Spik-NeRF v2: Pushing the Limit of Spiking Neural Radiance Fields with $\pm$I-LIF

Spiking Neural Networks (SNNs) offer a promising energy-efficient alternative to Artificial Neural Networks (ANNs) through event-driven, multiplication-free computation. However, when applied to downstream tasks such as Neural Radiance Fields (NeRF), SNNs suffer from significant information loss, resulting in a noticeable performance gap. In this paper, we present Spik-NeRF v2 to advance SNN-based neural rendering. We propose the $\pm$I-LIF spiking neuron, which extends I-LIF to support signed integer values during training, thereby mitigating information loss. During inference, it is converted to ternary spikes $\{-1,0,1\}$, preserving event-driven properties with addition-only operations. Furthermore, we introduce a re-parameterization technique that transforms a trained I-LIF-based Spik-NeRF with $t$ timesteps into an equivalent $\pm$I-LIF-based model with $t/2$ timesteps. This enables faster inference while preserving rendering quality, overcoming the suboptimal performance of directly trained $\pm$I-LIF models with reduced timesteps. Extensive experiments on synthetic and realistic datasets demonstrate that Spik-NeRF v2 surpasses existing SNN-based NeRF methods and achieves rendering quality comparable to ANN-based approaches.