NDLI: Understanding Bayesian Rooms Using Composite 3D Object Models

Content Provider	IEEE Xplore Digital Library
Author	Del Pero, L. Bowdish, J. Kermgard, B. Hartley, E. Barnard, K.
Copyright Year	2013
Description	Author affiliation: Univ. of Arizona, Tucson, AZ, USA (Del Pero, L.; Bowdish, J.; Kermgard, B.; Hartley, E.; Barnard, K.)
Abstract	We develop a comprehensive Bayesian generative model for understanding indoor scenes. While it is common in this domain to approximate objects with 3D bounding boxes, we propose using strong representations with finer granularity. For example, we model a chair as a set of four legs, a seat and a backrest. We find that modeling detailed geometry improves recognition and reconstruction, and enables more refined use of appearance for scene understanding. We demonstrate this with a new likelihood function that rewards 3D object hypotheses whose 2D projection is more uniform in color distribution. Such a measure would be confused by background pixels if we used a bounding box to represent a concave object like a chair. Complex objects are modeled using a set or re-usable 3D parts, and we show that this representation captures much of the variation among object instances with relatively few parameters. We also designed specific data-driven inference mechanisms for each part that are shared by all objects containing that part, which helps make inference transparent to the modeler. Further, we show how to exploit contextual relationships to detect more objects, by, for example, proposing chairs around and underneath tables. We present results showing the benefits of each of these innovations. The performance of our approach often exceeds that of state-of-the-art methods on the two tasks of room layout estimation and object recognition, as evaluated on two bench mark data sets used in this domain.
Starting Page	153
Ending Page	160
File Size	754198
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.27
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Three-dimensional displays Legged locomotion Image color analysis Solid modeling Geometry Cameras Context Bayesian modeling and inference Scene understanding Indoor scenes Object Recognition
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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