论文标题
在具有多元占用时间序列的固定设置中无监督的4D LIDAR移动对象细分
Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time Series
论文作者
论文摘要
在这项工作中，我们解决了从固定传感器中记录的4D LIDAR数据中无监督的移动对象分割（MOS）的问题，该传感器不涉及地面真相注释。基于深度学习的LIDAR MOS的最先进方法在很大程度上取决于注释的地面真实数据，这很昂贵，并且存在稀缺。为了在固定环境中缩小这一差距，我们提出了一种基于多元时间序列的新颖的4D激光雷达表示，这使无监督的MOS的问题放宽了时间序列聚类问题。更具体地说，我们建议通过多变量占用时间序列（MOTS）对体素的占用变化进行建模，该序列捕获了Voxel水平及其周围社区的时空占用变化。为了执行无监督的MOS，我们以自我监督的方式训练神经网络，将MOT编码为Voxel级特征表示，可以通过聚类算法将其划分为移动或静止。 RAW KITTI数据集的固定场景实验表明，我们完全无监督的方法实现了与受监督的最新方法相当的性能。
In this work, we address the problem of unsupervised moving object segmentation (MOS) in 4D LiDAR data recorded from a stationary sensor, where no ground truth annotations are involved. Deep learning-based state-of-the-art methods for LiDAR MOS strongly depend on annotated ground truth data, which is expensive to obtain and scarce in existence. To close this gap in the stationary setting, we propose a novel 4D LiDAR representation based on multivariate time series that relaxes the problem of unsupervised MOS to a time series clustering problem. More specifically, we propose modeling the change in occupancy of a voxel by a multivariate occupancy time series (MOTS), which captures spatio-temporal occupancy changes on the voxel level and its surrounding neighborhood. To perform unsupervised MOS, we train a neural network in a self-supervised manner to encode MOTS into voxel-level feature representations, which can be partitioned by a clustering algorithm into moving or stationary. Experiments on stationary scenes from the Raw KITTI dataset show that our fully unsupervised approach achieves performance that is comparable to that of supervised state-of-the-art approaches.
