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The goal of BOP is to capture the state of the art in estimating the 6D pose, i.e. 3D translation and 3D rotation, of rigid objects from RGB/RGB-D images. An accurate, fast, robust, scalable and easy-to-train method that solves this task will have a big impact in application fields such as robotics or augmented reality.

6D object pose estimation


M. Sundermeyer, T. Hodaň, Y. Labbé, Gu Wang, E. Brachmann, B. Drost, C. Rother, J. Matas,
BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects
IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW, CV4MR workshop) 2023, Vancouver.

T. Hodaň, M. Sundermeyer, B. Drost, Y. Labbé, E. Brachmann, F. Michel, C. Rother, J. Matas,
BOP Challenge 2020 on 6D Object Localization, European Conference on Computer Vision Workshops (ECCVW) 2020, Glasgow.

T. Hodaň, F. Michel, E. Brachmann, W. Kehl, A. G. Buch, D. Kraft, B. Drost, J. Vidal, S. Ihrke, X. Zabulis, C. Sahin, F. Manhardt, F. Tombari, T.-K. Kim, J. Matas, C. Rother,
BOP: Benchmark for 6D Object Pose Estimation, European Conference on Computer Vision (ECCV) 2018, Munich.
[PDF, SLIDES, POSTER, BIB] The online evaluation system has been developed by T. Hodaň and A. Melenovský.

BOP toolkit

Download the BOP toolkit with python scripts for reading the standard dataset format, rendering, evaluation etc.