3 citations · 5 across the 7 of their papers we have counts for
8 papers
StepCAD: Mesh-to-CAD Code Generation via LLM Policy and Geometry-Guided Search
Ghadi Nehme, Faez Ahmed
Recovering executable CAD programs from 3D meshes is challenging due to the compositional nature of CAD construction and the interaction between discrete modeling choices and conti…
CADBench: A Multimodal Benchmark for AI-Assisted CAD Program Generation
Anna C. Doris, Jacob Thomas Sony, Ghadi Nehme +3
Recovering editable CAD programs from images or 3D observations is central to AI-assisted design, but progress is difficult to measure because existing evaluations are fragmented a…
CADFit: Precise Mesh-to-CAD Program Generation with Hybrid Optimization
Ghadi Nehme, Eamon Whalen, Faez Ahmed
Despite recent progress, recovering parametric CAD construction sequences from geometric input, such as meshes or point clouds, is a key challenge for design and manufacturing, as…
FLARE: A Data-Efficient Surrogate for Predicting Displacement Fields in Directed Energy Deposition
Kittipong Thiamchaiboonthawee, Ghadi Nehme, Ram Mohan Telikicherla +5
Directed energy deposition (DED) produces complex thermo-mechanical responses that can lead to distortion and reduced dimensional accuracy of a manufactured part. Thermo-mechanical…
LAMP: Data-Efficient Linear Affine Weight-Space Models for Parameter-Controlled 3D Shape Generation and Extrapolation
Ghadi Nehme, Yanxia Zhang, Dule Shu +2
Generating high-fidelity 3D geometries under explicit parameter constraints is central to engineering design, yet current methods often require large datasets and fail to provide r…
VideoCAD: A Dataset and Model for Learning Long-Horizon 3D CAD UI Interactions from Video
Brandon Man, Ghadi Nehme, Md Ferdous Alam +1
Computer-Aided Design (CAD) is a time-consuming and complex process, requiring precise, long-horizon user interactions with intricate 3D interfaces. While recent advances in AI-dri…
Enhancing Robotic Manipulation: Harnessing the Power of Multi-Task Reinforcement Learning and Single Life Reinforcement Learning in Meta-World
Ghadi Nehme, Ishan Sabane, Tejas Y. Deo
At present, robots typically require extensive training to successfully accomplish a single task. However, to truly enhance their usefulness in real-world scenarios, robots should…
Safe Navigation: Training Autonomous Vehicles using Deep Reinforcement Learning in CARLA
Ghadi Nehme, Tejas Y. Deo
Autonomous vehicles have the potential to revolutionize transportation, but they must be able to navigate safely in traffic before they can be deployed on public roads. The goal of…