标题： BEV-VAE：用于自动驾驶的多视角图像生成与空间一致性 显示英文标题

标题： BEV-VAE: Multi-view Image Generation with Spatial Consistency for Autonomous Driving

摘要： 多视角图像生成在自动驾驶中需要跨相机视角的一致性3D场景理解。 大多数现有方法将此问题视为2D图像集生成任务，缺乏显式的3D建模。 然而，我们认为结构化表示对于场景生成至关重要，尤其是在自动驾驶应用中。 本文提出了BEV-VAE，用于一致且可控的视角合成。 BEV-VAE首先训练一个多视角图像变分自编码器，以获得紧凑且统一的BEV潜在空间，然后使用潜在扩散变换器生成场景。 给定相机配置，BEV-VAE支持任意视角生成，并且可选地生成3D布局。 在nuScenes和Argoverse 2（AV2）上的实验表明，在3D一致重建和生成方面表现出色。 代码可在以下地址获取：https://github.com/Czm369/bev-vae. 显示英文摘要

摘要： Multi-view image generation in autonomous driving demands consistent 3D scene understanding across camera views. Most existing methods treat this problem as a 2D image set generation task, lacking explicit 3D modeling. However, we argue that a structured representation is crucial for scene generation, especially for autonomous driving applications. This paper proposes BEV-VAE for consistent and controllable view synthesis. BEV-VAE first trains a multi-view image variational autoencoder for a compact and unified BEV latent space and then generates the scene with a latent diffusion transformer. BEV-VAE supports arbitrary view generation given camera configurations, and optionally 3D layouts. Experiments on nuScenes and Argoverse 2 (AV2) show strong performance in both 3D consistent reconstruction and generation. The code is available at: https://github.com/Czm369/bev-vae.