提出新方法，用稀疏多视角安检图像重建3D模型，提升安全检测泛化能力。
Fan-Beam CT Reconstruction for Unaligned Sparse-View X-ray Baggage Dataset
- 结合神经衰减场与推扫相机模型优化，实现非对齐稀疏视角重建。
- 通过颜色编码网络提升新视角渲染一致性，增强重建质量。
- 适用于缺乏旋转扫描设备的安检场景，适合安全检测领域研究者。
计算机断层扫描（CT）利用多角度X射线图像重建横截面图像。在医学CT中，数百张随源与探测器绕中心轴旋转采集的X射线图像用于精确重建。而在安检行李检查中，由于普遍采用固定式X射线系统，公开的重建数据有限，难以获取大规模带标签的3D体素数据用于训练。为解决此问题，本文提出一种基于未对齐稀疏多视角行李X射线数据集的校准与重建方法，该数据集具备丰富的2D标注。方法融合多光谱神经衰减场重建与线性推扫（LPB）相机模型位姿优化，通过颜色编码网络提升新视角的渲染一致性。旨在提升安全行李检查领域的模型泛化能力，该领域泛化尤为困难。
原文摘要 · Abstract (English)
Computed Tomography (CT) is a technology that reconstructs cross-sectional images using X-ray images taken from multiple directions. In CT, hundreds of X-ray images acquired as the X-ray source and detector rotate around a central axis, are used for precise reconstruction. In security baggage inspection, X-ray imaging is also widely used; however, unlike the rotating systems in medical CT, stationary X-ray systems are more common, and publicly available reconstructed data are limited. This makes it challenging to obtain large-scale 3D labeled data and voxel representations essential for training. To address these limitations, our study presents a calibration and reconstruction method using an unaligned sparse multi-view X-ray baggage dataset, which has extensive 2D labeling. Our approach integrates multi-spectral neural attenuation field reconstruction with Linear pushbroom (LPB) camera model pose optimization, enhancing rendering consistency for novel views through color coding network. Our method aims to improve generalization within the security baggage inspection domain, where generalization is particularly challenging.
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