High-Throughput and Accurate 3D Scanning of Cattle Using Time-of-Flight
  Sensors and Deep Learning
Gbenga Omotara Seyed Mohamad Ali Tousi Jared Decker Derek Brake Guilherme N. DeSouza
We introduce a high throughput 3D scanning solution specifically designed to
precisely measure cattle phenotypes. This scanner leverages an array of depth
sensors, i.e. time-of-flight (Tof) sensors, each governed by dedicated embedded
devices. The system excels at generating high-fidelity 3D point clouds, thus
facilitating an accurate mesh that faithfully reconstructs the cattle geometry
on the fly. In order to evaluate the performance of our system, we have
implemented a two-fold validation process. Initially, we test the scanner's
competency in determining volume and surface area measurements within a
controlled environment featuring known objects. Secondly, we explore the impact
and necessity of multi-device synchronization when operating a series of
time-of-flight sensors. Based on the experimental results, the proposed system
is capable of producing high-quality meshes of untamed cattle for livestock
studies.