Visual Tomography: Physically Faithful Volumetric Models of Partially
  Translucent Objects
David Nakath Xiangyu Weng Mengkun She Kevin Köser
When created faithfully from real-world data, Digital 3D representations of
objects can be useful for human or computer-assisted analysis. Such models can
also serve for generating training data for machine learning approaches in
settings where data is difficult to obtain or where too few training data
exists, e.g. by providing novel views or images in varying conditions. While
the vast amount of visual 3D reconstruction approaches focus on non-physical
models, textured object surfaces or shapes, in this contribution we propose a
volumetric reconstruction approach that obtains a physical model including the
interior of partially translucent objects such as plankton or insects. Our
technique photographs the object under different poses in front of a bright
white light source and computes absorption and scattering per voxel. It can be
interpreted as visual tomography that we solve by inverse raytracing. We
additionally suggest a method to convert non-physical NeRF media into a
physically-based volumetric grid for initialization and illustrate the
usefulness of the approach using two real-world plankton validation sets, the
lab-scanned models being finally also relighted and virtually submerged in a
scenario with augmented medium and illumination conditions. Please visit the
project homepage at www.marine.informatik.uni-kiel.de/go/vito