Distil-E2D: Distilling Image-to-Depth Priors for Event-Based Monocular Depth Estimation
Authors: Jie Long Lee
Gim Hee Lee
Venue: NeurIPS 2025 poster ()
Keywords: event camera
neuromorphic vision
monocular depth estimation
Primary area: applications
TL;DR: We propose Distil-E2D for event-based monocular depth estimation, using dense synthetic depths from foundational models, a confidence-guided calibrated loss for label alignment, and a novel architecture to improve encoder representation.

Abstract: Event cameras are neuromorphic vision sensors that asynchronously capture pixel-level intensity changes with high temporal resolution and dynamic range. These make them well suited for monocular depth estimation under challenging lighting conditions. However, progress in event-based monocular depth estimation remains constrained by the quality of supervision: LiDAR-based depth labels are inherently sparse, spatially incomplete, and prone to artifacts. Consequently, these signals are suboptimal for learning dense depth from sparse events. To address this problem, we propose Distil-E2D, a framework that distills depth priors from the image domain into the event domain by generating dense synthetic pseudolabels from co-recorded APS or RGB frames using foundational depth models. These pseudolabels complement sparse LiDAR depths with dense semantically rich supervision informed by large-scale image-depth datasets. To reconcile discrepancies between synthetic and real depths, we introduce a Confidence-Guided Calibrated Depth Loss that learns nonlinear depth alignment and adaptively weights supervision by alignment confidence. Additionally, our architecture integrates past predictions via a Context Transformer and employs a Dual-Decoder Training scheme that enhances encoder representations by jointly learning metric and relative depth abstractions. Experiments on benchmark datasets show that Distil-E2D achieves state-of-the-art performance in event-based monocular depth estimation across both event-only and event+APS settings.

Distil-E2D: Distilling Image-to-Depth Priors for Event-Based Monocular Depth Estimation
Jie Long Lee Gim Hee Lee Department of Computer Science, National University of Singapore jielong leecu. nus eus, gimhee leeenus. edu. sg
APS
DepthAnythingV2
Disti-E20 (E) (Ours)
Distil-E2D (E+I) (Ours)
LIDAR
Figure 1: Monocular depth estimation under low-light conditions. An image-based monocular depth estimator (DepthAnyythnngV: V2) fails to recover meaningful structure from nighttime APS frame due to poor lighting. In contrast, our weent-based Distil-E2D produces coherent depth predictions, highlighting the advantages of event sensing and our proposed depth prior transfer framework.
Abstract
Event cameras are neuromorphic vision sensors that asynchronously capture pixel- level intensity changes with high temporal resolution and dynamic range. These make them well suited for monocular depth estimation under challenging lighting conditions. However, progress event-based monocular depth estimation remains constrained by the quality of supervision: LiDAR-based depth labels are inherently sparse, spatially incomplete, and prone to artifacts. Consequently, these signals are suboptimal for learning dense depth from sparse events. To address this problem, we propose Distil-E2D, framework that distills depth priors from the image domain into the event domain by generating dense synthetic pseudolabels from co-recorded APS or RGB frames using foundational depth models. These pseudola- bels complement sparse LiDAR depths with dense semantically rich supervision informed by large-scale image-depth datasets. To reconcile discrepancies between synthetic and real depths, we introduce Confidence-Guut Guided Calibrated Depth Loss that learns nonlinear depth alignment and adaptively weights supervision by alignment confidence. Additionally, Context Transformer and employs Dual-Decoder Training scheme that enhances encoder representations by jointly learning metric and relative depth abstractions. Experiments on benchmark datasets show that Distil-E2D achieves state-of- state-of-the-eee performance in event-based monocular depth estimation across both event-only and event+APS settings. Code available at the project website
architecture integrates past predictions via
Introduction
Event cameras are neuromorphic vision sensors that asynchronously detect per-pixel brightness changes with microsecond precision and high dynamic range. Unlike conventional cameras, they are
httsss/ggihbbb.com//jjjiinnnggii
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

more resilient to motion blur and perform well in extreme lighting, enabling low-latency, high-speed vision. These properties of event cameras make them ideal for 3D perception tasks such as monocular depth estimation (MDE). Event-based monocular depth estimation (EMDE) provides an efficient and resilient approach to perceiving scene geometry under conditions involving high-speed motion, low illumination, and extreme dynamic range. EMDE applications span diverse domains, including autonomous driving [IH4], aerial robotics [5). robotic manipulation [6], and low-latency spatial reasoning in AR/VR systems 77.. These applications highlight the versatility and growing relevance of EMDE in real-time, real-world perception.
EMDE aims to recover dense depth from single-view event streams- an inherently ill ill-posed problem due to the absence of multi-view geometric constraints. Event data lack standard image-based depth cues such as absolute intensity, texture, shading, and color gradients. Additionally, the sparsity of events driven by scene and camera motion leads to uneven spatial coverage especially in static or low-motion regions. These factors cause EMDE to be significantly more challenging than its image-based counterpart. Recent learning- approaches [8-12 have shown promising results in predicting depth directly from event streams. However, the quality of supervision remains fundamental bottleneck. Most event-based depth datasets B 2
rely on LiDAR scans as ground truth, which poses several intrinsic challenges due to mismatched sensing modalities. LiDAR acquires depth via discrete angular laser scans to produce point clouds that give spatially sparse supervision. Furthermore, LiDAR utilizes mechanical scanning that integrates depth over a short but non -en-negiiiibbe temporal window. This temporal ambiguity introduces a mismatch with the microsecond precision of event data that complicates accurate event-depth association. Additionally, in dynamic scenes, the sequential scanning process introduces scanline artifacts- artifacts-paralle parallel streaks of occlusive depth measurements. Collectively, spatial sparsity, temporal misalignment, and scanline artifacts of LiDAR limit its effectiveness for training dense and temporally precise EMDE models.
Efforts to overcome supervision limitations through simulated data generation g 9 offer an alterna- tive by simulating both event streams and dense depth from virtual environments. These simulated datasets provide scalable means to generate diverse scenes with pixel-perfect labels, which circum- vent the hardware constraints and annotation challenges of real-world data collection. However, they come with significant drawbacks. The simulated scenes often lack the visual complexity, noise char- acteristics, and motion dynamics of real- wooldd environments. Moreover, simplified event generation models are used in simulation, resulting in event distributions that deviate from real-world event statistics. This introduces domain gaps in both appearance and signal distribution, which substantially degrades the generalization of models trained on simulated data when deployed in real-world settings.
On the other hand, image-based MDE has progressed rapidly in recent years, giving rise to foun- dational depth models [3319] with strong zero-shot generalization to diverse scenes. These foundational models are propelled by access to large-scale RGB-depth datasets (20+23) and robust self-supervised learning frameworks. These models learn strong geometric and semantic priors, enabling dense depth prediction with high spatial consistency and generalization across a wide range of environments. Despite their success, these rich priors have yet to be exploited in event-based based settings to overcome supervision limitations.
In this work, we address the supervision gap in event-based monocular depth estimation by proposing Distil-E2D, a novel distillation framework that transfers dense depth priors from foundational models into the event domain. Our key idea is to leverage co-recorded intensity images to generate high-quality synthetic depth pseudolabels using image-based foundational depth models. These pseudolabels provide dense and semantically rich supervision that complements sparse and noisy LiDAR ground truth for more effective learning of spatial structure from events. However, due to modality differences, synthetic depths exhibit scale and aligmment discrepancies with LiDAR. To mitigate this, we introduce the caafidence-guides Calibrated Depth Loss (CCDL), comprising: 1) a Nonlinear Depth Calibration (NDC) module that learns mapping to align synthetic and LiDAR depths, and 2) an Aligament-aware Confidence Estimator (ACE) that generates a pixel-wise confidence map by assessing local consistency to emphasize reliable supervision. Additionally, we propose a novel architecture featuring Context Transformer (CT) to integrate past prediction context via cross-attention, and Dual-Decoder Training scheme that encourages the encoder to learn richer spatial representations by jointly modeling metric and relative depth abstractions. Fig. [ shows the results from our Distil-E2D under challenging low-light conditions.
2

