SkyEvents: A Large-Scale Event-enhanced UAV Dataset for Robust 3D Scene Reconstruction
Authors: Wenzong Ma
Zhuoxiao Li
Jinjing Zhu
Tongyan Hua
Kanghao Chen
Zidong Cao
Da Yang
Peilun Shi
Yibo Zhou
Wufan Zhao
Hui Xiong
Venue: ICLR 2026 Poster (2026)
Keywords: Event
3D Scene Reconstruction
Primary area: datasets and benchmarks

Abstract: Recent advances in large-scale 3D scene reconstruction using unmanned aerial vehicles (UAVs) have spurred increasing interest in neural rendering techniques. However, existing approaches with conventional cameras struggle to capture consistent multi-view images of scenes, particularly in extremely blurred and low-light environments, due to the inherent limitations in dynamic range caused by long exposure and motion blur resulting from camera motion. As a promising solution, bio-inspired event cameras exhibit robustness in extreme scenarios, due to their high dynamic range and microsecond-level temporal resolution. Nevertheless, dedicated event datasets specifically tailored for large-scale UAV 3D scene reconstruction remain limited. To bridge this gap, we introduce SkyEvents, a pioneering large-scale event-enhanced UAV dataset for 3D scene reconstruction, incorporating RGB, event, and LiDAR data. SkyEvents encompasses 45 sequences, spanning over 8 hours of video, captured across a diverse set of illumination conditions, scenarios, and flight altitudes. To facilitate the event-based 3D scene reconstruction with SkyEvents, we propose the Geometry-constrained Timestamp Alignment (GTA) module to align timestamps between the event and RGB cameras. Furthermore, we introduce a Region-wise Event Rendering (RER) loss for supervising the rendering optimization. With SkyEvents, we aim to motivate and equip researchers to advance large-scale 3D scene reconstruction in challenging environments, harnessing the unique strengths of event cameras. Dataset and code will be available at https://github.com/Anthony-ECPKN/SkyEvent.

Published as conference paper ICLR 2026
SKYEVENTS: A LARGE-SCALE EVENT-ENHANCED UAV DATASET FOR ROBUST 3D SCENE RECONSTRUCTION
Wenzong Ma Zhuoxiao Li Jinjing Zhu Tongyan Hua 1 Kanghao Chen', Zidong Cao',
Da Yang?, Peilun Shi?, Yibo Zhou", Wufan Zhao Hui Xiong 1f
Hong Kong University of Science and Technology (Guangzhou) Guangdong Airace Technology Co., Ltd.
Chinese University of Hong Kong
Beihang University
RGB
1
Low Light
Norama Light
(b)
Figure 1: (a) Rendering and mesh under different light conditions. (b) Comparison and statistics of datasets.
(c) Dataset collection across varied illumination conditions, scenarios, and flight altitudes.
ABSTRACT
Recent advances in large-scale 3D scene reconstruction using unmanned aerial vehicles (UAVs) have spurred increasing interest in neural rendering techniques. However, ex- isting approaches with conventional cameras struggle to capture consistent multi-view images of scenes, particularly in extremely blurred and low-light environments, due the inherent limitations in dynamic range caused by long exposure and motion blur re- sulting from camera motion. As promising solution, bio-inspired event cameras ex- hibit robustness in extreme scenarios, due to their high dynamic range and microsecond- level temporal resolution. Nevertheless, dedicated event datasets specifically tailored for large-scale UAV 3D scene reconstruction remain limited. To bridge this gap, we in- troduce Events,,, pioneering large-scale event-enhanced UAV dataset for 3D scene reconstruction, incorporating RGB, event, and LiDAR data. SkyEvents encompasses 45 sequences, spanning over 8 hours of video, captured across a diverse set of illu- mination conditions, scenarios. and flight altitudes. To facilitate the event-based 3D
Equal contribution. Corresponding authors: (wufanzhac, xicnghui](ust..nt

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Published as a conference paper ICLR 2026
scene reconstruction with SkyEvents, we propose the Geometry-consttainee Timestamp Alignment (GTA) module to align timestamps between the event and RGB cameras. Furthermore, we introduce a Region-wise Event Rendering (RER) loss for supervis- ing the rendering optimization. With SkyEvents, we aim to motivate and equip re- searchers to advance large-scale 3D scene reconstruction in challenging environments, harnessing the unique strengths of event cameras. Dataset and code will be available at hitps: htop::///gitubb com/
INTRODUCTION
Large-scale 3D scene acquisition using unmanned aerial vehicles (UAVs) has become central tool for urban modeling, digital twins, and robotics applications. Driven by the growing demand for accurate and detailed reconstructions of city-scale environments, neural rendering techniques, such Neural Radiance
Fields (NcRF) (Mildenhall et al.] 2021) and 3D Gaussian Splatting (3DGS) (Kerbl et al. (2023), have been
extended from small object-centric captures (Barron et al. 2022 Müller al.) 2022 Li et al. [023]] Huang
et al.
2024
Yu et al.,
2024 Guédon & Lepetit 2023) to large-scale scenes Tancik et al. 2022 Turki
al.
2022
Mi & Xu
2023 Lin et al. 2024 Yu Chen
2024 Liu et al. 2025 Li al. 2025). However,
existing approaches still fundamentally rely on conventional CMOS-based RGB cameras, which remain
highly susceptible to the limitations in challenging conditions-especcillly in scenarios with motion blur and
insufficient illumination (Cladera et al. 2025). This inherent limitation often leads to degradation in the quality of 3D reconstructions under low-light and motion-blurred conditions (Zahid et al., [2025] Matta et al.,
2025
Zhang et al. 2025), thus limiting the achievable reconstruction fidelity (see Figure [a)).
To address this, event cameras have emerged as promising alternative visual sensing paradigm, providing a complementary modality for 3D reconstruction. Unlike conventional cameras, event-based sensors asyn- chronously record changes scene brightness at microsecond resolution, offering significantly improved temporal fidelity, a wide dynamic range, and robustness to motion blur (Xu et al. 2025 Zhu et al.) 2025). These advantages have made event cameras increasingly popular in 3D reconstruction, where they have been successfully combined with NeRF and 3DGS techniques to enhance the robustness of reconstructions
in dynamic and low-light environments (Yura et al. 2025; Zhang et al., 2025: Zhu et al., 2024).
Despite the growing efforts in event cameras for 3D reconstruction. key limitation remains: the lack of suitable, event-enhanced UAV datasets tailored for large-scale 3D scene reconstruction. While recent event-enhanced UAV datasets have laid important groundwork, they often lack the necessary modalities and ground truth required for high-fidelity city-scale 3D reconstruction (in Table []. For instance, datasets such MVSEC (Zhu et al. 2018) and UZH-FPV (Delmerico et al., 2019) include aerial sequences, but they lack synchronized high-resolutio rendering. Similarly, M3ED (Chaney et al. 2023) and NU-AIR (Iaboni et al.) (025) offer useful event streams and RGB imagery but do not provide the dense depth and 6-DoF pose supervision necessary for large-scale 3D reconstruction. The most recent work, EvMAPPER (Cladera et al., 2025), pioneers event- based orthomapping for high-altitude flights, but its focus on planar mosaics does not address the challenges of trajectory complexity and frame jitter that are inherent to low-altitude, large-scale 3D modeling.
