3D point cloud semantic segmentation is fundamental for autonomous driving.
Most approaches in the literature neglect an important aspect, i.e., how to
deal with domain shift when handling dynamic scenes. This can significantly
hinder the navigation capabilities of self-driving vehicles. This paper
advances the state of the art in this research field. Our first contribution
consists in analysing a new unexplored scenario in point cloud segmentation,
namely Source-Free Online Unsupervised Domain Adaptation (SF-OUDA). We
experimentally show that state-of-the-art methods have a rather limited ability
to adapt pre-trained deep network models to unseen domains in an online manner.
Our second contribution is an approach that relies on adaptive self-training
and geometric-feature propagation to adapt a pre-trained source model online
without requiring either source data or target labels. Our third contribution
is to study SF-OUDA in a challenging setup where source data is synthetic and
target data is point clouds captured in the real world. We use the recent
SynLiDAR dataset as a synthetic source and introduce two new synthetic (source)
datasets, which can stimulate future synthetic-to-real autonomous driving
research. Our experiments show the effectiveness of our segmentation approach
on thousands of real-world point clouds. Code and synthetic datasets are
available at this https URL.

本文主要研究了自动驾驶中 3D 点云语义分割的问题，提出了一种新的面向场景推理的算法框架，并针对领域变化以及使用合成数据时的挑战性问题进行了研究，提高了算法的稳定性和实际性能。