X-ray, known for its ability to reveal internal structures of objects, is expected to provide richer information for 3D reconstruction than visible light. Yet, existing neural radiance fields (NeRF) algorithms overlook this important nature of X-ray, leading to their limitations in capturing structural contents of imaged objects. In this paper, we propose a framework, Structure-Aware X-ray Neural Radiodensity Fields (SAX-NeRF), for sparse-view X-ray 3D reconstruction. Firstly, we design a Line Segment-based Transformer (Lineformer) as the backbone of SAX-NeRF. Linefomer captures internal structures of objects in 3D space by modeling the dependencies within each line segment of an X-ray. Secondly, we present a Masked Local-Global (MLG) ray sampling strategy to extract contextual and geometric information in 2D projection. Plus, we collect a larger-scale dataset X3D covering wider X-ray applications. Experiments on X3D show that SAX-NeRF surpasses previous NeRF-based methods by 12.56 and 2.49 dB on novel view synthesis and CT reconstruction. Code, models, and data will be released at https://github.com/caiyuanhao1998/SAX-NeRF

提出了一种基于X射线的稀疏视图3D重建框架SAX-NeRF，通过使用Line Segment-based Transformer模型化X射线中的物体内部结构，并使用Masked Local-Global ray sampling策略提取2D投影的上下文和几何信息，实验证明SAX-NeRF在新视角合成和CT重建方面优于以前的基于NeRF的方法。

结构感知稀疏视角X射线3D重建