BoundedGS: Memory-Efficient High-Resolution Training for 3D Gaussian Splatting

High-resolution 3D Gaussian Splatting training increases the number of pixels and Gaussian–tile intersections, expanding rasterization caches that overlap with projection and gradient state at the peak of a training view. A parameter update needs the gradients from the complete view, but the forward and backward caches of a local rasterization region can be released as soon as its contribution has been collected. Based on this observation, we introduce BoundedGS, which separates rasterization and projection backward passes and bounds the active local rasterization working set according to its measured load. Candidate-indexed projection and compact gradient storage address the view-level peaks exposed by this change, while CPU-assisted structure rebuilding and bounded image supply manage resources outside rendering. In a fixed-model resolution sweep with about 6M Gaussians, the measured rendering working set stays within 1.073–1.077 GiB from 1K to 16K; at the shared 11K setting, a warm pass takes 19.79 rather than 21.76 seconds. Across five native 5K outdoor scenes, full training reduces the mean rendering working set from 9.97 to 1.76 GiB (82.3%), retains similar reconstruction quality, and shortens training time from 36.7 to 33.9 minutes.