SynFlow: Scaling Up LiDAR Scene Flow Estimation with Synthetic Data

摘要

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.