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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
[NeurIPS 2025] SpatialLM: Training Large Language Models for Structured Indoor Modeling
| Date | Stars |
|---|---|
| 2026-07-31 | 4651 |
| 2026-08-02 | 4651 |
| 2026-08-04 | 4679 |
| 2026-08-06 | 4679 |
Today
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Momentum
0.0
growth rate 0.00%/day
# SpatialLM
<!-- markdownlint-disable first-line-h1 -->
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<div align="center">
<img src="figures/logo_light.png#gh-light-mode-only" width="60%" alt="SpatialLM" />
<img src="figures/logo_dark.png#gh-dark-mode-only" width="60%" alt="SpatialLM" />
</div>
<hr style="margin-top: 0; margin-bottom: 8px;">
<div align="center" style="margin-top: 0; padding-top: 0; line-height: 1;">
<a href="https://manycore-research.github.io/SpatialLM" target="_blank" style="margin: 2px;"><img alt="Project"
src="https://img.shields.io/badge/🌐%20Website-SpatialLM-ffc107?color=42a5f5&logoColor=white" style="display: inline-block; vertical-align: middle;"/></a>
<a href="https://arxiv.org/abs/2506.07491" target="_blank" style="margin: 2px;"><img alt="arXiv"
src="https://img.shields.io/badge/arXiv-Techreport-b31b1b?logo=arxiv&logoColor=white" style="display: inline-block; vertical-align: middle;"/></a>
<a href="https://github.com/manycore-research/SpatialLM" target="_blank" style="margin: 2px;"><img alt="GitHub"
src="https://img.shields.io/badge/GitHub-SpatialLM-24292e?logo=github&logoColor=white" style="display: inline-block; vertical-align: middle;"/></a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://huggingface.co/manycore-research/SpatialLM1.1-Qwen-0.5B" target="_blank" style="margin: 2px;"><img alt="Hugging Face"
src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-SpatialLM-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/></a>
<a href="https://huggingface.co/datasets/manycore-research/SpatialLM-Dataset" target="_blank" style="margin: 2px;"><img alt="Dataset"
src="https://img.shields.io/badge/%F0%9F%A4%97%20Dataset-Dataset-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/></a>
<a href="https://huggingface.co/datasets/manycore-research/SpatialLM-Testset" target="_blank" style="margin: 2px;"><img alt="Dataset"
src="https://img.shields.io/badge/%F0%9F%A4%97%20Dataset-Testset-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/></a>
</div>
## ✨ News
- [Sept, 2025] [SpatialLM-Dataset](https://huggingface.co/datasets/manycore-research/SpatialLM-Dataset) is now available on Hugging Face.
- [Sept, 2025] SpatialLM accepted at NeurIPS 2025.
- [Jun, 2025] Added finetuning instructions in [FINETUNE.md](./FINETUNE.md).
- [Jun, 2025] Check out our new models: [SpatialLM1.1-Llama-1B](https://huggingface.co/manycore-research/SpatialLM1.1-Llama-1B) and [SpatialLM1.1-Qwen-0.5B](https://huggingface.co/manycore-research/SpatialLM1.1-Qwen-0.5B), now available on Hugging Face. SpatialLM1.1 doubles the point cloud resolution, incorporates a more powerful point cloud encoder [Sonata](https://xywu.me/sonata/) and supports detection with user-specified categories.
- [Jun, 2025] SpatialLM [Technical Report](https://arxiv.org/abs/2506.07491) is now on arXiv.
- [Mar, 2025] We're excited to release the [SpatialLM-Llama-1B](https://huggingface.co/manycore-research/SpatialLM-Llama-1B) and [SpatialLM-Qwen-0.5B](https://huggingface.co/manycore-research/SpatialLM-Qwen-0.5B) on Hugging Face.
- [Mar, 2025] Initial release of SpatialLM!
## Introduction
SpatialLM is a 3D large language model designed to process 3D point cloud data and generate structured 3D scene understanding outputs. These outputs include architectural elements like walls, doors, windows, and oriented object bounding boxes with their semantic categories. Unlike previous methods that require specialized equipment for data collection, SpatialLM can handle point clouds from diverse sources such as monocular video sequences, RGBD images, and LiDAR sensors. This multimodal architecture effectively bridges the gap between unstructured 3D geometric data and structured 3D representations, offering high-level semantic understanding.Excerpt of 18,456 characters
Read on GitHub
19
Jia Zheng · @manycore-research
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1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8b286eb57fdaeec3, llm:Repository topics: mllm, point-clouds, scene-understanding, spatial-intelligence; description: 'SpatialLM: Training Large Language Models for Structured Indoor Modeling' (NeurIPS 2025).
matched fp:8b286eb57fdaeec3, llm:Repository topics: mllm, point-clouds, scene-understanding, spatial-intelligence; description: 'SpatialLM: Training Large Language Models for Structured Indoor Modeling' (NeurIPS 2025).
matched fp:8b286eb57fdaeec3, llm:Repository topics: mllm, point-clouds, scene-understanding, spatial-intelligence; description: 'SpatialLM: Training Large Language Models for Structured Indoor Modeling' (NeurIPS 2025).
matched fp:8b286eb57fdaeec3, llm:Repository topics: mllm, point-clouds, scene-understanding, spatial-intelligence; description: 'SpatialLM: Training Large Language Models for Structured Indoor Modeling' (NeurIPS 2025).