manycore-research/SpatialLM

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

4719 stars · 401 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

SpatialLM is a 3D large language model that processes point cloud data (from monocular video, RGBD images, or LiDAR) and generates structured 3D scene understanding outputs such as walls, doors, windows, and oriented object bounding boxes with semantic categories. It includes pretrained models, a training dataset, and finetuning instructions.

Use cases

• reconstruct indoor 3D layout from a monocular RGB video
• detect walls doors and objects in a 3D point cloud
• convert point clouds into structured scene representations
• spatial reasoning for embodied robotics and navigation
• finetune a 3D scene understanding LLM on custom data

When to choose

• you need structured indoor scene understanding from point clouds or video
• you want semantic 3D layouts without specialized scanning equipment
• you're researching spatial intelligence or multimodal LLMs

When to avoid

• you need outdoor or large-scale scene reconstruction
• you need a lightweight real-time pipeline without GPU inference
• you need a permissively licensed model for commercial use without checking the custom license

Facets

library · maturity active

machine-learning llm-inference computer-vision nlp computer-vision large-language-models artificial-intelligence robotics python point-cloud 3d-scene-understanding spatial-intelligence indoor-scene-reconstruction multimodal-llm scene-layout gpu linux

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markdown · JSON · MCP: product_card(name="manycore-research/SpatialLM")