Reference brains are indispensable tools in human brain mapping, enabling integration of multimodal data into an anatomically realistic standard space. Available reference brains, however, are restricted to the macroscopic scale and do not provide information on the functionally important microscopic dimension. We created an ultrahigh-resolution three-dimensional (3D) model of a human brain at nearly cellular resolution of 20 micrometers, based on the reconstruction of 7404 histological sections. "BigBrain" is a free, publicly available tool that provides considerable neuroanatomical insight into the human brain, thereby allowing the extraction of microscopic data for modeling and simulation. BigBrain enables testing of hypotheses on optimal path lengths between interconnected cortical regions or on spatial organization of genetic patterning, redefining the traditional neuroanatomy maps such as those of Brodmann and von Economo.

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This paper describes an environment to automatically or semi-automatically compute the precise mapping between a set of 2D images and a triangulated 3D model built from highresolution 3D range data. This environment is part of our Atelier3D framework for the modeling, visualization and analysis of large sensor-based datasets. This work was done to initially support three cultural heritage application projects: the modeling of the Grotta dei Cervi in Italy, of the Erechtheion in Athens, Greece, and of Leonardo's Mona Lisa. The proposed method combines image-based registration, feature matching, robust estimation techniques and advanced multi-resolution rendering with a powerful user interface.

Size and scale issues present a complexity problem in visualizing detailed 3D models built from sensor data. A model of Leonardo da Vinci's Mona Lisa, with its thin pictorial layer, illustrates the need for intuitive real-time processing tools that are seamlessly integrated with a multiresolution visualization environment.

/npsi/ctrl?action=rtdoc&an=15261152&lang=en http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?action=rtdoc&an=15261152&lang=fr READ THESE TERMS AND CONDITIONS CAREFULLY BEFORE USING THIS WEBSITE.http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/jsp/nparc_cp.jsp?lang=en Vous avez des questions? Nous pouvons vous aider. Pour communiquer directement avec un auteur, consultez la première page de la revue dans laquelle son article a été publié afin de trouver ses coordonnées. Si vous n'arrivez pas à les repérer, communiquez avec nous à PublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. Questions? Contact the NRC Publications Archive team atPublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. If you wish to email the authors directly, please see the first page of the publication for their contact information. NRC Publications Archive Archives des publications du CNRCFor the publisher's version, please access the DOI link below./ Pour consulter la version de l'éditeur, utilisez le lien DOI ci-dessous.http://doi.org/10.1109/CISS.2010.5464966 IEEE, pp. 1-6, 2010-03-19 Issues in Acquiring, Processing and Visualizing Large and Detailed 3D Models Abstract-Modelling from reality using active optical geometric sensing has been a very active research area in computer graphics and vision for the last twenty years. While most elements of the modelling pipeline have reached maturity and have been adopted in several application sectors, several issues remain, particularly in the modelling of large structures and environments, as well as in the management of large, complex and detailed 3D models. This paper describes some of these issues, and outlines some of the solutions that we have proposed. These methods and approaches, as well as their current limitations, are described using different example applications: a monument (the Erechtheion), a painting (Mona Lisa), and a terrain model.