SpikeStereoNet: A Brain-Inspired Framework for Stereo Depth Estimation from Spike Streams
Authors: Zhuoheng Gao
Yihao Li
Jiyao Zhang
Rui Zhao
Tong Wu
Hao Tang
Zhaofei Yu
Hao Dong
Guozhang Chen
Tiejun Huang
Venue: ICLR 2026 Poster (2026)
Keywords: Stereo depth estimation
Neuromorphic camera
Recurrent spiking neural network
Computer vision
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose a brain-inspired framework for spike cameras for stereo depth estimation from spike streams.

Abstract: Conventional frame-based cameras often struggle with stereo depth estimation in rapidly changing scenes. In contrast, bio-inspired spike cameras emit asynchronous events at microsecond-level resolution, providing an alternative sensing modality. However, existing methods lack specialized stereo algorithms and benchmarks tailored to the spike data. To address this gap, we propose SpikeStereoNet, a brain-inspired framework to estimate stereo depth directly from raw spike streams. The model fuses raw spike streams from two viewpoints and iteratively refines depth estimation through a recurrent spiking neural network (RSNN) update module. To benchmark our approach, we introduce a large-scale synthetic spike stream dataset and a real-world stereo spike dataset with dense depth annotations. SpikeStereoNet outperforms existing methods on both datasets by leveraging spike streams' ability to capture subtle edges and intensity shifts in challenging regions such as textureless surfaces and extreme lighting conditions. Furthermore, our framework exhibits strong data efficiency, maintaining high accuracy even with substantially reduced training data.

Published as a conference paper at ICLR 2026
SPIKESTEREONET A BRAIN-INSPIRED FRAME- WORK FOR STEREO DEPTH ESTIMATION FROM SPIKE STREAMS
Zhuoheng Gao', Yihao Li Jiyao Zhang", Rui Zhao', Tong Wu', Hao Tang',
Zhaofei Yu Hao Dong²*, Guozhang Chen Tiejun Huang!
1 National Key Laboratory for Multimedia Information Processing.
School of Computer Science, Peking University
2 Center Frontiers of Computing Studies, School of Computer Science, Peking University
3 Institute for Artificial Intelligence, Peking University
4 School of Engineering and Applied Science, University of Pennsylvania
https://github.. cww. comm/Citicality- Ciailly-Conniiie--CoC ComputaaionnnaaaSSppeeeeeeerrr
ABSTRACT
Conventional frame-based cameras often struggle with stereo depth estimation in rapidly changing scenes. In contrast, bio-inspired spike cameras emit asyn- chronous events microsecond-level resolution, providing ing modality. However, existing methods lack specialized stereo algorithms and benchmarks tailored to the spike data. To address this gap, we propose SpikeStere- oNet, brain- orain-inspired framework to estimate stereo depth directly from raw spike streams. The model fuses raw spike streams from two viewpoints and iteratively refines depth estimation through recurrent spiking neural network (RSNN) up- date module. To benchmark our approach, we introduce large-scale synthetic spike stream dataset and a real-world stereo spike dataset with dense depth anno- tations. SpikeStereoNee outperforms existing methods on both datasets by lever- aging spike streams' ability to capture subtle edges and intensity shifts in challeng- ing regions such as textureless surfaces and extreme lighting conditions. Further- more, our framework exhibits strong data efficiency, maintaining high accuracy even with substantially reduced training data.
alternative
INTRODUCTION
Depth perception is fundamental for navigating and interacting with the 3D world (Tosi al. 2025
driving applications from robotic manipulation (Ma et al., 2024: Li et al. 2024 Wen et al. 20256
Gao et al. (224) to autonomous navigation (Wei et al.) [024] Duba et al. [024]] Kalenberg al.
2024 Nahavandi et al. 2025 Wu et al. 2023 Xu et al. 2023c] Wang et al. (024b). Traditional stereo vision estimates depth from calibrated image pairs captured by frame-based cameras; how- ever, it suffers from motion blur and latency in dynamic scenes. Biological vision systems efficiently process visual imputs using sparse, asynchronous spikes, achieving remarkable speed and energy ef-
ficiency (Kundu al.
2021
Yang et al., 2024 Datta al. 2021 Göltz et al. 2021 Wang al.
2025
Wolf & Lappe 2021
Kucik & Meoni 2021). Neuromorphic spike cameras
(Zhao et al,
2024c
Zhang et al. 2022a
Zhao et al. 2021a b Hu et al. 2022 Zhao al. 2022 Chen et al.
2024a
Zhao et al. 2024a) implement this principle in hardware, delivering ultra-high temporal res-
olution (up to 40,000 Hz) and capturing rich luminance information through asynchronous binary
spike streams. This makes them suitable for robust perception tasks, such as stereo depth estimation, particularly highly dynamic scenarios where conventional methods are ineffective.
Despite their potential, spike cameras present distinct challenges for stereo depth estimation. Their asynchronous, binary, high-the -ighthhroughput streams conflict with frame-based algorithms that expect
synchronous, intensity-valued image pairs (Tankovich et al. 202T Lipson et al.) 2021 Li et al.)
2022 Zhao et al., 2023 Rao et al. 2023 Xu et al. 2023a Zeng et al. 2023 Guan et al. 2024
"Corresponding authors.

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Published as a conference paper at ICLR 2026
Wang et al. 2024a Xu et al.
2023b: Weinzaepfel el. 2023
Chen al. 2024b Cheng al. 2025
Jiang et al. 2025: Wen et al.
2025a). and any conversions to frames introduce temporal quantization
errors, motion blur, significant computational overhead (Zhao al. 2021b 2022 Chen et al.
2023). diminishing the sensor's intrinsic advantages. Although event- stereo methods have
been developed for DVS cameras (e.g. (Zhou et al., [202] Zhang et al.] [202bb] [ho et al. 202T
Lou et al.) [0244] Rançon et al. ).22)), they rely on temporal contrast, while spike cameras emit
integrated intensity streams that require tailored processing techniques. Critically, the field lacks
specialized algorithms and benchmarks designed for stereo depth estimation directly from raw spike
streams, hindering progress in this promising field.
Recent advances in spiking neural networks (SNNs) provide the foundation for our models de- sign. Classical neuron models offer compact yet expressive dynamics for spike-based computa- methods enable scalable training of
tion (Izhikevich) 2003). Building on these, surrogate-gradin deep SNNs with gradient-based optimization (Neftei et al., 2019). Adaptive recurrent SNNs fur- ther improve temporal modeling and efficiency time-domain tasks (Yin et al. 2021). In parallel, neuromorphic vision research demonstrates the benefits of spike-driven processing for object per- ception and recognition, highlighting the potential of spike-based architectures in real-world vision applications (Bi et al. 2019). Beyond SNNs-based, some work explores biologically inspired net- work architectures for vision. ClearSight (Lin al. 2025) proposes dual-drive hybrid model with neuron and synapse-based based attention for event-based motion deblurring. SABV-Depth (Wang et al.) 2023] integrates bio-inspired attention into monocular depth network enhance prediction ac- curacy. Earlier cortical models for object recognition, such as the neocognitron and hierarchical feedforward architectures (Fukushima 1980 Serre et al. 2007), further demonstrate how principles from visual neuroscience can guide the design of robust perception systems.
To bridge this gap, we introduce SpikeStereoNet (Fig. [ an end-to-end, biologically inspired frame- work for generating stereo depth directly from raw spike streams. SpikeStereoNet integrates a re- current spiking neural network (RSNN) into an iterative refinement loop (Teed Deng 2020) and models neuronal interactions (Henry) 1995) to capture spatiotemporal dynamics inherent in spike data. The analysis of ncuronal dynamics confirms the temporal stability, convergence, and expres- sive feature separation of the RSNN components. We introduce two novel benchmark datasets: large-scale synthetic dataset with diverse scenes and ground-truth depth, and a real-world dataset containing synchronized stereo spike streams and corresponding depth sensor measurements. Ex- perimental results demonstrate that SpikeStereoNet generalizes effectively, maintaining robust per- formance even when trained with substantially limited data. Our main contributions are as follows:
We present the large-scale synthetic and real-world raw spike stream datasets for stereo depth estimation offer an evaluation benchmark for this emerging field.
We propose novel biologically inspired RSNN-based SpikeStereoNet architecture that refines asynchronous spike data through iterative updates.
We analyze the dynamics of RSNN iterations to demonstrate the stability and convergence properties of the model.
We demonstrate the data efficiency of the proposed framework, highlighting the strong generalization even with limited training samples.
2 RELATED WORK
Spike Cameras and their Applications. Neuromorphic cameras, including event cameras (Moeys al., 2018 Posch et al., 2011T Huang et al.] 2017) and spike cameras (Huang al.) (023). are inspired by the primate retina and operate asynchronously at the pixel level, providing ultra- temporal resolution, wide dynamic range, low latency, and reduced energy consumption. Unlike event cameras, which follow differential sampling model and capture only luminance changes in logarithmic space, spike cameras employ an integral sampling model, accumulating photons until a threshold is reached and then firing spikes. Although event cameras tend to generate sparser output, they might lose scene textures, especially in static regions due to their change-base based design. In contrast, spike cameras high-speed imaging (Zhao et al.) 2024a). 3D reconstruction (Zhang et al. 2024b), motion deblurring (Zhang al. 2024d). optical flow (Zhao et al. 2022 2024h
capture richer spatial details, making them well suited for tasks such as
2025
Xia al. 2023), object detection
2

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Published as a conference paper at ICLR 2026
Parameter Setting
Image Dataset
Pre-training
Scene
Function Modules
Rendering Results
Image
Depth
Supervised
Object
Blender
Train
Video frame interpolation
Spiking Simulator
Spike Stream
Parameters
Input
Sensor
Sampling Process
Real World Scenes
Stereo System
Real Dataset
Transfer
Spike Camera
Real spike
Collect
Pretrestment
Finetune
Kinect
Depth
Figure 1: The overall pipeline is illustrated in the figure below. The upper path represents the training and evaluation process on synthetic dataset, while the lower path shows the transfer learning and testing procedure on real spike stream data by using the pre-trained model.
