12 citations · 33 across the 13 of their papers we have counts for
5 papers · 1 filter
TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds
Elona Dupont, Kseniya Cherenkova, Dimitrios Mallis +3
3D reverse engineering, in which a CAD model is inferred given a 3D scan of a physical object, is a research direction that offers many promising practical applications. This paper…
SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselines
Dimitrios Mallis, Sk Aziz Ali, Elona Dupont +6
Recent breakthroughs in geometric Deep Learning (DL) and the availability of large Computer-Aided Design (CAD) datasets have advanced the research on learning CAD modeling processe…
PvDeConv: Point-Voxel Deconvolution for Autoencoding CAD Construction in 3D
Kseniya Cherenkova, Djamila Aouada, Gleb Gusev
We propose a Point-Voxel DeConvolution (PVDeConv) module for 3D data autoencoder. To demonstrate its efficiency we learn to synthesize high-resolution point clouds of 10k points th…
SHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results
Alexandre Saint, Anis Kacem, Kseniya Cherenkova +7
The SHApe Recovery from Partial textured 3D scans challenge, SHARP 2020, is the first edition of a challenge fostering and benchmarking methods for recovering complete textured 3D…
A survey on Deep Learning Advances on Different 3D Data Representations
Eman Ahmed, Alexandre Saint, Abd El Rahman Shabayek +5
3D data is a valuable asset the computer vision filed as it provides rich information about the full geometry of sensed objects and scenes. Recently, with the availability of both…