Neural Pruning for 3D Scene Reconstruction: Efficient NeRF Acceleration

• URL: http://arxiv.org/abs/2504.00950v2
• Date: Mon, 07 Apr 2025 21:37:19 GMT
• Title: Neural Pruning for 3D Scene Reconstruction: Efficient NeRF Acceleration
• Authors: Tianqi Ding, Dawei Xiang, Pablo Rivas, Liang Dong,
• Abstract summary: This paper studies neural pruning as a strategy to address these concerns.<n>We compare pruning approaches, including uniform sampling, importance-based methods, and coreset-based techniques, to reduce the model size and speed up training.<n>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.
• Score: 0.2682592966402944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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