Neural Radiance Fields (NeRF) have become a popular 3D reconstruction approach in recent years. While they produce high-quality results, they also demand lengthy training times, often spanning days. This paper studies neural pruning as a strategy to address these concerns. We compare pruning approaches, including uniform sampling, importance-based methods, and coreset-based techniques, to reduce the model size and speed up training. Our findings show that coreset-driven pruning can achieve a 50% reduction in model size and a 35% speedup in training, with only a slight decrease in accuracy. These results suggest that pruning can be an effective method for improving the efficiency of NeRF models in resource-limited settings.
@article{arxiv.2504.00950,
title = {Neural Pruning for 3D Scene Reconstruction: Efficient NeRF Acceleration},
author = {Tianqi Ding and Dawei Xiang and Pablo Rivas and Liang Dong},
journal= {arXiv preprint arXiv:2504.00950},
year = {2025}
}
Comments
12 pages, 4 figures, accepted by International Conference on the AI Revolution: Research, Ethics, and Society (AIR-RES 2025)