GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction (2411.19454v2)

Abstract: 3D Gaussian Splatting has achieved impressive performance in novel view synthesis with real-time rendering capabilities. However, reconstructing high-quality surfaces with fine details using 3D Gaussians remains a challenging task. In this work, we introduce GausSurf, a novel approach to high-quality surface reconstruction by employing geometry guidance from multi-view consistency in texture-rich areas and normal priors in texture-less areas of a scene. We observe that a scene can be mainly divided into two primary regions: 1) texture-rich and 2) texture-less areas. To enforce multi-view consistency at texture-rich areas, we enhance the reconstruction quality by incorporating a traditional patch-match based Multi-View Stereo (MVS) approach to guide the geometry optimization in an iterative scheme. This scheme allows for mutual reinforcement between the optimization of Gaussians and patch-match refinement, which significantly improves the reconstruction results and accelerates the training process. Meanwhile, for the texture-less areas, we leverage normal priors from a pre-trained normal estimation model to guide optimization. Extensive experiments on the DTU and Tanks and Temples datasets demonstrate that our method surpasses state-of-the-art methods in terms of reconstruction quality and computation time.

• The paper introduces a novel method that integrates patch-match geometry guidance into 3D Gaussian splatting for accurate surface reconstruction from multiview images.
• It leverages normal prior-based optimization to enhance detail in texture-less areas, ensuring reliable geometry estimation and faster convergence.
• Extensive experiments on DTU and Tanks and Temples demonstrate superior reconstruction quality and efficiency compared to state-of-the-art methods.

Overview of GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction

The paper "GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction" introduces an innovative methodology for high-quality surface reconstruction capitalizing on 3D Gaussian splatting. The authors address the challenge presented by traditional 3D Gaussian approaches, which, despite their real-time rendering capabilities, often fail to detail fine surface structures as needed for accurate surface reconstruction from multiview images—a critical aspect for applications in computer graphics and vision domains such as animation, robotics, and augmented/virtual reality.

Methodological Advances

GausSurf builds upon the 3D Gaussian Splatting (GS) framework by integrating geometry guidance, distinguishing itself through two primary technical strategies:

1. Patch-Match-based Geometry Guidance: Utilizing the multi-view consistency obtainable through patch-match algorithms, GausSurf improves surface detail in texture-rich regions. This incorporation enables iterative refinement—rendering rough depth and normal maps, refining them using traditional Multi-View Stereo (MVS) concepts, and converging on high-quality surface representations with increased optimization efficiency. The authors leverage the inherent patch similarity checks to ensure multiview consistency and adapt efficiently through iterative propagation and random perturbations, yielding improved depth and normal quality over texture-rich areas.
2. Normal Prior-based Optimization: For managing texture-less areas which have typically been a stumbling block for reliable surface reconstruction, the paper advocates utilizing pre-trained normal estimation models. This approach provides additional supervision, ensuring that even in the absence of sufficient textural features, the geometry optimization remains reliable. This distinction of region-specific optimization—texture-rich versus texture-less—is highlighted as a unique attribute of the GausSurf method.

Results and Performance

Extensive experimentation on datasets such as DTU and Tanks and Temples demonstrates the superior performance of GausSurf over existing state-of-the-art GS-based approaches such as SuGaR, 2DGS, and PGSR, particularly in terms of reconstruction quality and computational efficiency. GausSurf handles single object reconstructions within less than ten minutes with notable accuracy improvements. Compared to implicit methods like NeuS and Neuralangelo, GausSurf offers comparable reconstruction quality while operating at a fraction of the time required by these methods.

Qualitative assessments further showcase GausSurf's ability to capture more refined geometry and smoother surfaces, even outperforming implicit methods due to its efficient integration of geometry guidance.

Implications and Future Directions

The efficient incorporation of geometry guidance into the Gaussian splatting framework has significant implications for the field. By bridging traditional MVS and neural rendering paradigms, GausSurf paves the way for enhanced real-time applications in dynamic and large-scale environments. However, despite its advancements, real-time applications like SLAM remain future targets for optimization to further decrease run-time without compromising quality. The iterative, geometry-guided paradigm shown here establishes a foundation upon which such real-time capabilities could be further developed.

By providing open access to the code and data, the authors encourage further research and development in surface reconstruction methodologies, potentially inviting exploration into integration with other geometric cues or hybrid systems for diversified applications. As such, GausSurf contributes a valuable approach to both academic exploration and practical application in complex visual computing environments.