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.

翻译：房间布局估计从单张全景图中预测布局。该方法需要大规模且形状多样的数据集来训练模型。然而，真实世界数据集存在显著不平衡性，包括布局复杂度维度、相机位置以及场景外观变化的不平衡。这些问题严重影响了模型训练性能。本文提出感知不平衡的房间布局估计（iBARLE）框架来解决这些问题。iBARLE包含：(1) 外观变化生成（AVG）模块，用于促进视觉外观域泛化；(2) 复杂结构混合（CSMix）模块，用于增强对房间结构泛化能力；(3) 基于梯度的布局目标函数，能够更有效地处理复杂布局中的遮挡问题。所有模块联合训练并相互促进以实现最优性能。基于ZInD数据集~\cite{cruz2021zillow}的实验和消融研究表明，与其他布局估计基线方法相比，iBARLE达到了最先进的性能。