SegTrans: Semantic Segmentation With Transfer Learning for MLS Point Clouds

3D point cloud semantic segmentation plays an essential role in fine-grained scene understanding from photogrammetry to autonomous driving. Although recent efforts have been made to push the 3D semantic segmentation forward, many solutions cannot generalize well to new data with different sensor configurations. For example, when transferring the segmentation model learned from terrestrial laser scanning (TLS) data to mobile laser scanning (MLS) data, the performance drops dramatically. Besides, rich-labeled data is usually required. However, labeling point cloud data is time-consuming and label-intensive in practice. In light of this, we propose SegTrans, an unsupervised domain adaption method for the point cloud semantic segmentation task, which largely improves the generalization performance from one labeled dataset (source domain) to another unlabeled dataset (target domain). Specifically, we first introduce a data selection module (DSM) to tackle the discrepancy between different datasets at the data level. Then an adversarial learning module (ALM) with an adversarial loss is iteratively implemented to align the domain-specific feature in both the source and target domains, which only consists of two fully connected layers. Experiments show the overall accuracy of the proposed method achieves 88% OA on the TUM City Campus dataset (MLS dataset) when trained on the Semantic3D dataset (TLS dataset).