InSpace: Structure-Aware 3D Indoor Scene Generation from a Single 360° Image

2026-07-04

AI Summary

Generating structurally coherent, asset-disentangled, and textured full 3D indoor scenes from a single 360° equirectangular projection (ERP) image remains highly challenging. This work proposes a three-stage approach: first estimating local geometry as a spatial prior, then introducing a viewpoint-selective cross-attention mechanism to produce a coarse scene layout, and finally synthesizing fine-grained, textured assets through a hybrid global–local attention module combined with a flow-matching strategy. To the best of our knowledge, this is the first method to achieve end-to-end generation of complete 3D indoor scenes from a single 360° image with both structural plausibility and disentangled assets. It significantly outperforms existing approaches across both 2D and 3D evaluation metrics, with its core innovations lying in the designs of viewpoint-selective attention and the global–local hybrid attention mechanism.

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/

Problem

• 3D indoor scene generation
• structure-aware
• 360° image
• scene layout
• single-image-to-3D

Innovation

• structure-aware generation
• 360-degree image
• 3D indoor scene
• equirectangular projection
• flow matching