Gaussian Splatting: 3D Reconstruction and Novel View Synthesis, a Review
It provides a comprehensive overview for researchers in computer vision and graphics, but is incremental as it reviews existing methods without introducing new results.
This review paper examines state-of-the-art techniques for 3D reconstruction and novel view synthesis, focusing on the Gaussian Splatting method, and discusses unresolved challenges and future directions in the field.
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.