A Synthetic Data Generation Pipeline for Point-Cloud-Based Rebar Segmentation
保存先:
| 出版年: | ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction vol. 42 (2025), p. 1137-1143 |
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| 第一著者: | |
| その他の著者: | , |
| 出版事項: |
IAARC Publications
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| オンライン･アクセス: | Citation/Abstract Full Text - PDF |
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| 抄録: | 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 92.1 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. |
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| ソース: | Advanced Technologies & Aerospace Database |