title: BOP Challenge 2024 on Model-Based and Model-Free 6D Object Pose
    Estimation
summary: We present the evaluation methodology, datasets and results of the BOP
  Challenge 2024, the sixth in a series of public competitions organized to
  capture the state of the art in 6D object pose estimation and related tasks. In
  2024, our goal was to transition BOP from lab-like setups to real-world
  scenarios. First, we introduced new model-free tasks, where no 3D object models
  are available and methods need to onboard objects just from provided reference
  videos. Second, we defined a new, more practical 6D object detection task where
  identities of objects visible in a test image are not provided as input. Third,
  we introduced new BOP-H3 datasets recorded with high-resolution sensors and
  AR/VR headsets, closely resembling real-world scenarios. BOP-H3 include 3D
  models and onboarding videos to support both model-based and model-free tasks.
  Participants competed on seven challenge tracks, each defined by a task, object
  onboarding setup, and dataset group. Notably, the best 2024 method for
  model-based 6D localization of unseen objects (FreeZeV2.1) achieves 22% higher
  accuracy on BOP-Classic-Core than the best 2023 method (GenFlow), and is only
  4% behind the best 2023 method for seen objects (GPose2023) although being
  significantly slower (24.9 vs 2.7s per image). A more practical 2024 method for
  this task is Co-op which takes only 0.8s per image and is 25X faster and 13%
  more accurate than GenFlow. Methods have a similar ranking on 6D detection as
  on 6D localization but higher run time. On model-based 2D detection of unseen
  objects, the best 2024 method (MUSE) achieves 21% relative improvement compared
  to the best 2023 method (CNOS). However, the 2D detection accuracy for unseen
  objects is still noticealy (-53%) behind the accuracy for seen objects
  (GDet2023). The online evaluation system stays open and is available at
  http://bop.felk.cvut.cz/
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paper_title: BOP Challenge 2024 on Model-Based and Model-Free 6D Object Pose
    Estimation
paper_summary: We present the evaluation methodology, datasets and results of the BOP
  Challenge 2024, the sixth in a series of public competitions organized to
  capture the state of the art in 6D object pose estimation and related tasks. In
  2024, our goal was to transition BOP from lab-like setups to real-world
  scenarios. First, we introduced new model-free tasks, where no 3D object models
  are available and methods need to onboard objects just from provided reference
  videos. Second, we defined a new, more practical 6D object detection task where
  identities of objects visible in a test image are not provided as input. Third,
  we introduced new BOP-H3 datasets recorded with high-resolution sensors and
  AR/VR headsets, closely resembling real-world scenarios. BOP-H3 include 3D
  models and onboarding videos to support both model-based and model-free tasks.
  Participants competed on seven challenge tracks, each defined by a task, object
  onboarding setup, and dataset group. Notably, the best 2024 method for
  model-based 6D localization of unseen objects (FreeZeV2.1) achieves 22% higher
  accuracy on BOP-Classic-Core than the best 2023 method (GenFlow), and is only
  4% behind the best 2023 method for seen objects (GPose2023) although being
  significantly slower (24.9 vs 2.7s per image). A more practical 2024 method for
  this task is Co-op which takes only 0.8s per image and is 25X faster and 13%
  more accurate than GenFlow. Methods have a similar ranking on 6D detection as
  on 6D localization but higher run time. On model-based 2D detection of unseen
  objects, the best 2024 method (MUSE) achieves 21% relative improvement compared
  to the best 2023 method (CNOS). However, the 2D detection accuracy for unseen
  objects is still noticealy (-53%) behind the accuracy for seen objects
  (GDet2023). The online evaluation system stays open and is available at
  http://bop.felk.cvut.cz/
paper_ai_summary: The BOP Challenge 2024 evaluates 6D object pose estimation and detection in real-world scenarios, introducing model-free tasks and high-resolution datasets, with improved methods for unseen object localization and detection.
paper_ai_keywords: 6D object pose estimation 6D object detection BOP Challenge model-free tasks high-resolution sensors AR/VR headsets BOP-H3 datasets FreeZeV2.1 GenFlow GPose2023 Co-op MUSE CNOS GDet2023