Imagine taking a single photo of a room and instantly getting a rough 3D sketch of the walls, floor, and ceiling without having to train or customize an AI model for each new building. This work proposes a way to do that using a generic, training‑free baseline and a benchmark to compare methods.
Professionals in architecture and interior design often need fast, approximate 3D understanding of existing spaces from photos (for space planning, renovation concepts, or virtual staging). Current methods can be fragile, require model training, or don’t generalize well to ‘in-the-wild’ photos. This research sets a standardized benchmark and a no-training baseline for estimating 3D room layouts from images, helping the ecosystem move toward more robust, plug‑and‑play 3D layout tools.
Unknown
Unknown
High (Custom Models/Infra)
Robustness and accuracy on diverse, cluttered real-world interiors; computational cost for running 3D geometry inference at scale on high-resolution imagery.
Early Adopters
Focuses on a training-free baseline and a formal benchmark for 3D layout estimation in unconstrained, ‘in-the-wild’ scenes, which differentiates it from prior methods that typically rely on dataset-specific training and more controlled imagery.