Title: EAG3R: Event-Augmented 3D Geometry Estimation for Dynamic and Extreme-Lighting Scenes
Venue: NeurIPS 2025
Accepted: yes
Keywords: Event Camera

3D Vision

Neuromorphic Computing
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

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 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.

1 Introduction

Estimating geometry from videos or images is a 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-based 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 in 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, on 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],

EAG3R

Input low light video

Input event stream

Lalign

Lflow

Lsmooth

Levent

Variables of

Optimization

{X, P, K}

Depth

Object Motion

4D Reconstruction

Trajectories

Pointmaps

and neural rendering [48, 25], but their integration into modern learning-based geometry pipelines remains limited.

In this paper, we propose EAG3R , an event-augemented MonST3R framework to enhance pointmapbased 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-Noise 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 a zero-shot nighttime setting.

Our main contributions are as follows:

We propose EAG3R , the first event-augmented pointmap-based geometry estimation framework, which integrates asynchronous event streams with RGB-based reconstruction to handle dynamic scenes under extreme low-light conditions.

We design a plug-in event perception module that integrates RGB and event data via: (1) a Retinex-based enhancer for visibility recovery and SNR map prediction; (2) a lightweight Swin-Transformer-based event adapter; and (3) an SNR-aware fusion scheme for adaptive feature integration.

We develop a novel event-based photometric consistency loss that guides global optimization 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-including monocular depth estimation, camera pose tracking, and dynamic reconstruction-and show it significantly outperforms existing RGB-based pose-free methods, even under zero-shot nighttime conditions.

2 Related Work

SfM and SLAM Traditional Structure-from-Motion (SfM) [1, 44, 45, 50, 53, 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 learning-based 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, DUSt3R and its variants

[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 is 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, 51], 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 eventguided 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 challenging 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 trajectories [28, 47]. 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 1 shows the overall pipeline of EAG3R. Our work addresses the critical challenge of robust monocular 3D scene reconstruction-encompassing dynamic geometry, camera pose, and depth estimation-under extreme lighting conditions where traditional RGB-based methods often fail. EAG3R enhances the MonST3R framework by synergistically integrating standard RGB video frames { I t ∈ R H × W × 3 } with asynchronous event streams {E t } (sequences of e k = ( x k , y k , t k , p k ) ). 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 ( X pm ∈ R H × W × 3 ), assigning a 3D coordinate per pixel, predicted for image pairs ( I a , I b ) by a Transformer model:

These encode relative geometry for depth and pose estimation and are refined via global optimization for multi-view consistency into a global point cloud X ∗ .

I

t

Enhance

E evt

E img

Cross

Attention

D

Head

E t

Fimg

Fevt

MSNR

Enhance

I

t

L illum

I

prior

N retinex

MSNR

C

Upstream

E

D

Head

I

t

E

D

Head

I

t'

Weights

Sharing

Cross

Attention

Reference View

Source View

I

lu

Extension to Dynamic Scenes. MonST3R adapts this for dynamic scenes by finetuning DUSt3R on dynamic datasets, predicting per-frame pointmaps. It optimizes a global scene model X ∗ global (comprising per-frame camera poses { P t } , intrinsics { K t } , and depth maps { D t } ) using an objective function:

guided by alignment, trajectory smoothness, and image-based optical flow ( L flow) terms.

However, the reliance of these image-based methods on clear visual information causes them to struggle in low-light settings: RGB images I t 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 t 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 a hybrid event-image architecture as is illustrated in Fig. 2. Our improvements begin with a Retinex-inspired enhancement module that operates on the raw input image I t to recover visibility in underexposed regions. This module also estimates a SNR map, M t snr , which serves as a spatial prior for confidence-aware fusion. Next, we introduce a lightweight event adapter based on a Swin Transformer backbone, designed to extract high-fidelity features from the sparse event stream E t . We also establish cross-modal interaction through a 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 t for downstream 3D reconstruction.

Retinex-based Image Enhancement. To enhance image visibility under low-light conditions and provide a spatial reliability prior, we introduce a lightweight Retinex-inspired [7] enhancement module. Given an input image I t , we estimate an illumination map L t illum using a shallow network N retinex with inputs I t and its channel-wise maximum projection I t prior = max c ( I t ) , and compute the enhanced image via element-wise multiplication:

To guide adaptive fusion, we compute a SNR map M t snr indicating local image reliability. We convert I t lu to grayscale I t g , apply mean filtering to obtain ˜ I t g , and define:

where ϵ ensures numerical stability. This SNR map emphasizes high-confidence regions and suppresses noise-dominated areas, enabling reliability-aware feature fusion downstream.

