Research map / Architectures and efficiency / Spiking networks
Spik-NeRF: Spiking Neural Networks for Neural Radiance Fields
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AI-extracted summary. Written by AI from the paper's abstract only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Introduces Spik-NeRF, extending spiking neural networks to neural radiance field rendering to exploit SNN energy efficiency.
Problem
Applying spiking neural networks to neural rendering, where binary spike activations of conventional SNNs appear to limit representational capacity for NeRF-style continuous outputs.
Approach
Extends SNNs to neural radiance fields (Spik-NeRF) to address the lack of expressiveness of binary spike activation maps for neural rendering; only the abstract's framing is available, so the specific mechanism is not stated.
Experimental setup
Builds on: NeRF
Closest papers by meaning
- SpikeNeRF: Learning Neural Radiance Fields from Continuous Spike Stream (CVPR 2024 · similarity 0.89)
- Rad-NeRF: Ray-decoupled Training of Neural Radiance Field (NeurIPS 2024 · similarity 0.88)
- Spiking NeRF: Representing the Real-World Geometry by a Discontinuous Representation (AAAI 2024 · similarity 0.88)
- Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks (AAAI 2025 · similarity 0.88)
- Ced-NeRF: A Compact and Efficient Method for Dynamic Neural Radiance Fields (AAAI 2024 · similarity 0.87)
- Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networks (NeurIPS 2024 · similarity 0.87)
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