Planner3D: LLM-enhanced Graph Prior Meets 3D Indoor Scene Explicit Regularization
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel method for generating realistic 3D indoor scenes from scene graphs using large language models and generative models. The approach enhances scene-level fidelity by jointly generating object shapes and spatial arrangements, overcoming limitations of prior methods.
Area Of Science
- Computer Vision
- Artificial Intelligence
- 3D Graphics
Background
- Compositional 3D scene synthesis is crucial for robotics, film, and gaming, but current methods struggle with shape diversity and scene-level fidelity.
- Existing generative models improve shape fidelity but often neglect joint layout-shape generation, leading to issues like object collisions.
Purpose Of The Study
- To develop a method for generating realistic and reasonable 3D indoor scenes directly from scene graphs.
- To address the under-explored area of scene-level fidelity in 3D scene synthesis.
Main Methods
- Utilized a large language model to enrich scene graph priors by aggregating global, node-wise, and edge-wise features.
- Employed a unified graph encoder to extract features for guiding joint layout-shape generation.
- Introduced additional regularization to explicitly constrain the generated 3D layouts.
Main Results
- Achieved improved 3D scene synthesis performance, particularly in scene-level fidelity, when benchmarked on the SG-FRONT dataset.
- Demonstrated the effectiveness of integrating large language models and generative models for complex scene generation.
Conclusions
- The proposed method successfully generates realistic 3D indoor scenes by effectively combining scene graph information with generative capabilities.
- The approach offers a significant advancement in synthesizing complex, collision-free 3D environments with high scene-level fidelity.
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