Loading [MathJax]/extensions/tex2jax.js

3D Reconstruction using Prior Knowledge

Research output: ThesisDoctoral thesis

Authors

  • Tom Hendrik Hachmann

Details

Original languageEnglish
QualificationDoctor of Engineering
Awarding Institution
Supervised by
Date of Award24 Jun 2024
Publication statusPublished - 2025

Abstract

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

Keywords

    Vorwissen, 3D reconstruction, Optimierung, optimization, 3D-Rekonstruktion, Prior Knowledge, Computer Vision

Cite this

3D Reconstruction using Prior Knowledge. / Hachmann, Tom Hendrik.
2025. 141 p.

Research output: ThesisDoctoral thesis

Hachmann, TH 2025, '3D Reconstruction using Prior Knowledge', Doctor of Engineering, Leibniz University Hannover.
Hachmann, T. H. (2025). 3D Reconstruction using Prior Knowledge. [Doctoral thesis, Leibniz University Hannover].
Hachmann TH. 3D Reconstruction using Prior Knowledge. 2025. 141 p.
Hachmann, Tom Hendrik. / 3D Reconstruction using Prior Knowledge. 2025. 141 p.
Download
@phdthesis{a8db01fd8ab848ffba0285f3d05c646d,
title = "3D Reconstruction using Prior Knowledge",
abstract = "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{\textquoteright} 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",
keywords = "Vorwissen, 3D reconstruction, Optimierung, optimization, 3D-Rekonstruktion, Prior Knowledge, Computer Vision",
author = "Hachmann, {Tom Hendrik}",
year = "2025",
language = "English",
school = "Leibniz University Hannover",

}

Download

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

M3 - Doctoral thesis

ER -

By the same author(s)