{"ID":5938032,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-07T19:04:33.587908931Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03990","arxiv_id":"2607.03990","title":"InSpace: Structure-Aware 3D Indoor Scene Generation from a Single 360° Image","abstract":"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 360° 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/","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2607.03990","url_pdf":"https://arxiv.org/pdf/2607.03990v1","authors":"[\"Gwanhyeong Koo\",\"Hyunsu Kim\",\"Youngji Kim\",\"Taejae Lee\",\"Siwoo Lim\",\"Sunjae Yoon\",\"Suyong Yeon\",\"Chang D. Yoo\"]","published":"2026-07-04T19:16:29Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}