Zheng et al. 2023), occlusion removal (Zhag et al. 2024c), monocular depth estimation (Zhang ct al. 2022a), and binocular depth estimation Wang et al. 2022).
Frame-Based Stereo Depth Estimation. Deep learning has significantly advanced stereo matching, leading to various methods with enhanced accuracy and generalization. The early methods (Fan
et al. 2021 Yin al. 2019) constructed cost volumes for each disparity and applied 3D CNNs for refinement. In contrast, recurrent refinement-baas based models (Rao et al. 2023 Tian et al. 2023)
inspired by RAFT-Stereo (Lipson al., 2021 Teed & Deng 2020) iteratively update the disparity without building a full 4D volume. It restricts 2D flow to the 1D disparity dimension and uses multi- level convolutional Gated Recurrent Unit (ConyGRU) for broader receptive fields, demonstrating strong generalization. Successor models such as DLNR (Zhao et al. 2023). IGEV-Stereo (Xu et al., 2023a) and Selective-Stereo (Wang al., 2024a) improve in-domain performance, RAFT-Stereo retains advantages in zero-shot settings (Lipson et al., 2021). Recent hybrid methods (Hamid et al.) 202T combine cost filtering and iterative refinement for better trade-offs. Meanwhile, transformer-
based solutions (Xu et al. 2023b) utilize cross-attention global self-attention.
Event-Based Stereo Depth Estimation. Event cameras capture pixel-level brightness changes asynchronously. offering significant advantages over conventional frame-hased cameras. Recently, event-based stereo depth estimation has advanced rapidly (Gallego et al. 2022, Tosi et al. 2025). with notable methods exploiting camera velocity (Zhang al.) 2022b) deriving depth without explicit event matching (Zhou et al. 2018). Deep learning approaches propose novel sequence em- bedding (Tulyakov et al., 20199 and fuse frame and event data (Nam et al. 2022 improve depth
estimates in challenging scenarios. Moreover, some works (Cho et al. 2023 leverage standard models from the image domain, exploiting the inherent connection between frame and event data to improve stereo depth estimation performance.
METHODS
This section describes the overall architecture of our proposed model (Fig. 2 Figs. 24. illustrate the design of the biologically inspired framework. Fig. [ shows the overall network architecture, which consists of the following four main components.

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Published as a conference paper at ICLR 2026
(a)
(b)
Corretation
Right Camera
Context festure
Left Camera
RSNN block
Context network
Spike Stereo (c)
Spike Stream Input
Spike Stereo Model
Output
RSNN update operator
Disparity
Spike camera
Left spike stream
Context feature
Left festure
Baseline
Transform
Wi4
W
feeture
Spike camera
Right spike streem
Correlation
Depth
The pipeline of the proposed solution. (a) and (b) The components of the biological visual system, each corresponding to a specific module the computational framework. (c) The overview of SpikeStereoNet: Multi-scale spike features first extracted to construct correlation pyramid, followed by a biologically inspired RSNN-based update operator that iteratively refines disparity using local cost volumes and contextual cues. The final disparity map is upsampled produce high-resolution depth estimates.
Figure 2:
Spike Camera Model. The spike camera, mimicking the retinal fovea, consists of a H x W pixel array in which each pixel asynchronously fires spikes report luminance intensity. Specifically, each pixel independently integrates the incoming light over time. At given moment if the ac- cumulated brightness at pixel (i,j) reaches or exceeds a fixed threshold C, spike is fired and the accumulator is reset: A(i.j.t) = ft dT Z C, where i, E ž, < H,j S W. Here, A(i, j. L) denotes the accumulated brightness at time I, T(i,j,T) r) is the instantaneous brightness at the pixel (i, and t, is the last firing time of the spike. t corresponds to the first spike, then = 0. Due circuit-level constraints, spike readout times quantized. Although firing asyn- chronous, all pixels periodically check their spike flags at discrete times nAt (n E Z), where At is short interval on the order of microseconds. As result, each readout forms binary spiking frame of size H x W. Over time, these frames constitute x W x N binary spike stream:
S(i.j.nAt) = {
if E [nn 1)4,, nd S.t. A(I.j,0) C, if [[n 1)At, ndt S.L. A(i.jt)) < C,
(1)
then we denote the generated spike stream S {0, NXHNW where H and W signify the height and width of the image, and N represents the temporal steps of the spike stream.
Spike Feature Extraction. The structure of spike feature extraction illustrated in Fig.Ba), which outlines the architecture of the feature network or the context network. The feature network re- ceives the left and right input spike streams Si(r) E N applying a 7 x convolution to downsample them to half-resolution. Then, series of residual blocks is used for spike feature extraction, followed by another downsampling layer to obtain 1/4-resolution features. For more expressive representations, noted {f.. e RC, M,, xW; }, E (4, 8, 16). Among these, ft, and fr. are used to construct the cost volume, while all levels used guide for the 3D regularization network.
further generate multi-scal features at 1/4, 1/8, and 1/16 scales, de-

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Published as a conference paper at ICLR 2026
Layert
Layer2
Layer3
Layeni
LayerS
ReLU
Feedforward Connection
Recurrent Connection
Layer
Conv
Norm
Layer
ResBlock a,
Adaptive variables
Spike Stream
Fecture
Sigmoid
s
Spike State
(a)
(c)
Local Correcation
Motion Encodor
RBNN Block
SIO
Up Mask
Motion
RSNNT
STA
RSNN2
R9NNS
Couppo Loo
Convex
Features
Delta
Upsample
B.Y
a.p.y
B.B.V
Current
Dispartty
Context Festure
Rofined Disparity
Figure 3: Illustration of the detailed structure of spike feature extraction and the RSNN-based update module. (a) Spike feature extraction: It comprises context network and two feature networks, which extract multi-scale correlation features, contextual features, and the initial hidden state from the spike streams. A single network structure is illustrated in the diagram. (b) RSNN-based update block: Local correlations and disparity fields are used to generate motion features, which update the RSNN hidden states through recurrent and feedforward connections. The RSNN the highest resolution is responsible for refining the disparity estimates. (c) Descriptions of key modules.
The context network shares similar backbone with feature networks. It consists of residual blocks and additional downsampling layers to produce multi-scale context features the original resolu- tion. These features are used to initialize and inject contexts into RSNNs at each recurrent iteration.
Correlation Volume. Given the left and right feature maps J0y and ((r), we first construct cor- relation cost volume of all pairs: Cijk En. farNs f(y)ae- Next, we build a 4-level correlation pyramid {C.) applying ID average pooling along the last dimension. Each level is obtained using a kernel size of 2 and stride of 2, progressively downsampling the disparity dimension.
capture multi-scale spatial and temporal information, three- layer RSNN is used as the update module. Each RSNN layer contains recurrent intra-layer connec- tions and feedforward inter-layer connections to facilitate hierarchical temporal processing (Fig. 3p, c). At 1/8 and 1/16 resolutions, RSNN modules receive inputs from the context features, the post- synaptic currents obtained by weights from the spike states at the same layer and the previous layer. These recurrent and feedforward connections expand the receptive field and facilitate feature prop- agation across scales. At 1/4 resolution level, the model additionally receives the current disparity estimate and the local cost volume derived from the correlation pyramid. A single RSNN updates as follows (the default parameters are the (-th layer network, the parameters from the previous layer explicitly indicated, and bold symbols represent vectors/matrices):
RSNN-Based Update Operator.
a2 = aCConv([s, (CConv([st_1,220 xz), Wa) + Ca),
B. o (Conv(( ([s Wg) c3).
a (Conv (CCon((st-1,,))) vz), W,) + Cy),
8, = [(8-11, (1-1) 0z 3er 7t),
where st is the spike state in layer and timestep t. Cas Cg, and Cyy context embeddings from the context network, and a is the sigmoid function. The number channels in the hidden states of RSNNs is 128, same as in the context feature. The function f(-) corresponds to proposed
5

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Published as a conference paper at ICLR 2026
adaptive leaky integrate-ande and-fire neuron model, defined as:
h = at Ut-1 + (1 - (Wic + ( W88 ( )
th Be Ppeuk.
(3)
0t ht 7t- 8t Vt
where et the membrane potential, "t is the firing threshold and ((-) is the Heaviside step func- tion. Wi and W are the recurrent and feedforward synaptic weights, respectively, implemented as convolutional kernels. And a, 33 and Y E [0, 1] are adaptive variables, representing retention of membrane potential, firing threshold, and soft reset in the previous step, respectively. This up- date mechanism draws biological inspiration from neuroscience findings showing that key neuronal properties such firing threshold, resting potential, and membrane time constant are not fixed but vary dynamically in biological neural circuits depending on neuronal state and contextual input.
Spatial Upsampling. Based the hidden state of final layer, network predicts both residual dispar- ity through two convolutional layers and then updates current disparity: de = d-- + Adg. Finally, the disparity 1/4 resolution is upsampled to full resolution using convex combination strategy.
Loss Function. We supervise the network for a composite loss function composed of the three terms: the main loss, the firing rate and the voltage regularization. The overall loss defined
L Laaroo + > Lyate
" + +Ny Ayy
(4)
Σ T I|de
delli As Σ (r ro)²
v;(1), 1 = 0.9.
The first term Liserroo is the main loss, which is the -nomo distance between all predicted disparities (de) and the ground truth disparity dy with increasing weights. To constrain the firing rate of neurons and promote temporal sparsity, the second term Lratereg is firing rate regularization, is the average firing rate of the i-th neuron during time T. and ro the target firing rate. Furthermore, the third term Lyrreg serves as the voltage regularization term, where N represents the total number of neurons. Both regularization terms encourage sparsity and benefit the performance.
EXPERIMENTS
4.1 DATASETS
Synthetic Dataset. To support supervised training for spike-based depth estimation, construct high-quality synthetic dataset using Blender. The dataset includes I 550 training scenes and 40 testing scenes, each containing 256 pairs of RGB images and corresponding ground-truth depth maps. The dataset build process and details are in the Appendix A.3
To generate dense spike streams with high temporal resolution in dynamic environments, we apply video interpolation method (Zhang et al. 2023) to synthesize 50 intermediate RGB frames between each pair of adjacent frames. These high-frequency image sequences are then converted into spike streams using biologically inspired spike generation mechanism that models the analog behav- ior of real spiking camera circuits. Noise modeling is also integrated further enhance realism. Our dataset provides synchronized high-resolution spike streams and pixel-wise depth ground truth, offering a comprehensive and reliable benchmark for training and testing.
