用神经衰减表面模型，仅凭2D X光片重建高精度3D结构。
NeAS: 3D Reconstruction from X-ray Images using Neural Attenuation Surface
- 引入神经衰减表面，联合建模几何与衰减场。
- 在模拟和真实数据上均实现精准3D表面重建。
- 适合医学影像重建，尤其低辐射场景应用。
从二维X射线图像重建三维结构是一种在医疗领域具有价值且高效的手段，相比计算机断层扫描可减少辐射暴露。近期基于隐式神经表示的方法虽提升了新视角合成的准确性，但表面形状估计仍不理想。为此，我们提出一种新型方法——神经衰减表面（NeAS），可同时捕捉场景的表面几何与衰减系数场。NeAS融合有符号距离函数（SDF），用于定义衰减场并辅助提取3D表面。我们在模拟与真实X射线图像上进行了实验，结果表明，仅需2D X射线图像，NeAS即可准确提取场景中的3D表面。
原文摘要 · Abstract (English)
Reconstructing three-dimensional (3D) structures from two-dimensional (2D) X-ray images is a valuable and efficient technique in medical applications that requires less radiation exposure than computed tomography scans. Recent approaches that use implicit neural representations have enabled the synthesis of novel views from sparse X-ray images. However, although image synthesis has improved the accuracy, the accuracy of surface shape estimation remains insufficient. Therefore, we propose a novel approach for reconstructing 3D scenes using a Neural Attenuation Surface (NeAS) that simultaneously captures the surface geometry and attenuation coefficient fields. NeAS incorporates a signed distance function (SDF), which defines the attenuation field and aids in extracting the 3D surface within the scene. We conducted experiments using simulated and authentic X-ray images, and the results demonstrated that NeAS could accurately extract 3D surfaces within a scene using only 2D X-ray images.
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