3D-UnOutDet: A Fast and Efficient Unsupervised Snow Removal Algorithm for 3D LiDAR Point Clouds

Abstract

In this work, we propose a novel, fast, and memory-efficient unsupervised statistical method, combined with an unsupervised deep learning (DL) model, for de-snowing 3D LiDAR point clouds in a fully unsupervised fashion. The results obtained on the real-scanned Winter Adverse Driving dataSet (WADS) show that our DL model achieves a 6.3% improvement in mIoU over the current state-of-the-art unsupervised DL methods and performs comparable to supervised counterparts, substantially narrowing the performance gap between supervised and unsupervised approaches. In addition to that, our model also outperforms its closest competitor by 12.8% mIoU when tested on our Canadian Adverse Driving Conditions (CADC) dataset annotations. Additionally, our de-snowing algorithm enhances downstream semantic segmentation and object detection tasks without even requiring any modifications to the base segmentation and detection models. The source code, trained models, and the online supplementary information are available at the following URL: https://sporsho.github.io/3DUnOutDet.