FruitBin: a tunable large-scale dataset for advancing 6D pose estimation in fruit bin-picking automation - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

FruitBin: a tunable large-scale dataset for advancing 6D pose estimation in fruit bin-picking automation

Guillaume Duret
Mahmoud Ali
  • Fonction : Auteur
Nicolas Cazin
  • Fonction : Auteur
  • PersonId : 964766
Danylo Mazurak
  • Fonction : Auteur
Anna Samsonenko
  • Fonction : Auteur
Alexandre Chapin
  • Fonction : Auteur
Florence Zara
Emmanuel Dellandréa
Jan Peters
  • Fonction : Auteur

Résumé

Bin picking, essential in various industries, depends on accurate object segmentation and 6D pose estimation for successful grasping and manipulation. Existing datasets for deep learning methods often involve simple scenarios with singular objects or minimal clustering, reducing 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 encompasses 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 occlusion levels to study occlusion robustness. Evaluating three 6D pose estimation 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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Dates et versions

hal-04683842 , version 1 (02-09-2024)

Identifiants

  • HAL Id : hal-04683842 , version 1

Citer

Guillaume Duret, Mahmoud Ali, Nicolas Cazin, Danylo Mazurak, Anna Samsonenko, et al.. FruitBin: a tunable large-scale dataset for advancing 6D pose estimation in fruit bin-picking automation. 9th International Workshop on Recovering 6D Object Pose (R6D), Sep 2024, Milan (Italie), France. ⟨hal-04683842⟩
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