Fast and Compact 3D Gaussian Splatting with Polarized Opacity Prior
3D Gaussian Splatting (3DGS) achieves state-of-the-art rendering quality at real-time speeds but suffers from "model bloat" - a large number of redundant, low-opacity Gaussians that inflate memory usage and training costs. This inefficiency stems from the standard "densify-then-prune" paradigm, which expands the model aggressively before relying on pruning to achieve compactness. To mitigate this problem, we present an efficient training framework that builds an intrinsically compact representation, replacing the conventional densify-then-prune cycle. Our method leverages a synergistic design: an L2 reconstruction loss to provide error-proportional gradients that stabilize optimization, a...
| Authors | Zi-Ming Wang, Kai-Wen Duan, Kowei Huang et al. |
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| Primary category | cs.CV |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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