Title: Spik-NeRF: Spiking Neural Networks for Neural Radiance Fields
Venue: NeurIPS 2025
Accepted: yes
Keywords: Spiking Neural Networks

Spiking NeRF

Neural Radiance Fields
Abstract: Spiking Neural Networks (SNNs), as a biologically inspired neural network architecture, have garnered significant attention due to their exceptional energy efficiency and increasing potential for various applications. In this work, we extend the use of SNNs to neural rendering tasks and introduce Spik-NeRF (Spiking Neural Radiance Fields). 
    We observe that the binary spike activation map of traditional SNNs lacks sufficient information capacity, leading to information loss and a subsequent decline in the performance of spiking neural rendering models. 
    To address this limitation, we propose the use of ternary spike neurons, which enhance the information-carrying capacity in the spiking neural rendering model. With ternary spike neurons, Spik-NeRF achieves performance that is on par with, or nearly identical to, traditional ANN-based rendering models.
    Additionally, we present a re-parameterization technique for inference that allows Spik-NeRF with ternary spike neurons to retain the event-driven, multiplication-free advantages typical of binary spike neurons. 
    Furthermore, to further boost the performance of Spik-NeRF, we employ a distillation method, using an ANN-based NeRF to guide the training of our Spik-NeRF model, which is more compatible with the ternary neurons compared to the standard binary neurons. 
    We evaluate Spik-NeRF on both realistic and synthetic scenes, and the experimental results demonstrate that Spik-NeRF achieves rendering performance comparable to ANN-based NeRF models.

Spik-NeRF:

Abstract

Spiking Neural Networks (SNNs), as a biologically inspired neural network architecture, have garnered significant attention due to their exceptional energy efficiency and increasing potential for various applications. In this work, we extend the use of SNNs to neural rendering tasks and introduce Spik-NeRF (Spiking Neural Radiance Fields with Ternary Spike). We observe that the binary spike activation map of traditional SNNs lacks sufficient information capacity, leading to information loss and a subsequent decline in the performance of spiking neural rendering models. To address this limitation, we propose the use of ternary spike neurons, which enhance the information-carrying capacity in the spiking neural rendering model. With ternary spike neurons, Spik-NeRF achieves performance that is on par with, or nearly identical to, traditional ANN-based rendering models. Additionally, we present a re-parameterization technique for inference that allows Spik-NeRF with ternary spike neurons to retain the event-driven, multiplication-free advantages typical of binary spike neurons. Furthermore, to further boost the performance of Spik-NeRF, we employ a distillation method, using an ANN-based NeRF to guide the training of our Spik-NeRF model, which is more compatible with the our ternary neurons compared to the standard binary neurons and other neuron forms. We evaluate Spik-NeRF on both realistic and synthetic scenes, and the experimental results demonstrate that Spik-NeRF achieves rendering performance comparable to ANN-based NeRF models.

1 Introduction

Spiking Neural Networks (SNNs) [36, 3, 9, 10, 35, 18, 17], known for their energy efficiency compared to Artificial Neural Networks (ANNs), have garnered significant attention due to their eventdriven computation mechanism and the energy-saving advantages of multiplication-free operations. SNNs have shown great potential in a wide range of applications. For instance, in [34], SNNs were applied to object detection and demonstrated substantial energy efficiency improvements, outperforming their ANN counterparts by orders of magnitude. In [16], SNNs were used to improve the image de-occlusion task. In [33], SNNs were employed for sequential learning, showing better performance and reduced energy consumption compared to ANNs with similar scale. Similarly, in [26], SNNs were utilized for Human Activity Recognition (HAR), achieving up to a 94% reduction in energy consumption while maintaining comparable accuracy to ANN-based models. Additionally, SNNs have been applied to pose tracking [45], 3D recognition [37], and even autonomous driving [39],

where LaneSNNs demonstrated low power consumption ( 1 W) in lane detection using event-based cameras.