Real Dataset. We collect real-world spike stereo dataset using two spike cameras and one depth camera (Kinect) in diverse environments to evaluate the generalization and practical performance of the model. The spike cameras have resolution of 400 250 with temporal resolution of 20,000 Hz, while the depth camera provides RGB-D data at 1280 x 720 resolution and 30 FPS. The captured scenes involve various objects of the real world recorded in indoor settings. The dataset includes over 3,000 stereo spike stream pairs (more than 300,000 frames) and depth maps and is divided into training and test sets. More details about real dataset and system are provided in Appendix A.4

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Published as a conference paper at ICLR 2026
Table 1: Quantitative results the of synthetic dataset, and all methods spike streams as input. Best results for each evaluation metric are bolded, second best are underlined. The "t" indicates that the lower the metrics, the better.
bad 1.0
bad 2.0
bad 3.0
AvgErr
Params
FLOPs
Method
(%)
(%) 4
(%) 4
(px) +
(M)
(B)
Stereospike (Rançon et al.
2022
14.10
9.82
5.35
1.10
5.10
569
ZEST (Lou et al. 20224)
11.10
4.94
3.50
0.62
340.75
989
CREStereo (I. al. 2022)
16.12
10.54
4.40
0.81
5.43
863
RAFT-Stereo (Lipson et al.
2021
9.67
4.64
2.76
0.48
11.41
798
GMStereo (Xu al. 20236
17.61
11.05
4.39
0.83
7.40
160
IGEV-Stereo [Xu el al. 2023a)
12.50
6.50
4.27
0.76
12.77
614
DLNR (Zhao al. 202.)
10.15
4.67
2.81
0.55
57.83
1580
MoCha CCen et al.
2024b)
11.93
5.67
4.39
0.73
20.96
935
Selective Wang al. 2024a)
9.24
4.57
2.66
0.45
13.32
957
MonSter (Cheng et al.
2025
9.23
4.64
2.72
0.46
388.69
1567
SpikeStereoNet (Ours)
8.41
4.13
2.38
0.42
12.15
473
Scene
Ground Truth
IGEV
DLNR
Selective-stereo
Ours
Figure 4: From left to right: synthetic scene images from the left view, ground-truth depth, depth prediction results from existing stereo methods and method.
4.2 IMPLEMENTATION DETAILS
We implemented SpikeStereoNet using PyTorch and performed all experiments on NVIDIA RTX 4090 GPUs. The model is trained using the AdamW optimizer with gradient clipping in the range of [-1, 1]. one-cycle learning rate schedule is adopted, with an initial learning rate set to x 10-4 During training, we apply 16 update iterations for each sample and set the batch size 8 for 300k steps. We use random horizontal and vertical flips as data augmentation in the training dataset.
4.3 EXPERIMENTAL RESULTS
Our method compared with various categories stereo depth estimation approaches. First, event- based stereo networks compared, such as ZEST (Lou et al. 2024). Next, state-of-the-an frame- based stereo networks are evaluated, including Selective-Stereo (Wang et al. 2024a),, RAFT-Stereo (Lipson et al. 2021), and GMStereo (Xu et al. (023b), etc. These methods cover different strategies, such as iterative refinement or transformer based global matching. All networks are re- ee-trained using the same settings and evaluated under identical conditions to ensure fair comparison.
Experiments on the Synthetic Dataset. We use the average end-point error (AvgErr) and the bad pixel ratio with different pixel thresholds to evaluate the quality of stereo depth estimation. The

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Published as a conference paper at ICLR 2026
Table 2: Quantitative results the real dataset, and all methods real spike streams input.
Method
bad 2.0 (%) L
bad 3.0 (%)
AvgEir
RAFT-Stereo Lipson et al.
2021
6.18
3.39
0.64
IGEV-Stereo Xu al. 202
(2)
8.39
5.72
0.88
Mocha-steree (Chen et al. 2024b
7.32
5,88
0.81
DLNR (Zhao et al. 2023)
5.64
3.38
0.61
Selective-Stereo (Wang et al.
2024a
5.50
3.43
0.58
SpikeStereoNet (Ours)
5.33
3.19
0.56
Scene
Kinect
RAFT-Stereo
Selective-stereo
Ours
Visual results of our method and competing approaches on the real dataset. The "Scene" refers to the camma-transformee temporal average of spike streams. "Kinect" represents the raw depths captured by the depth camera.
Figure 5:
results of the quantitative comparisons are presented Table As shown in the table, we achieve the best performance among the listed methods for almost all metrics in the synthetic dataset. Fig. E illustrates the scene, the spike stream, the ground truth of the depth, and the predicted depth maps of different methods. Our method significantly outperforms competing approaches, producing more accurate and sharper depth results. The sharpness and density of predicted depth maps demonstrate the effectiveness of our approach in spike stream inputs, especially in complex and cluttered scenes.
In addition to accuracy metrics, Table also reports the params and floating-poit operations (FLOPs) for each method. SpikeStereoNet achieves favorable balance between performance and efficiency, ranking second in FLOPs while outperforming most models in parameter number. This demonstrates that the additional computational cost, incurred to better capture the spatiotemporal characteristics and asynchrony of spike data is justified. Compared to other methods that han- dle complex temporal structures, such as Transformer-based approaches. SpikeStereoNet remains highly competitive in the efficiency. Additional comparison results are provided in Appendix A.S
Experiments the Real Dataset. To bridge the domain gap between synthetic and real data, apply domain adaptation strategy to fine-tune our model, which is initially trained on synthetic datasets. The visualization results are shown Fig. 5 Compared with other methods, our approach produces more accurate depth maps with sharper object boundaries and finer structural details. It performs especially well challenging regions, such as textureless surfaces and high-illumination areas, benefiting from the integration of spike domain characteristics. These results highlight the effectiveness of our domain adaptation strategy and the robustness our model real- weal-world stereo depth estimation from spike streams. The detailed results of different models are shown in Table 2, and we can see that our method outperforms other methods with the smallest AvgErr the real dataset. The results of experiments demonstrate that method can effectively handle real scenes.
Data Efficiency. To evaluate the generalizability our model, we conduct experiments by training it randomly selected subsets of the synthetic dataset, using 10% 50% of the full training data in
8

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ours
2.0
Selecive-SSeree
14
RAF T-Streo
AvgErr (px)
bad 2.0 2.0 (%)
GMSterea
12
1.5
10
1.0
0.1
0.2
0.3
0.4
0.5
0.1
0.2
0.3
0.4
0.5
Proportion of the training set
Figure 6: Data efficiency experiments on the synthetic dataset. The horizontal axis indicates the pro- portion of the training set, while the vertical axes represent the AvgErr and 2 px error, respectively.
10F
Augfir (P))
(lgg
Elgensalone
40
400
107
PGA-2
10
0.8
10.
0.4
-1.0
-200
0.3
-1.0
-0.5
0.5
10
-200
400
000
Number of lterations (T)
PCA-1
Figure 7: Visualization of network dynamics analysis. The sub-figures show, in order: the change in hidden state differences over time the RSNN, the eigenvalue spectrum of the network weights for stability analysis, and the PCA of different inputs over time.
10% increments. All models, including ours and the baselines, are tested on the complete testing set ensure a fair and consistent comparison. As illustrated in Fig. 6 our model consistently outper- forms other methods in all training data ratios. The performance gap becomes more significant as training data size decreases, demonstrating that our model generalizes better in data-scarce sce- narios. These results highlight the robustness and efficiency of our approach in learning meaningful representations, even with limited supervision.
Explanation of Iterative Refinement Structures. We analyze the dynamic behavior of our hybrid neural network to assess its temporal characteristics. As shown in Fig. 7 the left panel illustrates that hidden state differences decrease over time, indicating convergence. The middle panel displays the eigenvalue spectrum of the Jacobian matrix RSNN weights at the final time step, with all eigenvalues located within the unit circle, which confirms the stability of the system. The right panel presents a PCA of hidden states across input batches, showing increasing dispersion over time, which reflects the model's ' ability encode diverse temporal patterns. These results highlight temporal stability, robustness, and expressive power of the network. Further theoretical proof can be found in the Appendix A.5
Beyond validating our specific RSNN design, this dynamics analysis sheds light on why iterative re- finement, the core mechanism architectures like RAFT-Stereo (Lipson et al. [02T]) is effective. Our results explicitly show how a well-behaved iterative updater can maintain stability while pro- gressively integrating information over time steps to refine the estimate towards the correct solution. This provides dynamical grounding for the empirical success of iterative methods.
4.4 ABLATION STUDY
To evaluate the effectiveness of each component in our framework and analyze their contributions to the overall performance, conducted series of ablation studies in the synthetic dataset.
Connection Structure. We analyze the effect of connection structures within RSNN. When using feedforward connections (FFC) between layers recurrent connections (RC) within layers, there

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Table 3: Ablation studies of network architectures.
Setting of experiment
bad 2.0 (%) 4
bad 3.0 (%) +
AvgErr (px) +
w/o RC & FFC
12.07
6,46
1.29
w/o RC
11.05
4.48
0.83
w/o FFC
5.86
3,63
0.68
w/o GN module
7.49
3.11
0.58
w/o voltage regularization
7.33
2.97
0.57
w/o firing-rate regularization
6.38
2.85
0.51
w/o regularization
7.77
3,41
0.61
Ours (full)
4.13
2.38
0.42
Table 4: Ablation studies for the RSNN module.
