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An Efficient System for Multi-Perspective Imaging and Volumetric Shape Analysis

An Efficient System for Multi-Perspective Imaging and Volumetric Shape Analysis. High-performance Multi-perspective Vision Studio. Multi-view vision apps made user friendly Organize multi-view video data Efficient back-end processing & user-friendly front-end

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An Efficient System for Multi-Perspective Imaging and Volumetric Shape Analysis

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  1. An Efficient System for Multi-Perspective Imaging and Volumetric Shape Analysis E. Borovikov, A. Sussman, L. Davis, University of Maryland

  2. High-performance Multi-perspective Vision Studio • Multi-view vision apps made user friendly • Organize multi-view video data • Efficient back-end processing & user-friendly front-end • Portability across computing platforms • Parallel computers • Distributed systems • Independence from data acquisition facilities • Various sensor configurations, e.g. camera positioning • Images: color or b/w, at various resolutions • Extensibility • R&D friendly E. Borovikov, A. Sussman, L. Davis, University of Maryland

  3. Loader Back-end services Front-end services Database Multi-perspective Vision Studio DAF DAF DAF • Separation from data acquisition facilities • Generic multi-view sequence management • Extensible multi-perspective vision application framework App App App App E. Borovikov, A. Sussman, L. Davis, University of Maryland

  4. Multi-view Video Sequence Data • Acquisition: the Keck Lab • 64 cameras • 85 frames/sec • 1 min = 95GB • Distributed system • PC cluster • Storage 9TB • Parallel processing • Active Data Repository (ADR) E. Borovikov, A. Sussman, L. Davis, University of Maryland

  5. ADR-based Imaging System • Framework for multi-perspective imaging • ADR application structure • Customizing ADR framework: • back-end • ADR front-end user: • app front-end • clients E. Borovikov, A. Sussman, L. Davis, University of Maryland

  6. Multi-perspective Imaging Framework • Multi-perspective image data • Data set: multi-perspective video sequence • Data chunk: single image <camera, time> • Operations: associative and commutative • Parallel back-end (PBE) • Storage, retrieval, projection, aggregation • Camera index lookup • Application and ADR front-end (FE) • Maintain meta-information about datasets • Translate queries from clients to PBE E. Borovikov, A. Sussman, L. Davis, University of Maryland

  7. Recovery App Volume • multi-perspective • silhouette-based • visual cone intersection • octree encoding E. Borovikov, A. Sussman, L. Davis, University of Maryland

  8. Multiview Visual Cone Intersection • Global combine • collect occupancy maps • intersect them For each view • extract object’s silhouettes • build visual cones E. Borovikov, A. Sussman, L. Davis, University of Maryland

  9. Multiview Silhouette Extraction - = - = - = E. Borovikov, A. Sussman, L. Davis, University of Maryland

  10. Depth of Reconstruction E. Borovikov, A. Sussman, L. Davis, University of Maryland

  11. Experiments • Workload balancing • data distribution strategies • query scenarios • Scaling • speedup • scaled speedup E. Borovikov, A. Sussman, L. Davis, University of Maryland

  12. System Setup • Back-end: Linux PC cluster • 16 nodes • 450MHz dual-processor Pentium II • 256 MB RAM • Gigabit Ethernet • Front-end: Pentium II PC, same network • Client: 400 MHz dual-processor Pentium II PC, 100Mbit/s TCP/IP network E. Borovikov, A. Sussman, L. Davis, University of Maryland

  13. Test Queries Fixed • query universe: 2  2  2 [m] cube • camera calibration parameters Varying • # of frames: 1, 50, 100, 200, 400 • # of processors: 1, 2, 4, 8, 16 • depth of reconstruction: from 0 to 12 E. Borovikov, A. Sussman, L. Davis, University of Maryland

  14. Data Distribution Strategies • view per processor • round robin in a single dimension • view per processor • round robin in a single dimension • random • Hilbert space-filling curves • practical approach to multidimensional data distribution • robust, inexpensive declustering of compact regions • naïve • works OK when # of views = # of processors • poor workload balance otherwise • has a flavor of randomness, but • distributes data periodically, • worst case occurs when • dim_max_index mod processors_count = 0 • declusters data randomly • requires good n-D pseudo random generator E. Borovikov, A. Sussman, L. Davis, University of Maryland

  15. Workload Experiments E. Borovikov, A. Sussman, L. Davis, University of Maryland

  16. Scaling Experiments Query turnaround times (sec) E. Borovikov, A. Sussman, L. Davis, University of Maryland

  17. Summary • Goals • multi-perspective vision studio • separation between sensor net and vision application • Customized ADR for multi-perspective imaging • portability across parallel platforms • robustness in handling large datasets • Built multi-perspective imaging framework • good workload balance using Hilbert curve declustering • robust scaling • Utilized it for a volumetric shape analysis application • variable time step size volumetric sequences • variable space resolution E. Borovikov, A. Sussman, L. Davis, University of Maryland

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