Combining edge and texture information for real-time accurate 3D camera tracking
By Luca Vacchetti, Vincent Lepetit, Pascal Fua. Third IEEE and ACM International Symposium on Mixed and Augmented Reality, 2004
345 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/73f25d27b2ee0cbbf76d8275424d850a32d6e223
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
- Mark Billinghurst
- Changhyun Choi
- Henrik Iskov Christensen
- Gerhard Reitmayr
- Edward Rosten
- Henry Been Lirn Duh
- Tom Drummond
- Feng Zhou
In Computer Vision and Pattern Recognition
- Mark Billinghurst
- Henrik Iskov Christensen
- Changhyun Choi
- Henry Been Lirn Duh
- Gerhard Reitmayr
- Feng Zhou
- Tom Drummond
- Edward Rosten
Citations in the corpus
- Robust 3D visual tracking using particle filtering on the special Euclidean group: A combined approach of keypoint and edge features (2011, methodology): “To improve pose priors, there have been attempts to enhance the pose accuracy between frames by incorporating interest points [4], [5], [6] or employing additional sensors [7].”
- Fusing points and lines for high performance tracking (2005, methodology): “[9] uses a simple combination of measurements from the feature tracker, which minimizes a weighted sum of edge errors and feature errors.”
- Pose Estimation for Augmented Reality: A Hands-On Survey (2016, neutral): “An extension is proposed in [140] to integrate contribution of a model-”
- Trends in augmented reality tracking, interaction and display: A review of ten years of ISMAR (2008, methodology): “Since that time a range of different nature feature techniques have been applied to AR such as [18][68] and [88] (these features are often used to construct models, see below).”
- PL-SVO: Semi-direct Monocular Visual Odometry by combining points and line segments (2016, methodology): “…algorithms have been also used for both solving the problem of In low-textured environments, point-based algorithms usually fail due to difficulties in founding a large number of features, in contrast, line segments are usually abundant. tracking [12] [13] [14], and estimating the camera motion [15].”
- Going out: robust model-based tracking for outdoor augmented reality (2006, background): “These problems are well known for edge-based trackers as discussed in [29, 23].”