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<!DOCTYPE html>
<html class="no-js" lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Anna Vorontsova | CV </title>
<link rel="shortcut icon" href="favicon.ico" type="image/x-icon">
<link rel="icon" href="favicon.ico" type="image/x-icon">
<link href="https://fonts.googleapis.com/css?family=Lato:300,400,700,900" rel="stylesheet">
<link rel="stylesheet" href="libs/font-awesome/css/font-awesome.min.css">
<link href="css/bootstrap.min.css" rel="stylesheet">
<link href="css/styles.css" rel="stylesheet">
<link href="scss/styles.scss" rel="stylesheet">
</head>
<body>
<div id="mobile-menu-open" class="shadow-large">
<i class="fa fa-bars" aria-hidden="true"></i>
</div>
<!-- End #mobile-menu-toggle -->
<header>
<div id="mobile-menu-close">
<span>Close</span> <i class="fa fa-times" aria-hidden="true"></i>
</div>
<ul id="menu" class="shadow">
<li>
<a href="#about">About</a>
</li>
<li>
<a href="#experience">Experience</a>
</li>
<li>
<a href="#education">Education</a>
</li>
<li>
<a href="#projects">Projects</a>
</li>
<li>
<a href="#skills">Skills</a>
</li>
</ul>
</header>
<!-- End header -->
<div id="lead">
<div id="lead-content">
<h1>Anna Vorontsova</h1>
<h2>Data Scientist / AI Researcher, Computer Vision</h2>
<a href="Resume_AnnaVorontsova.pdf" class="btn-rounded-white">Download Resume</a>
</div>
<!-- End #lead-content -->
<div id="lead-overlay"></div>
<div id="lead-down">
<span>
<i class="fa fa-chevron-down" aria-hidden="true"></i>
</span>
</div>
<!-- End #lead-down -->
</div>
<!-- End #lead -->
<div id="about">
<h2 class="heading">About Me</h2>
<div class="container">
<div class="row">
<div class="personal-info col-md-4">
<div class="personal-image">
<img src="images/anna-vorontsova-medium.png"/>
</div>
</div>
<div class="col-md-8">
<p>
I am a Deep Learning Expert at NEURA Robotics. I received an M.Sc. in Data
Science, and a bachelor's degree in Applied Mathematics from one of the best Russian
universities. I have worked as a Research Scientist at Samsung Research for 5 years.
Overall, I have over 6 years of both industrial and research experience,
focusing on <b>2D and 3D computer vision</b> throughout my career.
I co-authored 15+ research papers accepted to
top-tier conferences, prepared a number of technical patents, and gained hands-on experience with
various deep learning models <b>(CNN, RNN, Transformer)</b> and frameworks <b>(PyTorch, Tensorflow)</b>.
</p>
</div>
</div>
</div>
</div>
<!-- End #about -->
<div id="experience" class="background-alt">
<h2 class="heading">Experience</h2>
<div id="experience-timeline">
<div data-date="May 2024 – now">
<h3>NEURA Robotics</h3>
<h4>Deep Learning Expert, 2D/3D Computer Vision</h4>
<p>
Solved various 2D and 3D computer vision tasks in robotic scenarios.
Adapted existing methods and/or developed new methods addressing 3D reconstruction,
object segmentation, antipodal and suction grasp generation.
Generated data for training and benchmarking developed methods.
Contributed to documentation on AI Safety,
wrote customer manuals and internal guides.
</p>
</div>
<div data-date="Oct 2018 – Apr 2024">
<h3>Samsung Research</h3>
<h4>AI Researcher, 2D/3D Computer Vision</h4>
<p>
Developed state-of-the-art algorithms addressing 2D and 3D computer vision tasks: SLAM,
visual and sensor-based localization, 3D reconstruction of indoor scenes, depth estimation,
object segmentation, 2D and 3D object detection.
Formulated scientific hypotheses and conducted experiments to prove them. Wrote a number
of academic papers accepted to top-tier CV and robotics conferences such as CVPR, ECCV,
WACV, IROS. Overall, contributed to 16 papers. <a href="https://neurips.cc/Conferences/2022/DatasetBenchmarkProgramCommittee">Outstanding Reviewer</a> at NeurIPS 2022
Datasets and Benchmarks track. Own several international patents on technical inventions.
Developed demos and PoCs: visual odometry, visual indoor navigation,
object weight measurement based on RGB-D data. Collected, labeled
and prepared data for prototyping and research purposes: visual navigation,
3D reconstruction of indoor scenes, visual analytics for retail.
