论文标题
假装它，混合，分段：桥接LIDAR传感器之间的域间隙
Fake it, Mix it, Segment it: Bridging the Domain Gap Between Lidar Sensors
论文作者
论文摘要
LIDAR数据的分割是一项为机器人或自动驾驶汽车环境提供丰富的，明显的信息的任务。目前，最佳性能用于LiDAR细分的神经网络对特定数据集进行了微调。切换激光雷达传感器而不在新传感器的大量注释数据上重新训练会产生域移动，从而导致网络性能急剧下降。在这项工作中，我们提出了一种针对LIDAR域自适应的新方法，其中我们使用带注释的圆锥激光雷达数据集并重新创建了不同LIDAR传感器的结构中记录的场景。我们通过从另一个域中重新创建全景数据并将生成的数据与标记为目标域数据的（伪）的一部分混合在一起，将域间隙缩小到目标数据。我们的方法在我们的半监视方法中，将努斯烯对semantickitti无监督域的适应性表现提高了15.2平均交点（MIOU），并提高了48.3 miou。我们证明了Semantickitti与Nuscenes域适应的类似改进，分别为21.8 miou和51.5 miou。我们将我们的方法与两种用于语义激光雷达分割域适应性的最先进的方法进行了比较，并为无监督和半监督域的适应性做出了重大改进。此外，我们成功地将我们提出的方法应用于两个完全未标记的数据集，这些数据集是两个最新的激光雷达传感器velodyne alpha prime和innoviztwo的数据集，并为两者培训了性能良好的语义细分网络。
Segmentation of lidar data is a task that provides rich, point-wise information about the environment of robots or autonomous vehicles. Currently best performing neural networks for lidar segmentation are fine-tuned to specific datasets. Switching the lidar sensor without retraining on a big set of annotated data from the new sensor creates a domain shift, which causes the network performance to drop drastically. In this work we propose a new method for lidar domain adaption, in which we use annotated panoptic lidar datasets and recreate the recorded scenes in the structure of a different lidar sensor. We narrow the domain gap to the target data by recreating panoptic data from one domain in another and mixing the generated data with parts of (pseudo) labeled target domain data. Our method improves the nuScenes to SemanticKITTI unsupervised domain adaptation performance by 15.2 mean Intersection over Union points (mIoU) and by 48.3 mIoU in our semi-supervised approach. We demonstrate a similar improvement for the SemanticKITTI to nuScenes domain adaptation by 21.8 mIoU and 51.5 mIoU, respectively. We compare our method with two state of the art approaches for semantic lidar segmentation domain adaptation with a significant improvement for unsupervised and semi-supervised domain adaptation. Furthermore we successfully apply our proposed method to two entirely unlabeled datasets of two state of the art lidar sensors Velodyne Alpha Prime and InnovizTwo, and train well performing semantic segmentation networks for both.
