Simultaneous Determination of Registration and Deformation Parameters among 3D Range Images

Masuda, Tohru; Hirota, Y.; Nishino, Ko; Ikeuchi, Katsushi

doi:10.1007/978-0-387-75807_8

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Our estimation is affected by various kinds of errors: range data measurement errors; initial registration errors; and the errors in the manually input initial shape parameter. We have already reported how the accuracy of our estimated parameter depended on such errors by using synthesized data computed using known parameters and adding Gaussian noise 30) . Additionally, here we investigated the combined effects of an initial translation, rotation, and specified shape parameter.…”

Section: Discussionmentioning

confidence: 99%

Registration and Deformation of 3D Shape Data through Parameterized Formulation

Masuda

Ikeuchi

2007

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In this paper, we investigate conventional registration implementation, consisting of rotation and translation, to design the most precise registration so as to accurately restore the 3D shape of an object. To achieve the most accurate registration, our registration implementation needs robustness against data noise, or initial pose and position of data. To verify the accuracy of our implemented registration, we compare the registration behavior with the registration behavior of conventional methods, and evaluate the numerical accuracy of transformation parameter obtained by our registration. However, registration by rigid-body transformation is not enough for modeling and shape comparison: registration with deformation is needed. In this paper, we extend our robust registration to simultaneously estimate the shape parameter as well as the rigid-body transformation parameter. This extension method assumes that the deformation is formulated strictly from the deformation mechanism. We additionally introduce the applications of our extension method.

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Section: Discussionmentioning

confidence: 99%

Registration and Deformation of 3D Shape Data through Parameterized Formulation

Masuda

Ikeuchi

2007

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“…It applies to data related to structures that are both visible and invisible from the ground-based sensor, and to data from overlapping regions. The 3DTM algorithm determines rotation and translation parameters and compares data in the overlapping regions of two range images, while the 3DA algorithm determines motion parameters in addition to rotation and translation parameters [22].…”

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Flying Laser Range Sensor for Large-Scale Site-Modeling and Its Applications in Bayon Digital Archival Project

et al. 2007

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We have been conducting a project to digitize the Bayon temple, located at the center of Angkor-Thom in the kingdom of Cambodia. This is a huge structure, more than 150 meters long on all sides and up to 45 meters high. Digitizing such a large-scale object in fine detail requires developing new types of sensors for obtaining data of various kinds related to irregular positions such as the very high parts of the structure occluded from the ground. In this article, we present a sensing system with a moving platform, referred to as the Flying Laser Range Sensor (FLRS), for obtaining data related to these high structures from above them. The FLRS, suspended beneath a balloon, can be maneuvered freely in the sky and can measure structures invisible from the ground. The obtained data, however, have some distortion due to the movement of the sensor during the scanning process. In order to remedy this issue, we have developed several new rectification algorithms for the FLRS. One method is an extension of the 3D alignment algorithm to estimate not only rotation and translation but also motion parameters. This algorithm compares range data of overlapping regions from ground-based sensors and our FLRS. Another method accurately estimates the FLRS's position by combining range data and image sequences from a video camera mounted on the FLRS. We evaluate these algorithms using a hardware-based method and verify that both methods achieve much higher accuracy than previous methods.

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“…• Window matching-based method 14) • 3D registration-based method 15), 22) • Structure from motion-based method 1) In Ref. 14), under the assumption that translation of the sensor is very small and within a plane parallel to the image plane without any rotation, the shape is recovered by using a video sequence image.…”

Section: Our Contributionsmentioning

confidence: 99%

Acquisition and Rectification of Shape Data Obtained by a Moving Range Sensor

Banno

Ikeuchi

2007

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"Modeling from Reality" techniques are making great progress because of the availability of accurate geometric data from three-dimensional digitizers. These techniques contribute to numerous applications in many areas. Among them, one of the most important and comprehensive applications is modeling cultural heritage objects. For a large object, scanning from the air is one of the most efficient methods for obtaining 3D data. We developed a novel 3D measurement system, the Floating Laser Range Sensor (FLRS), in which a range sensor is suspended beneath a balloon. The obtained data, however, have some distortions due to movement of the system during the scanning process. We propose two novel methods to rectify the shape data obtained by the moving range sensor. One method rectifies the data by using image sequences; the other rectifies the data without images. To test these methods, we have conducted a digital archiving project of a large-scale heritage object, in which our algorithms are applied. The results show the effectiveness of our methods. Moreover, both methods are applicable not only to our FLRS, but also to moving range sensors in general.

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Simultaneous Determination of Registration and Deformation Parameters among 3D Range Images

Abstract: Abstract

Cited by 4 publications

References 13 publications

Registration and Deformation of 3D Shape Data through Parameterized Formulation

Registration and Deformation of 3D Shape Data through Parameterized Formulation

Flying Laser Range Sensor for Large-Scale Site-Modeling and Its Applications in Bayon Digital Archival Project

Acquisition and Rectification of Shape Data Obtained by a Moving Range Sensor

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