MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting (2410.07707v1)

Abstract: Dynamic scene reconstruction is a long-term challenge in the field of 3D vision. Recently, the emergence of 3D Gaussian Splatting has provided new insights into this problem. Although subsequent efforts rapidly extend static 3D Gaussian to dynamic scenes, they often lack explicit constraints on object motion, leading to optimization difficulties and performance degradation. To address the above issues, we propose a novel deformable 3D Gaussian splatting framework called MotionGS, which explores explicit motion priors to guide the deformation of 3D Gaussians. Specifically, we first introduce an optical flow decoupling module that decouples optical flow into camera flow and motion flow, corresponding to camera movement and object motion respectively. Then the motion flow can effectively constrain the deformation of 3D Gaussians, thus simulating the motion of dynamic objects. Additionally, a camera pose refinement module is proposed to alternately optimize 3D Gaussians and camera poses, mitigating the impact of inaccurate camera poses. Extensive experiments in the monocular dynamic scenes validate that MotionGS surpasses state-of-the-art methods and exhibits significant superiority in both qualitative and quantitative results. Project page: https://ruijiezhu94.github.io/MotionGS_page

• The paper introduces explicit motion priors to guide 3D Gaussian deformation, improving accuracy in dynamic scene reconstruction.
• The optical flow decoupling module distinguishes between camera and motion flows, enabling precise supervision for Gaussian deformation.
• The camera pose refinement module iteratively optimizes poses, enhancing rendering quality and robustness in dynamic 3D environments.

MotionGS: Explicit Motion Guidance for Deformable 3D Gaussian Splatting

"MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting" presents an innovative approach to advancing dynamic scene reconstruction, a complex challenge in the domain of 3D computer vision. The proposed framework leverages deformable 3D Gaussian splatting, which has shown potential for high-quality scene representation and real-time rendering.

Contributions and Methodology

The primary innovation of MotionGS is the introduction of explicit motion priors to guide the deformation of 3D Gaussians, addressing the lack of motion constraints in current methods. This is achieved through two major components: an optical flow decoupling module and a camera pose refinement module.

1. Optical Flow Decoupling Module: This component plays a crucial role by distinguishing between camera flow and motion flow. Traditional methods often suffer from optimization difficulties as they do not separately account for the flow resulting from camera movement and dynamic object motion. By decoupling these flows, MotionGS provides more accurate supervision for Gaussian deformation. The motion flow is used to directly guide the deformation, synchronizing the model with actual object movements.
2. Camera Pose Refinement Module: The accuracy of camera poses is vital for rendering quality, particularly in dynamic scenes where camera movement is common. This module employs an alternating optimization strategy to refine camera poses iteratively, enhancing the consistency and reliability of scene reconstruction.

Experimental Evaluation

The efficacy of MotionGS was validated through extensive experimentation on monocular dynamic scenes. Noteworthy datasets such as NeRF-DS and HyperNeRF were employed to benchmark the model. MotionGS consistently demonstrated superior performance over state-of-the-art approaches, both qualitatively and quantitatively. For instance, improvements were observed in PSNR, SSIM, and LPIPS metrics, which are critical for assessing the quality of novel view synthesis.

Implications and Future Directions

The incorporation of motion priors into deformable 3D Gaussian frameworks addresses a significant gap in dynamic scene reconstruction. By enabling explicit motion guidance, MotionGS enhances model robustness and rendering quality, even under conditions of irregular object motion or imprecise initial camera poses. This advancement opens up new possibilities for applications in augmented reality, virtual reality, and interactive 3D content creation, where real-time and high-fidelity rendering are pivotal.

From a theoretical standpoint, this paper pushes the boundary of how dynamic information can be effectively integrated into static scene representations. Future research may build upon this foundation by further optimizing the computation of motion priors or exploring alternative representations that inherently incorporate temporal dynamics.

Conclusion

MotionGS represents a significant step forward in the field of dynamic scene reconstruction. By explicitly constraining 3D Gaussian deformation through robust motion priors and adaptive camera pose refinement, this approach offers a reliable solution to the challenges posed by dynamic environments. This framework not only improves the performance of existing 3D Gaussian methods but also enriches the toolkit available for researchers and practitioners working on complex scene reconstructions.