论文标题
PCSCNET：使用点卷积和稀疏卷积网络的LIDAR点云的快速3D语义分割
PCSCNet: Fast 3D Semantic Segmentation of LiDAR Point Cloud for Autonomous Car using Point Convolution and Sparse Convolution Network
论文作者
论文摘要
自动驾驶汽车必须迅速识别驾驶环境以安全驾驶。由于光检测和范围（LIDAR）传感器被广泛用于自动驾驶汽车，因此在传感器帧率内的点云中，LiDAR Point Cloud的快速语义分割，这是对驱动环境的认识，引起了人们的注意。尽管基于体素和基于融合的语义分割模型是点云语义分割中的最新模型，但由于高素的分辨率，它们的实时性能遭受了高计算负载。在本文中，我们使用点卷积和3D稀疏卷积（PCSCNET）提出了基于快速体素的语义分割模型。所提出的模型旨在使用基于点卷积的特征提取，以优于高和低体素分辨率。此外，提出的模型在特征提取后使用3D稀疏卷积加速了特征传播。实验结果表明，所提出的模型在Semantickitti和Nuscenes的语义分割中优于最新的实时模型，并在LIDAR点云推断中实现了实时性能。
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.