Method
bad 2.0 (%)
bad 3.0 (%) 4 AvgErr (px) L
Vanilla RNN
7.28
3.41
0.66
GRU
4.53
2.99
0.48
LSTM
4.77
2.94
0.49
Raw SNN LIF (fixed a, 8, 7)
11.05
4.48
0.83
7.05
4.06
0.69
ALIF RSNN (Ours)
4.13
2.38
0.42
is slight increase in runtime and parameter count. However, this structural enhancement leads to significantly better overall performance. As shown in the upper part of Table incorporating these connections results in improved accuracy in all evaluation metrics. The improved information flow across time and layers enables networks to capture richer temporal dynamics and finer spatial details, highlighting the importance of these connections in achieving accurate stereo depth estimation.
Regularization. We investigate the impact of removing regularization (Reg) terms and group nor- malization (GN). As listed in the lower part of Table removing either the regularization firing rate and membrane potential or the group normalization applied to adaptive input variables leads to clear drop in performance. The absence of regularization results in unstable training and increased prediction error, while removing group normalization slows convergence and reduces final accuracy. When both components are removed simultaneously, performance further degraded.
RSNN Structure. We conducted explicit replacement experiments in which the adaptive LIF (ALIF) neurons used in recurrent refinement
substituted with classical LIF implemen- tation that keeps all adaptive scalars fixed, where / and are fixed constants, not learnable or input dependent. In addition, we also removed spikes and replaced them with vanilla RNN, LSTM, and GRU blocks, which have the same number of neurons as ALIF-RSNN, This removes adaptivity to temporal patterns spike streams. We conducted an ablation study by replacing ALIF with the above models in the synthetic dataset. All other conditions of the experiment are the same, and the results are in Table 4 From the above table, it can be seen that after replacing the ALIF model, the overall performance significantly decreased. Adaptive gating markedly improves both stability and final accuracy with negligible overhead, justifying our choice the novel ALIF.
CONCLUSION
We present SpikeStereoNet,, the state-of-the-art deep learning framework for stereo depth estimation from spike streams of spike camera, with a biologically plausible update operator based on recurrent spiking neural networks (RSNNs). In addition, we construct synthetic and real-world stereo spike stream datasets with corresponding depth maps. Extensive experiments demonstrate that our method achieves SOTA performance, benefiting from the data efficiency and convergence of RSNN. Overall, StereStere represents an advance in neuromorphic stereo vision and demonstrates the potential of spike-based systems for efficient, accurate, and high-speed 3D perception.
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ETHICS STATEMENT
We affirm compliance with the Code of Ethics for all stages of this work. Our study does not involve interventions on human or animal subjects. We evaluate and report fairness indicators where applicable, and we avoid training releasing models that systematically disadvantage protected groups. All code, models, and preprocessing scripts will be released with documentation to support reproducibility while respecting data licenses and usage constraints. We also report computing settings to encourage consideration of environmental impact and resource use.
REPRODUCIBILITY STATEMENT
To facilitate reproduction of our results, we provide detailed descriptions of our methodology. All experiments were conducted under PyTorch with CUDA, and the exact environment is specified. We specify all model architectures, hyperparameters, training protocols, data-splitiing strategies, and evaluation metrics in the main body and the Appendix. All experiments were performed with fixed random seeds. We will release our complete dataset, code repository, and demo.
ACKNOWLEDGMENTS
This work supported by the National Natural Science Foundation of China (Grant No. 62576011) and the PKU-AI Robotics Joint Lab of Embodied Al.
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A APPENDIX
The appendix provides additional implementation details and extended experimental results for the SpikeStereoNet framework introduced in the main paper. It is organized into six distinct sections:
The Use of Large Language Models in Appendix [A.I] Theory Analysis and Method Appendix
A.2
Details of the Synthetic Dataset in Appendix A.3 Details of the Real Dataset in Appendix
More Experimental Results in Appendix
A.5 and Broader Impacts in Appendix A.6
A.1
THE USE OF LARGE LANGUAGE MODELS (LLMS)
During writing, we use large language models only for grammar and style polishing. The LLMs did not generate or rewrite technical content. All statements and citations were verified by the authors. No proprietary data or restricted code was shared with the LLMs. The authors assume full responsibility for the content of the paper.
A.2 THEORY ANALYSIS AND METIOО
A.2.1 PROBLEM STATEMENT
Given a continuous binary spike stream S E {0, 1] with a spatio-temporal resolution of H x W x N, centered on the timestamp f,, we divide it into three non-overlapping substreams S St. and S centered timestamps to. t1, and tg. respectively. The goal is to estimate the dis- parity map corresponding to t1. Here, 8 provides the essential spatial structure for estimating the disparity, while Shy and S provide temporal cues improve consistency and robustness. Letting S. E {0,1) 1] denote the n-th spike frame, predict the disparity at timestamp t from the full spike stream {S,), n = O,...,k, k, leveraging both spatial features and temporal continuity.
A.2.2 NOISE SIMULATION
The noise in spiking cameras comes mainly from the dark current. The accumulation of brightness in the pixel (i,j) over time can be expressed as:
A(i,jt) = [" ?
(I(i.).T) + Idunk (t, dr, 2
(5)
where A(),j,t) represents the integrated brightness, d is the previous firing time for the pixel (i, and Lank denotes the random noise component caused by the dark current. In our synthetic dataset, the dark currents in the circuits introduce thermal noise, which is the type of noise modeled this work.
A.2.3 VIDEO-TO-SPIKE PREPROCESSING PIPELINE
We introduce a two-stage preprocessing pipeline to convert conventional image data into temporally precise spike streams, comprising neural network-based frame interpolation and spike encoding.
Frame Interpolation for Enhanced Temporal Resolution. To enhance temporal resolution, raw frames from dynamic synthetic datasets are processed using pre-trained video frame interpolation model EMA-VFI (Zhang et al., 2023). The architecture comprises a hybrid CNN and transformer framework, and uses corre lorrelation information hidden within the attention map simultaneously enhance the appearance information and model motion.
For temporal expansion, applied frame rate upsampling factor of x50 the synthetic dataset. The output is represented as a 4D tensor of shape ,, H, W W,C), C). where T is the temporal length, Il W the spatial resolution and C denotes RGB channels.
Spike Encoding via Temporal Integration. The high-frame-ratt frame-rate RGB sequences are converted spike streams through temporal integration algorithm:
1. Convert RGB frames grayscale and normalize pixel intensities to [0, 1).
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2. Accumulate membrane potential over time as Vi Vi-1 + It.
3. Spike generation:
spike matrix(t, x; y) { if Vi(i 8) 2
0 otherwise
where threshold 0 = 5.0 and potential reset V <- V, 0 after spike.
4. Repeat until all frames are processed.
Spike streams are stored binary tensors of shape [T, H, W] using the StackToSpike func- tion with configurable noise mosse and threshold 0. For compact storage and compatibility for our framework, the Spik ToRke module compresses spikes (8 per byte) in the dat format, allowing lossless reconstruction during inference.
The proposed two-stage preprocessing pipeline effectively bridges conventional images and neuro- morphic vision processing. By combining deep learning-basse -hased frame interpolation with bio-inspired spike encoding, we achieve the following:
Temporal Super-Resolution: Neural interpolation extends the temporal sampling density by 50x through multi-scale optical flow and attention mechanisms, preserving physical consistency in dynamic scenes.
Biologically Plausible Encoding: The temporal integration algorithm emulates the neuron dynamics of the retina, converting intensity variations into sparse spike events with adaptive threshold control.
System Compatibility: Serialized spike data (.dat) with byte-level compression and struc- tured formatting ensure seamless integration into brain-inspired stereo depth estimation systems.
This pipeline facilitates the efficient transformation of dynamic scene datasets into spike-compatible formats, preserving configurable spatio-temporal characteristics and laying solid foundation for spike-based stereo depth estimation.
To assess the fidelity of the conversion of the dataset, we use the Texture From Interval (TFI) algo- rithm of the SpikeCV (Zheng al. 2026) library reconstruct grayscale images from spike streams with dimensions [T. H, W.. This method exploits the spatiotemporal sparsity and encoding capacity of spike streams approximate the textural structure of conventional images.
The core idea behind TFI is that the temporal interval between adjacent spikes encodes local texture intensity: shorter intervals correspond higher pixel activity and brighter intensity. The algorithm identifies the two nearest spike timestamps within bounded temporal window (+At) for each pixel and computes the grayscale of the value pixel based on their interval duration.
A.2.4 ADAPTIVE LEAKY INTEGRATE-AND FIRE NEURON MODEL
Common RSNN models often overlook several biologically relevant dynamic processes, particularly those occurring over extended time scales. To address this, incorporate neuronal adaptation into our RSNN design. Empirical studies, such as those in the Allen Brain Atlas, indicate that a significant proportion of excitatory neurons exhibit adaptation with varying time constants, Several approaches to fit models for adapting neurons to empirical data.
In this work, adopt simplified formulation. The membrane potential of each neuron is updated using the Exponential Euler Algorithm (Cachia 2004). The firing threshold of increases by fixed value B3 following each spike, while the peak voltage u!th Gr -ppak remains constant. At a discrete time step of At= = ms, the neuronal membrane potential update rule is defined as:
h = a4 Uz-1 + +(1 (1- aa) (Wiesst1 - Wys(--1) {
(6)
where a = cxp(-At) At/rra) and s E {0, 1} denotes the binary spike train of neuron. The retention of membrane potential is controlled by decay factor 7 In summary, the neuron model incorporates three adaptive parameters that govern the decay of the membrane potential, the firing threshold, and the reset potential, jointly determining the neuron's adaptive dynamics, respectively.
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A.2.5 APPLYING BPTT TO RSNN
Although backpropagation through time (BPTT) is biologically plausible, it serves as effec- tive optimization strategy for training RSNNs over extended temporal sequences, analogous to how evolutionary and developmental processes adapt biological systems to specific tasks. Prior work has applied backpropagation to feedforward spiking neural networks by introducing a seeuudoddervative to approximate the non-existent gradient at spike times. This surrogate derivative increases smoothly from 0 to 1 and then decays, allowing gradient-based learning. In our implementation, the pseudo- derivative's amplitude attenuated by factor less than 1, which improves BPTT stability and performance when training RSNNs over longer time horizons. Throughout, we use the following surrogate function g(z):
((x) sigmoid(az) 1 e-ax
the spiking function of the sigmoid gradient is used in backpropagation. The gradient is defined by:
g (x) * (1 sigmoid(axr)) sigmoid(axr),
where a a parameter that controls the slope of the surrogate derivative. Unless otherwise men- tioned, we 4.
