  Light scattering imposes a major obstacle for imaging objects seated deeply
in turbid media, such as biological tissues and foggy air. Diffuse optical
tomography (DOT) tackles scattering by volumetrically recovering the optical
absorbance and has shown significance in medical imaging, remote sensing and
autonomous driving. A conventional DOT reconstruction paradigm necessitates
discretizing the object volume into voxels at a pre-determined resolution for
modelling diffuse light propagation and the resulting spatial resolution of the
reconstruction is generally limited. We propose NeuDOT, a novel DOT scheme
based on neural fields (NF) to continuously encode the optical absorbance
within the volume and subsequently bridge the gap between model accuracy and
high resolution. Comprehensive experiments demonstrate that NeuDOT achieves
submillimetre lateral resolution and resolves complex 3D objects at 14
mm-depth, outperforming the state-of-the-art methods. NeuDOT is a non-invasive,
high-resolution and computationally efficient tomographic method, and unlocks
further applications of NF involving light scattering.
