GigaGS: Scaling up Planar-Based 3D Gaussians for Large Scene Surface Reconstruction (2409.06685v1)

Abstract: 3D Gaussian Splatting (3DGS) has shown promising performance in novel view synthesis. Previous methods adapt it to obtaining surfaces of either individual 3D objects or within limited scenes. In this paper, we make the first attempt to tackle the challenging task of large-scale scene surface reconstruction. This task is particularly difficult due to the high GPU memory consumption, different levels of details for geometric representation, and noticeable inconsistencies in appearance. To this end, we propose GigaGS, the first work for high-quality surface reconstruction for large-scale scenes using 3DGS. GigaGS first applies a partitioning strategy based on the mutual visibility of spatial regions, which effectively grouping cameras for parallel processing. To enhance the quality of the surface, we also propose novel multi-view photometric and geometric consistency constraints based on Level-of-Detail representation. In doing so, our method can reconstruct detailed surface structures. Comprehensive experiments are conducted on various datasets. The consistent improvement demonstrates the superiority of GigaGS.

• The paper introduces a novel partitioning strategy that groups cameras into overlapping blocks, enabling independent optimization and robust merging for large-scale reconstructions.
• It integrates multi-view photometric and geometric consistency constraints within a Level-of-Detail framework to preserve intricate details across varying scales.
• It employs a hierarchical plane representation to maintain surface quality and extract high-fidelity meshes, outperforming baseline methods on standard metrics.

GigaGS: Scaling up Planar-Based 3D Gaussians for Large Scene Surface Reconstruction

Introduction

The paper "GigaGS: Scaling up Planar-Based 3D Gaussians for Large Scene Surface Reconstruction" introduces GigaGS, an innovative method to tackle the challenging task of 3D surface reconstruction in large-scale scenes using 3D Gaussian Splatting (3DGS). Existing methods have demonstrated the efficacy of 3DGS in novel view synthesis and object-level surface reconstruction; however, their application to large-scale environments remains limited by high computational demands and inconsistencies in geometric and photometric representations. GigaGS advances the state-of-the-art by implementing a novel partitioning strategy and integrating various consistency constraints to improve reconstruction quality.

Key Contributions

1. Partitioning Strategy: A scalable partitioning strategy based on mutual visibility of spatial regions is introduced. This approach enables parallel processing by grouping cameras into overlapping blocks that are optimized independently before merging them seamlessly.
2. Multi-View Photometric and Geometric Consistency: The paper incorporates multi-view photometric and geometric consistency constraints within a Level-of-Detail (LoD) framework. This enhances the preservation of intricate geometric details across various scales, achieving robust reconstructions while mitigating artifacts.
3. Hierarchical Plane Representation: The authors employ a hierarchical structure to handle local Gaussian kernel sets and organize levels based on spatial distances. This structure is significant in maintaining output quality across varying granularity levels.

These innovations are collectively evaluated through exhaustive experiments that demonstrate superior performance over existing methods in terms of rendering quality and geometric fidelity.

Evaluation and Results

The proposed methodology is rigorously tested on several datasets comprising real-life aerial images of large-scale scenes, including Mill-19 and Urbanscene3d datasets. The results are evaluated on standard metrics such as SSIM, PSNR, and LPIPS, revealing substantial improvements over baseline approaches:

• Render Quality: When compared with novel view synthesis methods like MegaNeRF and VastGaussian, as well as surface reconstruction methods like NeRF, Neuralangelo, SuGaR, and PGSR, GigaGS shows competitive performance in rendering tasks. The metrics indicate that GigaGS achieves notably higher SSIM, PSNR values, and lower LPIPS scores across all tested scenes.
• Mesh Extraction: GigaGS demonstrates its capability to extract high-quality surface meshes in practical applications such as navigation and virtual reality, exhibiting clear advantages over existing methods in preserving geometric details and minimizing artifacts.

Implications and Future Directions

The advancements introduced in GigaGS have significant implications for the field of 3D surface reconstruction, especially in scenarios requiring large-scale scene processing. The ability to partition scenes for efficient parallel processing enables the handling of vast environments that were previously limited by computational constraints. Furthermore, the integration of consistency constraints across different granularities ensures robust and precise reconstructions, paving the way for applications in urban planning, autonomous navigation, and immersive virtual environments.

In terms of future developments, enhancements in 3D Gaussian performance, particularly in textureless regions, could further increase the robustness and applicability of the method. Moreover, integrating real-time feedback mechanisms and exploring scalability to even larger scenes could drive additional improvements.

Conclusion

The paper presents GigaGS, a methodology that significantly elevates the performance of 3D surface reconstruction in extensive scenes by addressing critical challenges related to computational efficiency and multi-view consistency. By fostering advancements in partitioning strategies and leveraging LoD frameworks, GigaGS provides a foundation for future research and practical applications in large-scale 3D scene reconstruction.