---

To our knowledge, this is the first approach to explicitly distill depth knowledge from the image domain into the event domain by leveraging naturally co-registered APS frames and co-calibrated external RGB cameras within real-world event datasets. Unlike synthetic simulation pipelines that suffer from event domain shift, our method operates entirely on real event data. This preserves the inherent dynamics and statistics of event streams, which result in models that are more robust and generalizable in practice, These innovations position our Distil-E2D as a principled framework for bridging the supervision gap in event-based depth learning with real-world robustness and practical scalability. Our contributions are summarized as follows:
We propose Distil-E2D, the first framework to distill dense depth priors from image-basee based foundational models into the event domain using co-registered APS or RGB frames.
We introduce Confidence-guidd thetic and real depth signals through learned Nonlinear Depth Calibration (NDC) and Alignment-aware Confidence Estimation (ACE).
Calibrated Depth Loss (CCDL) to reconcile syn-
We design novel architecture that incorporates Context Transformer (CT) to im- prove temporal modeling and a Dual-Decoder Training (DDT) strategy to improve spatial representation learning.
We demonstrate state-of-the-ard results across real-world benchmarks for both event-only and event+APS modalities, which validate the robustness and generalizability of our method.
2 Related Work
2.1 Event-based Monocular Depth Estimation
Early approaches to event-based monocular depth estimation relied on geometric methods 24 277, which estimated depth by optimizing constraints from cructure-from-moon or visual-inertial odometry. Despite being physically grounded, these methods require auxiliary data such accurate camera poses or IMU readings and are sensitive to noise. To overcome these limitations, later works explored supervised learning approaches [28] g which convert asynchronous event streams into dense spatiotemporal representations for convolutional networks to regress depth. To compensate for the sparsity of event data, several methods incorporate additional input from APS or RGB sensors 9 12 30 and fuse features from multiple modalities to improve prediction. More have been introduced for modeling long- range temporal dependencies via self- and cross-attention. However, the generalization ability of these learning-basee models limited due to their reliance small datasets with sparse LiDAR supervision.
recently, Transformer-based (31] architectures 11
12 29]
To mitigate data scarcity, some works leverage synthetic datasets by simulating event streams and corresponding depth maps from virtual environments [8 9 Although this enables large-scale training, such datasets suffer from two key domain gaps: 1) the event generation models often lack physical realism, and 2) the simulated scene structures fail to capture the complexity of real-world environments. These consequently lead degraded downstream performance. An alternative is self-supervised learning (30.. which uses estimated egomotion and photometric consistency to derive pseudo-depth labels. However, inaccuracies in pose estimation and image warping often result in noisy supervision that fails to capture fine-grained depth details. In contrast to synthetic or self- supervised approaches, our method retains the use of real-world event data while distilling dense and semantically rich supervision from powerful image-based depth priors to guide fine-grained depth estimation in the event domain.
2.2 Foundational Depth Models
Recent progress in foundational models has led to the development of highly generalizable monocular depth estimators [18] 19 17 15]
13 14 trained on diverse and large-scale datasets spanning indoor, outdoor, and synthetic scenes. These models depart from task-specific architectures by embracing scalable transformer or hybrid encoder-decoder designs capable of handling a broad range of environments without needing fine-tuning. Notable examples include DPT [8] and MiDaS [19]. which paved the way for using large pretraining datasets and cross-domain learning to enable zero-shot generalization.
32
3

---

Distil-EDD (E) Framework
Confidence-guidde Calibrated Depth Loss (CCDL)
Contest Ensader
ff
Cross Aftee
fest Mfya
A!
Encodee
Transformar
Decader
Synthetk Septh y
Distil-E2D (E+) Framework
81 - Feal""
Conteat Encader
Noniinsar Depth
MDd Prediction
#/bayddt- of
Event Enoader
Contect Transformer
Crews Attn
Dual Decoder Training (D01)
head ot's
Depth Anything
Deptt Encoder
Connent Transformer
MDE Decoder
WY Fou Iogtis "1 wne"
Algeement Aaaare Confidence Estimatio (843)
Figure 2: (Left) Distil-E2D architecture supporting event-only (E) and event+image (E+I) inputs. (Right) Overview of the Confidence guriedd Calibrated Depth Loss (CCDL).
Depth Anything V2 [4] builds on the foundation of Depth Anything [13], which leverages pretrained vision models such as DINO [33] and Segment Anything (34] within a modular encoder-decoder architecture trained Although the original model demonstrated strong generalization across diverse domains, Depth Anything V2 significantly advances the ofte-of-the-arr by addressing the limitations of real ground truth depth that is often sparse, noisy, or coarse. It proposes a novel strategy of generating high- quality pseudolabels from large-scale unlabeled real-world images augment training. These synthetic depths retain fine-grained geometry and realistic scene structure, offering more detailed and semantically rich supervision than conventional real labels. With the use of pseudolabeled real images, Depth Anything V2 enhances depth fidelity, semantic precision, and generalization to set new benchmark in monocular depth estimation.
curated mix of high-resolution synthetic and real-world depth datasets.
This approach inspires our own strategy of using calibrated, confidence-weighiee synthetic depths derived from real-world event data to augment sparse supervision and bridge the domain gap in event-based depth estimation. By aligning synthetic depths with real distributions and emphasizing reliable labels, create high-quality supervision signals that enable fine- ime-grained semantically-awwan depth estimation from events.
Our Method
Event cameras output asynchronous streams of events at the pixel level, capturing changes in log intensity with high temporal precision. Each pixel independently monitors log-intensity signal L(u, t). at pixel location u and time t. An event e; (u, 4, p4) is generated whenever the change in log-intensity exceeds contrast threshold Cames:
Formulation.
AL t4) = L(u, t4) - L(u,, t, - -t), (AL t+)l 2 Cinnes
(1)
with the polarity ps E {-1,+1] +1} indicating an increase or decrease in brightness. We represent window of events as:
E
where N is the number of events in the interval. Given window F(r-T,0) the aim in EMDE to predict a dense metric depth map
Fezd (F)-T.4) E R.R.R xxW
where Feza the learned event-to-depth model and x W the image resolution.
we convert the asynchronous event stream into grid voxel representation. Given stream of events {(u,, 4.,44.)))))))))) P.) within fixed temporal window AT, we divide this window into B temporal bins to construct voxel
Event Representation. Following prior works g
9
35
36

---

grid V E " where H and W are the spatial dimensions of the sensor, and B denotes the temporal resolution. Each event is first temporally normalized the range (0, B 1] using:
(B [)(4t -To)
AT
where To the start time of the event slice. We then perform linear interpolation to distribute the polarity of each event p; across its nearest temporal bins. The voxel value at (u, () is accumulated as:
V(u, 1) = Σ Pr max(0, (0, - 4 ( u,),
which ensures that contributions are smoothly distributed over time. This results a dense spatiotem- poral tensor that encodes both the spatial layout and fine-grained motion cues of the event stream for effective learning of dynamic structure in monocular depth estimation.
3.1 Dense Depth Prior Distillation
To enhance supervision in EMDE, we distill dense depth priors from pretrained image-based based foundational depth models. Specifically, we use DepthAnythingy2 [14, a state-of-the-art monocular depth predictor trained large-scale RGB-depth datasets to generate synthetic depth pseudolabels from image frames temporally aligned with events. These frames may be (i) APS images captured by hybrid event cameras (e.g., DAVIS240C) or (ii) RGB frames from synchronized external cameras. The resulting synthetic depths serve as auxiliary supervision signals during training. Fig 3 illustrates the su- pervision signals. Compared to the sparse and artifact- laden LiDAR depth maps, the synthetic pseudolabels are significantly denser, smoother, and more spatially coherent, especially around object boundaries. This enables more effective spatial learning from sparse event inputs. To maximize label quality, we apply DepthAnything V2 primarily well-lit daytime scenes. Formally, for an image frame I; at time f, we define the synthetic pseudolabel as:
Frame
LIDAR Depth
Synthetic Depth
Figure 3: LiDAR vs. synthetic depth. LiDAR is sparse, incomplete, and exhibits artifacts, while synthetic depths are dense and consistent.
(6) syu is then where Fan denotes the pretrained DepthAnythingV2s model. The resulting depth map ye used alongside sparse LiDAR ground truth sf to supervise the event-based depth prediction network. This cross-modal distillation transfers rich structural priors from the image domain into the event domain to improve depth estimation in regions where LiDAR is missing unreliable.
Fa)(L), syn yt
y/
3.2 Confidence-guided Calibrated Depth Loss (CCDL)
The Confidence-guided Calibrated Depth Loss (CCDL) aims to metrically align synthetic depth pseudolabels and emphasize reliable spatial regions to provide useful synthetic supervision. Illustrated Fig. 2] the CCDL comprises two subcomponents: 1) Nonlinear Depth Calibration (NDC) to metrically align synthetic depths to LiDAR depths, and 2) Alignment-awaar Confidence Estimation (ACE) to emphasize spatial supervision based on mapped depth agreement.
Nonlinear Depth Calibration (NDC). The dense predictions from DepthAnythingV2 are relative depth estimates (RDE) that lack metric scale. In contrast, EMDE supervised using metric depths derived from LiDAR. Consequently, synthetic depths cannot be directly used as pseudolabels without appropriate calibration. Existing methods invariant loss L.i 8 to address this mismatch, which assume global scale and shift discrepancy between relative and metric spaces. However, this scale-shift assumption often over-simplified. In practice, relative depths exhibit nonlinear scale compression, especially in scenes with large depth
12
I8
19 13
commonly rely on the scale-
5