A.2.6 DETAIL OF LOss FUNCTION
In our training framework, the total loss L is composed of three distinct components, each serving a specific purpose in optimizing the performance the model. Specifically, the total loss is defined
Lstereo + > Lrate reg + X reg:
where Lsterro represents stereo loss, Lrate reg denotes the firing rate regularization loss, and Lv rey corresponds to the velocity regularization loss. The parameters '5 and X are regularization coeffi- cients that control the influence of the respective regularization terms. Stereo loss Laterco is designed to measure the discrepancy between the ground-truse disparity dg and the predicted disparity d, over time t. It is formulated as follows:
Literroo Σ 1n delll.
(10)
t=1
where 7) 0.9 is discount factor that assigns more weight to recent disparities, ensuring that the model focuses more on recent predictions.
The firing rate regularization loss Leate reg aims to stabilize the firing rates of neurons by penalizing deviations from the targe firing rate ro. is defined as:
Lyate Σ (ra-vo))
ri T Σ S,(),
(II)
where r; is the average firing rate of the neuron over time T. this term encourages the model to maintain consistent firing rates across neurons, thereby enhancing stability and preventing over- activation.
The voltage regularization loss Lv.ng is introduced to regularize the temporal dynamics of the model by penalizing the high velocities the neurons. is expressed as
N
T
Lyyeg
ΣΣ
(1)1)
(12)
1=1 t=1
where (t) represents the membrane potential of neuron at time t, and is the number of neurons in the network. This regularization term helps smooth the temporal behavior of the model, ensuring that changes in neuronal states are gradual and controlled.
In general, the combination of these loss components allows for comprehensive optimization strat- egy that balances accuracy, firing rate stability, and temporal smoothness, thereby enhancing the robustness and performance of the model in stereo vision tasks.
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When frame- or event- based networks are re-training, we make certain adjustments to the hyper- parameters of these baseline models. Firstly, we did simply the default hyperparameters from the original papers because frame-based and event-based methods differ significantly from spike-based data, and using default settings would clearly disadvantage some baselines. There- fore, every baseline was re-trained with targeted hyperparameter adaptation for the spike modality. Secondly, we did not perform an exhaustive full hyperparameter search for every baseline. Many baselines (e.g., Snlective-Steren DLNR, RAFT-Stereo, ZEST) have large and complex hyperparam- eter spaces. Running full grid Bayesian search for each would require tens of thousands of GPU hours, far beyond the feasibility of this work. Thus, we adopt a practical, faimess-oriented tuning sensitive to data Published as a conference paper at ICLR 2026
A.2.7 NETWORK RE-TRAINING METHOD
protocol. For each baseline, we tune the hyperparameters that are known modality, including:
Trame-/Event-bart baselines:
learning rate (+2x around original)
batch size (to account for temporal dimension, 4-16)
temporal aggregation//ooelizatio hyperparameters
photometric/contrastive loss weights where applicable
number of update iterations (if models use iteration refinement, 16)
correlation pyramid resolution
random input crops used during training
weight decay in optimizer (10~5)
Event-based models:
time-bin size
event voxel grid normalization
temporal decay parameters
We select the best settings the synthetic validation dataset, and then retrain using the combined training set.
In summery, our objective rather than force them to operate with incompatible default settings. All baselines:
give each baseline reasonable and modality-aware advantage,
/ Retrain from scratch on the spike stereo dataset
/ Use moodality-adjuster hyperparameters
Evaluate under the same data splits and metries
This ensures fair comparison, even though a full hyperparameter search is not computationally feasible.
A.3 DETAILS OF THE SYNTHETIC DATASET
A conventional active stereo depth system typically comprises an infrared (IR) projector, a pair of IR stereo cameras (left and right) and a color camera. Depth measurement achieved by projecting dense IR dot pattern onto the scene, which is subsequently captured by the stereo IR cameras from different viewpoints. The depth values are then estimated by applying a stereo matching algorithm to the captured image pair, leveraging the disparity between them. We the method from (Dai jet al., 2002, Zhang et al. 20244) that replicates this process, containing light pattern projection, capture, and stereo matching. The simulator is implemented based on the Blender rendering engine.
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Table 5: Statistics of the training and test set of synthetic dataset.
Number of
Number of Spike Frames
Number of Scenes
Scene Indexes
Resolution
Depth Map
Training Dataset
0 149
38,250
400
250
76,500 20,400
150
Test Dataset
150 189
10,200
400 x 250
40
A.3.1 RANDOM SCENES
For each scene, we generate the corresponding 'meta' files to store structured metadata information. Additionally, for each object instance, high-quality "oobj' mesh file is created to represent its geom- etry. To simulate realistic camera motion, a continuous trajectory is generated by sampling camera poses uniformly from a virtual sphere with fixed radius of 0.6 meters. The representative camera viewpoints are separated by a constant angular increase of 3 9 ensuring smooth transitions along the trajectory. The objects rotated accordingly to maintain consistent spatial alignment. Fur- thermore, uniform background and consistent lighting configuration are applied across all scenes reduce domain-specifie biases and enhance rendering consistency. To further bridge the domain gap between synthetic and real- world data, we adopt domain randomization strategies by randomiz- ing object textures, material properties (from specular, transparent, to diffuse), object layouts, floor textures, and lighting conditions aligned with camera poses.
A.3.2 STEREO MATCHING
We perform stereo matching to obtain the disparity map, which can be transferred to the depth map leveraging the intrinsic parameters of the depth sensor. Specifically, matching cost volume is constructed along the epipolar lines between the left and right infrared (IR) images. The disparity each pixel is determined by selecting the position with the minimum matching cost. To enhance accuracy, sub-pixel disparity refinement is performed via quadratic curve fitting. In addition, several post-processing techniques are employed to improve depth quality, including left-right consistency checks, uniqueness constraints, and median filtering. These steps collectively yield more realistic and robust depth estimations. The calculation method from disparity depth is as follows:
BS
Z =
(13)
d (c21 Czu)
where the depth obtained, d the disparity, ʃ denotes the focal length of the camera, baseline distance between the stereo cameras and 6x1 Cau represents the horizontal offset between the principal points the right and left cameras.
A.3.3 SYNTHETIC DATASET
We make use of domain randomization and depth simulation and construct a large-scale synthetic dataset. In total, the dataset consists of two subsets: (1) Training set: Consists of 76,500 spike streams and 38.250 depth maps generated from 150 scenes composed randomized specular, transparent, and diffuse materials.
88 distinet objects, with (2) Test set: 20,400 spike streams and 10,200 depth maps generated from 40 new scenes containing 88 distinct objects. The scenes in the dataset are shown in Fig. 8 and Fig. g and the detailed statisties of dataset shown in Table 5
A.4 DETAILS OF THE REAL DATASET
A.4.1 STEREO CAMERA SYSTEM
We constructed a stereo imaging system using two spike cameras and one RGB-D camera (Kinect) to simultaneously capture left/right spike streams and the corresponding ground-truth depth images. As illustrated in Fig. 10 the two spike cameras are mounted on a high-precision alignment rig to ensure accurate baseline calibration. The spatial resolution of the spike camera is 400 x 250, and the temporal resolution is 20,000 Hz. The baseline between two spike cameras is 0.08 with focal length of 16 mm for both. The resolution of the RGB-D camera is 1280 720, and the frequency is 30 FPS. Compared with LiDAR, the RGB-D sensor provides dense depth annotations within its
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Figure 8: Scenes for generating the training set of the synthetic dataset. Selected five scenes, each displayed as in which the images correspond to different camera viewpoints of the same scene (partial views).
Figure 9: Scenes for generating the test set of the synthetic dataset. Selected scenes, each displayed as row in which the images correspond to different camera viewpoints of the same scene (partial views).
effective range, avoiding the sparse and incomplete depth output typically associated. To ensure spatial alignment between modalities, intrinsic and extrinsic calibrations are performed between the spike cameras and the depth camera. The captured depth maps then geometrically warped to the coordinate frame the left spike camera for accurate correspondence.
A.4.2 STEREO SYSTEM CALIBRATION
For the calibration of spike cameras and Kinect, we used the following methods and programs. When calibrating with spike cameras, necessary capture images of standard chessboard calibration pattern. Approximately 20 sets of images should be taken from different positions and angles (covering left-right and up-down tilts of 15-45 degrees, various distances, and the entire field of view). During shooting, the calibration board must remain stationary, be fully visible in the field of view both spike cameras, and both cameras should capture images simultaneously. Sufficient time should be allowed each position ensure image clarity. Additionally, when reconstruct- ing images using TFI/TFP (Zheng et al. 2026), the hardware and software settings (including the TFI/TFP parameters) of the two spike cameras must be kept consistent. To reduce noise interfer-
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ence, the process should start with completely static scenes and longer THI windows, ensuring that the scenes captured by both cameras are identical except for perspective differences. Adequate lighting should be provided, and scenes with clear, and strong edges should be selected to improve calibration success rates. OpenCV's calibration algorithms are used for stereo rectification (the pri- mary APIs are "cv.stereoRecttff Uncaaibrrt and "ev.stereoRectiff") The rectification is applied to the TFITTP images reconstructed by the spike cameras, and the rectified images serve as input to the binocular stereo algorithm.
A.4.3 STEREO SYSTEM SYNCHRONIZATION MECHANISM
We employ a hybrid hardware-trigger + software algorithmic synchronization pipeline that ensures stereo alignment suitable for downstream training and evaluation as follows:
Hardware-level coarse synchronization. Both spike cameras are connected to the same host ma- chine via high-speed USB. During recording, we use shared software trigger initiate acquisition on both sensors: ((1h) z (()) where (( and ( are the left/right start timestamps. This provides coarse alignment, typically within tens of microseconds.
Empirical estimation of residual time offsets Even with shared triggering, small timing mis- matches remain due independent internal clocks. To measure the remaining timing error, place a fast-switching LED in both cameras' fields of view and compare the spike timestamps of each illumination transition. We collect over 2,000 pairs of real stereo sequences and estimate the residual time offsets:
At
(R)
i= 1..... N.
