10 papers
BRepCLIP: Contrastive Multimodal Pretraining on BRep Primitives for CAD Understanding
Muhammad Usama, Didier Stricker, Mohammad Sadil Khan +1
Learning representations of CAD models is a largely open problem. While 3D representation learning has flourished around point clouds and meshes, the native format of CAD - boundar…
VVitCutLER: Towards Unsupervised Object Detection and Segmentation in Videos
Zhijing Lu, Khurram Azeem Hashmi, Didier Stricker +1
Unsupervised pixel-level video understanding remains challenging in real-world scenarios, where motion blur, occlusion, and fast object dynamics often cause temporal drift and flic…
ReConText3D: Replay-based Continual Text-to-3D Generation
Muhammad Ahmed Ullah Khan, Muhammad Haris Bin Amir, Didier Stricker +1
Continual learning enables models to acquire new knowledge over time while retaining previously learned capabilities. However, its application to text-to-3D generation remains unex…
SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completion
Danish Nazir, Marcus Liwicki, Didier Stricker +1
Depth completion involves recovering a dense depth map from a sparse map and an RGB image. Recent approaches focus on utilizing color images as guidance images to recover depth at…
DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces
Mohammad Sadil Khan, Muhammad Usama, Rolandos Alexandros Potamias +4
Computer-Aided Design (CAD) relies on structured and editable geometric representations, yet existing generative methods are constrained by small annotated datasets with explicit d…
NURBGen: High-Fidelity Text-to-CAD Generation through LLM-Driven NURBS Modeling
Muhammad Usama, Mohammad Sadil Khan, Didier Stricker +1
Generating editable 3D CAD models from natural language remains challenging, as existing text-to-CAD systems either produce meshes or rely on scarce design-history data. We present…