Virtual Staging of Indoor Panoramic Images via Multitask Learning and Inverse Rendering

Capturing indoor environments with 360$^\circ$∘ images provides a cost-effective method for creating immersive content. However, virtual staging-removing existing furniture and inserting new objects with realistic lighting-remains challenging. We present virtual staging pipeline for vIrtual staging for panoramic indoor images (VISPI), a framework that enables interactive restaging of indoor scenes from a single panoramic image. Our approach combines multitask deep learning with real-time rendering to extract geometric, semantic, and material information from cluttered scenes. The system includes: first, a vision transformer that simultaneously predicts depth, normals, semantics, albedo, and material properties; second, spherical Gaussian lighting estimation; third, real-time editing for interactive object placement; lastly, stereoscopic multicenter-of-projection generation for head mounted display exploration. The framework processes input through two pathways: extracting clutter-free representations for virtual staging and estimating material properties, including metallic and roughness signals. We evaluate VISPI on Structured3D and FutureHouse datasets, demonstrating potential applications in real estate visualization, interior design, and virtual environment creation.