Tomo-NeRF takes the two-view, four-view, or six-view images as the input (Figure ) and reconstructs a 3D image as the output (Figure ). We visualize the 2D prediction images by further making projections on the 3D output in both the same and different directions of the input images (i.e., validation view and novel view). The novel view represents the model reconstruction capability in a direction that is different from the projecting direction of the input images. Other than visual comparison, we quantitively evaluate the reconstruction accuracy with 3D structural similarity (SSIM), voxel-wise accuracy and the standard deviation of localized porosity (see Methods) by using the 3D ground truth sample as the reference. Reconstructing 'real XCT' data (Experiment # 1) results in lower reconstruction accuracy.