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arXiv · 2504.00950

Neural Pruning for 3D Scene Reconstruction: Efficient NeRF Acceleration

Tianqi Ding·Dawei Xiang·Pablo Rivas·Liang Dong
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Abstract

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.

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Tianqi Ding, Dawei Xiang, Pablo Rivas, Liang Dong. 2025-04-01. Neural Pruning for 3D Scene Reconstruction: Efficient NeRF Acceleration. https://arxiv.org/abs/2504.00950

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