کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4947374 1439576 2017 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multi-attribute statistics histograms for accurate and robust pairwise registration of range images
ترجمه فارسی عنوان
هیستوگرام آمار چند ویژگی برای ثبت دقت و صحت عکسهای محدوده
کلمات کلیدی
محور مرزی محلی، چندین ویژگی، برآورد تبدیل، تطبیق ویژگی، ثبت دامنه دامنه،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Registration of range images based on local shape features is widely adopted due to its validated effectiveness and robustness, while most existing local shape descriptors struggle to simultaneously achieve a pleasurable and balanced performance in terms of distinctiveness, robustness and time efficiency. This paper proposes a novel representation of 3D local surfaces, called multi-attribute statistics histograms (MaSH), for automatic registration of range images. MaSH comprises both spatial and geometric information characterizations. The characterization of spatial information is achieved via radial partitions in the 3D local support volume around the keypoint based on a local reference axis (LRA), creating a set of subspaces. While the encoding the shape geometry is performed by calculating statistical histograms of multiple faint correlated geometric attributes (i.e., local depth, normal deviation, and surface variation angle) for each subspace, so as to achieve information complementarity. Then, a robust rigid transformation estimation algorithm named 2-point based sample consensus with global constrain (2SAC-GC) is presented to tackle the problem of calculating an optimal transformation from the correspondence set with outliers. Finally, a coarse-to-fine registration method based on MaSH and 2SAC-GC is proposed for aligning range images. Experiments on both high-resolution (Laser Scanner) and low-resolution (Kinect) datasets report that, our method achieves a registration accuracy of 90.36% and 80.39% on the two datasets, respectively. It also exhibits strong robustness against noise and varying mesh resolutions. Furthermore, feature matching experiments show the over-all superiority of the proposed MaSH descriptor against the state-of-the-arts including the spin image, snapshots, THRIFT, FPFH, RoPS, LFSH and RCS descriptors.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 251, 16 August 2017, Pages 54-67
نویسندگان
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