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

An intern at TechRealm x PreserveMy.World compared five 3D reconstruction methods for preserving cultural heritage sites, including COLMAP, NeRF, Gaussian Splatting, monocular depth estimation, and multi-view stereo. The intern wrote a Python script simulating monocular depth estimation and generated a depth map, confirming the pipeline concept. The nonprofit uses AI to document historical landmarks like Lahore's Badshahi Masjid.

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