用雷达多普勒数据自监督训练激光雷达场景流，10%标注数据达90%监督性能。
DoGFlow: Self-Supervised LiDAR Scene Flow via Cross-Modal Doppler Guidance
- 通过雷达多普勒实时生成运动伪标签，跨模态迁移至激光雷达
- 在MAN TruckScenes上仅用10%标注数据达到监督方法90%精度
- 适合缺乏标注的自动驾驶动态环境感知任务
精准的3D场景流估计对自动驾驶系统安全导航至关重要，但大规模人工标注数据集的构建仍是发展鲁棒感知模型的主要瓶颈。现有自监督方法在长距离和恶劣天气场景下性能远逊于全监督方法，而监督方法因依赖昂贵的人工标注难以扩展。本文提出DoGFlow，一种无需任何人工真值标注的新型自监督框架，可恢复激光雷达场景流中的完整3D物体运动。该方法通过跨模态标签迁移：实时从4D雷达多普勒测量中计算运动伪标签，并利用动态感知关联与歧义消除传播将其转移至激光雷达域。在挑战性MAN TruckScenes数据集上，DoGFlow显著优于现有自监督方法，使激光雷达主干网络仅使用10%真值数据即可实现超过90%的全监督性能。
原文摘要 · Abstract (English)
Accurate 3D scene flow estimation is critical for autonomous systems to navigate dynamic environments safely, but creating the necessary large-scale, manually annotated datasets remains a significant bottleneck for developing robust perception models. Current self-supervised methods struggle to match the performance of fully supervised approaches, especially in challenging long-range and adverse weather scenarios, while supervised methods are not scalable due to their reliance on expensive human labeling. We introduce DoGFlow, a novel self-supervised framework that recovers full 3D object motions for LiDAR scene flow estimation without requiring any manual ground truth annotations. This paper presents our cross-modal label transfer approach, where DoGFlow computes motion pseudo-labels in real-time directly from 4D radar Doppler measurements and transfers them to the LiDAR domain using dynamic-aware association and ambiguity-resolved propagation. On the challenging MAN TruckScenes dataset, DoGFlow substantially outperforms existing self-supervised methods and improves label efficiency by enabling LiDAR backbones to achieve over 90% of fully supervised performance with only 10% of the ground truth data. For more details, please visit https://ajinkyakhoche.github.io/DogFlow/
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