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Carnegie Mellon Robotics Institute

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Abstract

n this paper, we present an adaptive data fusion model that robustly integrates depth and image only perception. Combining dense depth measurements with images can greatly enhance the performance of many computer vision algorithms, yet degraded depth measurements (e.g., missing data) can also cause dramatic performance losses to levels below image-only algorithms. We propose a generic fusion model based on maximum likelihood estimates of fused image-depth functions for both available and missing depth data. We demonstrate its application to each step of a state-of-the-art image-only object instance recognition pipeline. The resulting approach shows increased recognition performance over alternative data fusion approaches.

Notes

Associated Center(s) / Consortia: Quality of Life Technology Center

Text Reference

David Fouhey, Alvaro Collet Romea, Martial Hebert, and Siddhartha Srinivasa, "Object Recognition Robust to Imperfect Depth Data," 2nd Workshop on Consumer Depth Cameras for Computer Vision in conjunction with ECCV 2012, October, 2012.

BibTeX Reference

@inproceedings{Fouhey_2012_7252,
   author = "David Fouhey and Alvaro {Collet Romea} and Martial Hebert and Siddhartha Srinivasa",
   title = "Object Recognition Robust to Imperfect Depth Data",
   booktitle = "2nd Workshop on Consumer Depth Cameras for Computer Vision in conjunction with ECCV 2012",
   month = "October",
   year = "2012",
}

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