DoGFlow：基于跨模态多普勒引导的自监督LiDAR场景流
DoGFlow: Self-Supervised LiDAR Scene Flow via Cross-Modal Doppler Guidance
AI总结：
针对自主系统3D场景流估计中人工标注数据集的瓶颈问题，提出DoGFlow自监督框架，通过跨模态多普勒引导迁移运动伪标签，无需人工标注即可提升LiDAR场景流性能，在MAN TruckScenes数据集上大幅超越现有自监督方法并提高标签效率。
摘要：
精确的3D场景流估计对自主系统安全导航动态环境至关重要，但创建所需的大规模人工标注数据集仍是开发鲁棒感知模型的重大瓶颈。当前自监督方法难以匹配全监督方法的性能，尤其在挑战性的长距离和恶劣天气场景中，而监督方法因依赖昂贵的人工标注而无法扩展。我们提出DoGFlow，一种无需任何人工真值标注即可恢复LiDAR场景流估计中完整3D物体运动的新型自监督框架。本文介绍了跨模态标签迁移方法：DoGFlow直接从4D雷达多普勒测量实时计算运动伪标签，并通过动态感知关联和歧义消除传播将其迁移至LiDAR域。在挑战性的MAN TruckScenes数据集上，DoGFlow大幅优于现有自监督方法，且仅用10%的真值数据即可使LiDAR骨干网络达到全监督性能的90%以上。更多细节请访问https://ajinkyakhoche.github.io/DogFlow/
英文摘要：
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/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
