4D Gaussian Splatting for Real-Time Dynamic Scene Rendering (2310.08528v3)

Abstract: Representing and rendering dynamic scenes has been an important but challenging task. Especially, to accurately model complex motions, high efficiency is usually hard to guarantee. To achieve real-time dynamic scene rendering while also enjoying high training and storage efficiency, we propose 4D Gaussian Splatting (4D-GS) as a holistic representation for dynamic scenes rather than applying 3D-GS for each individual frame. In 4D-GS, a novel explicit representation containing both 3D Gaussians and 4D neural voxels is proposed. A decomposed neural voxel encoding algorithm inspired by HexPlane is proposed to efficiently build Gaussian features from 4D neural voxels and then a lightweight MLP is applied to predict Gaussian deformations at novel timestamps. Our 4D-GS method achieves real-time rendering under high resolutions, 82 FPS at an 800$\times$800 resolution on an RTX 3090 GPU while maintaining comparable or better quality than previous state-of-the-art methods. More demos and code are available at https://guanjunwu.github.io/4dgs/.

• The paper introduces a 4D Gaussian Splatting framework that models both motion and shape deformations for dynamic scenes.
• It employs decomposed voxel encoding and a lightweight MLP to efficiently predict Gaussian deformations across timestamps.
• Experiments demonstrate real-time rendering at up to 82 FPS with quality metrics of 34.05 dB PSNR and 0.98 SSIM.

Overview of 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering

The paper "4D Gaussian Splatting for Real-Time Dynamic Scene Rendering" introduces an advanced framework for the efficient representation and rendering of dynamic scenes. Drawing from the foundational concepts of 3D Gaussian Splatting (3D-GS), this paper extends the method into a four-dimensional space, offering a paradigm capable of real-time performance while maintaining high quality.

Core Contributions

The authors propose a novel representation, termed as 4D Gaussian Splatting (4D-GS), designed to model dynamic scenes holistically rather than processing each frame independently. The representation employs both 3D Gaussians and 4D neural voxels, amalgamating explicit and implicit paradigms for enhanced performance. Central to this methodology is a decomposed voxel encoding inspired by HexPlane, facilitating the construction of Gaussian features from 4D neural voxels. A lightweight multilayer perceptron (MLP) is then utilized to predict Gaussian deformations for new timestamps.

Key contributions of this framework can be enumerated as follows:

• Efficient Framework: Introduction of a robust 4D Gaussian splatting framework that facilitates modeling of both motion and shape deformations of Gaussians over time.
• Enhanced Encoding: Development of a multi-resolution encoding method that efficiently connects adjacent 3D Gaussians, enabling rich feature construction via a spatial-temporal structure encoder.
• Real-Time Rendering: Demonstrated capability to render dynamic scenes in real-time, achieving up to 82 FPS at 800×800 resolution for synthetic datasets, and 30 FPS at 1352×1014 for real datasets.

Experimental Evaluation

The framework's efficacy is substantiated through comprehensive experimental evaluation. Notably, 4D-GS exhibits superior performance in rendering speed and quality compared to state-of-the-art techniques. The paper reports real-time rendering capabilities and efficient training convergence, with the model completing training significantly faster than traditional methods. Notable metrics include achieving 34.05 dB PSNR and 0.98 SSIM in synthetic datasets, underscoring the advancement in rendering quality.

Theoretical and Practical Implications

The introduction of 4D Gaussian Splatting presents significant implications for both theoretical exploration and practical applications in dynamic scene rendering. Theoretically, the framework enhances understanding of high-dimensional rendering models, offering insights into the integration of explicit and implicit representations. Practically, this approach provides substantial advancements for applications in virtual reality (VR), augmented reality (AR), and cinematic productions, where speed and quality are paramount.

Future Directions

While the results are promising, the paper acknowledges the constraints in handling large motions and the necessity for further research on more complex scenes or diverse camera setups. Future exploration could address the integration of additional priors, such as depth or optical flow, to enhance the framework’s robustness against challenging scenarios, including monocular inputs with significant motion.

The paper sets a promising direction for future research into efficient, high-quality dynamic scene rendering, merging innovative representation methods with real-world applicability.