This comprehensive survey on 3D content generation aims to consolidate recent advancements in the field, categorizing existing approaches into three types: 3D native generative methods, 2D prior-based 3D generative methods, and hybrid 3D generative methods. The survey covers approximately 60 papers and discusses the limitations and open challenges in current 3D content generation techniques. It highlights the importance of 3D representation and diffusion models, and provides a detailed overview of each category of generative methods. The survey also outlines future directions, including the need for more effective 3D representations, large-scale datasets, and robust evaluation metrics. The project website, available at https://github.com/hitslj/Awesome-AIGC-3D, offers resources for further research in this area.This comprehensive survey on 3D content generation aims to consolidate recent advancements in the field, categorizing existing approaches into three types: 3D native generative methods, 2D prior-based 3D generative methods, and hybrid 3D generative methods. The survey covers approximately 60 papers and discusses the limitations and open challenges in current 3D content generation techniques. It highlights the importance of 3D representation and diffusion models, and provides a detailed overview of each category of generative methods. The survey also outlines future directions, including the need for more effective 3D representations, large-scale datasets, and robust evaluation metrics. The project website, available at https://github.com/hitslj/Awesome-AIGC-3D, offers resources for further research in this area.