UMass-Embodied-AGI/3D-LLM

Health v2 · maintenance only

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Adoption not part of the score

1212 stars · 77 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

3D-LLM is the research code for a large language model that takes 3D representations (objects and scenes) as input, built on BLIP-2/LAVIS. It includes pretrained and finetuned checkpoints for 3D visual question answering tasks like ScanQA, SQA3d, and 3DMV_VQA.

Use cases

• run inference on 3D objects and scenes with a language model
• finetune an LLM on 3D visual question answering datasets
• answer questions about 3D scenes like ScanQA or SQA3d
• experiment with injecting 3D features into large language models
• reproduce NeurIPS 2023 3D-LLM paper results

When to choose

• you need an LLM that accepts 3D scene or object representations
• you want to benchmark on 3D VQA datasets like ScanQA or SQA3d
• you are researching multimodal 3D-language models

When to avoid

• you need a production-ready or maintained product with active updates
• you want a simple pip-installable library or Hugging Face integration
• you work with 2D images only

Facets

library · maturity maintenance

llm-inference llm-training machine-learning deep-learning large-language-models artificial-intelligence computer-vision python 3d-representations multimodal visual-question-answering research-code neurips-2023 point-clouds blip2 natural-language-processing gpu linux

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