RGB frames or volumetric ground truth, limiting their utility for neural
To address it, we introduce SkyEvents, the first dataset specifically designed for event-enhanced UAV 3D re- construction. SkyEvents brings together challenging low-light and motion-blurred conditions, synchronized RGB frames, dense fer-frame depth supervision, high-quality 3D ground-trutt -ruth reconstructions, and accurate 6-DoF UAV poses within a benchmark dataset. The data is collected using a DJI Matrice 350 equipped with a centimeter-accurac
real-time kinematics system, flying over five distinct areas altitudes ranging from 70 to 100 meters (see Figure Πc)). DJI L2 LiDAR data serves as the ground truth due to its illumination- invariant nature and high precision. In total, the dataset contains 45 sequences (spanning over 8 hours)
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Datsset
Low-lighthwight
(RCR/Event)
3D Sup. (DNG/P
MYSEC (Hesacopteri Zha et 2018
X 50/50 Hi
Xia3
DIKP
346x2260
63
UZH-FFV Delmerion
2019
186"/120"
XIXIP
346 x 260
23
52"/
DIKIP
1280x720
NU-AIR
Laboni et al. 305
X770
XIXIX
640xx880
71
EVMAPPEK
714
XIXIX
12805220
161
SkyEvents
715
D/G/Y
1280x720
481
3D Sup. (D/G/P) denotes the availability of Depth, Geometry ground truth, and 6-DoF Pose," In minutes, dusk-only, "multiple illumination conditions. Table 1: Comparison of SkyEvents with previous event-based UAV datasets.
of paired RGB and event data, along with 0.72 km of point cloud data capturing at 2.64 cm/pixel GSD, enabling the robust development of perception algorithms under real-world conditions (see Figure Ib).
To enable event-based 3D reconstruction with SkyEvents, provide two supporting components: GTA module, which aligns event and RGB data based on temporal constraints, and RER loss, which optimizes event-based rendering. We evaluate the effectiveness of the proposed GTA module and RER loss with existing neural rendering techniques using the SkyEvents dataset. Our experiments demonstrate the potential of event data for 3D scene reconstruction, particularly in challenging environments.
Contributions. (1) We present SkyEvents, a first large-scale event-enhanced UAV dataset for 3D scene reconstruction, including synchronized RGB and event data, LiDAR data, and accurate 6-DoF UAV poses. (2) We provide two supporting components for integrating event modality into neural rendering-based 3D reconstruction: GTA and RER modules. (3) Experiments demonstrate that event-guided neural rendering outperforms RGB -nly baselines, achieving higher texture fidelity and geometric accuracy in lcrge-scale 3D reconstructions under low-light and notion-bburred environments.
2 RELATED WORKS
2. 3D SCENE RECONSTRUCTION
3D scene reconstruction seeks to recover the geometric structure of scene from multi-view images or other modality data. Neural Radiance Fields (NeRF) (Deng al., 2022 Garbin al., 2021 Mildenhall al., 2021 Hua & Wang 2024) have demonstrated high-fidelity novel view synthesis through an implicit repre- sentation, but are constrained by slow optimization and limited geometric precision. contrast, 3D Gaussian Splatting (3DGS) (Kerbl et al.) (023) leverages explicit point-based representation to improve computa- tional efficiency. Each Gaussian in 3DGS is characterized by parameters such as center, opacity, covariance, and color. Despite these improvements, the geometric accuracy and visual fidelity in 3DGS reconstructions can degrade under challenging conditions, such as
or low-light environments (Zahid al., Matta al.) 2025). To address these challenges, recent advancements in event-based 3DGS (Yura et al. 2025] Zhang et al. 2025) have shown promising results in enhancing reconstruction quality by utiliz- ing event cameras. However, notable gap in the existing works the lack of relevant event datasets for large-scale 3D scene reconstruction. To fill this gap, we introduce the first dataset specifically collected for event-enhanced UAV 3D scene reconstruction.
2025
2.2 EVENT-BASED 3D RECONSTRUCTION
Event cameras Zheng et al., 2023 Xu et al. 2025) are bio-inspired sensors that capture asynchronous brightness changes, in contrast traditional cameras, which capture images at fixed frame rate. This unique sensing mechanism provides event cameras with distinct advantages, including exceptional temporal resolution, low latency, and resilience motion blur and challenging lighting conditions. These attributes
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have spurred significant research into the application of event cameras across various computer vision tasks, such as object detection (Mitrokhin et al., 2018), depth estimation (Pan et al.
2024 [Shi et al.
2023). se-
mantic segmentation (Chen al. 2024; Kong et al. 2024), video enhancement Kim et al.
2024
Jing et al.]
2021 Tulyakov et al. 2 and, notably, y. 3D reconstruction (Chen et al., 2025; Wu et al.,
2024
Cannici &
Scaramuzza 2024 Han et al., 2024, Cladera et al. 2025 Ye et al. 2025 Han et al. 2024
Yu al., 2025).
In the domain of 3D reconstruction, early methods (Rudnev et al., 2023] Zhu et al.) |024]] Low & Lee| 2023)
combined NeRF with wvent-based data, utilizing volumetric rendering techniques guided by event supervi-
sion. More recent works (Zahid et al., 2202 Matta al. 2025; Yura et al. 2025 Zhang al. 2025 have
explored the integration of 3DGS with event data to enhance the reconstruction process. However, these prior approaches often face robustness issues, partieularly in challenging conditions such as low-light and
motion-blurred environments. To address these shortcomings, Dark-EvGS (Wu et al. (025) introduces an event-guided 3DGS pipeline, facilitating bright frame synthesis from arbitrary viewpoints low-light sce- narios. Although this approach represents a significant advancement, it remains primarily focused on small objects and does not fully capture the complexities of real-world 3D reconstruction tasks, which typically involve large-scale scenes. In this work, we introduce a new dataset specifically designed for large-scale UAV 3D scene reconstruction, overcoming the limitations of previous methods in city-scale scenes.
2.3 COMPARISON TO EXISTING EVENT-GUIDED DATASETS
Early UAV-related event datasets, such as MVSEC Zhu et al. 2018) and UZH-FPV (Delmerico et al. 2019)) established important baselines by providing stereo events with aggressive flight trajectories and precise ground truth. However, these datasets are not curated for texture-rich city-scale 3D modeling (e.g., limited image resolution/coverage and a focus on odometry rather than volumetric ground truth). Subsequent multi- platform corpora like M3ED (Chaney et al.) (023) include aerial sequences and synchronized modalities, but primarily target high-speed robotics rather than low-altitude urban reconstruction with dense geometry. The NU-AIR dataset (laboni et al. (2025) advances urban perception with aerial event streams and extensive detection labels, while lacking synchronized hig--resolution RGB and per-frame dense depth required for neural rendering benchmarks. EvMAPPER (Cladera et al., 2025 represents pioneering effort in event- based orthomapping at high altitudes, yet it generates only planar mosaics and does not capture the pose jitter and parallax needed for volumetric modeling low altitudes. Beyond datasets focused on UAVs, event camera datasets and benchmarks from other domains, including automotive driving (Gehrig et al. 2021 Binas al., 2017) and indoor robotics (Fischer & Milford 2020 Mitrokhin al. Burner et al. [0022] have advanced event vision, but they are not tailored to aerial 3D reconstruction at city scale because they lack synchronized RGB, dense per-frame depth, and low altitude capture protocols. To bridge this gap, in this work, we introduce an event geent-enaanced UAV dataset tailored for large-scale 3D scene reconstruction.
