# Sharpening Your Density Fields: Spiking Neuron Aided Fast Geometry Learning

arXiv: https://arxiv.org/abs/2412.09881 · HTML: https://mlsanity.com/paper/2412.09881/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 13 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [NeRF++: Analyzing and Improving Neural Radiance Fields](https://mlsanity.com/paper/2010.07492/) (2020)
- [SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World](https://mlsanity.com/paper/2309.10987/) (2023)
- [BioNeRF: Biologically Plausible Neural Radiance Fields for View Synthesis](https://mlsanity.com/paper/2402.07310/) (2024)
- [Spiking GS: Towards High-Accuracy and Low-Cost Surface Reconstruction via Spiking Neuron-based Gaussian Splatting](https://mlsanity.com/paper/2410.07266/) (2024)

## Built on by (most cited first, 2 of 2)

- [Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields](https://mlsanity.com/paper/2507.23033/) (2025)
- [Detecting AI-Generated Videos with Spiking Neural Networks](https://mlsanity.com/paper/2605.05895/) (2026)

## Abstract (arXiv)

Neural Radiance Fields (NeRF) have achieved remarkable progress in neural rendering. Extracting geometry from NeRF typically relies on the Marching Cubes algorithm, which uses a hand-crafted threshold to define the level set. However, this threshold-based approach requires laborious and scenario-specific tuning, limiting its practicality for real-world applications. In this work, we seek to enhance the efficiency of this method during the training time. To this end, we introduce a spiking neuron mechanism that dynamically adjusts the threshold, eliminating the need for manual selection. Despite its promise, directly training with the spiking neuron often results in model collapse and noisy outputs. To overcome these challenges, we propose a round-robin strategy that stabilizes the training process and enables the geometry network to achieve a sharper and more precise density distribution with minimal computational overhead. We validate our approach through extensive experiments on both synthetic and real-world datasets. The results show that our method significantly improves the performance of threshold-based techniques, offering a more robust and efficient solution for NeRF geometry extraction.