@inproceedings{
ye2024d,
title={3D Gaussian Rendering Can Be Sparser: Efficient Rendering via Learned Fragment Pruning},
author={Zhifan Ye and Chenxi Wan and Chaojian Li and Jihoon Hong and Sixu Li and Leshu Li and Yongan Zhang and Yingyan Celine Lin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IVqzbuLfoL}
}
3D Gaussian splatting has recently emerged as a promising technique for novel view synthesis from sparse image sets, yet comes at the cost of requiring millions of 3D Gaussian primitives to reconstruct each 3D scene. This largely limits its application to resource-constrained devices and applications.
Despite advances in Gaussian pruning techniques that aim to remove individual 3D Gaussian primitives, the significant reduction in primitives often fails to translate into commensurate increases in rendering speed, impeding efficiency and practical deployment. We identify that this discrepancy arises due to the overlooked impact of fragment count per Gaussian (i.e., the number of pixels each Gaussian is projected onto). To bridge this gap and meet the growing demands for efficient on-device 3D Gaussian rendering, we propose fragment pruning, an orthogonal enhancement to existing pruning methods that can significantly accelerate rendering by selectively pruning fragments within each Gaussian. Our pruning framework dynamically optimizes the pruning threshold for each Gaussian, markedly improving rendering speed and quality. Extensive experiments in both static and dynamic scenes validate the effectiveness of our approach. For instance, by integrating our fragment pruning technique with state-of-the-art Gaussian pruning methods, we achieve up to a 1.71$\times$ speedup on an edge GPU device, the Jetson Orin NX, and enhance rendering quality by an average of 0.16 PSNR on the Tanks\&Temples dataset. Our code is available at https://github.com/GATECH-EIC/Fragment-Pruning.
3D Gaussian Rendering Can Be Sparser: Efficient Rendering via Learned Fragment Pruning
Zhifan Ye Chenxi Wan Chaojian Li Jihoon Hong Sixu Li Leshu Li Yongan Zhang Yingyan Celine Lin