Given these successes, the question naturally arises: Can SNNs be adapted to the more complex task of neural rendering, such as rendering neural radiance fields (NeRF)?

In this paper, we introduce SpikNeRF , a spiking neural network approach tailored for neural rendering tasks, the first one directly-trained SNN-based NeRF model building upon the initial NeRF framework [31]. However, we found that applying SNNs to neural rendering tasks led to suboptimal performance. This is primarily due to the limited information capacity of the binary spike activation maps in SNNs. Unlike the rich activation maps of ANNs, the binary spike maps in SNNs fail to retain enough useful information during the quantization process, resulting in significant information loss and a decrease in performance. A more detailed explanation is provided in Sec. 3.3.

To address this challenge, we propose the ternary spike neuron for the Spik-NeRF, which extends the traditional binary spike representation ( { 0 , 1 } ) to a ternary form ( { 0 , 1 , 2 } ). This new approach significantly increases the information capacity of SNNs, as detailed in Sec. 3.3. Furthermore, in the inference phase, we introduce a reparameterization technique that transforms the ternary spikes ( { 0 , 1 , 2 } ) into the set of ( {-1 , 0 , 1 } ), preserving the multiplication-free and event-driven advantages of SNNs. The overall workflow of the proposed Spik-NeRF, along with the ternary spike neuron and re-parameterization technique, is illustrated in Fig. 1.

Additionally, to further improve the performance of Spik-NeRF, we propose using an ANN-based NeRF model for distillation. This technique, leveraging the superior capabilities of ANN-based NeRF models, is particularly well-suited for our ternary spike neuron, offering additional performance gains.

In summary, the main contributions of this work are as follows:

We present Spik-NeRF, a spiking neural network for rendering neural radiance fields. To our best knowledge, this is the first directly-trained SNN model building on the original NeRF framework [31].

We demonstrate, with theoretical justification, that binary spike activation maps in SNNbased NeRF are insufficient in carrying information, leading to performance degradation. To solve this issue, we propose the ternary spike neuron, which effectively increases the information capacity while retaining the multiplication-free and event-driven advantages of standard SNNs in the inference, aided by our re-parameterization technique.

We introduce a distillation method using an ANN-based NeRF teacher, which is more suitable for our ternary neuron compared to other spike neurons, to further enhances the performance of Spik-NeRF.

We evaluate Spik-NeRF on both realistic and synthetic scenes. The experimental results demonstrate that Spik-NeRF achieves rendering performance comparable to ANN-based NeRF models.

Spik-NeRF

TrainingPhase

1

5D Input

0

MLPs

Tx{0,1,2}Spikes

Re-parameterization

InferencePhase

1

(o, c) Output

Tx{-1,0,1}Spikes

2 Related Work

2.1 Spiking Neural Networks

There are generally three primary methods for training SNNs [13]: (1) spike-timing-dependent plasticity (STDP) [1] approaches, (2) ANN-to-SNN conversion approaches [22, 21, 29, 6, 8, 2, 19, 24], and (3) direct training methods [5, 32, 42, 35, 25, 41, 40].

The STDP method is biologically inspired [20, 7] and updates synaptic weights using an unsupervised learning algorithm called Hebbian learning [23]. However, this approach is still limited to small-scale datasets.

The ANN-to-SNN conversion method [6, 24, 22, 21] involves converting a well-trained ANN model to an SNN counterpart. This method is advantageous because training an ANN is faster than training an SNN. Consequently, the ANN-to-SNN conversion offers a quick way to obtain an SNN without using gradient descent. However, the converted SNN essentially mimics the original ANN and lacks learned features, thus not fully exploiting the benefits of SNNs. Additionally, this method typically requires many time steps to achieve high accuracy.

Direct training methods, on the other hand, aim to find an alternative function to replace the firing function of spiking neurons during backpropagation. These methods can significantly reduce the number of time steps needed, sometimes even to fewer than five [12, 25, 14], and have attracted considerable attention recently. However, finding an appropriate surrogate function for SNNs with large time steps remains a challenging problem. Our work focuses on addressing this issue.

