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We present 360-DFPE, a sequential floor plan estimation method that directlytakes 360-images as input without relying on active sensors or 3D information.Our approach leverages a loosely coupled integration between a monocular visualSLAM solution and a monocular 360-room layout approach, which estimate cameraposes and layout geometries, respectively. Since our task is to sequentiallycapture the floor plan using monocular images, the entire scene structure, roominstances, and room shapes are unknown. To tackle these challenges, we firsthandle the scale difference between visual odometry and layout geometry viaformulating an entropy minimization process, which enables us to directly align360-layouts without knowing the entire scene in advance. Second, tosequentially identify individual rooms, we propose a novel room identificationalgorithm that tracks every room along the camera exploration using geometryinformation. Lastly, to estimate the final shape of the room, we propose ashortest path algorithm with an iterative coarse-to-fine strategy, whichimproves prior formulations with higher accuracy and faster run-time. Moreover,we collect a new floor plan dataset with challenging large-scale scenes,providing both point clouds and sequential 360-image information. Experimentalresults show that our monocular solution achieves favorable performance againstthe current state-of-the-art algorithms that rely on active sensors and requirethe entire scene reconstruction data in advance.

360-DFPE: Leveraging Monocular 360-Layouts for Direct Floor Plan Estimation