用事件相机实现无运动模糊的全景图像重建
E2Pano: Learning Event-to-Panorama Image Reconstruction
- 基于几何引导的端到端学习框架，保留球面坐标
- 合成与实测数据上重建质量优于传统方法
- 适合事件相机全景成像研究者使用
事件相机具备微秒级时间分辨率和高动态范围，有望实现高速旋转扫描下的无运动模糊全景成像。然而，现有基于优化的方法计算开销大，而以往基于学习的重建方法多针对透视图像，缺乏对全景输出的几何感知支持。本文提出E2Pano，一种几何引导的事件到全景图像重建流水线，包含端到端可学习的光度重建阶段。该框架在整个流程中保持真实的球面坐标，采用轻量级增强模块结合频域监督以弥合事件-图像域差异，并利用带3D位置编码的球面Transformer进行光度重建。在合成数据和真实旋转扫描上的实验表明，相比优化基线，E2Pano在重建质量与光度重建成本上均有提升，且在仅在合成数据上训练的情况下仍能良好迁移到真实采集数据。此外，我们构建了PanoScan数据集，包含4,370个合成和30个真实世界的全景场景及其对应的事件流。相关数据与代码将公开。
原文摘要 · Abstract (English)
Event cameras offer microsecond-level temporal resolution and high dynamic range, potentially facilitating motion-blur-free panoramic imaging from fast rotational scanning. Nonetheless, existing optimization-based methods remain computationally demanding, while prior learning-based reconstruction methods are largely designed for perspective imagery and lack geometry-aware support for panoramic outputs. We present E2Pano, a geometry-guided event-to-panorama pipeline with an end-to-end learnable photometric reconstruction stage. Our framework preserves real spherical coordinates from geometric mapping throughout the pipeline, employs a lightweight enhancement module with frequency-domain supervision to bridge the event-image domain gap, and leverages a spherical Transformer with 3D positional embeddings for photometric reconstruction. Experiments on synthetic data and captured rotational scans show improved reconstruction quality and lower photometric reconstruction cost than optimization-based baselines, together with encouraging transfer to real captures under our acquisition protocol despite training purely on synthetic data. Additionally, we construct PanoScan, a dataset with 4,370 synthetic and 30 real-world panoramic scenes paired with event streams. Our dataset and code will be released.
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