G$^2$ARD-GS: Geometry-Guided Anchor-Regularized Gaussian Splatting Distillation
Dense colored LiDAR maps provide accurate city-scale geometry, but lifting them into 3D Gaussian Splatting (3DGS) retains millions of primitives, making the resulting models costly to store, transmit, render, and adapt. Aggressive primitive reduction alleviates this burden, but can remove the local surface support needed for stable novel-view synthesis and downstream geometric use. We introduce G$^2$ARD-GS, a geometry-guided distillation method that converts a dense Gaussian prior instantiated either as a training-free point-cloud lift or a trained GS model into a compact, reusable representation. G$^2$ARD-GS progressively consolidates the prior into surface-aware representatives, then re...
| Authors | Puyuan Zhang, Jianming Huang, Wenkai Ye et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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