2 code implementations • ICCV 2019 • Sergey Zakharov, Ivan Shugurov, Slobodan Ilic
An additional RGB pose refinement of the initial pose estimates is performed using a custom deep learning-based refinement scheme.
Ranked #8 on 6D Pose Estimation using RGB on LineMOD
1 code implementation • 27 Sep 2022 • Hao Yu, Ji Hou, Zheng Qin, Mahdi Saleh, Ivan Shugurov, Kai Wang, Benjamin Busam, Slobodan Ilic
More specifically, 3D structures of the whole frame are first represented by our global PPF signatures, from which structural descriptors are learned to help geometric descriptors sense the 3D world beyond local regions.
no code implementations • 5 Apr 2019 • Roman Kaskman, Sergey Zakharov, Ivan Shugurov, Slobodan Ilic
We also present a set of benchmarks to test various desired detector properties, particularly focusing on scalability with respect to the number of objects and resistance to changing light conditions, occlusions and clutter.
no code implementations • 9 Mar 2022 • Fu Li, Hao Yu, Ivan Shugurov, Benjamin Busam, Shaowu Yang, Slobodan Ilic
Pose estimation of 3D objects in monocular images is a fundamental and long-standing problem in computer vision.
no code implementations • CVPR 2022 • Ivan Shugurov, Fu Li, Benjamin Busam, Slobodan Ilic
We present a novel one-shot method for object detection and 6 DoF pose estimation, that does not require training on target objects.
no code implementations • 6 Jul 2022 • Ivan Shugurov, Ivan Pavlov, Sergey Zakharov, Slobodan Ilic
This paper introduces a novel multi-view 6 DoF object pose refinement approach focusing on improving methods trained on synthetic data.
no code implementations • 6 Jul 2022 • Ivan Shugurov, Sergey Zakharov, Slobodan Ilic
The main conclusions is that RGB excels in correspondence estimation, while depth contributes to the pose accuracy if good 3D-3D correspondences are available.