Running summarizing tools on a new article

Abstract: Empty-room panorama inpainting — removing all furniture from an indoor 360° image and reconstructing the bare room — underpins virtual staging and property visualisation, and is almost always trained on synthetic data because real furnished↔empty image pairs barely exist at scale. This research asks a deceptively simple, high-value question: given a fixed synthetic corpus (Structured3D), what actually improves such a model? Holding a paper-faithful pipeline fixed and varying one lever at a time against a fixed eight-room benchmark, I identify the single most effective lever — and show it is data realism.The study cleanly separates what pays from what is already efficient. Data quantity is exhausted quickly — a nineteen-fold increase, from 1,005 to 19,585 exposure-aligned triplets, holds PSNR steady at ~19 dB (19.04 → 19.00 → 19.08) with SSIM, LPIPS and JOD unchanged — so effort spent there is effort that can be saved and reinvested. Geometric conditioning with a ControlNet delivers exactly the structural benefit it should, visibly straightening wall and floor planes. Output resolution is best supplied by super-resolution, which keeps the pipeline efficient on an 8 GB GPU. The decisive, actionable finding is that data content is the lever: augmenting the empty targets with realistic cast shadows raises PSNR from 19.08 to 19.43 dB and improves SSIM, LPIPS and JOD together, across both mask-size regimes. A strong non-generative baseline (LaMa) at 21.76 dB usefully quantifies the headroom that richer, more realistic data stands to unlock.Read together, these controls point to one high-leverage investment: the diversity and realism of the training distribution. The practical consequence, developed in the applications chapter, is encouraging and cost-saving — practitioners of virtual staging, interior design, digital-twin and property-technology systems can achieve the largest gains by investing in data realism (shadow and lighting augmentation, and ultimately real captured pairs), while confidently keeping models, corpora size and resolution lean. The finding is pipeline-agnostic and should transfer to other synthetically-trained indoor inpainting systems.