Kreutz, Thomas ; Mühlhäuser, Max ; Sanchez Guinea, Alejandro (2023):
Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time Series.
In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV'23), pp. 1644-1653,
IEEE, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Hawaii, USA, 03.-07.01.2023, [Conference or Workshop Item]

Abstract

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 anno- tated ground truth data, which is expensive to obtain and scarce in existence. To close this gap in the stationary set- ting, we propose a novel 4D LiDAR representation based on multivariate time series that relaxes the problem of un- supervised 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 al- gorithm into moving or stationary. Experiments on station- ary scenes from the Raw KITTI dataset show that our fully unsupervised approach achieves performance that is com- parable to that of supervised state-of-the-art approaches.

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