S4D: Streaming 4D Real-World Reconstruction with Gaussians and 3D Control Points (2408.13036v2)

Abstract: Dynamic scene reconstruction using Gaussians has recently attracted increased interest. Mainstream approaches typically employ a global deformation field to warp a 3D scene in canonical space. However, the inherent low-frequency nature of implicit neural fields often leads to ineffective representations of complex motions. Moreover, their structural rigidity can hinder adaptation to scenes with varying resolutions and durations. To address these challenges, we introduce a novel approach for streaming 4D real-world reconstruction utilizing discrete 3D control points. This method physically models local rays and establishes a motion-decoupling coordinate system. By effectively merging traditional graphics with learnable pipelines, it provides a robust and efficient local 6-degrees-of-freedom (6-DoF) motion representation. Additionally, we have developed a generalized framework that integrates our control points with Gaussians. Starting from an initial 3D reconstruction, our workflow decomposes the streaming 4D reconstruction into four independent submodules: 3D segmentation, 3D control point generation, object-wise motion manipulation, and residual compensation. Experimental results demonstrate that our method outperforms existing state-of-the-art 4D Gaussian splatting techniques on both the Neu3DV and CMU-Panoptic datasets. Notably, the optimization of our 3D control points is achievable in 100 iterations and within just 2 seconds per frame on a single NVIDIA 4070 GPU.

• The paper introduces a novel method that uses discrete 3D control points with Gaussians to decouple and accurately model dynamic motions.
• It proposes an efficient streaming framework that modularizes segmentation, control point generation, motion manipulation, and residual compensation.
• Empirical results on Neu3DV and CMU-Panoptic datasets demonstrate superior reconstruction accuracy and speed, achieving significant improvements over state-of-the-art methods.

S4D: Streaming 4D Real-World Reconstruction with Gaussians and 3D Control Points

The paper "S4D: Streaming 4D Real-World Reconstruction with Gaussians and 3D Control Points" introduces an innovative approach to dynamic scene reconstruction by leveraging discrete 3D control points alongside Gaussian representations. This method addresses several limitations of existing techniques, particularly in handling complex motions and varying scene resolutions.

The authors begin by acknowledging the substantial progress made through 3D Gaussian Splatting (3D-GS), which has demonstrated impressive performance in static scene reconstructions. However, they note the limitations of existing methods in dynamic scenes. Traditional neural fields for dynamic scene reconstruction often struggle with representing high-frequency details due to their inherent low-frequency nature and structural rigidity. This paper proposes an alternative that capitalizes on the discrete structure of 3D-GS, ensuring efficient and flexible scene representation.

Key Contributions

1. Discrete 3D Control Points: The authors introduce a novel method to discretely model 3D motions of dynamic objects by combining traditional graphics principles with learnable pipelines. This approach decouples 3D motion into observable and hidden components, with the former being bound to optical flow and the latter acquired through learning. This method, referred to as "3D control points," enhances convergence speed and reconstruction accuracy.
2. Efficient Stream Processing: A generalized framework is developed to decompose the streaming 4D reconstruction process into four independent modules: 3D segmentation, 3D control points generation, object-wise motion manipulation, and residual compensation. This modular approach ensures robust and efficient processing.
3. Strong Empirical Results: The methodology was evaluated on the Neu3DV and CMU-Panoptic datasets, where it outperformed state-of-the-art 4D Gaussian Splatting techniques. Additionally, the optimization of 3D control points was notably efficient, requiring just 2 seconds per frame on a single NVIDIA 4070 GPU.

Methodology

Local 6-DoF Motion Decoupling

The core innovation lies in the introduction of a local motion decoupling system that uses 3D control points to model 6-degrees-of-freedom (6-DoF) motions. The system employs optical flow to separate motion into observable and hidden parts. The rotation attributes are represented using Euler angles for decoupling and quaternions for interpolation, ensuring precise and flexible motion representation.

Object-wise Motion Manipulation

This module transforms the motion-related attributes of Gaussians object-wise, ensuring that each Gaussian is influenced only by control points belonging to the same object category. This selective approach enhances the precision of the motion representation and accommodates topological changes in dynamic scenes.

Residual Compensation

To mitigate error accumulation and ensure stable long-term reconstruction, the method incorporates a residual compensation block using a keyframe strategy. This approach updates Gaussian attributes only at keyframes, allowing for comprehensive adjustments while retaining overall efficiency.

Experimental Results

The proposed methodology was rigorously tested on several dynamic scene datasets. Notably, it achieved superior performance metrics on the Neu3DV dataset, demonstrating higher PSNR, SSIM, and lower LPIPS scores compared to existing methods. The CMU-Panoptic dataset evaluations further underscored the robustness and efficiency of the proposed approach, especially in handling complex dynamic motions.

Implications and Future Directions

The implications of this research are substantial for both theoretical advancements and practical applications in AI and computer graphics:

• Practical Applications: The methodology can be employed in real-time applications requiring high-fidelity 4D reconstructions, such as virtual reality, augmented reality, and dynamic scene rendering in gaming and simulations.
• Theoretical Advancements: The introduction of discrete 3D control points represents a significant theoretical contribution, offering a new avenue for efficiently handling dynamic scenes with complex motions.

Looking forward, several avenues for further exploration remain. Enhancing the initial 3D reconstruction quality, extending support for monocular video inputs, and optimizing the implementation for even faster processing times are potential directions for future research.

In summary, the paper presents a robust solution for 4D real-world scene reconstruction, leveraging the discrete nature of 3D control points to efficiently handle complex dynamic motions. The promising experimental results and potential for further optimization make this a notable contribution to the field.