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：扩散模型的最新进展显著提升了具备二维控制能力的数据合成效果。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）仍面临挑战。具体而言，将鸟瞰图作为主要条件常会引发几何控制难题（例如高度控制），从而影响物体形状、遮挡模式及路面高程的表示，而这些要素对感知数据合成（尤其是三维目标检测任务）至关重要。本文提出MagicDrive——一个通过定制化编码策略实现多样化三维几何控制（包括相机位姿、道路地图、三维边界框）与文本描述联合控制的街景生成新框架。此外，我们的设计还引入了跨视图注意力模块，确保多相机视角间的一致性。借助MagicDrive，我们实现了可捕捉精细三维几何特征与多样化场景描述的高保真街景合成，有效提升了BEV分割与三维目标检测等任务的性能。