Graph-Guided Scene Reconstruction from Images with 3D Gaussian Splatting (2502.17377v1)

Abstract: This paper investigates an open research challenge of reconstructing high-quality, large 3D open scenes from images. It is observed existing methods have various limitations, such as requiring precise camera poses for input and dense viewpoints for supervision. To perform effective and efficient 3D scene reconstruction, we propose a novel graph-guided 3D scene reconstruction framework, GraphGS. Specifically, given a set of images captured by RGB cameras on a scene, we first design a spatial prior-based scene structure estimation method. This is then used to create a camera graph that includes information about the camera topology. Further, we propose to apply the graph-guided multi-view consistency constraint and adaptive sampling strategy to the 3D Gaussian Splatting optimization process. This greatly alleviates the issue of Gaussian points overfitting to specific sparse viewpoints and expedites the 3D reconstruction process. We demonstrate GraphGS achieves high-fidelity 3D reconstruction from images, which presents state-of-the-art performance through quantitative and qualitative evaluation across multiple datasets. Project Page: https://3dagentworld.github.io/graphgs.

• The paper introduces GraphGS, a novel graph-guided framework for optimizing 3D Gaussian Splatting that achieves efficient and high-quality 3D reconstruction of large, sparsely surveyed scenes from images.
• Key methodological innovations include spatial prior-based scene structure estimation, camera graph optimization for multi-view consistency, and an adaptive sampling strategy for faster convergence.
• GraphGS demonstrates state-of-the-art performance on challenging datasets, showing significant quantitative improvements (PSNR, SSIM, LPIPS) and approximately a 50% reduction in training time, enabling practical applications in AR/VR.

Graph-Guided Scene Reconstruction from Images with 3D Gaussian Splatting

The paper "Graph-Guided Scene Reconstruction from Images with 3D Gaussian Splatting" introduces a novel framework called GraphGS, aimed at efficient and high-quality 3D reconstruction of large open scenes from images. The paper addresses and overcomes significant limitations observed in existing methodologies, such as the requirement for precise camera poses and dense viewpoints, which often become impractical, especially in expansive outdoor environments.

Summary of Method

The proposed framework, GraphGS, integrates a graph-guided approach to optimize 3D Gaussian Splatting (3DGS), allowing for more effective synthesis in complex and sparsely surveyed scenes. The authors propose several key innovations:

1. Spatial Prior-Based Scene Structure Estimation: The framework employs this method to quickly estimate the initial structure of the scene utilizing rough camera poses, enhancing both the speed and the accuracy of subsequent optimization steps.
2. Camera Graph and Optimization: A camera graph is constructed, representing the spatial topology between different camera viewpoints. This graph is leveraged to impose a multi-view consistency constraint through the optimization process, ensuring better reconstruction quality by preventing issues like overfitting to specific sparse viewpoints.
3. Adaptive Sampling Strategy: The authors introduce an adaptive approach to sampling Gaussian points, allowing the system to dynamically allocate computational resources more effectively throughout the iterative optimization process. This accelerates the convergence and reduces the training time while maintaining high-fidelity reconstructions.

Key Results

The research showcases strong quantitative and qualitative state-of-the-art performance improvements over previous methods across multiple challenging datasets, including Waymo and KITTI. The paper reports notable numerical results, highlighting:

• Significant improvements in metrics such as PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index), and LPIPS (Learned Perceptual Image Patch Similarity) across datasets.
• Approximately 50% reduction in training time due to the efficient sampling and optimization strategies.

Implications and Future Directions

GraphGS not only sets a new benchmark for 3D scene reconstruction from images but also suggests potential extensions of these techniques to broader applications in computer vision. By eliminating the dependence on precise inputs and reducing processing time without sacrificing output quality, this research opens avenues for practical applications in fields like Augmented Reality (AR) and Virtual Reality (VR).

Looking forward, advancements could explore adaptive, context-aware graph constructions to further enhance processing efficiency. Additionally, expansion into real-time adaptive systems for dynamic environments and integration with LiDAR or other sensory data types could broaden the practical scope of Autonomous Driving Systems and Smart City infrastructure analysis.

In conclusion, GraphGS demonstrates a compelling step forward in computer vision methodologies, fostering advancements in real-time environmental modeling and practical AI-enhanced solutions in complex, unbounded settings.