Pose tracking from natural features on mobile phones

By Daniel Wagner, Gerhard Reitmayr, Alessandro Mulloni, Tom Drummond, Dieter Schmalstieg. 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality, 2008

529 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/93611a7f0f51fe7db35893a6fad8b92eeb025d36

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Revisiting Trends in Augmented Reality Research: A Review of the 2nd Decade of ISMAR (2008–2017) (2018, background): “[142], which optimized and extended computer vision feature descriptors to work on mobile phones.”
Mobile Augmented Reality Survey: From Where We Are to Where We Go (2017, methodology): “[216] replaced conventional Difference of Gaussians (DoGs) with FAST corner detector, 4*4 sub-regions with 3*3 sub-regions and k-d tree with multiple Spill Trees to reduce SIFT computation.”
BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth (2017, methodology): “Keypoint-based methods [16, 23] were popular for a long time and perform well but only on very textured objects.”
Segmentation-Driven 6D Object Pose Estimation (2018, methodology): “Traditionally, it has been handled by establishing correspondences between the object’s known 3D model and 2D pixel locations, followed by using the Perspective-n-Point (PnP) algorithm to compute the 6 pose parameters [19, 38, 43].”
AprilTag: A robust and flexible visual fiducial system (2011, methodology): “The performance of ARTag inspired several improvements to ARToolkit, which evolved into ARToolkitPlus [2], and finally Studierstube Tracker [12].”
Real-Time Detection and Tracking for Augmented Reality on Mobile Phones (2010, background): “To the best of our knowledge, our own previous work [20] represents the only published real-time 6DOF natural feature tracking system on mobile phones so far.”
Large scale mining and retrieval of visual data in a multimodal context (2008, background): “Only just recently client-side applications have been proposed [Wagner et al., 2008] which allow real-time extraction of SIFT features on client-side devices through heavy optimization.”
BRIEF: Computing a Local Binary Descriptor Very Fast (2012, methodology): “Furthermore, for larger point sets, it would be easy to incorporate a more elaborate matching strategy [37].”
Pose Estimation for Augmented Reality: A Hands-On Survey (2016, unknown): “Such an approach has been successfully used for vehicle localization [110] and augmented reality [81], [143], [144] (see Fig.”
Real-Time Seamless Single Shot 6D Object Pose Prediction (2017, methodology): “There are many fast keypoint and edge-based methods [21, 31, 36] that are effective for textured objects.”