The workshop covers topics related to estimating the 6D object pose (3D translation and 3D rotation) from RGB/RGB-D images, which is an important
problem for application fields such as robotic manipulation, augmented reality and autonomous driving. The introduction
of RGB-D sensors, advent of deep learning, and novel data generation pipelines led to substantial improvements in
object pose estimation. Yet there remain challenges to address such as robustness against occlusion and clutter,
scalability to multiple objects, effective synthetic-to-real domain transfer, fast and reliable object learning/modeling,
and handling non-rigid objects and object categories. Addressing these challenges is necessary for achieving reliable solutions that can be deployed in real-world settings.
In conjunction with the workshop, we organize the BOP Challenge 2023, the fifth in a series
of public competitions with the goal of capturing the status quo in the field of object pose estimation.
The 2023 challenge introduces new tasks of detection, segmentation and pose estimation of objects unseen during
training. By introducing these tasks, we wish to encourage development of practical methods that can
learn novel objects on the fly just from provided 3D models, which is an important capability for industrial setups.
The workshop features invited talks by experts in the field, presentation of the BOP Challenge 2023 results, and oral/poster presentations of
accepted workshop papers and of papers invited from the main conference.
The workshop is expected to be attended by people working on related topics in both academia and industry.
Previous workshop editions: 1st edition (ICCV 2015), 2nd edition (ECCV 2016), 3rd edition (ICCV 2017), 4th edition (ECCV 2018), 5th edition (ICCV 2019), 6th edition (ECCV 2020), 7th edition (ECCV 2022).