EAG3R: Event-Augmented 3D Geometry Estimation for Dynamic and Extreme-Lighting Scenes
Authors: Xiaoshan Wu
Yifei Yu
Xiaoyang Lyu
Yi-Hua Huang
Bo Wang
Baoheng Zhang
Zhongrui Wang
XIAOJUAN QI
Venue: NeurIPS 2025 spotlight ()
Keywords: Event Camera
3D Vision
Neuromorphic Computing
Primary area: applications
TL;DR: EAG3R synergistically integrates standard RGB video frames with asynchronous event streams to achieve robust 3D geometry estimation in dynamic and extreme-lighting scenes where conventional RGB-only approaches struggle.

Abstract: Robust 3D geometry estimation from videos is critical for applications such as autonomous navigation, SLAM, and 3D scene reconstruction. Recent methods like DUSt3R demonstrate that regressing dense pointmaps from image pairs enables accurate and efficient pose-free reconstruction. However, existing RGB-only approaches struggle under real-world conditions involving dynamic objects and extreme illumination, due to the inherent limitations of conventional cameras. In this paper, we propose \textbf{EAG3R}, a novel geometry estimation framework that augments pointmap-based reconstruction with asynchronous event streams. Built upon the MonST3R backbone, EAG3R introduces two key innovations: (1) a retinex-inspired image enhancement module and a lightweight event adapter with SNR-aware fusion mechanism that adaptively combines RGB and event features based on local reliability; and (2) a novel event-based photometric consistency loss that reinforces spatiotemporal coherence during global optimization. Our method enables robust geometry estimation in challenging dynamic low-light scenes without requiring retraining on night-time data. Extensive experiments demonstrate that EAG3R significantly outperforms state-of-the-art RGB-only baselines across monocular depth estimation, camera pose tracking, and dynamic reconstruction tasks.

EAG3R: Event-Augmented 3D Geometry Estimation for Dynamic and Extreme-Lighting Scenes
Xiaoshan Wu!.", Yifei Yu!.* Xiaoyang Lyu', Yihua Huang', Bo Wang', Baoheng Zhang', Zhongrui Wang?d Xiaojuan Qi 1 The University of Hong Kong Southern University of Science and Technology Equal contribution, ordered alphabetically, Project lead, Corresponding author
Abstract
Robust 3D geometry estimation from videos is critical for applications such autonomous navigation, SLAMM, and 3D scene reconstruction. Recent methods like DUS(3R demonstrate that regressing dense pointmaps from image pairs en- ables accurate and efficient pose-free reconstruction. However, existing RGB-only approaches struggle under real-world conditions involving dynamic objects and extreme illumination, due to the inherent limitations of conventional cameras. In this paper, we propose EAG3R, novel geometry estimation framework that augments pointmap- basee reconstruction with asynchronous event streams. Built upon the MonST3R backbone. EAG3R introduces two key innovations: (1) retinex-inspired image enhancement module and a lightweight event adapter with SNR-aware fusion mechanism that adaptively combines RGB and event features based on local reliability: and (2) novel event-based photometric consistency loss that reinforces spatiotemporal coherence during global optimization. Our method enables robust geometry estimation in challenging dynamic low-light scenes with- out requiring retraining on night-time data. Extensive experiments demonstrate that EAG3R significantly outperforms state-of-the-art RGB-only baselines across monocular depth estimation, camera pose tracking, and dynamic reconstruction tasks.
Introduction
Estimating geometry from videos or images is fundamental problem in 3D vision, with broad applications in camera pose estimation, novel view synthesis, geometry reconstruction, and 3D perception. These capabilities are crucial in downstream scenarios such as autonomous driving, SLAM, virtual environments, and robotic navigation. Recent methods like DUSt3R [64] have shown that regressing dense pointmaps from image pairs using transformer-ban
foundation models enables accurate and efficient pose-free 3D reconstruction. This paradigm has sparked a growing trend toward addressing various challenging scenarios, such as longer image sequences [59, 62, 60]. dynamic scenes [72, 10, 27, 55], and integration with techniques like Gaussian Splatting [16, 52, 18].
However, in real-world applications such as autonomous driving the wild, which often involve fast motion and rapidly changing illumination, RGB cameras dependent on long exposure times for imaging face significant challenges, including blur, out-of-focus artifacts, overexposure, and underexposure. Consequently, the resulting low-quality images hinder reliable geometry estimation.
Event cameras, the other hand, provide asynchronous measurements of pixel-level brightness changes with high temporal resolution and dynamic range. They have demonstrated strong resilience in challenging conditions such as fast motion and extreme illumination [17, 28, 47]. Prior work has leveraged event streams in 3D tasks such as depth estimation [4, 79, 40], surface reconstruction [8, 9],
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Laiga
Depth
Camera Pose
Liwns
Inpur low light video
EAG3R
Camera Intrinsics
Smoooth
|N, P K] Variables of Optimization
Trajectories
4D Reconstruction
Imput event streum
Object Motion
Pointmags
Figure I: EAG3R pipeline for event-augmented dynamic 3D reconstruction. EAG3R processes low-light video and corresponding event stream within temporal window, extracting pairwise pointmaps for each frame pair. These pointmaps are jointly optimized under alignment, flow, smoothness, and event-based consistency losses to recover global dynamic point cloud and per-frame camera poses and intrinsics (X, P. K). This unified representation enables efficient downstream tasks such as depth estimation and camera pose estimation, under challenging lighting conditions.
and neural rendering [48, 25], but their integration into modern Saaning-based geometry pipelines remains limited.
In this paper, we propose EAG3R, an event-augemented MonST3R framework to enhance pointmap- based 3D geometry estimation under dynamic and extremely low-light conditions. Built upon the MonST3R [72] backbone, EAG3R introduces two key innovations: (1) a lightweight event adapter with Signal-to-Non -ogioe- Ratio (SNR)-aware fusion mechanism that adaptively integrates event and image features based on local reliability, and (2) an event-based photometric consistency loss that enforces alignment between predicted motion-induced brightness changes and event-observed brightness changes during global optimization. These components enable EAG3R to remain robust in scenarios where conventional RGB-only pipelines fail.
We evaluate EAG3R on the MVSEC dataset [78], conducting extensive comparisons on depth estimation, camera pose tracking, and dynamic reconstruction in extreme low-light conditions. Results show that EAG3R significantly outperforms existing baselines, including DUSt3R [64], MonST3R [72], and Easi3R [10] variants, even in zero-shot nighttime setting.
Our main contributions are as follows:
We propose EAG3R, the first event- augmented pointmap- bointmap-based geometry estimation frame- work, which integrates asynchronous event streams with RGB-based reconstruction to handle dynamic scenes under extreme low-light conditions.
We design plug-in event perception module that integrates RGB and event data via: (1) Retinex- enhancer for visibility recovery and SNR map prediction; (2) a lightweight feature integration.
event adapter; and (3) SNR-aware fusion scheme for adaptive
We develop a novel event-based photometric consistency loss that guides global opti- mization by aligning predicted motion-induced brightness changes with event-observed measurements, improving spatiotemporal coherence under low light.
We validate EAG3R across multiple challenging 3D vision tasks-ineluding monocular depth estimation, camera pose tracking, and dynamic reconstruction and show it signifi- cantly outperforms existing RGB-based pose-fres methods, even under zero-shot nighttime conditions.
2 Related Work
50, SfM and SLAM Traditional Structure-fromtte Motion (SfM) [1, 44, 45, 50, 53,55) 54] and Simultaneous
Localization and Mapping (SLAM) [11, 15, 38, 41] methods estimate 3D structure and camera motion by establishing 2D correspondences [5, 12, 36, 38, 49] or minimizing photometric errors [14, 15], followed by bundle adjustment (BA) [2, 6, 61]. While effective with dense views, these often struggle with sparse or ill-conditioned data. Recent learnin-bbased approaches aim to improve robustness and efficiency: DUSt3R [64], directly regress dense point maps from image pairs using Transformer architectures [13] trained on large-scale 3D datasets. However, DUS(3R and its variants

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[35, 39, 56, 59, 60] are primarily designed for static scenes and their performance degrades with dynamic content.
Pose-free Dynamic Scene Reconstruction Reconstructing dynamic scenes without known poses a core challenge in SLAM. Classical methods rely on joint pose estimation and dynamic region
filtering via semantics [71] or optical flow [75], but depend heavily on accurate segmentation and
tracking. Some methods estimate temporally consistent depth using geometric constraints [37]
or generative priors [24, 51J, yet suffer from fragmented reconstructions due to missing camera trajectories. Recent works jointly optimize depth and pose by refining pretrained depth models [46]
using flow [65] and masks [23], e.g., in CasualSAM [74], Robust-CVD [30], and MegaSAM [32].
the latter integrating DROID-SLAM [58] and diverse priors [43, 69]. Recent approaches employ
direct pointmap regression, including MonST3R [72]. DAS3R [67], CUT3R [62], and Easi3R [10),
which leverage optical flow, segmentation, or attention for motion disentanglement.
Low-light Enhancement Low-light image enhancement (LIE) aims to improve image quality under poor illumination. Traditional methods like histogram equalization [3] and Retinex-based algorithms [21] have limited adaptability, while deep learning approaches [66, 7, 63] achieve better results but still struggle in extreme darkness. Event cameras, with high dynamic range and temporal resolution, enable structural information preservation in very low light [76, 73], inspiring event- guided LIE methods [26, 34]. However, robust fusion of frame and event data under noise remains challenging. EvLight [33] addresses this with adaptive event-image feature fusion.
Event-based 3D Vision Event cameras have enabled progress in 3D reconstruction under chal- lenging lighting and fast motion [17]. Early work used stereo setups for disparity and multi-view stereo [8, 42, 77], followed by monocular methods based on geometric priors like camera trajecto- ries [28, 47J. Recent approaches apply deep learning to stereo [40] and monocular [4, 9] settings, producing dense outputs such as meshes or voxels. Multimodal fusion with structured light [31] or RGB-D sensors [79] further improves robustness. Latest advances adapt NeRF [29, 48] and Gaussian Splatting [22, 25] to event data, enabling high-fidelity scene reconstruction and novel view synthesis.
3 Methods
Overview Figure shows the overall pipeline of EAG3R. Our work addresses the critical challenge of robust monocular 3D scene reconstruction- easttrrcttoon-eeccmmasiiiii dynamic geometry, camera pose, and depth estimation-unde extreme lighting conditions where traditional RGGB-based methods often fail. EAG3R enhances the MonST3R framework by synergistically integrating standard RGB video frames {1' E #x x33 with asynchronous event streams {E') (sequences of és (Iy,, yk, (R,P)) Pk)). ). This is achieved through two primary strategies: adaptive event-image feature fusion guided by signal quality, and an event-augmented global optimization that incorporates event-derived cues for static region masking and consistency loss.
The subsequent sections provide a detailed exposition: Section 3.1 reviews the foundational DUSt3R and MonST3R architectures. Section 3.2 then describes our Event-data Integration and Feature Fusion approach, including techniques for RGB image enhancement, the design of a lightweight event adapter, and our core SNR-aware fusion mechanism. Finally, Section 3.3 details the Global Optimization with Event Consistency, explaining how event-based consistency loss are integrated to achieve enhanced spatio-temporal coherence across both RGB and event data.
3.1 Preliminary
Our work builds upon DUSt3R and its dynamic extension MonST3R, which employ pointmaps for direct, dense 3D geometry estimation from images, facilitating pose-free monocular reconstruction.
Pointmap-based Static Reconstruction. DUSt3R utilizes pointmaps (Xpm Hx assign- ing a 3D coordinate per pixel, predicted for image pairs (T; by Transformer model:
x x = Model(1
(X
(I)
These encode relative geometry for depth and pose estimation and are refined via global optimization for multi-view consistency into global point cloud X".
3

