InSpace: Structure-Aware 3D Indoor Scene Generation from a Single 360deg Image
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Abstract (arXiv)
Recent advances in single image-to-3D generation have enabled high-quality asset synthesis, yet extending these capabilities to indoor scene generation remains challenging. Existing methods focus on asset-level generation while neglecting the structural layout, which is essential for downstream applications and serves as the spatial anchor for grounding assets. However, a single image with a limited field of view lacks the spatial coverage to recover a coherent global layout. To this end, we use a 360deg image represented in equirectangular projection (ERP) and propose InSpace, a structure-aware framework for 3D indoor scene generation. InSpace comprises three stages: (1) estimating partial scene geometry as spatial priors, (2) generating coarse scene structure with view-selective cross-attention, and (3) producing detailed layout and asset geometry with textures through a global-local hybrid attention, using flow matching. We also propose ERP-FRONT, a paired ERP-Image-to-3D indoor scene dataset based on 3D-FRONT. Experiments show that InSpace generates complete 3D indoor scenes with structural layout, along with separate textured assets from a single ERP image, achieving strong performance across 3D and 2D metrics. Project Page: https://kookie12.github.io/InSpace-Project-Page/
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 70 of its 78 references that are in the map. First posted on arXiv 4 Jul 2026.
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38 more
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- 2025MVRoom: Controllable 3D Indoor Scene Generation with Multi-View Diffusion Models
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- 2026AnchoredDream: Zero-Shot 360deg Indoor Scene Generation from a Single View via Geometric Grounding
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