Mastered all kinds of writing: academic manuscripts, annual reports, patents, tasks
for data annotators, documentation, and internal guides.
</p>
</div>
<div data-date="June 2017 – Oct 2018">
<h3>Rambler&Co</h3>
<h4>Research Intern / Junior Data Scientist, Computer Vision</h4>
<p>
Contributed to a project on cinema visitor monitoring based on video surveillance data.
Developed algorithms based on deep neural networks (segmentation, classification, detection,
tracking). Collected, labeled, and prepared training data. Conducted experiments and
presented the results in the form of reports and slides.
What started as a small toy project run by one intern (me), was considered so successful
that it convinced top management to create a computer vision department, mostly to develop
and maintain the cinema monitoring system. The implemented solution was used to collect
statistics in over 700 cinema halls in Russia.
</p>
</div>
</div>
</div>
<!-- End #experience -->
<div id="education">
<h2 class="heading">Education</h2>
<div class="education-block">
<h3>HSE University</h3>
<span class="education-date">Sep 2018 - June 2020</span>
<h4>Master of Data Science</h4>
<p>
Completed courses: Bayesian Networks, Functional Analysis,
Convex Optimization, Autonomous Driving
<br>
Thesis: Visual Odometry with Ego-motion Sampling
<br>
GPA: 4.5 (8.68 / 10)
</p>
</div>
<!-- End .education-block -->
<div class="education-block">
<h3>Yandex School of Data Analysis </h3>
<span class="education-date">Sep 2018 - June 2020</span>
<h4>Data Science, Advanced track</h4>
<p>
</p>
</div>
<!-- End .education-block -->
<div class="education-block">
<h3>HSE University</h3>
<span class="education-date">Sep 2014 - June 2018</span>
<h4>Bachelor of Applied Mathematics, Machine Learning and Applications track</h4>
<p>
Completed courses: Machine Learning, Deep Learning,
Statistical Learning Theory, NLP, Computer Vision, Reinforcement Learning,
Bayesian ML, Advanced Algorithms and Data Structures,
Probability Theory and Statistics
<br>
Thesis: Person Re-identification Based on Visual Attributes
<br>
GPA: 4.69 (8.1 / 10)
</p>
</div>
<!-- End .education-block -->
</div>
<!-- End #education -->
<div id="projects" class="background-alt">
<h2 class="heading">Projects & Publications</h2>
<div class="container">
<div class="row">
<div class="project">
<div class="project-image">
<img src="images/projects/unidet3d.png" />
</div>
<!-- End .project-image -->
<div class="project-info">
<h3>UniDet3D: One Transformer for Unified Point Cloud Segmentation </h3>
<h4>2024 Conference on Computer Vision and Pattern Recognition (CVPR) </h4>
<p class="paper-authors">M. Kolodiazhnyi, <b>A. Vorontsova</b>, A. Konushin, D. Rukhovich </p>
<p class="paper-abstract">
UniDet3D is a 3D object detection model trained on a mixture of indoor
datasets. By unifying various label spaces, UniDet3D learns a strong
representation across multiple datasets through a supervised joint training scheme, thus
achieving generalization in various indoor environments.
It outperforms existing 3D object detection methods in 6 indoor benchmarks.
</p>
<div class="social">
<ul>
<li>
<a href="https://arxiv.org/abs/2409.04234" target="_blank">Paper</a>
</li>
<li>
<a href="https://github.com/filaPro/unidet3d" target="_blank"><i class="fa fa-github" aria-hidden="true"></i></a>
</li>
</ul>
</div>
</div>
<!-- End .project-info -->
</div>
<!-- End .project -->
<div class="project">
<div class="project-image">
<img src="images/projects/oneformer3d.png" />
</div>
<!-- End .project-image -->
<div class="project-info">
<h3>OneFormer3D: One Transformer for Unified Point Cloud Segmentation </h3>
<h4>2024 Conference on Computer Vision and Pattern Recognition (CVPR) </h4>
<p class="paper-authors">M. Kolodiazhnyi, <b>A. Vorontsova</b>, A. Konushin, D. Rukhovich </p>
<p class="paper-abstract">
OneFormer3D is a unified, simple, and effective model jointly solving
semantic, instance, and panoptic segmentation of 3D point clouds. The model
is trained end-to-end in a single run with panoptic annotations, and achieves
top performance on all three tasks simultaneously, thereby setting a new state-of-the-art
in several 3D segmentation benchmarks.
