In addition to the above-mentioned variations between the experiment and modeling, non-uniform information density within the 3D decoder (Figure ) can also impact the re- construction quality near the outer surfaces of the 3D predictions. We visualize the prediction error of the six-view reconstructions for the synthetic irregular sphere sample (Figure ) and the real sample (Figure ) on the selected horizontal slices. The model is less likely to accurately discern and reconstruct the particles at the bottom of the sample which makes the particles look fused together. This issue is more exacerbated with real XCT data (Experiment #1, Figure ). When the distance from the bottom/top is more than four voxels, the reconstruction quality improves. During the transposed convolution operation, the voxels further away from the edge are able to receive a contribution from the adjacent voxel, i.e., overlapping information. The lack of overlapping information at the edge of the sample forms an information density gradient. The information density gradient becomes exacerbated as it propagates in the multilayer transposed convolution neural network. Furthermore, the particle projections near the top and bottom edges of the radiographic images are incomplete, thereby more irregular than the particles of the complete shape. The low information density and the more irregular shapes cause the low-quality reconstruction region near the top and bottom of the sample. This can be potentially resolved by subsequently feeding partially overlapping radiographic images into the model, and then stitching together the central part of the reconstruction (with a higher prediction accuracy). The error map for the region away from the bottom/top shows that Tomo-NeRF manages to restore the position and general shape of the particles. However, errors primarily occur at the surface of the particles, indicating that the restoration of shape details is insufficiently accurate, particularly for the real-experiment sample.