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Large Language Models Enter the 3D World!

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Summary

This article is an overview of a new model of artificial intelligence, 3D-LLM, which is able to interact with the 3-dimensional world and answer questions related to it. To create this model, the authors built a new dataset with 3D-text pairs, using three different generation steps. This dataset was used to train a model called Perceiver, which is able to translate the 3D scene into a representation that the language model can understand. Finally, the model was able to answer questions about the 3-dimensional world, with impressive results.

Q&As

What is 3D-LLM and how does it work?
3D-LLM is a new model that is able to understand the 3-dimensional world and interact with it. It is able to understand point clouds and language, and it can answer questions related to the environment with commonsense reasoning.

How is 3D-LLM able to understand the 3-dimensional world?
3D-LLM is able to understand the 3-dimensional world by extracting features from 3D data points representing spatial coordinates of objects or environments. It is also able to interact with the real world in three dimensions.

How did the authors create the dataset for 3D-LLM?
The authors created the dataset for 3D-LLM by prompting a text-only GPT model for generating the data they needed. They used three different approaches, such as Box-Demonstration-Instruction based Prompting, taking multiple photos of the 3D scenes and using ChatGPT to ask questions, and generating question and answer pairs from text descriptions of scenes.

What techniques were used to process 3D point clouds?
The techniques used to process 3D point clouds include rendering the scene in different views, segmenting the scene to get all the objects present in it, and using a model called Perceiver, which is a Transformer model, to process the information of varying sizes and translate it into a fixed-size representation.

What is the purpose of the Perceiver model when it comes to 3D-LLM?
The purpose of the Perceiver model when it comes to 3D-LLM is to translate the 3D scene into a 2D world that the LLM will understand. It acts as a translator from the 3D scene to the 2D world that the LLM will understand.

AI Comments

👍 This article provides a great overview on the development of 3D-LLM, a large language model that is able to understand and interact with the 3-dimensional world. It is an impressive feat and the engineering prowesses of the authors should be applauded.

👎 The 3D-LLM is still limited in its scope, only understanding 3 dimensions and text. It is far from being able to understand the full complexity of our world.

AI Discussion

Me: It's about a new Artificial Intelligence model called 3D-LLM that is able to understand our world in three dimensions and through text. It's the first large language model that is able to interact with the world we live in.

Friend: That's really fascinating! What implications can this have?

Me: Well, this could open up a lot of opportunities for AI applications, particularly in areas like autonomous driving, robotics and augmented reality. It could also help us better understand the world around us and allow us to interact with it in new ways. Additionally, it could help us make more informed decisions and provide us with insights that weren't available before.

Action items

Read the paper referenced in the article for more details on the implementation.
Watch the video demo of the 3D-LLM project.
Explore the code available on the 3D-LLM GitHub page.

Technical terms

Large Language Models (LLMs): Artificial intelligence models that can process text, code, and images.
3D-LLM: A new model that is able to understand the 3 dimensions and text, allowing it to interact with the world.
Point Clouds: Collections of 3D data points representing spatial coordinates of objects or environments.
ChatGPT: A text-only GPT model used to generate data.
BLIP-2: A model trained with both images and text with the purpose of answering questions about the images.
CLIP: A model able to extract features from images that are similar to text features, allowing comparison of the image with text.
Perceiver: A Transformer model used to process information of varying sizes and translate it into a fixed-size representation.
Vision-LLM: A pre-trained model used to process the perceiver’s response into proper language.