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Object Recognition Robust to Imperfect Depth Data
David F. Fouhey, Alvaro Collet, Martial Hebert, and Siddhartha Srinivasa
Robotics Institute, Carnegie Mellon University, USAdfouhey@cs.cmu.edu
acollet@cs.cmu.edu
hebert@cs.cmu.edu
siddh@cs.cmu.edu
Abstract. In 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.
LNCS 7584, p. 83 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012