SynFlow: Scaling Up LiDAR Scene Flow Estimation with Synthetic Data

Reliable 3D dynamic perception requires models that can anticipatemotion beyond predefined categories, yet progress is hindered by the scarcityof dense, high-quality motion annotations. While self-supervision on unlabeledreal data offers a path forward, empirical evidence suggests that scaling unla-beled data fails to close the performance gap due to noisy proxy signals. In thispaper, we propose learning robust real-world motion priors entirely from scal-able simulation. We introduce SynFlow, a data generation pipeline for large-scalesynthetic LiDAR scene flow. Unlike prior works that prioritize sensor-specificrealism, SynFlow employs a motion-oriented strategy to synthesize diverse kine-matic patterns across 4,000 sequences (∼940k frames), termed SynFlow-4k. Thisrepresents a 34× scale-up in annotated volume over existing real-world bench-marks. Our experiments demonstrate that SynFlow-4k provides a highly domain-invariant motion prior. In a zero-shot regime, models trained only on our syntheticdata generalize across multiple real-world benchmarks, comparable to in-domainsupervised baselines on nuScenes and outperforming state-of-the-art methodson TruckScenes by 31.8%. Furthermore, SynFlow-4k serves as a label-efficientfoundation: fine-tuning with only 5% of real-world labels surpasses models trainedfrom scratch on the full available budget. We open-source the pipeline and datasetto facilitate research in generalizable 3D motion estimation. More detail can befound at https://kin-zhang.github.io/SynFlow.