There are three reasons for further making projections on the 3D predictions. Firstly, it enables Tomo-NeRF to learn from both 3D labels and 2D labels, allowing for learn the scene prior to reconstruction. In addition, for the testing or experimental validation process, in which only radiographic images are available, the trained network optimizes itself through image features and the grayscale label from the input of projection views. Furthermore, for the actual experiments (e.g., in situ imaging) that lack 3D labels to verify the fidelity of the deep reconstruction results, we may compare the 2D predictions on the same projection angle with the input images and assess the novel view quality.