ColumbiaCGUI/PolXR
Oct 2024 – Oct 2024
1 Month active
Languages Used
Python
Technical Skills
3D ModelingData ProcessingGeospatial DataScripting
Qazi Ashikin
PROFILE
Qazi Ashikin
Qazi Ashikin developed a Python-based mesh generation workflow for the ColumbiaCGUI/PolXR repository, focusing on automating the conversion of geospatial data products such as DEMs and radar data into Unity-ready mesh assets. Leveraging skills in 3D modeling, data processing, and scripting, Qazi created scripts that output OBJ, MTL, and PNG files, streamlining asset preparation for both Unity and Blender pipelines. This work reduced manual steps in the data visualization process, enabling faster and more consistent asset generation for stakeholder demos. The project emphasized pipeline reliability and reproducibility, laying a foundation for automated asset deployment without major bug fixes required.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
708
Activity Months1
Your Network
6 people
Work History
October 2024
1 Commits • 1 Features
Oct 1, 20242024-10 Monthly summary for ColumbiaCGUI/PolXR focusing on delivering data-visualization mesh assets and pipeline automation. Key work delivered a Python-based mesh generation workflow converting data products (DEMs and radar data) into Unity-ready mesh assets (OBJ, MTL, PNG) to support a Unity data-visualization pipeline. The deliverable enables ingestion into Blender and Unity, enabling faster visualization asset generation and richer previews for stakeholders. Major bugs fixed: No major bugs reported in this period; work concentrated on feature delivery and pipeline reliability. Overall impact: Accelerated the end-to-end data visualization pipeline, enabling consistent mesh assets from diverse data sources, reducing manual steps, and laying groundwork for automated asset generation and deployment in Unity. Reinforced data processing and visualization capabilities for stakeholder demos and product demonstrations. Technologies/skills demonstrated: Python scripting for data preparation, mesh generation (OBJ/MTL/PNG), handling of DEMs and radar data, Unity and Blender asset pipelines, version control, and reproducible data workflows.
1 Commits • 1 Features
Oct 1, 20242024-10 Monthly summary for ColumbiaCGUI/PolXR focusing on delivering data-visualization mesh assets and pipeline automation. Key work delivered a Python-based mesh generation workflow converting data products (DEMs and radar data) into Unity-ready mesh assets (OBJ, MTL, PNG) to support a Unity data-visualization pipeline. The deliverable enables ingestion into Blender and Unity, enabling faster visualization asset generation and richer previews for stakeholders. Major bugs fixed: No major bugs reported in this period; work concentrated on feature delivery and pipeline reliability. Overall impact: Accelerated the end-to-end data visualization pipeline, enabling consistent mesh assets from diverse data sources, reducing manual steps, and laying groundwork for automated asset generation and deployment in Unity. Reinforced data processing and visualization capabilities for stakeholder demos and product demonstrations. Technologies/skills demonstrated: Python scripting for data preparation, mesh generation (OBJ/MTL/PNG), handling of DEMs and radar data, Unity and Blender asset pipelines, version control, and reproducible data workflows.
October 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
3D ModelingData ProcessingGeospatial DataScripting
Repositories Contributed To
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