3 DATASET
We present SkyEvents, the first and large-scale UAV dataset that integrates tri-modal sensing across event camera streams, RGB videos, and LiDAR point clouds. This dataset spans five distinct environments, each characterized by unique architectural structures and diverse activity patterns. To address the critical chal- lenge of accurate temporal synchronization across RGB and event data, propose GTA module. The GTA module ensures precise alignment of timestamps between event and RGB cameras, leveraging geometric constraints to optimize synchronization (see Figure]
3.1 PLATFORM AND DATA COLLECTION
We build a UAV sensor rig using a DJI Matrice 350 RTK equipped with a Prophesee Gen4EVK event camera, DJI Osmo Action 4 RGB camera, and an onboard mini-PC for synchronized logging (Figure 2 specs in
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Power supply
Meclunically lastened
Sclf-designed power
for data storage
Path planning
Attindee Control
DJI Matrice 390 KTK
Cuntreller
Capturo
EVI Events
Mlgani Evmenii
GARE
LEBAR RGGE
RGG Powe
Actions RGB
Event/GBB Alignment Via GTA Module
Figure 2:
Data collection and rendering pipelines. The data acquisition platform consists of an UAV pay- load, event camera, 120HZ RGB camera, and Mini PC. After collecting paired RGB and event data, we utilized the proposed GTA module to synchronize timestamps and warp between the event and RGB cameras.
Appendix Table 4). The mini-PC triggers both cameras under shared time base and records activation timestamps for coarse synchronization. SkyEvents contains 45 sequences spanning over hours across five areas (1.41 km²), captured at 70-100 m altitude, together with a DJI L2 LiDAR survey (0.72 km²) used reference geometry and to derive dense depth. Per-sequence statistics and distributions are provided Figure 3 and Appendix Table
$1
48
41 41.3%
6.1%
12.7%
11.5%
7.0%
Complee Building Structure
Alturooon
Astoeured Scurning Raste
Bulding
Morning
Open Fbroer Scrnes High-rise Bell ding
50m
Figure 3: Data statistics from left to right: flight path, scenarios types, illumination, and height.
3.2 GEOMETRY-CONSEE CONSTRAINED TIMESTAMP ALIGNMENT
In our system, observe that the event to the RGB stream. Consequently, we need to ensure frame-accurate synchronization between event camera and RGB camera. Let #x denote the RGB image at time and let E, # x3 be the event- rendered image at time Given RGB sampling times (tx K j= (c.g., Is interval), we search a symmetric window [t A, t A] with step ō and select the event time that maximizes a geometry score T
stream is typically delayed by approximately mss relative
T arg max S(Ie Er), TET,
Tk {tx A, tu
A S..., ... t A}.
(1)
where 100ms the half-w size, 8 8.333ms is the temporal step, Ty the candidate set around tg. and -(:,) the geometry consistency score.
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For pair (Er, (t we obtain putative correspondences {(x,, 1 with x;
(u, 04 E and x
(ul, vf) E It via the dense matcher MatchhAnyyhing/ROMM (He et al. 2025
Edstedt et al. 2024 and
estimate robust homography H R3x3 from RGB to event via MAGSAC (Barath al. 2019):
A
H
A *
(2)
where H maps RGB pixels to Event pixels, [u, u, 1] and [u', v', 1JT are homogeneous coordinates in Event and RGB, and À is the projective scale.
Let m E (0. 1 be the inlier mask and [[ y, w) w]] = (x/w,y/w). The per-inlier reprojection error
E, [H[[; 1]) x:l12- We then score the pair as
N
S(h,, F..)
m a max (1, -.=
2i=1 mi
a 0,
where m; indicates whether correspondence i an inlier, &; is the Euclidean reprojection error for inlier and a balances inlier support and normalized error (invalid H yields non-informative score).
To avoid per-pair perspective warping while preserving alignment, we approximate the RGB->Event ho- mography H on regular grid by a diagonal affine map (xr vl ~ D(x', y/] + t with D = diag (8., 8y) and t= (fz, ty)T and estimate (8z, Sy, lzz (2y via a single linear least-squares, fir. Concretely, we sample M grid points (z'), vf) in RGB, project them to Event by (I,, By) = [[ v/-- 1]7), and solve ming ||A - bl2. where the unknown f (Sz, faz; 8y, byy matrix A stacks RGB coordinates with axis-wise structure, and b stacks the corresponding Event coordinates (first all Tj. then all fj). Given event and RGB resolutions (Wo, Ho) and (W1, H1). we derive an RGB crop window (æo, 3/0 + (x1,y1) by stabilizing scales with 3, = max($2,6 t), 8y = max(sg, e), back-computing the crop origin from (-tz/82: -ly/5,) with clamp- ing to valid bounds, and setting the crop size so that bilinear resize matches the event resolution, i.e., widths/heights proportional to Wo/3z and Ho/šy. We log H, O, and the crop coordinates for exact repro- ducibility, where > 0 small stabilizer.
To enforce global 1 second cadence and suppress local noise, we jointly refine the sequence by maximizing geometric consistency penalized by interval deviations:
K +==
arg
Σ S( Er.)
Σ 11 1s|
B 0.
(4)
where F& are the refined event timestamps; ,3 trades off the global second cadence against geometric fit, and the absolute deviation term penalizes interval drift.
3.3 LIDAR ALIGNMENT
As demonstrated in Figure we align LiDAR and RGB in unified 3D coordinate and then transfer LiDAR geometry to each RGB image as metrically accurate depth. In our setup, DJI Zenmuse L2 LiDAR payload and an RGB camera are flown in separate missions over the same area, so the raw trajectories are not temporally synchronized. To recover a consistent geometry, we adopt unified structure-frommmotii pipeline using commercial photogrammetry software RealityScan. We first rasterize the LiDAR point cloud into perspective "LiDAR RGB" images by projecting the laser returns onto virtual pinhole cameras whose intrinsics and extrinsics are derived from the LiDAR trajectory. RealityScan generates the poses of these LiDAR images, and we treat them as fixed anchor views. We then import the calibrated UAV RGB images and run joint SfM and global bundle adjustment over all images, keeping the LiDAR-derivel camera poses
(SfM)
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With Event
Without Event
Scenes
Conditions
Methods
SSIM
PSNRT
LPIPST
SSIM
PSNK
LPUSS
Low-light
Luminanco-GS
0.1257
5,2060
0.5850
0.1214
4,7870
0.3990
Scenel
Impeovedd-GS
0.8044
27.4368
0.2701
0.8095
27.3554
0.2757
Blur
Impreved-GS++ecneel
0.8625
28,2600
0,2107
0.8655
28.1146
0.2045
Low-light
Laminance-GS
0.1417
5,7650
0,5402
1408
5,7000
0.5411
Socne2
Improved-GS
0.7951
26.4789
0.2482
0.7807
25.8635
0.2653
Blur
Imprevedd-GS+eered
0.2608
11.5674
0.6783
0.2488
11.5002
0.6816
Table Rendering performance comparison across different conditions and scenarios, with and without
event data.
Improved-GS' Improved-GS+RER Figure 4: Comparison of 3D scene reconstruction with Improved-Cis and Improved-GS+RRE in blurred environments.
