# 3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform Source: [3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform]() Collection index: [llms.txt]() Data updated: 2025-12-22T09:34:32.953Z Authors: Yining Zhao, Chao Wen, Zhou Xue, Yue Gao Published: 2022-07-19T14:22:28.000Z ## Abstract Significant geometric structures can be compactly described by global wireframes in the estimation of 3D room layout from a single panoramic image. Based on this observation, we present an alternative approach to estimate the walls in 3D space by modeling long-range geometric patterns in a learnable Hough Transform block. We transform the image feature from a cubemap tile to the Hough space of a Manhattan world and directly map the feature to the geometric output. The convolutional layers not only learn the local gradient-like line features, but also utilize the global information to successfully predict occluded walls with a simple network structure. Unlike most previous work, the predictions are performed individually on each cubemap tile, and then assembled to get the layout estimation. Experimental results show that we achieve comparable results with recent state-of-the-art in prediction accuracy and performance. Code is available at https://github.com/Starrah/DMH-Net. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Room Layout Estimation on MatterportLayout (test)](): Room Layout Estimation; MatterportLayout (test); 2D IoU: 81.25. ## Original sources and implementation [Original paper]() [PDF]()