Three-dimensional x-ray imaging is an important materials characterization approach which enables non-destructive and sub-surface evaluation of a wide range of materials. There is a growing trend in 4-D imaging approaches which combine 3D imaging with time. The latter is specifically useful for observing dynamic operating conditions also known as in situ or operando. One of the limitations in 4D imaging is the need for 1000s of images which severely limits the temporal resolution of such a technique. Herein, we introduce a sparse-view reconstruction algorithm known as Tomo-NeRF. Tomo-NeRF is a physics based learning approach which enables the reconstruction of 3D images with limited 2D views (<10 views). The results show the ability of Tomo-NeRF to reliably restore the structure of 3D two-phase granular media from two-view 2D radiographic images in a high structural similarity of 0.9971 for real XCT images and 0.9973 for synthetic images with irregular particle shape. The voxel-wise accuracy for real images (81.83-89.59%) is lower than synthetic images (86.40-95.37%). The deviation is likely because of experimental considerations which do not obey model assumptions (e.g., low incident energy, differences in attenuation coefficients, polychromatic and cone beams, and sample shifting). Reconstruction of complex structures with high fidelity with single-digit radiographic images can significantly impact the temporal resoulution of 4D-imaging. Future work aims to test the resilency of these algorithms on dynamic imaging experiments.