High-quality Surface Reconstruction using Gaussian Surfels (2404.17774v2)

Abstract: We propose a novel point-based representation, Gaussian surfels, to combine the advantages of the flexible optimization procedure in 3D Gaussian points and the surface alignment property of surfels. This is achieved by directly setting the z-scale of 3D Gaussian points to 0, effectively flattening the original 3D ellipsoid into a 2D ellipse. Such a design provides clear guidance to the optimizer. By treating the local z-axis as the normal direction, it greatly improves optimization stability and surface alignment. While the derivatives to the local z-axis computed from the covariance matrix are zero in this setting, we design a self-supervised normal-depth consistency loss to remedy this issue. Monocular normal priors and foreground masks are incorporated to enhance the quality of the reconstruction, mitigating issues related to highlights and background. We propose a volumetric cutting method to aggregate the information of Gaussian surfels so as to remove erroneous points in depth maps generated by alpha blending. Finally, we apply screened Poisson reconstruction method to the fused depth maps to extract the surface mesh. Experimental results show that our method demonstrates superior performance in surface reconstruction compared to state-of-the-art neural volume rendering and point-based rendering methods.

• The paper introduces Gaussian Surfels which flatten 3D Gaussian points into 2D ellipses to resolve surface alignment and normal ambiguity.
• It proposes a self-supervised normal-depth consistency loss and a volumetric cutting method that improve reconstruction quality and training speed.
• The approach is applicable in VR, AR, and real-time graphics, offering practical benefits through computational efficiency and high fidelity.

Enhanced 3D Surface Reconstruction with Gaussian Surfels

Introduction and Motivation

The field of 3D surface reconstruction has seen considerable advancements with the advent of technologies like 3D Gaussian Splatting (3DGS). However, existing approaches such as 3DGS face challenges including the representation of complex geometries with high fidelity, specifically in maintaining close alignment with actual surfaces, and efficiently handling specular reflections and background segmentation.

In addressing these challenges, the paper introduces a novel representation technique called "Gaussian Surfels," which effectively combines the flexibility of 3D Gaussian points (used in 3DGS) with the surface alignment properties of surfels. This hybrid approach aspires to enhance the geometric fidelity and computational efficiency of the reconstruction process.

Key Contributions

The paper brings forward several substantial contributions:

• Gaussian Surfels Representation:
  • This new representation flattens 3D Gaussian points into a 2D ellipse, directly addressing the previous limitations related to normal direction ambiguity and the alignment with the actual surface.
• Self-Supervised Normal-Depth Consistency Loss:
  • To compensate for the derivative issues caused by the aforementioned flattening, a novel loss function is introduced. It ensures consistency between rendered depth maps and normals, significantly enhancing the optimization procedure.
• Volumetric Cutting Method:
  • An innovative approach is proposed to refine the depth maps further. This technique focuses on eliminating the erroneous points by aggregating information from Gaussian surfels, which improves the geometric quality.
• Comparative Analysis:
  • The paper presents a comprehensive experimental evaluation, demonstrating that the Gaussian Surfels outperform existing neural volume rendering and point-based rendering methods in terms of reconstruction quality and training speed.

Theoretical and Practical Implications

Theoretical Implications:

• The introduction of Gaussian surfels represents a significant theoretical advancement in point-based geometric representations. It resolves the inherent issues related to surface alignment and normal direction ambiguity in 3DGS.
• The proposed normal-depth consistency loss introduces self-supervised mechanisms into the training of surface reconstruction models, which could be explored further in future research.

Practical Implications:

• The practical applications of this research are evident in fields such as virtual reality, augmented reality, and professional graphics rendering, where high-quality 3D reconstructions are crucial.
• The efficiency improvements with the Gaussian Surfels can lead to faster rendering times, making high-fidelity 3D modeling more accessible for real-time applications.

Future Research Directions

The paper proposes several future directions that could potentially enhance the robustness and applicability of the Gaussian Surfels:

• Handling Specular Reflections:
  • Future iterations could focus on extending the model to handle specular reflections better by encoding view-dependent appearance features within the Gaussian surfels.
• Integration with Depth Sensors:
  • Integrating depth sensing technologies could provide an additional layer of precision, particularly beneficial for capturing environments with minimal texture variations.

Conclusion

This research marks a considerable step forward in the domain of 3D surface reconstruction. By introducing Gaussian surfels, it not only addresses several limitations of the prior art but also opens up new avenues for creating more accurate and computationally efficient models. The proposed method’s ability to maintain high-quality reconstruction with enhanced stability and speed holds substantial promise for both academic research and practical applications in creating digital representations of complex real-world environments.