Computed Tomography (CT) plays a vital role in inspecting the internal structures of industrial objects. Furthermore, achieving high-quality CT reconstruction from sparse views is essential for reducing production costs. While classic implicit neural networks have shown promising results for sparse reconstruction, they are unable to leverage shape priors of objects. Motivated by the observation that numerous industrial objects exhibit rectangular structures, we propose a novel \textbf{N}eural \textbf{A}daptive \textbf{B}inning (\textbf{NAB}) method that effectively integrates rectangular priors into the reconstruction process. Specifically, our approach first maps coordinate space into a binned vector space. This mapping relies on an innovative binning mechanism based on differences between shifted hyperbolic tangent functions, with our extension enabling rotations around the input-plane normal vector. The resulting representations are then processed by a neural network to predict CT attenuation coefficients. This design enables end-to-end optimization of the encoding parameters -- including position, size, steepness, and rotation -- via gradient flow from the projection data, thus enhancing reconstruction accuracy. By adjusting the smoothness of the binning function, NAB can generalize to objects with more complex geometries. This research provides a new perspective on integrating shape priors into neural network-based reconstruction. Extensive experiments demonstrate that NAB achieves superior performance on two industrial datasets. It also maintains robust on medical datasets when the binning function is extended to more general expression. The code will be made available.

翻译：计算机断层扫描（CT）在检测工业物体内部结构方面发挥着至关重要的作用。此外，从稀疏视角实现高质量的CT重建对于降低生产成本至关重要。尽管经典的隐式神经网络在稀疏重建方面已展现出有前景的结果，但它们无法有效利用物体的形状先验。基于众多工业物体呈现矩形结构的观察，我们提出了一种新颖的**神经自适应分箱**（**NAB**）方法，该方法将矩形先验有效整合到重建过程中。具体而言，我们的方法首先将坐标空间映射到分箱向量空间。该映射依赖于一种基于平移双曲正切函数差值的创新分箱机制，并通过我们的扩展实现了绕输入平面法向量的旋转。随后，生成的表示由神经网络处理以预测CT衰减系数。该设计实现了编码参数（包括位置、尺寸、陡度和旋转）通过投影数据梯度流的端到端优化，从而提升了重建精度。通过调整分箱函数的平滑度，NAB能够泛化至具有更复杂几何形状的物体。本研究为将形状先验整合到基于神经网络的重建中提供了新视角。大量实验表明，NAB在两个工业数据集上取得了优越的性能。当分箱函数扩展至更一般表达式时，其在医学数据集上也保持了鲁棒性。代码将公开提供。