Abstract
3D autonomous driving semantic segmentation using deep learning has become, a well-studied subject, providing methods that can reach very high performance. Nonetheless, because of the limited size of the training datasets, these models cannot see every type of object and scenes found in real-world applications. The ability to be reliable in these various unknown environments is called domain generalization. Despite its importance, domain generalization is relatively unexplored in the case of 3D autonomous driving semantic segmentation. To fill this gap, this paper presents the first benchmark for this application by testing state-of-the-art methods and discussing the difficulty of tackling LiDAR domain shifts. We also propose the first method designed to address this domain generalization, which we call 3DLabelProp. This method relies on leveraging the geometry and sequentiality of the LiDAR data to enhance its generalization performances by working on partially accumulated point clouds. It reaches a mIoU of 52.6% on SemanticPOSS while being trained only on SemanticKITTI, making it state-of-the-art method for generalization (+7.4% better than the second best method). The code for this method will be available on Github.
Abstract (translated)
URL
https://arxiv.org/abs/2212.04245
