Free-Range Gaussians: Non-Grid-Aligned Generative 3D Gaussian Reconstruction

We present Free-Range Gaussians, a multi-view reconstruc-tion method that predicts non-pixel, non-voxel-aligned 3D Gaussiansfrom as few as four images. This is done through flow matching overGaussian parameters. Our generative formulation of reconstruction allowsthe model to be supervised with non-grid-aligned 3D data, and enables itto synthesize plausible content in unobserved regions. Thus, it improveson prior methods that produce highly redundant grid-aligned Gaussians,and suffer from holes or blurry conditional means in unobserved regions.To handle the number of Gaussians needed for high-quality results, weintroduce a hierarchical patching scheme to group spatially related Gaus-sians into joint transformer tokens, halving the sequence length whilepreserving structure. We further propose a timestep-weighted renderingloss during training, and photometric gradient guidance and classifier-freeguidance at inference to improve fidelity. Experiments on Objaverse andGoogle Scanned Objects show consistent improvements over pixel andvoxel-aligned methods while using significantly fewer Gaussians, withlarge gains when input views leave parts of the object unobserved.