no code implementations • 21 Mar 2024 • Shun Iwase, Katherine Liu, Vitor Guizilini, Adrien Gaidon, Kris Kitani, Rares Ambrus, Sergey Zakharov
We present a 3D shape completion method that recovers the complete geometry of multiple objects in complex scenes from a single RGB-D image.
no code implementations • 20 Feb 2024 • Takuya Ikeda, Sergey Zakharov, Tianyi Ko, Muhammad Zubair Irshad, Robert Lee, Katherine Liu, Rares Ambrus, Koichi Nishiwaki
This paper addresses the challenging problem of category-level pose estimation.
no code implementations • 19 Oct 2023 • Mayank Lunayach, Sergey Zakharov, Dian Chen, Rares Ambrus, Zsolt Kira, Muhammad Zubair Irshad
In this work, we address the challenging task of 3D object recognition without the reliance on real-world 3D labeled data.
1 code implementation • ICCV 2023 • Muhammad Zubair Irshad, Sergey Zakharov, Katherine Liu, Vitor Guizilini, Thomas Kollar, Adrien Gaidon, Zsolt Kira, Rares Ambrus
NeO 360's representation allows us to learn from a large collection of unbounded 3D scenes while offering generalizability to new views and novel scenes from as few as a single image during inference.
Ranked #1 on Generalizable Novel View Synthesis on NERDS 360
no code implementations • CVPR 2023 • Stephen Tian, Yancheng Cai, Hong-Xing Yu, Sergey Zakharov, Katherine Liu, Adrien Gaidon, Yunzhu Li, Jiajun Wu
Learned visual dynamics models have proven effective for robotic manipulation tasks.
no code implementations • ICCV 2023 • Vitor Guizilini, Igor Vasiljevic, Jiading Fang, Rares Ambrus, Sergey Zakharov, Vincent Sitzmann, Adrien Gaidon
In this work, we propose to use the multi-view photometric objective from the self-supervised depth estimation literature as a geometric regularizer for volumetric rendering, significantly improving novel view synthesis without requiring additional information.
1 code implementation • CVPR 2023 • Nick Heppert, Muhammad Zubair Irshad, Sergey Zakharov, Katherine Liu, Rares Andrei Ambrus, Jeannette Bohg, Abhinav Valada, Thomas Kollar
We present CARTO, a novel approach for reconstructing multiple articulated objects from a single stereo RGB observation.
1 code implementation • ICCV 2023 • Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, Carl Vondrick
We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image.
no code implementations • 12 Dec 2022 • Sergey Zakharov, Rares Ambrus, Katherine Liu, Adrien Gaidon
Compact and accurate representations of 3D shapes are central to many perception and robotics tasks.
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).
2 code implementations • 27 Jul 2022 • Muhammad Zubair Irshad, Sergey Zakharov, Rares Ambrus, Thomas Kollar, Zsolt Kira, Adrien Gaidon
A novel disentangled shape and appearance database of priors is first learned to embed objects in their respective shape and appearance space.
3D Shape Reconstruction From A Single 2D Image 6D Pose Estimation +4
no code implementations • 22 Jul 2022 • Prafull Sharma, Ayush Tewari, Yilun Du, Sergey Zakharov, Rares Ambrus, Adrien Gaidon, William T. Freeman, Fredo Durand, Joshua B. Tenenbaum, Vincent Sitzmann
We present a method to map 2D image observations of a scene to a persistent 3D scene representation, enabling novel view synthesis and disentangled representation of the movable and immovable components of the scene.
no code implementations • 12 Jul 2022 • Colton Stearns, Davis Rempe, Jie Li, Rares Ambrus, Sergey Zakharov, Vitor Guizilini, Yanchao Yang, Leonidas J Guibas
In this work, we develop a holistic representation of traffic scenes that leverages both spatial and temporal information of the actors in the scene.
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.
no code implementations • 8 May 2022 • Cameron Smith, Hong-Xing Yu, Sergey Zakharov, Fredo Durand, Joshua B. Tenenbaum, Jiajun Wu, Vincent Sitzmann
Neural scene representations, both continuous and discrete, have recently emerged as a powerful new paradigm for 3D scene understanding.
no code implementations • CVPR 2022 • Vitor Guizilini, Rares Ambrus, Dian Chen, Sergey Zakharov, Adrien Gaidon
Experiments on the KITTI and DDAD datasets show that our DepthFormer architecture establishes a new state of the art in self-supervised monocular depth estimation, and is even competitive with highly specialized supervised single-frame architectures.
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 • 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 • 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 • ICCV 2019 • Sergey Zakharov, Wadim Kehl, Slobodan Ilic
We present a novel approach to tackle domain adaptation between synthetic and real data.
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
no code implementations • 9 Oct 2018 • Benjamin Planche, Sergey Zakharov, Ziyan Wu, Andreas Hutter, Harald Kosch, Slobodan Ilic
Applying our approach to object recognition from texture-less CAD data, we present a custom generative network which fully utilizes the purely geometrical information to learn robust features and achieve a more refined mapping for unseen color images.
no code implementations • 16 May 2018 • Mai Bui, Sergey Zakharov, Shadi Albarqouni, Slobodan Ilic, Nassir Navab
By combining the strengths of manifold learning using triplet loss and pose regression, we could either estimate the pose directly reducing the complexity compared to NN search, or use learned descriptor for the NN descriptor matching.
no code implementations • 24 Apr 2018 • Sergey Zakharov, Benjamin Planche, Ziyan Wu, Andreas Hutter, Harald Kosch, Slobodan Ilic
With the increasing availability of large databases of 3D CAD models, depth-based recognition methods can be trained on an uncountable number of synthetically rendered images.
no code implementations • 27 Feb 2017 • Benjamin Planche, Ziyan Wu, Kai Ma, Shanhui Sun, Stefan Kluckner, Terrence Chen, Andreas Hutter, Sergey Zakharov, Harald Kosch, Jan Ernst
Recent progress in computer vision has been dominated by deep neural networks trained over large amounts of labeled data.