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A survey on surface reconstruction based on 3D Gaussian splatting.
Zheng Xu1, Gang Chen1, Feng Li1
1Institute of Geographical Spatial Information, Information Engineering University, Zhengzhou, China.
Peerj. Computer Science
|September 24, 2025
Summary
This survey explores 3D surface reconstruction methods, focusing on 3D Gaussian splatting (3D GS) and neural radiance fields (NeRFs). It provides a roadmap for understanding 3D representations, reconstruction techniques, and future research directions.
Area of Science:
- Computer Graphics
- 3D Vision
- Geometric Modeling
Background:
- Surface reconstruction is crucial in computer graphics.
- Recent advancements include neural radiance fields (NeRFs) and 3D Gaussian splatting (3D GS).
- The rapid growth necessitates a structured overview of current methodologies.
Purpose of the Study:
- To present core methodologies for 3D model surface reconstruction.
- To establish a structured roadmap covering 3D representations, reconstruction methods, datasets, and applications.
- To provide valuable resources for researchers in the field.
Main Methods:
- Focuses on 3D representations using 3D Gaussians as a central framework.
- Provides a comprehensive overview of surface reconstruction methods based on 3D Gaussian splatting.
- Categorizes algorithms into scene representation, Gaussian optimization, and surface structure extraction.
Main Results:
- Introduces 3D Gaussians as a key representation for surface reconstruction.
- Details evolving reconstruction methods leveraging 3D Gaussian splatting.
- Reviews available datasets, applications, and identifies current challenges.
Conclusions:
- The survey offers a structured roadmap for 3D surface reconstruction research.
- It highlights the significance of 3D Gaussian splatting in recent advancements.
- It suggests future research directions to foster progress in 3D reconstruction technologies.
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