Compact 3D Gaussian Representation for Radiance Field
(2311.13681)Abstract
Neural Radiance Fields (NeRFs) have demonstrated remarkable potential in capturing complex 3D scenes with high fidelity. However, one persistent challenge that hinders the widespread adoption of NeRFs is the computational bottleneck due to the volumetric rendering. On the other hand, 3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussisan-based representation and adopts the rasterization pipeline to render the images rather than volumetric rendering, achieving very fast rendering speed and promising image quality. However, a significant drawback arises as 3DGS entails a substantial number of 3D Gaussians to maintain the high fidelity of the rendered images, which requires a large amount of memory and storage. To address this critical issue, we place a specific emphasis on two key objectives: reducing the number of Gaussian points without sacrificing performance and compressing the Gaussian attributes, such as view-dependent color and covariance. To this end, we propose a learnable mask strategy that significantly reduces the number of Gaussians while preserving high performance. In addition, we propose a compact but effective representation of view-dependent color by employing a grid-based neural field rather than relying on spherical harmonics. Finally, we learn codebooks to compactly represent the geometric attributes of Gaussian by vector quantization. With model compression techniques such as quantization and entropy coding, we consistently show over 25$\times$ reduced storage and enhanced rendering speed, while maintaining the quality of the scene representation, compared to 3DGS. Our work provides a comprehensive framework for 3D scene representation, achieving high performance, fast training, compactness, and real-time rendering. Our project page is available at https://maincold2.github.io/c3dgs/.
Overview
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Neural Radiance Fields (NeRF) are used to create photorealistic 3D images but require substantial computational resources.
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3D Gaussian splatting (3DGS) offers faster rendering but traditionally uses large amounts of memory.
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A new compact 3D Gaussian framework has been developed, minimizing Gaussians and compressing attributes to enhance efficiency.
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Experimental validations show notable improvements in storage reduction and rendering speed using the compact framework.
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This advancement eases the computational barriers to NeRF adoption, making high-quality, real-time 3D rendering more accessible.
Introduction to Neural Radiance Fields
Neural Radiance Fields (NeRF) have emerged as a transformative technology in the realm of computer vision and graphics. With a focus on synthesizing photorealistic 3D scenes, NeRFs offer an impressive ability to construct high-fidelity images from a set of 2D inputs. Despite their prowess, they are computationally intensive primarily due to the requirement of volumetric rendering, which demands dense point sampling along rays to render a single pixel. This has presented a significant barrier to their adoption for real-time applications on devices with limited computational power.
Advancements in 3D Scene Representation
Addressing the need for faster rendering without compromising image quality, 3D Gaussian splatting (3DGS) has come to the fore as an alternative representation strategy. 3DGS replaces the volumetric rendering with a rasterization-based technique, which together with optimized computation, achieves exceptional rendering speeds. The caveat, however, is that 3DGS relies on a large number of 3D Gaussians, leading to exorbitant memory and storage demands – a key challenge that the latest research efforts seek to tackle.
Compact 3D Gaussian Framework
Recent developments in the field have introduced a compact 3D Gaussian framework designed to significantly enhance the efficiency of memory and storage while maintaining high-quality scene reconstructions. The approach embraces two main objectives: minimizing the number of required Gaussians and compressing their attributes. A novel masking strategy is employed to filter out redundant Gaussians, and a technique involving a grid-based neural field circumvents the traditional reliance on spherical harmonics for storing color data. Moreover, the utilization of vector quantization codebooks for geometric attributes rounds out the components of this compact representation. The resultant framework strikes a balance between performance and efficiency, demonstrating a remarkable reduction in storage and an increase in rendering speed in extensive experimental validations.
Results and Benchmarks
The testing of this compact representation across diverse datasets, encompassing both real and synthetic scenes, illustrates a consistent improvement in storage efficiency and rendering speed, achieving over tenfold storage reduction and enhanced rendering performance. Notably, on real-world datasets, the approach sets a new benchmark in reconstruction quality while boasting significant storage efficiency and an increase in rendering speed. All these advancements are integrated into a comprehensive framework that provides a pathway towards high-performance, fast-training, compact, and real-time rendering of 3D scenes.
Conclusion and Future Implications
The development of this compact 3D Gaussian representation addresses critical issues in memory and storage that have previously limited the practical applications of NeRFs and 3DGS. By facilitating high-quality rendering at an accelerated pace and with reduced storage needs, the framework is poised to accelerate the adoption of neural rendering in interactive applications and beyond. The project's contributions mark a significant stride towards overcoming computational barriers and enhancing the accessibility of advanced 3D scene rendering technologies.
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