  As large language models (LLMs) evolve, their integration with 3D spatial
data (3D-LLMs) has seen rapid progress, offering unprecedented capabilities for
understanding and interacting with physical spaces. This survey provides a
comprehensive overview of the methodologies enabling LLMs to process,
understand, and generate 3D data. Highlighting the unique advantages of LLMs,
such as in-context learning, step-by-step reasoning, open-vocabulary
capabilities, and extensive world knowledge, we underscore their potential to
significantly advance spatial comprehension and interaction within embodied
Artificial Intelligence (AI) systems. Our investigation spans various 3D data
representations, from point clouds to Neural Radiance Fields (NeRFs). It
examines their integration with LLMs for tasks such as 3D scene understanding,
captioning, question-answering, and dialogue, as well as LLM-based agents for
spatial reasoning, planning, and navigation. The paper also includes a brief
review of other methods that integrate 3D and language. The meta-analysis
presented in this paper reveals significant progress yet underscores the
necessity for novel approaches to harness the full potential of 3D-LLMs. Hence,
with this paper, we aim to chart a course for future research that explores and
expands the capabilities of 3D-LLMs in understanding and interacting with the
complex 3D world. To support this survey, we have established a project page
where papers related to our topic are organized and listed:
https://github.com/ActiveVisionLab/Awesome-LLM-3D.