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Reference View
Upstream
Enhance
E
Head
+ Weighta Staring
Attentionn
Fir
Fine
Heed
Erihance
Max
Figure 2: EAG3R network. Left: The DUSt3R (MonST3R) architecture with reference and source views processed via VIT encoder-decoder structure. Middle: Our method (only the upstream branch for the reference image shown), which includes a lightweight event encoder and fuses event and image features with cross-attention. Right: The Retinex-bass based enhancement module estimates an illumination map and an SNR confidence map to guide adaptive fusion.
Extension to Dynamic Scenes. MonST3R adapts this for dynamic scenes by finetuning DUSt3R on dynamic datasets, predicting per-frame pointmaps. It optimizes global scene model X global (comprising per-frame camera poses {P*), intrinsics {K*}, J. and depth maps {Df) using objective function:
MMnSTTRR Xilbbal = Lalign + Wsmoooth Smoothh + Wiloww Lnow
guided by alignment, trajectory smoothness, and image-based optical flow (Crow) terms.
However, the reliance of these image-based methods on clear visual information causes them to struggle in low-light settings: RGB images It lose crucial detail, and MonST3R's flow estimation with RAFT [57] can become unstable. Our EAG3R addresses these limitations by integrating asynchronous event data E' into both the feature extraction and global optimization stages, aiming for robust 3D reconstruction performance even in such challenging lighting conditions.
3.2 Event-data Integration and Feature Fusion
To enable robust geometry estimation in low-light scenarios, we redesign the encoding pipeline with hybrid event-image architecture is illustrated in Fig. 2. Our improvements begin with Retinex-inspired enhancement module that operates on the raw input image 1' to recover visibility in underexposed regions. This module also estimates SNR map. M which serves as spatial prior for confidence-aware fusion. Next, we introduce a lightweight event adapter based on a Swin Transformer backbone, designed extract high-fidelity features from the sparse event stream Et We also establish cross cross-modal interaction through a cross cross-attention mechanism between event and image features. Finally, we propose an SNR-aware fusion strategy that adaptively balances image and event features based on local SNR, favoring images in well-lit areas and events in low-visibility regions. This yields a more informative and robust representation F for downstream 3D reconstruction.
based Image Enhancement. To enhance image visibility under low- low-light conditions and provide spatial reliability prior, we introduce lightweight Retinex inspired [7] enhancement module. Given an input image 7c, we estimate an illumination map / using shallow network Nretinex with inputs I' and its channel-wist maximum projection T = maxx(1') (T'), and compute the enhanced image via element-wise multiplication:
L
Nretinex (I', , Ipriots 1/ I' Litum-
To guide adaptive fusion, we compute a SNR map M sur indicating local image reliability. We convert J to grayscale Ig. apply mean filtering to obtain 7 and define:
7
M snn
g
(4)
I: g
T +
where e ensures numerical stability. This SNR map emphasizes high-confidence regions and sup- presses noise-dominated areas, enabling reliability-awar feature fusion downstream.

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Lightweight Event Adapter. To effectively hamnsss asynchronous event streams El for dense geometric prediction, we introduce a lightweight event adapter by employing a Swin Transformer backbone initialized with weights from self-sup context-based pre-training regimen on event data [70]. The input events from E' are voxelized into a spatiotemporal grid and processed by the pre-trained Swin Transformer encoder, yielding hierarchical event features {F (F Corre- sponding hierarchical image features / are extracted at every layers from intermediate representations within the pre-trained image encoder. At each hierarchical stage I, the event features F are spatially aligned and simension-matched matched with their respective image counterparts m'mg.""
We then apply cross- cross-attentionn68) using event features as queries and image features keys and values:
F CrossAtttnQ = F K = F ing.ts
F (ing.))
Importantly, the image encoder remains frozen, and only the event adapter is updated. This training strategy ensures efficient adaptation without disrupting the pretrained image backbone, while allowing the event pathway to learn compensate for degraded or missing visual cues.
SNR-aware Feature Aggregation. We combine the final features from the image (Fit (ing-fina)) and event (For evt-final) encoders into a unified representation F", guided by the normalized SNR map M sur Specifically, weight image features by M and event features by its complement (1 -M followed by concatenation:
Ftt (Fi
img final C (M || (Fth (1-MM M
(6)
where @ denotes element- wise features F undergo a projection to match the input dimensionality of the downstream decoder.
wise multiplication with channel-wise broadcasting. The concatenated
This adaptive feature aggregation dynamically prioritizes image features under high-SNR conditions and event features under low-light scenarios, yielding robust and illumination invariinn representations for effective downstream 3D reconstruction.
3.3 Eyent-Enhanced Global Optimization
To enhance the performance of MonST3R's 3D reconstruction and camera pose estimation, particu- larly in challenging low-light environments where image-based cues are compromised, our approach augments its global optimization framework. The primary enhancement is the introduction of an event-based photometric consistency loss, Leven- This integration leverages the inherent advantages of event cameras- such as their high dynamic range and ability capture dynamics even with minimal illumination- to provide robust supervisory signals. The subsequent sections detail the formulation of this Levent from raw event data (Section 3.3.1) and its incorporation into the joint optimization process (Section 3.3.2).
3.3.1 Event-Basee Bastd Photometric Consistency Loss
The event-based photometric consistency loss, Levent is formulated to evaluate the alignment of brightness change patterns observed within salient image patches. achieves this by comparing two distinct representations of these patterns: the first, ALp.. (u), is derived directly from the raw event stream ε; the second, Air... (w; X Kgohha) is synthesized by integrating photometric information from an intensity image with scene motion inferred from the global state estimate Xglobal- The process of computing this loss is visualized in Fig.
Observed Brightness Increments from Events: Given an event stream corresponding the time interval between frames I' and 1" we compute the observed brightness increment within each salient image patch Pn, by aggregating events en = (æk, yke tR. Pk) occurring in both space and time over the patch and interval AT -t
A Ap, (u)
Σ
pjō(u Uz),
(7)
tyE(e,)) (E)
5

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Polarity Integration
Observed Brightness Increment
Events [ex]
Inpul image framne
Predicted Drighteess Increment
Levent
Patch
ai.
Dot Product vÂ
Palch
(ir pl,pi
Detected Harris corners
intrinsics and extrinsies
Figure 3: Event-based photometric consistency loss. Harris corners are detected on the input image define salient patches. Observed brightness increments are computed by integrating event polarities, while predicted increments are synthesized from image gradients and motion. The loss Levent measures their alignment.
where (ar, y) denotes local coordinates within the patch. Each patch Pm.. is centered at Harris corner detected on the reference intensity image Ie and covers small spatial neighborhood around the corner location. This ensures that the selected regions exhibit strong intensity gradients and are thus well-suited for event-based tracking. The aggregation Equation (7) yields polarity- event accumulation image representing the measured brightness changes within each patch.
To estimate the brightness change within salient image patch P... adopt predictive model derived from the principle of brightness constancy. This model synthesizes the expected brightness increment ALp... (u; Xglobal by combining photometric and geometric cues, specifically:
Brightness Increment Model from Intensity and Motion:
az (ū) Pm is computed over the patch
Local Intensity Gradient:
The spatial gradient P., from the intensity image I' at time
Inter-frame Pixel Motion: The motion field Autoi (ū, Xgloha) represents the per- per-pixel displacement between frames & and t', computed by projecting 3D points using the depth map D and the camera intrinsics and extrinsies (K*, Kt² Pt, pt) contained in the global state gloohul
Assuming locally constant optical flow and small inter-frame displacements, the predicted brightness increment is expressed as:
OI
ALP... (ū; Xglohat)
grad
(ū)lr...
Au
1++t' (ū, Xglobai)| AT
Ou
where AT denotes the integration interval and C is the contrast sensitivity threshold intrinsic the event sensor. This expression follows the generative model introduced in [20].
Event-Based Loss Objective: While Equation (8) provides explicit formulation, the scale factor Ar C' is unknown and varies across sensors and operating conditions. To eliminate this ambiguity, we normalize both the observed and predicted brightness increment patches to unit I norm before computing the residual. This yields an objective that is invariant to the unknown contrast scale and focuses solely on the alignment of gradient directions:
ALp (w)
ALP, (u; Xglobal)
Levent Levent(Xplotee (Xylohal) =
3ΣΣ ΣNEP
ATp. (u)ll
ΣMEp.n. ALPp (w; X Xglobal)
Pm DEP
This loss enforces that brightness variations predicted from image gradients and estimated motion are consistent with real event stream observations, thereby providing principled supervision signal for optimizing Xglobal-

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3.3.2 Joint Optimization with Event- Event-Based Constraints
The event-based loss Leven is integrated into MonST3R's global optimization objective (Eq. 2). The augmented objective to find the optimal global scene model X scales (o*) becomes:
global pairwise alignments {PW W and
X gloobal {Pw), {o-)==-=== arg
min Xataa }
(Caligul (aaggunXKaaaa global {Pw), + smooth Cmmooth (- (X (Xglbbal)
WBee Rrow (X glohaa) + Wevent Wevent Ceeeeveenxggiiee Kglobal)
(10)
where Wevent is the Wevent_ base scaled by the mean of (1 Sporms where Sworn are the normalized corner SNR values.
fevent provides more dependable constraint on geometry and motion by leveraging informative event patterns in salient patches. Minimizing this augmented objective refines the state estimate X global by ensuring that brightness changes modeled from intensity and motion, ALp,, (u: Xlloba),, align closely with observed event patterns ALP,,. (u). This synergy enhances the accuracy and robustness of 3D reconstruction and pose estimation, particularly when conventional image quality is poor.
4 Experiments
We evaluate our method in variety of tasks, including depth estimation (Section 4.2), camera pose estimation (Section 4.3) and 4D reconstruction (Section 4.4). We perform ablation studies in Section 4.5, We compare EAG3R with state-of-the-an pose free learning-based reconstruction method, including DUSt3R [64], MonST3R [72], and Easi3R [10].
4.1 Experiment Details
For training, we fine-tune the MonST3R baseline by training its ViT- Base decoder, DPT heads, Enhancement Net, and the Event Adapter. The Event Adapter is pre-trained the Aiartan dataset. Fine-tuning is performed for 25 epochs, using 8,000 image-event pairs per epoch. We employ the AdamW optimizer with learning rate of x 10-5 and mini-hatch size of per GPU. The training process completes in approximately 24 hours on 4 NVIDIA RTX 3090 GPUs. For global optimization, we adopt the same setting MonST3R, with hyperparameters W Wmmooth 0.01, Wflow 0.01, and 0.01. We use the Adam optimizer for 300 iterations with a learning rate of 0.01.
Wevent
For dataset selection, initially attempted to fine-tune the MonST3R baseline using events generated via Vidco-to- to--tentss (V2E)[19] from MonST3R's 's fine-tuning datasets. However, the noise in V2E- generated events led to gradient explosion during training, prompting to switch datasets with real event captures and ground truth (GT) depth. Given the scarcity of such data, we selected the Multi Vehicle Stereo Event Camera (MVSEC) dataset [78]. It provides synchronized stereo events and reliable LiDAR-derived depth GT. To ensure fair zero-shot evaluation of low-light performance, all models were trained exclusively on MVSEC's outdoor _aydoor_day22 sequence (normal daylight) and tested on the challenging outdoor_night 1-3 sequences (extreme low-light).
4.2 Monocular Depth Estimation
We evaluate monocular depth estimation on the MVSEC outdoor -ittootinngthh sequences, which feature extreme low-light conditions with significant noise and underexposure. All models are trained solely the MVSEC outdoor _atddorr__y22ooo sequence and tested zero-shot on these nighttime scenes to ensure a fair comparison. We report results using standard metrics: Absolute Relative Error (Abs Rel 1). Scale-invariant RMSE log (RMSE log 1), and the threshold accuracy 1.25 (1), where lower better for error metrics and higher is better for accuracy.
As shown in Tab. 1, DUSt3R performs poorly due to the severe degradation of visual cues at night. However, applying RetinexFormerr, a widely used image enhancement network, as a preprocessing light-up step (denoted as (LightUp)) does not yield significant improvements and, in some cases, de- grades performance, indicating that image enhancement alone is insufficient for this task without joint optimization with the downstream model. Fine-tuning MonST3R improves its performance across
7

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Table 1: Monocular depth estimation performance nighttime scenes. Evaluation is conducted on the MVSEC Nightl, Night2, and Night3 sequences. Standard metrics are used: Abs Rel 4. δ 1.25 1, and RMSE log 4. Best performing method in bold, second best underlined.
Method
Nightl
Night2
Night3
Abs Rel 1 6<1.251
RMSE log
Abs Rcl
6c1.25 1
RMSE log
Abs Rel 4
8<1.25
RMSE log
DUSER[64]
0.407
0.393
0.534
0.415
0.384
0.495
0.463
0.335
0.534
MonST33[72]
0.370
0.373
0.497
0.309
0.469
0.409
0.317
0.453
0.418
DUS@R (LightUp)
0.425
0.351
0.548
0.462
0.347
0.537
0,525
0.293
0.592
MonST3R (LightUp)
0.370
0,369
0.503
0.316
0.451
0,431
0.329
0.441
0,444
MonST3R (Pinctunc)
0.376
0.426
0.478
0.328
0.472
0.415
0.302
0.509
0.401
EAG3R
0.353
0.491
0.416
0.307
0.518
0.383
0.288
0.533
0.371
Table 2: Camera pose estimation on all MVSEC nighttime sequences. Evaluation is conducted on the MVSEC Night1, Night2, and Night3 sequences. Standard metrics are used: ATE 4, RPE trans 4. and RPE rot 4. Best performing method in bold, second best underlined.
Method
Night1
Night2
Night3
ATE
RPE trans
RPE rot
ATE
RPE trans
RPE rot
ATE
RPE trans
RPE rot
DUS(3R[64]
1.474
0.914
2.995
3.921
2.207
10.761
4.109
2.401
11.309
MonST3R[72]
0.559
0.317
0.369
0.626
0.341
1.460
0.733
0.427
1.371
East3Rannd 10]
1.505
0.953
3.024
3.884
2.205
10.608
4.102
2.398
11,306
0,550
0,303
0.362
0.606
0.328
1.462
0.712
0.414
1.369
MonST3R (Finetunc)
0,580
0.284
0.214
0.467
0.210
0.374
0,402
0.183
0.370
Ensi3R (Finetune)
0,540
0.263
0.214
0.448
0.202
0.374
0,394
0.178
0.371
EAG3R (Ours)
0.482
0.201
0.143
0.428
0.207
0.342
0.409
0.201
0.320
most metrics, demonstrating the benefit of domain adaptation. Our method, EAG3R, outperforms all baselines across all three nighttime sequences, indicating both accurate and reliable depth predictions. EAG3R leverages asynchronous event signals that remain informative in such challenging low-light settings, which enables strong generalization capabilities despite the model never having been trained on nighttime data. These results highlight the distinct advantage of incorporating event-based cues for robust depth estimation under extreme illumination conditions, where conventional RGB-bart based methods-ever when augmented with fine-tuning or pre-enhancementt-stnggg to perform reliably.
4.3 Camera Pose Estimation
We evaluate camera pose estimation on the challenging MVSEC nighttime sequences using standard metrics (ATE, RPE trans, RPE rot; lower is better), following consistent zero-shot protocol (trained on outdoor_day? as in our depth experiments.
As shown in Tab. 2, RGB-only baselines such as DUSt3R fail under extreme low-light conditions, while MonST3R offers improved results. Fine-tuning MonST3R leads to substantial gains, partic- ularly in RPE trans and RPE rot, with further improvements from Easi3R variants. Despite these enhancements, our proposed EAG3R consistently achieves the best performance across most metries and sequences. This advantage comes from EAG3R's effective use of asynchronous event data, which provides reliable motion cues even when RGB inputs are heavily degraded. As a result, EAG3R maintains robust tracking and delivers more accurate camera trajectories, highlighting the strength of event-based sensing in scenarios where conventional methods often fail. As illustrated in Fig. 4, the predicted trajectory from EAG3R exhibits lower drift and aligns more closely with the ground truth compared to DUSt3R and MonST3R, further demonstrating its superiority in precise pose estimation.
4.4 Dynamic Reconstruction
We evaluate dynamic 3D reconstruction on the MVSEC nutdoor_nighh1-1 sequences. Prior methods such as DUSt3R and MonST3R serve as bGB-dased baselines, with MonST3R extending pointmap prediction to dynamic scenes and Fasi3R variants incorporating motion-aware masking. However, all remain limited under low-light conditions due to their reliance degraded RGB inputs.
8

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DUSBR
MonST3R
EAG3R
Figure 4: Comparison of estimated camera trajectories. The predicted trajectories (solid blue) from DUS3R, MonST3R, and EAG3R are evaluated against the ground truth (dashed gray). Notably, EAG3R demonstrates a trajectory that more closely aligns with the ground truth.
Table 3: Ablation study on depth estimation performance on the Night3 sequence. Modules are incrementally added to the MonST3R baseline. Each addition improves performance, with the full EAG3R system achieving the best results.
Method
Abs Rel 4
8 1.25
RMSE log
MonST3R (Baseline)
0.317
0,453
0.418
MonST3R (Finetune)
0,302
0,509
0.401
Event
0.297
0.518
0.396
Event LightUp
0.291
0.523
0.388
Event + LightUp++ SNR Fusion (Full)
0.288
0.533
0.371
In contrast, EAG3R directly integrates asynchronous event streams into the 4D reconstruction pipeline, allowing for improved motion handling and robustness illumination changes. Qualitative results, provided in the appendix, show that EAG3R produces cleaner, more complete reconstructions and better preserves dynamic scene details compared to purely frame-based methods.
4.5 Ablation Study
To better understand the contribution of each design component in EAG3R, we conduct systematic ablation study on the MVSEC outdoor_ iigghtornnghh33aoo sequence for monocular depth estimation. Starting from the MonST3R baseline, we incrementally add our proposed modules: event inputs, the LightUp enhancement network, and the SNR-aware fusion mechanism. Results are shown in Tab.
Each component contributes positively to the final performance. The introduction of event streams already leads to substantial improvement, validating the value of asynchronous visual signals in low-light scenarios. Incorporating the LightUp module provides additional gains by improving the quality of underexposed RGB inputs. Finally, the SNR-guided fusion further boosts robustness by adaptively emphasizing reliable features from either modality, particularly in noisy or degraded regions. The combination of these modules leads to the strongest performance, confirming the effectiveness of our full EAG3R design.
Conclusion
We presented EAG3R, event-augmented framework for robust 3D geometry estimation under dy- namic and low-light conditions. Built on the MonST3R backbone, EAG3R introduces a lightweight event adapter and a retinex-inspired light-up module, SNR-aware fusion mechanism, and an event- based photometric consistency loss. These components enable reliable depth and pose estimation where conventional RGB-only methods struggle. EAG3R achieves strong zero-shot generalization to nighttime scenes, consistently outperforming state-of-the--e baselines in depth, camera pose estima- tion, and dynamic reconstruction tasks. Our results highlight the value of integrating asynchronous event signals into geometry pipelines. We discuss limitations and broader impact in the appendix.
6 Acknowledgements
This work has been supported by the National Key R&D Program of China (Grant No. 2022YFB33083000, Hong Kong Research Grant Council Early Career Scheme (Grant No. 27209621), General Research Fund Scheme (Grant No. 17202422, 17212923, 17215025), Theme- based Research (Grant No. T45-701/22-R) R) and Shenzhen Science and Technology Innovation

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Commission ( 1405040)) Part of the described research work is conducted in the JC STEM Lab of Robotics for Soft Materials funded by The Hong Kong Jockey Club Charities Trust. This research was partially conducted by ACCESS AT Chip Center for Emerging Smart Systems, supported by the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government,
10

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A Technical Appendices and Supplementary Material
A.1 Dataset Processing
The Multi-Vehicle Stereo Event Camera (MVSEC) dataset integrates three distinct sensor modalities, each with independent timestamps: Active Pixel Sensor (APS) for frame-based images, Dynamic Vision Sensor (DVS) for event-based data, and Velodyne Puck LITE for depth measurements. These modalities operate at different frequencies. necessitating careful synchronization to ensure data consistency. The Velodyne Puck LITE provides depth data at a fixed frequency of 20 Hz, while the APS captures frames at approximately 100 Hz during daytime and 10 Hz during nighttime. The DVS generates asynchronous event streams, which are temporally aggregated into voxel grids for processing.
To align these modalities, we project depth measurements to image timestamps and voxelize event data between consecutive timestamps. The alignment process varies between daytime and nighttime sequences due to differences in frame frequency relative to depth data.
A.1.1 Daytime Sequence Processing
For daytime sequences, where APS operates at 100 Hz, associate each depth ground truth from the Velodyne Puck LITE with the temporally closest APS frame. This is achieved through the following steps:
1. Pose Interpolation for Images:
Interpolate camera poses at all APS image timestamps Given discrete pose measurements P(tx) at times fgp we compute the interpolated pose P(t() at image timestamp & using linear interpolation for translation and spherical linear interpolation (SLERP) for rotation:
t 4-1 111-4, lx
P(tz) P(tx) +
(P(t)+1)- P(tt)).
where P(t) ((t)) represents the rotation R(t) E SO(3) and translation T(t) E 3
2. Pose Interpolation for Depth:
Similarly, interpolate poses at depth timestamps ta obtain P(ta), using the same interpolation method as above.
3. Timestamp Matching: For each depth timestamp tu identify the nearest image timestamp
t by minimizing the temporal difference:
t arg min Ita
tyl.
where T7 is the set of all image timestamps.
4. Depth Warping: Warp the depth data to the selected image timestamp 4; using the relative transformation between poses P(ta) and P(t,). For a 3D point Xa in the depth sensor's coordinate frame at tu the warped point X, at t, is computed as:
X; R(ta) -X - T(ta)) + T(t,).
A.1.2 Nighttime Sequence Processing
During nighttime sequences, the APS frame rate drops Hz, resulting in multiple depth measure- ments (at 20 Hz) per APS frame. We process the depth data as follows:
1. Depth Projection 3D: Project all depth measurements within the temporal window of single APS frame into 3D point cloud. For depth measurement d at timestamp ta the 3D point Xa obtained using the sensor's 's intrinsic calibration and pose P(ta):
X P(ta) unproject(d).
where unproject converts the depth measurement to 3D point in the sensor's local frame. 2. Selection of Closest Depth: For each APS frame at timestamp 4, select the depth measure- ment from the point cloud that is temporally closest I,, determined by the minimum temporal difference Ita tyl.
A-1

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Table A.I: Video depth estimation performance nighttime scenes. Evaluation is conducted on the MVSEC Night1, Night2, and Night3 sequences. Standard metrics are used: Abs Rel 4. δ 1.25 1, and Log RMSE 4. Best performing method in bold, second best underlined.
Method
Night1
Night2
Night3
Abs Rel 4
5c1.25
Log RMSE
Abs Rel
6c1.25
Log RMSE
Abs Rel J 6c1.25
Log RMSE
DU663R[64]
0.432
0.374
0,547
0.410
0.397
0.493
0.510
0.322
0.554
M77ST3R[72]
0.380
0.388
0.486
0.299
0.494
0.388
0.296
0.499
0.392
Basi3Rnaa(100
0.427
0.388
0.549
0.435
0.376
0.515
0.504
0.324
0.566
Rasi3R [10]
0.375
0.381
0.484
0.308
0.490
0.397
0.314
0.465
0.404
EAG3R (Ours)
0.357
0.482
0.494
0.512
A.1.3
Rectification and Hole Filling
To enhance data quality, we use rectified APS frames, DVS event data, and depth measurements. Rectification of APS frames introduces sparse regions ("holes") due the transformation process. We address this by applying spatial interpolation to fill these holes, ensuring a continuous image. Specifically, for pixel (x, y) in sparse region, estimate its value [I y) using weighted average of neighboring valid pixels:
[(x, v) EleADEN (((l.//)(((
Eiewnee w(r',(")
where N is the set of neighboring valid pixels, and wo s/) is distance-based weight (e.g., inverse Euclidean distance). This ensures the rectified frames are suitable for downstream tasks such as scene
understanding and 3D reconstruction.
This processing pipeline ensures robust temporal and spatial alignment across the APS, DVS, and depth modalities, enabling effective utilization of the MVSEC dataset.
A.2 Video Depth Estimation Results MVSEC
We evaluate video depth estimation under extreme low-light conditions using the MVSEC outdoor_ figgtornigght,,,. outdoor _oightr, and aatdoor_nights sequences. All models are trained solely on the outdoor_des _aao2 sequence and tested in a zero-shot nighttime setting. Following stan- dard protocol, we report Absolute Relative Error (Abs Rel 4), RMSE log and 5 < 1.25 (f) over all frames in each sequence.
As shown in Tab. A.1, conventional RGB-based baselines such DUSt3R and MonST3R suffer from errors due to degraded visual signals at night, and even Easi3R exhibit limited temporal consistency. In contrast, EAG3R achieves superior performance across all sequences and metries. By combining RGB-based pointmaps with temporally precise event cues, EAG3R effectively preserves geometric detail and improves depth stability over time. The event-based supervision introduces an additional constraint on spatiotemporal coherence, which regularizes the optimization even when image content weak or noisy. These results demonstrate that augmenting pointmap-based reconstruction with event streams enables robust, lemporally-consisten video depth estimation in dynamic and low-light environments.
A.3 Dynamic Reconstruction Results
We present dynamic 4D reconstruction results of EAG3R on challenging real-world sequences involving fast-moving objects and adverse lighting. As illustrated in Fig. A. 1, our method produces temporally coherent and geometrically accurate 3D point clouds, even under degraded RGB conditions and rapid scene changes.
Notably, in the highlighted sequence, a car passes through the middle of the scene a moment where RGB-based methods fail to produce stable or even visible reconstructions due to motion blur and low illumination. EAG3R, powered by event-based cues, successfully reconstructs the moving vehicle with sharp geometry and consistent motion across frames. This showcases the unique advantage of leveraging asynchronous event data for robust dynamic scene understanding.
A-2

