Towards Occlusion-Robust and Ambiguity-Resilient Panoramic Room Layout Estimation via A Layout Reliability Field

Estimating room layout from a panoramic image is fundamental to downstream tasks such as embodied navigation and scene reasoning, and has attracted extensive research attention. However, existing works are challenged by severe object occlusions and inherent ambiguity of layout annotations. In this paper, we propose an occlusion-robust and ambiguity-resilient layout estimation framework, which follows a paradigm that decomposes the task into horizontal-depth estimation and room height estimation. The horizontal-depth estimation is obtained through the fusion of depth priors and a layout reliability field. The reliability field is constructed under the supervision of geometric depth evidence and semantic textual priors. Specifically, we develop a depth-consistency-driven occlusion representation scheme that leverages depth-scale normalization to stably model object-induced occlusions across diverse scenes. In addition, a candidate-layout reasoning scheme is introduced by designing text-guided prompts and leveraging a multimodal large language model (MLLM) to capture semantic representations of layout ambiguity. Extensive experiments on three widely recognized datasets demonstrate that the proposed method significantly reduces boundary misclassification.