The autonomous car must recognize the driving environment quickly for safe driving. As the Light Detection And Range (LiDAR) sensor is widely used in the autonomous car, fast semantic segmentation of LiDAR point cloud, which is the point-wise classification of the point cloud within the sensor framerate, has attracted attention in recognition of the driving environment. Although the voxel and fusion-based semantic segmentation models are the state-of-the-art model in point cloud semantic segmentation recently, their real-time performance suffer from high computational load due to high voxel resolution. In this paper, we propose the fast voxel-based semantic segmentation model using Point Convolution and 3D Sparse Convolution (PCSCNet). The proposed model is designed to outperform at both high and low voxel resolution using point convolution-based feature extraction. Moreover, the proposed model accelerates the feature propagation using 3D sparse convolution after the feature extraction. The experimental results demonstrate that the proposed model outperforms the state-of-the-art real-time models in semantic segmentation of SemanticKITTI and nuScenes, and achieves the real-time performance in LiDAR point cloud inference.
翻译:自主汽车必须快速识别驱动环境, 以便安全驾驶。 由于独立汽车广泛使用光探测和射程传感器(LiDAR)传感器,作为传感器框架速率中点云的点云的点分解的快速静语分解模式吸引了对驱动环境的注意。虽然基于 voxel 和 聚合 的语义分解模型是最近点云语分解中最先进的模型,但由于高voxel 分辨率,它们的实时性能受到高计算负荷的影响。 在本文中,我们建议使用基于点共振和 3D 弧变异的快速 voxel 语义分解模式(PCSCNet) 。 拟议的模型旨在利用基于点的分流特征分解分解的分解,在高分解中超越高低的 voxel 分解。 此外, 拟议的模型加速了特征传播,在特征提取后使用了 3D 稀薄的分流。 实验结果显示, 拟议的模型超越了以状态- 艺术 实时分解模型在静脉磁段中实现Smantical- Stal- 和 级的流路段。