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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.13494 |
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| _version_ | 1866917713244323840 |
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| author | Nakath, David Weng, Xiangyu She, Mengkun Köser, Kevin |
| author_facet | Nakath, David Weng, Xiangyu She, Mengkun Köser, Kevin |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_13494 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Visual Tomography: Physically Faithful Volumetric Models of Partially Translucent Objects Nakath, David Weng, Xiangyu She, Mengkun Köser, Kevin Computer Vision and Pattern Recognition 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 |
| title | Visual Tomography: Physically Faithful Volumetric Models of Partially Translucent Objects |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.13494 |