2D & 3D VIDEO PROCESSING FOR IMMERSIVE APPLICATIONS

1. 2D & 3D VIDEO PROCESSING FOR IMMERSIVE APPLICATIONS Emerging Convergence of Video, Vision & Graphics Harpreet S. Sawhney Rakesh Kumar

2. ACKNOWLEDGEMENTS Collaborative Work with: Hai Tao Yanlin Guo Steve Hsu Supun Samarasekera Keith Hanna Aydin Arpa Rick Wildes

3. TECHNICAL SUCCESS OF CONVERGENCE TECHNOLOGIES PC based near real-time mosaicing Image based modeling for Entertainment Automated Video Enhancement: VHS-to-DVD Real-time Video Insertion Iris recognition, active vision

4. Immersive and Interactive TelepresenceModes of Operation Observation Mode Conversation Mode Interaction Mode User observes a remote site from any perspective. User “walks” through site to view activities of interest “up close”. Example: security, facility guards, sports & entertainment Users talk and observe one another as if in the same room. Users walk around yet maintain eye contact. Example: immersive tele- conferencing Remote users share a common work space. Users observe each other’s hands as they manipulate shared objects, such as war room wall displays. Example: mission planning, remote surgery

5. Quality of Service for Tele-presenceCritical Issues • High quality for immersive experience • Artifact free recovery of 3D shape from video streams • Efficient 3D video representation and compression • High quality rendering of new views using 3D shape and video streams • Bandwidth available in the Next Generation Internet • Low latency for interactive applications • Real time 3D geometry recovery at the content server end • Real time new view rendering at the browser client end • Adaptive Stream management to handle user requests and network loads • Error resilience and concealment to fill in missing packets

6. Convergence Technologies… for immersive & interactive visual applications ... • Vision algorithms: High-quality 3D shape recovery • and dynamic scene analysis • ASICs, high performance hardware: Real-time video processing • Compact, low-cost cameras: CMOS cameras • Low latency and high quality compression: Error resilience • Real time view synthesis : Standard platforms, e.g. PCs • Immersive Displays

7. Vision algorithm performance over time Immersive Telepresence High Quality 3d shape extraction 2000 Geo-registration visual databases Video registration to 3D site models 1998 Face Finding for Iris Recognition Algorithm Complexity Coarse 3D Depth Recovery 1995 Real-time insertion in Live TV 2D Video Insertion 1993 Mosaicing for entertainment & surveillance 2D Stabilization 1990 Time

8. HW Performance/Size/Cost over time ACADIA ASIC 2000 VFE-100 1992 VFE-200 1997 • Sarnoff ACADIA ASIC performance • 100 MHz system clock, processes 100 million pixels/sec in each processing element • 10 billion operations / sec total IC performance • 800 MB/sec SDRAM interface using 64-bit bus • Enables building smart 3D cameras for immersive applications.

9. Application Performance • Parametric Motion : Stabilization & Mosaicing • 720x240 fields @ 60 Hz OR 720x480 frames @ 30 Hz • Pyramid based Fusion : Dynamic Range, Focus Enhancement • 720x240 fields @ 60 Hz OR 720x480 frames @ 30 Hz • Stereo Depth Extraction • 720x240 field 32 disparity levels in 4 ms (250 Hz) • 720x240 field 60 disparity levels in 10 ms (100 Hz) • 60 disparities on 1k x 1k images at 55 ms (18 Hz)

10. Sarnoff Compression Technology… Required algorithm components for tele-presence are emerging ... MPEG4, Progressive Encoding E-vue 1999 Low Latency MPEG2 multiplexing service ICTV 1998-1999 Just Noticeable Difference (JND): MPEG2 Encoding and Quality Measurement Tektronix Algorithm Complexity 1997-1998 VideoPhone: H.263 LG Electronics 1997-1998 MPEG2: Encoding and Transmission DIREC-TV & HDTV 1993- 1996 Pyramid & Wavelet based Encoding Still Image Compression 1988-1993 Time

11. A FRAMEWORK FOR VIDEO PROCESSING ALIGN 2D & 3D MODELS OF MOTION & STRUCTURE MODEL-BASED IMAGE SEQUENCE ALIGNMENT TEST WARP/RENDER WITH 2D/3D MODELS TEST ALIGNMENT QUALITY SYNTHESIZE CREATE OUTPUT REPRESENTATIONS

12. Highlights of Sarnoff’s Video Analysis Technologies … framework applied to a create immersive representations ... 2D Immersive & Layered Representations Model-centric Video Visualization • Dynamic model & video • visualization • Geo-registration with reference • image database • Spherical Mosaics • Dynamic & Synopsis Mosaics Core Vision Algorithms for (Real-time) Motion & 3D Video Analysis Stereo & Video Sequence Enhancement Multi-camera Immersive Dynamic Rendering • Hi-Q IBR based mixed resolution synthesis • Video Quality Enhancement for efficient compression • Hi-Q Depth extraction • Image-based rendering with dynamic • depth