---

variation. To address this issue, we propose learning a nonlinear calibration function using small MLP Feal. which maps synthetic log-depths log yt" calibrated log-depths log ".
log Dt Feal (log ")" Leal || log Vi"" log yt 112.
where Leal the loss function. This allows us to correct systematic nonlinear distortions in the synthetic depths for better alignment to real-world metric scales. Using logarithmic depth represen- tation improves numerical stability and emphasizes relative depth relationships, which is important for aligning synthetic and LiDAR depths. Driving scenes span a large depth range, from near objects under distant backgrounds of meters away. The logarithmic transform compresses this range into a bounded interval (approximately 0-1 in our normalized setup), allowing both near and far regions contribute comparably the loss.
The calibrated depths 11 are subsequently used to super- vise the metric depth predictions di from our Distil-E2D network using an alignment loss:
Non-linear Depth Mapping
1.0
MVSEC
Calign( (d ")" Lui DT"") + (d , ")" (8)
DSEC
0.8
where Lym denotes the multi-scale gradient matching loss from [8. Concurrently, metric predictions also receive supervision from sparse LiDAR depths:
log depth (MDE)
0.6
femde (d,",, .u²)) La(d,", vft) + (y
0.4
The use of calibrated synthetic depths provides dense and semantically rich supervision that complements sparse LiDAR signals. This encourages better generalization by exposing the model to a broader range of depth cues during training. Dense supervision helps the network learn fine- grained spatial structures, such as object boundaries and scene layout, which are difficult infer from sparse labels alone.
0.2
log depth (RDE)
Figure 4: Learned nonlinear maps for
relative-to-metric depth alignment.
Alignment-Aware Confidence Estimation (ACE). However, nonlinear calibration may leave residual misalignments that make some synthetic depth regions less reliable. To address this issue, we introduce an Alignment-Awar Confidence Estimator (ACE) that assigns per-pixel reliability scores given a calibrated depth map V/." and sparse LiDAR depth map yf":
w = exp( log utth - log vi1))
w RR NW
(10)
The ACE formulation ensures that: 1) well-aligned regions receive higher weights. and 2) fixed absolute error at larger ground- truth depth results in smaller penalization. Due to the spar- sity of yf' the initial confidence map wi is also sparse. Therefore, we train lightweight CNN Foonf to propagate the sparse confidences to produce a dense confidence map w.::
wf² = FFo syn m.)
(11)
LIDA Depth
Callbrated Depth
Confidence Map
where mf the binary validity mask indicating valid depth locations yff
Figure 5: ACE confidence maps at epoch (top row) and epoch 200 (bottom row).
The network leverages the synthetic depth map to provide spatial context and uses m to provide LiDAR location cues. The resulting dense confidence map wil then used to weigh Caligns emphasizing supervision in spatially reliable regions.
To prevent collapse of wg, we incorporate two stabilizing mechanisms: base clamping and sparse confidence regularizer. Specifically, reparametrize:

---

पंن a + a w
(12)
with a = 0.1. This enforces पर E [0.1, 1] to avoid vanishing gradients. Additionally, we apply an L1
regularization loss:
f ||
ri: 1l,,
(13)
where vig is the min-max normalized sparse confidence map from section 3.2
This regularizer
encourages consistency with high-confidence regions and promotes non-trivial and spatially adaptive weighting.
Figure 5 illustrates the evolution of the ACE confidence maps from epoch (top) to epoch 200 (bottom). Initially, the confidence weights poorly defined due to the lack of reliable correspondence between synthetic and ground-truth depths. As training progresses, the confidence maps converge toward coherent spatial patterns that align closely with scene geometry. highlighting regions where depth alignment is more consistent. This progression shows that Feonf learns to propagate confidence from sparse LiDAR locations to structurally meaningful areas, enabling adaptive weighting of Latign- Consequently, the network focuses training on geometrically reliable regions, leading to more stable and accurate depth learning.
Combining NDC and ACE, we define the final training objective as CCDL:
Lesl = Leal + प (dligndf"'. "g"")
(df"", + mmie y²t).
(14)
This composite loss combines dense calibrated synthetic priors, sparse metric supervision, and adaptive weighting to effectively guide the learning of EMDE.
3.3 Network Architecture
As illustrated in Fig. (left), we introduce two model variants: Distil-E2D(E) (left, top) for event-only input, and Distil-E2D(E+D) (left, bottom) for configurations including co-recorded APS or RGB frames. Distil-F2DD(F) comprises two main branches: 1) an event branch that employs a recurrent convolutional encoder process event voxel sequences, and 2) context branch that encodes depth prediction of the previous timestep along with the current event voxel. 3) An image branch is introduced in Distil-E2D(E+I),, which uses DepthAnythingVV2 [4] the frame encoder. Both models incorporate a Context Transformer for temporal integration and a Dual-Decoder Training regime improve the quality of the encoder representation. The encoders in the context, event and image branches produce feature tensors fü, fū, and fi, respectively.
Context Transformer (CT). As highlighted in green in Fig we introduce the CT modules in the bottleneck region of our network. The CT module connects the event branch to the context branch, and also the image branch to the event branch in Distil-E2D(E+1D To incorporate past context into current predictions, we refine the event features fi by attending to the context features fi via cross-attention:
QK Vdx
CrossAttention((( Q, K, V) = softmax
V,
= SWW, K K= V= JCW.
(15)
where fi serves as the query and fi supplies the keys and values. Multi-head cross-attention is performed within Transformer module placed at the bottleneck of the event branch. The refined event features are denoted by:
Fe E
(16)
where Fevr denotes the event-branch transformer module. In Distil D2Dt:E) the image branch updates fi with refined event features fg:
ft fc). R MERE
(17)
image-branch where Fine is the image- branch transformer module. We use two Transformer blocks per branch with a multi-head self-attention layer followed by a multi-head cross-attention layer, respectively. Attention is applied in a low-resolution latent space for per-pixel reasoning. Positional encodings are learned for each latent pixel to preserve spatial consistency. This architecture allows efficient extrac- tion of relevant temporal and semantic context for more accurate and consistent depth predictions.
7

---

Ovents
Dintk 420
PvT+
Diet 420 (Dun)
Figure 6: (Left) Qualitative results on MVSEC Dataset.
Table I: Quantitative results on MVSEC Dataset.
day
nightl
night2
night3
10m
20m
30m
10m
20m 1. 30m
10m
20m J
30m J
10m J.
20m L 30m
runtime(ms)
Event-only
ULODE
2.72
3.84
4.10
3.13
1.022
4.89
2.19
3.15
3.92
2.86
4.16
5.05
MDDE+
1.85
2.64
3.13
3.38
3.82
4.46
1.67
2.63
3.58
1.42
2.33
3.18
8.0
EMD
2.65
2.18
2.70
3.64
2.06
2.76
3.42
2.09
2.82
3.92
5.3
ERE 11
1.44
2.23
2.75
1.85
2.45
3.24
1.66
2.53
3.15
1.49
2.37
2.97
EvT+ (E) !
1.31
1.92
2.32
1.54
2.31
2.96
1.47
2.22
2.92
1.36
2.13
2.84
35.0
Ours
1.14
2.16
2.75
1,33
2.06
2.70
1,13
2,04
2.79
10.5
Event ponnt-Iman Image (E+1)
RAMNe
1.39
2.76
2.50
3.19
3.82
1,21
2.31
3.28
1.01
2.34
3.43
6.2
(15+)
2.36
1.45
2.10
2.88
1.48
2.13
2.90
1.38
2.03
2.27
52.0
Ours (E+1)
1.04
1.44
1.75
1.35
2.02
2.79
1.20
2.04
2.62
0.99
1.96
2,71
47.5
Dual-Decoder Training (DDT). As highlighted in pink in Fig. 2 we introduce two decoders: the MDE Decoder for metric depth estimation and the RDE Decoder for relative depth estimation to enrich the feature representations of the encoder. The MDE Decoder predicts d," and is supervised using CCDL (section 3.2). and the RDE Decoder predicts di and is trained with uncalibrated synthetic depths yy via:
Lrde "²*) = Lai(d, "²² +
"/"")
The RDE Decoder used only during training provides dense supervision accelerate convergence and guide the encoder to focus on fine-grained scene details: object boundaries and edge consistency, which are often missed with sparse LiDAR supervision. Using separate MDE and RDE Decoders also prevents gradient interference and allows each to specialize in its respective depth abstraction while jointly regularizing the encoder towards robust and generalizable feature space.
4 Experiments
4.1
Experimental Setup
We evaluate our proposed Distil-F22 framework under two settings: (E) using event data only, and
(E+I) using both event and image (APS or RGB). We benchmark Distil-E2D (E) against five prior event-only methods: ULODE 28), MDDE+ 8) EMD[30), ERE(I]] and EvT+ [2) For the (E+I) configuration, we compare against multi-modal methods RAMNet and EvI+ (E+D)[2). We evaluate against two weal-world driving datasets:
1) MYSEC.
This dataset 2 is standard benchmark for event-based depth estimation, comprising synchronized events, grayscale APS images, and LiDAR depth from outdoor driving sequences recorded with a DAVIS 240C (260 346). Following prior work, we use the autdoor_day__. sequence for training and evaluate the remaining four sequences.
2) DSEC. This dataset 3 provides high-resolution stereo event and RGB data (480 x 640 events, 1080 x 1440 RGB frames) along with LiDAR depths across 41 driving sequences under diverse

