<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> This repository serves as a comprehensive, curated list of research papers focused on spatial intelligence within Vision-Language Models (VLMs). It addresses the growing need for organized resources in this rapidly advancing field, providing researchers and engineers with a centralized index to track state-of-the-art methods, datasets, benchmarks, findings, and applications, thereby accelerating progress in VLM spatial reasoning capabilities.

How It Works

The project functions as a living bibliography, meticulously categorizing and listing relevant academic publications. Resources are organized into distinct sections: Methods (detailing various approaches and models), Datasets & Benchmarks (for evaluation), and Findings & Applications (showcasing research outcomes and practical uses). Each entry typically includes a title, publication date, and a link to associated code repositories where available, facilitating direct access to foundational research and implementations.

Quick Start & Requirements

This section is not applicable as the repository is a curated list of papers and not a software project with installation instructions.

Highlighted Details

• Offers extensive coverage of spatial intelligence in VLMs, encompassing methods, datasets, benchmarks, findings, and applications.
• Entries feature publication dates (up to 2025) and direct links to GitHub repositories for many listed research papers, enabling quick access to implementations.
• Actively encourages community contributions via Pull Requests to maintain its currency and breadth.

Maintenance & Community

• The project is maintained by "mll-lab-nu".
• It explicitly welcomes community contributions via Pull Requests, indicating an open and collaborative approach to curation.
• No specific community channels (Discord, Slack) or roadmap links are provided in the README.

Licensing & Compatibility

• The README does not specify a license for the curated list itself.
• Compatibility for commercial use or closed-source linking would depend on the licenses of the individual papers and their associated code repositories, which are not detailed here.

Limitations & Caveats

• The repository is a curated list, not a software package, and does not provide direct tools or code for implementing spatial intelligence in VLMs.
• Information on the specific licenses of the listed papers and their code is not provided, requiring users to investigate each resource individually.
• While comprehensive, the list's currency depends on ongoing community contributions and curation efforts.