Abstract
We present an automatic approach to segment an object in calibrated
images acquired from multiple viewpoints. Our system starts with a new piecewise
planar layer-based stereo algorithm that estimates a dense depth map that
consists of a set of 3D planar surfaces. The algorithm is formulated using an energy
minimization framework that combines stereo and appearance cues, where
for each surface, an appearance model is learnt using an unsupervised approach.
By treating the planar surfaces as structural elements of the scene and reasoning
about their visibility in multiple views, we segment the object in each image independently.
Finally, these segmentations are refined by probabilistically fusing
information across multiple views. We demonstrate that our approach can segment
challenging objects with complex shapes and topologies, which may have
thin structures and non-Lambertian surfaces. It can also handle scenarios where
the object and background color distributions overlap significantly.
Datasets
The evaluation sequences are now available for download. Ground truth segmentations and camera calibration parameters are included.
MultiviewCosegDatasetsECCV2012.zip
@inproceedings {Kowdle-ECCV12,
author = "Adarsh Kowdle, Sudipta N. Sinha and Richard Szeliski",
title = "Multiple View Object Cosegmentation using Appearance and Stereo Cues",
booktitle = "European Conference on Computer Vision (ECCV 2012)",
location = "Firenze, Italy",
month = "October",
year = "2012",
}
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