ReconDreamer: Crafting World Models for Driving Scene Reconstruction via Online Restoration

TL;DR

ReconDreamer is a novel method that improves driving scene reconstruction by online restoration and progressive data updates, enabling accurate rendering of complex maneuvers like lane changes in autonomous driving simulations.

Contribution

It introduces ReconDreamer, the first approach to effectively render large maneuvers in driving scene reconstruction through incremental world model integration and online restoration.

Findings

Outperforms Street Gaussians in NTA-IoU, NTL-IoU, and FID metrics.

Surpasses DriveDreamer4D with PVG in large maneuver rendering.

Achieves significant improvements in rendering accuracy and user satisfaction.

Abstract

Closed-loop simulation is crucial for end-to-end autonomous driving. Existing sensor simulation methods (e.g., NeRF and 3DGS) reconstruct driving scenes based on conditions that closely mirror training data distributions. However, these methods struggle with rendering novel trajectories, such as lane changes. Recent works have demonstrated that integrating world model knowledge alleviates these issues. Despite their efficiency, these approaches still encounter difficulties in the accurate representation of more complex maneuvers, with multi-lane shifts being a notable example. Therefore, we introduce ReconDreamer, which enhances driving scene reconstruction through incremental integration of world model knowledge. Specifically, DriveRestorer is proposed to mitigate artifacts via online restoration. This is complemented by a progressive data update strategy designed to ensure high-quality rendering for more complex maneuvers. To the best of our knowledge, ReconDreamer is the first method to effectively render in large maneuvers. Experimental results demonstrate that ReconDreamer outperforms Street Gaussians in the NTA-IoU, NTL-IoU, and FID, with relative improvements by 24.87%, 6.72%, and 29.97%. Furthermore, ReconDreamer surpasses DriveDreamer4D with PVG during large maneuver rendering, as verified by a relative improvement of 195.87% in the NTA-IoU metric and a comprehensive user study.