Taming 3DGS: High-Quality Radiance Fields with Limited Resources
(2406.15643)Abstract
3D Gaussian Splatting (3DGS) has transformed novel-view synthesis with its fast, interpretable, and high-fidelity rendering. However, its resource requirements limit its usability. Especially on constrained devices, training performance degrades quickly and often cannot complete due to excessive memory consumption of the model. The method converges with an indefinite number of Gaussians -- many of them redundant -- making rendering unnecessarily slow and preventing its usage in downstream tasks that expect fixed-size inputs. To address these issues, we tackle the challenges of training and rendering 3DGS models on a budget. We use a guided, purely constructive densification process that steers densification toward Gaussians that raise the reconstruction quality. Model size continuously increases in a controlled manner towards an exact budget, using score-based densification of Gaussians with training-time priors that measure their contribution. We further address training speed obstacles: following a careful analysis of 3DGS' original pipeline, we derive faster, numerically equivalent solutions for gradient computation and attribute updates, including an alternative parallelization for efficient backpropagation. We also propose quality-preserving approximations where suitable to reduce training time even further. Taken together, these enhancements yield a robust, scalable solution with reduced training times, lower compute and memory requirements, and high quality. Our evaluation shows that in a budgeted setting, we obtain competitive quality metrics with 3DGS while achieving a 4--5x reduction in both model size and training time. With more generous budgets, our measured quality surpasses theirs. These advances open the door for novel-view synthesis in constrained environments, e.g., mobile devices.
Overview
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The paper addresses the resource demands and efficiency limitations of 3D Gaussian Splatting (3DGS) for novel-view synthesis (NVS) by proposing methodological enhancements.
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It introduces a score-based, guided densification approach and optimized backpropagation strategy to control model size and reduce training time while maintaining high-quality results.
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These improvements make 3DGS feasible for constrained environments such as edge devices and mobile platforms, achieving significant reductions in model size and training time.
Taming 3DGS: High-Quality Radiance Fields with Limited Resources
"Taming 3DGS: High-Quality Radiance Fields with Limited Resources" addresses the limitations of 3D Gaussian Splatting (3DGS) for novel-view synthesis (NVS), specifically concerning its resource demands and efficiency. The authors propose several methodological enhancements to optimize 3DGS for constrained devices, achieving competitive quality metrics while significantly reducing compute and memory requirements.
The paper identifies two main operational issues with 3DGS: the excessive memory consumption due to an uncontrolled number of Gaussian primitives and the prolonged training times. These constraints hinder the practicability of 3DGS in real-world applications, particularly on devices with limited computational resources.
To mitigate these challenges, the authors introduce a score-based, guided densification approach. This method iteratively increases the model size in a controlled manner, according to a user-defined budget. Their densification process is designed to be purely constructive, thus avoiding redundant Gaussian primitives and ensuring efficient resource utilization. This approach steers the addition of new Gaussians based on their contribution to reconstruction quality, leveraging training-time priors.
The paper further enhances 3DGS by optimizing the training process. By analyzing the original 3DGS pipeline, the authors derive faster, numerically equivalent solutions for gradient computation and attribute updates. Notably, they propose an alternative parallelization strategy for the backpropagation step, mitigating the bottleneck of gradient computation by avoiding serialized atomic operations.
The authors' evaluation demonstrates substantial improvements over standard 3DGS. Under a fixed budget, their method achieves a 4--5$\times$ reduction in both model size and training time while maintaining or surpassing the original 3DGS quality. These results underscore the method's suitability for applications in constrained environments, such as edge devices or mobile platforms.
Key Contributions
Controlled Gaussian Densification:
- The authors introduce a predictable model growth curve based on the densification patterns observed in 3DGS. This curve ensures that the number of Gaussians added in each step adheres to user-defined limits, allowing for precise control over the final model size.
Score-Based Sampling for Densification:
- The densification process is guided by a scoring function that combines positional gradients, image loss, and other Gaussian attributes. This score determines the priority of Gaussians for densification, ensuring high quality within the predefined budget. This approach emphasizes the importance of areas with high reconstruction error and salient image features.
High-Opacity Gaussians:
- By incorporating high-opacity Gaussians, the method enhances the expressiveness of the model, particularly for opaque surfaces. This adjustment allows for better surface representation with fewer primitives.
Optimized Backpropagation:
- The authors propose a per-splat parallelization strategy for backpropagation, minimizing the contention for gradient updates and significantly accelerating the training process. This optimization reduces the training time dramatically, making 3DGS feasible for time-sensitive applications.
Implications and Future Directions
The enhancements proposed in this paper present significant practical and theoretical implications. By addressing the resource constraints of 3DGS, the method expands the usability of high-quality NVS to environments previously considered infeasible. This has direct applications in mobile AR/VR systems, remote sensing, and real-time 3D reconstruction for telepresence.
Future developments could explore further optimizations in sample placement and occupancy predictions, potentially enhancing the generalizability and efficiency of the method. As the authors suggest, steering strategies guided by user input or focus regions could be pivotal for interactive applications, where prioritizing certain areas (e.g., faces in telepresence) can improve user experience without overburdening the system's resources.
This paper contributes a robust framework that significantly reduces the computational overhead of 3DGS. It opens paths for integrating high-fidelity NVS into a broader range of applications, paving the way for more responsive and resource-effective 3D rendering solutions.
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