No More Ambiguity in 360deg Room Layout via Bi-Layout Estimation

Yu-Ju Tsai, Jin-Cheng Jhang, Jingjing Zheng, Wei Wang, Albert Y. C. Chen, Min Sun, Cheng-Hao Kuo, Ming-Hsuan Yang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 28056-28065

Inherent ambiguity in layout annotations poses significant challenges to developing accurate 360deg room layout estimation models. To address this issue we propose a novel Bi-Layout model capable of predicting two distinct layout types. One stops at ambiguous regions while the other extends to encompass all visible areas. Our model employs two global context embeddings where each embedding is designed to capture specific contextual information for each layout type. With our novel feature guidance module the image feature retrieves relevant context from these embeddings generating layout-aware features for precise bi-layout predictions. A unique property of our Bi-Layout model is its ability to inherently detect ambiguous regions by comparing the two predictions. To circumvent the need for manual correction of ambiguous annotations during testing we also introduce a new metric for disambiguating ground truth layouts. Our method demonstrates superior performance on benchmark datasets notably outperforming leading approaches. Specifically on the MatterportLayout dataset it improves 3DIoU from 81.70% to 82.57% across the full test set and notably from 54.80% to 59.97% in subsets with significant ambiguity.