DrivingScene: A Multi-Task Online Feed-Forward 3D Gaussian Splatting Method for Dynamic Driving Scenes

• URL: http://arxiv.org/abs/2510.24734v1
• Date: Tue, 14 Oct 2025 03:32:46 GMT
• Title: DrivingScene: A Multi-Task Online Feed-Forward 3D Gaussian Splatting Method for Dynamic Driving Scenes
• Authors: Qirui Hou, Wenzhang Sun, Chang Zeng, Chunfeng Wang, Hao Li, Jianxun Cui,
• Abstract summary: We propose DrivingScene, an online framework that reconstructs 4D dynamic scenes from only two consecutive surround-view images.<n>Our key innovation is a lightweight residual flow network that predicts the non-rigid motion of dynamic objects per camera.
• Score: 11.532584276783105
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Real-time, high-fidelity reconstruction of dynamic driving scenes is challenged by complex dynamics and sparse views, with prior methods struggling to balance quality and efficiency. We propose DrivingScene, an online, feed-forward framework that reconstructs 4D dynamic scenes from only two consecutive surround-view images. Our key innovation is a lightweight residual flow network that predicts the non-rigid motion of dynamic objects per camera on top of a learned static scene prior, explicitly modeling dynamics via scene flow. We also introduce a coarse-to-fine training paradigm that circumvents the instabilities common to end-to-end approaches. Experiments on nuScenes dataset show our image-only method simultaneously generates high-quality depth, scene flow, and 3D Gaussian point clouds online, significantly outperforming state-of-the-art methods in both dynamic reconstruction and novel view synthesis.

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