Putting Objects in Perspective

Image understanding requires not only individually estimating elements of the visual world but also capturing the interplay among them. We provide a framework for placing local object detection in the context of the overall 3D scene by modeling the interdependence of objects, 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 refine geometry and vice-versa. Our framework allows painless substitution of almost any object detector and is easily extended to include other aspects of image understanding.

This work the latest of an on-going effort in Geometrically Coherent Image Interpretation. In our SIGGRAPH'05 paper Automatic Photo Pop-up, we show how to construct simple "pop up" 3D models from a single image. In our ICCV'05 paper Geometric Context from a Single Image, we provide a quantitative analysis of our system and extend our work by subclassifying vertical regions and using the geometric labels as context for object detection.


Dataset

Data is available here.


Publications

D. Hoiem, A.A. Efros, and M. Hebert, "Putting Objects in Perspective", to appear in CVPR 2006. pdf




Home


Some of this material is based upon work supported by the National Science Foundation under CAREER Grant No. ISS-0546547. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This work is also partially supported by a Microsoft Research Fellowship awarded in 2006.


GoStats.com