VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction (2406.05774v2)

Abstract: Although 3D Gaussian Splatting has been widely studied because of its realistic and efficient novel-view synthesis, it is still challenging to extract a high-quality surface from the point-based representation. Previous works improve the surface by incorporating geometric priors from the off-the-shelf normal estimator. However, there are two main limitations: 1) Supervising normals rendered from 3D Gaussians effectively updates the rotation parameter but is less effective for other geometric parameters; 2) The inconsistency of predicted normal maps across multiple views may lead to severe reconstruction artifacts. In this paper, we propose a Depth-Normal regularizer that directly couples normal with other geometric parameters, leading to full updates of the geometric parameters from normal regularization. We further propose a confidence term to mitigate inconsistencies of normal predictions across multiple views. Moreover, we also introduce a densification and splitting strategy to regularize the size and distribution of 3D Gaussians for more accurate surface modeling. Compared with Gaussian-based baselines, experiments show that our approach obtains better reconstruction quality and maintains competitive appearance quality at faster training speed and 100+ FPS rendering.

• The paper presents a novel D-Normal regularizer that couples normal vectors with geometric parameters via depth gradients for enhanced 3D reconstruction.
• It introduces a confidence-term weighting mechanism that promotes high-confidence predictions and mitigates inconsistencies across multi-view normal estimations.
• Experimental results demonstrate competitive real-time rendering (100+ FPS) and superior accuracy on benchmarks such as Tanks and Temples and Replica.

A Formal Overview of VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction

The paper "VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction" presents a novel approach to enhance surface reconstruction using 3D Gaussian splatting. This technique stands out owing to its efficiency and remarkable reconstruction capabilities, particularly in large-scale scenes, without compromising rendering quality.

Key Contributions

The contributions of this paper are fourfold:

1. D-Normal Regularizer: The authors propose a D-Normal regularizer that couples normal vectors with other geometric parameters, enabling comprehensive updates to these parameters. This is in contrast to previous methods where normal supervision only updated rotation parameters.
2. Confidence-Term Weighting: To address the inherent inconsistencies in normal predictions across multiple views, a confidence term is devised. This term differentially weights the depth-normal regularizer, reinforcing the impact of high-confidence predictions and downplaying low-confidence ones.
3. Densification and Splitting Strategy: New methods for densifying and splitting 3D Gaussians are introduced. They optimize the size and distribution of Gaussians to improve surface precision, addressing the depth errors and surface bumps that affect quality.
4. Performance and Application: The proposed method achieves competitive results in terms of rendering performance (maintaining 100+ FPS) and reconstruction accuracy, outperforming several Gaussian-based baselines on benchmarks like Tank and Temples, Replica, MipNeRF360, and DTU datasets.

Methodological Insights

The crux of the proposed method lies in its innovative regularization strategies, particularly the D-Normal regularizer. Traditional methods reliant on normal estimators are limited to updating only rotation parameters. The D-Normal regularizer, however, derives normals from the gradient of rendered depth maps. This integration not only enables the update of geometric parameters but also substantially mitigates inaccuracies in depth computation.

Geometric Properties and Computation

The proposed method calculates the depth as the intersection of rays with compressed Gaussian splats rendered in a view-consistent manner:

• Intersection Depth: By introducing a loss that flattens Gaussian splats into planar representations, the intersection depth calculation becomes more accurate, benefiting ultimately from better surface normal propagation during regularization.
• D-Normal Computation: The normal map derived from depth gradients allows effective feedback, thereby ensuring normals and their related geometric attributes are optimized concurrently.

Experimental Validation

In their experiments, the authors compare VCR-GauS against both implicit methods like NeuS and explicit methods including SuGaR and 2DGS. Notably, the F1-score improvements on the Tanks and Temples dataset underline superior surface reconstruction capabilities. The method also excels in novel view rendering quality and speed, equivalent to sophisticated methods such as Mip-NeRF360.

Implications and Future Work

From a theoretical standpoint, the integration of dense geometric priors into real-time rendering pipelines via Gaussian splatting is a significant advancement. Practically, this research could bolster applications in virtual reality, robotics, and beyond, where rapid yet accurate 3D surface reconstructions are paramount.

Looking ahead, future work could extend to addressing edge cases where normal predictions fail severely. Additionally, exploring this approach for reconstructing semi-transparent or reflective surfaces could further broaden its applicability and robustness.

Conclusion

The VCR-GauS framework offers a comprehensive solution to Gaussian surface reconstruction, characterized by exceptional accuracy, efficiency, and novel integration of depth-normal regularizers with confidence weighting. This paper undoubtedly marks a significant step towards more reliable and scalable 3D surface reconstruction methods.

By effectively attending to the dual challenges of geometric parameter coupling and view consistency, VCR-GauS sets a new standard in 3D surface modeling while maintaining high operational efficiency and rendering quality.