2D Floorplan Generation from Panoramic Images
Date Issued
2015
Date
2015
Author(s)
Liao, Pei-Cheng
Abstract
This thesis proposes a semi-automatic method that generates a 2D floorplan from cylindrical panoramic images in indoor public area. Nowadays, digital maps are common for outdoor, but people also need indoor map when entering a new building. Popular reconstruction methods like structure form motion (SfM) often need sufficient and robust features, which are lacked in many indoor environments. Our purposed method is improved by the shape generation algorithm [1] can overcome featureless conditions. The input panoramic images are separated by corner selection from a user and generate corner rays from the panorama centers to corner positions. The shape generation algorithm connects adjacent corner rays to generate shapes, and then the shapes are combined to the floorplan. Nevertheless, the said algorithm has some problems; for example, it cannot find the correspondence to combine and is limited to the Manhattan world assumption. In order to solve these problems, we present two methods: wall matching and floorplan refinement. Wall matching will correlate the wall correspondence that the wall is separated by corners. We apply the geometry and image similarity to find the best wall correspondence, and then combine the shapes to the floorplan. However, when our method computes shape combination, the error is generated from shape generation and propagates to next shape, leading overall floorplan to the inaccurate result. Therefore, we develop the corner-line minimization in least square method that is a robust floorplan refinement algorithm to get more accurate result. Finally, the method can be used to generate indoor floorplan in featureless and non-Manhattan world scene. We also demonstrate results on several challenging datasets, and the results of floorplan are similar to ground truth.
Subjects
Panoramic image
2D floorplan generation
Manhattan world assumption
Type
thesis