Lightweight Event Adapter. To effectively harness asynchronous event streams E t for dense geometric prediction, we introduce a lightweight event adapter by employing a Swin Transformer backbone initialized with weights from a self-supervised, context-based pre-training regimen on event data [70]. The input events from E t are voxelized into a spatiotemporal grid and processed by the pre-trained Swin Transformer encoder, yielding hierarchical event features { F t evt ,l } 4 l =1 . Corresponding hierarchical image features { F t img ,l } 4 l =1 are extracted at every 6 layers from intermediate representations within the pre-trained image encoder. At each hierarchical stage l , the event features F t evt ,l are spatially aligned and dimension-matched with their respective image counterparts F t img ,l .

We then apply cross-attention[68], using event features as queries and image features as keys and values:

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 to compensate for degraded or missing visual cues.

SNR-aware Feature Aggregation. We combine the final features from the image ( F t img-final ) and event ( F ′ t evt-final ) encoders into a unified representation F t , guided by the normalized SNR map ˆ M t snr . Specifically, we weight image features by ˆ M t snr and event features by its complement (1 -ˆ M t snr ) , followed by concatenation:

where ⊙ denotes element-wise multiplication with channel-wise broadcasting. The concatenated features F t cat undergo a projection to match the input dimensionality of the downstream decoder.

This adaptive feature aggregation dynamically prioritizes image features under high-SNR conditions and event features under low-light scenarios, yielding robust and illumination-invariant representations for effective downstream 3D reconstruction.

3.3 Event-Enhanced Global Optimization

To enhance the performance of MonST3R's 3D reconstruction and camera pose estimation, particularly 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, L event. This integration leverages the inherent advantages of event cameras- such as their high dynamic range and ability to capture dynamics even with minimal illumination- to provide robust supervisory signals. The subsequent sections detail the formulation of this L event from raw event data (Section 3.3.1) and its incorporation into the joint optimization process (Section 3.3.2).

3.3.1 Event-Based Photometric Consistency Loss

The event-based photometric consistency loss, L event, is formulated to evaluate the alignment of brightness change patterns observed within salient image patches. It achieves this by comparing two distinct representations of these patterns: the first, ∆ L P m ( u ) , is derived directly from the raw event stream E ; the second, ∆ ˆ L P m ( u ; X global ) , is synthesized by integrating photometric information from an intensity image with scene motion inferred from the global state estimate X global. The process of computing this loss is visualized in Fig. 3.

Observed Brightness Increments from Events: Given an event stream E corresponding to the time interval between frames I t and I t ′ , we compute the observed brightness increment within each salient image patch P m by aggregating events e k = ( x k , y k , t k , p k ) occurring in both space and time over the patch and interval ∆ τ = t ′ -t :

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Input image frame

Patch

Motion estimated from

intrinsics and extrinsics

where u = ( x, y ) denotes local coordinates within the patch. Each patch P m is centered at a Harris corner detected on the reference intensity image I t and covers a 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 in Equation (7) yields a polarity-weighted event accumulation image representing the measured brightness changes within each patch.

Brightness Increment Model from Intensity and Motion: To estimate the brightness change within a salient image patch P m , we adopt a predictive model derived from the principle of brightness constancy. This model synthesizes the expected brightness increment ∆ ˆ L P m ( u ; X global ) by combining photometric and geometric cues, specifically:

Local Intensity Gradient: The spatial gradient ∂ I t grad ∂u (ˆ u ) | P m is computed over the patch P m from the intensity image I t at time t .

Inter-frame Pixel Motion: The motion field ∆ u t → t ′ cam (ˆ u, X global ) represents the per-pixel displacement between frames t and t ′ , computed by projecting 3D points using the depth map D t and the camera intrinsics and extrinsics ( K t , K t ′ , P t , P t ′ ) contained in the global state X global .

Assuming locally constant optical flow and small inter-frame displacements, the predicted brightness increment is expressed as:

where ∆ τ denotes the integration interval and C is the contrast sensitivity threshold intrinsic to the event sensor. This expression follows the generative model introduced in [20].

Event-Based Loss Objective: While Equation (8) provides an explicit formulation, the scale factor ∆ τ · 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 L 2 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:

This loss enforces that brightness variations predicted from image gradients and estimated motion are consistent with real event stream observations, thereby providing a principled supervision signal for optimizing X global .

3.3.2 Joint Optimization with Event-Based Constraints

The event-based loss L event is integrated into MonST3R's global optimization objective (Eq. 2). The augmented objective to find the optimal global scene model X ∗ global , pairwise alignments { P ∗ W } , and scales { σ ∗ } becomes:

where w event is the w event_base scaled by the mean of (1 -S norm ) , where S norm are the normalized corner SNR values.

