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Fast depth estimation from single image using structured forest | IEEE Conference Publication | IEEE Xplore

Fast depth estimation from single image using structured forest


Abstract:

Depth estimation from single image is an important component of many vision systems, including robot navigation, motion capture and video surveillance. In this paper, we ...Show More

Abstract:

Depth estimation from single image is an important component of many vision systems, including robot navigation, motion capture and video surveillance. In this paper, we propose to apply a structure forest framework to infer depth information from single RGB image. The core idea of our approach is to exploit the structure properties exhibit in local patches of depth map to learn the depth level for each pixel. We formulate the problem of depth estimation in a structured learning framework based on random decision forests. Each trained forest infers a patch of structured labels that are accumulated across the image to obtain the final depth map. Moreover, we systematically investigate a variety of depth-relevant features and the regression forest framework automatically determines the best feature combination and uses as input of structure forest. Our approach achieves quasi real-time performance that is orders of magnitude faster than state-of-the-art approaches, while also achieving state-of-the-art depth estimation results on the Make3D dataset.
Date of Conference: 25-28 September 2016
Date Added to IEEE Xplore: 19 August 2016
ISBN Information:
Electronic ISSN: 2381-8549
Conference Location: Phoenix, AZ, USA

References

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