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Dust3R
Monst3R
EAG3R
Figure A.I: Qualitative comparison dynamic scenes. Our method reconstructs consistent 31) geometry even when a fast-moving vehicle passes through the scene. RGB-only methods fail to capture this motion reliably.
A.4 Summary of Existing Event-RGB Datasets
Our choice of the MVSEC dataset was guided by the strict requirements of our task: robust 3D geometry estimation in dynamic scenes under extreme lighting. This demands datasets with aligned RGB, event data, and accurate ground truth for both depth and pose combination that is rarely available. As shown in Table A.2, few existing datasets satisfy the necessary conditions for evaluating dynamic reconstruction under challenging lighting.
Table A.2: Comparison of RGB-event datasets.
Dataset
Low-Hight
Dynamic
RGB
Depth Seusor
GT Pose
Platform
Enviroument
DSEC
LiDAR-16
Car
Outdoor
UZH-FPV
MoCap
Indooe/Ourdoor
DAVIS 240C
X
MoCap
Handheld
Indocn/Ouddoor
GENI
Outdoor
Prophesee IMP
Car
Outdoor
TUM VIE
x
MoCap
Handheld Head mounted
Indocr/Outdoor
LiDAR-16
MoCap / Cartograpber
Car Drone
Indoco/Uudoor
M3ED
6
LIDAR 64
LIO
Car Legged Robot Drone
Indocn/Ouddor
A.5
Ablations on Design Rationales
This section presents ablation studies analyzing the design rationales of EAG3R. Although EAG3R built upon the pointmap-based reconstruction framework, it is the first incorporate asynchronous event streams, enabling robust and pose-free 4D reconstruction under extreme lighting conditions. Three key design aspects are examined: the fetinex-inspired confidence estimation, the lightweight event adapter, and the multi-stage feature fusion.
A.5.1 Addressing Extreme-Light Challenges for Geometric Estimation
straightforward strategy for handling low-light conditions is apply image enhancement preprocessing step. However, this approach is found to be suboptimal for geometric estimation due to introduced artifacts and the lack of structural consistency preservation.
As shown in Table A.3, applying RetinexFormer ("LightUp")) before reconstruction provides limited even negative benefit, indicating that conventional enhancement does effectively support cross-modal fusion.
Table A.3: Ablation image enhancement under extreme lighting. Evaluation is conducted on the MVSEC Nighti-8 sequences. Metrics: Abs Rel 5 < 1.25 1, RMSE log 4. Best in bold.
Method
Night1
Night2
Night3
Abs Rel J
<; 1.25
RMSE log
Abs Rel J
<x 1.25
RMSE log
Abs Rel
<x 1.25 1
RMSE log
MonST3R
0.370
0.373
0.497
0.309
0.469
0.409
0.317
0.453
0.418
MonST3R (LighhUp)
EAC3R (Ours)
0.353
0.491
0.416
0.307
0.518
0.383
0.288
0,533
0.371
To address this limitation, EAG3R introduces Retinex-inspiree SNR estimation module that computes spatially varying confidence map rather than directly enhancing images. This map guides
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the adaptive fusion of RGB and event features, assigning higher confidence to events in dark or noisy regions and RGB inputs where illumination is reliable. This constitutes, to the best of our knowledge, the first use of a Retinex-based confidence mechanism in event- geettg geometric reconstruction.
A.5.2 Efficient and Effective Event Adapter
Events are inherently sparse and asynchronous, posing challenges for efficient feature adaptation. A direct reuse of heavy RGB encoder with zero-convolution initialization, leads to inefficient training and limited gains (as is compared in A.4).
Table A.4: Ablation event adapter design. Comparison between zero-convolution initialization and the proposed lightweight Swin Transformer-basee adapter. Evaluation is conducted on the MVSEC Night1-3 sequences.
sequences,,,, Metrics: Abs Rel 4, 1.25 t. RMSE log 4.
Method
Abs Rel
8 < 1.25 1
RMSE log
Zero Conv
0.377 00.323 00.302 0.446 / /0.78 / 0.485 0.449 0.399/ 0.379 0.353 / 0.307 0.288 0.491 / 0.518// 0.533 0.416/ 0.383 0.371
EAG3R (Ours)
Our adapter is pretrained large-scale event-only datasets using self-supervised objectives, enabling the transfer of general motion and edge priors. This design ensures fast convergence and strong generalization, particularly under data-scarce extreme-light conditions.
A.5.3 Robust and Principled Feature Fusion
RGB and event modalities differ substantially in spatial density, temporal resolution, and noise char- acteristics. Naive strategies, such feature concatenation single-layer attention, are insufficient to capture their complementary information.
Table A.5: Ablation on fusion strategies. Comparison between simple additive, single-layer attention, and the proposed multi-stage adaptive fusion. Evaluation conducted on the MVSEC Ni Night1-3 sequences. Metries: Abs Rel 4. d < 1.25 1. RMSE log 4.
Nightl
Night2
Night3
Rel
5<
RMSE log
Abs Rol
5
log |
J
< 1.25
RMSE log
Feature Add
0.357
0.482
0.477
0.312
0.519
0.384
0.295
0.535
0.373
Last-layer Attention
0.361
0.495
0.426
0.322
0.486
0.396
0.296
0.515
0.380
EAG3R (Ours)
0.491
0.416
0.307
0.383
0.288
0.371
EAG3R employs a multi-stage adaptive fusion mechanism that combines:
Cross-attention within the encoder, where event features query multi-scale RGB features, enabling nonlinear and context-aware interactions.
SNR-guided feature aggregation followed by a learnable nonlinear projection, enhancing robustness against local noise and illumination variation.
We compare different feature fusion strategies in A.5, demonstrating the superiority of our approach.
A.5.4 Feature Strategy for Global Optimization
To improve the stability of global optimization, the feature selection strategy in EAG3R focuses on Harris corners, which represent sparse yet highly stable points with strong image gradients. These features provide high-confidence geometric constraints and enhance convergence in the op- timization of camera pose and structure. Three strategies are compared: Harris corners (ours), SuperPoint(learned detector), and random sampling.
It is observed in A.6 that random sampling introduces noisy gradients by selecting unreliable regions, thereby degrading optimization stability. In contrast, learned detectors such as SuperPoint are compu- tationally expensive and prone to overfitting when illumination varies significantly. The proposed Harris-based strategy provides a balanced solution, introducing stable and targeted supervision signals that improve convergence while maintaining computational efficiency.
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Table A.6: Comparison of feature selection strategies for global optimization. Evaluation is conducted on the MVSEC Night1-3 sequences subsets. Metrics include ATE J. RPE trans 4. and RPE rot J. The proposed Harris-corner approach achieves the best trade-off between accuracy and computational efficiency.
Method
ATE +
RPE trans 4
RPE rot +
Computation Cost
Random Sampling
0.687
0.261
0.153
Low
SuperPoint [12]
0.685
0.260
0.153
High
Harris Corner (Ours)
0.655
0.236
0.152
Medium
A.6
Runtime and Memory Analysis
To assess the computational efficiency of EAG3R, we conduct a detailed runtime and memory analysis. Our framework is designed introduce minimal overhead while maintaining strong reconstruction performance. This efficiency stems from its modular and lightweight architecture components.
Runtime Analysis. Table A.7 reports the resource consumption for single image event pair inference, while Table A.8 summarizes the memory usage during global optimization. Compared MonST3R, EAG3R introduces only a minor overhead of approximately +0.4 GB VRAM, +0.11 TFLOPs, and +1.2 per forward pass. Even when incorporating event loss in the global optimization stage, the increase remains modest. These results demonstrate that EAG3R achieves robustness and multimodal integration with minimal computational cost.
Table A.7: Runtime and memory analysis for single inage-event pair inference.
Method
VRAM (GB)
TFLOPs
Forward Time (s)
MonST3R (Baseline)
2.165
1.284
~1.9
+LightUp
2.1
1.287
~2.6
+LightUp Event Adapter + Fusion (Full)
2.562
1.398
~3.1
Table A.8: Memory usage during global optimization (sequence length 20).
Method
VRAM (GB)
MonST3R (Baseline)
10.99
EAG3R (w/o event loss)
12.08
EAG3R (w/ event loss)
14.02
Scalability to Longer Sequences. We further analyze scalability with respect to sequence length, following the reviewer's suggestion. EAG3R employs a sliding-windar window optimization scheme, where only pairwise pointmaps and loss terms within a fixed temporal window are computed. This design avoids the quadratic complexity of fully connected graph optimization, maintaining constant problem size per iteration regardless of video length (as is reported in A.9). Consequently, both runtime and memory cost scale linearly with the total number of frames, as confirmed by our experiments running on an NVIDIA A100 GPU.
Table A.9: Scalability analysis: peak memory usage vs. sequence length.
Sequence Length
40
60
100
Max Memory (GB)
14.02
20.19
27.78
37.40
46.49
Overall, the results confirm that EAG3R maintains near-linear computational growth with respect to sequence length and introduces only marginal overhead compared the baseline.
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A.7 Generalization to More Datasets
To demonstrate EAG3R's scalability, we conducted additional experiments on MVSEC indoor and M3ED datasets, covering diverse environments (indoor, outdoor, night, HDR), sensor platforms (drones, robots, cars), and motion types, including complex aerial and ambulatory trajectories.
Models were trained under normal lighting and evaluated in low-lightHHD conditions in a zero-shot setting, confirming EAG3R's robustness beyond vehicle-centric scenes.
HDR Environments (Robot Dog). To assess the model's performance high-dynamic-range (HDR) conditions, we evaluated EAG3R the challenging M3ED robot dog dataset Latpl_plaaaaaaaa split, which features rapid motion and severe illumination fluctuations. As shown in Table A.10, EAG3R achieves substantially higher pose estimation accuracy than both the MonST3R baseline and its scene-fined cinetffiettued variant across all key metrics.
Table A.10: Pose estimation performance on the M3ED robot dog dataset under HDR lighting conditions.
Method
ATE
RPE trans &
RPE rot
MonST3R MonST3R (finetune)
0.3425
0.1919
2.3003
0,1853
0.0981
1.8493
EAG3R (Ours)
0.1361
0.0632
0.6086
High-Speed Outdoor Drone Scenarios. To further evaluate robustness under extreme motion and complex lighting, we tested EAG3R on the M3ED high-speed drone dataset. As summarized in Ta- ble 1, EAG3R consistently outperforms the baseline all three outdoor sequences, demonstrating reliable pose estimation even in high-speed and high-contrast conditions.
Table A.11:
Pose estimation results on the M3ED high-speed outdoor drone sequences.
High Beams
Penno Packing
Penno Parking 2
Method
ATE J.
RPE trans
RPE rot J
ATE 1
RPE (trans)
RPE (rot) 1
ATE 4
RPE (trans)
RPE (rot) 1
MonST3R
0.0668
1.0852
0.9023
EAG3R (Ours)
0.1111
0.0572
0.6450
0.1189
0,0748
0,5380
0.3089
0.1572
0,9032
High-Speed Indoor Drone Scenarios. We further evaluated EAG3R's s depth estimation performance on the indoor sequences of the MVSEC dataset. As reported in Table A.12, EAG3R achieves the best results across all key depth metrics, surpassing both the baseline and its finetuned variant by large margin.
A.8 Statistical Analysis and Robustness Validation
To further assess robustness, we performed statistical analysis across four independent training runs. As summarized in Table A.13, the results exhibit low variance and consistent performance across all key metrics, confirming the model's 's robustness and stability during optimization.
These results indicate that EAG3R maintains consistent performance across multiple random initial- izations, with all reported metrics exhibiting low variance and statistically significant stability.
A.9 Limitations
Despite the strong empirical performance of EAG3R, several limitations remain:
Limited dataset availability. Currently, there a scarcity of public datasets that simultaneously provide real event data, RGB videos, and accurate ground-truth geometry. To address this, our future work aims to curate diverse dataset featuring high-quality, real-world event-RGB pairs across varied lighting and motion scenarios.
Dependence on event data quality. Our approach assumes access to temporally aligned, high- quality event streams. Although the SNR- fusion mechanism mitigates some effects of noise, the
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Table A.12: Depth estimation performance on MVSEC indoor drone sequences.
Method
Abs Rel
8 1.25 t
RMSE log
MonST3R
0.097
0.918
0.146
MonST3R (finetune)
0.307
0.429
0.331
EAG3R (Ours)
0.041
0.972
0.094
Table A.13: Statistical evaluation of EAG3R across four independent runs. Reported metrics include absolute relative (Abs Rel J), accuracy under threshold (6 1.25 f), and log RMSE 4 Results indicate low standard deviation and statistically significant stability across all sequences.
Metric
Night1
Night2
Night3
Abs Rel
f
RMSE log
Rel
RMSE log
RMSE
Mcan
0.4851
