Layout Anything: One Transformer for Universal Room Layout Estimation
Md Sohag Mia, Muhammad Abdullah Adnan

TL;DR
Layout Anything introduces a transformer-based framework that unifies geometric structure prediction for indoor room layouts, achieving state-of-the-art accuracy and real-time inference without complex post-processing.
Contribution
The paper adapts the OneFormer's universal segmentation architecture for room layout estimation, integrating novel data augmentation and geometric loss modules for improved performance.
Findings
Achieves state-of-the-art accuracy on multiple benchmarks.
Runs at 114ms inference speed, enabling real-time applications.
Effectively enforces geometric constraints during training.
Abstract
We present Layout Anything, a transformer-based framework for indoor layout estimation that adapts the OneFormer's universal segmentation architecture to geometric structure prediction. Our approach integrates OneFormer's task-conditioned queries and contrastive learning with two key modules: (1) a layout degeneration strategy that augments training data while preserving Manhattan-world constraints through topology-aware transformations, and (2) differentiable geometric losses that directly enforce planar consistency and sharp boundary predictions during training. By unifying these components in an end-to-end framework, the model eliminates complex post-processing pipelines while achieving high-speed inference at 114ms. Extensive experiments demonstrate state-of-the-art performance across standard benchmarks, with pixel error (PE) of 5.43% and corner error (CE) of 4.02% on the LSUN, PE…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · 3D Shape Modeling and Analysis · Advanced Neural Network Applications
