VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction
(2402.17427)Abstract
Existing NeRF-based methods for large scene reconstruction often have limitations in visual quality and rendering speed. While the recent 3D Gaussian Splatting works well on small-scale and object-centric scenes, scaling it up to large scenes poses challenges due to limited video memory, long optimization time, and noticeable appearance variations. To address these challenges, we present VastGaussian, the first method for high-quality reconstruction and real-time rendering on large scenes based on 3D Gaussian Splatting. We propose a progressive partitioning strategy to divide a large scene into multiple cells, where the training cameras and point cloud are properly distributed with an airspace-aware visibility criterion. These cells are merged into a complete scene after parallel optimization. We also introduce decoupled appearance modeling into the optimization process to reduce appearance variations in the rendered images. Our approach outperforms existing NeRF-based methods and achieves state-of-the-art results on multiple large scene datasets, enabling fast optimization and high-fidelity real-time rendering.
Overview
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VastGaussian is a novel approach to 3D Gaussian Splatting (3DGS) for efficient and high-fidelity reconstruction of large scenes with real-time rendering.
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The methodology includes progressive data partitioning for parallel optimization, decoupled appearance modeling to handle variations in training images, and seamless scene merging for coherent large-scale reconstructions.
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Experimental results demonstrate superior performance over existing NeRF-based approaches with better SSIM, PSNR, and LPIPS metrics, highlighting its effectiveness in detailed scene reconstruction.
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Future prospects involve enhancing scalability, memory efficiency, rendering speeds, and the potential for dynamic object reconstruction within large scenes.
VastGaussian: Enhancing 3D Gaussian Splatting for Large Scene Reconstruction with Real-time Rendering Capabilities
Introduction to VastGaussian
In the domain of large scene reconstruction, the pursuit of achieving both high-quality visual fidelity and real-time rendering performance has ushered in various methodologies, predominantly revolving around neural radiance fields (NeRF) and their adaptations. The advent of 3D Gaussian Splatting (3DGS) marked a significant advancement, particularly in rendering small-scale, object-centric scenes with notable efficiency and visual quality. However, when it comes to large-scale environments, the scalability of 3DGS encounters considerable challenges, such as constraints on video memory, excessive optimization durations, and conspicuous appearance variations. Addressing these pivotal issues, the study introduces VastGaussian, a novel method that scales 3DGS for large scene reconstructions, facilitating fast optimization alongside real-time, high-fidelity rendering.
Key Innovations and Methodology
The crux of VastGaussian's methodology lies in a series of strategic enhancements to the conventional 3DGS framework, tailored to surmount the limitations observed in large scene reconstructions:
- Progressive Data Partitioning: The scene is divided into smaller cells allowing for parallel optimization and simplifying the task per cell. The strategy includes considerate assignment of training data to these cells, mitigated by an airspace-aware visibility criterion ensuring detailed optimization with minimized artifacts.
- Decoupled Appearance Modeling: A novel technique that addresses the challenge of appearance variations across training images, resulting from variations in illumination, exposure, etc. Unlike previous approaches that integrate appearance variations into the modeling process directly, VastGaussian decouples this, applying adjustments only during the optimization phase, which are then discarded to maintain real-time rendering performance.
- Seamless Scene Merging: Post-optimization, the individually processed cells are merged to form a coherent scene. By ensuring overlapping training data among adjacent cells, VastGaussian guarantees seamless transitions devoid of visual discontinuities.
Experimental Outcomes
VastGaussian sets a new benchmark across multiple large scene datasets, showcasing superior performance over existing NeRF-based approaches. Emphasized by strong numerical results, the method demonstrates significantly improved SSIM, PSNR, and LPIPS metrics, indicative of its ability to generate detailed, high-quality reconstructions subliminally surpassing prior art. Moreover, it retains a satisfactory balance between video memory usage and rendering speeds, promoting its applicability in real-world scenarios.
Theoretical Implications and Future Prospects
The approach delineates a substantial forward leap in reconstructing complex, large-scale scenes. Theoretical implications include validating the efficacy of scalable splatting techniques in 3D reconstruction and novel insights into appearance variation handling, which could be broadly applicable in other domains of generative AI and computer vision. Practically, VastGaussian promises enhancements in applications ranging from virtual reality, film production, to autonomous navigation, all of which benefit from rapid, accurate scene reconstructions.
Foreseeing future expansions, the adaptability of VastGaussian to even more extensive scenes, improvements in memory efficiency, and further accelerations in rendering speeds emerge as intriguing avenues of research. Additionally, exploring the integration of dynamic object reconstructions within these large-scale scenes could further augment the method's applicability, providing a more comprehensive solution to 3D scene reconstruction challenges.
Conclusion
VastGaussian emerges as a pioneering methodology in the realm of large-scale 3D scene reconstructions, adeptly tackling the trilemma of quality, speed, and scalability that has long challenged existing frameworks. By innovating on data partitioning strategies, appearance modeling, and scene merging techniques, it paves the way for future advancements in the field, illustrating a scalable, efficient path forward for high-fidelity 3D scene reconstructions.
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