Scalable Cloud-Native Pipeline for Efficient 3D Model Reconstruction from Monocular Smartphone Images

TL;DR

This paper introduces a scalable cloud-native pipeline that automatically reconstructs detailed 3D models from smartphone images, integrating AI, machine learning, and microservices for industrial and educational applications.

Contribution

It presents a novel, modular cloud-based system combining AI and pose estimation to efficiently generate customizable 3D models from monocular images.

Findings

Efficient 3D reconstruction from smartphone images.

Modular microservices architecture enables flexibility.

Embedded textures and materials enhance model realism.

Abstract

In recent years, 3D models have gained popularity in various fields, including entertainment, manufacturing, and simulation. However, manually creating these models can be a time-consuming and resource-intensive process, making it impractical for large-scale industrial applications. To address this issue, researchers are exploiting Artificial Intelligence and Machine Learning algorithms to automatically generate 3D models effortlessly. In this paper, we present a novel cloud-native pipeline that can automatically reconstruct 3D models from monocular 2D images captured using a smartphone camera. Our goal is to provide an efficient and easily-adoptable solution that meets the Industry 4.0 standards for creating a Digital Twin model, which could enhance personnel expertise through accelerated training. We leverage machine learning models developed by NVIDIA Research Labs alongside a custom-designed pose recorder with a unique pose compensation component based on the ARCore framework by Google. Our solution produces a reusable 3D model, with embedded materials and textures, exportable and customizable in any external 3D modelling software or 3D engine. Furthermore, the whole workflow is implemented by adopting the microservices architecture standard, enabling each component of the pipeline to operate as a standalone replaceable module.