---

Table 2: Ablations on MVSEC Dataset.
day
night
night2
SYN
NDC
ACE
CT
DDT
10m
20m 4
30m 4
10m. |
20m J
30m |
10m |
20m
30m
10m J
20m J
30m.
2.89
4.01
3.91
3.53
4.01
5.52
2.63
3.56
4.01
2.29
3.12
4.11
2.63
3.52
3,68
5.96
4.63
5.11
2.91
3,85
3.96
2.62
2.73
X
X
1.47
1.95
2.41
1.74
2.45
3.22
1.60
2.43
3.33
1.35
2.23
2.97
1.35
1.76
2.01
1.53
2.32
3,04
1,47
2.17
3.01
1.21
2.14
2.82
1.59
1.86
2.78
V
1.14
1.56
1.81
1.41
2.16
2.78
1.33
2,06
2,70
1.13
2.04
2.79
lighting conditions. We 28 sequences for training and 13 for testing. Since 330 is the only work reporting on DSEC, we compare directly with their results.
4.2 Quantitative Results
Table rrppots the quantitative results on MVSEC following the standard evaluation protocol of the prior works with average depth enrors at cutoff distances of 10m, 20m, and 30m. Our Distil-E2D consistently outperforms all supervised baselines. This demonstrates that the use dense and high- quality calibrated pseudolabels distilled from foundation depth models yields greater performance gains than solely relying on sparse LiDAR supervision.
Table 3: Results DSEC Dataset.
Distil-E2D (E) and (E+1) exceed the performance of MDDE+ & and RAMNet(9), both which utilize large- scale simulated event data for training. This highlights the ability of our framework to bridge the synthetic-te -o-relll gap more effectively by combining real-world event data with calibrated confidence-weigee bels. Comparing with self-supervised methods, Distil-E2D (E) surpasses EMD 30 This shows the superiority of depth priors distilled from image foundation models over photometric www.amttrrrc-cossieeeeyey based gpeudo-supervise
10m 4
20m +
30m 1
EMD 30
0.96
1.55
2.21
Ours
0.64
1.11
1.61
synthetic pseudola-
Distil-F2D also outperforms resolution event data and its compatibility with both APS and RGB modalities. As shown in Figure] 7 higlerone image frames yield finer and sharper depth estimates which further validates our approach of principled distillation of dense depth priors from foundational models.
30 on DSEC, which shows the scalability of our method to higher
4.3 Qualitative Results
Qualitative results are shown in Fig- 6 for MVSEC and Fig. for DSEC. On MVSEC, Distil-E2D produces sharper object boundaries such cars, trees, and lampposts compared the next best method EvT+ [12) which remains visibly blurred. For example, in row 1, our Distil-E2D clearly delineates the parked vehicles. On the other hand, methods trained only with sparse LiDAR [1] yield oversmoothed predictions. This is because EMDE inherently requires semantic understanding and boundary precision, which are better captured by dense and semantically rich pseudolabels generated from foundational models than by sparse LiDAR supervision. As a result, our Distil-E21 more accurately segments and assigns depth fine scene structures.
Similar patterns are observed in the
DSEC results (Fig 7 Although
parisons with EMD 30 remain quan- titative due unavailable code, our Distil-E2D produces visually accurate predictions. For example, in Fig. (row 3, column 4), LiDAR fails to capture the full extent of the safety bollard and road barrier. In contrast, our Distil-E2D reconstructs them ac- curately. This underscores the value of foundation model-based pseudo- label supervision for EMDE.
Figure 7: (Left) Qualitative results on DSEC Dataset.

---

4.4 Ablation Study
We present ablations in Tab. to quantify the contribution of each core component in Distil-E2D:
NDC. Training on synthetic depth without NDC (row 1) causes the largest drop in performance, underperforming the LiDAR-only baseline (row 2). With NDC (row 3) depth accuracy improves significantly. This validates the role of NDC in correcting scale and distribution mismatches between synthetic pseudolabels and the LiDAR ground truth. The result also confirms that scale-invariant losses alone are insufficient to bridge the nonlinear relative-metric scale gap. and that explicit nonlinear calibration is necessary for effectively leveraging dense synthetic supervision.
ACE. Introducing ACE (row 4) yields further gains, confirming its effectiveness in weighting super- vision towards more reliable regions. This mitigates the impact of misaligned noisy pseudolabels, allowing the model to prioritize cleaner training signals.
CT. Adding CT (row 5) leads to additional improvements, validating its ability to incorporate temporal context. By attending to prior predictions, CT enhances performance in frames with sparse events or ambiguous geometry.
DDT. Incorporating DDT (row also improves performance. This shows that joint supervision on both metric and relative depth encourages the encoder to learn richer and more generalizable features with better semantic structure and spatial precision.
Overall, these results confirm that each component contributes uniquely to model performance. Together, they enable Distil-E2D to achieve strong EMDE results through more effective use of synthetic supervision, calibrated integration, and temporal reasoning.
Conclusion
present Distil-E22D, a framework for event-based monocular depth estimation that distills dense priors from pretrained image-based depth models. By leveraging synthetic pseudolabels, our method surpasses the limitations of sparse LiDAR supervision. Our key contributions include Nonlinear Depth Calibration (NDC) for scale alignment, Alignment-aware Confidence Estimation (ACE) for robust supervision, a Context Transformer (CT) for temporal modeling, and Dual-Decoder Training (DDT) for feature generalization. These components collectively enable Distil-E2D to achieve state-of-the-as performance. Our results highlight the potential of foundation model distillation to advance generalizable depth perception in the event domain.
Acknowledgments
This research/ / project is supported by the National Research Foundation, Singapore, under its NRF. Investigatorship Programme (Award ID. NRF-NRFF09-0008) and Al Singapore Programme (AISG Award No: AISG2-PhD/2021-00-0011 and the Tier grant T1-25 11-221RRESS RES2305 from the Singapore Ministry of Education.
References
[1] A. I. Maqueda, A. Loquercio, G. Gallego, N. García, and D. Scaramuzza, Eevent-based vision meets deep learning on steering prediction for self-driving cars," in Proceedings the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5419-5427, 2018
[2] A. Z. Zhu, D. Thakur, T. Ozaslan, B. Pfrommer, V. Kumar, and K. Daniilidis, "The multi vehicle stereo event camera dataset: An event camera dataset for 3d perception." " in IEEE Robotics and Automation Letters, vol. 3, pp. 2032-2039 2018.
[3] D. Gehrig, A. Loquercio, H. Rebecq, and D. Scaramuzza, "Dsec: A stereo camera dataset for driving scenarios," IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 4947-4954, 2021.
[4] K. Huang, S. Zhang, Zhang, and D. Tao, "Event-based simultaneous localization and mapping: A comprchensive survey," 2024.
10

---

[5] D. Falanga, K. Kleber, and D. Scaramuzza, "Dynamic obstacle avoidance for quadrotors with event cameras," Science Robotics, vol. 5, no. 40, p. caaz9712, 12, 2020.
[6] A. Z. Zhu, D. Falanga, G. Gallego, and D. Scaramuzza, "Event-based vision for manipulation: survey," arXiv preprint arXiv:2208. 05960, 2022.
[7] J. Hidalgo-Cariis T. Schöps, D. Gehrig, H. Rebeoq, and D. Scaramuzza, "Low-latency visual slam with event cameras for ar/vr headsets," " in IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 11-20, IEEE, 2021.
[8] D. G. Javier Hidalgo-Carrio and D. Scaramuzza, "I Learning monocular dense depth from events," IEEE International Conference on 3D Vision.(3IVV. 2020.
(9] M. J. H.-C. Daniel Gehrig, Michelle Rüegg and D. Scaramuzza, "Combining events and frames using recurrent asynchronous multimodal networks for monocular depth prediction," IEEE Robotic and Automation Letters. (RA-L), 2021.
[10] L. Wang, Y. Chae, and K.-J. Yoon, "Dual transfer learning for event-based end-task prediction via pluggable event to image translation." 2021.
[11] X. Liu, J. Li, Fan, and Tian, "Event-based monocular dense depth estimation with recurrent transformers," arXiv preprint arXiv:2212.02791, 2022.
[12] A. Sabater, Montesano, and A. C. Murillo, "Event transformert. a multi purpose solution for efficient event data processing," arXiv preprint arXiv:2211.12222 2022.
multi-purpose
[13] L. Yang, B. Kang, Z. Huang, X. Xu, J. Feng, and H. Zhao, "Depth anything: Unleashing the power of large-scale unlabeled data," in CVPR, 2024.
[14] L. Yang, B. Kang, Z. Huang, Z. Zhao, X. Xu, J. Feng, and H. Zhao, "Depth anything v2," arXiv: 24X1:2446.009414 2024.
[15] B. Ke, A. Obukhov, S. Huang, N. Metzger, R. C. Daudt, and K. Schindler, "Repurposing diffusion-based image generators for monocular depth estimation." in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
[16] Z. Li and N. Snavely, "Megadepth: Learning single-view depth prediction from internet photos." in Computer Vision and Pattern Recognition (CVPR), 2018.
[17] S. F. Bhat, R. Birkl, D. Wofk, Wonka, and M. Müller, "Zoedepth: Zero-shot transfer by combining relative and metric depth," arXiv preprint 22t.::2332.228888 2023.
[18] R. Ranftl, A. Bochkovskiy, and Koltun, "Vision transformers for dense prediction," ICCV, 2021.
[19] R. Ranftl, K. Lasinger, D. Hafner, K. Schindler, and V. Koltun, "Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer," IEEE Transactions Pattern Analysis and Machine Intelligence, vol. 44, no. 3, 2022.
[20] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, "Vision meets robotics: The kitti dataset." International Journal of Robotics Research (IJRR), 2013.
[21] K. Nathan Silberman, Derek Hoiem and R. Fergus, "Indoor segmentation and support inference from rgbd images," in ECCV, 2012.
[22] T. Schöps, L. Schönberger, S. Galliani, T. Sattler, K. Schindler, M. Pollefeys, and A. Geiger, "A multi-view stereo benchmark with high-resolutin images and multi-camera videos," in 2017 IEFE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3538-254F 2017.
[23] I. Vasiljevic, N. Kolkin, S. Zhang, R. Luo, H. Wang, F. Z. Dai, A. F. Daniele, M. Mostajabi, Basart, M. R. Walter, and Shakhnarovich, "DIODE; A Dense Indoor and Outdoor DFpth Dataset," CoRR, vol. abs/1908 htts//990.0006. 000633, 2019.

