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KinectFusion : Real-Time Dense Surface Mapping and Tracking

KinectFusion : Real-Time Dense Surface Mapping and Tracking. by Richard.A Newcombe et al . Presenting: Boaz Petersil. Motivation. Augmented Reality. Robot Navigation. 3d model scanning. Etc. Related Work. Tracking (&sparse Mapping). Bundle-adjustment(offline ). PTAM.

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KinectFusion : Real-Time Dense Surface Mapping and Tracking

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  1. KinectFusion: Real-Time Dense Surface Mapping and Tracking by Richard.ANewcombeet al. Presenting: Boaz Petersil

  2. Motivation Augmented Reality Robot Navigation 3d model scanning Etc..

  3. Related Work Tracking (&sparse Mapping) Bundle-adjustment(offline) PTAM Tracking&Mapping Kinect Fusion DTAM (RGB cam!) Dense Mapping

  4. Challenges • Tracking Camera Precisely • Fusing and De-noising Measurements • Avoiding Drift • Real-Time • Low-Cost Hardware

  5. Proposed Solution • Fast Optimization for Tracking, Due to High Frame Rate. • Global Framework for fusing data • Interleaving Tracking & Mapping • Using Kinect to get Depth data (low cost) • Using GPGPU to get Real-Time Performance (low cost)

  6. How does kinect work?

  7. Method

  8. Tracking • Finding Camera position is the same as fitting frame’s Depth Map onto Model Tracking Mapping

  9. Tracking – ICP algorithm • icp = iterative closest point • Goal: fit two 3d point sets • Problem: What are the correspondences? • Kinect fusion chosen solution: • Start with • Project model onto camera • Correspondences are points with same coordinates • Find new T with Least - Squares • Apply T, and repeat 2-5 until convergence Tracking Mapping

  10. Tracking – ICP algorithm • Assumption: frame and model are roughly aligned. • True because of high frame rate Tracking Mapping

  11. Mapping • Mapping is Fusing depth maps when camera poses are known • Problems: • measurements are noisy • Depth maps have holes in them • Solution: • using implicit surface representation • Fusing = estimating from all frames relevant Tracking Mapping

  12. Mapping – surface representation • Surface is represented implicitly - using Truncated Signed Distance Function (TSDF) Voxel grid • Numbers in cells measure voxel distance to surface – D Tracking Mapping

  13. Mapping Tracking Mapping

  14. Mapping d= [pixel depth] – [distance from sensor to voxel] Tracking Mapping

  15. Mapping Tracking Mapping

  16. Mapping Tracking Mapping

  17. Mapping Tracking Mapping

  18. Mapping • Each Voxel also has a weight W, proportional to grazing angle • Voxel D is the weighted average of all measurements sensor2 sensor1 Tracking Mapping

  19. Handling drift • Drift would have happened If tracking was done from frame to previous frame • Tracking is done on built model Tracking Mapping

  20. Results & Applications

  21. Pros & Cons • Pros: • Really nice results! • Real time performance (30 HZ) • Dense model • No drift with local optimization • Robust to scene changes • Elegant solution • Cons : • 3d grid can’t be trivially up-scaled

  22. Limitations • doesn’t work for large areas (Voxel-Grid) • Doesn’t work far away from objects (active ranging) • Doesn’t work out-doors (IR) • Requires powerful Graphics card • Uses lots of battery (active ranging) • Only one sensor at a time

  23. Future Ideas • Keep Voxel Grid only for near sensor aera • Compress out of sensor reach model into ‘smart’ vertices map

  24. Thank you!

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