{"id":5101,"date":"2025-02-12T19:03:59","date_gmt":"2025-02-12T18:03:59","guid":{"rendered":"https:\/\/mecatron.rma.ac.be\/?page_id=5101"},"modified":"2025-02-12T19:05:14","modified_gmt":"2025-02-12T18:05:14","slug":"marnix-enting","status":"publish","type":"page","link":"https:\/\/mecatron.rma.ac.be\/index.php\/people\/marnix-enting\/","title":{"rendered":"Marnix Enting"},"content":{"rendered":"

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Researcher<\/span><\/h4>\n

Robotics & Autonomous Systems,
Royal Military Academy<\/p>\n

Address<\/h4>\n

Avenue De La Renaissance 30, 1000 Brussels, Belgium<\/p>\n

Contact Information<\/h4>\n

Email<\/strong>: Marnix.Enting@mil.be<\/a><\/p>\n

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Marnix is a researcher in the Robotics and Autonomous Systems unit of the Department of Mechanics at the Royal Military Academy of Belgium. His research is focused on developing multi-spectral 3D reconstruction solutions to enhance operator control for Explosive Ordnance Disposal.<\/p>\n

He earned his Bachelor\u2019s in Aerospace Engineering at Delft University of Technology<\/a>, followed by a Master\u2019s in Space Engineering and finally Robotics at the same institution, graduating in 2024. For his master\u2019s thesis, Marnix developed a Neural Radiance Field (NeRF) method for 3D reconstruction of autonomous driving scenarios, with a focus on assessing its compression capabilities compared to traditional methods.<\/p>\n

During his studies, he developed an interest in Robotics and Computer Vision while participating in various extracurricular activities. This hands-on experience sparked his curiosity about perception systems and their role in autonomous decision-making.<\/p>\n

In 2025, Marnix joined the Belgian Royal Military Academy to work on the GUIDED<\/a> project, which focuses on making EOD operations safer, more efficient, and user-friendly, particularly by improving threat detection and reducing the mental burden on operators.<\/p>\n

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