A review of heuristic optimization techniques applied for 3D body reconstruction from anthropometric measurements

Abstract

Reconstructing 3D human models has a variety of applications in areas such as entertainment, medical, manufacturing, and design. Reconstruction techniques are classified based on characteristics such as data input, devices used, and algorithms employed, in which using anthropometric measurements is one of the most widely used methods. Traditional methods of 3D human reconstruction from anthropometric measurements rely on technologies like Convolutional Neutral Network (CNN), and Linear Regression to generate an accurate model in a reasonable amount of time. This paper presents a picture of heuristic optimization methods to find the optimal solution in 3D body reconstructions from anthropometric measurements. In terms of output accuracy, the methods discussed in this paper have the potential to outperform CNN and similar technologies. Results are verified and validated on a real dataset to evaluate the performances of each method.