Accurate reconstruction of heterogeneous scenes for high-fidelity rendering in an efficient manner remains a crucial but challenging task in many Virtual Reality and Augmented Reality applications. The recent 3D Gaussian Splatting (3DGS) has shown impressive quality in scene rendering with real-time performance. However, for heterogeneous scenes with many weak-textured regions, the original 3DGS can easily produce numerously wrong floaters with unbalanced reconstruction using redundant 3D Gaussians, which often leads to unsatisfied scene rendering. This paper proposes a novel multi-plane Gaussian Splatting (MPGS), which aims to achieve high-fidelity rendering with compact reconstruction for heterogeneous scenes. The key insight of our MPGS is the introduction of a novel multi-plane Gaussian optimization strategy, which effectively adjusts the Gaussian distribution for both rich-textured and weak-textured regions in heterogeneous scenes. Moreover, we further propose a multi-scale geometric correction mechanism to effectively mitigate degradation of the 3D Gaussian distribution for compact scene reconstruction. Besides, we regularize the Gaussian distributions using normal information extracted from the compact scene learning. Experimental results on public datasets demonstrate that the proposed MPGS achieves much better rendering quality compared to previous methods, while using less storage and offering more efficient rendering. To our best knowledge, MPGS is a new state-of-the-art 3D Gaussian splatting method for compact reconstruction of heterogeneous scenes, enabling high-fidelity rendering in novel view synthesis, especially improving rendering quality for weak-textured regions. The code will be released at https://github.com/wanglids/MPGS.