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When created faithfully from real-world data, Digital 3D representations ofobjects can be useful for human or computer-assisted analysis. Such models canalso serve for generating training data for machine learning approaches insettings where data is difficult to obtain or where too few training dataexists, e.g. by providing novel views or images in varying conditions. Whilethe vast amount of visual 3D reconstruction approaches focus on non-physicalmodels, textured object surfaces or shapes, in this contribution we propose avolumetric reconstruction approach that obtains a physical model including theinterior of partially translucent objects such as plankton or insects. Ourtechnique photographs the object under different poses in front of a brightwhite light source and computes absorption and scattering per voxel. It can beinterpreted as visual tomography that we solve by inverse raytracing. Weadditionally suggest a method to convert non-physical NeRF media into aphysically-based volumetric grid for initialization and illustrate theusefulness of the approach using two real-world plankton validation sets, thelab-scanned models being finally also relighted and virtually submerged in ascenario with augmented medium and illumination conditions. Please visit theproject homepage at www.marine.informatik.uni-kiel.de/go/vito

Visual Tomography: Physically Faithful Volumetric Models of Partially Translucent Objects