By continuously minimizing cross-entropy (Equation S2 in Supporting Information) and MSE (Equation S3 in Supporting Information) losses during the training process, Tomo-NeRF can learn to increase the probability of predicting the voxel phase from 3D labels and six views. Learning from one projection direction can serve as relevance and share knowledge for other directions. The model can rely more directly on the input image feature if the query projection direction and input orientation are similar; otherwise, the model needs to leverage the learned prior.