Optimizing 3D Gaussian Splatting for Sparse Viewpoint Scene Reconstruction (2409.03213v1)

Abstract: 3D Gaussian Splatting (3DGS) has emerged as a promising approach for 3D scene representation, offering a reduction in computational overhead compared to Neural Radiance Fields (NeRF). However, 3DGS is susceptible to high-frequency artifacts and demonstrates suboptimal performance under sparse viewpoint conditions, thereby limiting its applicability in robotics and computer vision. To address these limitations, we introduce SVS-GS, a novel framework for Sparse Viewpoint Scene reconstruction that integrates a 3D Gaussian smoothing filter to suppress artifacts. Furthermore, our approach incorporates a Depth Gradient Profile Prior (DGPP) loss with a dynamic depth mask to sharpen edges and 2D diffusion with Score Distillation Sampling (SDS) loss to enhance geometric consistency in novel view synthesis. Experimental evaluations on the MipNeRF-360 and SeaThru-NeRF datasets demonstrate that SVS-GS markedly improves 3D reconstruction from sparse viewpoints, offering a robust and efficient solution for scene understanding in robotics and computer vision applications.

• The paper presents the SVS-GS framework that overcomes sparse viewpoint limitations by integrating 3D Gaussian smoothing, local adaptive density scaling, and score distillation sampling loss.
• It leverages depth priors, dynamic depth masks, and DGPP loss to enhance geometric consistency and detail preservation in 3D reconstructions.
• Empirical results on the MipNeRF-360 and SeaThru-NeRF datasets show superior PSNR, SSIM, and LPIPS scores compared to existing methods.

Optimizing 3D Gaussian Splatting for Sparse Viewpoint Scene Reconstruction

The paper "Optimizing 3D Gaussian Splatting for Sparse Viewpoint Scene Reconstruction" by Shen Chen, Jiale Zhou, and Lei Li presents a novel framework termed SVS-GS that addresses the inherent limitations of 3D Gaussian Splatting (3DGS) in sparse viewpoint scenarios. The SVS-GS framework enhances the 3D reconstruction process by integrating multiple advanced techniques to mitigate artifacts and improve geometric consistency, thereby pushing the boundaries of scene reconstruction in robotics and computer vision.

Introduction

The SVS-GS framework is motivated by the need for efficient 3D scene reconstruction from sparse viewpoints, which is a common limitation in resource-constrained environments. Traditional Neural Radiance Fields (NeRF) have shown promise in novel view synthesis but are computationally intensive and struggle with sparse input data. Conversely, 3DGS provides a more computationally efficient alternative by employing an explicit representation of 3D Gaussians. However, 3DGS is susceptible to high-frequency artifacts and doesn't perform optimally with sparse viewpoints.

Methodology

To overcome these challenges, the SVS-GS framework introduces several key components:

1. 3D Gaussian Smoothing Filter: This filter is implemented to regulate the diffusion range of Gaussian primitives in both 3D space and their 2D projections. This step ensures better preservation of details, especially for small and thin structures.
2. Local Adaptive Density Scaling Module: This module addresses the issue of low point cloud density from sparse viewpoints by dynamically increasing the density of Gaussian primitives. This ensures a denser set of 3D Gaussian primitives, enhancing detail representation.
3. Score Distillation Sampling (SDS) Loss: SDS loss is employed to integrate 3DGS with 2D diffusion, leveraging depth prior information to constrain the positions and sizes of the 3D Gaussians. This helps in reducing noise and maintaining geometric consistency.
4. Dynamic Depth Mask and Depth Gradient Profile Prior (DGPP) Loss: These components contribute to sharpening edges in the depth maps and enhance geometric accuracy by selectively retaining critical depth information.

Results

Empirical evaluations on MipNeRF-360 and SeaThru-NeRF datasets demonstrate that SVS-GS markedly outperforms existing methods in reconstructing scenes from sparse viewpoints. For instance, on the MipNeRF-360 dataset, SVS-GS achieves the highest PSNR and SSIM scores, indicating its superior capability in maintaining geometric and textural details. Moreover, it registers the lowest LPIPS score, further attesting to its visual fidelity. On the SeaThru-NeRF dataset, SVS-GS effectively mitigates the challenges posed by complex underwater environments, preserving scene details better than its counterparts.

Discussion

The strong numerical results underscore the robustness of SVS-GS in various challenging scenarios. Notably, the integration of depth priors and DGPP loss substantially enhances edge sharpness and detail preservation. The dynamic depth mask and adaptive density scaling module effectively address the limitations posed by sparse viewpoint data, ensuring a more consistent and detailed 3D reconstruction.

Implications and Future Work

Practically, the SVS-GS framework opens new avenues for 3D scene reconstruction in environments where obtaining dense multi-view data is impractical. This has significant implications for applications in autonomous vehicle navigation and robotic vision systems operating in complex terrains. Theoretically, the innovative use of 3D Gaussian smoothing and SDS loss in scene reconstruction could inspire future research to explore more sophisticated filtering and optimization techniques.

Future developments could focus on further enhancing the framework's adaptability to extremely sparse datasets and integrating additional sensory inputs (e.g., LiDAR) to further improve reconstruction accuracy. Additionally, exploring the impact of different types of priors and loss functions could yield even more robust and versatile reconstruction frameworks.

Conclusion

The SVS-GS framework represents a significant advance in 3D scene reconstruction from sparse viewpoints, integrating cutting-edge techniques to enhance detail fidelity and geometric consistency. Its robust performance across various datasets underscores its potential applicability in both robotics and broader computer vision tasks, offering an effective solution for high-quality 3D scene understanding in resource-constrained environments.