Kavli Affiliate: Zheng Zhu
| First 5 Authors: Guosheng Zhao, Chaojun Ni, Xiaofeng Wang, Zheng Zhu, Xueyang Zhang
| Summary:
Closed-loop simulation is essential for advancing end-to-end autonomous
driving systems. Contemporary sensor simulation methods, such as NeRF and 3DGS,
rely predominantly on conditions closely aligned with training data
distributions, which are largely confined to forward-driving scenarios.
Consequently, these methods face limitations when rendering complex maneuvers
(e.g., lane change, acceleration, deceleration). Recent advancements in
autonomous-driving world models have demonstrated the potential to generate
diverse driving videos. However, these approaches remain constrained to 2D
video generation, inherently lacking the spatiotemporal coherence required to
capture intricacies of dynamic driving environments. In this paper, we
introduce DriveDreamer4D, which enhances 4D driving scene representation
leveraging world model priors. Specifically, we utilize the world model as a
data machine to synthesize novel trajectory videos, where structured conditions
are explicitly leveraged to control the spatial-temporal consistency of traffic
elements. Besides, the cousin data training strategy is proposed to facilitate
merging real and synthetic data for optimizing 4DGS. To our knowledge,
DriveDreamer4D is the first to utilize video generation models for improving 4D
reconstruction in driving scenarios. Experimental results reveal that
DriveDreamer4D significantly enhances generation quality under novel trajectory
views, achieving a relative improvement in FID by 32.1%, 46.4%, and 16.3%
compared to PVG, S3Gaussian, and Deformable-GS. Moreover, DriveDreamer4D
markedly enhances the spatiotemporal coherence of driving agents, which is
verified by a comprehensive user study and the relative increases of 22.6%,
43.5%, and 15.6% in the NTA-IoU metric.
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