CF-GISS: Collision-Free Generative 3D Indoor Scene Synthesis with Controllable Floor Plans and Optimized Layouts

SUCHONG, Yingbin Fu, Jing Yang, Param Hanji, wangshaojun, zhaoxuan, Fangcheng Zhong

Keywords: Indoor Scene Synthesis, 3D scene generation, Procedural generation, Generative models

TL;DR: A novel framework for generative 3D indoor scene synthesis that ensures collision-free layouts while conditioning on controllable floor plans, along with a novel indoor dataset

Abstract:

We introduce CF-GISS, a novel framework for generative 3D indoor scene synthesis that ensures collision-free scene layouts by incorporating an image-based intermediate layout representation. In contrast to existing methods that directly construct the scene graph or object list, our approach facilitates substantially more effective prevention of collision artifacts as out-of-distribution (OOD) scenarios during generation. Furthermore, CF-GISS conditions layout generation on floor plans controllable via images or textual descriptions, enabling the production of coherent, house-wide layouts that are robust to variations in geometric and semantic structures. Our framework demonstrates state-of-the-art performance on the 3D-FRONT dataset, delivering high-quality, collision-free scene synthesis while offering flexibility in accommodating a range of floor plan structures. Additionally, we propose a novel dataset with significantly expanded coverage of household items and room configurations, as well as improved data quality.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 9376