用两张图实时重建动态驾驶场景，精度效率双提升
DrivingScene: A Multi-Task Online Feed-Forward 3D Gaussian Splatting Method for Dynamic Driving Scenes
- 基于静态场景先验+轻量残差光流网络，逐相机预测非刚性运动
- 在nuScenes上实现图像级实时重建，深度、光流、3D点云同步输出
- 适合自动驾驶感知与实时渲染，尤其擅长复杂动态场景
实时高保真动态驾驶场景重建面临复杂运动与视点稀疏的挑战，现有方法难以兼顾质量与效率。本文提出DrivingScene，一种在线前馈框架，仅需两帧环视图像即可重建4D动态场景。核心创新在于轻量级残差光流网络，在学习到的静态场景先验基础上，对每台相机预测动态物体的非刚性运动，通过场景光流显式建模动态。同时引入粗到精训练范式，避免端到端方法常见的不稳定性。在nuScenes数据集上的实验表明，该图像仅方法可在线生成高质量深度图、场景光流和3D Gaussian点云，显著优于当前最优方法，在动态重建与新视角合成方面表现突出。
原文摘要 · Abstract (English)
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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