RGB
Event Bluryy RGB
frozen. This procedure rigidly registers all RGB cameras into the LiDAR coordinate system, yielding single globally consistent Euclidean frame shared by LiDAR and RGB.
Given the optimized camera intrinsics and extrinsics, perform global multi-view stereo (MVS) recon- struction in which LiDAR depth acts as a strong geometric prior. Specifically, we fuse the LiDAR point cloud with MVS-derived depth estimates to obtain dense, continuous mesh, where LiDAR stabilizes depth in texture-poor or repetitive regions and suppresses multi-viww ambiguities. We treat this fused surface as the ground-truth (GT) geometry. Finally, for each RGB frame, we back-project the GT mesh into the cor- responding camera using the calibrated intrinsics and extrinsics, producing dense, metrically accurate depth maps that are pixel-wise aligned with the original RGB images.
4 BENCHMARKS
4.1 BENCHMARK METHOD
To evaluate the effectiveness of incorporating event modality for 3D reconstruction, we leverage two state- of-the-art 3DGS pipelines, Luminance-GS (Cui et al., (025) and Improved-GS (Deng al. (022).. for benchmark experiments.
Input. Each event e; (x, f. P:) is triggered microsecond timestamp & when the brightness at pixel x changes by more than a contrast threshold C (i.e., [[(x, f++1) - L(x, &.] 2 C), where L(x, t;) is the logarithmic brightness, and p; E {-1, represents either an increase or decrease in the logarithmic brightness L(x,4) 44). In parallel, RGB image It, is captured at discrete time 4,
4.2 RIGION-WISE EVENT RENDERING LOSS
To encourage 3DGS to recover accurate geometry and appearance, we introduce an event-based brightness- change consistency loss Zhang et al. (2025). Given timestamps t and fz, accumulate events within (4,, (2) into an event image
Σ p: = S
which approximates the loggbrightness change the interval. We compare it with the synthesized bright- ness change, computed as the logarithmic difference between two rendered images I,, and I,z- Since the
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Improved
-GS
Luminance
-GS
w/o Event w/ Event Figure 5: Comparison of 3D scene reconstruction using existing 3D GS methods (Improved-GS and Luminance-GS), with and without event enhancement. The integration of event modality through RER markedly enhances rendering quality.
RGB
Event
RGB and event sensors have different footprints and intrinsics, naively warping and undistorting RGB im- ages leads to misalignment with the event frame. Instead, we estimate the warp between the two sensors and define region-aligned event supervision loss that constrains the brightness change only within their overlapping regions. To ensure spatial support is consistent with the event frame, we apply the region-wise alignment and cropping derived in equation | we approximate the RGB->event homography by diago- nal affine map Co with parameters = (sg. Sy. tz. ty), derive a crop window, and resample to the event resolution. We then convert the aligned renderings to log space and define the loss
Levent =
[llogCo - ClogCo( - E(4,, 12)
(6)
4.3 IMPLEMENTATION DETAILS AND EVALUATION METRICS
Implementation Details. For the training setup, we employ Luminance-GS (Cui et al. 2025 state-of- the-art (SOTA) 3DGS pipeline designed for complex illumination scenes, and Improved-GS (Dng al.) 2025), the SOTA neural rendering pipeline, to assess the effectiivness of incorporating event modality. Both models were trained on single NVIDIA RTX 4090 using the Adam optimizer. Training is conducted for total of 30,000 iterations, with Event refinement starting at 8,000 iterations. For other settings, such Gaussian reset steps (Kerbl et al. 2023), we follow the default configuration.
Low-light Image Generation. Although UAVs can be programmed to repeat nearly identical RTK-guided flight paths across different light conditions, to build a unified model, we rely on mnneture-from-mottee (SfM), which requires reliable feature detection and matching across views. Under low-light conditions, however, many images exhibit weak textures and low contrast, causing feature matching to fail. As re- sult, substantial portion low-light images cannot be registered in the SfM pipeline, preventing consis- tent multi-temporal 3D reconstruction. To evaluate performance of methods in low light-conditions, we utilize daytime sequenees our experiments and generate synthetic low-light versions. This ensures pixel- level correspondence, enabling controlled training and ablation studies. Following ÇZhang et al.) [02T] Liang et al., 2023), we generate low-light frames from corresponding normal-light frames using gamma correction and linear scaling, employing identical parameter settings. The process is formalized as follows: (p) = 3 (a x (p))) ,where represents the gamma correction factor, which is sampled from a uniform distribution U(2, 3.5). The parameters and are linear scaling factors, drawn from U(0.9, 1) and U(0.5, 1), respectively.
2024). a motion- bhurred image is physically Blurry Image Generation. Following prior work (Li al.
generated by accumulating photons over the exposure time, ensuring that the resulting blurred image remains differentiable with respect to both the parameters of NeRF and the motion trajectory.
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BRISQUE 4
NIQE
ELVID
7,1075
0.2141
6,109%
5.4571
FTENer
22,9101
0,2885
5,4518
PieeNer
14.3349
0.2276
4,5584
12,7435
4.2509
Hypo
7,6182
5,7898
SPADE-E2VID
14,7458
0.2444
7.0057
SSI-EIVID
59,9326
0.1701
9.0612
Event
E2Depth
GT
Table 3: Quantitative comparison of event-to-video methods.
Figure 6: Depth Estimation.
Evaluation Metries. To thoroughly evaluate the performance of 3DGS methods, we employ standard quan- titative metrics, including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) (Zhang al. 2018).
4.4 RESULTS
In this section we focus on novel view synthesis from joint RGB and event input using three dimensional Gaussian splatting backbones. The goal is answer concrete question: given fixed rendering pipeline, does adding events improve reconstruction quality in low light and motion blurred UAV scenes?
Table 2 summarizes the quantitative results
representative scenes. For the low light setting with I Luminance GS (Cui et al. 2,23), events provide small but consistent gains. On both scenes, adding event supervision increases PSNR and slightly reduces LPIPS, while keeping SSIM essentially unchanged, which indicates that events act as a stabilizing cue under extreme illumination where RGB alone is severely un- derexposed. For the blurred setting with Improved GS (Deng et al., 2025), the benefit of events is more pronounced. On the larger Scene 2 with more than 800 training images, the gains are clearer: PSNR in- creases by about half decibel and LPIPS decreases by noticeable margin for the event driven model compared to the RGB only baseline. These trends hold both for the plain Improved GS variant and for the simple gamma recovery kernel, which shows that event cues remain helpful even when exposure is partially corrected on the RGB branch. Figure 4 visualizes the baseline comparison that underlies the blurred rows for Improved GS in Table 2 When events are used, double contours and ghosting around moving or blurred regions notably reduced and details appear sharper, which matches the quantitative improvements in PSNR and LPIPS. Under normal lighting, minor camera jitters that cause ambiguities the RGB only reconstructions are also mitigated once event constraints are introduced.
Overall, these results suggest that events supply high-frequency constraints that particularly aid deblurring, and also benefit log-light reconstruction, especially in challenging or large-scale settings.
4.5 OTHER TASKS TO EXPLORE
Monocular Depth Estimation. We further evaluate event-based monocular depth estimation on SkyEvents by running inference with the SOTA E2Depth model. As illustrated in Figure 6 depth predictions exhibit noticeable artifacts and loss of fine structures in aerial event streams, highlighting that current models do not adequately address bavebased event depth estimation and underlining the potential value of our dataset for this frontier task.
