- The paper introduces RTGS, a system that integrates efficiency-aware pruning with foveated rendering to deliver real-time neural rendering on mobile devices.
- It employs a novel Computational Efficiency metric and scale decay during training to prune low-impact points without sacrificing photorealism.
- Extensive experiments show RTGS outperforms state-of-the-art models by up to 7.9 times in speed while maintaining competitive PSNR and subjective visual quality.
RTGS: Real-Time Neural Rendering with Efficiency-Aware Pruning and Accelerated Foveated Rendering
This paper introduces "RTGS," an advanced Point-Based Neural Rendering (PBNR) system designed to achieve real-time neural rendering on mobile devices while maintaining human visual quality. PBNR techniques, especially those related to 3D Gaussian Splatting, offer photorealistic rendering by learning light-matter interactions from data. Despite their promise, achieving real-time PBNR on mobile platforms remains challenging due to significant computational demands.
Main Innovations
The authors of this paper propose two key techniques to tackle this challenge: efficiency-aware pruning and a novel Foveated Rendering (FR) method tailored for PBNR. These innovations collectively enable the RTGS system to execute in real-time (above 100 FPS) on mobile devices like the Nvidia Jetson Xavier board.
Efficiency-Aware Pruning
Existing pruning methods reduce the number of points in a PBNR model but fall short in terms of improving rendering speed due to their agnostic approach to computational costs. By contrast, RTGS introduces a "Computational Efficiency" (CE) metric that considers both the visual contribution of a point and its impact on computational efficiency. Points with low CE are pruned to ensure that the model's performance yield matches its computational load.
Additionally, a "scale decay" technique is integrated into the training process. This technique decays the size of the ellipses associated with points, reducing their computational footprint while minimally impacting visual quality. The combination of these methods leads to substantial speed improvements, making RTGS seven times faster than existing state-of-the-art models while retaining high visual quality.
Foveated Rendering for PBNR
Foveated Rendering exploits the human visual system's reduced sensitivity in the peripheral visual field to relax rendering quality. The RTGS approach involves rendering different image regions at varying quality levels corresponding to the eye's focus and periphery. The novelty in RTGS lies in its efficient data representation that ensures points used for rendering at higher quality levels are subsets of those used at lower levels. Selective multi-versioning of critical parameters, such as opacity and spherical harmonics coefficients, further refines the quality without significantly increasing storage requirements.
The training framework for RTGS is guided by the Human Visual System-aware Quality (HVSQ) metric. HVSQ accurately models human visual perception at varying eccentricities, ensuring that the relaxed visual quality aligns well with subjective human judgments. This meticulous training allows RTGS to maintain consistent visual quality while reducing computational load in the peripheral regions of the image.
Numerical Results and Evaluation
The effectiveness of the RTGS system is validated through comprehensive experiments and comparisons. User studies confirm that the subjective quality of the RTGS system is statistically no worse than the high-quality but slower Mini-Splatting-D model. Quantitative evaluations show that RTGS variants significantly outperform existing PBNR models, achieving real-time performance on mobile GPUs with subjective quality retained, if not improved.
Specifically, the fastest RTGS variant achieves up to 7.9 times the speed of the dense 3DGS model while maintaining a competitive PSNR. The paper also includes an extensive ablation paper that validates the individual contributions of efficiency-aware pruning, scale decay, and foveated rendering.
Implications and Future Work
The implications of RTGS extend to various practical applications in AR/VR, smart cities, and digital healthcare, where real-time, photorealistic rendering on mobile platforms is crucial. The system's compatibility with mobile GPUs opens avenues for more immersive and interactive experiences in these domains.
From a theoretical perspective, this work challenges conventional approaches to model pruning and rendering by emphasizing computational efficiency over mere reduction in model size. The innovative integration of human vision models into the neural rendering pipeline sets a precedent for future research focusing on perceptual quality over purely objective metrics.
Future developments may explore even more refined human vision models, greater hardware optimizations, and extensions to other neural rendering paradigms. As mobile computing power continues to grow, the principles and techniques introduced by RTGS could very well set the stage for new benchmarks in real-time rendering efficiency.
In conclusion, RTGS presents a highly efficient, human perception-aware system for real-time neural rendering on mobile devices, promising substantial performance gains without sacrificing visual quality. This work marks a significant step forward in the field of neural rendering, aligning computational efficiency with human-centric quality metrics.