DLWM: Dual Latent World Models enable Holistic Gaussian-centric Pre-training in Autonomous Driving
Vision-based autonomous driving has gained much attention due to its low costs and excellent performance. Compared with dense BEV (Bird's Eye View) or sparse query models, Gaussian-centric method is a comprehensive yet sparse representation by describing scene with 3D semantic Gaussians. In this paper, we introduce DLWM, a novel paradigm with Dual Latent World Models specifically designed to enable holistic gaussian-centric pre-training in autonomous driving using two stages. In the first stage, DLWM predicts 3D Gaussians from queries by self-supervised reconstructing multi-view semantic and depth images. Equipped with fine-grained contextual features, in the second stage, two latent worl...
| Authors | Yiyao Zhu, Ying Xue, Haiming Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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