1 INTRODUCTION

2 PRELIMINARIES  
2.1 Neural Implicit Field  
2.2 Point-based Rendering  
2.3 3D Gaussian Splatting  

3 OPTIMIZATION OF 3D GAUSSIAN SPLATTING  
3.1 Efficiency  
3.2 Photorealism  
3.3 Generalization and Sparse Views  

4 APPLICATIONS OF 3D GAUSSIAN SPLATTING  
4.1 Human Reconstruction  
4.2 Artificial Intelligence-Generated Content (AIGC)  
4.3 Autonomous Driving  

5 EXTENSIONS OF 3D GAUSSIAN SPLATTING  
5.1 Dynamic 3D Gaussian Splatting  
5.2 Surface Representation  
5.3 Editable 3D Gaussian Splatting  
5.4 Semantic Understanding  
5.5 Physics Simulation  

6 TECHNICAL CLASSIFICATION  
6.1 Initialization  
6.2 Attribute Expansion  
6.3 Splatting  
6.4 Regularization  
6.5 Training Strategy  
6.6 Adaptive Control  
6.7 Post-Processing  
6.8 Integration with Other Representations  
6.9 Guidance by Additional Prior  

7 INTERRELATIONSHIPS AND CHALLENGES  
7.1 Suboptimal Data Challenges  
7.2 Generalization Challenges  
7.3 Physics Challenges  
7.4 Realness and Efficiency Challenges  

8 OPPORTUNITIES  
8.1 Suboptimal Training Data  
8.2 Generalization  
8.3 Physics Problems  
8.4 Realness and Efficiency  
8.5 Application  
8.6 Technical Improvements  

9 CONCLUSION