vault backup: 2025-12-07 22:22:01

This commit is contained in:
Simon Lübeß
2025-12-07 22:22:01 +01:00
parent 5c7e6ab2c6
commit cb6ce89fe1
8 changed files with 339 additions and 114 deletions

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@ -39,16 +39,18 @@
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3
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@ -0,0 +1,3 @@
# WSL
1. Install Blender `sudo snap install blender --classic` (--classic hebt die isolation auf, die normalerweise bei snap verwendet wird)
2. Install UV `curl -LsSf https://astral.sh/uv/install.sh | sh`

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%%

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Basiert auf Unity Eyes (duh).
- Unterstützt Konfiguration der Wahrscheinlichkeitsverteilung von Eye Pse, Came intrinsic and extrinsic parameters, multi-camera setups, unterschiedliche Beleuchtungsbedingungen
- GUI für scnelles Prototyping der Data Distributions
- Zeigt: training mit Kamera-spezifischen Datensätzen ermöglicht besseren Transfer zur echten Welt
- TODO: UnitityEyes 1 kann wohl nur 18 Environments, 20 Gesichter sowie yaw und pitch von camera und Auge kontrollieren
- Camera Intrinsics:
- Image width, Height
- Focal length (x und y)
- image center (c und y)
- Einheit: alles in pixel
- Die Parameter können anhand von standard-Toolboxen kalibriert werden (https://ieeexplore.ieee.org/document/888718)
- Generated output:
- Random generated images (basierend auf User-Settings)
- Ground-truth locations von:
- Pupil center
- normalized vector representing optical axis
- center of globe of the eye
- Alle 2D-Parameter, die UnityEyes ausgespuckt hat
- Generiert auf einem M3 Max 85,7 Bilder pro Sekunde
Sie nutzen EfficientNetV2 (https://arxiv.org/abs/2104.00298) für Pupil center und gaze vector estimation.
Trainiert mit 10000 Samples von UnityEyes (1) und UnityEyes 2
UnityEyes 2 ist deutlich besser als UnityEyes 1

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# Motivation
Deep learning Methoden sind vielversprechende Ansätze um zuverlässige Eye-Tracking und Pupillen-Erkennungs-Anwendungen zu implementieren. Wie im maschinellen Lernen und insbesondere beim deep-learning üblich, ist eine große Menge von Daten nötig, um robuste Modelle trainieren zu können. Das bedeutet, dass viele Bilder von Augen mit dazugehöriger Segmentierungsmaske vorhanden sein müssen. Abgesehen von der Menge an Daten, muss diese auch von guter Qualität sein und am besten so nah an der später verwendeten Hardware sein, um die sogenannte Domain-Gap (oder auch sim-to-real Gap), also die ..., so gering wie möglich zu halten. Nur so ist es möglich effektive und zuverlässige neuronale Netze zu trainieren. Diese Datensätze können manuell erstellt werden, sind aber sehr Aufwändig. Des Weiteren ist es wichtig, dass der verwendete Datensatz so Divers wie möglich ist. Das beginnt im Falle von Augenbildern mit einfachen dingen, wie Acessoirs (Brillen) oder Schminke. Aber selbstverständlich auch auf den Unterschieden zwischen ethnischen Gruppen.
Ein anderer Ansatz um solche Datensätze zu erstellen ist es, sie synthetisch zu erzeugen....

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# 1. Direkte related works lesen:
- 1. Lesen
- 2. Zusammenfassen
- [ ] UnityEyes
- [ ] RIT-Eyes (haben wir schon)
- [ ] NVGaze
- [ ] SynthEyes
- [ ] Mehr suchen:
- [ ] Selbst suchen
- [ ] Gemini
- [ ] Perplexity
- [ ] ChatGibbidy
- [ ] Gucken, was Jan, Meiko und Timm so hatten
# 2. Parameter suchen
Wir haben uns wohl oder übel dafür entschieden, es mit Shapekeys zu machen. Das bedeutet, wir sollten endlich mal suchen, was es so für Parameter gibt.