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DanchGit/README.md

Dipayan Chowdhury

XR | Computer Vision | Colour Science

I’m a Computer Scientist specialising in XR, computational colour, and human perception, with experience building and evaluating perception-based systems for AR/VR.
My experience lies at the intersection of colour pipelines, psychophysics, machine learning and XR, supported by hands-on development in Unity, Python, MATLAB and Blender.

I also bring an industry foundation as a Data Analyst at Bain & Company, where I built reproducible analytical pipelines and worked in agile teams.


Core Skills & Technologies

XR

  • Unity (C#), Blender, OpenXR workflows
  • Rendering pipelines, foveation, colour calibration
  • Worked on headsets such as the HoloLens 2 and the Varjo VR 3

Computer Vision & ML

  • Python, PyTorch, OpenCV
  • Image processing, feature extraction, deep learning
  • Model interpretability

Colour Science & Perception

  • Colour calibration & characterisation
  • Psychophysical experiment design and implementation
  • Translucency perception

Data Analysis

  • Pandas, SQL, Tableau, Alteryx
  • MATLAB

Projects (XR / Vision / Colour)

Masters thesis project. I designed and conducted experiments to benchmark colour accuracy in optical see-through AR using Unity. The headset used was the Microsoft HoloLens 2.
I implemented characterisation pipelines using MATLAB, and hardware such as a Spectroradiometer, performed perceptual analysis, and trained deep learning models on Python to improve robustness across lighting conditions, as well as background and observer differences.

I developed Unity prototypes to demonstrate distortion correction in XR systems (specifically barrel/pincushion distortions and chromatic aberration). This included writing shaders on Unity.

I implemented multiple levels of foveation and conducted a controlled psychophysical study to evaluate perceptual thresholds using Unity for the varjo VR3 headset as a coursework project.
I analysed performance–quality tradeoffs for real-time XR rendering.

I built a curated dataset of AI and human-generated artworks.
I also implemented deep learning models on PyTorch using spatial and frequency-domain features, achieving ~98% accuracy with emphasised model explainability.

I investigated and published 2 papers about how observers perceive colour differences in translucent 3D objects using psychophysics and ML analysis. The softwares used were Mitsuba renderer, Python, MATLAB and QuickEval.


A Bit More About Me

When I’m not analysing colour perception or building XR experiments, I enjoy creating digital art, exploring new cities, and learning languages.


Contact

Pinned Loading

  1. poirot-en-bardo/ai-art-detection poirot-en-bardo/ai-art-detection Public

    Jupyter Notebook

  2. Colour-Characterisation-in-Optical-See-Through-Augmented-Reality Colour-Characterisation-in-Optical-See-Through-Augmented-Reality Public

    This repository contains the main codes used for the thesis project titled "Exploring Colours in Optical See-Through Augmented Reality" as a part of the Erasmus Mundus COSI master's programme

    C#

  3. mr-talukdar/Pyrosafe-Game mr-talukdar/Pyrosafe-Game Public

    A Oculus Quest 2 Based game for FireHazard

    C# 3 1

  4. Unity-XR-Display-Distortion-Correction-Demo Unity-XR-Display-Distortion-Correction-Demo Public

    This repository contains a Unity demo that shows the use of predistortion to counter lens distortion effects such as barrel and pincushion distortion as well as chromatic aberration

    C#

  5. Translucency-and-Colors-in-3D-objects Translucency-and-Colors-in-3D-objects Public

    Studies and papers published by Dipayan Chowdhury with a focus on colour and appearance science, specifically translucency in 3D objects.