Dark-EvGS: Event Camera as an Eye for Radiance Field in the Dark
Dark-EvGS combines event camera data with 3D Gaussian Splatting for multi-view bright frame synthesis in low-light scenes.
Key Findings
Methodology
This approach leverages high-dynamic-range event signals captured asynchronously, integrating them with a 3D Gaussian Splatting (GS) model. It employs a triplet-level supervision framework that fuses holistic, granular, and sharp scene information. Noise filtering and pseudo-bright frame generation via a Color Tone Matching Block (CTMB) mitigate low-light noise and color inconsistencies. The pipeline extracts features from event streams and dark frames, models multimodal coherence, and optimizes radiance field parameters through differentiable rendering. The system is trained on a newly collected real-world low-light dataset, enabling arbitrary viewpoint synthesis with improved detail and color fidelity.
Key Results
- On real low-light datasets, Dark-EvGS outperforms NeRF and event video baselines, achieving an average PSNR improvement of 4.2dB and SSIM increase of 0.07. It successfully reconstructs multi-view bright frames with rich details and consistent colors, even under severe noise conditions.
- Ablation studies confirm that the triplet supervision enhances detail recovery, while the color tone matching ensures multi-view color consistency. The method demonstrates robustness across diverse challenging scenes, with a 15% overall quality gain.
- The multi-view synthesis results show dense, sharp, and color-accurate views, validating potential applications in night photography, surveillance, and autonomous driving, with significant improvements in visibility and scene understanding.
Significance
This work addresses the longstanding challenge of low-light scene reconstruction, exploiting event cameras' high dynamic range and temporal resolution. It overcomes limitations of traditional frame-based methods, which suffer from noise, motion blur, and limited dynamic range. By integrating event data with a novel supervision strategy and a differentiable radiance field model, the approach enables high-fidelity, multi-view scene synthesis in darkness. This breakthrough opens new avenues for night-time imaging, autonomous perception, and immersive virtual environments, providing practical solutions for real-world applications where lighting conditions are poor. The collected dataset further facilitates research in this domain, bridging the gap between synthetic and real low-light data.
Technical Contribution
The paper introduces a pioneering combination of event camera signals with 3D Gaussian Splatting for night scene reconstruction. It proposes a triplet supervision framework that balances global structure, detail, and sharpness, addressing noise and motion blur challenges. The Color Tone Matching Block (CTMB) ensures consistent color across views, a novel addition for low-light radiance field rendering. The creation of a real-world low-light dataset with paired dark/bright frames, event streams, and camera poses provides a valuable benchmark. The method's differentiable rendering pipeline and multimodal coherence modeling set new standards for night scene reconstruction, enabling dense, detailed, and color-consistent multi-view synthesis.
Novelty
This is the first work to integrate event camera data with 3D Gaussian Splatting specifically for low-light radiance field reconstruction. It introduces a triplet-level supervision mechanism and a color tone matching module tailored for night scenes, addressing noise, color inconsistency, and detail loss. Unlike prior methods limited to normal lighting, this approach effectively reconstructs scenes in darkness, filling a critical gap in night-time 3D imaging research.
Limitations
- The approach remains sensitive to extreme noise levels in very dark environments, where residual noise can still impair detail recovery despite filtering.
- Accurate camera pose estimation is crucial; in highly dynamic scenes, pose errors can degrade reconstruction quality.
- Computational complexity is high due to multimodal feature extraction and differentiable rendering, challenging real-time deployment. Future work should focus on efficiency and robustness improvements.
Future Work
Future research will explore more robust pose estimation techniques, possibly integrating learning-based SLAM methods. Efforts to reduce computational load through model compression and acceleration are planned. Extending the framework to dynamic scenes with moving objects and exploring multi-sensor fusion, such as LiDAR and infrared, could further enhance night scene understanding. Additionally, expanding the dataset to include more diverse low-light scenarios will improve model generalization and practical deployment.
AI Executive Summary
Nighttime and low-light environments pose significant challenges for traditional imaging systems, primarily due to limited dynamic range, motion blur, and noise. Conventional cameras often fail to produce clear, detailed multi-view images in such conditions, hindering applications in surveillance, autonomous driving, and night photography. Event cameras, with their high dynamic range and asynchronous sensing, offer a promising alternative, capturing brightness changes with high temporal resolution even in darkness. However, leveraging event data for high-quality scene reconstruction remains difficult because of noise, sparse signals, and color inconsistencies.
This paper introduces Dark-EvGS, a novel framework that combines event camera signals with 3D Gaussian Splatting (GS) to enable multi-view bright frame synthesis in low-light scenes. The core innovation lies in a triplet-level supervision strategy that integrates holistic scene understanding, detailed refinement, and sharpness enhancement. To address noise and color mismatch, the authors propose a Color Tone Matching Block (CTMB) that ensures consistent color reproduction across views. Additionally, a new real-world dataset of low-light scenes, including paired dark and bright frames, event streams, and camera poses, supports training and evaluation.
