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