Event-to- Video Reconstruction. Table and Figure summarize the performance of event-to-video recon- struction methods on our SkyEvents. Overall, methods originally trained on ground-level data struggle to generalize baV-d event streams. Methods such ET-Net, SPADE-E2VID, and SSL-E2VID exhibit

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Gray Image
Events
E2VID+
FireNet
SSL-E2VID
Figure 7: Video reconstruction performance comparison: E2VID+ (Stoffregen et al. 2020 FireNet (Scheer-
linck et al. 2020), and SSL-E2VID (Paredes- Vallés & De Croon 2021
significantly degraded image quality, underscoring the challenge posed by acrial, low-light event data and the need for dedicated training in this regime.
5 CONCLUSION
We introduce SkyEvents, the first large-scale, event-enhanced UAV dataset specifically designed for 3D scene reconstruction using RGB, event, and LiDAR data. The dataset spans wide range of conditions, including variations in illumination, scenario, and flight height, comprising 45 sequences (>-h) of paired RGB and event data, along with 0.72 km of LiDAR point cloud data capturing around 2.64 cm/pixel GSD. To fully harness the potential of this large-scale dataset, we propose the GTA module to effectively synchronize RGB-event timestamps, and the RER loss guide rendering optimization. Experimental results underscore the significant contribution of event data to 3D scene reconstruction. In conclusion, hope SkyEvents will catalyze further research and innovation the domain of event-enhanced 3D reconstruction, particularly in extreme scenarios.
Limitation and Future Work. Due to current hardware constraints, the RGB and event data are not per- feetly aligned spatiotemporally. In the future, aim upgrade the sensor platform to obtain larger, more precise, and higher-quality paired dataset, enabling more accurate synchronization under increasingly challenging scenarios. This enhanced dataset will serve foundation for exploring broader range of computer vision tasks, such as depth estimation and video frame interpolation.
ACKNOWLEDGMENTS
This work was supported in part by the National Key R&D Program of China (Grant No. 2023YFF0725001,
in part by the National Natural Science Foundation of China (Grant No. 92370204 and No. 42401567), in part by the Guangdong Basic and Applied Basic Research Foundation (Grant No. 153815151200577 part by the Key-Area Special Project of Guangdong Provincial Ordinary Universities (Grant No. 2024ZDZX1007) in part by the AI Research and Learning Base of Urban Culture, Guangdong Provin- cial Department of Education (Grant No. 2023W7JD008), and in part by the Guangdong Provincial Project (Grant No. 2024QN1 GO95).
The authors would like thank Guangdong Airace Technology Co., Ltd. and All-Airspace Intelligent Lab for their support and providing the experimental environment for this research.
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A APPENDIX
A.1 UAV PLATFORM & SENSOR SYSTEM DESIGN
The data acquisition system was built around a DJI Matrice 350 RTK unmanned aerial vehicle, selected for its robust payload capacity of 2.7 kg and extended flight endurance of approximately 45 minutes, shown in Figure 8 This platform integrates RTK GNSS positioning with centimeter-level accuracy, ensuring precise georeferencing throughout all missions. The airframe's IP55 weather resistance enables operations under varying environmental conditions.
(a) Front view
Side view
(c) Rear view
Figure 8: Multi-modal UAV data collection platform overview. The DJI Matrice 350 RTK is equipped with a synchronized sensor suite and An onboard mini-PC.
custom-designed sensor suite was mounted vibration-damped carbon fiber plate minimize motion artifacts, shown in Figure 9 The core vision system consists of two synchronized cameras: Prophesee EVK4 HD event camera capturing asynchronous events 1280x720 resolution with sub-millseecond la- tency, and DJI Osmo Action 4 RGB camera recording 4K video at 120 fps with global shutter. Temporal alignment between these sensors was maintained with microsecond-level precision.
A.2 DATA COLLECTION SYSTEM KEY COMPONENTS
An onboard Xiaomi Mini PC served as the central computing unit, handling real-time data acquisition from both cameras through USB 3.2 interfaces. With sustained write speeds exceeding 500 MB/s, this system reliably captured the bigh-bandwidth event streams (up to 10 Mevts/s) alongside uncompressed RGB video. The complete setup represents balanced integration of commercial components and custom mounting solutions, providing reproducible platform for multi-modal aerial data collection across diverse urban environments. Platform detail listed in Table
A.3 LARGE-SCALE SCENARIO DIVERSITY
The dataset encompasses five distinct scenes at Hong Kong University of Science and Technology (Guangzhou), shown in Figure 10 captured in sequential order: (1) Main Teaching Building (2) North Dormitory, (3) Data Center, (4) Sports Field, (5) South Dormitory. Details are listed in Figure I0 and Table I and each scene presents unique architectural and environmental challenges for aerial perception:
Main Teaching Complex: vast interconnected structure comprising 10 buildings with glass facades, featuring a central aerial garden with dense foliage, ground-level water bodies, and tree clusters.
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(a) Carbon fiber mounting plate
(h) Power conversion module
(c) Flight controller with route planning
Figure 9: Key components of the UAV data collection system. (a) Custom carbon fiber plate provides rigid mounting for sensors and computing unit while damping vibrations. (b) Power conversion module regulates DJI battery output to stable 12V/5V for onboard electronics. (c) Remote controller with pre-loaded KML routes enables fully autonomous flight operations.
North/South Dormitories: High-rise residential buildings with rich texture patterns. The North Dormitory was sampled at around 70m, while the taller South Dormitory required 100m flight alti- tude for comprehensive coverage. Both feature intricate shadow dynamics from vertical structures.
Data Center: Three isolated white-toned buildings with minimal color variation, creating texture- poor surfaces ideal for testing feature extraction algorithms. Uniform 70m sampling height.
Sports Field: Complementary low-texture environments characterized by monochromatic surfaces. The outdoor Sports Field features uniform turf patterns, while the indoor Gymnasium contains fast- moving human activities under variable artificial lighting.
A.4 MATCHING RESULTS
Figure 11 shows the matching results with the proposed GTA module on SkyEvents and MVSEC datasets. And as shown in the figure, the event streams aligned by our GTA module exhibit a pixel-perfect correspon- dence with the RGB frames' geometric edges. Moreover, the successful alignment MVSEC confirms that our module generalizes well and achieves high temporal precision.
A.5 OTHER TASKS TO EXPLORE
Figure 13 shows the performance of existing event-to-video reconstruction methods F2VID (Rehecq al. 2019 E2VID+ (Stoffregen et al. 2020). FireNet (Scheerlinck et al., 2020). ET-Net (Weng et al. 202 FireNet+ (Stoffregen et al. 2020). HyperE2VID (Ercan et al. (2024), SPADE-E2VID (Cadena al. 2021 and SSL-E2VID (Paredes- Vallés & De Croon 2021) when applied to our SkyEvents dataset.
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Figure 10: Campus region overview. The map shows five distinct areas: (1) Main Building complex, (2)
North Dormitory, (3) Data Center, (4) Playground, and (5) South Dormitory, covering diverse urban scenar- ios for multi-modal data collection.
Maschel Key Points
Warpel Imnages
SkyEveet Danset
MVSIC
Figure 11: Matching results with the proposed GTA module on SkyEvents and MVSEC datasets.
