From Pixels to Polygons: A Lightweight AI Framework for Single-Image 3D Reconstruction and WebAR Visualization
TL;DR
A lightweight, end-to-end artificial intelligence framework that automatically reconstructs a textured 3D polygon model from a single RGB photograph and renders it interactively through a browser-native WebAR interface and offers a scalable, cost-efficient route to AI-powered 3D asset generation.
Abstract
Immersive digital experiences rely increasingly on high-quality 3D content, yet traditional 3D authoring demands specialized expertise, multi-camera capture rigs, and prolonged processing pipelines that remain out of reach for most developers. This paper presents a lightweight, end-to-end artificial intelligence framework that automatically reconstructs a textured 3D polygon model from a single RGB photograph and renders it interactively through a browser-native WebAR interface. The system integrates a deep learning model utilizing convolutional layers to capture representative patterns and features. with a monocular depth-estimation module, Marching-Cubes mesh synthesis, UV-texture projection, and progressive mesh-simplification optimized for delivery over A-Frame, Three.js, and the WebXR Device API. Empirical evaluation confirms a reconstruction accuracy of 96.8% across benchmark image sets, with processing times within practical thresholds on commodity mobile hardware. The proposed architecture eliminates dedicated application installation and offers a scalable, cost-efficient route to AI-powered 3D asset generation.