A Survey on 3D Gaussian Splatting
(2401.03890)Abstract
3D Gaussian splatting (GS) has recently emerged as a transformative technique in the realm of explicit radiance field and computer graphics. This innovative approach, characterized by the utilization of millions of learnable 3D Gaussians, represents a significant departure from mainstream neural radiance field approaches, which predominantly use implicit, coordinate-based models to map spatial coordinates to pixel values. 3D GS, with its explicit scene representation and differentiable rendering algorithm, not only promises real-time rendering capability but also introduces unprecedented levels of editability. This positions 3D GS as a potential game-changer for the next generation of 3D reconstruction and representation. In the present paper, we provide the first systematic overview of the recent developments and critical contributions in the domain of 3D GS. We begin with a detailed exploration of the underlying principles and the driving forces behind the emergence of 3D GS, laying the groundwork for understanding its significance. A focal point of our discussion is the practical applicability of 3D GS. By enabling unprecedented rendering speed, 3D GS opens up a plethora of applications, ranging from virtual reality to interactive media and beyond. This is complemented by a comparative analysis of leading 3D GS models, evaluated across various benchmark tasks to highlight their performance and practical utility. The survey concludes by identifying current challenges and suggesting potential avenues for future research in this domain. Through this survey, we aim to provide a valuable resource for both newcomers and seasoned researchers, fostering further exploration and advancement in applicable and explicit radiance field representation.
Overview
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3D Gaussian Splatting (3D GS) is an emerging technique in computer graphics that maps 3D spatial coordinates to pixel values, facilitating real-time rendering and editable control.
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3D GS employs explicit radiance fields, projecting optimized 3D Gaussians onto a 2D plane, reducing computational demands compared to methods like neural radiance fields (NeRF).
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The technique is practical for a wide range of applications, including virtual reality and real-time cinematic rendering, and offers enhanced control over scene dynamics.
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Future research will likely address data efficiency, memory usage, rendering improvements, and integrating additional attributes into 3D GS.
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This survey establishes 3D GS's importance and encourages further research to unlock its full potential in 3D reconstruction and representation.
Understanding 3D Gaussian Splatting: A Comprehensive Survey
Introduction
The field of explicit radiance fields and computer graphics is witnessing a significant transformation with the advent of 3D Gaussian Splatting (3D GS), a technique distinguished by the use of millions of 3D Gaussians. This approach implies mapping spatial coordinates to pixel values, a process that promises not only real-time rendering capabilities but also intricate levels of control and editability. This innovation positions 3D GS to potentially revolutionize how we undertake 3D reconstruction and representation tasks.
The Fundamentals of 3D Gaussian Splatting
3D GS operates on the principle of projecting optimized 3D Gaussians onto a 2D image plane for rendering, a process known as "splatting." Unlike traditional methods that utilize implicit scene representations like neural radiance fields (NeRF), which require extensive computational resources, 3D GS employs an explicit method that streamlines computation. By doing so, 3D GS maintains the quality of continuous volumetric radiance fields necessary for high-fidelity image synthesis while sidestepping the computational overhead typical of NeRF methods. This survey aims to systematically explore the recent developments in 3D GS, evaluating its practicality across various applications, and identifying challenges and future research directions.
Practical Applications and Performance
The versatility of 3D GS allows it to encompass a vast array of applications, enhancing everything from virtual reality experiences to real-time cinematic rendering. Its explicit scene representation introduces newfound levels of control over scene dynamics critical for complex environments involving intricate geometries and varying light conditions. Moreover, it maintains efficiency during rendering, signifying a noteworthy leap in shaping the future of related fields.
Future Prospects and Challenges
While 3D GS shows impressive advancements, it is not without its challenges and areas ripe for further exploration. Future research may focus on improving data efficiency, memory utilization, rendering techniques, optimization processes, and regularizing the system's performance. Moreover, 3D GS can be enhanced by integrating additional attributes like linguistic and physical properties, expanding its application domain. As 3D GS is still in its nascency, this survey emphasizes the need for continuous research and innovation, suggesting that the full potential of 3D GS is still unfolding.
Conclusion
3D Gaussian Splatting emerges as a game-changing technology with substantial implications for 3D reconstruction and representation, offering a dynamic and highly efficient alternative to traditional rendering methods. This survey serves as a foundational resource, aiming to foster the exploration and progression in this swiftly evolving domain, bridging the gaps and contributing to the enrichment of this nascent field.
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