Gaussian Splatting: 3D Reconstruction and Novel View Synthesis, a Review

TL;DR

This review comprehensively covers recent advancements in Gaussian Splatting for 3D reconstruction and novel view synthesis, highlighting key techniques, challenges, and future research directions in the rapidly evolving field.

Contribution

It provides a thorough overview of Gaussian Splatting methods, including input types, model structures, and training strategies, and discusses unresolved challenges and future directions.

Findings

Summarizes recent developments in Gaussian Splatting techniques.

Identifies key challenges and potential future research areas.

Highlights the importance of comprehensive algorithm reviews in this domain.

Abstract

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.