ACD-GS: Asymmetric Curvature-aware Densification for 3D Gaussian Splatting

Abstract

Geometry-aware densification at its source

3D Gaussian Splatting (3DGS) has emerged as a powerful paradigm for real-time photorealistic novel view synthesis. However, its training-time densification remains largely heuristic, often resulting in redundant Gaussian proliferation and inaccurate geometric reconstruction. Existing methods predominantly rely on first-order screen-space gradients to trigger cloning and splitting, which fail to distinguish true geometric discontinuities from high-frequency textures or noisy residuals. To address this limitation, we propose ACD-GS, an Asymmetric Curvature-aware Densification framework for compact and geometry-aware 3D Gaussian optimization. Our key insight is that second-order depth responses provide a more reliable structural prior for density control, enabling asymmetric regulation of cloning and splitting to better capture geometric discontinuities. Building on this, we introduce a multi-view photometric consistency constraint to suppress transient single-view errors, together with a capacity-aware adaptive regulation strategy that balances densification and pruning during training. Unlike post-hoc compression or quantization-based approaches, ACD-GS directly prevents redundant Gaussian generation during training, yielding inherently compact representations. Extensive experiments demonstrate that our method reduces nearly 60% of Gaussian count and storage while maintaining competitive rendering fidelity, consistently outperforming existing methods across multiple datasets.

Compared with the vanilla 3DGS baseline. See the paper for full per-scene results.

Citation

BibTeX

@inproceedings{zhong2026acdgs,
  title     = {ACD-GS: Asymmetric Curvature-aware Densification
               for 3D Gaussian Splatting},
  author    = {Zhong, Houqiang and Zhu, Tianchi and Hu, Qiang and
               Zhang, Xiaoyun and Song, Li and Zhang, Wenjun},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year      = {2026}
}