DoGFlow: Self-Supervised LiDAR Scene Flow via Cross-Modal Doppler Guidance
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918130581766144 |
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| author | Khoche, Ajinkya Zhang, Qingwen Cai, Yixi Mansouri, Sina Sharif Jensfelt, Patric |
| author_facet | Khoche, Ajinkya Zhang, Qingwen Cai, Yixi Mansouri, Sina Sharif Jensfelt, Patric |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_18506 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DoGFlow: Self-Supervised LiDAR Scene Flow via Cross-Modal Doppler Guidance Khoche, Ajinkya Zhang, Qingwen Cai, Yixi Mansouri, Sina Sharif Jensfelt, Patric Computer Vision and Pattern Recognition 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/ |
| title | DoGFlow: Self-Supervised LiDAR Scene Flow via Cross-Modal Doppler Guidance |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.18506 |