2.2 Spiking Neural Networks for NeRF

Some research has explored applying SNNs to Neural Radiance Fields, but these studies differ from our approach. For instance, hybrid ANN-SNN models were proposed in Spiking NeRF [28] and Spiking Nerfacto [11]. These works employ non-linear, non-spike functions to post-process the density-related outputs of the original ANN-based NeRF models. In contrast, our work focuses on utilizing a fully SNN-based model for neural radiance fields. In Spiking-Nerf [27], an ANN-SNN model is proposed to develop energy-efficient spiking neural rendering using the ANN-to-SNN conversion approach, which, as mentioned earlier, requires many time steps. In comparison, our work concentrates on developing a direct training SNN method for NeRF. Another relevant study, SpikingNerf [43], presents a spiking neural radiance field model based on the DVGO [38] and TensoRF [4] frameworks. These frameworks enhance the original NeRF model by integrating various innovations. Except for these, this method requires numerous time steps as each sampled point on the ray is associated with a particular time step and represented in a hybrid manner.

Our approach, however, aims to develop a spiking neural radiance field model based on the original NeRF framework [31], which could pave the way for future advancements in this field. Additionally, we aim to achieve competitive performance with fewer time steps, which is directly related to the energy efficiency of the model.

3 Preliminary and Methodology

In this paper, we primarily apply the SNN to the NeRF framework [31], which is the first deep learning model that represents a scene as a neural radiance field and renders novel views from this representation. We then modify it to create Spik-NeRF. First, we provide a detailed introduction to NeRF and the widely used SNN neuron model, the Leaky Integrate-and-Fire (LIF) model. Subsequently, we address the information loss issue when applying SNN to NeRF. Finally, we present the Spik-NeRF model, which is based on ternary spike neurons to resolve the aforementioned problem, along with an isomorphic network knowledge distillation method to further enhance performance.

3.1 NeRF

In contrast to conventional explicit 3D reconstruction techniques that employ discrete voxel grids or point clouds, NeRF [31] introduces a novel continuous implicit representation through differentiable volumetric rendering. The core innovation lies in encoding the 3D scene as a 5D neural radiance

field using a multi-layer perceptron (MLP), which maps spatial coordinates p = ( x, y, z ) ∈ R 3 and viewing directions v = ( θ, ϕ ) ∈ S 2 to volume density σ ∈ R + and view-dependent RGB color c = ( r, g, b ) ∈ [0 , 1] 3 . This parametric representation is formalized through two cascaded MLP components:

where θ and γ denote network parameters, e ∈ R N represents intermediate feature embeddings, and σ corresponds to the differential opacity at point p . These networks are all composed of several fully connected layers. Each fully connected layer implements the transformation:

where a is the activation from the previous layer.

The rendering process employs classical volume rendering principles [30] with neural adaptation. For a camera ray r ( g ) = o + g d with near/far bounds [ g n , g f ] , we sample K stratified points { g i } K i =1 and compute the pixel color via numerical integration:

Here G i represents the transmittance probability for ray segment [ g n , g i ] , and α i denotes the alphacompositing weight for the i -th sample. The hierarchical sampling strategy combines coarse and fine networks to importance-sample along rays.

The model is optimized through photometric reconstruction over a set of rays R using an L 2 loss between rendered and observed pixel colors:

where ˆ C c and ˆ C f denote outputs from the coarse and fine networks respectively.

