# Visual Tomography: Physically Faithful Volumetric Models of Partially Translucent Objects

arXiv: https://arxiv.org/abs/2312.13494 · HTML: https://mlsanity.com/paper/2312.13494/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 51 references that are in the map.
- First posted on arXiv 21 Dec 2023.

## Builds on (most cited first, 13 of 13)

- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis](https://mlsanity.com/paper/2012.03927/) (2020)
- [Neural Reflectance Fields for Appearance Acquisition](https://mlsanity.com/paper/2008.03824/) (2020)
- [Neural Volumes: Learning Dynamic Renderable Volumes from Images](https://mlsanity.com/paper/1906.07751/) (2019)
- [Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects](https://mlsanity.com/paper/2110.14217/) (2021)
- [GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF](https://mlsanity.com/paper/2210.06575/) (2022)
- [Learning Object-Centric Neural Scattering Functions for Free-Viewpoint Relighting and Scene Composition](https://mlsanity.com/paper/2303.06138/) (2023)
- [NeAT: Neural Adaptive Tomography](https://mlsanity.com/paper/2202.02171/) (2022)
- [Inverse Transport Networks](https://mlsanity.com/paper/1809.10820/) (2018)

## Abstract (arXiv)

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