  The remarkable potential of multi-modal large language models (MLLMs) in
comprehending both vision and language information has been widely
acknowledged. However, the scarcity of 3D scenes-language pairs in comparison
to their 2D counterparts, coupled with the inadequacy of existing approaches in
understanding of 3D scenes by LLMs, poses a significant challenge. In response,
we collect and construct an extensive dataset comprising 75K
instruction-response pairs tailored for 3D scenes. This dataset addresses tasks
related to 3D VQA, 3D grounding, and 3D conversation. To further enhance the
integration of 3D spatial information into LLMs, we introduce a novel and
efficient prompt tuning paradigm, 3DMIT. This paradigm eliminates the alignment
stage between 3D scenes and language and extends the instruction prompt with
the 3D modality information including the entire scene and segmented objects.
We evaluate the effectiveness of our method across diverse tasks in the 3D
scene domain and find that our approach serves as a strategic means to enrich
LLMs' comprehension of the 3D world. Our code is available at
https://github.com/staymylove/3DMIT.
