3D shape measurement techniques for human body reconstruction

Abstract

<p>In this work the performances of three different techniques for 3D scanning have been investigated. In particular two commercial tools (smartphone camera and iPad Pro LiDAR) and a structured light scanner (Go!SCAN 50) have been used for the analysis. First of all, two different subjects have been scanned with the three different techniques and the obtained 3D model were analysed in order to evaluate the respective reconstruction accuracy. A case study involving a child was then considered, with the main aim of providing useful information on performances of scanning techniques for clinical applications, where boundary conditions are often challenging (i.e., non-collaborative patient). Finally, a full procedure for the 3D reconstruction of a human shape is proposed, in order to setup a helpful workflow for clinical applications.</p>

Anomaly detection in plant growth in a controlled environment using 3D scanning techniques and deep learning

“…A fixed camera is used a reference and its performance is compared with those of three scanning techniques, namely structured light scanner (SL), LIDAR (L), photogrammetry with high resolution camera (Pc), and an autonomous, low-cost photogrammetry system (Pl). The performances in reproducing the plant shape are assessed with the purpose of developing and standardizing a low-cost procedure to monitor the crop growth, saving time and resources [7,8]. Indeed, while 2D measurement performed by a fixed camera allows for a real-time monitoring thanks to sub-hourly image acquisition, on the other hand it would probably not be a feasible solution in a greenhouse, especially in vertical farms.…”