Estimating 3D occupancy and motion at the vehicle's surroundings is essential for autonomous driving, enabling situational awareness in dynamic environments. Existing approaches jointly learn geometry and motion but rely on expensive 3D occupancy and flow annotations, velocity labels from bounding boxes, or pretrained optical flow models. We propose a self-supervised method for 3D occupancy flow estimation that eliminates the need for human-produced annotations or external flow supervision. Our method disentangles the scene into separate static and dynamic signed distance fields and learns motion implicitly through temporal aggregation. Additionally, we introduce a strong self-supervised flow cue derived from features' cosine similarities. We demonstrate the efficacy of our 3D occupancy flow method on SemanticKITTI, KITTI-MOT, and nuScenes.
@article{arxiv.2602.23894,
title = {SelfOccFlow: Towards end-to-end self-supervised 3D Occupancy Flow prediction},
author = {Xavier Timoneda and Markus Herb and Fabian Duerr and Daniel Goehring},
journal= {arXiv preprint arXiv:2602.23894},
year = {2026}
}
Comments
Accepted version. Final version is published in IEEE Robotics and Automation Letters, DOI: 10.1109/LRA.2026.3665447