<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> This repository is a curated list of academic papers focused on Computer-Aided Design (CAD), covering diverse sub-fields like geometric deep learning, LLMs for CAD, reconstruction, generation, representation, and recognition. It serves as a valuable resource for researchers, engineers, and students seeking to navigate the latest advancements and academic contributions in CAD. The primary benefit is the organized consolidation of relevant literature, significantly reducing the effort required to discover foundational and cutting-edge work.

How It Works

This repository serves as a curated index of academic research papers focused on Computer-Aided Design (CAD). It organizes papers into distinct categories, including Geometric Deep Learning, Large Language Models for CAD, CAD Reconstruction, Generation, Representation, and Recognition. This structured approach allows researchers and practitioners to efficiently discover and explore the latest advancements in specific sub-fields of CAD. The inclusion of links to project pages and code repositories, where available, further enhances its value by providing direct access to supplementary materials and potential implementations, fostering reproducibility and deeper engagement with the research.

Quick Start & Requirements

This repository is a list of papers and does not involve direct software installation or execution. Users can access the listed papers by following the provided links to their respective publication venues (e.g., arXiv, conference proceedings) or project pages. No specific software prerequisites or setup are required to browse or utilize this list.

Highlighted Details

• Features comprehensive surveys on emerging areas like Geometric Deep Learning and Large Language Models for CAD.
• Curates papers with direct links to project pages and code repositories, facilitating deeper dives into research and potential reproducibility.
• Covers a broad spectrum of CAD research, from foundational geometric learning and reconstruction to advanced generative models and recognition tasks.

Maintenance & Community

Information regarding active maintenance, specific contributors, sponsorships, or community channels (like Discord/Slack) is not provided in the README. The list appears to be a static compilation of research papers.

Licensing & Compatibility

The README does not specify a software license for the curated list itself. The licensing of individual papers or associated code/datasets would be governed by their respective publishers, authors, or repositories. Compatibility for commercial use or closed-source linking would depend on the terms of each individual paper's publication and any linked resources.

Limitations & Caveats

This repository is a curated list of research papers and does not provide direct software tools or implementations. The scope is limited to the papers selected by the curator and may not be exhaustive. Entries marked "arXiv 2025" represent forthcoming or pre-print works whose final content may differ. The availability and maintenance of external project pages and code repositories are beyond the scope of this list.