Flow4R: Unifying 4D Reconstruction and Tracking with Scene Flow

• URL: http://arxiv.org/abs/2602.14021v1
• Date: Sun, 15 Feb 2026 06:58:08 GMT
• Title: Flow4R: Unifying 4D Reconstruction and Tracking with Scene Flow
• Authors: Shenhan Qian, Ganlin Zhang, Shangzhe Wu, Daniel Cremers,
• Abstract summary: Flow4R predicts a minimal per-pixel property set-3D point position, scene flow, pose weight, and confidence-from two-view inputs using a Vision Transformer.<n> trained jointly on static and dynamic datasets, Flow4R achieves state-of-the-art performance on 4D reconstruction and tracking tasks.
• Score: 61.297800738187355
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Reconstructing and tracking dynamic 3D scenes remains a fundamental challenge in computer vision. Existing approaches often decouple geometry from motion: multi-view reconstruction methods assume static scenes, while dynamic tracking frameworks rely on explicit camera pose estimation or separate motion models. We propose Flow4R, a unified framework that treats camera-space scene flow as the central representation linking 3D structure, object motion, and camera motion. Flow4R predicts a minimal per-pixel property set-3D point position, scene flow, pose weight, and confidence-from two-view inputs using a Vision Transformer. This flow-centric formulation allows local geometry and bidirectional motion to be inferred symmetrically with a shared decoder in a single forward pass, without requiring explicit pose regressors or bundle adjustment. Trained jointly on static and dynamic datasets, Flow4R achieves state-of-the-art performance on 4D reconstruction and tracking tasks, demonstrating the effectiveness of the flow-central representation for spatiotemporal scene understanding.

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