An Invitation to 3-D Vision: From Images to Geometric Models
An Invitation to 3-D Vision: From Images to Geometric Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Randomized Trees for Real-Time Keypoint Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Online camera pose estimation in partially known and dynamic scenes
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Feature management for efficient camera tracking
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
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Camera-based tracking systems which reconstruct a feature map with structure from motion or SLAM techniques highly depend on the ability to track a single feature in different scales, different lighting conditions and a wide range of viewing angles. The acquisition of high quality features is therefore indispensable for a continuous tracking of a feature with a maximum possible range of valid appearances. We present a tracking system where not only the position of a feature but also its surface normal is reconstructed and used for precise prediction and tracking recovery of lost features. The appearance of a reference patch is also estimated sequentially and refined during the tracking, which leads to a more stable feature tracking step. Such reconstructed reference templates can be used for tracking a camera pose with a great variety of viewing positions. This feature reconstruction process is combined with a feature management system, where a statistical analysis of the ability to track a feature is performed, and only the most stable features for a given camera viewing position are used for the 2D feature tracking step. This approach results in a map of high quality features, where the the real time capabilities can be preserved by only tracking the most necessary 2D feature points.