Search Results for author: Sergey Zakharov

Found 11 papers, 2 papers with code

Unsupervised Discovery and Composition of Object Light Fields

no code implementations8 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.

Novel View Synthesis Scene Understanding

Multi-Frame Self-Supervised Depth with Transformers

no code implementations15 Apr 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.

Frame Monocular Depth Estimation

Autolabeling 3D Objects with Differentiable Rendering of SDF Shape Priors

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.

3D Object Instance Recognition and Pose Estimation Using Triplet Loss with Dynamic Margin

no code implementations9 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.

Pose Estimation

HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects

no code implementations5 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.

6D Pose Estimation 6D Pose Estimation using RGB

DPOD: 6D Pose Object Detector and Refiner

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.

3D Object Detection 6D Pose Estimation +1

Seeing Beyond Appearance - Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition

no code implementations9 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.

Denoising Domain Adaptation +1

When Regression Meets Manifold Learning for Object Recognition and Pose Estimation

no code implementations16 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.

Multi-Task Learning Object Recognition +2

Keep it Unreal: Bridging the Realism Gap for 2.5D Recognition with Geometry Priors Only

no code implementations24 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.

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