  Muon scattering tomography utilises muons, typically originating from cosmic
rays to image the interiors of dense objects. However, due to the low flux of
cosmic ray muons at sea-level and the highly complex interactions that muons
display when travelling through matter, existing reconstruction algorithms
often suffer from low resolution and high noise. In this work, we develop a
novel two-stage deep learning algorithm, $\mu$-Net, consisting of an MLP to
predict the muon trajectory and a ConvNeXt-based U-Net to convert the
scattering points into voxels. $\mu$-Net achieves a state-of-the-art
performance of 17.14 PSNR at the dosage of 1024 muons, outperforming
traditional reconstruction algorithms such as the point of closest approach
algorithm and maximum likelihood and expectation maximisation algorithm.
Furthermore, we find that our method is robust to various corruptions such as
inaccuracies in the muon momentum or a limited detector resolution. We also
generate and publicly release the first large-scale dataset that maps muon
detections to voxels. We hope that our research will spark further
investigations into the potential of deep learning to revolutionise this field.
