AGS-Mesh: Adaptive Gaussian Splatting and Meshing with Geometric Priors for Indoor Room Reconstruction Using Smartphones (2411.19271v2)

Abstract: Geometric priors are often used to enhance 3D reconstruction. With many smartphones featuring low-resolution depth sensors and the prevalence of off-the-shelf monocular geometry estimators, incorporating geometric priors as regularization signals has become common in 3D vision tasks. However, the accuracy of depth estimates from mobile devices is typically poor for highly detailed geometry, and monocular estimators often suffer from poor multi-view consistency and precision. In this work, we propose an approach for joint surface depth and normal refinement of Gaussian Splatting methods for accurate 3D reconstruction of indoor scenes. We develop supervision strategies that adaptively filters low-quality depth and normal estimates by comparing the consistency of the priors during optimization. We mitigate regularization in regions where prior estimates have high uncertainty or ambiguities. Our filtering strategy and optimization design demonstrate significant improvements in both mesh estimation and novel-view synthesis for both 3D and 2D Gaussian Splatting-based methods on challenging indoor room datasets. Furthermore, we explore the use of alternative meshing strategies for finer geometry extraction. We develop a scale-aware meshing strategy inspired by TSDF and octree-based isosurface extraction, which recovers finer details from Gaussian models compared to other commonly used open-source meshing tools. Our code is released in https://xuqianren.github.io/ags_mesh_website/.

• The paper introduces an adaptive Gaussian splatting method with geometric priors to enhance 3D reconstruction accuracy from smartphone data.
• It refines depth and normal estimates through Depth Normal Consistency and Adaptive Normal Regularization to improve mesh quality.
• Experimental results show significant gains in mesh accuracy, completeness, and novel-view synthesis compared to baseline techniques.

AGS-Mesh: Adaptive Gaussian Splatting and Meshing for Indoor Room Reconstruction

The paper "AGS-Mesh: Adaptive Gaussian Splatting and Meshing with Geometric Priors for Indoor Room Reconstruction Using Smartphones" presents a novel system for enhancing 3D reconstruction accuracy using smartphone-collected data. The research primarily aims to address the limitations of traditional mesh reconstruction methods by incorporating Adaptive Gaussian Splatting (AGS) techniques with geometric priors. Through this approach, the researchers aim to improve both mesh estimation and novel-view synthesis quality for challenging indoor environments, leveraging mobile devices equipped with depth sensors and monocular geometry estimators.

Methodology Overview

The proposed methodology builds upon recent advancements in the field of 3D Gaussian Splatting (3DGS). It introduces an adaptive filtering and optimization framework to incorporate low-resolution depth and normal estimates accurately. These enhancements improve the geometric accuracy of existing Gaussian Splatting methods, particularly in complex and detail-rich indoor scenes.

Key elements of the methodology include:

1. Depth Normal Consistency (DNC): This measure adaptively filters unreliable depth estimates by examining their consistency with monocular normal predictions, hence refining erroneous depth readings that often plague consumer-grade mobile devices.
2. Adaptive Normal Regularization (ANR): This strategy focuses on refining normal estimates by mitigating regularization in regions where predictors are less reliable, hence preserving multi-view consistency and geometric precision.
3. IsoOctree-Based Meshing: Inspired by Truncated Signed Distance Fields (TSDF) and octree-based isosurface extraction, the proposed scale-aware meshing strategy aims to recover scene details with higher fidelity and smoother surface representation.

Experimental Results

The authors substantiate their claims through rigorous experimentation on datasets such as MuSHRoom and ScanNet++, showcasing significant performance improvements in mesh metrics such as accuracy, completeness, and F-score. Notably, the proposed methods demonstrate substantial gains over baseline techniques, indicating that the adaptive regularization strategies have successfully integrated geometric priors into the 3DGS framework.

Quantitatively, the results highlight enhanced mesh accuracy and completeness, with improvements reported in the Chamfer-$L_1$ distance and normal consistency. Additionally, the method achieves better performance in novel-view synthesis tasks, reflected in metrics like PSNR and SSIM, thereby underscoring the efficacy of the proposed filtering and surface recovery strategies.

Implications and Future Directions

The implications of this research are twofold. Practically, it offers a viable pathway to utilize consumer-grade technology for detailed and accurate 3D reconstruction applications. This can democratize access to high-quality 3D modeling, extending its applications in virtual reality, augmented reality, and video game development. Theoretically, the work opens avenues for deeper exploration into adaptive geometric regularization and its role in enhancing real-world scene reconstruction. Future developments could further refine these techniques, particularly in handling dynamic scenes or integrating additional sensory modalities.

In conclusion, "AGS-Mesh" represents a meaningful contribution to the field of computer vision and 3D reconstruction, setting a precedent for future research endeavors aiming to leverage widely accessible technology for complex geometric modeling tasks.