Recently, many approaches have been proposed through single or multiple representations to improve the performance of point cloud semantic segmentation. However, these works do not maintain a good balance among performance, efficiency, and memory consumption. To address these issues, we propose DRINet++ that extends DRINet by enhancing the sparsity and geometric properties of a point cloud with a voxel-as-point principle. To improve efficiency and performance, DRINet++ mainly consists of two modules: Sparse Feature Encoder and Sparse Geometry Feature Enhancement. The Sparse Feature Encoder extracts the local context information for each point, and the Sparse Geometry Feature Enhancement enhances the geometric properties of a sparse point cloud via multi-scale sparse projection and attentive multi-scale fusion. In addition, we propose deep sparse supervision in the training phase to help convergence and alleviate the memory consumption problem. Our DRINet++ achieves state-of-the-art outdoor point cloud segmentation on both SemanticKITTI and Nuscenes datasets while running significantly faster and consuming less memory.

翻译：最近,通过单一或多个表达方式提出了许多方法,以改善点云语系分化的性能,然而,这些工程在性能、效率和内存消耗之间没有保持良好的平衡。为了解决这些问题,我们提议DRINet++,通过增强点云的广度和几何特性,并采用 voxel-as-point 原则,扩展DRINet++。为了提高效率和性能,DRINet++主要由两个模块组成: 浅色地貌和偏地地地地测量特征增强。 简简法的精华分解器提取了每个点的当地背景信息, 简化的几何特征增强通过多尺度的分散投影和细微多尺度的聚变异来增强点云的几何特性。 此外,我们提议在培训阶段进行深入的细小监督,以帮助聚合和缓解记忆消耗问题。我们的DRINet+在SmanticITTI和Nuscenes数据集上都实现了最先进的户外点云分分化,同时运行速度和消耗较少的记忆。