DLWM：双潜变量世界模型实现自动驾驶中全面的高斯中心预训练
DLWM: Dual Latent World Models enable Holistic Gaussian-centric Pre-training in Autonomous Driving
提出以3D语义高斯为场景表示的双潜变量世界模型预训练框架，可复用于基于高斯表示的感知与规划任务，其高斯流引导的潜变量预测思路对时序特征学习有借鉴意义。
原文依据 DLWM predicts 3D Gaussians from queries by self-supervised reconstructing multi-view semantic and depth images.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论基于视觉的自动驾驶因其低成本和优异性能而备受关注。与密集的鸟瞰图或稀疏查询模型相比，高斯中心方法通过用3D语义高斯描述场景，提供了一种全面而稀疏的表示。本文提出DLWM，一种新颖的双潜变量世界模型范式，专门设计用于通过两个阶段实现自动驾驶中全面的高斯中心预训练。在第一阶段，DLWM通过自监督重建多视图语义和深度图像，从查询中预测3D高斯。在第二阶段，借助细粒度上下文特征，分别训练两个潜变量世界模型进行时序特征学习，包括用于下游占用感知和预测任务的高斯流引导潜变量预测，以及用于运动规划的自车规划引导潜变量预测。在SurroundOcc和nuScenes基准上的大量实验表明，DLWM在高斯中心3D占用感知、4D占用预测和运动规划任务上均取得了显著性能提升。
英文原文摘要 1150 字符
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 world models are trained separately for temporal feature learning, including Gaussian-flow-guided latent prediction for downstream occupancy perception and forecasting tasks, and ego-planning-guided latent prediction for motion planning. Extensive experiments in SurroundOcc and nuScenes benchmarks demonstrate that DLWM shows significant performance gains across Gaussian-centric 3D occupancy perception, 4D occupancy forecasting and motion planning tasks.