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Table 4: Precise Sensor Suite Specifications and Integration Details
DJI Matrice 350 RTK (UAV Platform)
Category
Specification Details
Role
Primary aerial vehicle
Dimensions
Folded: 430 420 430 (with props)
Unfolded: 810
430 mm
Weight
Without battery: 3.77 kg: With dual battery: 6.47 kg
Payload
Capacity: 2.7 kg (max takeoff weight 9.2 kg): Gimbal load: 960 max
Flight
Endurance: 555 min (unloaded, 8 m/s cruise)
Max speed: 23 m/s (horizontal), m/s (ascent), 5 m/s (descent)
Rotation rate: Pitch: 300 /s, Yaw: 100%/s
Positioning
System: RTK GNSS
Accuracy: cm +1 ppm (horiz), 1.5
cm +1 ppm (vert)
Environmental
Rating: IP55 (weather resistant); Max altitude: 5000 (with 21 10s props)
Safety
ADS B receiver, dual battery redundancy
Integration
Custom vibration-damper carbon fiber plate
Prophesee EVK4 HD (Event Camera)
Category
Specification Details
Role
Asynchronous capture
Resolution
1280 720 (HD)
Pixel size
4.86 x 4.86 Jmm
Temporal
Latency: 220 jas; Event Rate: 10 Mevts's (million events second)
Dynamic Range
86 dB (up 120 dB under low light)
Spectral
Response: 400-1000 nm (visible to NIR)
Power
Consumption: 0.5 (USB powered)
Interface
Data: USB 3.0 Type-C: Syne: IX Connector Type B (sync in/out, trigger in)
Mechanical
Dimensions: 30 x 30 36 mm; Weight: 40 (excluding lens)
Mounting
Rigid co-location with RGB camera
Optics
Included: C-mount 1/2.5" lens (FOV 47.7%) Temporal-spatial calihration with RGB
Calibration
DJI Osmo Action (RGB Camera)
Category
Specification Details
Role
Synchronized RGB video capture
Resolution
4K UHD (3840 2160) 120 fps
Shutter
Global shutter (eliminates rolling shutter artifacts)
Sensor
Size: 1/1.3" CMOS; Pixel Size: 2.4 am
Dynamic Range
D-Log M (10-bit color depth)
Lens
FOV: (wide mode)
Synchronization
Accuracy: <100 μs relative to event camera
Xiaomi Mini PC (Compute & Data Logger)
Category
Specification Details
Role
Central data acquisition hub
CPU
Intel N N100 (4-core, 3.4GHz Turbo)
Memory
16GB DDR4 RAM
Storage
512GB NVMe SSD
I/O
Ports: 4 x USB 3.2 Gen 2 (10Gbps), 1 x HDMI 2.0, x 2.5G Ethernet
OS
Windows 10 Professional
Power
Consumption: 12W TDP (powered via UAV battery)
Data Rate
Capacity: ~500 MB/s sustained write
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Gray Image
Events
E2VID
E2VID+
FireNet
ET-Net
FireNet
HlperdE2VID
SPADE-E2VID
SSI-E2VID
Figure 12: Video reconstruction with E2VID, E2VID+, FireNet, ET-Net, FireNet+, HYpeEE2VID,SPADEE E2VID, and SSL-E2VID.
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A.6 DATASET STATISTICS AND EXAMPLES
A.6.1 DATASET STATISTICS
The SkyEvents dataset comprises multi-modal aerial data collected across five distinct urban scenarios, including complex building structures, low-rise and high-rise buildings, and open planar scenes. Data acquisition was performed using UAV platform equipped with synchronized RGB (3840x2160), event (1280x720), and LiDAR sensors. Flights were conducted at altitudes of 70-100 m under varied illumina- tion conditions (morning, noon, afternoon, dusk), with both automated routes and manual circular paths employed to ensure comprehensive spatial coverage. The dataset spans total duration of 28,866 seconds, amounting to 2.02 TB of data. Detailed specifications for each sequence -including sensor types, illumi- nation, resolution, flight parameters, and data size- are summarized in Table S This diverse collection supports development and evaluation of pereeption algorithms under real-world urban scenarios.
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Table SkyEvents Dataset Collection Statistics
Sequence
Sensor
Illumin.
(sr(s)
Resolution
Scenario
Flight Path
Height(m)
Data Size(GB)
07061519
RGB
Afternoo
1056
3840x2160
Medium-rise building
Auto flight
70
16.00
07061519
Event
Afeemoon
1435
1280x720
Medium-rise building
Auto flight
70
115.00
07061537
RGB
Afteroon
379
3840x2160
Medium-rise building
Auto flight
70
5,54
07061635
RGB
Aftemoon
1056
3840>2160
Low-rise building
Auto flight
70
16,00
07061635
Event
Aftemoon
1056
1280x720
Low-rise building
Auto flight
70
141.00
07071011
RGB
Morning
1056
3840>2160
Open planar scenes
Auto flight
70
16.00
07071011
Eveut
Morning
1523
1280x720
Open planar scenes
Auto flight
70
103.00
07071029
RGB
Morning
467
3840x2160
Open planar scenes
Auto flight
70
7.04
07071146 146
RGB
Noon
1056
3840x2160
High-rise building
Auto flight
100
16.00
0707 146
Event
Morning
1820
1280x720
High-rise building
Auto flight
100
150.00
07071204
RGB
Noon
764
3840x2160
High-rise building
Auto flight
100
11.50
07071500
RGB
Afiernoon
1056
3840x2160
Complex huilding
flight
100
16.00
07071500
Event
Afermoon
1628
1280x720
Complex huilding
Auto flight
100
145.00
07071518
RGB
Afermoon
572
3840x2160
Complex building
Auto flight
100
8.68
07071631
Event
Afternoon
1618
1280x720
Complex huilding
Auto flight
100
139.00
0707163
Afeemoon
1056
3840x2160
Complex huilding
flight
100
07071649
RGB
Afermoon
562
3840x2160
Complex building
Auto flight
100
8.47
07071800
RGB
Dusk
1056
3840x2160
Low-rise building
Auto flight
70
16.00
07071800
Event
Dusk
1183
1280x720
Low-rise building
Auto flight
70
95.70
07071817
RGB
Dusk
127
3840x2160
Open planar scenes
Auto flight
70
1.92
07071936
RGB
Dusk
1454
3840x2160
Medium-rise building
Auto flight
70
13.40
07071936
Event
Dusk
1454
1280x720
Medium-rise building
Auto flight
70
40.20
0911 1183
RGB
Dusk
546
3840x2160
Complex building
Manual circle
70
8,21
09111836
Event
Dusk
546
1280x720
Complex building
Manual circle
70
7,89
09121548
RGB
Afternoon
3840x2160
Open planar scenes
Manual circle
80
14,50
09121549
Event
Aftemoon
969
1280x720
Open planar scenes
Manual circle
80
12.80
09121611
RGB
A Afernoon
911
3840x2160
Open planar scenes
Auto flight
80
13.60
09121612
Event
Aferroon
911
1280x720
Open planar scenes
Auto flight
80
30.40
09141514
RGB
Afiemoon
1053
3840x2160
Complex huilding
flight
100
16.00
09141514
Event
Afeemoo
1913
1280x720
Complex huilding
Auto flight
100
72.50
09141532
RGB
A Aferrioon
860
3840x2160
Complex building
Auto flight
100
12.40
09141551
RGB
Aferroon
752
3840x2160
Complex building
Auto flight
100
10,80
09141551
Eveut
Afternoo
752
1280x720
Complex building
Auto flight
100
23.80
09141605
RGB
Afleroon
1066
3840x2160
Complex building
Manual circle
80
15.90
09141605
Event
Aflernoon
1066
1280x720
Complex building
Manual circle
80
56.30
09141759
RGB
Dusk
1053
3840>2160
Complex building
Auto flight
100
16.00
09141759
Event
Dusk
1803
1280x720
Complex building
Auto flight
100
104.00
09141816
RGB
Dusk
750
3840x2160
Complex building
Auto flight
100
10.90
09191513
RGB
Afternoon
782
3840x2160
Low-rise building
Manual circle
100
11.30
09191513
Event
Afternoon
782
1280x720
Low-rise building
Manual circle
100
22,80
09191530
RGB Afternoon
599
3840x2160
Medium-ris risuu building
Manual circle
100
8.76
09191530
Aflernonn
1280x720
Medium-rive building
Manual circle
100
24.10
09191543
RGB
Afteroon
1053
3840x2160
High-rise building
Manual circle
100
16.00
09191543
Event
A Afeenoon
1208
1280x720
High-rise building
Manual circle
100
$2,20
09191601
RGB
Aflerooon
155
3840x2160
High-rise building
Manual circle
100
2.08
0922
LIDAR
6600
All regions
Auto flight
100
362.00
Total Duration & Size
28866s
2022.69GB
RGB: 3840x21600 @00p H.265; Event: 1280x720 @ variable rate; LiDAR: Full area coverage
22

---

Published as conference paper ICLR 2026
Gray Image
Events
E2VID
E2VID+
FireNet
ET-Net
FireNet+
HyperE2VID
SPADE-E2VID
SSL-E2VID
Figure 13: Video reconstruction with E2VID, E2VID+, FireNet, ET-Net, FireNett, HyperE2VID, SPADE E2VID, and SSL-E2VID.
