Abstract
The proposed method given in this article is prepared for analysis of data in the form of cloud of points directly from 3D measurements. It is designed for use in the end-user applications that can directly be integrated with 3D scanning software. The method utilizes locally calculated feature vectors (FVs) in point cloud data. Recognition is based on comparison of the analyzed scene with reference object library. A global descriptor in the form of a set of spatially distributed FVs is created for each reference model. During the detection process, correlation of subsets of reference FVs with FVs calculated in the scene is computed. Features utilized in the algorithm are based on parameters, which qualitatively estimate mean and Gaussian curvatures. Replacement of differentiation with averaging in the curvatures estimation makes the algorithm more resistant to discontinuities and poor quality of the input data. Utilization of the FV subsets allows to detect partially occluded and cluttered objects in the scene, while additional spatial information maintains false positive rate at a reasonably low level.
Highlights
Prevalence of 3D data acquisition optical systems demands development of dedicated algorithms for an efficient analysis of such data in various fields of civil engineering, entertainment, and industry
Topology of the analysed data is exploited by another group of algorithms related to Non-rigid shapes [17], where statistical significance measure uses geodesic metrics as partial similarity criterion [18], or where 3D object is characterized by a set of signatures which allows to determine similarity between objects as a multiplication of the pair wise histogram comparison results [19]
Each reference object is presented as a set of directional point clouds acquired from various points of view
Summary
Prevalence of 3D data acquisition optical systems demands development of dedicated algorithms for an efficient analysis of such data in various fields of civil engineering, entertainment, and industry. Topology of the analysed data is exploited by another group of algorithms related to Non-rigid shapes [17], where statistical significance measure uses geodesic metrics as partial similarity criterion [18], or where 3D object is characterized by a set of signatures (histograms of geometric distances, diffusion distances, the ratio of diffusion and geodesic distances, and two curvature-related histograms) which allows to determine similarity between objects as a multiplication of the pair wise histogram comparison (with χ2 measure) results [19] They are not suitable for data in the form of single directional point clouds. Topology of an object represented by a directional point cloud may be changed due to the presence of noise or occlusion This would introduce significant errors in the recognition process that use such type of algorithms. As a part of built-in 3D scanner software, may be optional, but in result significant operator support in critical situations
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
