The field of 3D reconstruction and rendering is rapidly advancing, with a focus on improving the accuracy and efficiency of surface characterization and geometry learning. Recent developments have introduced novel frameworks and methods for incorporating photoclinometry techniques, surface normal and albedo estimation, and specular-suppressed inputs to accurately represent complex surfaces. The use of Gaussian splats and neural networks has also become increasingly popular, enabling the reconstruction of high-frequency surface textures and the generation of zoomable maps with rivers and fjords. Furthermore, researchers have made significant progress in addressing the transparency-depth dilemma in 3D reconstruction, enabling the accurate reconstruction of transparent surfaces. Noteworthy papers include PMNI, which achieves state-of-the-art performance in the reconstruction of reflective surfaces, and TSGS, which significantly outperforms current leading methods in transparent surface reconstruction. Additionally, the introduction of methods such as ARAP-GS and CAGE-GS has enabled flexible and efficient deformation of 3D Gaussian Splatting scenes, while CompGS++ has achieved substantial compression of Gaussian splatting data for static and dynamic scenes.