---

[24] H. Rebecq, G. Gallego, E. Mueggler, and D. Scaramuzza, "EMVS: Event-based multi-view stereo-33 3D reconstruction with an event camera in real-time," Int. Comput. Vis., vol. 126, pp. 1414, Dec. 2018
[25] Y. Zhou, G. Gallego, H. Rebecq, L. Kneip, H. Li, and D. Scaramuzza, Semi-dense 3D Re- construction with a Stereo Event Camera, p. 242-258. Springer International Publishing, 2018.
[26] H. Kim, S. Leutenegger, and A. J. Davison, "Real-time 3d reconstruction and 6-dof tracking with an event camera," in European Conference on Computer Vision, 2016.
[27] A. Mitrokhin, C. Fermuller, C. Parameshwara, and Y. Aloimonos, "Event-based moving object detection and tracking," in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Oct. 2018.
[28] A. 7. Zhu, I.. Yuan, K. Chaney, and K. Daniilidis, "Unsupervised event-based learning of optical flow, depth, and egomotion," 2018.
[29] A. Devulapally, M. F. F. Khan, Advani, and V. Narayanan, "Multi-modal fusion of event and rgb for monocular depth estimation using unified transformer-bast based architecture." in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2081-2089, 2024.
[30] Zhu, L. Liu, B. Jiang, F. Wen, H. Zhang, W. Li, and Y. Liu, "Self-supervised event-based monocular depth estimation using cross-modal consistency," 2024.
[31] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, "Attention all you need," 2023.
[32] A. Bochkovskii, A. Delaunoy, H. Germain, M. Santos, Y. Zhou, S. R. Richter, and V. Koltun, "Depth pro: Sharp monocular metric depth in less than second," in International Conference on Learning Representations, 2025.
[33] M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, "Emerg- ing properties in self-supervised vision transformers." in Proceedings of the International Conference on Computer Vision (ICCV), 2021.
[34] A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao. S. Whitehead, A. C. Berg, W.-Y. Lo, Dollár, and R. Girshick, "Segment anything," arXiv:2304.00 02643, 2023.
[35] H. Rebecq, Ranftl, V. Koltun, and D. Scaramuzza, "High speed and high dynamic range video with an event camera," IEEE Transactions Puttern Analysis and Machine Intelligence, vol. 41, no. 6, pp. 1377-1391, 2019.
[36] B. Ercan, O. Eker, C. Saglam, A. Erdem, and E. Erdem, "Hypere2vid: Improving event-based video reconstruction via hypemetworks."" IEEE Transactions Image Processing, vol. 33, p. 1826,1837, 2024.
12

---

A Supplementary Material
A.1 Limitations
Although Distil-E2D achieves state-of-the-art results across multiple benchmarks, it is subject to sev- eral limitations. First, the framework is dependent on the availability and quality of dense, calibrated pscudolabels derived from image-based foundation models. Inaccurate or biased depth estimates from these models, especially low-light environments or textureless regions, can degrade the effectiveness of the training signal. Second, although Distil-E2D improves the quality of supervision through dense synthetic depths, the quantity and diversity of event-based training data remain limited. This constrains the model's 's ability to generalize to complex, real-world driving conditions with varied lighting, geometry, and motion patterns.
Future work could mitigate these issues by leveraging ensembles of depth foundation models to reduce pseudolabel bias, integrating online refinement techniques for self-correction during training, and employing video-to-event (vid2e) generation strategies to synthesize realistic event data from large-scale monocular video datasets. Distil-E2D enables promising direction: By coupling with video-to-event (vid2e) conversion strategies, it can transform large-scale monocular video datasets into synthetic event-depth datasets, significantly reducing the need for laborious data collection and opening new avenues for scalable event-base weent-based depth learning.
A.2 Implementation Details
Data Preparation. Each depth map is paired with events from the preceding 50ms window: To address frame rate differences between the LiDAR and RGB/APS modalities, we match each depth map the nearest available image frame within +50ms margin. If valid match is found, exclude the image frame from synthetic depth generation and loss computation. These images are passed through DepthAnythingV2-LLaa discretized into spatiotemporal voxel grids with B = 5 bins for MVSEC and B = 15 bins for DSEC accommodate varying event densities due to sensor resolution. During training, we apply horizontal flipping to events, images, and depth maps for data augmentation. All code, pre-trained weights, and training configurations will be released facilitate reproducibility.
generate dense synthetic pseudolabels. Event data is
Encoder Architecture We use DepthAnyttinggv--L for the frame encoder in the E+I setting. The architecture of our event encoder, context encoder, and RDE/MDE decoder is as follows:
Event Encoder (Recurrent):
Input Head: Conv2d(5, 32, kernel size 5, stride 1, padding 2)
Encoder O: Conv2d(32, 64, kernel size 5, stride 2) + ConvLSTM(128, 256, kernel size 3)
Encoder 1: Conv2d(64, 128, kernel size 5, stride 2) + ConvLSTTM(256, 512, kernel size 3)
Encoder Conv2d(128, 256, kernel size 5, stride 2) ConvLSTTM(12,, 1024, kernel size
3)
Encoder 3: Conv2d(256, 512, kernel size 5, stride 2) + ConvLSTTM(1024, 2048, kernel size
3)
Context Encoder (Feedforward):
Input Head: Conv2d(6, 32, kernel size 5, stride 1, padding 2)
Encoder O: Conv2d(32, 64, kernel size 5, stride 2)
Encoder 1: Conv2d(64, 128, kernel size 5, stride 2)
Encoder 2: Co222d(128, 256, kernel size 5, stride 2)
Encoder 3: Conv2d(256. 512, kernel size 5, stride 2)
Decoder (Shared for RDE and MDE Heads):
Decoder Upsample Conv2d(512, 256, kernel size 5, stride
Decoder 1: Upsample Conv2d(256, 128, kernel size 5, stride 1)
13

---

Decoder 2: Upsample + Conv2d(128, 64, kernel size 5, stride 1) Decoder 3: Upsample + Conv2d(64, 32, kernel size 5, stride 1)
Output Head: Conv2d(32, 1, kernel size I)
Skip connections are applied between the event encoder and RDE/MDE decoders.
Nonlinear Depth Calibrator (NDC). The NDC module is three-layer multilayer perceptron with a ID input and output. contains a single hidden layer of 64 units and learns a nonlinear mapping function that transforms relative synthetic depths to the metric scale of ground-truth LiDAR. This component plays a crucial role in reconciling distributional differences between synthetic and real depths.
Alignment-awarr Confidence Estimator (ACE). The ACE module is a four-layer convolutional neural network with 3-channel input (concatenation of the sparse confidence map, synthetic depth, and the sparse LiDAR depth mask), 32-channel hidden representation, and 1-channel confidence filters with padding preserve spatial resolution. final
output. All convolutions use sigmoid activation ensures that confidence weights fall within [0, 1], modulating the contribution of each pixel the loss based on alignment quality.
Training Configuration. Distil-E2D is implemented using PyTorch 2.0 and trained with the AdamW optimizer at a learning rate x 10-4, A OneCycleLR scheduler dynamically adjusts the learning rate. Training is conducted over 250 epochs with full precision and batch size of 16, using sequences of 10 frames corresponding to a 500ms event window.
A.3 Analysis of Context-Induced Distortions under Accelerated Motion.
We analyze whether the context branch introduces distortions under acceleration the MVSEC test set using event-only input. The test frames are segmented into low, medium, and high motion bins based the average ground truth optical flow magnitude with thresholds defined by the 33rd and 66th percentiles. For each bin, we report the mean depth error at 30m.
Motion Bin Baseline (no context) Ours (w/ context)
A
Low Medium High
2.66
2.34
0.32
2.71
2.52
0.19
2.80
2.69
0.11
The results show that with the context branch, the accuracy decreases with increasing motion due the growing misalignment between frames, which reduces the effectiveness of temporal fusion. Nonetheless, the context branch continues to provide valuable temporal cues and structural continuity. yielding improved accuracy over the bascline across all motion bins.
A.4 Compute Resources
All experiments were performed on single NVIDIA RTX A6000 GPU with 48 GB of memory. Generating pseudolabels from DepthAnything -2- required 4-6 hours per dataset. Training Distil-E2D took approximately 72 hours for MVSEC and 96 hours for DSEC. We did not use distributed training or multi-GPU setups. The entire workflow executable within compute budgets accessible to most academic institutions.
A.5 License and Credit
We acknowledge the use of publicly available models and datasets, each used compliance with their respective open-source licenses:
Depth Anything V2: Apache 2.0 License
MVSEC Dataset: Creative Commons BY 4.0 License
DSEC Dataset: Creative Commons BY-SA 4.0 License
14

