  Designing 3D indoor layouts is a crucial task with significant applications
in virtual reality, interior design, and automated space planning. Existing
methods for 3D layout design either rely on diffusion models, which utilize
spatial relationship priors, or heavily leverage the inferential capabilities
of proprietary Large Language Models (LLMs), which require extensive prompt
engineering and in-context exemplars via black-box trials. These methods often
face limitations in generalization and dynamic scene editing. In this paper, we
introduce LLplace, a novel 3D indoor scene layout designer based on lightweight
fine-tuned open-source LLM Llama3. LLplace circumvents the need for spatial
relationship priors and in-context exemplars, enabling efficient and credible
room layout generation based solely on user inputs specifying the room type and
desired objects. We curated a new dialogue dataset based on the 3D-Front
dataset, expanding the original data volume and incorporating dialogue data for
adding and removing objects. This dataset can enhance the LLM's spatial
understanding. Furthermore, through dialogue, LLplace activates the LLM's
capability to understand 3D layouts and perform dynamic scene editing, enabling
the addition and removal of objects. Our approach demonstrates that LLplace can
effectively generate and edit 3D indoor layouts interactively and outperform
existing methods in delivering high-quality 3D design solutions. Code and
dataset will be released.
