iBARLE: imBalance-Aware Room Layout Estimation
Taotao Jing, Lichen Wang, Naji Khosravan, Zhiqiang Wan, Zachary, Bessinger, Zhengming Ding, Sing Bing Kang

TL;DR
iBARLE introduces a comprehensive framework for room layout estimation that addresses dataset imbalance issues through appearance variation, structural mix-up, and occlusion-aware objectives, achieving state-of-the-art results.
Contribution
The paper proposes the iBARLE framework with novel modules for appearance variation, structural mix-up, and occlusion-aware training, improving model robustness and accuracy.
Findings
iBARLE outperforms existing methods on ZInD dataset
Modules improve generalization to diverse room structures
Effective handling of occlusions in complex layouts
Abstract
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…
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Videos
iBARLE: imBalance-Aware Room Layout Estimation· youtube
Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Video Surveillance and Tracking Methods · Advanced Vision and Imaging