---

A.6 Broader Societal Impact
This work advances event-based depth perception, enabling power-efficient, low-latency applications in domains such as autonomous robotics, AR/VR, and assistive systems. Although these capabiliiies are beneficial in fast-motion low-light environments, they also introduce significant risks. Real-time depth inference using covert, low-data-rate event cameras may facilitate surveillance, unauthorized tracking, or military use. Furthermore, distillation of pretrained models can propagate existing biases or inadvertently expose sensitive information from uncurated datasets. These risks highlight the importance of transparency dataset provenance, ethical deployment practices, and model auditing. We advocate for interdisciplinary oversight, fairness evaluations, and responsible use, especially in socially sensitive or surveillance-aajacen applications.
15

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: Each contribution in the abstract/inttroduced is clearly detailed in the method section and validated by ablations sec 4.4.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
Justification:
The limitations are discussed in the supplementary.
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g.. independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't 't acknowledged judgment and recognize that individual actions in favor of transparency play impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
the paper. The authors should use their best
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
16

---

Justification:
Our work does not include theoretical results.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced the statement of any theorems.
The proofs can either appear the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Question:
Answer: [Yes]
Justification: Formulation details, architecture, and experiment settings are described in the paper. Code will also be released upon acceptance.
Guidelines:
The answer NA means that the paper does include experiments.
If the paper includes experiments, No answer this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make it clear how reproduce that algorithm. (b) If the contribution is primarily
model architecture, the paper should describe the architecture clearly and fully.
(c) If the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome deseribe the particular way they provide for reproducibility, In the case of closed-source models, it may be that access to the model is limited in some way (e.g., registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
17

---

Answer: [Yes]
Justification: We use publicly available datasets, pretrained models, and we will release code upon acceptance.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (https://nips... cc/
for more details.
While we encourage the release of code and data, we understand that this might not be possible, so "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to to reproduce the results. See the NeurIPS code and data submission guidelines (https
httpol/iiigggddde/oooooee http://uddde/ooeSuubntb ssicnyolicy) for more details.
The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification:
The experiment settings are detailed in sec 4.1 of the paper.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, or as supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer:
[No]
Justification: We follow evaluation procedures used by existing prior works which do not include error bars.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by emror bars, confi- dence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.
The factors of variability that the error bars capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
18

---

It should be clear whether the error bar is the standard deviation or the standard error of the mean.
is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out range (e.g. negative enror rates).
If error bars reported in tables plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
Question:
Answer: [Yes]
Justification: we include inference runtime comparisons the experiments section, and the compute requirements in the supplementary.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (c.g., preliminary or failed experiments that didn't 't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics
Answer: [Yes]
Justification:
We have reviewed and complied with the NeurIPS Code of Ethics.
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., there a special consid- eration due to laws or regulations in their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: we discuss societal impact the supplementary.
Guidelines:
The answer NA means that there no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has no societal impact why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
19

---

The conference expects that many papers will be foundational research and not tied particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how system Iearns from feedback over time, improving the efficiency and accessibility ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data or models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: proposed model is not of such nature.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
recognize that providing effective safeguards challenging, and many papers do not require this, but encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper. properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification: we have properly credited and reviewed the license and terms of use of the datasets and code we use. The MVSEC Dataset uses the CC-BY 4.0 license, the DSEC Dataset the CC-BY SA 4.0 license, and DepthAnything uses the Apache License 2.0.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode com/ datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
20

---

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if has changed) should be provided.
If this information is not available online, the authors are encouraged to reach out to the asset's s creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification:
we do not release new assets in this paper.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create anonymized URL or include anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
Answer: [NA]
Justification:
we do not use crowdsourcing in this paper.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According the NeurIPS Code of Bthies, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based the requirements of your country or institution) were obtained?
Answer: [NA]
Justification:
We do human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurlPS Code of Ethics and the guidelines for their institution.
For initial submissions, not include any information that would break anonymity (if applicable), such the institution conducting the review.
21

---

16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology. scientific rigorousness, originality of the research, declaration is not required.
Answer: [NA]
Justification:
We do not use LLMs in this paper.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to L.LM policy htttaa/s/eaaarrsssec////ooeeeeee for what should or should not be described.
22


