Egocentric 6-DoF Tracking of Small Handheld Objects
arXiv:1804.05870
Abstract
Virtual and augmented reality technologies have seen significant growth in the past few years. A key component of such systems is the ability to track the pose of head mounted displays and controllers in 3D space. We tackle the problem of efficient 6-DoF tracking of a handheld controller from egocentric camera perspectives. We collected the HMD Controller dataset which consist of over 540,000 stereo image pairs labelled with the full 6-DoF pose of the handheld controller. Our proposed SSD-AF-Stereo3D model achieves a mean average error of 33.5 millimeters in 3D keypoint prediction and is used in conjunction with an IMU sensor on the controller to enable 6-DoF tracking. We also present results on approaches for model based full 6-DoF tracking. All our models operate under the strict constraints of real time mobile CPU inference.
References in corpus (6)
- Adam: A Method for Stochastic Optimization
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- Axiomatic Attribution for Deep Networks
- Real-time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor
- BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis
- First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations