Presentation on theme: "KinectFusion : Real-Time Dense Surface Mapping and Tracking IEEE International Symposium on Mixed and Augmented Reality 2011 Science and Technology Proceedings."— Presentation transcript:
KinectFusion : Real-Time Dense Surface Mapping and Tracking IEEE International Symposium on Mixed and Augmented Reality 2011 Science and Technology Proceedings (Best paper reward)
Outline Introduction Motivation Background System diagram Experiment results Conclusion
Introduction Passive camera Simultaneous localization and mapping (SLAM) Structure from motion (SFM) – MonoSLAM (ICCV 2003) MonoSLAM – Parallel Tracking and Mapping  (ISMAR 2007) Parallel Tracking and Mapping Disparity – Depth model  (2010) Depth model Pose of camera from Depth models  (ICCV 2011) Pose of camera from Depth models
Motivation Active camera : Kinect sensor Pose estimation from depth information Real-time mapping – GPU
Background- Camera sensor Kinect Sensor – Infra-red light Input Information – RGB image(1) – Raw depth data – Calibrated depth image(2) (1)(2)
Background – Pose estimation Depth maps from two views Iterative closest points (ICP)  Point-plane metric  ICP
Background – Pose estimation Projective data association algorithm 
Background – Scene Representation Volume of space Signed distance function 
Experiment Results : Processing time Pre-processing raw data, data-associations; pose optimisations; raycasting the surface prediction and surface measurement integration Demo
Conclusion Robust tracking of camera pose by all aligning all depth points Parallel algorithms for both tracking and mapping
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