Oleg Voynov, Alexey Artemov, Vage Egiazarian, Alexander Notchenko, Gleb Bobrovskikh, Evgeny Burnaev, Denis Zorin
International Conference on Computer Vision 2019
We address the problem of depth map super-resolution with the focus on visual quality of the corresponding 3D geometry. We demonstrate that basing the loss function on deviation of 3D surface rendering instead of direct depth deviation yields significantly improved results as measured by a number of perceptual metrics.
Maria Taktasheva, Albert Matveev, Alexey Artemov, Evgeny Burnaev
Analysis of Images, Social networks and Texts 2019
Reconstruction of directional fields is a need in many geometry processing tasks, such as image tracing, extraction of 3D geometric features, and finding principal surface directions. We propose a deep learning-based approach and study the expressive power and generalization ability.
Maria Kolos, Anton Marin, Alexey Artemov, Evgeny Burnaev
International Symposium on Neural Networks 2019
We propose a simple and efficient method for creating realistic targeted synthetic datasets in the remote sensing domain, leveraging the opportunities offered by game development engines. Our evaluations demonstrate that our pipeline helps to improve the performance and convergence of deep learning models when the amount of real-world data is severely limited.
Alexey Bokhovkin, Evgeny Burnaev
International Symposium on Neural Networks 2019
Typical semantic segmentation losses (IoU, cross-entropy) are not sensitive enough to some misalignment of boundaries. As segment is fully explained with its boundary, we propose to use differentiable surrogate of metric BF1 to better account pixels on the edge of a segment.
Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, Daniele Panozzo
Conference on Computer Vision and Pattern Recognition 2019
We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction.
Ivan Barabanau, Alexey Artemov, Evgeny Burnaev, Vyacheslav Murashkin
arXiv 2019
We augment a conventional object detection framework with a keypoint detection module and a multi-view consistency loss to make it a robust 3D keypoint estimator, that we use for predicting 3D objects in KITTI road scenes.
Aibek Alanov, Max Kochurov, Denis Volkhonskiy, Daniil Yashkov, Evgeny Burnaev, Dmitry Vetrov
arXiv 2019
We propose a novel multi-texture synthesis model based on generative adversarial networks (GANs) with a user-controllable mechanism. The user control ability allows us to explicitly specify the texture which should be generated by the model. This property follows from using an encoder part which learns a latent representation for each texture from the dataset.
Denis Volkhonskiy, Ekaterina Muravleva, Oleg Sudakov, Denis Orlov, Boris Belozerov, Vladislav Krutko, Evgeny Burnaev, Dmitry Koroteev
arXiv 2019
We propose a novel deep learning architecture for three-dimensional porous media reconstruction from two-dimensional slices. Given central slices, we recover the three-dimensional structure around such slices as the most probable one.