no code implementations • 20 Jul 2016 • Wadim Kehl, Fausto Milletari, Federico Tombari, Slobodan Ilic, Nassir Navab
We present a 3D object detection method that uses regressed descriptors of locally-sampled RGB-D patches for 6D vote casting.
no code implementations • 20 Jul 2016 • Wadim Kehl, Federico Tombari, Nassir Navab, Slobodan Ilic, Vincent Lepetit
We present a scalable method for detecting objects and estimating their 3D poses in RGB-D data.
no code implementations • 26 Aug 2016 • Wadim Kehl, Tobias Holl, Federico Tombari, Slobodan Ilic, Nassir Navab
Volume-based reconstruction is usually expensive both in terms of memory consumption and runtime.
1 code implementation • ICCV 2017 • Wadim Kehl, Fabian Manhardt, Federico Tombari, Slobodan Ilic, Nassir Navab
We present a novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot.
Ranked #1 on 6D Pose Estimation using RGBD on Tejani
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.
1 code implementation • ECCV 2018 • Fabian Manhardt, Wadim Kehl, Nassir Navab, Federico Tombari
We present a novel approach for model-based 6D pose refinement in color data.
no code implementations • CVPR 2019 • Fabian Manhardt, Wadim Kehl, Adrien Gaidon
We present a deep learning method for end-to-end monocular 3D object detection and metric shape retrieval.
no code implementations • ICCV 2019 • Sergey Zakharov, Wadim Kehl, Slobodan Ilic
We present a novel approach to tackle domain adaptation between synthetic and real data.
no code implementations • 9 Apr 2019 • Sergey Zakharov, Wadim Kehl, Benjamin Planche, Andreas Hutter, Slobodan Ilic
In this paper, we address the problem of 3D object instance recognition and pose estimation of localized objects in cluttered environments using convolutional neural networks.
no code implementations • CVPR 2017 • Wadim Kehl, Federico Tombari, Slobodan Ilic, Nassir Navab
We present a novel method to track 3D models in color and depth data.
1 code implementation • CVPR 2020 • Sergey Zakharov, Wadim Kehl, Arjun Bhargava, Adrien Gaidon
We present an automatic annotation pipeline to recover 9D cuboids and 3D shapes from pre-trained off-the-shelf 2D detectors and sparse LIDAR data.
no code implementations • 22 Jun 2020 • Hiroharu Kato, Deniz Beker, Mihai Morariu, Takahiro Ando, Toru Matsuoka, Wadim Kehl, Adrien Gaidon
Deep neural networks (DNNs) have shown remarkable performance improvements on vision-related tasks such as object detection or image segmentation.
no code implementations • ECCV 2020 • Deniz Beker, Hiroharu Kato, Mihai Adrian Morariu, Takahiro Ando, Toru Matsuoka, Wadim Kehl, Adrien Gaidon
3D object detection from monocular images is an ill-posed problem due to the projective entanglement of depth and scale.
3D Object Detection 3D Object Detection From Monocular Images +5
no code implementations • 23 Oct 2022 • Sergey Zakharov, Rares Ambrus, Vitor Guizilini, Wadim Kehl, Adrien Gaidon
In this paper, we show that the recent progress in neural rendering enables a new unified approach we call Photo-realistic Neural Domain Randomization (PNDR).
no code implementations • CVPR 2023 • Lijin Yang, Quan Kong, Hsuan-Kung Yang, Wadim Kehl, Yoichi Sato, Norimasa Kobori
Compositional temporal grounding is the task of localizing dense action by using known words combined in novel ways in the form of novel query sentences for the actual grounding.