Object pose recovery has gained increasing attention in the computer vision field as it has
become an important problem in rapidly evolving technological areas related to autonomous …

Nowadays, computer vision with 3D (dimension) object detection and 6D (degree of
freedom) pose assumptions are widely discussed and studied in the field. In the 3D object …

Abstract 6D pose estimation from a single RGB image is a fundamental task in computer
vision. The current top-performing deep learning-based methods rely on an indirect strategy …

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 …

We present a new method for estimating the 6D pose of rigid objects with available 3D
models from a single RGB input image. The method is applicable to a broad range of …

We address the task of 6D multi-object pose: given a set of known 3D objects and an RGB or
RGB-D input image, we detect and estimate the 6D pose of each object. We propose a new …

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 …

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 …