L event provides a 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, ∆ ˆ L P m ( u ; X global ) , align closely with observed event patterns ∆ L P m ( 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 a 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-art 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 on the ETartanAir dataset. Fine-tuning is performed for 25 epochs, using 8,000 image-event pairs per epoch. We employ the AdamW optimizer with a learning rate of 5 × 10 -5 and a mini-batch size of 4 per GPU. The training process completes in approximately 24 hours on 4 NVIDIA RTX 3090 GPUs. For global optimization, we adopt the same setting as MonST3R, with hyperparameters w smooth = 0 . 01 , w flow = 0 . 01 , and w event _ base = 0 . 01 . We use the Adam optimizer for 300 iterations with a learning rate of 0.01.

For dataset selection, we initially attempted to fine-tune the MonST3R baseline using events generated via Video-to-Events (V2E)[19] from MonST3R's fine-tuning datasets. However, the noise in V2Egenerated events led to gradient explosion during training, prompting us to switch to 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 a fair zero-shot evaluation of low-light performance, all models were trained exclusively on MVSEC's outdoor_day2 sequence (normal daylight) and tested on the challenging outdoor_night1-3 sequences (extreme low-light).

4.2 Monocular Depth Estimation

We evaluate monocular depth estimation on the MVSEC outdoor_night1-3 sequences, which feature extreme low-light conditions with significant noise and underexposure. All models are trained solely on the MVSEC outdoor_day2 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 ↓ ), Scale-invariant RMSE log (RMSE log ↓ ), and the threshold accuracy δ < 1 . 25 ( ↑ ), where lower is 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 RetinexFormer, a widely used image enhancement network, as a preprocessing light-up step (denoted as (LightUp)) does not yield significant improvements and, in some cases, degrades 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

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-based methods-even when augmented with fine-tuning or pre-enhancement-struggle 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 a consistent zero-shot protocol (trained on outdoor_day2 ) 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, particularly 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 metrics 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 outdoor_night1-3 sequences. Prior methods such as DUSt3R and MonST3R serve as RGB-based baselines, with MonST3R extending pointmap prediction to dynamic scenes and Easi3R variants incorporating motion-aware masking. However, all remain limited under low-light conditions due to their reliance on degraded RGB inputs.

MonST3R

EAG3R

DUSt3R

297.5

Ground Truth

297.0

297.0

(w)

297.0

296.5

296.0

Predicted

E

296.5

296.0

295.5

Ground Truth

Predicted

(w)

296.5

296.0

295.5

Ground Truth

Predicted

96

97

98

99

100

101

102

103

96

97

98

99

100

101

102

EOT

96

97

86

99

100

101

102

103

z (m)

z (m)

z (m)

In contrast, EAG3R directly integrates asynchronous event streams into the 4D reconstruction pipeline, allowing for improved motion handling and robustness to 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 a systematic ablation study on the MVSEC outdoor_night3 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. 3.

Each component contributes positively to the final performance. The introduction of event streams already leads to a 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.

5 Conclusion

We presented EAG3R , a event-augmented framework for robust 3D geometry estimation under dynamic and low-light conditions. Built on the MonST3R backbone, EAG3R introduces a lightweight event adapter and a retinex-inspired light-up module, an SNR-aware fusion mechanism, and an eventbased 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-art baselines in depth, camera pose estimation, 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.

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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, we associate each depth ground truth from the Velodyne Puck LITE with the temporally closest APS frame. This is achieved through the following steps:

Pose Interpolation for Images : Interpolate camera poses at all APS image timestamps t i . Given discrete pose measurements P ( t k ) at times t k , we compute the interpolated pose P ( t i ) at image timestamp t i using linear interpolation for translation and spherical linear interpolation (SLERP) for rotation:

where P ( t ) = ( R ( t ) , T ( t )) represents the rotation R ( t ) ∈ SO (3) and translation T ( t ) ∈ R 3 .

Pose Interpolation for Depth : Similarly, interpolate poses at depth timestamps t d to obtain P ( t d ) , using the same interpolation method as above.

Timestamp Matching : For each depth timestamp t d , identify the nearest image timestamp t i by minimizing the temporal difference:

where T I is the set of all image timestamps.

Depth Warping : Warp the depth data to the selected image timestamp t i using the relative transformation between poses P ( t d ) and P ( t i ) . For a 3D point X d in the depth sensor's coordinate frame at t d , the warped point X i at t i is computed as:

A.1.2 Nighttime Sequence Processing

During nighttime sequences, the APS frame rate drops to 10 Hz, resulting in multiple depth measurements (at 20 Hz) per APS frame. We process the depth data as follows:

Depth Projection to 3D : Project all depth measurements within the temporal window of a single APS frame into a 3D point cloud. For a depth measurement d at timestamp t d , the 3D point X d is obtained using the sensor's intrinsic calibration and pose P ( t d ) :

where unproject converts the depth measurement to a 3D point in the sensor's local frame.