A.7 OTHER TASKS TO EXPLORE
Figure 13 shows the performance of existing event-to-video reconstruction methods E2VID (Rebecg et al. 2019) E2VID+ (Stoffregen et al. 2020). FireNet (Scheerlinck et al. 2020), ET-Net (Weng et al. 202 FireNet+ (Stoffregen et al. 2020). HyperE2VID (Ercan et al. (024)) SPADE-E2VID (Cadena al. 202 and SSL-E2VID (Paredes- Vallés & De Croon 2021) when applied to our SkyEvents dataset.
23


Reviews:
[{"review_id": "OZTge1614B", "reviewer": "Reviewer_jye9", "summary": "This work proposes a large-scale multimodal UAV dataset (i.e., SkyEvents) for 3D scene reconstruction, which includes RGB images, events, and LiDAR data. This newly built dataset provides 45 hybrid sequences considering light changes, scene diversity, and flight altitudes. Besides, the authors present a GTA module to align timestamps between two streams and design an RER loss function for supervising the rendering optimization. I believe the large-scale dataset will provide a challenging benchmark for event-based 3D reconstruction using agile drones.", "questions": "1. How were the event camera (e.g., EKV4) and the frame-based camera (e.g., DJI Action 4) physically integrated and synchronized to ensure spatiotemporal alignment, particularly under the vibrations experienced during UAV flight?\n\n2. The authors suggest that the primary motivation for introducing an event camera in UAV scenarios is to address high-speed motion blur. Under what specific conditions of flight altitude and velocity would an event camera provide a clear advantage over a standard 20 FPS RGB camera, which is susceptible to such motion blur?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The topic of event-based 3D reconstruction using agile drones is very interesting.\n\n2. The large-scale multimodal UAV dataset, which includes events, frames, and point clouds, will benefit the event-based vision community.\n\n3. The writing is clear and easy to understand.", "weaknesses": "1. Figure 1 in the manuscript is very impressive and nicely illustrates large-scale 3D scene reconstruction. The authors are encouraged to include more examples of large-scale 3D scenes in the visualization results, ideally covering high-speed or low-light scenarios, to highlight the advantages of event-based modalities compared to RGB frames and RGB–event multimodal settings.\n\n2. As a dataset or benchmark paper, it would be beneficial to evaluate more existing event-based 3D reconstruction methods for comparison. At least incorporating some open-source algorithms would make the dataset more useful and accessible to a broader research community.\n\n3. Given that this work mainly focuses on presenting a large-scale dataset, it might not be ideal to introduce new components such as the GTA module and RER loss in the same paper, as this could make the focus of the paper less clear.\n\n4. The inclusion of LiDAR point clouds in the dataset is an excellent idea. However, the manuscript currently lacks sufficient experimental results or demonstrations showing the value or impact of the point cloud data.\n\n5. For a dataset paper, Table 1 is particularly important. The authors are encouraged to further enrich this table and clearly articulate the unique contributions and advantages of their dataset compared to existing ones. This would make the paper more solid and convincing.\n\n6. Minor suggestions in the future work:\n\na. In the Related Work section, the titles of Sections 2.1 and 2.2 could be revised, as their contents overlap. Section 2.2 could be renamed to Event-based 3D Reconstruction for clarity.\n\nb. For large-scale UAV datasets, it is suggested that future work could employ stereo event cameras, as in [1,2]. The authors might even consider multi-UAV collaboration to collect coordinated datasets for 3D scene reconstruction, potentially leveraging event camera simulators such as [3].\n\nc. The authors could also refer to and cite more recent works on event-based 3D reconstruction, e.g., [4].\n\n[1] Active Event-based Stereo Vision, CVPR 2025.\n\n[2] Enhanced event-based dense stereo via cross-sensor knowledge distillation, ICCV 2025.\n\n[3] Physical-based event camera simulator, ECCV 2024.\n\n[4] EvaGaussians: Event stream assisted Gaussian splatting from blurry images, ICCV 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HjDv1ORRAg", "reviewer": "Reviewer_WeKv", "summary": "This paper introduces SkyEvents, a large-scale multimodal UAV dataset incorporating RGB, event, and LiDAR data, with particular emphasis on the role of event cameras for 3D scene reconstruction in urban environments. While the dataset is valuable in its focus on event-based sensing under challenging conditions, the paper's primary contribution remains at the data collection and preliminary fusion level.", "questions": "1. How did you address synchronization challenges between RGB, event, and LiDAR sensors?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "The strengths of this work include the creation of the comprehensive SkyEvents dataset, which features diverse challenging conditions and multimodal data (RGB, event, LiDAR). It potentially enables improved 3D reconstruction and perception algorithms, introduces effective synchronization (GTA) and rendering techniques (RER), and demonstrates the beneficial impact of event data in various challenging scenarios like low-light and motion blur.", "weaknesses": "The main limitation is the lack of in-depth task-specific analysis demonstrating the concrete benefits of event data for downstream perception tasks such as depth estimation, semantic segmentation, or real-time scene understanding. The experimental results are mostly qualitative and do not sufficiently quantify the actual improvements brought by event modalities. As a result, the paper falls short of establishing clear performance gains or providing comprehensive evaluations that would justify its impact as a benchmark resource.\n\nFurthermore, insufficient details on calibration, synchronization, and data processing hinder reproducibility and broader adoption. Without extensive task-oriented experiments and rigorous benchmarking, the work remains at an early exploratory stage, limiting its immediate utility for advancing event-based perception research.\n\nOverall, while the dataset is a promising resource, the paper does not demonstrate enough analysis or experimental validation to warrant publication in its current form. Significant additional work is necessary to substantiate the practical advantages of the provided data for perception tasks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TT3efzRwTp", "reviewer": "Reviewer_r6JB", "summary": "This paper introduces SkyEvents, a large-scale 3D scene reconstruction dataset that integrates multi-domain signals, including RGB video, event stream, and LiDAR point clouds. The dataset spans 481 minutes, making it three times longer than the previously largest dataset, EvMAPPER. To handle temporal misalignment between RGB and event signals, this paper proposes a Geometry-constrained Timestamp Alignment (GTA) method. Additionally, it introduces a Region-based Event Rendering (RER) loss to improve event-based rendering optimization. Experiments on SkyEvents include comparisons with and without event inputs in rendering, qualitative analyses of the RER loss, and evaluations against existing reconstruction baselines.", "questions": "Is it typical to use LiDAR point clouds as ground truth for scene reconstruction? While LiDAR provides higher accuracy than other sensing modalities, it still contains measurement noise and unobserved regions from the camera’s viewpoint, which may lead to inaccurate quantitative evaluation. How is this limitation addressed or considered in the evaluation process?