Crossmodal Few-shot 3D Point Cloud Semantic Segmentation

Recently, few-shot 3D point cloud semantic segmentation methods have been introduced to mitigate the limitations of existing fully supervised approaches, i.e., heavy dependence on labeled 3D data and poor capacity to generalize to new categories. However, those few-shot learning methods need one or few labeled data as support for testing. In practice, such data labeling usually requires manual annotation of large-scale points in 3D space, which can be very difficult and laborious. To address this problem, in this paper we introduce a novel crossmodal few-shot learning approach for 3D point cloud semantic segmentation. In this approach, the point cloud to be segmented is taken as query while one or few labeled 2D RGB images are taken as support to guide the segmentation of query. This way, we only need to annotate on a few 2D support images for the categories of interest. Specifically, we first convert the 2D support images into 3D point cloud format based on both appearance and the estimated depth information. We then introduce a co-embedding network for extracting the features of support and query, both from 3D point cloud format, to fill their domain gap. Finally, we compute the prototypes of support and employ cosine similarity between the prototypes and the query features for final segmentation. Experimental results on two widely-used benchmarks show that, with one or few labeled 2D images as support, our proposed method achieves competitive results against existing few-shot 3D point cloud semantic segmentation methods.