no code implementations • 28 Jan 2020 • Caner Sahin, Guillermo Garcia-Hernando, Juil Sock, Tae-Kyun Kim
In this paper, we present the first comprehensive and most recent review of the methods on object pose recovery, from 3D bounding box detectors to full 6D pose estimators.
no code implementations • 11 Mar 2019 • Caner Sahin, Guillermo Garcia-Hernando, Juil Sock, Tae-Kyun Kim
6D object pose estimation is an important task that determines the 3D position and 3D rotation of an object in camera-centred coordinates.
no code implementations • 9 Oct 2018 • Tomas Hodan, Rigas Kouskouridas, Tae-Kyun Kim, Federico Tombari, Kostas Bekris, Bertram Drost, Thibault Groueix, Krzysztof Walas, Vincent Lepetit, Ales Leonardis, Carsten Steger, Frank Michel, Caner Sahin, Carsten Rother, Jiri Matas
The workshop featured four invited talks, oral and poster presentations of accepted workshop papers, and an introduction of the BOP benchmark for 6D object pose estimation.
1 code implementation • ECCV 2018 • Tomas Hodan, Frank Michel, Eric Brachmann, Wadim Kehl, Anders Glent Buch, Dirk Kraft, Bertram Drost, Joel Vidal, Stephan Ihrke, Xenophon Zabulis, Caner Sahin, Fabian Manhardt, Federico Tombari, Tae-Kyun Kim, Jiri Matas, Carsten Rother
We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image.
no code implementations • 1 Aug 2018 • Caner Sahin, Tae-Kyun Kim
Intra-class variations, distribution shifts among source and target domains are the major challenges of category-level tasks.
no code implementations • 11 Jun 2018 • Juil Sock, Kwang In Kim, Caner Sahin, Tae-Kyun Kim
Our architecture jointly learns multiple sub-tasks: 2D detection, depth, and 3D pose estimation of individual objects; and joint registration of multiple objects.
no code implementations • ICCV 2017 • Vassileios Balntas, Andreas Doumanoglou, Caner Sahin, Juil Sock, Rigas Kouskouridas, Tae-Kyun Kim
In this paper we examine the effects of using object poses as guidance to learning robust features for 3D object pose estimation.
no code implementations • 10 Jun 2017 • Caner Sahin, Tae-Kyun Kim
A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modality.
no code implementations • 9 Jan 2017 • Caner Sahin, Rigas Kouskouridas, Tae-Kyun Kim
The iterative refinement is accomplished based on finer (smaller) parts that are represented with more discriminative control point descriptors by using our Iterative Hough Forest.
no code implementations • 8 Mar 2016 • Caner Sahin, Rigas Kouskouridas, Tae-Kyun Kim
State-of-the-art techniques proposed for 6D object pose recovery depend on occlusion-free point clouds to accurately register objects in 3D space.