</p>
<div class="social">
<ul>
<li>
<a href="https://openaccess.thecvf.com/content/CVPR2024/html/Kolodiazhnyi_OneFormer3D_One_Transformer_for_Unified_Point_Cloud_Segmentation_CVPR_2024_paper.html" target="_blank">Paper</a>
</li>
<li>
<a href="https://github.com/filaPro/oneformer3d" target="_blank"><i class="fa fa-github" aria-hidden="true"></i></a>
</li>
</ul>
</div>
</div>
<!-- End .project-info -->
</div>
<!-- End .project -->
<div class="project">
<div class="project-image">
<img src="images/projects/tetris.png" />
</div>
<!-- End .project-image -->
<div class="project-info">
<h3>TETRIS: Towards Exploring the Robustness of Interactive Segmentation</h3>
<h4>2024 AAAI Conference on Artificial Intelligence (AAAI) </h4>
<p class="paper-authors">A. Moskalenko, V. Shakhuro, <b>A. Vorontsova</b>, A. Konushin, A. Antonov, A. Krapukhin, D. Shepelev, K. Soshin</p>
<p class="paper-abstract">
We conducted a user study of clicking patterns and found that
the standard assumption made in the common evaluation strategy may not hold, making
the accuracy and robustness of existing methods questionable. We propose a novel evaluation
strategy providing a more comprehensive analysis of a model’s performance. Besides, we
introduce a novel benchmark for measuring the robustness of interactive segmentation,
and report the results of an extensive evaluation of numerous models.
</p>
<div class="social">
<ul>
<li>
<a href="https://arxiv.org/abs/2402.06132" target="_blank">Paper</a>
</li>
</ul>
</div>
</div>
<!-- End .project-info -->
</div>
<!-- End .project -->
<div class="project">
<div class="project-image">
<img src="images/projects/super.png" />
</div>
<!-- End .project-image -->
<div class="project-info">
<h3>SUPER: Selfie Undistortion and Head Pose Editing with Identity Preservation</h3>
<h4>2024 International Conference on Image Processing (ICIP) </h4>
<p class="paper-authors">P. Karpikova, A. Spiridonov, <b>A. Vorontsova</b>, A. Yaschenko, E. Radionova, I. Medvedev, A. Limonov</p>
<p class="paper-abstract">
Selfies captured from a short distance might look unnatural due to heavy distortions
and improper posing. We propose SUPER, a novel method of correcting distortions and adjusting
head poses in selfies. SUPER combines generative and rendering approaches
to ensure correct geometry while preserving identity.
</p>
<div class="social">
<ul>
<li>
<a href="https://arxiv.org/abs/2406.12700v1" target="_blank">Paper</a>
</li>
</ul>
</div>
</div>
<!-- End .project-info -->
</div>
<!-- End .project -->
<div class="project">
<div class="project-image">
<img src="images/projects/fawn.png" />
</div>
<!-- End .project-image -->
<div class="project-info">
<h3>FAWN: Floor-And-Walls Normal Regularization for Direct Neural TSDF Reconstruction</h3>
<h4>2024 International Conference on Image Processing (ICIP) </h4>
<p class="paper-authors">A. Sokolova, <b>A. Vorontsova</b>, B. Gabdullin, A. Limonov </p>
<p class="paper-abstract">
FAWN is a modification of truncated signed distance function (TSDF)
reconstruction methods. FAWN takes the standard scene structure in account by detecting
walls and floor in a scene, and penalizing their normals for deviating from the horizontal
and vertical directions. We add FAWN to state-of-the-art TSDF reconstruction
methods and demonstrate a quality gain in a number of indoor benchmarks.
</p>
<div class="social">
<ul>
<li>
<a href="https://arxiv.org/abs/2406.12054" target="_blank">Paper</a>
</li>
</ul>
</div>
</div>
<!-- End .project-info -->
</div>
<!-- End .project -->
<div class="project">
<div class="project-image">
<img src="images/projects/medea.png" />
</div>
<!-- End .project-image -->
<div class="project-info">
<h3>MEDeA: Multi-View Efficient Depth Alignment</h3>
<h4>2024 International Conference on Image Processing (ICIP) </h4>
<p class="paper-authors">M. Artemyev, <b>A. Vorontsova</b>, A. Sokolova, A. Limonov </p>
<p class="paper-abstract">
Single-view depth estimation methods cannot guarantee consistency throughout a sequence of frames.
Minimizing discrepancy across multiple views takes hours, making these methods infeasible.
