Real-Time 3D Shape of Micro-Details

TL;DR

This paper introduces a real-time 3D shape reconstruction method that combines photometric stereo, advanced reflectance modeling, and optimization techniques to produce highly detailed micro-prints from consumer-level images.

Contribution

It presents a novel hybrid approach integrating multiple components for accurate, real-time 3D reconstruction of micro-details using simple, mobile phone images.

Findings

Successful 3D reconstruction of micro-prints with high detail

Outperforms existing state-of-the-art methods

Works with consumer-level mobile phone images

Abstract

Motivated by the growing demand for interactive environments, we propose an accurate real-time 3D shape reconstruction technique. To provide a reliable 3D reconstruction which is still a challenging task when dealing with real-world applications, we integrate several components including (i) Photometric Stereo (PS), (ii) perspective Cook-Torrance reflectance model that enables PS to deal with a broad range of possible real-world object reflections, (iii) realistic lightening situation, (iv) a Recurrent Optimization Network (RON) and finally (v) heuristic Dijkstra Gaussian Mean Curvature (DGMC) initialization approach. We demonstrate the potential benefits of our hybrid model by providing 3D shape with highly-detailed information from micro-prints for the first time. All real-world images are taken by a mobile phone camera under a simple setup as a consumer-level equipment. In addition, complementary synthetic experiments confirm the beneficial properties of our novel method and its superiority over the state-of-the-art approaches.