1 / 21

High-Performance Multi-Perspective Vision Studio for Efficient 3D Shape Analysis

The Multi-Perspective Vision Studio developed by Eugene Borovikov, Alan Sussman, and Larry Davis at UMCP offers an advanced system for multi-perspective imaging and 3D shape analysis. Utilizing 64 cameras to achieve 85 frames per second, this scalable, affordable solution enhances volumetric shape reconstruction, silhouette extraction, and texture mapping. Key features include extensible frameworks based on ADR and DataCutter, effective data de-clustering via Hilbert curves, and parallel processing capabilities, allowing robust handling of large datasets while ensuring high performance.

quynn-norman
Télécharger la présentation

High-Performance Multi-Perspective Vision Studio for Efficient 3D Shape Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A High-performanceMulti-perspective Vision Studio An Efficient System for Multi-Perspective Imaging and 3D Shape Analysis Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  2. Multi-view vision • interesting • affordable • challenging • distributed Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  3. Multi-perspective environments Keck Lab • 64 cameras • 85 frames/sec • 1 min = 95GB Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  4. Volume reconstruction • multi-perspective • silhouette-based • visual cone intersection • special octree encoding Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  5. Volume reconstruction Background subtraction Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  6. Volume reconstruction Multi-perspective silhouette extraction - = - = - = Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  7. Volume reconstruction Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  8. Volume reconstruction image plane Visual cone construction Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  9. Volume reconstruction 3D occupancy map as octree image plane Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  10. Volume reconstruction resolution=8depth Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  11. Data Capture Data Capture Loader Back-end services Front-end services Database Client Client Multi-perspective Vision Studio • Features • abstraction from data acquisition • multi-view sequence management • extensible application framework • based on ADR and DataCutter • Applications • Volumetric shape reconstruction • 3D density-based model fitting • Texture mapping surface meshes Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  12. Data de-clustering based on Hilbert space-filling curve 1 2 3 4 time index 5 6 7 8 1 2 3 4 5 6 7 8 camera index Customizable Studio Server • Data elements (chunks): image<cam-ndx,time-ndx> • Loader: Hilbert curve based de-clustering algorithm • Parallel back-end: database engine • index: (x,y,z,t) -> (cam,time) • aggregation: associative&commutative • Application front-end: gateway • query: application dependent • result: AppFE node is optional Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  13. Client GUI Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  14. Server performance Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  15. Constant work load performance 12000 10000 8000 seconds 6000 4000 2000 0 2 4 16 number of processors 8 8 frame group size 4 16 2 Server performance Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  16. A density fitting example Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  17. (consistency) (conservation) Density based shape modeling given a volume V, fit a density f by solving Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  18. Hierarchical fitting Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  19. Density based modeling results Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  20. Mesh texture coloring Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

  21. Conclusions Multi-perspective vision studio • abstracting vision application from sensor array • portability across parallel platforms • robustness in handling large datasets • expandable functionality High performance comes from • effective data de-clustering (Hilbert curve) • frame grouping to improve workload balance • efficient voxel projection strategy Eugene Borovikov, Alan Sussman and Larry Davis, UMCP

More Related