The recent advancements in the field of 3D Gaussian Splatting have significantly pushed the boundaries of novel view synthesis, scene understanding, and 3D editing. Researchers are increasingly focusing on integrating semantic constraints, depth priors, and physical properties into 3D Gaussian representations to enhance the accuracy and efficiency of 3D scene reconstruction and editing. Notably, there is a strong emphasis on developing training-free frameworks and leveraging multi-modal data, such as combining text and image features, to improve the contextual understanding and generalizability of 3D models. Additionally, the field is witnessing innovations in accelerating the optimization process for high-quality radiance fields, with methods that balance computational efficiency and rendering quality. Interactive and intuitive 3D editing tools are also emerging, enabling localized edits with minimal user input, which is crucial for real-time applications in AR/VR. Furthermore, the integration of open-vocabulary features and the distillation of language features into 3D space are opening new avenues for semantic segmentation and scene understanding, making 3D models more versatile and adaptable to various downstream tasks. Overall, the trend is towards more intelligent, efficient, and user-friendly 3D modeling techniques that can handle complex and diverse real-world scenarios.