(14)
We model the empirical distribution
At (us
(15)
where in practice: Psr within 3-7 jas,
ahe depends on lighting and spike density but remains i 15 jus.
This analysis characterizes the realistic synchronization noise between the two spike streams.
Temporal cost matching, Before building the cost volume, we apply a lightweight alignment pro- cedure:
(( + 5 + &t),
(16)
where Át is estimated via maximizing temporal correlation:
At = arg max
Σ (S, (()
(17)
2,9
This provides fine temporal adjustment without modifying the spike stream statistics.
Domain Randomization training, To ensure robustness to synchronization jitter, we inject the measured At distribution into the synthetic spike generator:
S(L) * syn(l) = S(L.) ideal(t + e), 6 e~ E~N(H,, 220)
(18)
This domain randomization teaches SpikeStereoNet to become invariant to microsecond-leww off- sets.
Fine-tuning on real spike data. Because the network is pre-trained with jitter matching the real distribution, fine-tuning on real data automatically adapts to the actual synchronization statisties:
Greal E support(N (aaattNNasss (pan,
(19)
As a result, the RSNN refinement module effectively compensates for minor mismatches during iterative updates.
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RGB-Depth Camera (Kinect)
Splias 5 2
Spike Camera
Spike Camera
FOTOFANS
Figure 10:
The photograph of our hybrid imaging system. The RGB-D camera is fixed on the top plane of the stereo spike cameras. The extrinsic parameters between the cameras are calibrated.
A.4.4 REAL DATASET COLLECTION
The real dataset comprises some video sequences captured under diverse environmental conditions, including varying ambient illumination levels (e.g., low, medium, and high brightness) and a range of dynamic scenarios characterized by distinct object arrangements. These scenes encompass dif- ferent spatial configurations and relative motions of multiple objects (e.g., camera shake and object motion), introducing varied degrees of motion blur. The following is the specific collection process of our real dataset:
Dataset Acquisition Setup. Our real dataset is collected using a custom-buill hardware system consisting of:
Two spike cameras arranged in stereo configuration, with baseline of 80 mm (calibrated for epipolar alignment).
A Kinect depth camera through hardware triggering, used capture ground-truth depth for alignment.
Calibration of the Stereo Spike Camera System. Stereo spike cameras are calibrated following rigorous protocol:
A matte chessboard calibration target (10x 14 squares, 25 mm per square) is used, posi- tioned statically across 20+ viewpoints (covering 15-45% tilts and 0.5-2 distances).
Both spike cameras captured synchronized spike streams of the target, which are converted TFI (Temporal Filtered Integration) images with a 50 window ensure clarity.
The calibration quality is validated by checking that the reprojection emoos for the 3D chessboard corners are pixel across viewpoints.
Dataset Content, The dataset includes indoor real-world scenes:
Objects:
20+ everyday items, including textureless objects (e.g., plain plastic basin, white mugs) and reflective objects (e.g., metal box). Each scene contains 5-8 randomly arranged objects to simulate clutter.
Lighting Conditions: 8 controlled lighting setups with direct light sources (LED panels) at angles of 30 45% 60%. and 90º relative to the scene, with intensities ranging from 300- 1500 lux to induce varying levels of specularity and shadow.
Data Modalities: For each scene, we provide raw stereo spike streams and Kinect-aligned depth maps (ground truth).
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A.5
MORE EXPERIMENTAL RESULTS
A.5.1
THEORETICAL ANALYSIS OF NETWORKS
We further supplement the theoretical analysis in the article with the explicit convergence of RSNN and Lipschitz stability analysis. Here is the complete proof:
Constructing Fixed Point Iteration and Mapping. For clarity, rewrite the RSNN update step in vector form:
u, = -F( with u u: (h, v.s. RAN,
(20)
aG + (1 (wrc v W (
Bellpeak 0(h
F(u)- =
(21)
vih)
0- v
The parameters Ort, Be, Yt are all adaptive variables regulated by the firing rate, and after the activa- tion function, their ranges all (0, 1), that is, 0 < Or < 1, < Be < 1, < Yt 1.
Lipschitz Continuity. The Lipschitz constant is determined by the activation function 0(-) (hard threshold/ReLU) and linear operator. If is 1-Lipschitz ReLU, the mapping globally Lipschitz in U₁ then:
( - F(u'll || = Lilu - u'll.
(22)
where
of + (1 are) Yt. 3r Upeak [|Wrelll
(23)
and all the ranges of variables in this equation are (0, 1), hence the RSNN update contraction with constant L < 1.
Iterative Refinement and Convergence. Because is a contraction in the entire metric space II-I), and according to the Banach Fixed-Point theorem (Gordji et al. 2017)) the error satisfies.
u"
(1) (
(24)
Hence, the sequence } linear exponentially unique fixed point u*. The bound agrees with the empirical spectral-radius curves reported earlier (Fig. 7).
Practical Implications.
No Exploding States:
The membrane potentials remain bounded by --z ||u ()))
Training Stability: Gradients passed through time have an upper bound L _T which miti- gates the exploding BPTT signals.
Noise Resilience: Any perturbation d at step k decays t
Under the condition that ar2, Bt Yt bounded and the weight satisfies L 1, the iterative u, = F(u,-1) RSNN linearly converges to unique fixed point, providing a theoretical guarantee for continuous and contraction Lipschitz mapping. The above theoretical proof supplements the empirical stability study already proposed (Fig. 7b.
A.5.2 ADDITIONAL COMPARATIVE RESULTS
We present a complete stereo depth estimation sequence for a single scene within the synthetic dataset, capturing the full motion trajectory of objects throughout dynamic environment. The different scene images below are obtained by rotating the stereo spike camera system from different views, and we have extracted some of the views equal intervals for display. As illustrated Fig. and Fig. [2] the predicted depth maps effectively reconstruct the continuous evolution of the scene, accurately reflecting both spatial structure and temporal consistency. These results demonstrate the model's 's ability to preserve coherent geometry across time, validating its effectiveness in handling dynamic visual inputs. The supplementary materials in the zip file include continuously changing videos.
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Scene
Spike (TFI)
Ground Truth
Estimation
Figure 11: Video results of our method on the synthetic dataset in the single scene. From left to right, the figure shows the sequential scene images, the visualized spike stream using the TFI method, the ground-truth depth maps, and the depth estimation results produced by proposed method.
View 0
View 20
View 40
View 60
View 80
View 100
View 120
View 140
View 160
View 180
View 200
View 220
Figure 12:
Visualization results of SpikeStereoNet on the synthetic dataset in a single scene. We presented depth estimation results of 12 views in the scene.
In addition, we conducted qualitative comparison between SpikeStereoNet and several state-of- the-art methods the synthetic dataset. Our method consistently produces more accurate and visu- ally coherent depth maps in various scenes. Further qualitative results are provided to demonstrate the robustness and effectiveness of our approach.
A.5.3 SENSITIVE OF THRESHOLD AND TEMPORAL QUANTIZATION
We conducted additional experiments quantify how the spike threshold and temporal quantization affect SpikeStereoNet. The model shows robustness to wide range of settings, largely due to the adaptive LIF dynamics and the RSNN refinement stage.
During spike generation, the threshold controls how many potentials should accumulate be- fore a spike is triggered. We evaluate thresholds in the range: "peak { 0.7 1.0tpeaks 3. where Upeak is the nominal threshold. Spike streams are discretized into (7) bins: {0.5 ms, 1.0 ms, 2.0 ms, ms). and At 1.0 ms the default time step of the model. The comparison results are shown in Table
Overall, the impact of the two parameters on the model follows:
Low threshold for denser spikes and slightly more noise.
High threshold for sparser spikes and fewer temporal cues.
Very fine bins (< 0.5 ms) increase sparsity but yield negligible improvement.
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Table 6: Sensitivity analysis of the spike threshold and the temporal quantization step during spike
generation.
Settings bad 2.0 (%)
bad 3.0 (%))
AvgErr (px) +
Variation
0.7 Upeak
4.56
2.61
0.44
+3.5%
p.akk
4.78
2.62
0,45
+5.9%
1. 1.62pcak
4.92
2.78
0,46
+8.3%
Upeak, At =1 ms
4.13
2.38
0.42
At =0.5 ms
4.44
2.58
0,43
+1.2%
At -2.0 ms
4.93
2.67
0.44
+3.6%
At =4.0 ms
5.01
2.80
0.47
+10.7%
Table 7: The comparison results of the computational efficiency.
Method
Equivalent FPS
FLOPs (B)
Method
Equivalent FPS
FLOPs (B)
CREStereo
1.67
863
GMSterco
2.28
160
RAFT-Sterco
1,80
798
DLNR
1.79
1580
ZEST
1.63
989
MoCha
1.31
935
IGEV-Stereo
1.53
614
MonSter
1.72
1567
Selective-Stereo 1.76
957
Ours
1.91
473
Coarse bins (> 4 ms) lose spike timing, which is important for handling motion.
The performance of the model varies within +5.9% thought the threshold range.
Within 0.5-2 ms, the model is stable with the AvgErr variation of less than 3.6%.
This robustness arises because the dynamics of ALIF incorporates adaptive membrane thresholds 3eUpeak and learned reset gates Yt that automatically compensate for moderate changes in spike density, even when the raw spike discretization changes.
SpikeStereoNet not strongly sensitive to moderate shifts threshold or temporal step because: The ALIF neuron's adaptive threshold dynamically regulates firing rates. The RSNN recurrence reconstructs the missing temporal structure from the context. Firing-rate and voltage regulariza- tion constrain membrane dynamics, ensuring stable behavior even under different spike densities. Overall, only extreme settings (very coarse temporal step or high thresholds) noticeably degrade performance.
A.5.4 COMPUTATIONAL EFFICIENCY
We conducted additional experiments measure the FPS or Inference Latency of the networks listed in Table 1 using the same hardware configuration: NVIDIA RTX 3090 GPU, paired with an Intel Core 17-13700K CPU and 32GB RAM. All models are averaged over 100 inference runs to minimize variability. The results are summarized, the FPS and FLOPs of inference refer to depth map estimated using set of stereo spike streams (T 50).
