Abstract
This paper describes a 3D object classification method by 3D-3D comparison using the numerical surface point signatures on interest points of 3D objects point cloud. Interest or salient points of 3D point cloud were found by Heat Kernel Signature method. The numerical point signatures used for classification were composed only on these points. To investigate the objects classification resistance to the data measurement noise, additionally to original 3D data was added 1.5 % of continuity distributed noise. Object classification was carried out using forty three 3D objects point cloud database. Study of 3D object interest points recognition has shown that the standard Surface Point Signatures methodology is sensitive to the normal vector used for signature composition as well as the object’s surface normal is very sensitive to objects mesh error. In order to reduce the sensitivity to the object surface measurement error we have proposed to use one constant vector as average from all object mesh normal’s. Such approach on average improved interest point’s recognition rate by ~16 % and allowed to reach 95.9 % of classification accuracy on used 43 objects database. DOI: http://dx.doi.org/10.5755/j01.eee.20.6.7259
Highlights
Three dimensional object recognition by 3D-3D template matching is a young research field extensively pursued in recent decade
Study of 3D object interest points recognition has shown that the standard Surface Point Signatures methodology is sensitive to the normal vector used for signature composition as well as the object’s surface normal is very sensitive to objects mesh error
In order to reduce the sensitivity to the object surface measurement error we have proposed to use one constant vector as average from all object mesh normal’s
Summary
Three dimensional object recognition by 3D-3D template matching is a young research field extensively pursued in recent decade. Several applications have been proposed for automated object recognition task with high enough accuracy. The 2D object recognition in images has been extensively investigated with significant success [1]. These methods cannot be directly extended and applied on range data for object recognition; new. 3D objects classification are successful used for produced components sorting and identification [8]. Various devices have been developed as an attempt to access the 3D information of the physical world, such as Time-Of-Flight (TOF) camera [9], stereo vision camera, laser range scanner, and the structured light
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