Global-Local Tree Search for Language Guided 3D Scene Generation
Large Vision-Language Models (VLMs), such as GPT-4, have achieved remarkable
success across various fields. However, there are few studies on 3D indoor
scene generation with VLMs. This paper considers this task as a planning
problem subject to spatial and layout common sense constraints. To solve the
problem with a VLM, we propose a new global-local tree search algorithm.
Globally, the method places each object sequentially and explores multiple
placements during each placement process, where the problem space is
represented as a tree. To reduce the depth of the tree, we decompose the scene
structure hierarchically, i.e. room level, region level, floor object level,
and supported object level. The algorithm independently generates the floor
objects in different regions and supported objects placed on different floor
objects. Locally, we also decompose the sub-task, the placement of each object,
into multiple steps. The algorithm searches the tree of problem space. To
leverage the VLM model to produce positions of objects, we discretize the
top-down view space as a dense grid and fill each cell with diverse emojis to
make to cells distinct. We prompt the VLM with the emoji grid and the VLM
produces a reasonable location for the object by describing the position with
the name of emojis. The quantitative and qualitative experimental results
illustrate our approach generates more plausible 3D scenes than
state-of-the-art approaches. Our source code is available at
https://github.com/dw-dengwei/TreeSearchGen .
67e29a0af38c0d7de45c28ee
Wei Deng
claimed_verified
2025-03-25T12:18:58.901Z
65816205e6b9dfd2cd2cff14
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/yfzfGuD0PRYiumnI-Ei3-.jpeg
Wei
user
WeiDeng1999
67e29a0af38c0d7de45c28ef
Mengshi Qi
67e29a0af38c0d7de45c28f0
Huadong Ma
Global-Local Tree Search for Language Guided 3D Scene Generation
Large Vision-Language Models (VLMs), such as GPT-4, have achieved remarkable
success across various fields. However, there are few studies on 3D indoor
scene generation with VLMs. This paper considers this task as a planning
problem subject to spatial and layout common sense constraints. To solve the
problem with a VLM, we propose a new global-local tree search algorithm.
Globally, the method places each object sequentially and explores multiple
placements during each placement process, where the problem space is
represented as a tree. To reduce the depth of the tree, we decompose the scene
structure hierarchically, i.e. room level, region level, floor object level,
and supported object level. The algorithm independently generates the floor
objects in different regions and supported objects placed on different floor
objects. Locally, we also decompose the sub-task, the placement of each object,
into multiple steps. The algorithm searches the tree of problem space. To
leverage the VLM model to produce positions of objects, we discretize the
top-down view space as a dense grid and fill each cell with diverse emojis to
make to cells distinct. We prompt the VLM with the emoji grid and the VLM
produces a reasonable location for the object by describing the position with
the name of emojis. The quantitative and qualitative experimental results
illustrate our approach generates more plausible 3D scenes than
state-of-the-art approaches. Our source code is available at
https://github.com/dw-dengwei/TreeSearchGen .
A novel global-local tree search algorithm leverages large vision-language models to generate plausible 3D indoor scenes by decomposing the scene into hierarchical levels and using emojis to guide object placement.
VLMs GPT-4 3D indoor scene generation global-local tree search algorithm spatial constraints layout common sense constraints scene structure hierarchy room level region level floor object level supported object level top-down view space emoji grid