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

Putting Objects in Perspective

Derek Hoiem, Alexei A. Efros, and Martial Hebert
Proc. IEEE Computer Vision and Pattern Recognition (CVPR), June, 2006, pp. 2137 - 2144.

Download

Adobe portable document format (pdf) (4MB) 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

Image understanding requires not only individually esti- mating elements of the visual world but also capturing the interplay among them. In this paper, we provide a frame- work for placing local object detection in the context of the overall 3D scene by modeling the interdependence of ob- jects, surface orientations, and camera viewpoint. Most object detection methods consider all scales and locations in the image as equally likely. We show that with probabilistic estimates of 3D geometry, both in terms of surfaces and world coordinates, we can put objects into perspective and model the scale and location variance in the image. Our approach reflects the cyclical nature of the problem by allowing probabilistic object hypotheses to re- fine geometry and vice-versa. Our framework allows pain- less substitution of almost any object detector and is easily extended to include other aspects of image understanding. Our results confirm the benefits of our integrated approach.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center Associated Project(s): Geometrically Coherent Image Interpretation Number of pages: 8

Text Reference

Derek Hoiem, Alexei A. Efros, and Martial Hebert, "Putting Objects in Perspective," Proc. IEEE Computer Vision and Pattern Recognition (CVPR), June, 2006, pp. 2137 - 2144.

BibTeX Reference

@inproceedings{Hoiem_2006_5467,    author = "Derek Hoiem and Alexei A. Efros and Martial Hebert",    title = "Putting Objects in Perspective",    booktitle = "Proc. IEEE Computer Vision and Pattern Recognition (CVPR)",    pages = "2137 - 2144",    month = "June",    year = "2006",    volume = "2", }