0.4171
0.3137
0.4969
0.3939
0.2904
0.5144
0.3796
0.0211
0.0059
0.0163
0.0340
0.0113
0.0059
0.0321
0.0099
p-value
0.0009
<0,0001
<0,0001
<0,00001
0.0013
<0,0001
<0.0001
0.0010
<0.0001
performance of EAG3R can still degrade when event data are excessively sparse, noisy, or misaligned. In particular, we attempted to train our model using synthetic events generated by V2E [20]. but observed that the low fidelity of these generated events caused optimization instability, including gradient explosion and failure to converge.
A.10 Broader Impacts
Positive impact. EAG3R improves 3D perception in challenging environments involving dynamic content and poor illumination. This systems such as drones, mobile robots, and vehicles, particularly in low-light or fast-motion settings. Additionally, our approach may benefit applications in assistive technology, remote exploration, and AR/VR, where robust scene understanding under non-ideal conditions critical.
potential to enhance safety and reliability in autonomous
Potential risks and misuse. with many vision-based systems, there exists a risk of misuse in surveillance or privacy-invasivy applications. EAG3R's ability to reconstruct geometry from dark or partially visible scenes could be leveraged in ways that compromise individual privacy. Moreover, due to reliance event cameras, which remain expensive and less common, the technology may be disproportionately accessible to well-funded institutions, potentially widening gaps in accessibility.
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Reviews:
[{"review_id": "hNba58AcCH", "reviewer": "Reviewer_DUi3", "summary": "This paper introduces EAG3R, a novel 3D geometry estimation framework that augments pose-free pointmap-based reconstruction with asynchronous event streams. Built on the MonST3R backbone, EAG3R integrates a Retinex-inspired image enhancement module, a lightweight Swin Transformer-based event adapter with SNR-aware fusion, and an event-based photometric consistency loss into the 3D geometry optimization pipeline.", "questions": "1. The method assumes perfectly time-synchronized, high-SNR event data aligned with RGB frames. But in practice event cameras can introduce more sensor noise relative to conventional cameras. Have you studied the effect of event noise on your method?\n1. Do you have a runtime analysis and GPU memory usage comparison against MonST3R to assess practical feasibility?\n1. Can you include a qualitative failure-case analysis or limitations section focused on known challenges with event data?\n1. Could you provide examples of situations where EAG3R fails or performs poorly? For example, does it degrade in highly deformable motion, complete darkness with no RGB signal, or heavy event noise scenarios? A qualitative analysis would clarify boundaries of robustness.\n1. How does EAG3R handle regions with few or no events, such as textureless walls or static background areas in dynamic scenes? Does the SNR-aware fusion default to purely RGB features in such regions, and if so, is there a risk of low-quality depth in underexposed areas?", "limitations": "Further study into the following would illuminate potential limitations of the method:\n1. Analysis on out-of-distribution motion patterns: MVSEC mostly covers automotive driving scenarios with relatively predictable ego-motion and scene dynamics. It’s unclear how the method handles motion outside of this range.\n1. Reliance on event data quality and the method's failure modes: analysis of the method's dependence on event data quality, sensor properties, and failure cases in event-sparse or highly noisy scenarios would be valuable.\n1. Scalability to longer sequences/larger scenes: the method performs global optimization over per-frame pointmaps, camera poses, and intrinsics, which is computationally intensive. While the paper reports 300 optimization iterations, it doesn’t quantify how this scales with longer sequences.", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths\n1. Relevant problem: 3D geometry estimation under dynamic, extreme low-light conditions is an important challenge for applications like autonomous navigation and AR/VR.\n1. Novel technical contributions: retinex-inspired image enhancement module, SNR-aware fusion mechanism between event-based and image-based features and event-based photometric consistency loss incorporated into global optimization.\n1. Clear integration of event streams into a contemporary pointmap-based framework (MonST3R), a meaningful step for practical scene reconstruction.\n\n## Weaknesses\n1. Limited dataset diversity: all experiments are conducted only on MVSEC outdoor sequences. While the paper justifies this, testing on additional diverse benchmarks (like E3D or open event datasets) would strengthen generalizability claims.\n1. While the method’s zero-shot performance on different lighting conditions is a strength, its generalization to other types of scene dynamics (e.g., different motion types, varied object categories, or other outdoor/indoor environments) isn’t demonstrated. A limitation if claiming \"general-purpose dynamic reconstruction\".\n1. Ablation completeness: while ablation studies are helpful, including runtime analysis (added compute/memory overhead from events, fusion, optimization) would clarify practical trade-offs.\n1. Event data limitations: the method's performance in highly noisy or sparse event streams (common in commercial-grade sensors) isn’t deeply evaluated.\n1. Although the paper claims statistical rigor in the checklist, the reported tables (Tables 1–3) don't visibly include error bars, confidence intervals, or p-values.", "was_revised": true, "final_justification": "The authors have clearly and thoroughly addressed the raised concerns with strong empirical evidence and detailed analysis. EAG3R shows robust performance under noisy and sparse event conditions, as well as strong generalization across datasets and motion patterns. While the method introduces some non-negligible computational overhead, the authors provide runtime and scalability analyses that support its practical feasibility. Statistical validation from multiple training runs confirms that the results are stable and reproducible. The adaptive fusion mechanism effectively handles regions with few or no events, and the paper includes a transparent discussion of limitations and failure cases. Overall, this is a well-executed and meaningful contribution to event-augmented 3D reconstruction.", "rebuttal": "> W1: Limited dataset diversity\nW2: General-purpose dynamic reconstruction\nL1: Analysis on out-of-distribution motion patterns\n\nPlease refer to our reply to jzhF W3.\n\n> W3: Ablation completeness: ... clarify practical trade-offs.\nQ2: Do you have a runtime analysis ... assess practical feasibility?\n\n**Reply:** We thank the reviewer for suggesting runtime analysis.\n\n1. **Lightweight event fusion**\n  - **Event Adapter**: Uses efficient Swin Transformer, freezing the image encoder and updating only the adapter/decoder, minimizing computation.\n  - **Enhancement Module**: Shallow, Retinex-inspired design ensures high efficiency.\n\n2. **Runtime analysis**\n  - **Table 1**: For single image-event pairs, EAG3R adds ~0.4 GB VRAM, ~0.11 TFLOPs, and ~1.2s inference time over MonST3R, with potential CUDA optimization.\n  - **Table 2**: For global optimization (sequence length 20), event-guided approach adds ~1 GB VRAM overhead compared to baseline without event loss.\n\n\nOur design introduces minimal computational overhead while maintaining efficiency.\n\n| Method | VRAM Usage (GB) | TFLOPS | Forward Pass Time |\n| :--- | :--- | :--- | :--- |\n| MonST3R (Baseline) | 2.165 | 1.284 | ~1.9s |\n| +Lightup | 2.192 | 1.287 | ~2.6s |\n| +Light up + Event Adapter + Fusion (Full) | 2.562 | 1.398 | ~3.1s |\n\n| Method | VRAM Usage (GB) |\n| :--- | :--- |\n| MonST3R (Bseline) | 10.99 |\n| EAG3R (w/o event loss) | 12.08 |\n| EAG3R (w/ event loss) | 14.02 |\n\n> W4: Event data limitations:...... isn’t deeply evaluated.\nQ1: The method assumes perfectly time-synchronized, ...... the effect of event noise on your method?\nL2: Reliance on event data quality ...... noisy scenarios would be valuable.\n\n**Reply:** We thank the reviewer for their comment on event data quality challenges. Our response covers two aspects:\n\n1. **MVSEC Noise**: MVSEC’s real-world data, collected with a commercial DAVIS346 sensor, includes inherent noise and ~90% sparsity. EAG3R’s strong performance on this noisy input demonstrates its robustness.\n\n2. **Noise and Sparsity Simulations**:\n\t- **Noise**: We injected 5%, 10%, and 15% Poisson noise into event streams to mimic sensor noise, testing EAG3R’s performance.\n\t- **Sparsity**: We simulated low-contrast scenes by randomly dropping 5%, 10%, and 15% of events to evaluate robustness.\n\nThese tests confirm EAG3R’s resilience to practical noise and sparsity challenges.\n\n| | | **night1** | | | **night2** | | | **night3** | | |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| **Type** | **level** | **Abs Rel** | **δ<1.25** | **RMSE log** | **Abs Rel** | **δ<1.25** | **RMSE log** | **Abs Rel** | **δ<1.25** | **RMSE log** |\n| | **Real** | 0.3526 | 0.4906 | 0.4160 | 0.3066 | 0.5181 | 0.3829 | 0.2875 | 0.5336 | 0.3710 |\n| **Noise** | **+5%** | 0.3526 | 0.4909 | 0.4161 | 0.3066 | 0.5184 | 0.3830 | 0.2876 | 0.5336 | 0.3711 |\n| **Noise** | **10%** | 0.3524 | 0.4923 | 0.4162 | 0.3066 | 0.5188 | 0.3830 | 0.2878 | 0.5338 | 0.3713 |\n| **Noise** | **15%** | 0.3521 | 0.4951 | 0.4164 | 0.3067 | 0.519 | 0.3832 | 0.2881 | 0.5339 | 0.3715 |\n| **Sparsity** | **5%** | 0.3527 | 0.4896 | 0.4161 | 0.3066 | 0.5167 | 0.3831 | 0.2874 | 0.5323 | 0.3710 |\n| **Sparsity** | **10%** | 0.3527 | 0.4883 | 0.4161 | 0.3067 | 0.5150 | 0.3833 | 0.2872 | 0.5310 | 0.3710 |\n| **Sparsity** | **15%** | 0.3527 | 0.4869 | 0.4160 | 0.3067 | 0.5130 | 0.3835 | 0.2871 | 0.5293 | 0.3709 |\n\nModern event cameras (e.g., Prophesee GenX320) have higher dynamic range and lower noise than the DAVIS346 used in MVSEC, suggesting EAG3R’s performance could improve further with newer hardware.\n\n> W5: Although the paper claims ...... confidence intervals, or p-values.\n\n**Reply:** We thank the reviewer for suggesting statistical analysis. While large-scale 3D vision models like ours, DUSt3R, and MonST3R typically report single-run results due to high computational costs, we conducted 4 additional independent training runs from random initialization to validate robustness. Results show stable performance in depth estimation, with low standard deviation across key metrics.\n\n| | **night1** | | | **night2** | | | **night3** | | |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| | **Abs Rel** | **δ<1.25** | **RMSE log** | **Abs Rel** | **δ<1.25** | **RMSE log** | **Abs Rel** | **δ<1.25** | **RMSE log** |\n| **Mean** | 0.3546 | 0.4851 | 0.4171 | 0.3137 | 0.4969 | 0.3939 | 0.2904 | 0.5144 | 0.3796 |\n| **Std** | 0.0211 | 0.0059 | 0.0163 | 0.0076 | 0.034 | 0.0113 | 0.0059 | 0.0321 | 0.0099 |\n| **p-value**| 0.0009 | < 0.0001 | < 0.0001 | < 0.0001 | 0.0013 | < 0.0001 | < 0.0001 | 0.001 | < 0.0001 |\n\n\n> Q3: Can you include a qualitative failure ...... with event data?\nQ4: Could you provide examples of situations ...... boundaries of robustness.\n\n**Reply**:  We thank the reviewer for their insightful comments on our method’s operational boundaries. Our analysis of EAG3R’s performance on challenging MVSEC and M3ED datasets clarifies its robustness:\n1. **MVSEC Performance**: In nighttime sequences, large motion (e.g., sharp turns) and sparse co-located RGB/event signals (e.g., dark, static areas) challenge performance. However, EAG3R’s degradation is less severe than MonST3R, showing the advantage of event-augmentation.\n\n2. **M3ED Generalization**: Tests on diverse indoor and M3ED datasets (e.g., drone and quadruped robot motion) confirm EAG3R’s robustness across complex trajectories, outperforming baselines and avoiding overfit to MVSEC’s vehicular patterns.\n\n3. **Failure Cases**: Extreme conditions like complete darkness or heavy event noise may cause failures due to limited information.\n \nIn summary, while aggressive motion and sparse events pose challenges, EAG3R consistently outperforms pure visual methods and generalizes well across diverse real-world scenarios.\n\n> Q5: How does EAG3R handle regions with few or no events, such as textureless walls or static background areas in dynamic scenes? Does the SNR-aware fusion default to purely RGB features in such regions, and if so, is there a risk of low-quality depth in underexposed areas?\n\n**Reply**: Reply: We thank the reviewer for their insightful comment on our fusion design’s performance. EAG3R’s multi-stage fusion robustly handles event-sparse regions by:\n\n- **Well-lit static areas**: Few events but high-quality RGB input; SNR-aware fusion prioritizes reliable image features.\n\n- **Underexposed static regions**: Low event and RGB quality; early-stage cross-attention fusion uses broader image context to enhance weak local patches.