3.2 Vanilla SNN for NeRF (Denoted as Spiking NeRF)

SNNs use the spiking neuron, which is inspired by the brain's natural mechanisms, to transmit information. A spiking neuron will receive input spike trains from the previous layer neuron models along times to update its membrane potential, u . In the paper, we adopt the widely used leaky integrate and fire (LIF) neuron model. The LIF neuron model governs membrane potential u ( t ) evolution through time t :

where Θ( · ) denotes the Heaviside step function, τ m is the membrane time constant, and V th the firing threshold. For practical implementation, we adopt the discrete-time formulation:

where W denotes synaptic weights and ⊙ the Hadamard product. Parameters follow biological constraints: τ = 4 , u reset = 0 , V th = 0 . 5 [25].

We transform NeRF's MLP layers to spiking domains through temporal unfolding and potential accumulation:

Each spiking MLP layer implements temporal-aware computation:

where s t k -1 denotes spike inputs from layer k -1 at timestep t . The membrane potential u t k tracks temporal dependencies across layers.

The spiking radiance field outputs are integrated through:

where T is the total timestep of the Spiking NeRF. Then volumetric rendering then follows Eq. (4) with σ i = ¯ σ i and c i = ¯ c i .

3.3 Spik-NeRF

3.3.1 Information Loss in Spiking NeRF

While employing binary spike feature embeddings in Spiking NeRF offers substantial energy efficiency, it inherently has a limited representational capacity compared to the high-precision feature embeddings utilized in ANN-based NeRF. This limitation ultimately restricts its performance. To better illustrate this issue, we begin by providing a theoretical analysis based on the concept of entropy. Given a set X , its representational capability, denoted as C ( X ) , can be quantified by the maximum entropy of X , as expressed below:

where p X ( x ) represents the probability of observing a sample x from X . The following proposition can be easily derived:

Proposition: Given a set X , we have C ( X ) = max H ( X ) = max ( -∑ x ∈ X p X ( x ) log p X ( x ) ) . When the probability distribution is defined as p X ( x ) = 1 M , where M represents the total number of samples in X , the entropy H ( X ) reaches its maximum value of log( M ) . Therefore, it follows that C ( X ) = log( M ) .

Next, we calculate the representational capacities of the binary spike feature embeddings in Spiking NeRF and the real-valued feature embeddings in the ANN-based counterpart. Let E B ∈ B C × N represent the binary feature embeddings of the Spiking NeRF, and E R ∈ R C × N denote the realvalued feature embeddings of the ANN-based NeRF. A binary spike output s can be represented by 1 bit, with two possible samples from s . Therefore, the number of samples in E B is 2 ( C × N ) , and the corresponding representational capacity is:

In contrast, a real-valued output requires 32 bits, leading to 2 32 possible samples. Hence, the representational capacity for the real-valued embeddings is:

This comparison clearly demonstrates that the representational capacity of the binary spike feature embeddings is substantially limited, which consequently results in degraded performance for Spiking NeRF.

3.3.2 Ternary Spike Neuron Mechanism for Spik-NeRF

Our theoretical analysis reveals that enhancing the information capacity of spike neuron activations directly correlates with improved task performance. To capitalize on this insight, we propose a novel

ternary spike neuron formulation that forms the foundation of our Spik-NeRF architecture. The membrane dynamics and spike generation mechanism operate through three distinct phases:

where ∆ v represents our adaptive threshold margin (fixed at 1 in implementation). Obviously, this ternary formulation significantly enhances the representational capacity of Spik-NeRF. To quantitatively analyze the representational advantage, we also resort to the information entropy theory. Let E T ∈ T C × N denote as a ternary feature embedding in our Spik-NeRF. The ternary spike embeddings E T consists of 3 C × N samples. Hence,

The ≈ 58 . 5% increase in theoretical information capacity directly translates to enhanced scene representation capabilities, which benefits performance improvement.

3.3.3 Training-Inference Decoupling via Spike Reparameterization

While ternary spike neurons enhance representational capacity, their direct implementation introduces computational challenges: the { 0 , 1 , 2 } activation space prevents efficient conversion of weightactivation multiplications to additions, a critical advantage in SNN acceleration. To resolve this fundamental efficiency conflict, we propose a novel spike re-parameterization technique that preserves both the information richness of ternary signals and the computational benefits of binary networks.

