The Robotics Institute

Carnegie Mellon Robotics Institute

Menu

• About RI
• People
• Research
• Education
• Careers
• News & Media
• Events

• RI History

• Contact Us

• Maps & Directions

• About this Site

• Faculty

• Adjunct Faculty

• Students

• Postdoctoral Fellows

• Project Staff

• Institute Staff

• Visitors

• Alumni

• All RI Members

• Faculty Research Interests

• Research Centers

• Consortia

• Labs & Groups

• Projects

• Publications

• Academic Programs

• Admissions

• Policies and Forms

• Courses

• Student Services

• Student Life

• Faculty Positions

• Staff Positions

• Postdoctoral Positions

• Internships

• Employers Seeking Graduating Students

• Graduate Students Seeking Employment

• Official Press Releases

• Recent News

• Videos

• Events and Activities

• Talks and Seminars

Fully Automatic Registration of Multiple 3D Data Sets

Daniel Huber and Martial Hebert
IEEE Computer Society Workshop on Computer Vision Beyond the Visible Spectrum(CVBVS 2001), December, 2001.

Download

Adobe portable document format (pdf) (1MB) Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

This paper presents a method for automatically registering multiple three dimensional (3D) data sets. Previous approaches required manual specification of initial pose estimates or relied on external pose measurement systems. In contrast, our method does not assume any knowledge of initial poses or even which data sets overlap. Our automatic registration algorithm begins by converting the input data into surface meshes, which are pair-wise registered using a surface matching engine. The resulting matches are tested for surface consistency, but some incorrect matches may be locally undetectable. A global optimization process searches a graph constructed from these potentially faulty pair-wise matches for a connected sub-graph containing only correct matches, employing a global consistency measure to detect incorrect, but locally consistent matches. From this sub-graph, the final poses of all views can be computed directly. We apply our algorithm to the problem of 3D digital reconstruction of real world objects and show results for a collection of automatically digitized objects.

Notes

Sponsor: Eastman Kodak Company Associated Center(s) / Consortia: Vision and Autonomous Systems Center Associated Lab(s) / Group(s): 3D Computer Vision Group Associated Project(s): Automatic 3D Modeling from Range Images

Text Reference

Daniel Huber and Martial Hebert, "Fully Automatic Registration of Multiple 3D Data Sets," IEEE Computer Society Workshop on Computer Vision Beyond the Visible Spectrum(CVBVS 2001), December, 2001.

BibTeX Reference

@inproceedings{Huber_2001_3886,    author = "Daniel Huber and Martial Hebert",    title = "Fully Automatic Registration of Multiple 3D Data Sets",    booktitle = "IEEE Computer Society Workshop on Computer Vision Beyond the Visible Spectrum(CVBVS 2001)",    month = "December",    year = "2001", }