The Manhattan World Assumption: Regularities in Scene Statistics Which Enable Bayesian Inference
Abstract
Preliminary work by the authors made use of the so-called "Man(cid:173) hattan world" assumption about the scene statistics of city and indoor scenes. This assumption stated that such scenes were built on a cartesian grid which led to regularities in the image edge gra(cid:173) dient statistics. In this paper we explore the general applicability of this assumption and show that, surprisingly, it holds in a large variety of less structured environments including rural scenes. This enables us, from a single image, to determine the orientation of the viewer relative to the scene structure and also to detect target ob(cid:173) jects which are not aligned with the grid. These inferences are performed using a Bayesian model with probability distributions (e.g. on the image gradient statistics) learnt from real data.
Cite
Text
Coughlan and Yuille. "The Manhattan World Assumption: Regularities in Scene Statistics Which Enable Bayesian Inference." Neural Information Processing Systems, 2000.Markdown
[Coughlan and Yuille. "The Manhattan World Assumption: Regularities in Scene Statistics Which Enable Bayesian Inference." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/coughlan2000neurips-manhattan/)BibTeX
@inproceedings{coughlan2000neurips-manhattan,
title = {{The Manhattan World Assumption: Regularities in Scene Statistics Which Enable Bayesian Inference}},
author = {Coughlan, James M. and Yuille, Alan L.},
booktitle = {Neural Information Processing Systems},
year = {2000},
pages = {845-851},
url = {https://mlanthology.org/neurips/2000/coughlan2000neurips-manhattan/}
}