Ackermann, Jens and Ritz, Martin and Stork, André and Goesele, Michael (2012):
Removing the Example from Example-based Photometric Stereo.
In: Lecture Notes in Computer Science (LNCS); 6554, Springer, Berlin, Heidelberg, New York, In: Trends and Topics in Computer Vision, pp. 197-210, DOI: 10.1007/978-3-642-35740-4₁₆,
[Conference or Workshop Item]
Abstract
We introduce an example-based photometric stereo approach that does not require explicit reference objects. Instead, we use a robust multi-view stereo technique to create a partial reconstruction of the scene which serves as sceneintrinsic reference geometry. Similar to the standard approach, we then transfer normals from reconstructed to unreconstructed regions based on robust photometric matching. In contrast to traditional reference objects, the scene-intrinsic reference geometry is neither noise free nor does it necessarily contain all possible normal directions for given materials.We therefore propose several modifications that allow us to reconstruct high quality normal maps. During integration, we combine both normal and positional information yielding high quality reconstructions. We show results on several datasets including an example based on data solely collected from the Internet.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2012 |
| Creators: | Ackermann, Jens and Ritz, Martin and Stork, André and Goesele, Michael |
| Title: | Removing the Example from Example-based Photometric Stereo |
| Language: | English |
| Abstract: | We introduce an example-based photometric stereo approach that does not require explicit reference objects. Instead, we use a robust multi-view stereo technique to create a partial reconstruction of the scene which serves as sceneintrinsic reference geometry. Similar to the standard approach, we then transfer normals from reconstructed to unreconstructed regions based on robust photometric matching. In contrast to traditional reference objects, the scene-intrinsic reference geometry is neither noise free nor does it necessarily contain all possible normal directions for given materials.We therefore propose several modifications that allow us to reconstruct high quality normal maps. During integration, we combine both normal and positional information yielding high quality reconstructions. We show results on several datasets including an example based on data solely collected from the Internet. |
| Series Name: | Lecture Notes in Computer Science (LNCS); 6554 |
| Publisher: | Springer, Berlin, Heidelberg, New York |
| Uncontrolled Keywords: | Business Field: Virtual engineering, Business Field: Visual decision support, Research Area: Generalized digital documents, 3D Scene reconstruction, Photometry, Multi-view stereo, Computer vision, Surface reconstruction, 3D Scanning, 3D Reconstruction |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Interactive Graphics Systems |
| Event Title: | Trends and Topics in Computer Vision |
| Date Deposited: | 12 Nov 2018 11:16 |
| DOI: | 10.1007/978-3-642-35740-4₁₆ |
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