DrivingScene: 動的運転シーン向けマルチタスクオンラインフィードフォワード3Dガウシアンスプラッティング
DrivingScene: A Multi-Task Online Feed-Forward 3D Gaussian Splatting Method for Dynamic Driving Scenes
2枚の連続した全方位画像から動的な運転シーンをオンラインで4D再構成するフィードフォワード型3Dガウシアンスプラッティング手法を提案。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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著者: Qirui Hou, Wenzhang Sun, Chang Zeng, Chunfeng Wang, Hao Li, Jianxun Cui
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
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.
