Stable real-time 3D tracking using online and offline information

By Luca Vacchetti, Vincent Lepetit, Pascal Fua. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004

441 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/0e0b810f73d3560d5e6e8543dc124185f5bd6c03

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Multiple 3D Object tracking for augmented reality (2008, methodology): “Our approach is somehow related to [12] but is much more robust since ours exploits feature recognition.”
HERB: a home exploring robotic butler (2010, methodology): “6.1 Related work Reliable object recognition, pose estimation and tracking are critical tasks in robotic manipulation (Taylor and Kleeman 2003; Ekvall et al. 2005; Zickler and Veloso 2006; Mittrapiyanuruk et al. 2004) and augmented reality applications (Vacchetti et al. 2004; Gordon and Lowe 2006).”
Fusing points and lines for high performance tracking (2005, methodology): “Point features can be used with [18] or without [2] a surface model.”
Real-Time Seamless Single Shot 6D Object Pose Prediction (2017, methodology): “Other related methods include 3D model-based registration [17, 20, 35], Hausdorff matching [9], oriented Chamfer matching for edges [18] and 3D chamfer matching for aligning 3D curve-based models to images [26].”
ASIFT: A New Framework for Fully Affine Invariant Image Comparison (2009, methodology): “The SIFT method and its variants have been popularly applied for scene recognition [10, 40, 50, 61, 15, 52, 65, 41] and detection [14, 46], robot localization [6, 53, 47, 43], image registration [64], image retrieval [16], motion tracking [59, 20], 3D modeling and reconstruction [49, 62], building p”
Tracking-Learning-Detection (2012, background): “Planarity [34], [36] or a full 3D model [37] is typically exploited.”
Object recognition and pose estimation using color cooccurrence histograms and geometric modeling (2005, methodology): “Computing all six parameters for objects with complex textural properties has proven to be a difficult problem, [32, 31].”
Object recognition and full pose registration from a single image for robotic manipulation (2009, methodology): “Reliable object recognition, pose estimation and tracking are critical tasks in robotic manipulation [6], [7], [8], [9] and augmented reality applications [10], [1].”