Pairwise Registration by Local Orientation Cues

• Published in: Computer graphics forum Vol. 35; no. 6; pp. 59 - 72
• Main Authors: Petrelli, Alioscia, Di Stefano, Luigi
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.09.2016
• Subjects: 3-D graphics; 3D local reference frame; 3D shape matching; Analysis; Cameras; Computation; computer vision-3D shape registration; Criteria; Cues; Feature extraction; I.3.5 [Computer Graphics]: Computational Geometry and Object Modelling-Geometric algorithms; I.3.5 [Computer Graphics]: Computational Geometry and Object Modelling—Geometric algorithms, languages and systems; I.4.3 [Image Processing and Computer Vision]: Enhancement-Registration; I.4.6 [Image Processing and Computer Vision]: Segmentation-Edge and feature detection; Image processing systems; languages and systems; Lasers; Pipelines; Registration; Scanners; Studies; Three dimensional models
• ISSN: 0167-7055; 1467-8659; 1467-8659
• DOI: 10.1111/cgf.12732

Inspired by recent work on robust and fast computation of 3D Local Reference Frames (LRFs), we propose a novel pipeline for coarse registration of 3D point clouds. Key to the method are: (i) the observation that any two corresponding points endowed with an LRF provide a hypothesis on the rigid motion between two views, (ii) the intuition that feature points can be matched based solely on cues directly derived from the computation of the LRF, (iii) a feature detection approach relying on a saliency criterion which captures the ability to establish an LRF repeatably. Unlike related work in literature, we also propose a comprehensive experimental evaluation based on diverse kinds of data (such as those acquired by laser scanners, Kinect and stereo cameras) as well as on quantitative comparison with respect to other methods. We also address the issue of setting the many parameters that characterize coarse registration pipelines fairly and realistically. The experimental evaluation vouches that our method can handle effectively data acquired by different sensors and is remarkably fast. Inspired by recent work on robust and fast computation of 3D Local Reference Frames (LRFs), we propose a novel pipeline for coarse registration of 3D point clouds. Key to the method are: (i) the observation that any two corresponding points endowed with an LRF provide a hypothesis on the rigid motion between two views, (ii) the intuition that feature points can be matched based solely on cues directly derived from the computation of the LRF, (iii) a feature detection approach relying on a saliency criterion which captures the ability to establish an LRF repeatably.