Core Viewpoint - The article discusses the development of DriveVGGT, a 4D reconstruction framework specifically designed for autonomous driving, which integrates prior knowledge to enhance geometric prediction consistency and inference efficiency in multi-camera systems [3][4][7]. Group 1: Key Innovations - DriveVGGT incorporates three critical new priors for autonomous driving: low camera view overlap, known intrinsic and extrinsic camera parameters, and fixed relative positions of cameras [3]. - The framework features a Temporal Video Attention (TVA) module that processes multi-camera video independently to leverage the temporal continuity of single-camera sequences [4]. - A Multi-Camera Consistency Attention (MCA) module is introduced to establish consistency across different cameras while ensuring that each token focuses only on adjacent frames, balancing effectiveness and efficiency [4]. Group 2: Application and Impact - The explicit introduction of relative camera pose priors in DriveVGGT significantly improves the geometric prediction consistency and inference efficiency in autonomous driving scenarios [7]. - The framework aims to address the challenges faced by traditional visual geometric models in low-overlap multi-camera environments, enhancing the overall performance of autonomous driving systems [7].