Selection of Closest Depth : For each APS frame at timestamp t i , select the depth measurement from the 3D point cloud that is temporally closest to t i , as determined by the minimum temporal difference | t d -t i | .

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 to the transformation process. We address this by applying spatial interpolation to fill these holes, ensuring a continuous image. Specifically, for a pixel ( x, y ) in a sparse region, we estimate its value I ( x, y ) using a weighted average of neighboring valid pixels:

where N is the set of neighboring valid pixels, and w ( x ′ , y ′ ) is a 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 on MVSEC

We evaluate video depth estimation under extreme low-light conditions using the MVSEC outdoor_night1 , outdoor_night2 , and outdoor_night3 sequences. All models are trained solely on the outdoor_day2 sequence and tested in a zero-shot nighttime setting. Following standard protocol, we report Absolute Relative Error (Abs Rel ↓ ), RMSE log ↓ , and δ < 1 . 25 ( ↑ ) over all frames in each sequence.

As shown in Tab. A.1, conventional RGB-based baselines such as 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 metrics. 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 is weak or noisy. These results demonstrate that augmenting pointmap-based reconstruction with event streams enables robust, temporally-consistent 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.

Dust3R

Monst3R

EAG3R

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 - a 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.

A.5 Ablations on Design Rationales

This section presents ablation studies analyzing the design rationales of EAG3R . Although EAG3R is built upon the pointmap-based reconstruction framework, it is the first to incorporate asynchronous event streams , enabling robust and pose-free 4D reconstruction under extreme lighting conditions. Three key design aspects are examined: the Retinex-inspired confidence estimation, the lightweight event adapter, and the multi-stage feature fusion.

A.5.1 Addressing Extreme-Light Challenges for Geometric Estimation

A straightforward strategy for handling low-light conditions is to apply image enhancement as a 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 or even negative benefit, indicating that conventional enhancement does not effectively support cross-modal fusion.

To address this limitation, EAG3R introduces a Retinex-inspired SNR estimation module that computes a spatially varying confidence map rather than directly enhancing images. This map guides

the adaptive fusion of RGB and event features, assigning higher confidence to events in dark or noisy regions and to 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-guided 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 a heavy RGB encoder with zero-convolution initialization, leads to inefficient training and limited gains (as is compared in A.4).

Our adapter is pretrained on 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 or extreme-light conditions.

A.5.3 Robust and Principled Feature Fusion

RGB and event modalities differ substantially in spatial density, temporal resolution, and noise characteristics. Naive strategies, such as feature concatenation or single-layer attention, are insufficient to capture their complementary information.

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 optimization 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 computationally 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.

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 to 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 to MonST3R, EAG3R introduces only a minor overhead of approximately +0 . 4 GB VRAM, +0 . 11 TFLOPs, and +1 . 2 s 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.

Scalability to Longer Sequences. We further analyze scalability with respect to sequence length, following the reviewer's suggestion. EAG3R employs a sliding-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 a 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.

Overall, the results confirm that EAG3R maintains near-linear computational growth with respect to sequence length and introduces only marginal overhead compared to the baseline.

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-light/HDR conditions in a zero-shot setting, confirming EAG3R's robustness beyond vehicle-centric scenes.

HDREnvironments (Robot Dog). To assess the model's performance in high-dynamic-range (HDR) conditions, we evaluated EAG3R on the challenging M3ED robot dog dataset penno_plaza_lights 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-finetuned variant across all key metrics.

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 Table A.11, EAG3R consistently outperforms the baseline in all three outdoor sequences, demonstrating reliable pose estimation even in high-speed and high-contrast conditions.

High-Speed Indoor Drone Scenarios. We further evaluated EAG3R'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 a 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 robustness and stability during optimization.

These results indicate that EAG3R maintains consistent performance across multiple random initializations, 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 is 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 a 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, highquality event streams. Although the SNR-aware fusion mechanism mitigates some effects of noise, the

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 has the potential to enhance safety and reliability in autonomous 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 is critical.

Potential risks and misuse. As with many vision-based systems, there exists a risk of misuse in surveillance or privacy-invasive 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 on 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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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 1, "DATE": "2025-06-09"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nPositive:\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.\n\nQUESTIONS:\nAs 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.\n\nLIMITATIONS:\nYes, this is presented in the work.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nPlease refer to the Weaknesses and Limitations parts.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nFurther 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}