\n\n- **Extreme cases**(e.g., night sky): Unreliable depth estimates are masked out in benchmarks like MVSEC, minimally impacting metrics.\n\nEAG3R’s fusion, combining early cross-modal interaction and SNR-aware aggregation, ensures resilience in low-activity regions, avoiding catastrophic failure under challenging lighting.\n\n> L3: Scalability to longer sequences/larger scenes: the method performs global optimization over per-frame pointmaps, camera poses, and intrinsics, which is computationally intensive. While the paper reports 300 optimization iterations, it doesn’t quantify how this scales with longer sequences.\n\nReply: We appreciate the reviewer’s comment on scalability. Our EAG3R framework ensures scalability for long video sequences using a sliding window optimization strategy, like MonST3R, computing pairwise pointmaps and loss terms only within the window, avoiding the costly fully connected graph of all frame pairs.\n\n* Constant Problem Size: The sliding window ensures that the optimization problem's size and complexity remain constant at each step, regardless of the total length of the video sequence.\n* Decoupled Iteration Cost: Because the optimization problem is fixed in size, the number of iterations required for convergence does not need to increase for longer videos. The cost per iteration is effectively decoupled from the total sequence length.\n\nThe computational cost for processing a long video scales linearly with frame count, not quadratically, ensuring high scalability. Supplementary experiments on an A100 GPU confirm this linear relationship between sequence length and cost.\n\n| Sequence length | Max Memory (GB) |\n| :--- | :--- |\n| 20 | 14.02 |\n| 40 | 20.19 |\n| 60 | 27.78 |\n| 80 | 37.40 |\n| 100 | 46.49 |"}, {"review_id": "Jnk0VooRDm", "reviewer": "Reviewer_b25i", "summary": "This paper presents EAG3R, an event-augmented framework for robust 3D geometry estimation in dynamic and extreme-lighting scenes. Leveraging asynchronous event streams, EAG3R enhances the MonST3R backbone with two key innovations: a Retinex-inspired image enhancement module combined with a lightweight event adapter for SNR-aware feature fusion, and an event-based photometric consistency loss to enforce spatiotemporal coherence during global optimization. This approach enables effective integration of event and RGB features, adapting to local reliability to maintain performance in low-light conditions without retraining for nighttime scenarios. Experiments on the MVSEC dataset demonstrate that EAG3R outperforms state-of-the-art RGB-only baselines in monocular depth estimation, camera pose tracking, and dynamic reconstruction, achieving up to 10.7% lower Absolute Relative Error in extreme low-light scenes and showcasing strong zero-shot generalization to nighttime environments.", "questions": "1. The method relies on Harris corners for event patches, which may miss structural details in texture-sparse regions (e.g., walls, smooth surfaces). This could lead to incomplete reconstructions—validate with alternative patch selection (e.g., random sampling) to quantify bias.\n2. Experiments only use MVSEC,  more validation is necessary.\n3. The paper claims dynamic scene support but doesn’t quantify errors for moving objects (e.g., vehicles). \n4. The SNR-aware fusion assumes linear weightings, but lighting nonlinearities (e.g., saturation) may invalidate this. Introduce nonlinear fusion models (e.g., attention-based) and compare via ablation.\n5. Ignoring negative events (only using positive polarity) might lose intensity decrease signals in sudden lighting changes.", "limitations": "Please refer to the Weaknesses and Limitations parts.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths\nEAG3R effectively integrates event streams with RGB data, using a Retinex-inspired module to enhance low-light images and an SNR-aware fusion strategy to adaptively combine features. This allows robust 3D geometry estimation in dynamic, extreme-lighting scenes without retraining for nighttime conditions. The event-based photometric consistency loss improves spatiotemporal coherence, and experiments on MVSEC show significant performance gains over RGB-only baselines in depth estimation, pose tracking, and dynamic reconstruction.\nWeaknesses\nThe method relies on Harris corners for event patch selection, which may miss structural details in texture-sparse areas. The framework does not explicitly address reconstruction errors for moving objects, leaving room for improvement in handling dynamic scenes.", "was_revised": false, "final_justification": "", "rebuttal": "> Q1: The method relies on Harris corners...... to quantify bias.\n\nReply: We thank the reviewer for the valuable comment. While Harris corners may overlook texture-sparse regions, our design deliberately uses event loss as a **sparse, high-confidence supplement**, not the primary geometric constraint. These regions are already addressed by our global optimization, which integrates alignment, smoothness, and optical flow losses to ensure spatial-temporal consistency.\n\n1. Why choose Harris corners\nUnder extreme lighting where optical flow supervision becomes unreliable (due to degraded RAFT performance), the event loss becomes crucial. However, applying it indiscriminately in low-gradient regions like walls can lead to unstable or uninformative gradients. Hence, we restrict it to Harris corners—locations with strong gradients and dense, reliable event activity—maximizing both **signal quality** and **numerical stability**.\n\n2. Comparing Harris corner-based path selection with other feature selection approaches\nTo assess potential bias, we conducted an ablation comparing Harris-based and random patch sampling. As is summarized in the following Table, random sampling caused noticeable degradation, as many sampled patches fell into low-texture regions, introducing noisy gradients that hindered convergence. This empirically confirms that our targeted strategy enhances effectiveness while avoiding spurious influence from weak or invalid supervision.\n\n| Method | ATE | RPE trans | RPS rot |\n| :--- | :---: | :---: | :---: |\n| Harris corner | 0.655 | 0.236 | 0.152 |\n| Random Sampling | 0.687 | 0.261 | 0.153 |\n| Superpoint | 0.685 | 0.260 | 0.153 |\n\n> Q2: Experiments only use MVSEC, more validation is necessary.\n\n**Reply**: Please refer to our reply to reviewer jzhF W3.\n\n> Q3: The paper claims dynamic scene support but doesn’t quantify errors for moving objects (e.g., vehicles).\n\nReply: We thank the reviewer for the insightful question regarding evaluation on moving objects. The main challenge lies in the lack of dense annotations (e.g., masks) for dynamic objects in current datasets, which makes direct evaluation difficult. Nonetheless, we provide several indirect but effective metrics in the paper to assess performance in dynamic scenes.\n\n* On one hand, in dynamic environments, the accuracy of monocular pose estimation serves as a strong indirect quantitative indicator of the model's ability to disentangle ego-motion from object motion. Poor handling of moving objects would result in incorrect camera motion estimates. As shown in Table 2, our model significantly outperforms the baseline in low-light dynamic settings.\n* On the other hand, we quantify the video depth metric in Table A1 of the Appendix (below), which evaluates the depth estimation accuracy across all pixels in the video sequence. This naturally includes dynamic objects in the scene, thus indirectly reflecting the model’s performance on moving objects. \n\n| Method | Night 1 Abs Rel ↓ | δ<1.25 ↑ | Log RMSE ↓ | Night 2 Abs Rel ↓ | δ<1.25 ↑ | Log RMSE ↓ | Night 3 Abs Rel ↓ | δ<1.25 ↑ | Log RMSE ↓ |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| DUST3R | 0.432 | 0.374 | 0.547 | 0.410 | 0.397 | 0.493 | 0.510 | 0.322 | 0.554 |\n| MonST3R | 0.380 | 0.388 | 0.486 | 0.299 | 0.494 | 0.388 | 0.296 | 0.499 | 0.392 |\n| Easi3Rdust3r | 0.427 | 0.388 | 0.549 | 0.435 | 0.376 | 0.515 | 0.504 | 0.324 | 0.566 |\n| Easi3Rmonst3r | 0.375 | 0.381 | 0.484 | 0.308 | 0.490 | 0.397 | 0.314 | 0.465 | 0.404 |\n| EAG3R (Ours) | 0.372 | 0.443 | 0.480 | 0.304 | 0.514 | 0.403 | 0.281 | 0.542 | 0.390 |\n\n\n> Q4: The SNR-aware fusion assumes linear weightings, but lighting nonlinearities (e.g., saturation) may invalidate this. Introduce nonlinear fusion models (e.g., attention-based) and compare via ablation.\n\nReply: We thank the reviewer for this insightful comment regarding our feature fusion strategy. We would like to clarify that our framework does not limit fusion to a single linear step. Instead, we adopt multi-stage, nonlinear and adaptive mechanisms to robustly integrate RGB and event features: \n\n* Nonlinear fusion in the encoder: We embed a multi-level cross-attention mechanism within the encoder, where event features query RGB features across resolutions. This enables content-aware, nonlinear fusion early in the network, allowing dynamic emphasis on salient information from both modalities.\n* Learnable nonlinear projection after SNR-guided aggregation: The initial SNR-based weighting is followed by a learnable nonlinear projection applied to concatenated features. This step adaptively reweights and transforms the representation, helping address challenges like local lighting nonlinearity.\n\nTo directly address the reviewer's suggestion, we conducted new ablation experiments on depth estimation comparing different fusion strategies: linear addition, attention-based fusion at the final stage, and our proposed multi-stage fusion. The results show that our approach outperforms all other choices.\n\n| Methods | night1 Abs Rel ↓ | δ<1.25 ↑ | RMSE log ↓ | night2 Abs Rel ↓ | δ<1.25 ↑ | RMSE log ↓ | night3 Abs Rel ↓ | δ<1.25 ↑ | RMSE log ↓ |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| Feature Add | 0.357 | 0.482 | 0.417 | 0.312 | 0.519 | 0.384 | 0.295 | 0.535 | 0.373 |\n| Last Layer Attention | 0.361 | 0.495 | 0.426 | 0.322 | 0.486 | 0.396 | 0.296 | 0.515 | 0.380 |\n| Ours | 0.353 | 0.491 | 0.416 | 0.307 | 0.518 | 0.383 | 0.288 | 0.533 | 0.371 |\n\n> Q5: Ignoring negative events (only using positive polarity) might lose intensity decrease signals in sudden lighting changes.\n\nReply: We appreciate the reviewer’s concern. However, we would like to clarify that our method does not ignore negative events. Both positive and negative polarity events are used throughout our pipeline.\nTo avoid any further confusion, we will revise the manuscript to clearly state that both polarities of events are utilized during feature extraction and fusion.\n\n> Paper Formatting Concerns: The reference formatting is non-standard. For instance, some journal names are presented in full, while others use abbreviations.\n\nReply: We thank the reviewer for your thorough review and valuable feedback. We sincerely apologize for the inconsistencies in the citation formatting. In the revised version of the paper, we will carefully review and standardize all references to ensure consistency."}, {"review_id": "o0Nh294adv", "reviewer": "Reviewer_jzhF", "summary": "The paper proposes an evolution of DUSt3R / MonST3R that augments those pipelines with event data. Integrations are done in two primary ways, i.e. via (i) an event perception module and (ii) an event-based photometric consistency loss. The method demonstrates clear performance improvements, particularly in low light scenarios.", "questions": "As noted in the previous section, my main concerns revolve around the writing style of the paper. That being said, the work is overall novel and produces clearly good results. I therefore believe it should be accepted, so my preliminary score is borderline accept.\n\nOn a separate note, I do wonder if the authors could expand the experiment to include more generic (i.e. non-event-based) datasets, by simulating the event system. For example, it would be interesting to know if a variant of their photometric loss could improve performance when e.g. computed over pairs of RGB images instead of pairs of events and RGB detections.", "limitations": "Yes, this is presented in the work.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Positive:\n- The contribution is novel -- to my knowledge this is the first work to integrate event data within a dust3r-like formulation.\n- The benefits of the event system are fairly well proven by the experimental section. While the tables of quantitative results in the main paper are useful, I found the extra results in the supplementary material section particularly interesting.\n\nNegative:\n- I find the paper somewhat difficult to read. For much of the paper, the authors simply describe the architecture and losses, without much intuition. For example, the lightweight event adapter in lines 151-160 effectively describes the figure and architecture, but provide little reason as to why the authors did things the way they did. In my view, more intuition would make the paper easier to read.