Abstract: Dynamic multi-view collaborative perception aims to address the problem of global perception in scenarios involving multiple mobile platforms, heterogeneous viewpoints, and continuously evolving environments. Its core challenge lies in achieving cross-view spatial alignment, identity-consistent reasoning, and high-level semantic understanding under conditions of time-varying relative poses, abrupt changes in target visibility, and unstable collaborative relationships. This paper takes “geometric dynamics, observation dynamics, and collaborative dynamics” as a unified analytical thread and provides a systematic survey of representative studies on dynamic multi-view collaborative perception. For geometric dynamics, this paper reviews dynamic pose estimation, complementary-view connection, shared bird’s-eye-view representation, and unified spatiotemporal representation methods such as dynamic neural radiance fields and four-dimensional Gaussian splatting. For observation dynamics, this paper analyzes research directions including cross-view association and tracking, air-ground collaborative perception, vehicle-infrastructure collaborative perception, and collaborative understanding between first-person and third-person views. For collaborative dynamics, this paper discusses temporal asynchronous calibration, topology-varying systems, and distributed collaborative mechanisms under communication constraints. On this basis, this paper further summarizes representative public datasets, core evaluation metrics, and public benchmark results that reflect the characteristics of geometric dynamics, observation dynamics, and collaborative dynamics, and analyzes the applicable conditions, performance advantages, and failure boundaries of different technical routes. Finally, this paper discusses future research directions from four aspects: unified spatiotemporal representation and online spatial alignment, open-category and cross-domain robust association, asynchronous topology-adaptive collaborative mechanisms, and system-level evaluation benchmarks for real-world deployment.