论文标题
KPRNET：改善基于投影的激光雷达语义细分
KPRNet: Improving projection-based LiDAR semantic segmentation
论文作者
论文摘要
语义分割是自动驾驶汽车感知系统中的重要组成部分。在这项工作中，我们采用了图像和点云分段的最新进展，以在分割激光扫描的任务中获得更好的准确性。 KPRNET改善了2D投影方法的卷积神经网络架构，并利用KPCONV用可学习的点组件替换常用的后处理技术，从而使我们能够获得更准确的3D标签。通过这些改进，我们的模型优于Semantickitti基准测试的当前最佳方法，达到63.1的MIOU。
Semantic segmentation is an important component in the perception systems of autonomous vehicles. In this work, we adopt recent advances in both image and point cloud segmentation to achieve a better accuracy in the task of segmenting LiDAR scans. KPRNet improves the convolutional neural network architecture of 2D projection methods and utilizes KPConv to replace the commonly used post-processing techniques with a learnable point-wise component which allows us to obtain more accurate 3D labels. With these improvements our model outperforms the current best method on the SemanticKITTI benchmark, reaching an mIoU of 63.1.
