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arXiv Computer Vision By Sheng-Hsiang Hung, Ting-Yu Yen, Wei-Fang Sun, Simon See, Shih-Hsuan Hung, Hung-Kuo Chu

LoBE-GS: Load-Balanced and Efficient 3D Gaussian Splatting for Large-Scale Scene Reconstruction

Read the original on arXiv Computer Vision →

LoBE-GS is a Load‑Balanced and Efficient 3D Gaussian Splatting framework designed for large‑scale scene reconstruction. It introduces a load‑balanced KD‑tree partitioning scheme that balances camera counts per block, uses depth‑based back‑projection to speed up camera assignment, and applies visibility cropping and selective densification to cut training costs. Experiments on urban and outdoor datasets show up to an order‑of‑magnitude faster scene‑partitioning and a 3.2× reduction in worst‑block fine‑training time compared to CityGaussian, while keeping reconstruction quality competitive.

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