Putting objects in perspective

被引：279

作者：

Hoiem, Derek ^{[1
]}

Efros, Alexei A. ^{[1
]}

Hebert, Martial ^{[1
]}

机构：

[1] Carnegie Mellon Univ, Inst Robot, Pittsburgh, PA 15213 USA

来源：

INTERNATIONAL JOURNAL OF COMPUTER VISION | 2008年 / 80卷 / 01期

基金：

美国国家科学基金会;

关键词：

scene understanding; object recognition; object detection; camera calibration; 3D reconstruction; surface estimation; viewpoint estimation;

D O I：

10.1007/s11263-008-0137-5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Image understanding requires not only individually estimating elements of the visual world but also capturing the interplay among them. In this paper, 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. Our results confirm the benefits of our integrated approach.

引用

页码：3 / 15

页数：13