Reviews:
[{"review_id": "3jdLnovFst", "reviewer": "Reviewer_Uz19", "summary": "This paper presents a model trained to predict dense depth maps from event-based cameras. It works by distillating priors about geometry of dense depth maps by using zero-shot depth prediction from a foundation model (DepthAnything-V2) on co-registered grayscale or RGB images that are \"synchronized\" with the event signals, gathered within a 3d block of event voxels over a fixed time interval. \nTo transform the relative depth maps predicted by DepthAnything-V2 to metric (absolute) depth maps, a calibration model is trained, supervised by LIDAR depth projected images, to learn a non linear calibration function. The calibrated synthesized dense depth maps are then used to supervise the event-base depth prediction model.\nThe proposed architectures uses a context branch fed by the past dense depth map prediction, that is combined to the input event data through a cross attention module performed by a transformer at the end of the decoder, before going through the decoder that outputs the dense depth maps.", "questions": "Could you observe or try to analyze the possible distortions induced by the context branch in terms of excessive dependence to the past (in particular in case of accelerations)?\nPlease introduce and make clear the APS acronym and the distinction with RBG images. \nIt is not clear BTW why the APS image appears as binary (saturated?) in Figure 1.\nThere is a notation problem in equation (5), since i is the sum index, it should be \"The voxel value at (\\mathbf{u},t)...\" and \"V(\\mathbf{u},t) =\" instead of (\\mathbf{u}_i,t)", "limitations": "One word of discussion about generalization and the possible strong dependence to the context could be added.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "The paper is clear and nicely written. The problem of estimating depth maps in nighttime or tunnel conditions is important so the subject is relevant. The proposed architecture and distillation setup make sense. The experimental part is well done, the results are good and the ablation study shows the benefits of the different components of the approach.  \nOne possible weakness of the approach is the strong dependence to the context branch. One might wonder if this method could generalize to different contexts (e.g. handheld or aerial, with fast viewpoint changes), and how the past depth maps possibly distort (blur?) the current prediction.    \nAside from this minor point I don't see major issue in this paper, except a few clarifications to add (see questions).", "was_revised": true, "final_justification": "After reading the reviews and rebuttal, I am totally satisfied with the answers of the authors, and in particular to the slight concerns I had initially, I therefore maintain my \"Accept\" score. \nBest regards,", "rebuttal": "**Q1: Potential distortions induced by the context branch under accelerated motion.**\n\nWe analyze whether the context branch introduces distortions under acceleration on the MVSEC test set using event-only input. The test frames are segmented into low, medium, and high motion bins based on the average ground truth optical flow magnitude with thresholds defined by the 33rd and 66th percentiles. For each bin, we report the mean depth error at 30m.\n\n| Motion Bin | Baseline (no context branch) | Ours (w/ context branch) | $\\Delta$ Delta |\n|-----------|-----------|-----------|-----------|\n| Low | 2.66 | $\\textbf{2.34}$ | 0.32 |\n| Medium | 2.71 | $\\textbf{2.52}$ | 0.19 |\n| High | 2.80 | $\\textbf{2.69}$ | 0.11 |\n\nThe results show that with the context branch, the accuracy decreases with increasing motion due to the growing misalignment between frames, which reduces the effectiveness of temporal fusion. Nonetheless, the context branch continues to provide valuable temporal cues and structural continuity, yielding improved accuracy over the baseline across low to high motion magnitudes.\n\n\n**Q2: Clarification on APS, its distinction from RGB, and the appearance of saturation in Figure 1.**\n\nAPS (Active Pixel Sensor) frames are captured from the same imaging plane as events and are inherently co-aligned. In contrast, RGB frames are acquired from an external camera and require extrinsic calibration. As seen in Figure 1, APS frames may appear saturated under challenging lighting due to limited dynamic range. Although APS and events share the same imaging plane, they operate on distinct circuitry and sensing principles. Specifically, event data more robust to extreme illumination since APS measures absolute intensity while events encode log-intensity changes.\n\n\n**Q3: Notation error in Equation (5).**\n\nThank you for highlighting this. We will correct the notation in the camera-ready version.\n\n\n**Q4: Discussion about model generalization.**\n\nSimilar to existing EMDE models, our method currently does not generalize to open-world scenes and remains dataset-specific. This work focuses primarily on improving the quality of supervision through dense depth distillation. However, we acknowledge that the lack of large-scale event datasets remains a key bottleneck. We hope that our work motivates future efforts to acquire such datasets. This would enable our work and other future work to be extended to generalizable event-based monocular depth estimation."}, {"review_id": "iaQVSaA6ig", "reviewer": "Reviewer_qS9Q", "summary": "The missing of large volume and high-quality datasets for event-based tasks hinders the spreading of related applications such as Event Monocular Depth Estimation (EMDE).\nFor example, depth foundation models (VFM) such as Depth Anything V2 or MoGe leverage millions of high-quality labelled data to achieve astonishing results, while this kind of training is still unfeasible for the event domain, since high-quality-high-volume synthetic or real datasets are missing.\n\nIn particular with respect to EMDE, previous literature trained the models with real datasets such as MVSEC, where ground truth is collected using LiDAR, thus producing sparse and noisy labels.\nThis paper focuses on the high-quality part of the aforementioned problem, trying to overcome the problem by exploiting frame-based depth foundational models -- i.e., Depth Anything V2.\n\nThe core idea is to leverage aligned frame and event data -- e.g., using a DVS sensor that produces intrinsically aligned frame-event information -- to add an additional proxy-supervision signal from the estimations of the foundational model.\nIn other words, given an aligned frame, we can estimate an aligned proxy depth map using the foundation model and use it to complete the LiDAR information.\n\nAdditionally, the authors introduce a series of techniques to fuse LiDAR and proxy relative depth maps: 1) a MLP model to create a non linear depth mapping to convert the proxy labels to a metric scale; 2) a CNN confidence estimator model to weight the supervision of proxy labels; 3) a double head for metric and relative depth estimation.\nThe enhanced supervision is used to train a novel transformer-based architecture for both event-only monocular depth estimation and event-frame monocular depth estimation.\n\nThe authors validated the effectiveness of the proposal with two standard benchmarks -- i.e., MVSEC and DSEC -- with state-of-the-art performance.", "questions": "- Could you provide additional information regarding the network implementation as requested in the previous section?\n- Could you confirm the inference times of MDDE+ and ERE models?\n- Instead of using relative VFM, have the authors tried to use metric foundational models?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n- **The paper is well written and easy to follow**: the authors present their methodology in a clear and structured way, with well-organized sections and illustrative figures that guide the reader through the key concepts.\n- **Interesting idea to partially overcome to the event-data issue**: The paper addresses an important problem in the event-based literature -- i.e., missing high-quality-high-volume datasets -- with particular effort regarding the high-quality part. The proposed distillation framework is a novel paradigm to leverage the knowledge of frame-based VFM to increase the quality of event datasets.\n- **SOTA performance**: The results show the effectiveness of the proposal against several competitors using two standard benchmarks -- i.e., MVSEC and DSEC.\n\nWeaknesses\n\n- **Fairness of the E+I experiments**: Given Fig. 2, the proposed Distil-E2D (E+I) Framework uses frozen DAv2 for estimation, thus leveraging (of course indirectly) millions of labeled images, while the other competitors do not. This raises concerns regarding the fairness of the E+I evaluation protocol.\n- **Missing details about implementations**: I would like to see more details regarding the network implementation, since there are few details about it. For example, the size of the frozen Frame Encoder -- i.e., DAv2-S or DAv2-B or DAv2-L -- is missing. Also, there are no details about the encoders. I suggest that the authors include additional information, such as figures and implementation details in the supplementary.\n- **Concerns about inference times**: I tried both MDDE+ and ERE models, but I found that MDDE+ is quite faster than ERE.\n- **Usage of only one VFM for proxy-labelling**: The authors tried only one VFM for proxy supervision and Image Encoder. I suggest in future work to try other new VFM such as MoGe, DepthPro, and Lotus.\n- **Missing study regarding the high-quantity dataset issue**: The authors missed the opportunity to study the effectiveness of the proposal regarding the high-quantity problem cited in the Summary section. In particular, the authors could have tried to increase the robustness of the network with a proxy pre-training using a large amount of aligned frame-event unlabelled data and proxy supervision from VFMs (leveraging the RDE head). I suggest this as future work for the authors.\n\nGiven the Strengths and Weaknesses of the proposal, I give an initial score of \"Accept\". In my opinion, the event-based high-quality-high-quantity dataset issue is a huge problem for the event community that hinders its diffusion.\nThe authors managed to handle the problem partially with an interesting solution.\nFurthermore, I did not find any major weaknesses, thus confirming my positive score.", "was_revised": true, "final_justification": "Given the Strengths and Weaknesses of the proposal, I give a score of \"Accept\". In my opinion, the event-based high-quality-high-quantity dataset issue is a huge problem for the event community that hinders its diffusion. The authors managed to handle the problem partially with an interesting solution. Furthermore, the authors replied to my concerns, thus confirming my positive score.", "rebuttal": "**Q1: Fairness of using DAV2 in E+I experimental protocol.**\n\nWe thank the reviewer for raising this point regarding the fairness of the E+I experiments. Using DAV2 as the  image encoder is a natural and principled design choice. It avoids the need to train a separate image encoder from scratch with limited data by using the rich priors of foundational depth models. Importantly, incorporating DAV2 does not trivially solve the depth estimation problem. APS frames often yield inaccurate depth predictions when relying solely on DAV2, particularly in challenging nighttime scenes. Additional challenges arise from depth misalignment between synthetic supervision and real-world data. In our E+I framework, events play a crucial role by offering high-temporal-resolution cues that complement DAV2 features. This leads to improved performance in scenarios where image-only models struggle. We thus view our approach as a meaningful extension of existing models instead of being an unfair advantage. This is because events remain essential to achieving accurate depth estimation.\n\n\n**Q2: Missing implementation details on encoder and DAV2 architecture.