Generative 3D Gaussians with Learned Density Control
We present Density-Sampled Gaussians (DeG), a novel 3D representation designed to bridge the gap between adaptive rendering primitives and scalable generative modeling. Unlike existing approaches that constrain 3D Gaussians to fixed voxel grids or arrays, DeG models Gaussian centers as samples from a learnable probability density function defined over an octree. This formulation provides a rigorous mathematical framework for adaptive density control: by jointly optimizing the spatial density and Gaussian attributes under rendering supervision, our model naturally concentrates primitives in regions of high geometric complexity. We achieve this via a new render loss contribution gradient th...
| Authors | Runjie Yan, Yan-Pei Cao, Peng Wang et al. |
|---|---|
| Primary category | cs.GR |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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