语义分段
MVP-Net: Multiple View Pointwise Semantic Segmentation of Large-Scale Point Clouds
论文作者
论文摘要
3D点云的语义分割是自主驾驶环境感知的重要任务。大多数点云语义分割方法的管道包括点采样，邻居搜索，特征聚合和分类。邻居搜索方法（例如K-Nearest邻居算法，KNN）已被广泛应用。但是，KNN的复杂性始终是效率的瓶颈。在本文中，我们提出了一个端到端的神经体系结构，即多点网，MVP-NET，以有效并直接推断大型室外点云，而无需KNN或任何复杂的预/后/后处理。取而代之的是，引入了基于假设的空间填充曲线和点云方法的多旋转，以扩大点特征聚集和接受场的扩展。数值实验表明，所提出的MVP-NET比最有效的语义分割方法randla-net快11倍，并且在大规模基准Semantickitti数据集上达到了相同的精度。
Semantic segmentation of 3D point cloud is an essential task for autonomous driving environment perception. The pipeline of most pointwise point cloud semantic segmentation methods includes points sampling, neighbor searching, feature aggregation, and classification. Neighbor searching method like K-nearest neighbors algorithm, KNN, has been widely applied. However, the complexity of KNN is always a bottleneck of efficiency. In this paper, we propose an end-to-end neural architecture, Multiple View Pointwise Net, MVP-Net, to efficiently and directly infer large-scale outdoor point cloud without KNN or any complex pre/postprocessing. Instead, assumption-based space filling curves and multi-rotation of point cloud methods are introduced to point feature aggregation and receptive field expanding. Numerical experiments show that the proposed MVP-Net is 11 times faster than the most efficient pointwise semantic segmentation method RandLA-Net and achieves the same accuracy on the large-scale benchmark SemanticKITTI dataset.
