This week marked the beginning of my internship at TechRealm x PreserveMy.World —
a nonprofit using AI to preserve cultural heritage sites in 3D. I'm on the Core Tech
track, focused on Platform & Web Engineering.
PreserveMy.World is a nonprofit initiative that uses AI and 3D reconstruction to
document and preserve historical sites — starting with Lahore's heritage landmarks
like Badshahi Masjid.
I compared five methods for reconstructing heritage sites in 3D:
1. COLMAP (Structure-from-Motion) Takes multiple photos and produces a 3D point cloud. Great starting point for PMW.
2. NeRF (Neural Radiance Fields) Creates photorealistic 3D scenes from images. Needs a GPU but produces stunning results.
3. Gaussian Splatting
Fast, high-quality 3D rendering — ideal for web platforms like PreserveMy.World.
4. Monocular Depth Estimation
Works from a single image or video. Most practical for field use with limited equipment.
5. Multi-View Stereo (MVS) Dense reconstruction from multiple calibrated images. Good for building facades.
I wrote a Python script simulating monocular depth estimation and generated a depth
map output. Small experiment, but it confirmed the pipeline concept works.
The first attempt at down a heritage image in Python returned a 403 error —
so I switched to a simulated depth map instead. Real-world data acquisition will need
proper API access or local images.
I'll continue researching 3D reconstruction methods, run more experiments,
This post is part of my TechRealm x PreserveMy.World internship journey. GitHub: https://github.com/Anfey-SE/PMW-day1