8 citations · 20 across the 11 of their papers we have counts for
15 papers
MiCADangelo: Fine-Grained Reconstruction of Constrained CAD Models from 3D Scans
Ahmet Serdar Karadeniz, Dimitrios Mallis, Danila Rukhovich +3
Computer-Aided Design (CAD) plays a foundational role in modern manufacturing and product development, often requiring designers to modify or build upon existing models. Converting…
CAD-Recode: Reverse Engineering CAD Code from Point Clouds
Danila Rukhovich, Elona Dupont, Dimitrios Mallis +3
Computer-Aided Design (CAD) models are typically constructed by sequentially drawing parametric sketches and applying CAD operations to obtain a 3D model. The problem of 3D CAD rev…
CAD-Assistant: Tool-Augmented VLLMs as Generic CAD Task Solvers
Dimitrios Mallis, Ahmet Serdar Karadeniz, Sebastian Cavada +5
We propose CAD-Assistant, a general-purpose CAD agent for AI-assisted design. Our approach is based on a powerful Vision and Large Language Model (VLLM) as a planner and a tool-aug…
DAVINCI: A Single-Stage Architecture for Constrained CAD Sketch Inference
Ahmet Serdar Karadeniz, Dimitrios Mallis, Nesryne Mejri +3
This work presents DAVINCI, a unified architecture for single-stage Computer-Aided Design (CAD) sketch parameterization and constraint inference directly from raster sketch images.…
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…
PICASSO: A Feed-Forward Framework for Parametric Inference of CAD Sketches via Rendering Self-Supervision
Ahmet Serdar Karadeniz, Dimitrios Mallis, Nesryne Mejri +3
This work introduces PICASSO, a framework for the parameterization of 2D CAD sketches from hand-drawn and precise sketch images. PICASSO converts a given CAD sketch image into para…
CAD-SIGNet: CAD Language Inference from Point Clouds using Layer-wise Sketch Instance Guided Attention
Mohammad Sadil Khan, Elona Dupont, Sk Aziz Ali +3
Reverse engineering in the realm of Computer-Aided Design (CAD) has been a longstanding aspiration, though not yet entirely realized. Its primary aim is to uncover the CAD process…
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…
SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes
Kseniya Cherenkova, Elona Dupont, Anis Kacem +3
3D scanning as a technique to digitize objects in reality and create their 3D models, is used in many fields and areas. Though the quality of 3D scans depends on the technical char…
CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representations
Elona Dupont, Kseniya Cherenkova, Anis Kacem +4
3D reverse engineering is a long sought-after, yet not completely achieved goal in the Computer-Aided Design (CAD) industry. The objective is to recover the construction history of…
Disentangled Face Identity Representations for joint 3D Face Recognition and Expression Neutralisation
Anis Kacem, Kseniya Cherenkova, Djamila Aouada
In this paper, we propose a new deep learning-based approach for disentangling face identity representations from expressive 3D faces. Given a 3D face, our approach not only extrac…
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…
3DBooSTeR: 3D Body Shape and Texture Recovery
Alexandre Saint, Anis Kacem, Kseniya Cherenkova +1
We propose 3DBooSTeR, a novel method to recover a textured 3D body mesh from a textured partial 3D scan. With the advent of virtual and augmented reality, there is a demand for cre…
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…