Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Object recognition and full pose registration from a single image for robotic manipulation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Computer Vision: Algorithms and Applications
Computer Vision: Algorithms and Applications
The MOPED framework: Object recognition and pose estimation for manipulation
International Journal of Robotics Research
Indoor segmentation and support inference from RGBD images
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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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.