High-Throughput and Accurate 3D Scanning of Cattle Using Time-of-Flight Sensors and Deep Learning
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arXiv
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| Format: | Preprint |
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2023
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| author | Omotara, Gbenga Tousi, Seyed Mohamad Ali Decker, Jared Brake, Derek DeSouza, Guilherme N. |
| author_facet | Omotara, Gbenga Tousi, Seyed Mohamad Ali Decker, Jared Brake, Derek DeSouza, Guilherme N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_03861 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | High-Throughput and Accurate 3D Scanning of Cattle Using Time-of-Flight Sensors and Deep Learning Omotara, Gbenga Tousi, Seyed Mohamad Ali Decker, Jared Brake, Derek DeSouza, Guilherme N. Computer Vision and Pattern Recognition 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. |
| title | High-Throughput and Accurate 3D Scanning of Cattle Using Time-of-Flight Sensors and Deep Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2308.03861 |