  Room layout estimation predicts layouts from a single panorama. It requires
datasets with large-scale and diverse room shapes to train the models. However,
there are significant imbalances in real-world datasets including the
dimensions of layout complexity, camera locations, and variation in scene
appearance. These issues considerably influence the model training performance.
In this work, we propose the imBalance-Aware Room Layout Estimation (iBARLE)
framework to address these issues. iBARLE consists of (1) Appearance Variation
Generation (AVG) module, which promotes visual appearance domain
generalization, (2) Complex Structure Mix-up (CSMix) module, which enhances
generalizability w.r.t. room structure, and (3) a gradient-based layout
objective function, which allows more effective accounting for occlusions in
complex layouts. All modules are jointly trained and help each other to achieve
the best performance. Experiments and ablation studies based on
ZInD~\cite{cruz2021zillow} dataset illustrate that iBARLE has state-of-the-art
performance compared with other layout estimation baselines.
