ULSR-GS: Ultra Large-scale Surface Reconstruction Gaussian Splatting with Multi-View Geometric Consistency (2412.01402v3)

Abstract: While Gaussian Splatting (GS) demonstrates efficient and high-quality scene rendering and small area surface extraction ability, it falls short in handling large-scale aerial image surface extraction tasks. To overcome this, we present ULSR-GS, a framework dedicated to high-fidelity surface extraction in ultra-large-scale scenes, addressing the limitations of existing GS-based mesh extraction methods. Specifically, we propose a point-to-photo partitioning approach combined with a multi-view optimal view matching principle to select the best training images for each sub-region. Additionally, during training, ULSR-GS employs a densification strategy based on multi-view geometric consistency to enhance surface extraction details. Experimental results demonstrate that ULSR-GS outperforms other state-of-the-art GS-based works on large-scale aerial photogrammetry benchmark datasets, significantly improving surface extraction accuracy in complex urban environments. Project page: https://ulsrgs.github.io.

• The paper presents ULSR-GS, a novel Gaussian Splatting method for large-scale surface reconstruction using point-to-photo partitioning and multi-view consistency.
• It incorporates multi-view consistency constraints during densification to achieve richer geometric precision in reconstructed 3D models.
• ULSR-GS outperforms state-of-the-art methods in large-scale scenarios, achieving improved F1 scores and scalability with multi-GPU support.

An Expert Overview of "ULSR-GS: Ultra Large-scale Surface Reconstruction Gaussian Splatting with Multi-View Geometric Consistency"

The paper introduces "ULSR-GS," a novel framework designed to address the challenges of large-scale surface reconstruction using Gaussian Splatting (GS). This method improves upon existing GS techniques by focusing on accurate mesh extraction in expansive, complex urban environments. With a clear intent to enhance the fidelity and usability of large-scale scene models, the authors have pushed the boundaries of scalable Gaussian Splatting beyond the capabilities of existing methods.

Core Contributions

The paper makes the following key contributions:

1. Innovative Partitioning Strategy: The authors present a point-to-photo scene partition method, replacing traditional camera-position-based partitioning. This approach utilizes multi-view optimal matching to select the most informative images for training in each sub-region. By focusing image selection on point-cloud data rather than image locations alone, ULSR-GS ensures higher quality reconstruction by removing computational redundancies and irrelevant data.
2. Multi-View Geometric Consistency: ULSR-GS introduces a densification strategy that incorporates multi-view consistency constraints. During training, it applies geometric consistency across views to refine the surface details, ensuring that the resultant 3D models have richer geometric precision.
3. Performance and Scalability Improvements: Experimental results demonstrate that ULSR-GS markedly outperforms state-of-the-art GS-based methods in large-scale aerial photogrammetry scenarios. This is achieved by maintaining high reconstruction accuracy while simultaneously ensuring scalability through a multi-GPU setup.

Numerical and Technical Insights

ULSR-GS presents compelling numerical results. For instance, when tested on the GauU-Scene dataset, the method achieved substantial improvements in F1 scores, precision, and recall for mesh extraction compared to existing methods. By leveraging a multi-GPU setup, ULSR-GS reduced training time without compromising on the fidelity of the reconstruction, outperforming single-GPU benchmarks like 2DGS, GOF, and PGSR in both efficiency and accuracy.

Theoretical and Practical Implications

Theoretically, ULSR-GS advances the domain of 3D surface reconstruction by integrating geometric consistency into the densification process—a technique which proves crucial for maintaining model integrity across large, complex urban landscapes. Practically, the method's scalability and its optimal image selection make it well-suited for applications requiring high-precision 3D modeling from vast datasets, such as urban planning, smart city simulations, and immersive virtual environments.

Future Directions

While ULSR-GS represents a significant stride forward in large-scale surface reconstruction, there remain opportunities to enhance the method. The authors identify room for improvement in regions prone to reflective errors, such as water surfaces or glass. Future research could focus on refining depth projection in such challenging areas, potentially enhancing the robustness of ULSR-GS in even more diverse environments.

Conclusion

Overall, "ULSR-GS" offers a substantial contribution to both the theory and application of Gaussian Splatting in large-scale surface extraction tasks. The framework's novel partitioning and multi-view strategies enable it to produce superior results compared to its predecessors, marking a potential shift in how large-scale urban reconstructions might be approached in future research and engineering projects.