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "- The large-scale real-world SkyEvents dataset is a valuable asset for the scene reconstruction community, providing diverse multi-domain signals.\n\n- Beyond the dataset itself, the methodology for large-scale aerial data collection and the inclusion of hardware details offer practical guidance for future dataset collection efforts.\n\n- The proposed temporal alignment strategy between RGB and event signals enhances the dataset’s usability and convenience for potential users.", "weaknesses": "**Incremental Contributions**\n\na. The proposed Geometry-constrained Timestamp Alignment (GTA) is practically useful for potential users of the SkyEvents dataset. However, in terms of novelty, it appears to be an extension of Dynamic Time Warping (DTW) [1] rather than a fundamentally new formulation. A discussion or comparison with DTW would strengthen the paper.\n\n[1] Berndt, Donald J., and James Clifford. “Using Dynamic Time Warping to Find Patterns in Time Series.” In AAAI Workshop on Knowledge Discovery in Databases, 1994.\n\nb. The motivation and formulation of the Region-based Event Rendering (RER) loss are difficult to follow. Equation (5) is unclear since $\\mathcal{C} _ {\\theta}$ is not defined anywhere in the paper, and the function $C$ in the definition of $E$ is also unspecified. Even assuming that this loss is intended to minimize the displacement difference of certain features between the initial and final frames (i.e., $\\log \\mathcal{C} _ {\\theta} (\\hat{I} _ {t_1}) - \\log \\mathcal{C} _ {\\theta} (\\hat{I} _ {t_2})$) and the accumulation of event activities ($E(t _ 1, t _ 2)$), it remains unclear how this formulation guides or benefits event rendering optimization.\n\nc. Excluding GTA and RER, the main contribution is limited to the dataset itself. However, the supplementary information supporting the usefulness of SkyEvents is quite limited. The paper should elaborate on the design philosophy behind the dataset—specifically, why certain characteristics were selected and how they differ from existing datasets. Furthermore, a comprehensive comparison with other datasets in terms of these characteristics is necessary to better demonstrate the dataset’s uniqueness and value.\n\n**Immature Presentation**\n\na. As mentioned earlier, some functions used in the definition of the RER loss (i.e., $C_{\\theta}$ and $C$) are not defined. Furthermore, the connection between the mathematical definition of RER and its underlying design intuition is not sufficiently explained, making it difficult to understand the motivation behind this formulation.\n\nb. The related work section primarily focuses on datasets. If GTA and RER loss are intended to be core contributions of the paper, a more comprehensive review of related methods should be included to demonstrate the distinct features of the proposed methods compared to existing ones, and to clarify why these components are crucial for the given task, i.e., multi-modal dataset construction and scene reconstruction.\n\nc. The evaluation structure requires revision.\n\n- It is unclear what the main results of the paper are in Section 4.4 (Results), and how these differ from the results presented in Section 4.5.\nThe authors should clarify what readers are expected to focus on in the baseline comparison shown in Figure 6.\n\n- In Figure 4, it is not explained why comparisons are made with and without GS, and what specific conclusion can be drawn from this comparison.\n\n**No Benefit in Evaluation**\n\na. In Figure 4, it is unclear what benefit the RER loss provides; the difference between the results with and without RER is not evident.\n\nb. In Table 2, the use of event data does not consistently improve performance. The purpose or takeaway of this comparison should be clarified—what should the reader conclude about the effectiveness of incorporating event signals?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Ox5bTeIjOF", "reviewer": "Reviewer_3NrW", "summary": "1. **Originality-wise**: the paper proposes a large-scale UAV event-based dataset for robust 3D reconstruction.\n2. **Quality-wise**: the proposed dataset has varied large-scale scenes including different flight path, scenarios types, illumination, and height settings.\n3. **Clarity-wise**: the manuscript is clearly written, with well-structured methodology, detailed explanations, and intuitive visualizations that enhance understanding.", "questions": "Please refer to Weaknesses part.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. A Pioneering and Highly Valuable Dataset: The most significant contribution of this work is the SkyEvents dataset itself. SkyEvents is not only large in scale (over 8 hours, covering 0.72 km²) but also rich in modalities (RGB, Event, LiDAR) and provides accurate 6-DoF poses, filling a major gap. \n2. Solves a Critical Practical Problem: The proposed GTA module directly addresses a very challenging yet common real-world problem: precise timestamp alignment between multiple sensors, especially in low-cost setups without hardware synchronization", "weaknesses": "1. **Regarding the GTA Module**: While the paper demonstrates the module's effectiveness indirectly through its positive impact on 3D reconstruction and other downstream tasks, it lacks a more direct and intuitive quantitative evaluation. To compellingly validate the module's performance, I suggest the authors conduct experiments on existing datasets with pre-aligned ground-truth timestamps (e.g., DSEC, MVSEC). This would allow for a direct measurement of the method's alignment error and a quantitative comparison of its effectiveness against other approaches.\n2. **Regarding Low-Light Image Generation**: The approach of generating synthetic low-light images is practical for settings where scenes cannot be re-captured. However, aligning event data captured directly under bright sunlight with these synthetically darkened images could introduce a significant data mismatch. Event cameras are known to exhibit different noise characteristics, such as leaky events, in bright conditions compared to true dark environments. This raises concerns about the fidelity of the alignment. I am curious if the authors implemented any specific procedures to address this potential distortion.\n3. **Regarding Evaluation Metrics**: I agree that using the same metrics as the original 3DGS is valid for the motion deblur task. However, for low-light or over-exposed conditions, these reference-based metrics may become ineffective due to the quality degradation of the ground-truth images themselves. An evaluation based on a flawed reference is not meaningful. I recommend that the authors incorporate no-reference image quality assessment metrics (e.g., BRISQUE, HyperIQA) to ensure the validity and intuitiveness of the evaluation results.\n4. The video visualization is lacking and this reduces the credibility of the real effect.\n\n---\nI have listed my concerns, and the score will be adjusted based on the author's response.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)