Real-world point cloud datasets have made significant contributions to the development of LiDAR-based perception technologies, such as object segmentation for autonomous driving. However, due to the limited number of instances in some rare classes, the long-tail problem remains a major challenge in existing datasets. To address this issue, we introduce a novel, synthetic point cloud dataset named RareBoost3D, which complements existing real-world datasets by providing significantly more instances for object classes that are rare in real-world datasets. To effectively leverage both synthetic and real-world data, we further propose a cross-domain semantic alignment method named CSC loss that aligns feature representations of the same class across different domains. Experimental results demonstrate that this alignment significantly enhances the performance of LiDAR point cloud segmentation models over real-world data.