Our solution employs a train-infer decoupling strategy with affine transformation of spike representations. During training, we maintain the native { 0 , 1 , 2 } spike formulation for gradient stability. For inference, we apply a linear transformation to the spike tensor:

This shifts the spike space to {-1 , 0 , 1 } while preserving ordinal relationships. The membrane potential update equations consequently adapt as follows:

where w b = W1 constitutes a pre-computable bias term. Thus in the inference, the linear layer will only consist of addition operations and keep the event-driven advantage.

Note that we can also use the {-1 , 0 , 1 } activation spike [15] during training. However, we observe that its performance is inferior to that of the { 0 , 1 , 2 } spike. Additionally, the { 0 , 1 , 2 } spike activation resembles ReLU activation more closely, making it better suited for ANN-SNN distillation compared to the {-1 , 0 , 1 } spike activation. With these two reasons, we chose this form of neuron for our work.

3.3.4 Isomorphic Network Knowledge Distillation

To further increase the performance, we propose an isomorphic distillation framework that transfers knowledge from an ANN-based NeRF (teacher) to our Spik-NeRF (student). We establish direct supervision on both density and color predictions through mean squared error (MSE) distillation. For any 3D point p and viewing direction v ,

The final training objective combines photometric reconstruction loss with distillation is

where λ d and λ c control the distillation strength for density and color respectively. We set them as 0.5 in the paper.

4 Experiment

We assess the rendering performance of Spik-NeRF on both synthetic and real-world datasets [31]. The synthetic dataset includes eight scenes featuring different objects. For each scene, there are 100 views used for training and 200 views for testing, with each view image having a resolution of 400 × 400 pixels. The real-world dataset consists of eight scenes captured with mobile phones. Each scene contains between 20 and 60 images, and the images are resized to 400 × 400 pixels in this paper. Additionally, one-eighth of the images are reserved for testing.

The network architecture follows the design outlined in NeRF [31]. All models are trained using the Adam optimizer for 300,000 iterations with a batch size of 1,024 rays. We initialize the learning rate at 5 × 10 -4 , which is decayed exponentially as training progresses. For synthetic scenes, the number of sampled points is set to 64 for the coarse network and 128 for the fine network. Similarly, for real-world scenes, 64 and 128 sampled points are used for the coarse and fine networks, respectively. The total number of timesteps for both Spiking NeRF and our Spik-NeRF is set to 2, while for the Spiking-NeRF, it is 8 timesteps. Since our Spik-NeRF with 2 timesteps achieves rendering performance comparable to the ANN-based NeRF model, we did not explore larger timesteps.

4.1 Rendering Performance

We evaluate the rendering performance of Spik-NeRF both quantitatively and qualitatively. Tables 1 and 2 present per-scene quantitative results from the synthetic and realistic datasets, respectively.

As mentioned earlier, although previous work has explored the application of SNNs to NeRF, these studies differ from our approach. Spiking-NeRF [27], an ANN-SNN hybrid model, uses the same NeRF [31] framework as ours, and is thus selected for comparison. Additionally, we implemented the original NeRF [31] and a spiking NeRF based on a vanilla binary spike neuron for comparison, referred to as ANN-based NeRF and Spiking NeRF, respectively.

For performance evaluation, we adopt standard metrics: PSNR and SSIM (higher values are better), and LPIPS [44] (lower values are better), as used in NeRF [31]. Since Spiking-Nerf [27] only reports PSNR, we present SSIM and LPIPS results for ANN-based NeRF, Spiking NeRF, and Spik-NeRF.