From the additional results in the Table 7 can be seen that our model also outperforms most in inference speed and FLOPs.
A.5.5 HYBRID MODALITY FUSION
Hybrid neuromorphic-fran frame sensing becoming increasingly common, and we have conducted preliminary hybrid experiments and discussed integration strategies below.
Spike+TFI Image Fusion. We generated TFI images (Temporal Frame Integration, 10-50 win- dows) from the raw spike stream:
lot Σ t=
TFK(,,))
S(x,y. y.
(25)
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Table 8: The quantitative results of Spike+THI fusion the synthetic dataset.
Method
bad 2.0 (%) 4
bad 3.0 (%)
Avglir (px)
Spike+TFI (At = 10)
6.18
3.69
0.67
Spike+TFI (AI -30)
5.35
3.09
0.56
Spike+ TFI (At 50)
4.98
2.87
0.54
Ours
4.13
2.38
0.42
Table 9: The quantitative results of Spike+RGB fusion the real dataset.
Method
bad 2.0 (%)
bad 3.0 (%)
AvgErr (px) L
Rpi (Kinect)
5.08
3,43
0.59
Ours
5.33
3.19
0.56
To evaluate fusion, adapted our architecture by adding a TFI branch and merging its features with spike features via: channel-wise concatenation, and context fusion (applying a shallow CNN to extract context features before cost-volume refinement). The experimental results on synthetic datasets are as Table
Compared to only spike input, TFI provides limited benefits in motion situations where spike time crucial. Quantitative evaluation shows that performance has decreased by about 12% in average scenes. Because using TFI for reconstruction into frame images additionally introduces noise.
Experiments With Spike+RGB Frames. We also performed preliminary fusion with synchronous RGB frames from the Kinect. Following prior hybrid designs, evaluated two fusion strategies. (I) Early Fusion. The RGB data are downsampled and concatenated with the spike feature map, which has the advantages of simplicity and disadvantages of misalignment and HDR mismatch. (2) context Fusion. RGB is processed by separate context encoder, producing Figb Ligb (RGB), Spike spike (S), followed by Frused Cony([Figb. Fsikel). This version is consistently more stable. The experimental results on real datasets are as Table D In summary, spike+RGB yields an improvement in object details, with slightly better global structure consistency, but with small degradation in motion (RGB lagging behind spikes). In addition, due to the use of Kinect depth cameras to capture RGB images, frame rate and resolution blur output cannot avoided.
In summary, we conducted preliminary spike+TFI and spike+RGB fusion experiments. The hybrid inputs offer modest robustness improvements static illumination scenes. We describe a prelimi- nary architecture modification for hybrid fusion, inspired by the hybrid spike- RGB literature (Chang ct al. 2023).
A.5.6 ADDITIONAL RESULTS FOR ABLATION STUDY
We provide additional ablation studies to demonstrate the effectiveness of our designed modules.
RSNN Module. Below we disentangle the impact of group normalization (GN) from that of our RSNN refinement by re-running the key variants and reporting the results the same metrics used in Table 10 The RNN is the same number of neurons the original model.
These results demonstrate that both components, the RSNN architecture and the GN contribute to the performance, but neither alone is sufficient to achieve full model's results. Thus, the competitive performance of our framework arises from the synergy between the RSNN (optimized for spike-based temporal patterns) and the GN (stabilizing its dynamics).
Adaptation of Neuron Models. We conducted an explicit replacement experiment in which the adaptive LIF (ALIF) neurons used in recurrent refinement were substituted with classical LIF or raw SNN implementation that keeps all adaptive sealars fixed. In the following, we formalize the LIF classical, highlight the theoretical differences, and report quantitative results.
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Scene
Ground Truth
=
3
= 4
=
T 16
T 32
Remove RC
Remove FFC
Remove Reg
Remove GN
Visualization results ablation study evaluating the effectiveness of our framework on the synthetic dataset. Among them, T 32 is the baseline of our model.
Figure 13:
The raw SNN layer with no recurrent connections and fixed threshold (feedforward spiking layer) typically contains:
Ut 0-1/11 W Wys!-11 0vr Uth).
(26)
The classical LIF neuron, unlike our ALIF, uses fixed parameters (no adaptive variables a, Br. Yt)- Its dynamics can be written
h a Vt-1 + (1-) +
(Wror8t-1
tt B Uppaak
(27)
8t 0hh 1.
U, h4 7/t St th
where a, and y are fixed constants (e.g., 0.5, 1.0, 1.0 in our baselines), not learnable input dependent. This removes adaptivity to temporal patterns in spike streams. All other network com- ponents, learning rates, and losses were kept identical. We conducted ALIF with classical LIF or raw SNN in the synthetic dataset (the same architecture). All other conditions of the experiment are the same, and the results are in Table
ablation study by replacing
From the above table, it can be seen that the overall performance decreases significantly after re- placement with the LIF model. The ALIF is critical for our task because stereo spike streams require adaptive temporal processing. Adaptive gating markedly improves both stability and final accuracy with negligible overhead, justifying our choice of the novel ALIF over classical LIF or raw SNN.
Number of Iterations. We analyze the impact of iteration steps by varying the number of iterations our RSNN- -baed model. As shown in Table II our method achieves competitive or superior performance with significantly fewer iterations compared to existing approaches. Unlike conven- tional GRUs or transformer-basee update modules, RSNNs require fewer parameters and operations per iteration, thus maintaining lower computational cost.
In addition, to further support the ablation studies presented in the main experimental section, we provide the corresponding qualitative results in Fig. [3] These visualizations clearly demonstrate the effectiveness of each module in our proposed framework and validate their individual contributions the overall performance.
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Table 10: Ablation study for RSNN and group normalization.
Method
bad 2.0 (%) +
bad 3.0 (%)
AvgÊrr (px) 4
RNN RNN GN
7.88
4.04
0.76
7.28
3.41
0.66
RSNN
6,38
2.85
0.51
RSNN GN (Ours)
4.13
2.38
0.42
Table 11: Ablation study for number of iterations and runtime.
Number of Iterations (T)
3
8
32
AvgErr (px)
0.69
0.61
0.56
0.49
0.46
0,42
Runtime (s)
0,41
0.42
0.43
0.46
0.51
0.63
Image to Video. Regarding our model for generating continuous spike streams using the video frame interpolation method. Below we justify our design choice, quantify the interpolation error, and report a new control experiment based on Blender's 's native sub-frame rendering and optical flow supervision.
We use EMA-VFI (Enhanced Motion-Aware Video Frame Interpolation) (Zhang et al. 2023). a successor to FILM that achieves comparable or superior quality in small motion scenes. In our raw Blender renders (ground truth), the quantitative metrics of EMA-VFI (PSNR = 38.2 1.5 dB, SSIM =002 indicate minimal artifacts, with visual inspections confirming that interpolated frames preserve fine details critical for spike simulation. We computed the optical flow magnitude per pixel (in pixels) across the entire synthetic dataset.
From Table 12 more than 95% of the frames exhibit sub-pixel motion, far below the motion thresh- old (=s px) which EMA-VFI begins introduce visible artifacts. Thus interpolation artifacts are negligible for the vast majority of the data.
After interpolation, frames are passed through the spike simulator. The conversion from interpolated frames spike streams via the spike simulator further mitigates residual artifacts. The simulator introduces Poisson-like temporal noise and sparsity, which naturally smooths minor interpolation inconsistencies. Thus, even if an interpolated frame has minor inaccuracies, the temporal differ- ence between two adjacent bins remains dominated by photon shot noise injected by the simulator. Regarding ground-truth depth labels, which are extracted directly from Blender's z-buffer per orig- inal key-frame, and they do not rely on the interpolated RGB images. During spike simulation, the disparity is linearly blended between key-frames, guaranteeing label consistency regardless of interpolation artifacts.
To compare the effectiveness of the methods, re-rendered full sequences with ground-truth op- tical flow from Blender, and regenerated a "SubFrame-GT" variant. The ground-truth optical flow exported via Blender's Vector pass, generate image data and passed the spike simulator. We re-train the model from scratch using the same hyper-parameter, and the results are as Table I3
The performance difference < 2% / 0.1 pp, indicating that interpolation artifacts do not materi- ally influence training or final accuracy. Overall, the two methods represent different patterns and approaches that can be chosen for specific tasks and datasets.
Table 12: Motion statistics in the synthetic datasets.
Quantile
50%
75%
90%
95%
Motion (px)
0.31
0.57
1.12
1.89
30

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Published as a conference paper at ICLR 2026
Table 13: Ablation study for Video frame interpolation methods.
Dataset variant
bad 2.0 (%)
Avglirr (px)
SubFrame-GT (no interpolation)
0,44
4.43
Original (Ours)
0.44
4.52
A.6 BROADER IMPACTS
The proposed framework for stereo depth estimation from spike streams has strong potential to advance neuromorphic vision in diverse application scenarios. In autonomous driving, the enhanced depth estimation enabled by spike-based sensing can improve tection, and object location, thus contributing to more robust and reliable navigation systems. In the field robotics, the fine-grasn graiger depth perception afforded by this method supports precise manip- ulation, mapping, and motion planning, especially in highly dynamic high-speed environments where conventional cameras typically underperform. Moreover, this framework that can directly es- timate depth from raw stereo spike streams under supervised settings, enabling broader exploration of spike-based vision in both research and industrial applications.
scene understanding. obstacle de-
L Limitations. Our method is limited by the small scale of the real world dataset and the domain gap between the synthetic and real data. Although domain adaptation helps, its effectiveness is constrained by the quality real labeled data and cannot fully bridge distribution differences. We plan to extend our method to handle these questions in future work.
Future Works. We will strengthen the benchmark with broader coverage, and therefore we have launched follow-up data collection campaign that will: (1) Expand the variety of the scene: in- door, outdoor and remi-outdoor environments with larger depth ranges and moving platforms: (2) Increase lighting diversity: day-night sweeps and high dynamic range sequences; (3) Enrich object categories: targeting 250 physical objects 30 WordNet classes, including specular and transpar- ent materials. The existing and extended dataset will be released publicly under permissive license and the accompanying capture/annotation pipeline will be open-sourced to facilitate community contributions.