\n- Some of the algorithmic choices are not well well ablated. For example, the authors chose Harris feature to simulate event activations on RGB images. While I understand the overall reason for matching these detections to events, this should be validated experimentally (as there are many other types of features that could work well, or, indeed, the authors could train their own relevant feature detector).\n- The experiments have a wide breadth (i.e. across many domains), but are quite shallow (i.e. not much testing within each domain). This is probably explained by the relative low availability of event + RGB datasets.", "was_revised": true, "final_justification": "The rebuttal addressed many of my concerns, so I increased my final review score to Accept.", "rebuttal": "> W1: I find the paper somewhat difficult to read. ...... In my view, more intuition would make the paper easier to read.\n\nReply: We thank the reviewer for their valuable feedback on improving the readability of our paper. We appreciate the opportunity to clarify our design process, particularly for the event adapter and feature fusion strategy. Here we provide more intuitions and experimental considerations for our architecure and loss design, we will also revise out paper accordingly. \n\n### 1. Design Rationale for the Hybrid Architecture\n\nOur hybrid architecture design seeks an optimal balance between performance and efficiency. We initially evaluated an approach that duplicated the heavyweight RGB ViT encoder, but experiments showed it incurred significant computational overhead for limited performance gain. As shown in the table below, our final proposed architecture significantly outperforms this initial design.\n\n| | **Night1** | | | **Night2** | | | **Night3** | | |\n| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| | **Abs Rel ↓** | **δ<1.25 ↑** | **RMSE log ↓** | **Abs Rel ↓** | **δ<1.25 ↑** | **RMSE log ↓** | **Abs Rel ↓** | **δ<1.25 ↑** | **RMSE log ↓** |\n| **Zero_Conv** | 0.377 | 0.446 | 0.449 | 0.323 | 0.478 | 0.399 | 0.302 | 0.485 | 0.379 |\n| **Ours** | 0.353 | 0.491 | 0.416 | 0.307 | 0.518 | 0.383 | 0.288 | 0.533 | 0.371 |\n\nWe ultimately chose a **lightweight Swin Transformer**, pre-trained on large-scale event datasets, as the event backbone. The core advantage of this strategy is its ability to effectively transfer powerful, general-purpose features (e.g., motion patterns) from event data. This facilitates efficient model convergence and stronger generalization, especially in data-scarce, low-light scenarios.\n\n### 2. Design Rationale for the Event-Based Photometric Loss ($L_{event}$)\n\nOur event-based photometric consistency loss ($L_{event}$) is designed to provide a robust spatiotemporal supervision signal, particularly in scenarios where traditional image-based cues fail due to low light. Its effectiveness and stability stem from two key design choices:\n\n* **Geometrically-Derived Motion**: To circumvent the unreliability of 2D optical flow in low light, the motion field is generated directly via geometric projection from the global 3D state (depth map $D^t$ and camera transformations $P^t, K^t$). This method establishes a direct, physically consistent constraint between the 3D geometry being optimized and the event data.\n\n* **Scale-Invariant Formulation**: To address the unknown and variable contrast sensitivity threshold ($C$) of event cameras, we normalize both observed and predicted brightness-change patches to a unit $L^2$ norm. This makes the optimization objective focus on the **consistency of gradient directions** rather than their absolute magnitudes, ensuring robustness to sensor variations and stability during optimization.\n\n\n> W2: Some of the algorithmic choices ......, the authors could train their own relevant feature detector).\n\n**Reply**: We thank the reviewer for the constructive comment regarding ablation on the choice of Harris corners. We apologize if our rationale was not sufficiently articulated in the submission. Below, we provide both theoretical justification and empirical validation.\n\n**Theoretical Motivation**\n\nOur use of Harris corners is guided by two key considerations:\n\n- **Signal Quality:** Events are triggered by brightness changes, and Harris corners naturally lie in high-gradient regions (e.g., edges, textured areas), which produce dense and reliable event streams. These regions are ideal for enforcing event-based supervision tied to motion dynamics.\n\n- **Numerical Stability:** The event loss in our Levent formulation depends on the image gradient (via brightness constancy). In flat, low-texture areas, gradients approach zero, making the event loss unstable or uninformative. Harris corners ensure we apply the loss only where it is physically meaningful and robust.\n\nWhile learned detectors like SuperPoint can also identify salient features, Harris offers a simpler, interpretable, and gradient-aligned solution that matches the assumptions behind the event loss.\n\n**Empirical Validation**\n\nTo validate this design choice, we performed an ablation study on pose estimation for night1 sequences comparing three patch selection methods:\n\n1. **Harris corners** (ours)\n\n2. **SuperPoint corners** (learned detector)\n\n3. **Random sampling**\n\nResults show that Harris-based selection achieves the best trade-off between performance and stability. SuperPoint yields comparable but slightly lower results with higher computational cost, while random sampling degrades performance due to noisy gradients in textureless regions. This confirms the effectiveness and efficiency of our choice.\n\n| Method | ATE | RPE trans | RPS rot | Computational Cost |\n| :--- | :---: | :---: | :---: | :---: |\n| Harris corner | 0.655 | 0.236 | 0.152 | medium |\n| Random Sampling | 0.687 | 0.261 | 0.153 | low |\n| Superpoint | 0.685 | 0.260 | 0.153 | high |\n\n> W3: The experiments have a wide breadth (i.e. across many domains), but are quite shallow (i.e. not much testing within each domain). This is probably explained by the relative low availability of event + RGB datasets.\n\nReply: We thank the reviewer for the valuable feedback on dataset diversity. We first provide an overview of mainstream event-RGB datasets and explain our selection rationale. We then present additional experiments on MVSEC Indoor and M3ED, which cover a broad range of environments and motion patterns.\n\n### The reason for using MVSEC\nOur choice of the MVSEC dataset was guided by the strict requirements of our task: robust 3D geometry estimation in dynamic scenes under extreme lighting. This demands datasets with aligned RGB, event data, and accurate ground truth for both depth and pose — a combination that is rarely available.\nAs shown in the table below, few existing datasets satisfy the necessary conditions for evaluating dynamic reconstruction in challenging lighting.\n\n| Dataset | Low-light Scenes | Dynamic Scenes | RGB | Depth Sensor | Ground Truth Pose | Platform | Environment |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| DSEC | ✓ | ✓ | ✓ | LiDAR-16 | X | Car | Outdoor |\n| UZH-FPV | X | ✓ | ✓ | X | MoCap | Drone | Indoor/Outdoor |\n| DAVIS 240C | X | ✓ | ✓ | X | MoCap | Handheld | Indoor/Outdoor |\n| GEN1 | ✓ | ✓ | ✓ | X | X | Car | Outdoor |\n| Prophesee 1MP | ✓ | ✓ | ✓ | X | X | Car | Outdoor |\n| TUM-VIE | ✓ | ✓ | ✓ | X | MoCap | Handheld/Head-mounted | Indoor/Outdoor |\n| MVSEC | ✓ | ✓ | ✓ | LiDAR-16 | MoCap / Cartographer | Car/Drone | Indoor/Outdoor |\n| M3ED | ✓ | ✓ | ✓ | LiDAR-64 | LIO | Car/Legged Robot/Drone | Indoor/Outdoor |\n\n### Scalability of EAG3R\nTo demonstrate EAG3R’s scalability, we conducted additional experiments on MVSEC indoor and M3ED datasets, covering diverse environments (indoor, outdoor, night, HDR), sensor platforms (drones, robots, cars), and motion types, including complex aerial and ambulatory trajectories.\n\nModels were trained under normal lighting and evaluated in low-light/HDR conditions in a zero-shot setting, confirming EAG3R’s robustness beyond vehicle-centric scenes.\n\n#### Performance in HDR environments (robot dog)\n\nTo evaluate the model's performance in HDR scenarios, we conducted tests on the demanding pose estimation M3ED robot dog dataset featuring intense lighting changes. As shown in the table below, EAG3R's pose estimation accuracy is generally superior to both the MonST3R baseline and its scene-finetuned version, with substantial improvements across all key metrics.\n\n|  | penno_plaza_lights | | |\n| :--- | :--- | :--- | :--- |\n| | ATE | RPE trans | RPE rot | \n| MonST3R | 0.34253 | 0.19195 | 2.30028 | \n| MonST3r (finetune) | 0.18527 | 0.09806 | 1.84931 |\n| EAG3R | 0.13608 | 0.06317 | 0.60864 | \n\n#### Performance in high-speed outdoor drone scenarios\n\nTo further test the model's robustness under high-speed motion and complex lighting, we performed evaluations on M3ED high-speed drone dataset. EAG3R demonstrated robust performance in these highly challenging scenarios. For the demanding pose estimation, EAG3R shows clear improvements over the baseline.\n\n|  | High Beams | | | Penno Parking 1 | | | Penno Parking 2 | | |\n| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |\n| | ATE | RPE trans | RPE rot | ATE | RPE trans | RPE rot | ATE | RPE trans | RPE rot |\n| MonST3R | 0.19511 | 0.06677 | 1.08519 | 0.16074 | 0.09418 | 0.491 | 0.43969 | 0.25023 | 0.90225 |\n| EAG3R (ours) | 0.11108 | 0.05717 | 0.64498 | 0.1189 | 0.07483 | 0.53798 | 0.30893 | 0.15723 | 0.90318 |\n\n#### Performance in high-speed indoor drone scenarios\n\nWe also evaluated the depth estimation performance on the indoor sequence from the MVSEC dataset. As shown in the table below, our proposed method, EAG3R, achieves the best performance across all key metrics compared to the MonST3R baseline and its finetuned variant.\n\n| Method | Abs Rel ↓ | δ<1.25 ↑ | RMSE log ↓ |\n| :--- | :---: | :---: | :---: |\n| MonST3R | 0.097 | 0.918 | 0.146 |\n| MonST3R (finetune) | 0.307 | 0.429 | 0.331 |\n| EAG3R (Ours) | 0.041 | 0.972 | 0.094 |"}, {"review_id": "zYZGjPrNm9", "reviewer": "Reviewer_dFm7", "summary": "This paper proposes EAG3R, a method for robust 3D geometry estimation (including depth, camera pose, and 4D reconstruction) in challenging scenes with dynamic objects and extreme low-light conditions. The core idea is to augment an existing pointmap-based reconstruction approach, MonST3R, with information from an event camera. The contributions are twofold: 1) A feature fusion module that combines features from RGB images and event streams. 2) A event-based photometric consistency loss that is integrated into the global optimization stage. This loss enforces consistency between the motion predicted by the 3D model and the brightness changes directly observed by the event camera. Experiments on the MVSEC dataset show that EAG3R outperforms RGB-only baselines in zero-shot evaluations on nighttime sequences.", "questions": "- The main components of EAG3R appear to be adaptations of existing work (MonST3R backbone, Retinex/SNR from [8, 69], and the event loss model from [21]). Could you please elaborate on what you consider the core conceptual novelty of your work beyond the successful integration of these components? For instance, is there a non-obvious insight or a fundamental challenge in fusing event data with pointmap-based methods? \n\n- The event-based photometric loss is adapted from [21]. Could you discuss the specific challenges encountered when integrating this loss into the global optimization framework of MonST3R? The original work [21] used it for feature tracking. How does its behavior and impact change when used as a global constraint on a dense, globally optimized state (pointmaps, poses, intrinsics)? Were any non-trivial modifications required to make it stable and effective in this new context? \n\n- Your evaluation is limited to the MVSEC dataset, and you mention that training with V2E-generated events failed. This raises questions about the method's generalizability.\n    - How do you expect EAG3R to perform on other event-camera datasets, which may have different sensor noise characteristics or scene content?\n    - Could you provide more insight into why V2E-generated events caused training to fail?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 1, "strengths_and_weaknesses": "**Strengths**\n- The paper tackles a highly relevant and challenging problem in 3D vision: robust reconstruction in adverse conditions. The proposed method demonstrates significant and consistent quantitative improvements over strong, recent baselines (DUSt3R, MonST3R, Easi3R) on monocular depth and camera pose estimation tasks (Tables 1 and 2). The empirical results are strong, and the ablation study (Table 3) clearly validates the contribution of each component of the proposed system.