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Dimitrios Mallis

5 accepted papers

2025

CAD-Assistant: Tool-Augmented VLLMs as Generic CAD Task Solvers

Dimitrios Mallis, Ahmet Serda Karadeniz, Sebastian Cavada, Danila Rukhovich, Niki Foteinopoulou, Kseniya Cherenkova +2
ICCV 2025poster

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-augmentation paradigm using CAD-specific tools. CAD-Assistant addresses multimodal user queries by generating actions that…

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2025

CAD-Recode: Reverse Engineering CAD Code from Point Clouds

Danila Rukhovich, Elona Dupont, Dimitrios Mallis, Kseniya Cherenkova, Anis Kacem, Djamila Aouada
ICCV 2025poster

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 reverse engineering consists of reconstructing the sketch and CAD operation sequences from 3D representations such as point…

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2025

MiCADangelo: Fine-Grained Reconstruction of Constrained CAD Models from 3D Scans

Ahmet Serdar Karadeniz, Dimitrios Mallis, Danila Rukhovich, Kseniya Cherenkova, Anis Kacem, Djamila Aouada
NeurIPS 2025poster

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 3D scans into parametric CAD representations—a process known as CAD reverse engineering—remains a significant challenge…

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2024

TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds

Elona Dupont*, Kseniya Cherenkova, Dimitrios Mallis, Gleb A Gusev, Anis Kacem, Djamila Aouada
ECCV 2024poster

"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 proposes , an end-to-end transformer-based architecture that predicts the CAD sequence from a point cloud. leverages t…

Cited by 40Source ↗PDF ↗Scholar ↗
2020

Unsupervised Learning of Object Landmarks via Self-Training Correspondence

Dimitrios Mallis, Enrique Sanchez, Matthew Bell, Georgios Tzimiropoulos
NeurIPS 2020poster

This paper addresses the problem of unsupervised discovery of object landmarks. We take a different path compared to that of existing works, based on 2 novel perspectives: (1) Self-training: starting from generic keypoints, we propose a self-training approach where the goal is to learn a detector th…

Cited by 23Source ↗PDF ↗code ↗Scholar ↗