BOP Challenge 2024 on Model-Based and Model-Free 6D Object Pose
  Estimation
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/
67f4b54ddf6757586b52a4eb
Van Nguyen Nguyen
admin_assigned
2025-04-08T10:39:20.915Z
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Van Nguyen NGUYEN
user
nv-nguyen
67f4b54ddf6757586b52a4ec
Stephen Tyree
admin_assigned
2025-04-08T10:39:27.341Z
66d9c590947b2e8abcd21368
https://cdn-avatars.huggingface.co/v1/production/uploads/66d9c590947b2e8abcd21368/Kmcrrl7Kbv9Q1_ZsIy5d9.jpeg
Stephen Tyree
user
swtyree
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Andrew Guo
67f4b54ddf6757586b52a4ee
Mederic Fourmy
admin_assigned
2025-04-08T10:39:37.711Z
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Mederic Fourmy
user
mfourmy
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Anas Gouda
admin_assigned
2025-04-08T10:39:43.578Z
6698fc06d77a8b28703aaa67
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Anas Gouda
user
anas-gouda
67f4b54ddf6757586b52a4f0
Taeyeop Lee
admin_assigned
2025-04-08T10:39:49.136Z
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Taeyeop Lee
user
taeyeop
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Sungphill Moon
claimed_verified
2025-04-17T11:10:37.564Z
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sungphill.moon
user
sp9103
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Hyeontae Son
admin_assigned
2025-04-08T10:40:00.645Z
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Hyeontae Son
user
countywest
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Lukas Ranftl
67f4b54ddf6757586b52a4f4
Jonathan Tremblay
67f4b54ddf6757586b52a4f5
Eric Brachmann
67f4b54ddf6757586b52a4f6
Bertram Drost
67f4b54ddf6757586b52a4f7
Vincent Lepetit
admin_assigned
2025-04-08T10:40:35.068Z
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Vincent Lepetit
user
miettle
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Carsten Rother
67f4b54ddf6757586b52a4f9
Stan Birchfield
67f4b54ddf6757586b52a4fa
Jiri Matas
admin_assigned
2025-04-08T10:41:24.478Z
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Jiri Matas
user
jmatas
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Yann Labbe
67f4b54ddf6757586b52a4fc
Martin Sundermeyer
admin_assigned
2025-04-08T10:41:04.956Z
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Martin Sundermeyer
user
ma-sundermeyer
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Tomas Hodan
admin_assigned
2025-04-08T10:40:57.037Z
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Tomas Hodan
user
tomhodan
BOP Challenge 2024 on Model-Based and Model-Free 6D Object Pose
  Estimation
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/
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.
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