**\n\nWe use DepthAnythingv2-L for the frame encoder in the E+I setting and for synthetic depth generation. The architecture of our event encoder, context encoder, and RDE/MDE decoder is as follows:\n\n**Event Encoder**\n\n| Module     | Operation                                                                 |\n|------------|---------------------------------------------------------------------------|\n| Input Head | Conv2d(5, 32, kernel size 5, stride 1, padding 2)                         |\n| Encoder 0  | Conv2d(32, 64, kernel size 5, stride 2) + ConvLSTM(128, 256, kernel size 3) |\n| Encoder 1  | Conv2d(64, 128, kernel size 5, stride 2) + ConvLSTM(256, 512, kernel size 3) |\n| Encoder 2  | Conv2d(128, 256, kernel size 5, stride 2) + ConvLSTM(512, 1024, kernel size 3) |\n| Encoder 3  | Conv2d(256, 512, kernel size 5, stride 2) + ConvLSTM(1024, 2048, kernel size 3) |\n\n**Context Encoder**\n\n| Module     | Operation                                         |\n|------------|---------------------------------------------------|\n| Input Head | Conv2d(6, 32, kernel size 5, stride 1, padding 2) |\n| Encoder 0  | Conv2d(32, 64, kernel size 5, stride 2)           |\n| Encoder 1  | Conv2d(64, 128, kernel size 5, stride 2)          |\n| Encoder 2  | Conv2d(128, 256, kernel size 5, stride 2)         |\n| Encoder 3  | Conv2d(256, 512, kernel size 5, stride 2)         |\n\n**Decoder (RDE and MDE Decoders)**\n\n| Module      | Operation                                           |\n|-------------|-----------------------------------------------------|\n| Decoder 0   | Upsample x2 + Conv2d(512, 256, kernel size 5, stride 1) |\n| Decoder 1   | Upsample x2 + Conv2d(256, 128, kernel size 5, stride 1) |\n| Decoder 2   | Upsample x2 + Conv2d(128, 64, kernel size 5, stride 1)  |\n| Decoder 3   | Upsample x2 + Conv2d(64, 32, kernel size 5, stride 1)   |\n| Output Head | Conv2d(32, 1, kernel size 1) \n\nSkip connections are applied between the event encoder and RDE/MDE decoders. We will include these architectural details in the supplementary material.\n\n**Q3: Inference time comparison between ERE and MDDE+.**\n\nWe thank the reviewer for this observation. Upon revisiting our runtime measurements, we verified that\nruntime of ERE is as reported (35ms). However, we realized that our reported MDDE+ runtime (24.2ms) had mistakenly included pre-processing overhead. In our revised measurement, MDDE+ achieves approximately 8ms on our setup. \nThis confirms that it is indeed much faster than ERE. We will update the runtime results accordingly.\n\n**Q4: Limited evaluation of proxy supervision using only one VFM.**\n\nWe thank the reviewer for this insightful suggestion. Although our study focused on a single VFM to maintain a controlled analysis, exploring other VFMs such as MoGe, DepthPro, and Lotus, or even a mixture-of-experts design could potentially further improve depth distillation. We acknowledge this as a valuable direction for future work.\n\n**Q5: Scalability with large unlabelled datasets.**\n\nWe agree that proxy pretraining on large-scale unlabelled event-image data is a highly promising direction. Our framework is inherently compatible with such scaling and has a strong potential to yield generalizable EMDE models when applied to large-scale data. Although the construction and processing of such datasets requires resources beyond the scope of this work, we view this as a natural and impactful extension of our approach and an important direction for future research.\n\n**Q6: Use of relative versus metric foundational models for depth supervision.**\n\nWe ran some initial experiments with Depth Pro as a source of metric supervision. However, we found that the predicted depths often exhibited inconsistent global scaling and do not align well with the LiDAR ground truth across different scenes. In contrast, relative depth models such as Depth Anything V2 produced more temporally stable output with sharper object boundaries and more coherent segmentation of surfaces. These qualitative differences resulted in a better spatial structure, which we then calibrated to metric space using our NDC module. Our two-stage design, which first captures rich relative geometry before calibrating to metric space, offers a more reliable and modular alternative to directly relying on metric predictions from VFMs."}, {"review_id": "hSz3Ev8v4O", "reviewer": "Reviewer_jNtM", "summary": "This paper proposes the Distil-E2D, which is a framework utilizing the prior knowledge from foundational depths models. This work utilizes DepthAnythingV2 to generate pseudo labels and complements sparse LiDAR depth maps. It introduces a Confidence-Guided Calibrated Depth Loss to align the synthetic and real depths and weight the confidence. This work additionally employs a Context Transformer and a Dual-Decoder Training scheme. Experiments also prove the good performance of the Distil-E2D.", "questions": "1. Some parts of Figure 1 are confusing: the arrow from DepthAnythingV2 to the “MDE Prediction dtm” looks insufficient, and according to the formula, the coefficient in $L_{\\text{align}}$ between Calibrated Depth and LiDAR Depth should be $w_t^d$ rather than $W_c$.\n2. In the abstract, the first occurrence of “MDE” should be accompanied by its full form, “monocular depth estimation.” Additionally, “MDE” and “EMDE” must be used consistently throughout the paper, many places use “MDE” (e.g., line 154, line 168) when it should actually read “EMDE”. References [28] and [37] are identical, and the authors should delete the duplicate one.\n3. Why does Equation 7 use logarithmic depth instead of the raw depth? Is this based on a particular prior or intended to expand the effective depth range? Further explanation is needed.\n4. Further clarification is required on:\n   (a) the network architectures of the encoder and decoder;\n   (b) for the MVSEC and DSEC datasets, considering poorly lit scenarios such as nighttime where the APS/RGB modality degrades severely, did the training set include only well-illuminated sequences?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\nThis paper focuses on monocular depth estimation with event cameras and provides thorough consideration and design in terms of pseudo labels, loss function formulation, and network architecture. The experimental results demonstrate the superiority of the proposed approach, and the ablation studies confirm the effectiveness of each module. The writing is clear, the content is easy to understand, and the work exhibits strong originality.\n\nWeakness:\nThere are still some confusing aspects in the manuscript, which will be detailed below and require further clarification from the authors.", "was_revised": true, "final_justification": "The authors have addressed my concerns, and I think this solid work meets the acceptance requirements.  So I'm raising the overall score to 5.", "rebuttal": "**Q1: Inconsistencies in Figure 1 annotations and equations.**\n\nWe thank the reviewer for pointing out the errors in the figure. The coefficient in $L_{align}$ between Calibrated Depth and LiDAR Depth should be $w^d_t$  instead of $W_c$. Furthermore, the arrow between DepthAnythingV2 and MDE Prediction should not exist. We will have the figure corrected in the camera-ready paper.\n\n**Q2: Inconsistent acronym usage and duplicate references.**\n\nWe thank the reviewer for the feedback. We will align the naming and correct the references.\n\n**Q3: Justification for using logarithmic depth in Equation (7).**\n\nWe use log depth instead of true depth because it offers numerical stability and better captures relative depth relationships, which is crucial when aligning synthetic and LiDAR depths. Depth values in driving scenes can span several orders of magnitude from very close objects (e.g., less than 2 meters) to distant background regions (tens of meters). Applying a log transform compresses this dynamic range into a bounded interval (approximately 0 to 1 in our normalized setup). This ensures that both near and far depths contribute comparably to the loss.\n\n**Q4: Lack of detailed encoder and decoder architecture descriptions.**\n\nPlease refer to our response to Reviewer qS9Q Q2 for full architectural details, which will also be included in the final supplementary material.\n\n**Q5: Clarification on whether training data includes low-light sequences.**\n\nThe training includes only well-illuminated sequences. For example, \nwe use the `outdoor_day2` sequence in MVSEC for training, which is also the standard training set for other competing algorithms. We refrain from using nighttime sequences for training because of the degraded quality of monocular depth estimates in low light."}, {"review_id": "hB3oOMnyXE", "reviewer": "Reviewer_9Hdw", "summary": "The paper proposes to distill image-to-depth priors for event-based monocular depth estimation by generating dense pseudo ground truth with DepthAnythingV2. The main challenge is that DepthAnythingV2 provides relative depth, while the goal is to estimate metric depth. The authors address this by (1) training a small MLP (NDC module) to fit the dense relative depth to the sparse LiDAR metric depth; (2) estimating a confidence map with an ACE module; (3) combining pseudo ground truth loss and LiDAR ground truth loss via loss design; and (4) using a dual-decoder architecture to enhance metric-depth training with simultaneous relative-depth training. The proposed method achieves state-of-the-art performance on the DSEC and MVSEC datasets.", "questions": "Observing Equation (11) and (12), I see that if $\\mathcal{F}\\_{conf}$ learns to trivially predict all zero $w\\_t^d$, then the second term $w\\_t^d\\cdot \\mathcal{L}\\_{align}(d\\_t^m, \\hat{y}^{syn})$ of Equation (12) will be completely reduced to zero. (According to the supplementary materials, the last layer of $\\mathcal{F}\\_{conf}$ is a sigmoid layer, so exact zeros cannot be produced, but the outputs can approach zero.) How is this collapse avoided?\n\nIf this zero-collapse happens, then all effect of the calibrated depth will be lost, meaning that the model only learns from the LiDAR ground truth (through $\\mathcal{L}\\_{mde}$) and the raw DepthAnythingV2 depth (through $\\mathcal{L}\\_{rde}$).\n\nBeyond explanation of the zero-collapse prevention methodology, I would also like to see some visualizations or statistics of the confidence maps produced by the ACE module in the rebuttal stage.", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The motivation of the paper is clear, and the solution is straightforward.\n- The evaluation and ablation studies are comprehensive, showing the effectiveness of each component.\n- The proposed method achieves state-of-the-art performance on the DSEC and MVSEC datasets.\n\nWeaknesses:\n- The generalization of the method is not impressive: although in-domain performance is good, it would be better if a generally trained model could be used across different datasets.\n- It is not clear how ACE works (see \"Questions\").", "was_revised": true, "final_justification": "The authors have issued my concerns so I decide to maintain my original rating.", "rebuttal": "**Q1: Mechanisms to prevent trivial collapse in the confidence weighting module.**\n\nWe thank the reviewer for highlighting this potential failure mode. To prevent collapse of $w_t^d$, we incorporate two stabilizing mechanisms: **base clamping** and a **sparse confidence regularizer**.\n\nSpecifically, in base clamping we reparametrize:\n\n$\\tilde{w}_t^d = \\alpha + (1 - \\alpha) w_t^d,$\n\nwith $\\alpha = 0.1$, where $\\tilde{w}_t^d$ is the final confidence weights. This enforces $\\tilde{w}_t^d \\in [0.1, 1]$ to avoid vanishing gradients. Additionally, the sparse confidence regularizer applies an L1 regularization loss:\n\n$\\mathcal{L}_{\\text{reg}} = \\beta\\| w_t^d - \\hat{w}_t^s \\|_1,$\n\nwhere $\\beta=0.5$ and $\\hat{w}_t^s$ is the min-max normalized sparse confidence map from Equation (10). This regularizer encourages consistency with high-confidence regions and promotes spatially adaptive weighting. We will include these implementation details in the final supplementary material.\n\n**Q2: Request for visualizations or quantitative statistics of ACE confidence maps.**\n\nWe report summary statistics of the dense confidence map $w_t^d$ computed over the MVSEC training set:\n\n| Metric | Value |\n|-----------|-----------|\n| Mean confidence $w^d_t$ | 0.72|\n| Standard deviation $w^d_t$ | 0.247|\n| Min-max range $w^d_t$ | 0.21 - 0.98 |\n\nThese statistics demonstrate that $w_t^d$ exhibits variation and spans the full expected dynamic range, and does not collapse to a trivial solution. Although visualizations of the confidence maps cannot be presented in the rebuttal, we will provide them in the final supplementary material."}]

Author rebuttal:

Meta-review: The final ratings are 3 accepts and 1 borderline accept. The AC have read the reviews and rebuttal, and discussed the submission with the reviewers. The reviewers agreed that the paper presents an interesting idea to overcome data limitations. Nonetheless, they raised a number of points during the review phase including fairness of experiments, lack of implementation details, computational complexity, and clarity of presentation. The authors were able to clarify these points during the rebuttal and discussion phases. The AC recommends the authors to incorporate the feedback and suggestions provided by the reviewers, and the materials presented in the rebuttal, which would improve the next revision of the manuscript.
Decision: Accept (poster)