Automated rebar cage assembly and quality inspection require reliable rebar recognition. Although rebar segmentation from point clouds has been extensively studied, its generalizability remains limited. One key challenge is the scarcity of real data for training the segmentation models. To address this issue, we propose, for the first time, a pipeline for generating synthetic data for the rebar point cloud instance segmentation task. Using this pipeline, we applied the state-of-the-art Oneformer3D on rebar mesh instance segmentation. The model trained on our synthetic dataset achieved a 95.4 mAP in real-world experiments, showing strong synthetic-to-real transfer capability. By eliminating the need for manual data collection and annotation, the proposed method facilitates advancements in automated rebar cage assembly and dimensional quality inspection technologies.