Our MeDEA takes RGB frames with camera parameters and outputs temporally-consistent depth maps
orders of magnitude faster then previous test-time optimization approaches.
MeDEA sets a new state-of-the-art in indoor benchmarks and handles smartphone-captured data.
</p>
<div class="social">
<ul>
<li>
<a href="https://arxiv.org/abs/2406.12048" target="_blank">Paper</a>
</li>
</ul>
</div>
</div>
<!-- End .project-info -->
</div>
<!-- End .project -->
<div class="project">
<div class="project-image">
<img src="images/projects/td3d.png" />
</div>
<!-- End .project-image -->
<div class="project-info">
<h3>Top-Down Beats Bottom-Up in 3D Instance Segmentation</h3>
<h4>2024 Winter Conference on Applications of Computer Vision (WACV) </h4>
<p class="paper-authors">M. Kolodiazhnyi, D. Rukhovich, <b>A. Vorontsova</b>, A. Konushin </p>
<p class="paper-abstract">
Most 3D instance segmentation methods are bottom-up and typically include
resource-exhaustive post-processing. TD3D is a pioneering cluster-free,
fully-convolutional approach trained end-to-end.
This is the first top-down method outperforming bottom-up approaches in a 3D domain.
It demonstrates outstanding accuracy while being much up to 2.6x faster on inference
than the current state-of-the-art grouping-based approaches.
</p>
<div class="social">
<ul>
<li>
<a href="https://openaccess.thecvf.com/content/WACV2024/html/Kolodiazhnyi_Top-Down_Beats_Bottom-Up_in_3D_Instance_Segmentation_WACV_2024_paper.html" target="_blank">Paper</a>
</li>
<li>
<a href="https://github.com/SamsungLabs/td3d" target="_blank"><i class="fa fa-github" aria-hidden="true"></i></a>
</li>
</ul>
</div>
</div>
<!-- End .project-info -->
</div>
<!-- End .project -->
<div class="project">
<div class="project-image">
<img src="images/projects/negil.png" />
</div>
<!-- End .project-image -->
<div class="project-info">
<h3>Neural Global Illumination for Inverse Rendering</h3>
<h4>2023 International Conference on Image Processing (ICIP) </h4>
<p class="paper-authors">N. Patakin, D. Senushkin, <b>A. Vorontsova</b>, A. Konushin </p>
<p class="paper-abstract">
NeGIL the first neural inverse rendering approach capable of processing
inter-reflections. We formulate a novel neural global illumination model, which
estimates both direct environment light and indirect light as a surface light field,
and build a Monte Carlo differentiable rendering framework. Our framework effectively
handles complex lighting effects and facilitates the end-to-end reconstruction of
physically-based spatially-varying materials.
</p>
</div>
<!-- End .project-info -->
</div>
<!-- End .project -->
<div class="project">
<div class="project-image">
<img src="images/projects/tr3d.png" />
</div>
<!-- End .project-image -->
<div class="project-info">
<h3>TR3D: Towards Real-Time Indoor 3D Object Detection</h3>
<h4>2023 International Conference on Image Processing (ICIP) </h4>
<p class="paper-authors">D. Rukhovich, <b>A. Vorontsova</b>, A. Konushin </p>
<p class="paper-abstract">
TR3D is a fast fully-convolutional 3D object detection model trained
end-to-end, that achieves state-of-the-art results on the standard benchmarks.
Moreover, to take advantage of both point cloud and RGB inputs, we propose an
early fusion of 2D and 3D features. The versatile and efficient fusion module
can be applied to make a conventional 3D object detection method multimodal,
thereby improving its detection accuracy.
</p>
<div class="social">
<ul>
<li>
<a href="https://arxiv.org/abs/2302.02858" target="_blank">Paper</a>
</li>
<li>
<a href="https://github.com/SamsungLabs/tr3d" target="_blank"><i class="fa fa-github" aria-hidden="true"></i></a>
</li>
</ul>
</div>
</div>
<!-- End .project-info -->
</div>
<!-- End .project -->
<div class="project">
<div class="project-image">
<img src="images/projects/contours.jpg" />
</div>
<!-- End .project-image -->
<div class="project-info">
<h3>Contour-based Interactive Segmentation</h3>
<h4>2023 International Joint Conference on Artificial Intelligence (IJCAI) </h4>
<p class="paper-authors">P. Popenova, D. Galeev, <b>A. Vorontsova</b>, A. Konushin </p>
<p class="paper-abstract">
Interactive segmentation can be used to speed up and simplify image editing and labeling.
