3D-LLM

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3D-LLM is an academic research tool that injects the 3D world into Large Language Models. It is the first LLM capable of taking 3D representations as inputs, handling both object and scene data.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is 3D-LLM?

3D-LLM is a pioneering Large Language Model developed by UMass-Embodied-AGI, designed to process and understand 3D representations. This tool is unique as it's the first LLM capable of directly taking 3D data as input, encompassing both individual objects (like those from Objaverse) and complex scene data (such as ScanNet and HM3D). It offers functionalities for pretraining and finetuning, with released checkpoints for various tasks like ScanQA, SQA3d, and 3DMV_VQA. The project provides detailed instructions for installation, inference, finetuning, and generating 3DLanguage data through a three-step feature extraction process, making it a valuable resource for researchers in embodied AI and 3D vision-language understanding.

Best used for

Ideal for professors and researchers who need to advance the capabilities of Large Language Models by integrating 3D world understanding. Especially valuable for those working on embodied AI, 3D vision-language tasks, and developing models that can interpret and interact with complex 3D environments.

Common actions

integrate 3D data

train large language models

extract 3D features

conduct academic research

face swapping"AI Agents"collaborationgithub copilotautomated workflowworkflowsopen-sourcedeepfakelow-code/no-code

Capabilities

Key features

3D representation input
Object data handling
Scene data handling
Pretraining checkpoints
Finetuning checkpoints
3D feature extraction

Target Audience

professorresearcher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of 3D data can 3D-LLM process?

3D-LLM is designed to handle both object-level data, such as those found in Objaverse, and complex scene-level data from datasets like ScanNet and HM3D. This versatility allows for a broad range of applications in 3D vision-language understanding.

Are there pre-trained models available for 3D-LLM?

Yes, 3D-LLM provides pre-trained checkpoints for both pretraining and finetuning. These include finetuning checkpoints specifically for tasks like ScanQA, SQA3d, and 3DMV_VQA, which offer improved performance over earlier versions.

How can I generate 3D features for my own data using 3D-LLM?

3D-LLM offers a detailed three-step process for 3D feature extraction, applicable to both object and scene data. This involves rendering images, generating captions, and constructing 3D features from the rendered images, with support for tools like ChatCaptioner, Mask2Former, and Segment Anything.

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