Large-Scale Pseudo-Label Supervised 3D Segmentor with Image Priors

Large-scale point cloud segmentation plays a critical role in the remote sensing community. However, the challenge of high annotation costs has limited the release of datasets and the development of methods. Inspired by multi-modal fusion methods, we observe that semantic priors from images can significantly enhance point cloud understanding. To this end, we propose a Pseudo-Label-Supervised 3D Segmentor, named PLS3D, for large-scale point cloud semantic segmentation. PLS3D consists of two main components: Pseudo-Label Transfer via Projection Matching (PTPM) and a segmentation network supervised by pseudo-labels. We employ PTPM to generate pseudo-labels from remote sensing images and use these pseudo-labels to supervise point cloud semantic segmentation. Moreover, we design a residual attention module (RAM) to capture global distribution information, thereby refining the local geometry. We conduct the experiments on the N3C-California and 2Dinsight3D datasets. PLS3D achieves accuracies of 85.86% and 78.34%, respectively, which are comparable to point cloud annotation supervision methods. The experiments demonstrate the effectiveness and robustness of our PLS3D for large-scale point cloud segmentation without the need for substantial annotation costs.