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 between 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 learning-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 topological structures. The analysis indicates that future research should focus on developing multimodal fusion representation methods, optimizing high-precision conversion algorithms, developing dedicated conversion technologies for specific industries, and actively exploring AI-driven intelligent conversion paradigms to provide key technical support for the innovative development of fields such as intelligent manufacturing and digital twins.