# What I Learned About 3D Reconstruction in Week 1 of My AI Internship at PreserveMy.World

> Source: <https://dev.to/amna_hafeez/what-i-learned-about-3d-reconstruction-in-week-1-of-my-ai-internship-at-preservemyworld-32fd>
> Published: 2026-06-26 19:23:40+00:00

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 downloading 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*