4D-Rotor Gaussian Splatting: Towards Efficient Novel View Synthesis for Dynamic Scenes (2402.03307v3)

Abstract: We consider the problem of novel-view synthesis (NVS) for dynamic scenes. Recent neural approaches have accomplished exceptional NVS results for static 3D scenes, but extensions to 4D time-varying scenes remain non-trivial. Prior efforts often encode dynamics by learning a canonical space plus implicit or explicit deformation fields, which struggle in challenging scenarios like sudden movements or generating high-fidelity renderings. In this paper, we introduce 4D Gaussian Splatting (4DRotorGS), a novel method that represents dynamic scenes with anisotropic 4D XYZT Gaussians, inspired by the success of 3D Gaussian Splatting in static scenes. We model dynamics at each timestamp by temporally slicing the 4D Gaussians, which naturally compose dynamic 3D Gaussians and can be seamlessly projected into images. As an explicit spatial-temporal representation, 4DRotorGS demonstrates powerful capabilities for modeling complicated dynamics and fine details--especially for scenes with abrupt motions. We further implement our temporal slicing and splatting techniques in a highly optimized CUDA acceleration framework, achieving real-time inference rendering speeds of up to 277 FPS on an RTX 3090 GPU and 583 FPS on an RTX 4090 GPU. Rigorous evaluations on scenes with diverse motions showcase the superior efficiency and effectiveness of 4DRotorGS, which consistently outperforms existing methods both quantitatively and qualitatively.

• The paper demonstrates a novel extension of Gaussian splatting into a 4D temporal domain, enabling efficient dynamic scene synthesis.
• It leverages temporal slicing and CUDA optimization to render dynamic scenes at up to 277 FPS on RTX 3090 GPUs.
• Results show superior performance over state-of-the-art methods, balancing high-fidelity rendering with real-time processing speeds.

"4D-Rotor Gaussian Splatting: Towards Efficient Novel View Synthesis for Dynamic Scenes" (2402.03307)

Introduction

The quest for efficient novel view synthesis (NVS) of dynamic 3D scenes, particularly leveraging temporal versus static 3D representations, presents formidable challenges in the field of computer vision and graphics. Despite remarkable strides in static scene synthesis, the complexity of temporally varying scenes has limited the advancement of dynamic NVS techniques. This paper introduces "4D Gaussian Splatting" (4DGS), a methodology which extends the paradigm of 3D Gaussian splatting from static scenes to dynamic ones by incorporating a temporal dimension to create anisotropic 4D Gaussian splats. This approach not only models complex spatial-temporal movements but also achieves real-time rendering speeds with superior fidelity.

Methodology

The methodology centers around the extension of 3D Gaussian splatting to 4D by incorporating time as an additional dimension. This 4D model, represented as $XYZT$ Gaussians, captures dynamic scene changes effectively. The Gaussians are tailored to represent dynamics at individual timestamps through temporal slicing, thus creating dynamic 3D Gaussians that can be projected into image form seamlessly. Temporal slicing enables efficient rendering as it adapts the static scene modeling advances to complex dynamic behavior, ensuring robustness in scenarios with abrupt movements or highly detailed renderings. Further leveraging a CUDA-based optimization allows 4DGS to realize performance metrics like achieving 277 FPS on RTX 3090 GPUs, delineating its feasibility for real-time applications.

Figure 1: A simplified 2D illustration of the temporal slicing process for 4D Gaussians, demonstrating their conversion to dynamic 2D ellipses.

Results

The results substantiate 4DGS's proficiency in both efficiency and quality. Evaluations conducted on datasets with diverse motions, including Plenoptic Video Dataset and D-NeRF Dataset, underscore 4DGS's superior speed and rendering quality compared to existing state-of-the-art methods. The technique outperforms contemporary volumetric rendering techniques, which are inherently hamstrung by the need for dense sampling and high computational overhead. Numerically, 4DGS accomplishes a remarkable parsing of 1352x1014 videos at 583 FPS, demonstrating over twofold speed gains over its contemporaries without compromising on accuracy or detail.

Figure 2: Visualization of 4D Gaussian temporal evolution within dynamic sequences, highlighting movement fidelity over short time spans.

Discussion

4DGS's introduction offers transformative possibilities in areas requiring high-fidelity and real-time 3D scene rendering. The efficiency gains open up new potential for applications in virtual reality and gaming environments, where dynamic scene rendering capability is paramount. Theoretical implications suggest 4DGS's robustness in modeling high-entropy scenes which challenge traditional NVS methodologies. However, certain limitations, such as handling highly translucent objects and motion blur, warrant further investigation. Prospective enhancements in hybridization with machine learning models could address these nuances, propelling NVS technologies forward.

Conclusion

Overall, 4D-Rotor Gaussian Splatting ushers in a new era for dynamic scene synthesis, merging high-quality renderings with unprecedented processing speeds. By extending the dimensionality of Gaussian splats into the temporal domain, 4DGS surmounts the key obstacles of dynamic vs. static scene modeling, aligning well with real-world application constraints while fostering continuous evolution within the domain of neural radiance field methodologies.

Figure 3: Speed fields visualization, evidencing the proficient motion capture akin to the optical flow representation derived naturally through 4D Gaussian slicing.