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.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Cadena, C., McDonald, J., Leonard, J.J., Neira, J.: Place recognition using near and far visual information. In: IFAC World Congress (2011)
Lai, K., Bo, L., Ren, X., Fox, D.: A Large-Scale Hierarchical Multi-View RGB-D Object Dataset. In: ICRA (2011)
Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor Segmentation and Support Inference from RGBD Images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012)
Janoch, A., Karayev, S., Jia, Y., Barron, J., Fritz, M., Saenko, K., Darrell, T.: A category-level 3-D object dataset: Putting the kinect to work. In: Workshop on Consumer Depth Cameras in Computer Vision (in conjunction with ICCV) (2011)
Collet, A., Martinez, M., Srinivasa, S.S.: The MOPED framework: Object Recognition and Pose Estimation for Manipulation. International Journal of Robotics Research 30, 1284–1306 (2011)
Hackett, J., Shah, M.: Multi-sensor fusion: a perspective. In: ICRA (1990)
Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Szeliski, R.: Computer Vision: Algorithms and Applications. Springer (2011)
Chiu, W.C., Blanke, U., Fritz, M.: Improving the kinect by cross-modal stereo. In: BMVC (2011)
Collet, A., Berenson, D., Srinivasa, S.S., Ferguson, D.: Object recognition and full pose registration from a single image for robotic manipulation. In: ICRA (2009)
Lai, K., Fox, D.: A Scalable Tree-based Approach for Joint Object and Pose Recognition. In: Conference on Artificial Intelligence (2011)
Kootstra, G., Kragic, D.: Fast and Bottom-Up Object Detection, Segmentation, and Evaluation using Gestalt Principles. In: ICRA (2011)
Helmer, S., Lowe, D.: Using stereo for object recognition. In: ICRA (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fouhey, D.F., Collet, A., Hebert, M., Srinivasa, S. (2012). Object Recognition Robust to Imperfect Depth Data. In: Fusiello, A., Murino, V., Cucchiara, R. (eds) Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science, vol 7584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33868-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-33868-7_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33867-0
Online ISBN: 978-3-642-33868-7
eBook Packages: Computer ScienceComputer Science (R0)