SwinGS: Sliding Window Gaussian Splatting for Volumetric Video Streaming with Arbitrary Length (2409.07759v1)

Abstract: Recent advances in 3D Gaussian Splatting (3DGS) have garnered significant attention in computer vision and computer graphics due to its high rendering speed and remarkable quality. While extant research has endeavored to extend the application of 3DGS from static to dynamic scenes, such efforts have been consistently impeded by excessive model sizes, constraints on video duration, and content deviation. These limitations significantly compromise the streamability of dynamic 3D Gaussian models, thereby restricting their utility in downstream applications, including volumetric video, autonomous vehicle, and immersive technologies such as virtual, augmented, and mixed reality. This paper introduces SwinGS, a novel framework for training, delivering, and rendering volumetric video in a real-time streaming fashion. To address the aforementioned challenges and enhance streamability, SwinGS integrates spacetime Gaussian with Markov Chain Monte Carlo (MCMC) to adapt the model to fit various 3D scenes across frames, in the meantime employing a sliding window captures Gaussian snapshots for each frame in an accumulative way. We implement a prototype of SwinGS and demonstrate its streamability across various datasets and scenes. Additionally, we develop an interactive WebGL viewer enabling real-time volumetric video playback on most devices with modern browsers, including smartphones and tablets. Experimental results show that SwinGS reduces transmission costs by 83.6% compared to previous work with ignorable compromise in PSNR. Moreover, SwinGS easily scales to long video sequences without compromising quality.

• The paper introduces a novel framework integrating sliding-window Gaussian splatting with MCMC to adapt efficiently to dynamic 3D scenes.
• It achieves an 83.6% reduction in transmission costs and over 300 FPS rendering speeds while maintaining robust PSNR quality.
• The framework demonstrates scalability through a WebGL viewer that enables real-time playback on diverse devices including smartphones.

SwinGS: Sliding Window Gaussian Splatting for Volumetric Video Streaming with Arbitrary Length

Overview

The paper "SwinGS: Sliding Window Gaussian Splatting for Volumetric Video Streaming with Arbitrary Length" by Bangya Liu and Suman Banerjee addresses critical limitations in the streamability of 3D Gaussian Splatting (3DGS) models when applied to dynamic scenes. Despite the compelling advancements and potential utility of 3DGS in various applications—such as volumetric video, autonomous vehicles, and immersive technologies—the main challenges remain model size, video duration constraints, and content deviation.

Core Contributions

1. Novel Framework Integration: The authors introduce SwinGS, an innovative framework that merges spacetime Gaussians with Markov Chain Monte Carlo (MCMC) methods. This integration ensures the model adapts to different 3D scenes over varying frames, while employing a sliding window to capture Gaussian snapshots for each frame.
2. Enhanced Streamability: To facilitate real-time rendering and reduce transmission costs, SwinGS divides the transmission of a comprehensive model into smaller, manageable chunks containing per-frame new Gaussians. The experimental prototype demonstrates an 83.6% reduction in transmission costs with negligible compromise in PSNR.
3. Scalability and Practical Implementation: The framework showcases an interactive WebGL viewer, enabling real-time playback on most modern devices, including smartphones and tablets. This practical implementation proves the framework's scalability for long volumetric sequences.

Technical Highlights

Spacetime Gaussian and MCMC Integration

SwinGS builds on the premise of 3DGS-MCMC introduced by Kheradmand et al. (2024), leveraging Stochastic Gradient Langevin Dynamics (SGLD) for continuous model adaptation. The model maintains a constant number of Gaussians during training by relocating underutilized Gaussians to new positions, ensuring uniform representation and minimizing redundant computations.

Sliding Window Mechanism

The sliding window technique is central to SwinGS, ensuring efficient snapshot and Gaussian lifespan management. This mechanism, combined with Gaussian maturation, allows only recently relevant Gaussians to be optimized, thus reducing resource overhead and ensuring scalability.

Experimental Results

The authors benchmarked SwinGS against existing methods using datasets from ActorsHQ and DyNeRF. Results showed that SwinGS not only maintained high rendering quality with PSNR metrics comparable to other high-fidelity methods but also significantly outperformed in terms of rendering speed and streaming efficiency.

• Rendering Quality: While the PSNR was slightly lower than the highest-performing methods like SpacetimeGS and 3DGStream, SwinGS maintained a robust PSNR with substantial reduction in transmission overhead.
• Speed and Efficiency: The system reported over 300 FPS rendering speeds, highlighting over 300 FPS. This is a clear stark difference from NeRF-based methods which were limited by computational overheads.
• Streamability: SwinGS demonstrated practical and efficient streaming capabilities, leveraging WebGL-based viewers to facilitate playback on various devices.

Practical and Theoretical Implications

Practical Implications

1. Volumetric Video Applications: SwinGS can enable high-fidelity, real-time volumetric video streaming suitable for applications in VR, AR, and MR. The reduced bandwidth and enhanced scalability make it feasible for commercial deployment.
2. Autonomous Vehicles and Robotics: The ability to handle dynamic scenes and maintain model integrity over long durations make SwinGS suitable for real-time environments in autonomous vehicles and teleoperation in robotics vision.

Theoretical Implications

1. Gaussian Model Efficiency: The relocation and maturation strategy informs future research on optimizing Gaussian models for dynamic scene representation, striking a balance between quality and computational efficiency.
2. Model Adaptability: The integration of MCMC techniques with 3DGS offers a new approach to maintaining model adaptability and integrity over extended sequences, paving the way for further exploration into dynamic neural rendering models.

Future Directions

1. Bandwidth Optimization: Future work could refine the Gaussian maturation process to dynamically prioritize the most informative Gaussians, further optimizing bandwidth usage.
2. Parallel Training Efficiency: Exploring enhancements in parallel training processes or employing distributed computing techniques could significantly reduce the GPU hours required for extended volumetric video training.
3. Extended Applications: Investigating the application of SwinGS in other real-world scenarios, such as surveillance and live event streaming, could broaden its practical impact.

Conclusion

SwinGS represents an important step forward in the application of 3D Gaussian Splatting for volumetric video streaming. By effectively addressing the core challenges of model size, video duration, and content deviation, it paves the way for scalable and efficient real-time streaming applications. The framework's innovative use of a sliding window mechanism, combined with robust integration of MCMC techniques, ensures high rendering quality and practical deployability, affirming its potential to transform immersive media streaming and beyond.

This essay provides a professional and detailed analysis of the SwinGS framework, adhering strictly to the provided guidelines.