CityGS-X实现超大规模场景高效重建，5000+图像仅用4张4090显卡5小时完成。
CityGS-X: A Scalable Architecture for Efficient and Geometrically Accurate Large-Scale Scene Reconstruction
- 采用分层混合3D表示与批量多任务渲染，替代传统繁琐流程。
- 在4×4090下5小时完成5000+图像训练，其他方法因显存不足失败。
- 适合需要高精度、大规模场景重建的研究者和工业应用。
尽管3D高斯溅射在大规模场景重建中取得显著进展，但仍面临处理速度慢、计算成本高和几何精度有限等挑战，根源在于其固有的非结构化设计及缺乏高效并行化。为同时克服这些问题，我们提出CityGS-X，一种基于新型并行化分层混合3D表示（PH^2-3D）的可扩展架构。作为首次尝试，CityGS-X摒弃复杂的合并-分割过程，改用新设计的批处理级多任务渲染机制。该架构通过动态层级细节体素分配，实现高效的多GPU渲染，显著提升可扩展性与性能。大量实验表明，CityGS-X在训练速度、渲染容量和几何细节准确性方面均优于现有方法。特别地，仅用4张4090显卡，5小时内即可完成包含5000+图像的场景训练，而其他方法则因显存溢出（OOM）完全失败，证明其远超现有方法的能力边界。
原文摘要 · Abstract (English)
Despite its significant achievements in large-scale scene reconstruction, 3D Gaussian Splatting still faces substantial challenges, including slow processing, high computational costs, and limited geometric accuracy. These core issues arise from its inherently unstructured design and the absence of efficient parallelization. To overcome these challenges simultaneously, we introduce CityGS-X, a scalable architecture built on a novel parallelized hybrid hierarchical 3D representation (PH^2-3D). As an early attempt, CityGS-X abandons the cumbersome merge-and-partition process and instead adopts a newly-designed batch-level multi-task rendering process. This architecture enables efficient multi-GPU rendering through dynamic Level-of-Detail voxel allocations, significantly improving scalability and performance. Through extensive experiments, CityGS-X consistently outperforms existing methods in terms of faster training times, larger rendering capacities, and more accurate geometric details in large-scale scenes. Notably, CityGS-X can train and render a scene with 5,000+ images in just 5 hours using only 4 * 4090 GPUs, a task that would make other alternative methods encounter Out-Of-Memory (OOM) issues and fail completely. This implies that CityGS-X is far beyond the capacity of other existing methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。