Expokit: a software package for computing matrix exponentials
ACM Transactions on Mathematical Software (TOMS)
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Stereo Processing by Semiglobal Matching and Mutual Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
SBA: A software package for generic sparse bundle adjustment
ACM Transactions on Mathematical Software (TOMS)
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
KinectFusion: Real-time dense surface mapping and tracking
ISMAR '11 Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality
RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments
International Journal of Robotics Research
An Efficient Direct Approach to Visual SLAM
IEEE Transactions on Robotics
FrameSLAM: From Bundle Adjustment to Real-Time Visual Mapping
IEEE Transactions on Robotics
DTAM: Dense tracking and mapping in real-time
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Joint Depth and Color Camera Calibration with Distortion Correction
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this work, a real-time image-based camera tracker is designed for live television production studios. The major concern is to decrease camera tracking expenses by an affordable vision-based approach. First, a dense keyframe model of the static studio scene is generated using image-based dense tracking and bundle adjustment. Online camera tracking is then defined as registration problem between the current RGB-D measurement and the nearest keyframe. With accurate keyframe poses, our camera tracking becomes virtually driftless. The static model is also used to avoid moving actors in the scene. Processing dense RGB-D measurements requires special attention when aiming for real-time performance at 30Hz. We derive a real-time tracker from our cost function for a low-end GPU. The system requires merely a RGB-D sensor, laptop and a low-end GPU. Camera tracking properties are compared with KinectFusion. Our solution demonstrates robust and driftless real-time camera tracking in a television production studio environment.