Lego

Drums

Mic

Ship

Ground Truth

ANN-based

Spiking NeRF

Spik-NeRF

NeRF

On the synthetic dataset, Spiking-NeRF with 8 timesteps achieved an average PSNR of 11.93. In comparison, our directly trained Spiking NeRF achieved a significant improvement with an average PSNR of 28.95. More notably, our Spik-NeRF, utilizing ternary spikes, further boosts the average PSNRto 30.23, approaching the performance of the ANN-based NeRF (31.15 PSNR). Furthermore, in every scene, Spik-NeRF consistently outperforms both Spiking-NeRF and Spiking NeRF, highlighting the effectiveness of our approach.

On the realistic dataset, our method also surpasses Spiking-NeRF and Spiking NeRF. For instance, Spik-NeRF achieves a PSNR of 26.26, outperforming Spiking-NeRF and Spiking NeRF by 13.17 and 0.72 PSNR, respectively. In terms of SSIM and LPIPS, Spik-NeRF achieves scores of 0.817 and 0.121, while Spiking NeRF achieves 0.781 and 0.162, respectively.

Figure 2 illustrates the rendering results from synthetic datasets, including Lego, Drums, Mic, and Ship, for ANN-based NeRF, Spiking NeRF, and Spik-NeRF. It is evident that our method recovers fine details in both geometry and appearance, comparable to the ANN-based NeRF. This includes features such as the Drums' pedal, the Microphone's mesh grille, and the Ship's rigging. In contrast, Spiking NeRF produces blurry and distorted renderings, particularly for the Microphone's mesh grille.

We also include the rendering results for the realistic dataset in the appendix. As seen in the figures, our method consistently represents fine geometry more accurately across rendered views than Spiking NeRF.

4.2 Ablation Study for Knowledge Distillation

In this section, we evaluate the performance of our method with the proposed isomorphic network knowledge distillation on complex scenes, such as Room, Orchids, and Drums. The results are presented in Tab. 3.

As illustrated in Tab. 3, applying knowledge distillation to Spik-NeRF results in a noticeable improvement in performance, bringing its results much closer to those of the ANN-based NeRF. The quantitative analysis reveals two important findings: (1) Knowledge distillation effectively narrows the performance gap between Spik-NeRF and ANN-based NeRF, as demonstrated by the improvement in both PSNR and SSIM metrics. (2) Even in challenging scenes that involve specular reflections, such as Orchids, our method achieves rendering quality comparable to ANN-based NeRF, suggesting that knowledge distillation is

beneficial in maintaining high-quality renderings in complex environments.

These findings also highlight the compatibility of our Spik-NeRF with the ternary spike neuron model and the isomorphic network knowledge distillation, which appears to facilitate the optimization process and enhance performance significantly.

5 Conclusion

We present Spik-NeRF, achieving ANN-comparable rendering quality. Theoretical analysis reveals binary spikes' limited representational capacity for SNN-based NeRF. To address limitations of binary spike neurons based Spiking NeRF, we propose the ternary spike neuron for Spik-NeRF, which increase representational capacity by 58.5% using three activation states. We also propose a train-infer decoupling via spike reparameterization technique to keep the energy efficiency of SNNs. In addition, we also propose the isomorphic distillation method, which transfers knowledge from ANN-based NeRF to compensate for information loss. Our experiments show that Spik-NeRF achieves PSNR metric within 2.9% of ANN baselines with only 2 timesteps, while retaining energy efficiency through multiplication-free operations. Our work bridges the efficiency-performance gap in neural fields, enabling future energy-efficient 3D reconstruction.

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Wu, Y., Deng, L., Li, G., Zhu, J., Xie, Y., and Shi, L. Direct training for spiking neural networks: Faster, larger, better. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 33, pp. 1311-1318, 2019.

Yao, X., Hu, Q., Liu, T., Mo, Z., Zhu, Z., Zhuge, Z., and Cheng, J. Spikingnerf: Making bio-inspired neural networks see through the real world. arXiv preprint arXiv:2309.10987 , 2023.

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Zou, S., Mu, Y., Zuo, X., Wang, S., and Cheng, L. Event-based human pose tracking by spiking spatiotemporal transformer, 2023.

