3D Gaussian Splatting (3DGS) has established itself as an efficient representation for
real-time, high-fidelity 3D scene reconstruction. However, scaling 3DGS to large and
unbounded scenes such as city blocks remains difficult. Existing divide-and-conquer
methods alleviate memory pressure by partitioning the scene into blocks and training on
multiple, non-communicating GPUs, but introduce new bottlenecks: (i) partitions suffer
from severe load imbalance since uniform or heuristic splits do not reflect actual
computational demands, and (ii) coarse-to-fine pipelines fail to exploit the coarse stage
efficiently, often reloading the entire model and incurring high overhead.
In this work, we introduce LoBE-GS, a novel Load-Balanced and
Efficient 3D Gaussian Splatting framework that re-engineers the large-scale 3DGS
pipeline. Specifically, LoBE-GS introduces a load-balanced
KD-tree scene partitioning scheme with optimized cutlines that balance per-block camera
counts. To accelerate preprocessing, it employs depth-based back-projection for fast
camera assignment, reducing processing time from hours to minutes. It further reduces
training cost through two lightweight techniques: visibility cropping and selective
densification.
On large-scale urban and outdoor datasets, LoBE-GS reduces
scene-partitioning time by up to an order of magnitude and the worst-block fine-training
time by up to 3× relative to CityGaussian, yielding up to 9.1× faster
end-to-end training than prior partition-based methods while maintaining competitive
reconstruction quality. LoBE-GS provides a communication-free
alternative with favorable quality–efficiency trade-offs under matched GPU budgets.