13. TOPOLOGY INFERENCE & LOCAL-TO-GLOBAL ALIGNMENT SPHERICAL MOSAICS [Sawhney,Hsu,Kumar ECCV98, Szeliski,Shum SIGGRAPH98] Sarnoff Library Video Captures almost the complete sphere with 380 frames

14. SPHERICAL TOPOLOGY EVOLUTION

15. SPHERICAL MOSAIC Sarnoff Library

16. ACTIVE FOCUS OF ATTENTION WFOV/NFOV CONTROL

17. DYNAMIC MOSAICS Video Stream with deleted moving object Original Video Dynamic Mosaic Video

18. SYNOPISIS MOSAICS

19. ALIGNMENT & SYNTHESIS FOR HI-RES STEREO SYNTHESIS A HIGH END APPLICATION OF IBMR [Sawhney,Guo,Hanna,Kumar,Zhou,Adkins SIGGRAPH2001] Low-Res Left Synthesized High-Res Left Original High-Res Right

20. THE PROBLEM SCENARIO INPUT OUTPUT Left Eye (Typically 1.5K) Right Eye (Typically 6K)

21. 3D & Motion Alignment Based Stereo Sequence Processing w o t-2 w l w o t-1 o f l t-1 f w l s t e r e o o f l t t f s t e r e o f f l t+1 t+1 o l w f f l o t+2 o t+2 l w w o t+3 Left Right w Left Right • Highlights : • Scintillation effect is reduced. • Occlusion regions are better handled.

22. SYNTHESIS RESULT ON REAL FOOTAGE

23. IMPLICATIONS FOR IMMERSIVE IBMR CAMERA CONFIGURATIONS Lo-res camera Hi-res camera Multi-resolution camera configuration allows 3D capture at the highest resolution as well as user-controlled large range of zooms without the need for zoom control on the cameras.

24. Model-Centric Video VisualizationORVideo-Centric Model Visualization[Hsu,Supun,Kumar,Sawhney CVPR00] Original Video Site model Geo-registration of video to site model Re-projection of video after merging with model.

25. Video to Site Model Alignment • Model to frame alignment REFINE Correspondence-less exterior orientation from 3D-2D line pairs

26. 0° 45° 90° 135° Oriented Energy Pyramid • Goal: representation which indicates edge strength in the image at various orientations and scales • Orientation selectivity: reduce false matches • Coarse-to-fine: increase capture range

27. Pose Refinement Algorithm…iterative coarse to fine adjustment of pose ... This will be an animation of the gradual improvement of alignment during the coarse to fine iterations regsite_animation.avi

28. Geo-Registration Video to Reference Database Alignment[Wildes et al. ICCV01] Current Video 3D Reference Imagery

29. Registration : Radical Appearance Changes

30. Cross view depth checking Dynamic 3D Capture & Rendering…global modeling is not feasible... • Recovering depth from local views • Depth refinement across multiple local views • New view synthesis using multiple local views

31. 3D Shape/Depth Estimation from Multiple Views of a Scene Stereo Pair • Estimation of high quality, artifact free depth maps co-registered with video imagery for rendering new views. • Must work both outdoors and indoors

32. Multi-baseline depth estimation - requirements [Tao,Sawhney,Kumar WACV00, ICCV01] Accurate boundaries Accurate boundaries Thin structures Depth maps New view rendering Global matching method A traditional stereo algorithm

33. New view rendering using local depth estimation Multi-window plane+ parallax algorithm (1998) Local flow estim-ation (1992) Color segmentation based stereo algorithm (2000) New view rendering

34. Main ideas • Motivations • be able to handle textureless regions • handle object boundaries accurately • global visibility constraints should be enforced • Hypothesize reasonable depths for unmatched regions • Solutions • Global matching method - an analysis-by-synthesis approach • Representation - smooth depth representation in homogeneous region • Search method - neighborhood depth hypotheses generation • Efficient algorithm - incremental warping • Scene constraints - prior functions

35. Color Segmentation Original image (frame 12) Original image (left) Color segmentation [Comanicius 97]

36. New view rendering using local depth estimation Left image True depth Color segmentation based stereo algorithm new view rendering

37. Depth computation from 3 views Video frame 11 Video frame 12 Video frame 13 Color segmentation (frame 12) Depth map (frame 12)

38. Multiple View Depth Recovery and New View Rendering New view rendering from a single view. left: from frame212, right: from frame 215 New view rendering from multiple views.

39. Multiple view depth recovery and new view rendering Original 14 video frames (frame 04-17) New view rendering (71 frames) Depth map of frame 12 and 15

40. Immersive Visualization of a Dynamic Event • Temporally consistent motion and 3D shape extraction • Scintillation free dynamic high-quality rendering

41. AN IMMERSIVE IBMR GRAND CHALLENGE

42. AND IF WE DO IT RIGHT

43. The End