Most approaches use clicks, which might be inconvenient when selecting small objects.
We present a first-in-class contour-based interactive segmentation approach and demonstrate
that a single contour provides the same accuracy as multiple clicks, thus reducing the
number of interactions.
</p>
<div class="social">
<ul>
<li>
<a href="https://arxiv.org/abs/2302.06353" target="_blank">Paper</a>
</li>
</ul>
</div>
</div>
<!-- End .project-info -->
</div>
<!-- End .project -->
<div class="project">
<div class="project-image">
<img src="images/projects/fcaf3d.png" />
</div>
<!-- End .project-image -->
<div class="project-info">
<h3>FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection</h3>
<h4>2022 European Conference on Computer Vision (ECCV) </h4>
<p class="paper-authors">D. Rukhovich, <b>A. Vorontsova</b>, A. Konushin </p>
<p class="paper-abstract">
FCAF3D is a first-in-class fully convolutional anchor-free indoor 3D object detection method.
FCAF3D can handle large-scale scenes with minimal runtime through a single feed-forward pass.
Moreover, we propose a novel parametrization of oriented bounding boxes that consistently
improves detection accuracy. State-of-the-art on ScanNet, SUN RGB-D, and S3DIS datasets.
</p>
<div class="social">
<ul>
<li>
<a href="https://github.com/anonymous-fcaf3d/anonymous-fcaf3d" target="_blank"><i class="fa fa-github" aria-hidden="true"></i></a>
</li>
<li>
<a href="https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/6356_ECCV_2022_paper.php" target="_blank">Paper</a>
</li>
</ul>
</div>
</div>
<!-- End .project-info -->
</div>
<!-- End .project -->
<div class="project">
<div class="project-image">
<img src="images/projects/floorplan.png" />
</div>
<!-- End .project-image -->
<div class="project-info">
<h3>Floorplan-Aware Camera Poses Refinement</h3>
<h4>2022 International Conference on Intelligent Robots and Systems (IROS)</h4>
<p class="paper-authors">A. Sokolova, F. Nikitin, <b>A. Vorontsova</b>, A. Konushin </p>
<p class="paper-abstract">
A technical floorplan depicts walls, partitions, and doors, being a valuable source
of information about the general scene structure. We propose a novel floorplan-aware
3D reconstruction algorithm that extends bundle adjustment, and show that using a
floorplan improves 3D reconstruction quality on the Redwood dataset and our self-captured
data.
</p>
<div class="social">
<ul>
<li>
<a href="https://arxiv.org/abs/2210.04572" target="_blank">Paper</a>
</li>
</ul>
</div>
</div>
<!-- End .project-info -->
</div>
<!-- End .project -->
<div class="project">
<div class="project-image">
<img src="images/projects/imvoxelnet.png" />
</div>
<!-- End .project-image -->
<div class="project-info">
<h3>ImVoxelNet: Image to Voxels Projection for Monocular and Multi-view General-purpose 3D Object Detection</h3>
<h4>2022 Winter Conference on Applications of Computer Vision (WACV)</h4>
<p class="paper-authors">D. Rukhovich, <b>A. Vorontsova</b>, A. Konushin </p>
<p class="paper-abstract">
ImVoxelNet is a fully convolutional 3D object detection method that operates in
monocular and multi-view modes. ImVoxelNet takes an arbitrary number of RGB
images with camera poses as inputs. General-purpose: state-of-the-art
on outdoor (KITTI and nuScenes) and indoor (SUN RGB-D and ScanNet) datasets.
</p>
<div class="social">
<ul>
<li>
<a href="https://openaccess.thecvf.com/content/WACV2022/html/Rukhovich_ImVoxelNet_Image_to_Voxels_Projection_for_Monocular_and_Multi-View_General-Purpose_WACV_2022_paper" target="_blank">Paper</a>
</li>
</ul>
</div>
</div>
<!-- End .project-info -->
</div>
<!-- End .project -->
<div class="project">
<div class="project-image">
<img src="images/projects/gp2.png" />
</div>
<!-- End .project-image -->
<div class="project-info">
<h3>Single-Stage 3D Geometry-Preserving Depth Estimation Model Training on Dataset Mixtures with Uncalibrated Stereo Data</h3>
<h4>2022 Conference on Computer Vision and Pattern Recognition (CVPR)</h4>
<p class="paper-authors">N. Patakin, <b>A. Vorontsova</b>, M. Artemyev, A. Konushin </p>
<p class="paper-abstract">
GP2 is a General-Purpose and Geometry-Preserving scheme of training single-view
depth estimation models. GP2 allows training on a mixture of a small part of
geometrically correct depth data and voluminous stereo data. State-of-the-art
results in the general-purpose geometry-preserving single-view depth estimation.
