no code implementations • 27 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.
1 code implementation • 30 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.
no code implementations • 13 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.
no code implementations • 22 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.
no code implementations • 20 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.
no code implementations • 12 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.
no code implementations • 26 Oct 2020 • Alexandre Saint, Anis Kacem, Kseniya Cherenkova, Konstantinos Papadopoulos, Julian Chibane, Gerard Pons-Moll, Gleb Gusev, David Fofi, Djamila Aouada, Bjorn Ottersten
Additionally, two unique datasets of 3D scans are proposed, to provide raw ground-truth data for the benchmarks.
no code implementations • 23 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.
no code implementations • 4 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.