CoSurfGS:Collaborative 3D Surface Gaussian Splatting with Distributed Learning for Large Scene Reconstruction (2412.17612v1)

Abstract: 3D Gaussian Splatting (3DGS) has demonstrated impressive performance in scene reconstruction. However, most existing GS-based surface reconstruction methods focus on 3D objects or limited scenes. Directly applying these methods to large-scale scene reconstruction will pose challenges such as high memory costs, excessive time consumption, and lack of geometric detail, which makes it difficult to implement in practical applications. To address these issues, we propose a multi-agent collaborative fast 3DGS surface reconstruction framework based on distributed learning for large-scale surface reconstruction. Specifically, we develop local model compression (LMC) and model aggregation schemes (MAS) to achieve high-quality surface representation of large scenes while reducing GPU memory consumption. Extensive experiments on Urban3d, MegaNeRF, and BlendedMVS demonstrate that our proposed method can achieve fast and scalable high-fidelity surface reconstruction and photorealistic rendering. Our project page is available at \url{https://gyy456.github.io/CoSurfGS}.

• The paper introduces a novel distributed 3D reconstruction framework that compresses and aggregates local models for high-quality large-scale scene representation.
• It utilizes a three-layer device-edge-cloud learning strategy with adaptive Gaussian pruning to reduce memory demand and accelerate computational processes.
• Quantitative and qualitative evaluations show that CoSurfGS outperforms state-of-the-art methods in accuracy and photorealistic rendering, benefiting urban planning and autonomous systems.

CoSurfGS: Collaborative 3D Surface Gaussian Splatting with Distributed Learning for Large Scene Reconstruction

This paper presents CoSurfGS, a framework designed for the efficient 3D surface reconstruction of large-scale environments. Leveraging 3D Gaussian Splatting (3DGS) alongside a distributed learning strategy, CoSurfGS addresses practical challenges like high memory demand and excessive computational time that occur when traditional methods scale up.

Existing methods in 3DGS surface reconstruction have primarily focused on limited-size scenes or discrete objects. These methods invariably suffer when applied to larger environments due to memory constraints, processing overheads, and a subsequent lack of geometric detail. In response, CoSurfGS proposes a multi-agent system that accounts for these difficulties using a novel "device-edge-cloud" architecture.

Key Components

1. Local Model Compression (LMC) and Model Aggregation Scheme (MAS): CoSurfGS employs these novel processes to compress local models and then aggregate them into a coherent global representation. This strategy ensures high-quality surface representation for large scenes while simultaneously reducing the GPU memory footprint and training time.
2. Three-Layer Distributed Learning Framework:
   • Device Layer: Each device independently processes scene segments to create local Gaussian models.
   • Edge Layer: Local models are transmitted to edge servers, where LMC reduces redundancies before models are aggregated using MAS.
   • Cloud Layer: Aggregated models are further distilled and refined, producing a final, global reconstruction without compromising local privacy.
3. Adaptive Gaussian Pruning: During device-level training, the system adaptively prunes Gaussian points to maintain computational efficiency and memory usage. This ensures only the most relevant data is transmitted upwards through the framework.
4. Distributed Learning Efficiency: By enabling parallel model training across devices, the approach drastically reduces data transmission latency and accelerates the learning process. This demonstrates significant improvements in training speeds and resource efficiency, especially for large-scale scenarios.

Results and Implications

Quantitative and qualitative evaluations conducted on datasets such as Urban3d and MegaNeRF reveal that CoSurfGS outperforms existing state-of-the-art methods in both surface reconstruction accuracy and photorealistic rendering. Importantly, it achieves this with reduced computational demand and training time, illustrating the practicality of scaling 3DGS models to larger scenes.

The results indicate a robust framework that positions CoSurfGS as an effective solution for widespread applications such as urban planning and the development of autonomous vehicles, where large-scale scene reconstruction is critical.

Future Directions

With the current setup demonstrating promising improvements, future research could expand on several fronts:

• Refined Pruning Criteria: Further enhancement of pruning techniques could refine resource management and data reduction without sacrificing detail.
• Dynamic Task Allocation: Exploration into dynamic allocation of computational tasks and intelligent load balancing could address variability in scene complexity and device capabilities.
• Integration with SLAM Systems: Synergizing CoSurfGS with Simultaneous Localization and Mapping (SLAM) systems could broaden its applicability in real-time geographical navigation and virtual environment tasks.

CoSurfGS provides a significant contribution to the field of large-scale 3D reconstruction by deftly integrating distributed learning with geometric modeling, thus paving the path toward more scalable and efficient 3DGS approaches.