\n\n- The paper is very well-written and easy to follow. The method is presented logically, and the figures (especially Figs. 2 and 3) provide clear illustrations of the network architecture and the novel loss function. The motivation for using event cameras is well-established, and the overall narrative is compelling.\n\n- The proposed system is technically sound and thoughtfully designed. The idea of using an SNR map to adaptively fuse information from two different sensor modalities is intuitive. Similarly, incorporating a physically-grounded event consistency loss directly into the global optimization step is a clever way to leverage the strengths of event data to regularize the final reconstruction.\n\n**Weaknesses**\n- My main concern is that the work, while effective, appears to be a combination of existing techniques rather than proposing a fundamentally new concept. While the integration of these components into a unified framework produces good results, the paper lacks a core contribution in terms of a new algorithm or theoretical insight. I feel it is better suited for a robotics or systems conference like ICRA or RAL. In essence:\n    - The backbone (MonST3R) is an existing method.\n    - The Retinex-based enhancement and the concept of SNR-aware fusion are inspired by or directly adapted from prior work on low-light enhancement (e.g., [8], [69]).\n    - The formulation of the event-based brightness increment model (eq. 9), which is the core of the novel Levent loss, is explicitly stated to follow the generative model from Gehrig et al. [21].\n    \n- The experiments are conducted exclusively on the MVSEC dataset. While MVSEC is an appropriate benchmark for this task, relying on a single dataset makes it difficult to assess the true generalizability. The authors mention that they switched from using V2E-generated events due to \"gradient explosion,\" which suggests the method may be sensitive to the specific noise characteristics or data distribution of more complex scenes. Evaluating on other datasets with would have strengthened the paper's claims of robustness.\n\n- Related to the originality concern, the scientific significance is somewhat limited. The paper convincingly shows that event data improves pointmap-based reconstruction in the dark, which is an expected but arguably valuable confirmation. However, it offers less insight into the fundamental principles of multi-modal fusion for this task beyond what was already known. The contribution is more practical and system-oriented.", "was_revised": true, "final_justification": "The paper solves quite a challenging problem of reconstructing 3D scenes in very low light conditions. The rebuttal gave an explanation for the V2E failures. Overall, it's a good systems-level contribution although I would like to have seem more qualitative results of the reconstructions.", "rebuttal": "> W1: My main concern is  ...... follow the generative model from Gehrig et al. [21].\nW3: Related to the originality concern, ...... is more practical and system-oriented.\nQ1: The main components  ..... with pointmap-based methods?\n\nReply: We thank the reviewer for the feedback and would like to clarify the novelty of **EAG3R**. While based on existing frameworks, our work is the **first to integrate asynchronous event streams with the pointmap-based paradigm**, enabling **robust, pose-free 4D reconstruction** in extreme lighting conditions—where prior methods typically fail. This integration demanded resolving several key challenges, which form our core contributions.\n\n---\n\n### 1. Solving The Challenge of Extreme-Light Conditions for Geometric Tasks\n\n* **Challenge:** To enable the framework under extreme lighting conditions, a common workaround for low-light scenarios is to apply image enhancement. However, we found this **insufficient for geometric estimation**.\n\n* **Why Enhancement Fails:** Preprocessing with RetinexFormer (\"LightUp\") yields minimal or negative impact on downstream performance due to artifacts and failure to guide multimodal fusion.\n\n| Method               | Night1 Abs Rel ↓ | δ<1.25 ↑ | RMSE log ↓ | Night2 Abs Rel ↓ | δ<1.25 ↑ | RMSE log ↓ | Night3 Abs Rel ↓ | δ<1.25 ↑ | RMSE log ↓ |\n|----------------------|------------------|-----------|--------------|------------------|-----------|--------------|------------------|-----------|--------------|\n| MonST3R              | 0.370            | 0.373     | 0.497        | 0.309            | 0.469     | 0.409        | 0.317            | 0.453     | 0.418        |\n| MonST3R (light-up)   | 0.370            | 0.369     | 0.503        | 0.316            | 0.451     | 0.431        | 0.329            | 0.441     | 0.444        |\n| **Ours**             | **0.353**        | **0.491** | **0.416**    | **0.307**        | **0.518** | **0.383**    | **0.288**        | **0.533** | **0.371**    |\n\n- **Our Contribution:** We introduce a **Retinex-inspired SNR estimation module**, not just for enhancement but to compute a spatially-varying **confidence map**. To the best of our knowledge, this is the first time a Retinex-based method has been used to generate a confidence map to guide the adaptive fusion of RGB and event data for a 3D geometry task, trusting events more in dark/noisy regions and RGB more where signals are clear.\n\n---\n\n### 2. Designing an Efficient and Effective Event Adapter\n\n- **Challenge:** Events are sparse and asynchronous; adapting them effectively without excessive cost or large paired datasets is non-trivial.\n\n- **Why Naive Solutions Fail:** Using a heavy RGB encoder with zero convolution initialization is both inefficient and ineffective.\n\n|              | Abs Rel ↓ | δ<1.25 ↑ | RMSE log ↓ |\n|--------------|------------|-----------|--------------|\n| **Night1**   |            |           |              |\n| Zero_Conv    | 0.377      | 0.446     | 0.449        |\n| **Ours**     | **0.353**  | **0.491** | **0.416**    |\n| **Night2**   |            |           |              |\n| Zero_Conv    | 0.323      | 0.478     | 0.399        |\n| **Ours**     | **0.307**  | **0.518** | **0.383**    |\n| **Night3**   |            |           |              |\n| Zero_Conv    | 0.302      | 0.485     | 0.379        |\n| **Ours**     | **0.288**  | **0.533** | **0.371**    |\n\n- **Our Contribution:** We propose a **lightweight Swin Transformer-based event adapter**, pretrained via self-supervised learning on large-scale event-only datasets. This allows efficient transfer of **motion and edge priors**, enabling fast convergence and strong generalization under data scarcity.\n\n---\n\n### 3. Achieving Robust, Principled Feature Fusion\n\n- **Challenge:** RGB and event data differ significantly in density, noise characteristics, and timing. Simple fusion (e.g., concatenation) or nonlinear fusion (attention) is inadequate.\n\n- **Our Contribution:** A **multi-stage adaptive fusion strategy** that:\n  - Uses **cross-attention within the encoder**, where event features query RGB features at multiple levels, learning nonlinear, context-aware relationships.\n  - Combines this with **SNR-guided feature aggregation**, followed by a **learnable nonlinear projection** for further refinement. This ensures robustness to local noise and lighting variation.\n\n| Method               | Night1 Abs Rel ↓ | δ<1.25 ↑ | RMSE log ↓ | Night2 Abs Rel ↓ | δ<1.25 ↑ | RMSE log ↓ | Night3 Abs Rel ↓ | δ<1.25 ↑ | RMSE log ↓ |\n|----------------------|------------------|-----------|--------------|------------------|-----------|--------------|------------------|-----------|--------------|\n| Feature Add          | 0.357            | 0.482     | 0.417        | 0.312            | 0.519     | 0.384        | 0.295            | 0.535     | 0.373        |\n| Last Layer Attention | 0.361            | 0.495     | 0.426        | 0.322            | 0.486     | 0.396        | 0.296            | 0.515     | 0.380        |\n| **Ours**             | **0.353**        | **0.491** | **0.416**    | **0.307**        | **0.518** | **0.383**    | **0.288**        | **0.533** | **0.371**    |\n\n> Q2: The event-based photometric loss ...... make it stable and effective in this new context?\n\nReply: We thank the reviewer for the insightful comment. We emphasize that adapting event-based photometric consistency loss to a **global optimization framework** is not a trivial integration but a **conceptual and methodological innovation**. Our $L_{event}$ is not a minor extension for local tracking—it is specifically designed for the more complex task of **global 4D reconstruction**.\n\nOur contributions are highlighted by three fundamental differences: **application domain**, **motion modeling**, and **feature point strategy**.\n\n---\n\n### 1. Application Domain: From Local 2D Tracking to Global 4D Reconstruction\n\n- Prior work (e.g., Gehrig et al. [21]) focuses on **local 2D feature tracking**, operating on small patches in the image plane.\n- In contrast, EAG3R performs **global 4D scene reconstruction**. Our $L_{event}$ serves as a supervisory loss within a global optimization (similar to bundle adjustment), jointly refining:\n  - **Per-frame depth maps ($D^t$)**\n  - **3D camera poses ($P^t$)**\n  - **Camera intrinsics ($K^t$)**\n\nThis moves from localized 2D motion to **global scene-level optimization**.\n\n---\n\n### 2. Motion Modeling: From Optical Flow to Geometric Projection\n\n- Previous work models motion via **2D optical flow**, which is unreliable in low-light where RGB quality degrades.\n- Our approach bypasses this by computing the motion field **$\\Delta u_{\\text{cam}}$** **geometrically** from estimated 3D structure and camera poses:\n\n$$\n\\Delta u_{\\text{cam}}^{t \\rightarrow t'} = \\pi(K^{t'} (R^{t \\rightarrow t'} (D^t(x) \\cdot (K^t)^{-1} \\tilde{x}) + T^{t \\rightarrow t'})) - x\n$$\n\n- This **physically grounded projection** provides robust supervision even when RGB fails, linking observed events directly to 3D geometry.\n\n---\n\n### 3. Feature Strategy: High-Confidence Constraints for Global Optimization\n\n- We focus $L_{event}$ on **Harris corners**—sparse but stable points with strong gradients—introducing **high-quality constraints** into the global optimization.\n- We compared this with:\n  1. **Harris corners** (ours)\n  2. **SuperPoint** (learned detector)\n  3. **Random sampling**\n\n| Method           | ATE ↓ | RPE trans ↓ | RPE rot ↓ | Computation Cost |\n|------------------|:-----:|:-----------:|:---------:|:----------------:|\n| **Harris corner** | **0.655** | **0.236** | **0.152** | Medium            |\n| Random Sampling  | 0.687 | 0.261       | 0.153     | Low               |\n| SuperPoint       | 0.685 | 0.260       | 0.153     | High              |\n\n- Random sampling introduce noisy gradients by selecting unreliable regions, degrading optimization. Network-based detectors are computational expensive. Our Harris-based method ensures **stable, targeted supervision**.\n\n---\n\nIn summary, EAG3R significantly extends previous event-based tracking by redefining the application scope, introducing a geometry-driven motion formulation, and applying a tailored feature strategy—**transforming local principles into a globally optimized 3D reconstruction system**.\n\n\n> W2: The experiments are c ...... f robustness.\nQ3: Your evaluation is ......  caused training to fail?\n\nReply:  We thank the reviewer for the valuable feedback on dataset diversity. \n\n**Please refer to our reply to our reply to reviewer jzhF W3 for reasons for choosing MVSEC and additional experiments on other datasets.**\n\n#### Why V2E generated datasets fail?\n\nWe determined that the failure of V2E) synthesis in low-light conditions stems from two compounding issues within the V2E pipeline: degraded frame interpolation and unstable event triggering.\n 1. Low-Light Frame Interpolation\nThe V2E process relies on optical flow-based interpolation to generate high-framerate video. However, low-light footage suffers from high noise and low contrast, which severely degrades the quality of the optical flow estimation. This leads to inaccurate or artifact-ridden interpolated frames, making the synthesized brightness changes unreliable for the subsequent event generation step.\n\n#### 2. Unstable Log-Intensity Event Triggering\nV2E generates events when the change in log-intensity at a pixel crosses a set contrast threshold, $C$:\n\n$$ \\Delta L(x, y, t) = \\log(I(x, y, t)) - \\log(I_{\\text{ref}}(x, y)) \\geq \\pm C $$\n\nIn low-light conditions, pixel intensities $I(x, y, t)$ are very low and close to zero. This makes the $\\log(I)$ operation numerically unstable and highly sensitive to noise. As a result, the V2E process either fails to generate meaningful events from subtle motion or produces a stream of spurious events triggered by random noise."}]

Author rebuttal:

Meta-review: After rebuttal, the reviewers were satisfied with the responses from the authors and upgraded their ratings. 
Finally all the reviewers agreed to acceptance. 
The authors are encouraged to reflect the responses into the camera ready version.
Decision: Accept (spotlight)