arXiv cs.AI· Chuanzhi Xu, Langyi Chen, Haoxian Zhou, Zelin Zhang, Haodong Chen, Qiang Qu, Xiaoming Chen, Weidong Cai·· 3 小时前AI 评分31
EventSeeColor：从单色事件中学习重建颜色
EventSeeColor: Learning to Reconstruct Color from Monochrome Events
AI 导读
EventSeeColor 是一种从单色事件相机数据重建场景颜色的方法，利用事件重建的灰度图与对齐事件共同引导扩散上色模型。灰度语义上下文支持颜色推理，区域均衡的色度监督和局部 RGB 精修改善颜色分布、亮度与纹理。在两个真实事件数据集的联合评估中，该方法在外部对比方法中取得最优的结构相似度、感知评分与彩色区域色差。
正文 · 原文
Abstract:Event cameras capture visual brightness changes under challenging lighting conditions. However, traditional event-based image reconstruction targets grayscale intensity and produces images without scene color. Color distinguishes surfaces with similar intensity. Reconstructing color from events can reveal appearance differences that reconstructed grayscale images cannot express. Although monochrome event cameras do not directly measure color, their event-triggering patterns depend on surface reflectance, illumination, and the sensor's spectral response. These patterns may contain cues that support color reconstruction. In this paper, we present EventSeeColor, which uses event-reconstructed grayscale and aligned events to jointly guide a diffusion colorizer. Grayscale-derived semantic context supports color inference, while region-balanced chroma supervision and local RGB refinement improve color distribution, brightness, and texture. In the combined evaluation on two real event datasets, EventSeeColor achieves the best structural similarity, learned perceptual scores, and chromatic-region color difference among the external methods.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.40166 [cs.CV] |
| (or arXiv:2610.40166v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40166 arXiv-issued DOI via DataCite |
Submission history
From: Chuanzhi Xu [view email]
[v1]
Thu, 29 Oct 2026 14:38:48 UTC (3,908 KB)
来源：arXiv cs.AI · arxiv.org