The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Sumbangan kertas kerja adalah dua kali ganda: Pertama, kajian literatur pendaftaran set titik diberikan, dan kedua, formulasi kuasa dua terkecil berwajaran kovarians baru bagi masalah pendaftaran set titik pandangan berbilang dibentangkan. Data titik untuk pendaftaran permukaan biasanya diperolehi oleh penderia permukaan 3D bukan sentuhan seperti pencari julat laser mengimbas atau sistem cahaya berstruktur. Formulasi kami membenarkan spesifikasi taburan hingar 3D anisotropik dan heteroskedastik (bergantung kepada titik) untuk setiap titik yang diukur. Sebaliknya, algoritma sebelumnya secara amnya menganggap model hingar sensor isotropik, yang tidak dapat menerangkan dengan tepat ciri hingar sensor. Bagi kes di mana ukuran titik adalah heteroskedastik dan taburan anisotropik, keputusan pendaftaran yang diperoleh dengan kaedah yang dicadangkan menunjukkan ketepatan yang lebih baik berbanding yang dihasilkan oleh rumusan kuasa dua terkecil tidak berwajaran. Keputusan dibentangkan untuk set data sintetik dan sebenar untuk menunjukkan ketepatan dan keberkesanan teknik yang dicadangkan.
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Salinan
John WILLIAMS, Mohammed BENNAMOUN, "A Multiple View 3D Registration Algorithm with Statistical Error Modeling" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 8, pp. 1662-1670, August 2000, doi: .
Abstract: The contribution of the paper is two-fold: Firstly, a review of the point set registration literature is given, and secondly, a novel covariance weighted least squares formulation of the multiple view point set registration problem is presented. Point data for surface registration is commonly obtained by non-contact, 3D surface sensors such as scanning laser range finders or structured light systems. Our formulation allows the specification of anisotropic and heteroscedastic (point dependent) 3D noise distributions for each measured point. In contrast, previous algorithms have generally assumed an isotropic sensor noise model, which cannot accurately describe the sensor noise characteristics. For cases where the point measurements are heteroscedastically and anisotropically distributed, registration results obtained with the proposed method show improved accuracy over those produced by an unweighted least squares formulation. Results are presented for both synthetic and real data sets to demonstrate the accuracy and effectiveness of the proposed technique.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_8_1662/_p
Salinan
@ARTICLE{e83-d_8_1662,
author={John WILLIAMS, Mohammed BENNAMOUN, },
journal={IEICE TRANSACTIONS on Information},
title={A Multiple View 3D Registration Algorithm with Statistical Error Modeling},
year={2000},
volume={E83-D},
number={8},
pages={1662-1670},
abstract={The contribution of the paper is two-fold: Firstly, a review of the point set registration literature is given, and secondly, a novel covariance weighted least squares formulation of the multiple view point set registration problem is presented. Point data for surface registration is commonly obtained by non-contact, 3D surface sensors such as scanning laser range finders or structured light systems. Our formulation allows the specification of anisotropic and heteroscedastic (point dependent) 3D noise distributions for each measured point. In contrast, previous algorithms have generally assumed an isotropic sensor noise model, which cannot accurately describe the sensor noise characteristics. For cases where the point measurements are heteroscedastically and anisotropically distributed, registration results obtained with the proposed method show improved accuracy over those produced by an unweighted least squares formulation. Results are presented for both synthetic and real data sets to demonstrate the accuracy and effectiveness of the proposed technique.},
keywords={},
doi={},
ISSN={},
month={August},}
Salinan
TY - JOUR
TI - A Multiple View 3D Registration Algorithm with Statistical Error Modeling
T2 - IEICE TRANSACTIONS on Information
SP - 1662
EP - 1670
AU - John WILLIAMS
AU - Mohammed BENNAMOUN
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2000
AB - The contribution of the paper is two-fold: Firstly, a review of the point set registration literature is given, and secondly, a novel covariance weighted least squares formulation of the multiple view point set registration problem is presented. Point data for surface registration is commonly obtained by non-contact, 3D surface sensors such as scanning laser range finders or structured light systems. Our formulation allows the specification of anisotropic and heteroscedastic (point dependent) 3D noise distributions for each measured point. In contrast, previous algorithms have generally assumed an isotropic sensor noise model, which cannot accurately describe the sensor noise characteristics. For cases where the point measurements are heteroscedastically and anisotropically distributed, registration results obtained with the proposed method show improved accuracy over those produced by an unweighted least squares formulation. Results are presented for both synthetic and real data sets to demonstrate the accuracy and effectiveness of the proposed technique.
ER -