EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS
(2312.04564)Abstract
Recently, 3D Gaussian splatting (3D-GS) has gained popularity in novel-view scene synthesis. It addresses the challenges of lengthy training times and slow rendering speeds associated with Neural Radiance Fields (NeRFs). Through rapid, differentiable rasterization of 3D Gaussians, 3D-GS achieves real-time rendering and accelerated training. They, however, demand substantial memory resources for both training and storage, as they require millions of Gaussians in their point cloud representation for each scene. We present a technique utilizing quantized embeddings to significantly reduce per-point memory storage requirements and a coarse-to-fine training strategy for a faster and more stable optimization of the Gaussian point clouds. Our approach develops a pruning stage which results in scene representations with fewer Gaussians, leading to faster training times and rendering speeds for real-time rendering of high resolution scenes. We reduce storage memory by more than an order of magnitude all while preserving the reconstruction quality. We validate the effectiveness of our approach on a variety of datasets and scenes preserving the visual quality while consuming 10-20x lesser memory and faster training/inference speed. Project page and code is available https://efficientgaussian.github.io
Overview
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EAGLES presents a novel approach for 3D scene representation requiring lower memory and computational resources compared to traditional methods like NeRF.
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The technique uses quantized embeddings to efficiently reduce the memory footprint while maintaining high reconstruction quality.
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Key innovations of EAGLES include attribute quantization, progressive training, and controlled densification for improving efficiency without quality loss.
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EAGLES achieves comparable performance to state-of-the-art methods, with a significant reduction in memory usage and faster training and rendering speeds.
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The method is optimized for real-time applications and can operate in memory-constrained environments while preserving visual fidelity.
Background on 3D Scene Representations
3D scene representation is a critical area in computer vision that facilitates the generation of new views of a scene, often from different angles or perspectives not originally captured. Traditionally, this task involves considerable computational resources and storage, making it challenging to implement in real-time applications or on systems with limited memory. Neural Radiance Fields (NeRFs) have set a high standard for quality in scene reconstruction but are known for their demanding resource requirements.
Innovations in Efficient 3D Gaussians
A novel approach known as Efficient Accelerated 3D Gaussians with Lightweight Encoding (EAGLES) aims to mitigate the memory and computation intensity of previous methods. EAGLES leverages quantized embeddings to efficiently reduce memory storage while maintaining reconstruction quality. This approach results in scene representations that are lighter and faster, allowing for real-time rendering of high-resolution scenes with significantly reduced memory footprints.
Key Technical Contributions
To achieve a balance between efficiency and quality, EAGLES introduces several key techniques:
- Attribute Quantization: By compressing color and rotation attributes of Gaussian points in a scene, EAGLES considerably lowers memory requirements without substantial quality loss. A novel aspect includes the quantization of opacity coefficients, which enhances the optimization process and reduces visual artifacts.
- Progressive Training: In lieu of starting with full image resolution during training, EAGLES adopts a progressive schedule, beginning with lower resolutions and gradually increasing to the full scale. This strategy not only speeds up training but also reduces the introduction of artifacts during the optimization of Gaussian points.
- Controlled Densification: A careful management of the frequency at which Gaussian points are added during training (densification) effectively reduces the overall number and therefore storage, without significantly affecting the reconstruction performance.
Evaluation and Implications
Extensive evaluation of EAGLES on various datasets demonstrates comparable performance to state-of-the-art techniques like NeRF and other 3D-GS methods. Additionally, it significantly outperforms these methods in terms of training duration and frame rates during rendering. This performance is achieved with a more than tenfold reduction in memory storage, making EAGLES a highly efficient method for real-time applications and systems with memory constraints.
Conclusion
EAGLES offers an innovative solution to the challenge of real-time, high-quality 3D scene representation in memory-constrained environments. Its mix of quantization, progressive training, and controlled densification makes it a promising tool for real-world use cases that demand both efficiency and visual fidelity.