A Technical Appendices and Supplementary Material

A.1 Rendering Performance in Realistic Dataset

Figure 3 presents the rendering results from realistic datasets, including Flower, Room, T-rex, and Horns, for ANN-based NeRF, Spiking NeRF, and Spik-NeRF. As shown, our method captures fine details in both geometry and appearance, achieving results comparable to those of the ANN-based NeRF. In contrast, Spiking NeRF produces blurry and distorted renderings in certain areas.

Flower

Room

T-rex

Horns

Ground Truth

ANN-based

Spiking NeRF

Spik-NeRF

NeRF

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper explores the application of SNNs on the challenging rendering neural radiance fields (NeRF) tasks, and proposes the first directly trained SNN-based NeRF model called Spike-NeRF. To overcome the accuracy loss due to binary spikes, Spike-NeRF introduces ternary spiking neurons to enhance the model's information-carrying capacity. A reparameterization technique is further used to preserve efficient computation during inference. The authors additionally propose a distillation method to improve the performance of Spik-NeRF. Extensive experiments confirm the effectiveness and performance advantages of Spik-NeRF.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The paper addresses the critical challenge of information loss in SNNs by introducing novel ternary spike neurons, which use activation values of 0/1/2 during training and −1/0/1 during inference by reparameterization. This design enhances the expressive capacity of SNNs while preserving their computational efficiency and event-driven nature.\n\n2. The paper is well-written, with clear explanations of the methods, results, and conclusions. \n\n3. The authors provide code and examples, which is a great help for reproducibility and a deeper understanding of the proposed methods.\n\nWeaknesses:\n\n1. All experiments were reported at a timestep of T=2. Since SNN behaviour often varies substantially with temporal resolution, it would be beneficial to evaluate the model using additional timesteps.\n\n2. The comparison between models appears to be unfair. While the baseline NeRF model was trained for 200,000 iterations, the proposed model was trained for 300,000 iterations. This discrepancy in training duration could influence performance and may partially account for the observed improvements, rather than reflecting the superiority of the proposed method.\n\nQUESTIONS:\n1. Could the authors extend their experiments to include additional timesteps to better assess the temporal generalization of the proposed approach?\n\n2. What is the rationale for training the proposed model for 300,000 iterations, especially given that the baseline NeRF model was trained for only 200,000? Could this discrepancy have influenced the reported performance gains?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper explores the application of SNNs to the neural rendering task, specifically through the introduction of Spik-NeRF (Spiking Neural Radiance Fields with Ternary Spike). Traditional SNNs, which rely on binary spike activations, suffer from limited information capacity, leading to performance degradation in neural rendering models. To overcome this, the authors propose using ternary spike neurons, which significantly enhance the information-carrying capacity of the model. The result is a rendering performance in Spik-NeRF that is comparable to, or nearly identical with, traditional ANN-based NeRF models. Additionally, the authors introduce a re-parameterization technique to preserve the event-driven, multiplication-free advantages typical of binary spike neurons. Experimental results on both realistic and synthetic scenes show that Spik-NeRF achieves rendering performance comparable to ANN-based NeRF models.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This is one of the few works to apply SNNs to the NeRF task, potentially inspiring future research on low-power NeRF systems, which is a promising direction for energy-efficient rendering solutions.\n2. The paper introduces an effective solution to the problem of information loss in traditional binary spike SNNs by utilizing ternary spike neurons. This enhancement increases the information-carrying capacity, significantly boosting performance in spiking neural rendering models.\n3. The paper is well-written and clearly organized, making complex ideas accessible to a broader audience. The explanations of the methods and results are easy to follow.\n\nWeaknesses:\n1. The experiments conducted in the paper are only for a few timesteps. It would be good to test the proposed method with a broader range of timesteps to understand its performance across different configurations.