Details
| Original language | English |
|---|---|
| Place of Publication | Düsseldorf |
| Number of pages | 141 |
| ISBN (electronic) | 978-3-18-688810-5 |
| Publication status | Published - 2025 |
Publication series
| Name | Fortschritt-Berichte VDI |
|---|---|
| Volume | Nr. 888 |
| ISSN (Print) | 0178-9627 |
Abstract
Keywords
- Vorwissen, 3D reconstruction, Optimierung, optimization, 3D-Rekonstruktion, Prior Knowledge, Computer Vision
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Düsseldorf, 2025. 141 p. (Fortschritt-Berichte VDI; Vol. Nr. 888).
Research output: Book/Report › Monograph › Research › peer review
}
TY - BOOK
T1 - 3D Reconstruction using Prior Knowledge
AU - Hachmann, Tom Hendrik
PY - 2025
Y1 - 2025
N2 - This dissertation investigates how prior knowledge can improve optical reconstruction systems that enable the localizationand measurement of objects in three-dimensional space. Reconstruction algorithms are usually subject to information lossduring the initial image acquisition, affecting the results’ quality. This fundamental problem can be mitigated by exploiting prior knowledge about the scene. Four reconstructionsystems are presented in this dissertation, demonstrating theeffective utilization of prior knowledge. First, a deep convolutional neural network is used for matting dynamic scenes, exploiting synchronizedbackground color changes to reconstruct transparent foregrounds, even with imprecise backgroundcolors. Second, the exact positions of cochlear implant electrodes are localized using Markov random fields, utilizing priorknowledge of electrode distances and minimal bending radii, significantly improving positioning accuracy. Third, electroluminescent wires woven into hair help reconstruct braided hairstyles for special effects by using active curves to track guidehairs and create realistic 3D braid models. Finally, 3D reconstruction of the human spine during movement is
AB - This dissertation investigates how prior knowledge can improve optical reconstruction systems that enable the localizationand measurement of objects in three-dimensional space. Reconstruction algorithms are usually subject to information lossduring the initial image acquisition, affecting the results’ quality. This fundamental problem can be mitigated by exploiting prior knowledge about the scene. Four reconstructionsystems are presented in this dissertation, demonstrating theeffective utilization of prior knowledge. First, a deep convolutional neural network is used for matting dynamic scenes, exploiting synchronizedbackground color changes to reconstruct transparent foregrounds, even with imprecise backgroundcolors. Second, the exact positions of cochlear implant electrodes are localized using Markov random fields, utilizing priorknowledge of electrode distances and minimal bending radii, significantly improving positioning accuracy. Third, electroluminescent wires woven into hair help reconstruct braided hairstyles for special effects by using active curves to track guidehairs and create realistic 3D braid models. Finally, 3D reconstruction of the human spine during movement is
KW - Vorwissen
KW - 3D reconstruction
KW - Optimierung
KW - optimization
KW - 3D-Rekonstruktion
KW - Prior Knowledge
KW - Computer Vision
U2 - 10.51202/9783186888105
DO - 10.51202/9783186888105
M3 - Monograph
SN - 978-3-18-388810-8
T3 - Fortschritt-Berichte VDI
BT - 3D Reconstruction using Prior Knowledge
CY - Düsseldorf
ER -