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Publikation

FruitBin: A Tunable Large-Scale Dataset for Advancing 6D Pose Estimation in Fruit Bin-Picking Automation

Guillaume Duret; Mohamed Mahmoud Sayed Shelkamy Ali; Nicolas Cazin; Danylo Mazurak; Anna Samsonenko; Alexandre Chapin; Florence Zara; Emmanuel Dellandréa; Liming Chen; Jan Peters
In: Alessio Del Bue; Cristian Canton; Jordi Pont-Tuset; Tatiana Tommasi (Hrsg.). Computer Vision - ECCV 2024 Workshops - Milan, Italy, September 29-October 4, 2024, Proceedings, Part I. Computer Vision Systems (CVS), Pages 73-90, Lecture Notes in Computer Science, Vol. 15623, Springer, 2024.

Zusammenfassung

Bin picking, essential in various industries, depends on accu- rate object segmentation and 6D pose estimation for successful grasping and manipulation. Existing datasets for deep learning methods often in- volve simple scenarios with singular objects or minimal clustering, reduc- ing the effectiveness of benchmarking in bin picking scenarios. To address this, we introduce FruitBin, a dataset featuring over 1 million images and 40 million 6D poses in challenging fruit bin scenarios. FruitBin encom- passes all main challenges, such as symmetric and asymmetric fruits, textured and non-textured objects, and varied lighting conditions. We demonstrate its versatility by creating customizable benchmarks for new scene and camera viewpoint generalization, each divided into four occlu- sion levels to study occlusion robustness. Evaluating three 6D pose es- timation models—PVNet, DenseFusion, and GDRNPP—highlights the limitations of current state-of-the-art models and quantitatively shows the impact of occlusion. Additionally, FruitBin is integrated within a robotic software, enabling direct testing and benchmarking of vision models for robot learning and grasping. The associated code and dataset can be found on: https://gitlab.liris.cnrs.fr/gduret/fruitbin

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