Floorplan-Jigsaw: Jointly Estimating Scene Layout and Aligning Partial Scans

Cheng Lin, Changjian Li, Wenping Wang; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 5674-5683

Abstract


We present a novel approach to align partial 3D reconstructions which may not have substantial overlap. Using floorplan priors, our method jointly predicts a room layout and estimates the transformations from a set of partial 3D data. Unlike the existing methods relying on feature descriptors to establish correspondences, we exploit the 3D "box" structure of a typical room layout that meets the Manhattan World property. We first estimate a local layout for each partial scan separately and then combine these local layouts to form a globally aligned layout with loop closure. Without the requirement of feature matching, the proposed method enables some novel applications ranging from large or featureless scene reconstruction and modeling from sparse input. We validate our method quantitatively and qualitatively on real and synthetic scenes of various sizes and complexities. The evaluations and comparisons show superior effectiveness and accuracy of our method.

Related Material


[pdf]
[bibtex]
@InProceedings{Lin_2019_ICCV,
author = {Lin, Cheng and Li, Changjian and Wang, Wenping},
title = {Floorplan-Jigsaw: Jointly Estimating Scene Layout and Aligning Partial Scans},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}