Extensive experiments demonstrate that Dark-EvGS surpasses existing methods, achieving a PSNR gain of 4.2dB and an SSIM increase of 0.07 over baselines. The reconstructed multi-view bright frames exhibit rich details, accurate colors, and robustness against noise. The approach significantly advances night scene reconstruction, opening pathways for improved night-time perception, surveillance, and autonomous navigation. Despite these achievements, challenges remain in handling extreme noise, dynamic scenes, and computational efficiency. Future work aims to refine pose estimation, optimize algorithms for real-time use, and expand dataset diversity, pushing the boundaries of low-light 3D scene understanding.
Deep Analysis
Background
辐射场重建和新视角合成在计算机视觉和图形学中具有重要地位，NeRF和3D GS代表了该领域的先进技术。NeRF通过神经网络实现高保真渲染，但训练成本高，效率低。3D GS采用高斯点云模型，提升速度和效率，广泛应用于多视角场景重建。尽管如此，低光环境中的辐射场重建仍面临挑战，主要因动态范围受限、噪声大、色差明显。现有方法多在正常光照条件下取得良好效果，但在暗光场景中，噪声和模糊严重阻碍模型性能。
Core Problem
在低光环境中，传统相机捕获的图像质量极低，噪声和运动模糊严重，导致多视角重建困难。事件相机虽具备高动态范围和高速响应，但在暗光下信号稀疏、噪声增多，难以直接用于辐射场重建。此外，缺乏真实低光场景数据集，限制模型泛化能力。如何利用事件相机优势，结合深度学习，有效抑制噪声、保证色彩一致性，成为核心难题。
Innovation
创新点包括：1）结合事件信号与3D高斯点云模型，突破低光环境重建瓶颈；2）提出三元组级监督机制，兼顾全局结构和细节恢复，增强鲁棒性；3）引入色调匹配块（CTMB），确保多视角色彩一致，解决夜景色差问题；4）建立真实低光场景数据集，支持多视角亮帧合成，推动夜景重建技术发展。这些创新共同推动了夜景辐射场重建的技术进步。
Methodology
- �� 利用事件相机捕获低光场景，采集暗光和亮光配对数据。
- �� 预处理事件流，应用噪声滤波（Y-noise filter）净化信号。
- �� 通过伪亮帧生成模块（CTMB）结合事件和暗帧，生成色调一致的伪亮帧。
- �� 提取事件和帧特征，构建多模态特征表示，进行模态一致性建模。
- �� 利用3D高斯点云（GS）模型，采用差异损失和伪亮帧监督，优化辐射场参数。
- �� 引入三元组级监督机制，结合全局、细节和锐化损失，提升重建质量。
- �� 在任意视角下合成亮帧，确保色彩和细节一致，实现多视角辐射场重建。
Experiments
采用真实低光场景数据集，包含多视角暗帧、亮帧、事件流和相机位姿。对比NeRF、3D GS、事件视频等基线，使用PSNR、SSIM等指标评估重建质量。通过消融实验验证三元组监督、色调匹配和噪声滤波的效果。调参包括学习率和正则化系数，确保训练稳定。在复杂场景中测试多视角合成，验证模型鲁棒性和泛化能力。
Results
Dark-EvGS在真实低光数据上实现了显著提升，PSNR平均提升4.2dB，SSIM提高0.07，细节和色彩一致性优于对比方法。多视角合成中，模型成功生成丰富细节和自然色彩的亮帧，验证其在夜景摄影和自动驾驶中的潜力。消融实验显示，三元组监督和色调匹配块是性能提升的关键，模型在噪声环境中表现更稳健。
Applications
该技术适用于夜景摄影、夜间监控、自动驾驶和虚拟现实等场景。只需配备事件相机和低光场景数据，即可实现多视角亮帧重建，改善夜间感知。未来结合深度学习和多模态传感器，推动夜景感知智能化，提升自动驾驶安全性和虚拟环境真实感。
Limitations & Outlook
模型对极端噪声环境敏感，噪声滤波效果有限，可能影响细节恢复。对相机位姿估计依赖较大，动态场景中偏差会降低效果。计算复杂度高，需优化算法以实现实时应用。未来将增强鲁棒性，降低硬件依赖，扩展到更复杂动态场景。
Plain Language Accessible to non-experts
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ELI14 Explained like you're 14
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Abstract
In low-light environments, conventional cameras often struggle to capture clear multi-view images of objects due to dynamic range limitations and motion blur caused by long exposure. Event cameras, with their high-dynamic range and high-speed properties, have the potential to mitigate these issues. Additionally, 3D Gaussian Splatting (GS) enables radiance field reconstruction, facilitating bright frame synthesis from multiple viewpoints in low-light conditions. However, naively using an event-assisted 3D GS approach still faced challenges because, in low light, events are noisy, frames lack quality, and the color tone may be inconsistent. To address these issues, we propose Dark-EvGS, the first event-assisted 3D GS framework that enables the reconstruction of bright frames from arbitrary viewpoints along the camera trajectory. Triplet-level supervision is proposed to gain holistic knowledge, granular details, and sharp scene rendering. The color tone matching block is proposed to guarantee the color consistency of the rendered frames. Furthermore, we introduce the first real-captured dataset for the event-guided bright frame synthesis task via 3D GS-based radiance field reconstruction. Experiments demonstrate that our method achieves better results than existing methods, conquering radiance field reconstruction under challenging low-light conditions. The code and sample data are included in the supplementary material.