The field of 3D reconstruction and novel view synthesis is rapidly advancing with the development of new methods and techniques. A key direction in this field is the use of Gaussian-based scene representations, which have been shown to be effective in achieving robust tracking and high-fidelity mapping. Another area of research is the development of methods for underwater scene reconstruction, which is a challenging task due to the limited quality of underwater images. Researchers are also exploring the use of Neural Radiance Fields (NeRFs) for large-scale scene modeling, which has shown promising results in terms of scalability and accuracy. Furthermore, there is a growing interest in developing methods for sparse view reconstruction, which can be used in applications such as robotic platforms. Noteworthy papers in this area include AquaGS, which presents an SfM-free underwater scene reconstruction model, and Switch-NeRF++, which introduces a Heterogeneous Mixture of Hash Experts network for scalable NeRFs. Additionally, SparSplat presents a fast multi-view reconstruction method with generalizable 2D Gaussian Splatting, and GSsplat proposes a generalizable semantic Gaussian Splatting method for efficient novel-view synthesis.