Vehicle-centric Perception via Multimodal Structured Pre-training

This paper introduces VehicleMAE-V2, a novel pre-trained large model designed to improve vehicle-centric perception. The core innovation lies in leveraging multimodal structured priors (symmetry, contour, and semantics) to guide the masked token reconstruction process. The proposed modules (SMM, CRM, SRM) effectively incorporate these priors, leading to enhanced learning of generalizable representations. The approach addresses a critical gap in existing methods, which often lack effective learning of vehicle-related knowledge during pre-training. The use of symmetry constraints, contour feature preservation, and image-text feature alignment are promising techniques for improving vehicle perception in intelligent systems. The paper's focus on structured priors is a valuable contribution to the field.