Gaussian Splatting: 3D Reconstruction and Novel View Synthesis: A Review

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 re...

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Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.96797-96820
Main Authors: Dalal, Anurag, Hagen, Daniel, Robbersmyr, Kjell G., Knausgard, Kristian Muri
Format: Article
Language:English
Subjects:
3D reconstruction
Algorithms
Computational modeling
computer vision
Deep learning
Gaussian splatting
Image reconstruction
novel view synthesis
optimization
Point cloud compression
rendering
Solid modeling
State-of-the-art reviews
Three-dimensional displays
Training
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3408318