  Sparse-view computed tomography (SVCT) reconstruction aims to acquire CT
images based on sparsely-sampled measurements. It allows the subjects exposed
to less ionizing radiation, reducing the lifetime risk of developing cancers.
Recent researches employ implicit neural representation (INR) techniques to
reconstruct CT images from a single SV sinogram. However, due to ill-posedness,
these INR-based methods may leave considerable ``holes'' (i.e., unmodeled
spaces) in their fields, leading to sub-optimal results. In this paper, we
propose the Coordinate-based Continuous Projection Field (CoCPF), which aims to
build hole-free representation fields for SVCT reconstruction, achieving better
reconstruction quality. Specifically, to fill the holes, CoCPF first employs
the stripe-based volume sampling module to broaden the sampling regions of
Radon transformation from rays (1D space) to stripes (2D space), which can well
cover the internal regions between SV projections. Then, by feeding the
sampling regions into the proposed differentiable rendering modules, the holes
can be jointly optimized during training, reducing the ill-posed levels. As a
result, CoCPF can accurately estimate the internal measurements between SV
projections (i.e., DV sinograms), producing high-quality CT images after
re-projection. Extensive experiments on simulated and real projection datasets
demonstrate that CoCPF outperforms state-of-the-art methods for 2D and 3D SVCT
reconstructions under various projection numbers and geometries, yielding
fine-grained details and fewer artifacts. Our code will be publicly available.
