Recent advancements in diffusion models have significantly enhanced the data synthesis with 2D control. Yet, precise 3D control in street view generation, crucial for 3D perception tasks, remains elusive. Specifically, utilizing Bird's-Eye View (BEV) as the primary condition often leads to challenges in geometry control (e.g., height), affecting the representation of object shapes, occlusion patterns, and road surface elevations, all of which are essential to perception data synthesis, especially for 3D object detection tasks. In this paper, we introduce MagicDrive, a novel street view generation framework offering diverse 3D geometry controls, including camera poses, road maps, and 3D bounding boxes, together with textual descriptions, achieved through tailored encoding strategies. Besides, our design incorporates a cross-view attention module, ensuring consistency across multiple camera views. With MagicDrive, we achieve high-fidelity street-view synthesis that captures nuanced 3D geometry and various scene descriptions, enhancing tasks like BEV segmentation and 3D object detection.

翻译：近期扩散模型的进展显著提升了基于二维控制的数据合成能力。然而，在街景生成中实现精确的三维控制——这对三维感知任务至关重要——仍难以达成。具体而言，以鸟瞰图作为主要条件常导致几何控制（如高度）方面的挑战，影响目标形状、遮挡模式和路面高程的表示，而这些要素对于感知数据合成（尤其是三维目标检测任务）不可或缺。本文提出MagicDrive，一种新型街景生成框架，通过定制化编码策略，支持包括相机姿态、道路地图和三维边界框在内的多样化三维几何控制，并结合文本描述。此外，我们的设计引入跨视角注意力模块，确保多相机视图间的一致性。借助MagicDrive，我们实现了高保真度的街景合成，能够捕捉细微的三维几何特征与多样场景描述，从而提升鸟瞰图分割和三维目标检测等任务性能。