  Large language models (LLMs) and Vision-Language Models (VLMs) have been
proven to excel at multiple tasks, such as commonsense reasoning. Powerful as
these models can be, they are not grounded in the 3D physical world, which
involves richer concepts such as spatial relationships, affordances, physics,
layout, and so on. In this work, we propose to inject the 3D world into large
language models and introduce a whole new family of 3D-LLMs. Specifically,
3D-LLMs can take 3D point clouds and their features as input and perform a
diverse set of 3D-related tasks, including captioning, dense captioning, 3D
question answering, task decomposition, 3D grounding, 3D-assisted dialog,
navigation, and so on. Using three types of prompting mechanisms that we
design, we are able to collect over 300k 3D-language data covering these tasks.
To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that
obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as
our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism,
3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show
that our model outperforms state-of-the-art baselines by a large margin (e.g.,
the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore,
experiments on our held-in datasets for 3D captioning, task composition, and
3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative
examples also show that our model could perform more tasks beyond the scope of
existing LLMs and VLMs. Project Page: : https://vis-www.cs.umass.edu/3dllm/.
