Semantic Alignment Verification for Floorplan Reconstruction from Sparse Panoramas
ECCV 2022
John Lambert
Yuguang Li
Ivaylo Boyadzhiev
Lambert Wixson
Manjunath Narayana
Will Hutchcroft
James Hays
Frank Dellaert
Sing Bing Kang


We propose a new system for automatic 2D floorplan reconstruction that is enabled by SALVe, our novel pairwise learned alignment verifier. The inputs to our system are sparsely located 360◦ panoramas, whose semantic features (windows, doors, and openings) are inferred and used to hypothesize pairwise room adjacency or overlap. SALVe initializes a pose graph, which is subsequently optimized using GTSAM. Once the room poses are computed, room layouts are inferred using HorizonNet, and the floorplan is constructed by stitching the most confident layout boundaries. We validate our system qualitatively and quantitatively as well as through ablation studies, showing that it outperforms state-of-the-art SfM systems in completeness by over 200%, without sacrificing accuracy. Our results point to the significance of our work: poses of 81% of panoramas are localized in the first 2 connected components (CCs), and 89% in the first 3 CCs.




We make our code, pretrained models, and our ModifiedHorizonNet (MHNet) ZInD predictions publicly available.


Paper and Supplementary Material

J. Lambert, Y. Li, I. Boyadzhiev, L. Wixson, M. Narayana, W. Hutchcroft, J. Hays, F. Dellaert, S.B. Kang.
SALVe: Semantic Alignment Verification for Floorplan Reconstruction from Sparse Panoramas.
In European Conference on Computer Vision (ECCV), 2022.
(hosted on ArXiv)



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