High-Throughput and Accurate 3D Scanning of Cattle Using Time-of-Flight Sensors and Deep Learning

TL;DR

This paper presents a high-throughput 3D scanning system for cattle that combines time-of-flight sensors and deep learning to produce accurate, high-fidelity 3D models for livestock phenotyping.

Contribution

It introduces a novel multi-sensor 3D scanning system with validation for livestock phenotyping, enhancing accuracy and efficiency over previous methods.

Findings

High-quality 3D meshes of cattle achieved

Effective multi-device synchronization demonstrated

System accurately measures volume and surface area

Abstract

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.