  Image-based 3D reconstruction is a challenging task that involves inferring
the 3D shape of an object or scene from a set of input images. Learning-based
methods have gained attention for their ability to directly estimate 3D shapes.
This review paper focuses on state-of-the-art techniques for 3D reconstruction,
including the generation of novel, unseen views. An overview of recent
developments in the Gaussian Splatting method is provided, covering input
types, model structures, output representations, and training strategies.
Unresolved challenges and future directions are also discussed. Given the rapid
progress in this domain and the numerous opportunities for enhancing 3D
reconstruction methods, a comprehensive examination of algorithms appears
essential. Consequently, this study offers a thorough overview of the latest
advancements in Gaussian Splatting.