31


Reviews:
[{"review_id": "4hQrMwB3CG", "reviewer": "Reviewer_9ZHQ", "summary": "This work proposes SpikeStereoNet, a brain-inspired framework and the first to estimate stereo depth directly from raw spike streams. Moreover, this work introduces a large-scale synthetic spike stream dataset and a real-world stereo spike dataset with dense depth annotations. Results demonstrate that SpikeStereoNet outperforms existing methods.", "questions": "see weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "This work is clearly written, makes significant contributions, and proposes a new dataset and an SNN-based processing method. I believe it will be beneficial to the neuromorphic vision community.", "weaknesses": "1. This work used a spiking neural network, but it was not mentioned in the related work. What is the difference between a recurrent spiking layer and a raw spiking layer? I suggest the authors supplement their research on SNNs.\n\n2. In the experimental section, there is a lack of ablation experiments related to RSNN construction, which is puzzling as to why the proposed method is effective.\n\n3. What are the differences between adaptive LIF neurons and vanilla LIF neurons? If vanilla LIF is used, will there be a performance loss?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HkkBOcgLPG", "reviewer": "Reviewer_gap8", "summary": "This paper proposes SpikeStereoNet, a framework for stereo depth estimation directly from spiking camera data. Using a Recurrent Spiking Neural Network (RSNN), the method iteratively refines depth estimation. It provide synthetic and real-world datasets with depth labels for evaluation. Experiments show that SpikeStereoNet outperforms some existing methods on both datasets.", "questions": "1. For the real-world dataset, it is unclear how temporal synchronization between different cameras is achieved. The paper does not describe any synchronization circuitry or mechanism.\n\n2. It is unclear why event-based stereo methods are included for comparison. If such comparisons are necessary, why are only ZEST and StereoSpike selected, while other representative methods such as DDES[1] and Se-cff[2] are not considered?\n\n[1] Yeongwoo Nam, Mohammad Mostafavi, Kuk-Jin Yoon, and Jonghyun Choi. Stereo depth from events cameras: Concentrate and focus on the future. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 6114–6123, 2022.\n\n[2] Stepan Tulyakov, Francois Fleuret, Martin Kiefel, Peter Gehler, and Michael Hirsch. Learning an event sequence embedding for dense event-based deep stereo. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1527–1537, 2019.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper introduces large-scale synthetic and real-world spiking stereo datasets, filling an important gap in spike-based depth estimation.\n\n2. It proposes a biologically inspired RSNN-based architecture (SpikeStereoNet) for stereo depth estimation from spiking data.\n\n3. It provides a theoretical analysis of the RSNN’s iterative dynamics, demonstrating the model’s stability and convergence.", "weaknesses": "1. The paper overlooks prior studies and incorrectly claims to be the first to estimate stereo depth from spiking data. Actually, “Learning Stereo Depth Estimation with Bio-Inspired Spike Cameras” has already investigated the same problem. Consequently, the novelty and originality of this work are debatable.\n\n2. The paper does not clearly explain the model’s novelty. The proposed framework is similar to existing stereo methods, differing mainly in using an RSNN as the update module. However, the paper does not explain why an RSNN is necessary compared to existing update modules. And it also does not explain what makes SpikeStereoNet particularly suited for spiking data.\n\n3. The paper lacks a systematic organization and analysis of the experimental results. Although it demonstrates that the proposed method outperforms both event-based and image-based approaches, it does not analyze the source of this performance advantage. Moreover, the paper does not clearly justify the choice of comparison methods, explain their relevance, or indicate whether they represent state-of-the-art baselines.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "bjMNZv4QfQ", "reviewer": "Reviewer_qsqk", "summary": "This paper proposes SpikeStereoNet, a biologically inspired framework for stereo depth estimation directly from raw spike streams. The method integrates a recurrent spiking neural network (RSNN) into an iterative refinement structure and introduces two benchmark datasets, including a large-scale synthetic dataset and a real-world spike stereo dataset. The approach achieves state-of-the-art performance compared with both event-based and frame-based stereo methods and demonstrates strong generalization and data efficiency.", "questions": "1. How sensitive is SpikeStereoNet to the spike threshold and the temporal quantization step used during spike generation?\n\n2. Does the proposed method provide measurable computational or energy efficiency benefits?\n\n3. Does the adaptive LIF with input-dependent propose the deployment challenge? because all the parameters will change under different timesteps.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Novel and timely problem setting in neuromorphic stereo vision. The work explores stereo depth estimation directly from spike streams, an under-explored but promising topic.\n\n2. Valuable dataset contribution. The synthetic and real spike stereo datasets fill an existing gap and will likely benefit future research in this area.\n\n3. The proposed RSNN-based iterative update operator is biologically motivated and well integrated into the overall pipeline. The adaptive leaky integrate-and-fire neuron design with context-dependent parameters ($\\alpha$, $\\beta$, $\\gamma$) is innovative.\n\n4. The experimental design is comprehensive. Comparisons include both frame-based baselines such as RAFT-Stereo and Selective-Stereo, and event-based methods, such as ZEST. Results consistently show clear advantages of the proposed method.\n\n5. The data efficiency experiments demonstrate strong generalization under limited supervision, which is an important property for spike-based systems.\n\n6. Theoretical analysis of convergence and temporal stability adds rigor to the model design.", "weaknesses": "1. The real-world dataset remains small and limited to indoor scenes. Quantitative results for real data are not clearly presented in the main paper, which weakens the validation of real-world applicability.\n\n2. The claimed biological plausibility is conceptually appealing, but its computational or representational benefits compared with conventional recurrent architectures are not fully quantified. Some biological architecture works lack discussion [1, 2].\n \n3. The ablation study covers some components but does not isolate the contribution of the adaptive neuron model or explore the sensitivity to regularization weights.\n\n[1] ClearSight: Human Vision-Inspired Solutions for Event-Based Motion Deblurring[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2025: 7462-7471.\n\n[2] SABV-Depth: A biologically inspired deep learning network for monocular depth estimation. Knowledge-based systems, 263, 110301.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "fB7h3ECpY4", "reviewer": "Reviewer_u6CE", "summary": "This paper addresses the limited availability of methods and benchmarks for stereo depth estimation from raw spike camera streams, a sensor modality distinct from both frame-based and event cameras. To this end, the authors contribute:\n- Benchmarks: A synthetic dataset with dense ground truth and a real-world dataset captured using dual spike cameras and a Kinect.\n- SpikeStereoNet: An end-to-end framework that adapts iterative refinement (similar to RAFT-Stereo) by replacing GRU updates with a Recurrent Spiking Neural Network (RSNN) using adaptive Leaky Integrate-and-Fire neurons for native spike data processing.", "questions": "Can you please justify the claim that your method is designed for \"highly dynamic scenarios\" when the provided datasets appear to only contain tabletop scenes with simple, non-dynamic motion?\n\nAre there any scenes in your synthetic or real-world test sets that feature (a) fast egomotion, (b) multiple independently moving objects, or (c) complex non-planar environments? If not, how can you be sure the method generalizes beyond static tabletop/floor scenes?\n\nGiven that the real-world video is not available, can you provide more details on the \"object motion\" and \"camera shake\" mentioned in the appendix? Are these motions fast and challenging, or slow and simple?\n\nThe adaptive LIF model (Eq. 3, A.2.4) is a key component. How much does this adaptivity (i.e., learning) contribute to performance versus using a simpler, non-adaptive LIF with fixed (but tuned) hyperparameters?\n\nYou state that all baselines were \"re-trained using the same settings\" for fairness, which is excellent. Could you please clarify if this included a full hyperparameter search for each baseline on this new spike dataset, or were the default hyperparameters from their original papers used? Ensuring baselines are optimally tuned for this new data modality is crucial.\n\nYour work focuses purely on spike streams. However, hybrid sensor systems are common. Have you considered fusing the spike stream with either a reconstructed TFI image (from the spikes themselves) or with synchronous RGB frames (like those from the Kinect) to see if this further improves robustness? How might the network change to accommodate this?\n\nI strongly recommend bringing the quantitative results for the real dataset (Table 4) into the main paper. To make space for Table 4, perhaps the qualitative results (Fig. 4/5) could be slightly condensed.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper is clearly written, with detailed appendices that support reproducibility. The benchmark contributions are substantial, and the integration of spiking dynamics into a refinement framework is well aligned with the characteristics of spike data. The analysis is thorough, covering dynamical stability, theoretical convergence, and ablations that validate the design.", "weaknesses": "The paper's primary weakness is the disconnect between its claims and its evidence. It claims to be a solution for \"rapidly changing scenes\" and \"highly dynamic scenarios,\" but the new datasets appear to be limited to simple, object-centric tabletop scenes with non-complex motion (e.g., smooth camera panning/hovering, \"camera shake\"). This dataset is insufficient to validate the central claims of the paper.\n\nFollowing from previous point, the dataset seems to lack challenging, realistic scenarios. The scenes in Fig. 4, 5, 8, and 9, together with the supplemented video, all show static objects on a planar surface. The paper gives no evidence of testing on scenes with:\n\nFast egomotion (e.g., a car or drone navigating).\nMultiple independently moving objects.\nComplex, non-planar 3D environments (e.g., outdoor scenes, complex indoor rooms). This makes the benchmark's \"challenge\" questionable.\nThe adaptive LIF model (Eq. 3, Appendix A.2.4), with learned parameters, is a core component of the RSNN updater. However, the ablation study (Table~2) does not isolate its contribution, leaving it unclear how much adaptivity improves performance over a simpler, fixed LIF variant.\n\nWhile the paper outlines detailed calibration procedures (Appendix A.4), the use of a Kinect for real-world depth ground truth introduces noise. This is a known limitation and, despite the challenges of data collection, deserves acknowledgment.\n\nThe quantitative results for the real-world dataset (Table 4) are central to validating sim-to-real transfer, yet they are placed in the appendix. Given their importance, they should be included in the main paper, arguably more so than the extended qualitative results.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)