تشخیص تکرار هدایت شده با برجستگی
Abstract
I. Introduction
II. Related Works
III. Algorithm Overview
IV. Saliency Guided Window Repetition Detection
V. Experimental Results
Authors
Figures
References
Abstract
We present a saliency-guided algorithm for detecting the locations of repetitive structures on building facades. First, the global and local saliencies of each point are determined by measuring the global rarity and the local distinctness. The saliency map is utilized to adaptively extract the salient points. Second, the salient points are vertically sliced. A curve can be derived by counting the total number of points in each slice. Then, the curve is converted into a square wave to locate the vertical splitting position. Next, each segment is horizontally sliced, similar to the vertical splitting. The salient points are partitioned into repetitive candidates after the vertical and horizontal splitting. Finally, the repetitive candidates are refined according to the similarity of the neighborhood and the regularity of the arrangement. The experimental results demonstrate that our method can quickly and effectively extract repetitions from facade point clouds.
Introduction
Windows are important elements of a building facade. Accurate detection of 3D facade elements has become highly important for urban building modeling because the reconstructed models have been widely used for many important applications, such as virtual tourism, urban planning, and entertainment. There is an extensive literature on repetitive structure detection methods, which range from image-based methods [1]–[3] to 3D point-based methods [4]–[6]. Due to the loss of three-dimensional information in two-dimensional imaging and the inevitable influences of illumination, reflections and occlusions, detecting repetitive structures from images remains difficult. Recent advances in terrestrial laser scanning (TLS) provide a convenient approach for quickly collecting 3D point clouds of a building facade. Three-dimensional point clouds with high density and high accuracy can express the geometric details of objects. Several point cloud-based methods, such as slice-based methods [5], [6] and boundary-based methods [7], [8], were proposed for extracting the repetitions from facade point clouds. A data gap appears where the laser beam does not return a signal due to window glass or other openings. In order to detect the opening areas across the facade, slice-based methods must segment the point cloud of each planar facade using Random sample consensus (RANSAC) or a region growing method in advance.