Abstract: This paper systematically reviews common 3D model representation methods, including point clouds, voxels, meshes, boundary representations (B-Rep), and implicit representations such as signed distance fields (SDF) and neural radiance fields (NeRF), and introduces their differences in geometric accuracy, computational efficiency, and application scenarios. Furthermore, it explores conversion techniques be-tween point clouds and meshes, point clouds and B-Rep, voxels and meshes, meshes and B-Rep, as well as meshes and implicit representations, covering both traditional geometric algorithms and deep learn-ing-based intelligent approaches. The analysis evaluates the strengths and weaknesses of existing methods in terms of computational precision, topological consistency, and efficiency, highlighting key challenges such as low processing efficiency for high-resolution models and difficulties in preserving complex topo-logical structures. The analysis indicates that future research should focus on developing multimodal fu-sion representation methods, optimizing high-precision conversion algorithms, developing dedicated con-version technologies for specific industries, and actively exploring AI-driven intelligent conversion para-digms to provide key technical support for the innovative development of fields such as intelligent manu-facturing and digital twins.