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Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes (2404.10772v2)

Published 16 Apr 2024 in cs.CV

Abstract: Recently, 3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis results, while allowing the rendering of high-resolution images in real-time. However, leveraging 3D Gaussians for surface reconstruction poses significant challenges due to the explicit and disconnected nature of 3D Gaussians. In this work, we present Gaussian Opacity Fields (GOF), a novel approach for efficient, high-quality, and adaptive surface reconstruction in unbounded scenes. Our GOF is derived from ray-tracing-based volume rendering of 3D Gaussians, enabling direct geometry extraction from 3D Gaussians by identifying its levelset, without resorting to Poisson reconstruction or TSDF fusion as in previous work. We approximate the surface normal of Gaussians as the normal of the ray-Gaussian intersection plane, enabling the application of regularization that significantly enhances geometry. Furthermore, we develop an efficient geometry extraction method utilizing Marching Tetrahedra, where the tetrahedral grids are induced from 3D Gaussians and thus adapt to the scene's complexity. Our evaluations reveal that GOF surpasses existing 3DGS-based methods in surface reconstruction and novel view synthesis. Further, it compares favorably to or even outperforms, neural implicit methods in both quality and speed.

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Citations (9)
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Summary

  • The paper introduces a novel method that leverages ray-Gaussian intersections to directly compute opacity values for adaptive surface reconstruction.
  • It employs regularization strategies and marching tetrahedra to refine geometry extraction and generate compact, detailed meshes efficiently.
  • The method outperforms traditional approaches on datasets like Tanks and Temples and Mip-NeRF 360, paving the way for real-time VR and AR applications.

Gaussian Opacity Fields: A Novel Approach for Compact Surface Reconstruction from 3D Gaussians

Introduction

Advancements in 3D reconstruction from multi-view images have paved the way for significant progress in areas like robotics and virtual reality. Historically, methods like Neural Radiance Field (NeRF) and its extensions demonstrated notable novel view synthesis (NVS) results. However, these methods often struggle with the high computational costs and limited applicability to reconstructing unbounded scenes. Notably, surface reconstruction from 3D Gaussian splatting (3DGS) models has shown potential, but challenges remain in capturing detailed geometry and efficiently handling background areas.

Introducing Gaussian Opacity Fields (GOF), this paper sets forth an innovative method optimized for high-quality, efficient, and compact surface reconstruction, especially in unbounded scenes. By directly leveraging the geometric information encoded within 3D Gaussians, GOF methodically circumvents the limitations observed in prior approaches, mainly the disconnection between NVS performance and the explicitness in surface reconstruction applications.

Gaussian Opacity Fields (GOF)

At the core of our approach is the transition from projection-based rendering to a novel formulation that utilizes explicit ray-Gaussian intersections. This shift allows for the evaluation of opacity values along a ray, culminating in the definition of Gaussian Opacity Fields. The principal highlights of GOF include:

  • Direct Geometry Extraction: By computing the opacity values directly from the 3D Gaussians, we can extract the underlying geometry by identifying specific level sets without traditional methods like Poisson reconstruction or TSDF fusion.
  • Enhanced Geometry through Regularization: Approximating the surface normals as the normals at the ray-Gaussian intersection plane, coupled with regularization strategies, significantly refines the geometry extraction process.
  • Efficient Geometry Extraction Method: Utilizing marching tetrahedra, GOF employs a geometry extraction method where the tetrahedral grids adapt based on the scene's complexity, enabling the derivation of compact and detailed meshes.

These innovations not only enhance the fidelity of surface reconstruction but also ensure that the process remains efficient and adaptable to the geometric complexity of different scenes.

Evaluation and Implications

Extensive evaluations across multiple challenging datasets, such as Tanks and Temples and Mip-NeRF 360, demonstrate the superiority of GOF over existing methodologies. Remarkably, GOF not only matches the performance of state-of-the-art neural implicit methods in terms of quality but does so with significantly greater speed. These findings present an intriguing avenue for minimizing the computational overhead traditionally associated with high-quality surface reconstruction.

Furthermore, the capability of GOF to efficiently process unbounded scenes while generating detailed and compact meshes opens new pathways for its application in real-time rendering and beyond. With GOF, the possibilities extend to virtual and augmented reality applications, where dynamic, detailed, and computationally efficient surface reconstructions are essential.

Future Directions

The promising results achieved by GOF present several interesting directions for future research. Key among these is the optimization of the tetrahedral grid generation process for even greater efficiency. Additionally, exploring the integration of more advanced view-dependent appearance models could further enhance the fidelity of the reconstructed scenes.

Conclusion

GOF symbolizes a significant step forward in the quest for efficient, high-quality surface reconstruction. By addressing the explicit nature of 3D Gaussians and deploying a novel opacity field-based approach, GOF sets a new benchmark for compact and detailed mesh generation. It not only holds the potential to revolutionize surface reconstruction methodologies but also broadens the horizons for practical applications in technology and entertainment.