Elias Marks
Papers
12
Total Citations
182
H-Index
7
About
Elias Marks is a leading researcher at the intersection of agricultural robotics and computer vision, with a primary focus on developing robust perception systems for autonomous farming. His work addresses critical challenges in modern agriculture, including the need for sustainable intensification and labor automation. Marks’s major contributions center on 3D shape completion and reconstruction of fruits and plants under real-world field conditions—a notoriously difficult problem due to occlusions and variable lighting. His pioneering research, such as the highly cited "PhenoBench" dataset (48 citations) and "Contrastive 3D Shape Completion" (42 citations), has established benchmarks that enable robots to accurately estimate fruit volumes, poses, and semantic maps. He has also advanced metric-semantic mapping for long-term localization and developed multi-sensor, multi-temporal datasets for in-field phenotyping. With over 180 total citations across his top papers, Marks’s work directly supports applications from autonomous harvesting and yield estimation to high-throughput phenotyping. His creation of the BonnBeetClouds3D dataset and his transformer-based approaches for fruit reconstruction represent notable achievements, positioning him as a key innovator in making agricultural robots more perceptive, efficient, and sustainable.
Research Focus
Key Achievements
Top Papers
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- 5Target-Aware Implicit Mapping for Agricultural Crop Inspection13 citations · 2023
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- 7Fruit Tracking Over Time Using High-Precision Point Clouds10 citations · 2023
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