Symmetry Detection Using Feature Lines

Abstract

In this paper, we describe a new algorithm for detecting structural redundancy in geometric data sets. Our algorithm computes rigid symmetries, i.e., subsets of a surface model that reoccur several times within the model differing only by translation, rotation or mirroring. Our algorithm is based on matching locally coherent constellations of feature lines on the object surfaces. In comparison to previous work, the new algorithm is able to detect a large number of symmetric parts without restrictions to regular patterns or nested hierarchies. In addition, working on relevant features only leads to a strong reduction in memory and processing costs such that very large data sets can be handled. We apply the algorithm to a number of real world 3D scanner data sets, demonstrating high recognition rates for general patterns of symmetry.

Unsupervised discovery of repetitive objects

“…They focused on detection of symmetry or regular patterns in 3D with applications in graphics and image compression. Work of Bokeloh et al [9] is more closely related to this work. The authors proposed an algorithm for detecting structural redundancy by matching symmetric constellations of feature lines.…”

Segmentation and Unsupervised Part-based Discovery of Repetitive Objects

“…In 3D, discovery and utilization of repetition has been adressed in computer aided design and other synthetic models [7,8,9]. The work of Bokeloh et al [10] is more closely related to this work. The authors proposed an algorithm for detecting structural redundancy by matching symmetric constellations of feature lines.…”

Pattern-Driven Colorization of 3D Surfaces

“…Voting is limited to a fixed set of transformations. Instead of operating at the level of sample points, in [2] a graph-based approach is proposed, which works at the level of feature curves. While achieving substantial speedup, this approach is intended mostly for man-made objects, such as architectural models.…”