\n2. It does not compare its results to other works that have applied SNNs to NeRF. A comparison with existing studies in this area would help contextualize the significance and impact of this contribution.\n\nQUESTIONS:\n1. Could the authors extend their experimental evaluation to include other timestep values (e.g., 4) to assess how the ternary spike neurons perform across various temporal resolutions?\n\nLIMITATIONS:\nSee above.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Spik-NeRF, the first directly-trained Spiking Neural Network (SNN) approach for Neural Radiance Fields (NeRF). The authors identify the inherent limitation of binary spike activations in representing fine-grained scene information, which results in degraded rendering quality. To address this, they propose three technical contributions: 1) A ternary spike neuron to enhance representational capacity; 2) A re-parameterization scheme to convert ternary activations into a format amenable to efficient inference; 3) A distillation framework using a pre-trained ANN-based NeRF teacher to guide the SNN model. Extensive experiments on synthetic and real datasets demonstrate that Spik-NeRF approaches the rendering quality of ANN-based NeRF with significantly fewer timesteps, offering promise for energy-efficient 3D reconstruction.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. Novelty and Contribution: This is the first fully SNN-based method directly trained on NeRF, which is a meaningful step in extending SNNs to high-dimensional neural rendering tasks.\n\n2. Technical Insight: The paper provides solid theoretical justification for the use of ternary spike neurons, supported by entropy-based analysis. The proposed re-parameterization ensures energy-efficient inference without compromising the advantages of SNNs.\n\n3. Distillation Strategy: The ANN-SNN distillation strategy is well-motivated and leads to performance improvements, especially in challenging scenes.\n\n4. Clarity and Presentation: The paper is clearly written, well-organized, and easy to follow, with figures and tables that effectively support the claims.\n\nWeaknesses:\n\n1. The model is evaluated only at T=2. Since the energy-performance trade-off is central to SNN research, a more thorough analysis across different timesteps would be valuable.\n\n2. While comparisons with Spiking-NeRF (Li et al., 2025) and ANN-based NeRF are provided, more detailed ablations against other recent SNN-NeRF variants would strengthen the empirical contributions.\n\n3. The paper emphasizes energy efficiency, but no actual energy consumption or MAC operation estimates are reported. Including such results would better quantify the benefits of Spik-NeRF.\n\nQUESTIONS:\nWhy was only T=2 tested? How does Spik-NeRF quantitatively compare against other SNN-NeRF models?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Spik-NeRF, a novel framework that adapts a fully SNN-based model for NeRF training. Through a rigorous theoretical analysis of the vanilla Spiking NeRF, the authors make several key contributions:\n1.\tDesign of ternary spike neurons that significantly enhance the information-carrying capacity compared to standard binary activations.\n2.\tA reparameterization technique allowing ternary activations to be used efficiently in inference while retaining event-driven, multiplication-free advantages. \n3.\tAn isomorphic network knowledge distillation scheme transferring knowledge from ANN-based NeRFs to Spik-NeRF.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1.\tThe presentation of this paper is very clear and easy to understand.\n2.\tThe designs of the ternary spike neurons and the reparameterization technique are well-motivated and appear effective.\n\nWeaknesses:\n1.\tAlthough the authors claim in P3, L122, that the number of timesteps directly impacts the model’s energy efficiency, the experiments do not include any energy analysis. Compared to vanilla NeRF—which is now considered a relatively weak baseline—the approach may indeed consume less energy during inference, but the training overhead could be substantial due to the added complexity of ANN-NeRF distillation.\n2.\tThe experiments do not report training time or inference time. Another concern is whether the proposed methods are compatible with faster NeRF variants such as InstantNGP, Nerfacto, or TensoRF. Focusing exclusively on vanilla NeRF may limit the practical relevance of the work for SNN applications in neural rendering or NVS.\n\nQUESTIONS:\nIt is unclear whether the capacity can be further increased. For example, could quaternary spike neurons be used? Is there an upper bound on the number of discrete states that remain compatible with efficient SNN operation?\n\nLIMITATIONS:\nSee above.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}