Search Results for author: Kseniya Cherenkova

Found 9 papers, 1 papers with code

CAD-SIGNet: CAD Language Inference from Point Clouds using Layer-wise Sketch Instance Guided Attention

no code implementations27 Feb 2024 Mohammad Sadil Khan, Elona Dupont, Sk Aziz Ali, Kseniya Cherenkova, Anis Kacem, Djamila Aouada

Thanks to its auto-regressive nature, CAD-SIGNet not only reconstructs a unique full design history of the corresponding CAD model given an input point cloud but also provides multiple plausible design choices.

3D Reconstruction CAD Reconstruction

SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselines

1 code implementation30 Aug 2023 Dimitrios Mallis, Sk Aziz Ali, Elona Dupont, Kseniya Cherenkova, Ahmet Serdar Karadeniz, Mohammad Sadil Khan, Anis Kacem, Gleb Gusev, Djamila Aouada

In this paper, we define the proposed SHARP 2023 tracks, describe the provided datasets, and propose a set of baseline methods along with suitable evaluation metrics to assess the performance of the track solutions.

SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes

no code implementations13 Apr 2023 Kseniya Cherenkova, Elona Dupont, Anis Kacem, Ilya Arzhannikov, Gleb Gusev, Djamila Aouada

3D scanning as a technique to digitize objects in reality and create their 3D models, is used in many fields and areas.

CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representations

no code implementations22 Aug 2022 Elona Dupont, Kseniya Cherenkova, Anis Kacem, Sk Aziz Ali, Ilya Arzhannikov, Gleb Gusev, Djamila Aouada

3D reverse engineering is a long sought-after, yet not completely achieved goal in the Computer-Aided Design (CAD) industry.

Disentangled Face Identity Representations for joint 3D Face Recognition and Expression Neutralisation

no code implementations20 Apr 2021 Anis Kacem, Kseniya Cherenkova, Djamila Aouada

The proposed network consists of three components; (1) a Graph Convolutional Autoencoder (GCA) to encode the 3D faces into latent representations, (2) a Generative Adversarial Network (GAN) that translates the latent representations of expressive faces into those of neutral faces, (3) and an identity recognition sub-network taking advantage of the neutralized latent representations for 3D face recognition.

Face Recognition Generative Adversarial Network

PvDeConv: Point-Voxel Deconvolution for Autoencoding CAD Construction in 3D

no code implementations12 Jan 2021 Kseniya Cherenkova, Djamila Aouada, Gleb Gusev

This dataset is used to learn a convolutional autoencoder for point clouds sampled from the pairs of 3D scans - CAD models.

3DBooSTeR: 3D Body Shape and Texture Recovery

no code implementations23 Oct 2020 Alexandre Saint, Anis Kacem, Kseniya Cherenkova, Djamila Aouada

The texture is subsequently obtained by projecting the partial texture onto the template mesh before inpainting the corresponding texture map with a novel approach.

A survey on Deep Learning Advances on Different 3D Data Representations

no code implementations4 Aug 2018 Eman Ahmed, Alexandre Saint, Abd El Rahman Shabayek, Kseniya Cherenkova, Rig Das, Gleb Gusev, Djamila Aouada, Bjorn Ottersten

3D data is a valuable asset the computer vision filed as it provides rich information about the full geometry of sensed objects and scenes.

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