</p>
<div class="social">
<ul>
<li>
<a href="https://openaccess.thecvf.com/content/CVPR2022/html/Patakin_Single-Stage_3D_Geometry-Preserving_Depth_Estimation_Model_Training_on_Dataset_Mixtures_CVPR_2022_paper" target="_blank">Paper</a>
</li>
</ul>
</div>
</div>
<!-- End .project-info -->
</div>
<!-- End .project -->
<div class="project">
<div class="project-image">
<img src="images/projects/discoman.png" />
</div>
<!-- End .project-image -->
<div class="project-info">
<h3>DISCOMAN: Dataset of Indoor Scenes for Odometry, Mapping and Navigation</h3>
<h4>2019 International Conference on Intelligent Robots and Systems (IROS)</h4>
<p class="paper-authors">P. Kirsanov, A. Gaskarov, F. Konokhov, K. Sofiiuk, <b>A. Vorontsova</b>, I. Slinko, D. Zhukov, S. Bykov, O. Barinova, A. Konushin </p>
<p class="paper-abstract">
A synthetic dataset for training and benchmarking semantic SLAM. Contains 200
sequences of 3000-5000 frames (RGB images generated using physically-based
rendering, depth, IMU) and ground truth occupancy grids. In addition, we establish
baseline results for SLAM, mapping, semantic and panoptic segmentation on our dataset.
</p>
<div class="social">
<ul>
<li>
<a href="https://arxiv.org/abs/1909.12146" target="_blank">Paper</a>
</li>
</ul>
</div>
</div>
<!-- End .project-info -->
</div>
<!-- End .project -->
<div class="project">
<div class="project-image">
<img src="images/projects/robustness.png" />
</div>
<!-- End .project-image -->
<div class="project-info">
<h3>Measuring Robustness of Visual SLAM</h3>
<h4>2019 International Conference on Machine Vision Applications (MVA)</h4>
<p class="paper-authors">D. Prokhorov, D. Zhukov, O. Barinova, <b>A. Vorontsova</b>, A. Konushin </p>
<p class="paper-abstract">
A feasibility study of RGB-D SLAM. We extensively evaluate the popular ORBSLAM2
on several benchmarks, perform statistical analysis of the results, and find
correlations between the metric values and the attributes of the trajectories.
While the accuracy is high, robustness is still an issue.
</p>
<div class="social">
<ul>
<li>
<a href="https://arxiv.org/abs/1910.04755" target="_blank">Paper</a>
</li>
</ul>
</div>
</div>
<!-- End .project-info -->
</div>
<!-- End .project -->
<div class="project">
<div class="project-image">
<img src="images/projects/motionmaps.png" />
</div>
<!-- End .project-image -->
<div class="project-info">
<h3>Scene Motion Decomposition for Learnable Visual Odometry</h3>
<h4>SEMNAV 2019 : CVPR'19 Workshop on Deep Learning for Visual Navigation</h4>
<p class="paper-authors">I. Slinko, <b>A. Vorontsova</b>, F. Konokhov, O. Barinova, A. Konushin </p>
<p class="paper-abstract">
Instead of ego-motion estimation, we address a dual problem of estimating the
motion of a scene w.r.t a static camera. Using optical flow and depth, we
calculate the motion of each point of a scene in terms of 6DoF and create motion
maps, each one addressing a single degree of freedom. Such a decomposition
improves accuracy over naive stacking of depth and optical flow.
</p>
<div class="social">
<ul>
<li>
<a href="https://arxiv.org/abs/1907.07227" target="_blank">Paper</a>
</li>
</ul>
</div>
</div>
<!-- End .project-info -->
</div>
<!-- End .project -->
</div>
</div>
</div>
<!-- End #projects -->
<div id="skills">
<h2 class="heading">Skills</h2>
<ul>
<li>Python</li>
<li>PyTorch</li>
<li>Tensorflow</li>
<li>OpenCV</li>
<li>Open3D</li>
<li>blenderproc</li>
<li>scikit-learn</li>
<li>ultralytics</li>
<li>NumPy</li>
<li>SciPy</li>
<li>Pandas</li>
<li>Docker</li>
<li>Git</li>
</ul>
</div>
<!-- End #skills -->
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