Elias Marks

Elias Marks

Ph.D. student at the Photogrammetry and Robotics Lab of the University of Bonn. M.Sc. in Artificial Intelligence and Robotics at 'La Sapienza' University of Rome.

I’m a Ph.D. student in Computer Vision and Robotics at the Photogrammetry and Robotics Lab of the University of Bonn, where I develop 3D perception and reconstruction methods to help us understand and automate complex real-world environments such as agricultural environments.

My work bridges research and application by designing novel algorithms that transform multi-sensor data into 3D representations that can be leveraged to solve real-world problems autonomously. This includes experience with both deep learning and traditional methods for 3D vision, multi-view geometry, and large-scale field data collection and calibration.

My passion for robotics started with early Arduino projects and grew through a B.Eng. in Automation and Robotics in Germany, where I explored object detection and tracking and worked at Fraunhofer on localization and navigation for service robots. Later, during my M.Sc. in AI and Robotics in Rome, I deepened my expertise in computer vision and motion planning, completing a thesis on perception and path planning for mobile robots.

My research has been recognized with multiple Best Paper Awards at leading robotics and perception venues, including ICRA, IROS, and RA-L.

Passionate about developing new ideas to solve concrete problems, I aim to apply my expertise in 3D computer vision, perception, and neural reconstruction to build intelligent, real-world systems.

News

  • [Oct. 2025] IEEE/CVF ICCV - Our paper “Tree Skeletonization from 3D Point Clouds by Denoising Diffusion” got accepted at ICCV 2025.
  • [Oct. 2024] IEEE/RSJ IROS - Best Paper Award on Agri-Robotics for our work “BonnBeetClouds3D: A Dataset Towards Point Cloud-Based Organ-Level Phenotyping of Sugar Beet Plants Under Real Field Conditions”.
  • [May. 2024] IEEE ICRA - Finalist of the Best Service Robotics Paper for our work “Efficient and Accurate Transformer-Based 3D Shape Completion and Reconstruction of Fruits for Agricultural Robots”.
  • [Dec. 2023] IEEE RA-L - Honorable Mention for “High Precision Leaf Instance Segmentation in Point Clouds Obtained Under Real Field Conditions”.
  • [Jun. 2023] IEEE ICRA - Outstanding Automation Paper Award for “Target-Aware Implicit Mapping for Agricultural Crop Inspection”.
  • [Nov. 2022] IEEE/RSJ IROS - Finalist of the Best Paper Award on Agri-Robotics for our work “Constrastive 3D Shape Completion and Reconstruction for Agricultural Robots using RGB-D Frames”.
  • [May. 2022] IEEE ICRA - Outstanding Automation Paper Award for “Precise 3D Reconstruction of Plants from UAV Imagery Combining Bundle Adjustment and Template Matching”.