Density-Guided Semi-Supervised 3D Semantic Segmentation with Dual-Space Hardness Sampling

Densely annotating the large-scale point clouds is laborious. To alleviate the annotation burden, contrastive learning has attracted increasing attention for tackling semi-supervised 3D semantic segmentation. However, existing point-to-point contrastive learning techniques in literature are generally sensitive to outliers, resulting in insufficient modeling of the point-wise representations. To address this problem, we propose a method named DDSemi for semi-supervised 3D semantic segmentation, where a density-guided contrastive learning technique is explored. This technique calculates the contrastive loss in a point-to-anchor manner by estimating an anchor for each class from the memory bank based on the finding that the cluster centers tend to be located in dense regions. In this technique, an inter-contrast loss is derived from the perturbed unlabeled point cloud pairs, while an intra-contrast loss is derived from a single unlabeled point cloud. The derived losses could enhance the discriminability of the features and implicitly constrain the semantic consistency between the perturbed unlabeled point cloud pairs. In addition, we propose a dual-space hardness sampling strategy to pay more attention to the hard samples located in sparse regions of both the geometric space and feature space by reweighting the point-wise intra-contrast loss. Experimental results on both indoor-scene and outdoor-scene datasets demonstrate that the proposed method outperforms the comparative state-of-the-art semi-supervised methods.