We present the evaluation methodology datasets and results of the BOP Challenge 2023 the
fifth in a series of public com-petitions organized to capture the state of the art in model …

We present the evaluation methodology, datasets and results of the BOP Challenge 2022,
the fourth in a series of public competitions organized with the goal to capture the status quo …

This paper presents the evaluation methodology, datasets, and results of the BOP
Challenge 2020, the third in a series of public competitions organized with the goal to …

We present an approach to learn dense, continuous 2D-3D correspondence distributions
over the surface of objects from data with no prior knowledge of visual ambiguities like …

Six-dimensional object detection of rigid objects is a problem especially relevant for quality
control and robotic manipulation in industrial contexts. This work is a survey of the state of …

Current 6D pose estimation methods focus on handling objects that are previously trained,
which limits their applications in real dynamic world. To this end, we propose a geometry …

Recent learning methods for object pose estimation require resource-intensive training for
each individual object instance or category hampering their scalability in real applications …

We present a novel and efficient approach to estimate 6D object poses of known objects in
complex scenes represented by point clouds. Our approach is based on the well-known …

We propose a method for estimating the 6DoF pose of a rigid object with an available 3D
model from a single RGB image. Unlike classical correspondence-based methods which …

In this work we present a novel dense-correspondence method for 6DoF object pose
estimation from a single RGB-D image. While many existing data-driven methods achieve …