Scene wireframes sketching for Unmanned Aerial Vehicles. (February 2019)

• Record Type: Journal Article
• Authors: Santos, Roi; Pardo, Xose M.; Fdez-Vidal, Xose R.
• Is Part Of: Pattern recognition. Volume 86(2019:Feb.)
• Page Start: 354
• Page End: 367
• Publication Date: 2019-02
• Subjects: 3D abstraction -- Reconstruction -- Line-based sketching -- UAV; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.09.017
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: New method for obtaining a 3D reconstruction of objects and scenes based on lines. The model does not require previous result from other SfM pipelines. Resilient to low texture and a low number of images. Aimed for UAVs. Abstract: This paper introduces novel insights to improve the state-of-the-art line-based unsupervised observation and abstraction models of man-made environments. The increasing use of autonomous UAVs inside buildings and around human-made structures demands new accurate and comprehensive representation of their operation environments. Most of the 3D scene abstraction methods use invariant feature point matching, nevertheless some sparse 3D point clouds do not concisely represent the structure of the environment. The presented approach is based on observation and representation models using the straight line segments. The goal of the work is a complete method based on the matching of lines, that provides a complementary approach to state-of-the-art methods when facing 3D scene representation of poor texture environments for future autonomous UAV. Oppositely to other recently published methods obtaining 3D line abstractions, the proposed method features 3D segment abstraction in the absence of a previously generated point based reconstruction. Another advantage is the ability to group the resulting 3D lines according to different planes, for exploiting coplanar line intersections. These intersections are used like feature points in the reconstruction process. It has been proved that this method exclusively based on lines can obtain spatial information in the adverse situations when a SIFT-like SfM pipeline fails to generate a dense point cloud.