The field of 3D reconstruction and scene understanding is rapidly advancing, with a focus on developing more efficient, accurate, and generalizable methods. Recent research has explored the use of neural implicit surfaces, Gaussian splatting, and deep learning-based approaches to improve the quality and robustness of 3D reconstructions. These methods have shown promising results in handling challenging scenarios such as sparse views, low-quality images, and large parallax. Notable papers in this area include SparseRecon, which proposes a novel neural implicit reconstruction method for sparse views, and RobustGS, which introduces a general and efficient multi-view feature enhancement module to improve the robustness of feedforward 3DGS methods. Additionally, H3R and Uni3R have demonstrated state-of-the-art performance in generalizable 3D reconstruction and unified 3D scene reconstruction and understanding, respectively. Other notable papers include PIS3R, MuGS, Surf3R, and PixCuboid, which have made significant contributions to the field of 3D reconstruction and scene understanding.