TeFlow: Enabling Multi-frame Supervision for Self-Supervised Feed-forward Scene Flow Estimation

• URL: http://arxiv.org/abs/2602.19053v1
• Date: Sun, 22 Feb 2026 05:50:16 GMT
• Title: TeFlow: Enabling Multi-frame Supervision for Self-Supervised Feed-forward Scene Flow Estimation
• Abstract summary: Multi-frame supervision has the potential to provide more stable guidance by incorporating motion cues from past frames.<n>We present TeFlow, enabling multi-frame supervision for feed-forward models by mining temporally consistent supervision.<n>Our method performs on par with leading optimization-based methods, yet speeds up 150 times.
• Score: 14.239684633948746
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Self-supervised feed-forward methods for scene flow estimation offer real-time efficiency, but their supervision from two-frame point correspondences is unreliable and often breaks down under occlusions. Multi-frame supervision has the potential to provide more stable guidance by incorporating motion cues from past frames, yet naive extensions of two-frame objectives are ineffective because point correspondences vary abruptly across frames, producing inconsistent signals. In the paper, we present TeFlow, enabling multi-frame supervision for feed-forward models by mining temporally consistent supervision. TeFlow introduces a temporal ensembling strategy that forms reliable supervisory signals by aggregating the most temporally consistent motion cues from a candidate pool built across multiple frames. Extensive evaluations demonstrate that TeFlow establishes a new state-of-the-art for self-supervised feed-forward methods, achieving performance gains of up to 33\% on the challenging Argoverse 2 and nuScenes datasets. Our method performs on par with leading optimization-based methods, yet speeds up 150 times. The code is open-sourced at https://github.com/KTH-RPL/OpenSceneFlow along with trained model weights.

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