SelfOccFlow: Towards end-to-end self-supervised 3D Occupancy Flow prediction

• URL: http://arxiv.org/abs/2602.23894v1
• Date: Fri, 27 Feb 2026 10:42:01 GMT
• Title: SelfOccFlow: Towards end-to-end self-supervised 3D Occupancy Flow prediction
• Authors: Xavier Timoneda, Markus Herb, Fabian Duerr, Daniel Goehring,
• Abstract summary: Estimating 3D occupancy and motion at the vehicle's surroundings is essential for autonomous driving.<n>Existing approaches jointly learn geometry and motion but rely on expensive 3D occupancy and flow annotations.<n>We propose a self-supervised method for 3D occupancy flow estimation that eliminates the need for human-produced annotations or external flow supervision.
• Score: 2.012425476229879
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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