Deep Learning of 3D Scanned Data

By

Project description:

The general idea of this project is to investigate recent advances of geometric deep learning to leverage raw 3D scans to higher level representations. One of the main goals  is to infer Computer-Aided Design (CAD) models directly from 3D scans. To that aim, multiple aspects are being considered such as 3D scan refinement and parametrization, geometrical detail enhancement, etc. As geometric deep learning methods are the focus of this project, a unique dataset called CC3D dataset of more than 50k pairs of scans/CAD models has been collected and can be requested.

  • Starting date: 01/12/2020
  • Duration: 48 months
  • Funding source: Artec3D
  • Researchers: Dr. Anis Kacem, Dr. Dimitrios Mallis, Sk Aziz Ali, Elona Dupont, Ahmet Serdar Karadeniz, Prof. Djamila Aouada (PI)
  • Partners: Artec3D

Publications

Publications

Elona Dupont, Kseniya Cherenkova, Anis Kacem, Sk Aziz Ali, Ilya Arzhannikov, Gleb Gusev, and Djamila Aouada, “CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representations.” In IEEE International Conference of 3D Vision (3DV), 2022. [Project Page]