GaussianRoom: Improving 3D Gaussian Splatting with SDF Guidance and Monocular Cues for Indoor Scene Reconstruction (2405.19671v1)

Abstract: Recently, 3D Gaussian Splatting(3DGS) has revolutionized neural rendering with its high-quality rendering and real-time speed. However, when it comes to indoor scenes with a significant number of textureless areas, 3DGS yields incomplete and noisy reconstruction results due to the poor initialization of the point cloud and under-constrained optimization. Inspired by the continuity of signed distance field (SDF), which naturally has advantages in modeling surfaces, we present a unified optimizing framework integrating neural SDF with 3DGS. This framework incorporates a learnable neural SDF field to guide the densification and pruning of Gaussians, enabling Gaussians to accurately model scenes even with poor initialized point clouds. At the same time, the geometry represented by Gaussians improves the efficiency of the SDF field by piloting its point sampling. Additionally, we regularize the optimization with normal and edge priors to eliminate geometry ambiguity in textureless areas and improve the details. Extensive experiments in ScanNet and ScanNet++ show that our method achieves state-of-the-art performance in both surface reconstruction and novel view synthesis.

• The paper introduces GaussianRoom, which integrates neural SDF with 3D Gaussian Splatting to significantly enhance indoor scene reconstruction and rendering accuracy.
• It employs SDF-guided Gaussian densification and pruning along with Gaussian-guided point sampling to boost optimization efficiency.
• Experiments on ScanNet datasets demonstrate notable gains in reconstruction and rendering metrics, outperforming existing methods.

GaussianRoom: Integrating Neural Signed Distance Fields with 3D Gaussian Splatting for Enhanced Indoor Scene Reconstruction and Rendering

This paper introduces GaussianRoom, a novel framework that integrates Neural Signed Distance Fields (SDF) with 3D Gaussian Splatting (3DGS) to address challenges in indoor scene reconstruction and rendering. By leveraging the complementary strengths of both techniques, GaussianRoom achieves state-of-the-art performance in both surface reconstruction and novel view synthesis.

Introduction and Motivation

Indoor scene reconstruction from multi-view RGB images presents notable challenges, particularly due to the extensive presence of textureless areas. Traditional Multi-View Stereo (MVS) methods and recent neural-radiance-field-based approaches often struggle with geometric ambiguities and lengthy optimization times, respectively. While 3D Gaussian Splatting shows promise with its accelerated optimization and rendering capabilities, it suffers from noisy and incomplete reconstructions in textureless indoor scenes.

Recognizing the advantages of neural SDF in modeling surfaces and the efficiency of 3DGS, the authors propose GaussianRoom. This framework integrates a neural SDF field to guide the densification and pruning of Gaussians, improving the initialization of point clouds and the optimization process.

Methodology

GaussianRoom employs a unified strategy to allow mutual enhancement between 3DGS and neural SDF:

1. SDF-guided Primitive Distribution:
   • Global Densification: Gaussian primitives are deployed in regions with low SDF values, indicating proximity to the true scene surface.
   • Densification and Pruning: Gaussian primitives are adjusted based on their SDF values and opacity, ensuring proper spatial alignment and density.
2. Gaussian-guided Point Sampling:
   • Depth maps rendered from 3D Gaussians guide the sampling range for neural SDF, improving efficiency by focusing sample points around the surface regions.
3. Monocular Cues:
   • Edge Prior: Enhances focus on detailed areas through edge-detection methods, adjusting training weights accordingly.
   • Normal Prior: Provides geometric constraints in textureless areas using normal information to regularize geometry.

Key Findings and Results

Extensive experiments on ScanNet and ScanNet++ datasets validate the effectiveness of GaussianRoom:

• Reconstruction Quality: GaussianRoom achieves superior numerical results in accuracy, completion, precision, recall, and F-score metrics compared to both Gaussian-based and NeRF-based methods.
• Rendering Quality: Significant improvements are observed in SSIM, PSNR, and LPIPS metrics, highlighting the framework's ability to produce high-quality visual outputs.

The qualitative results further corroborate these findings, showing that GaussianRoom maintains scene integrity and detail, outperforming other methods in both texture-rich and textureless regions.

Implications and Future Directions

The integration of neural SDF with 3DGS presented in GaussianRoom opens new avenues for efficient and high-quality indoor scene reconstruction and rendering. The mutual learning strategy between 3DGS and neural SDF introduces a feedback loop that benefits both components, leveraging the geometric information in neural SDF to guide Gaussian primitives and, conversely, utilizing Gaussian depth maps to enhance SDF sampling efficiency.

Future research could explore extending GaussianRoom to more complex scenes, including those with dynamic elements or varying lighting conditions. Additionally, improving the monocular cues and expanding their application beyond indoor scenes may further enhance the robustness and versatility of the framework.

In conclusion, GaussianRoom presents a sophisticated approach to addressing the challenges of indoor scene reconstruction by successfully combining the strengths of neural SDF and 3D Gaussian Splatting, setting a new benchmark in the field.