3D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunities

TL;DR

This survey reviews 3D Gaussian Splatting (3DGS), highlighting its capabilities for efficient 3D reconstruction and real-time rendering, while analyzing current methods, challenges, and future research directions.

Contribution

It provides a comprehensive categorization of 3DGS techniques, summarizes key technical modules, and discusses challenges and opportunities for future advancements.

Findings

Categorized nine types of technical modules in 3DGS

Identified common challenges across 3DGS applications

Proposed potential research opportunities in 3DGS

Abstract

3D Gaussian Splatting (3DGS) has emerged as a prominent technique with the potential to become a mainstream method for 3D representations. It can effectively transform multi-view images into explicit 3D Gaussian through efficient training, and achieve real-time rendering of novel views. This survey aims to analyze existing 3DGS-related works from multiple intersecting perspectives, including related tasks, technologies, challenges, and opportunities. The primary objective is to provide newcomers with a rapid understanding of the field and to assist researchers in methodically organizing existing technologies and challenges. Specifically, we delve into the optimization, application, and extension of 3DGS, categorizing them based on their focuses or motivations. Additionally, we summarize and classify nine types of technical modules and corresponding improvements identified in existing works. Based on these analyses, we further examine the common challenges and technologies across various tasks, proposing potential research opportunities.