Video Analysis for Augmented and Mixed Reality. Kiyoshi Kiyokawa Osaka University
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1 Video Analysis for Augmented and Mixed Reality Kiyoshi Kiyokawa Osaka University
2 Introduction Who am I? A researcher on AR / MR / VR / 3DUI / CSCW / Wearable Comp. Visualization / Wearable computing / Context-awareness Tabletop display / Multi-touch interaction / New display HW / 3D User Interfaces 3D reconstruction / 3D remote collaboration / Remote robot control
3 Introduction What is Augmented Reality? Technology that augments the real world by computer information Feiner et al., KARMA (1991) Kiyokawa et al., VLEGO (1997) Kato et al., ARToolkit (1999) SCE, The Eye of Judgment (2007) Layar (2009)
4 Introduction Why Augmented Reality? Direct coupling of computer intelligence to human behavior provides intuitiveness and efficiency Information Retrieval Architectural Review Conventional User Interface Manual search on the web Photo-taking / Reviewing on monitor AR Interface Direct search Direct assessment
5 Introduction Why Augmented Reality? (cont d) AR-ready devices getting ubiquitous Smartphones / Tablets / Personal Game Consoles Display / Camera / Compass / GPS / Networking AR market expected to expand exponentially Global AR Market Forecast by Research and Market (2011) Nintendo 3DS Gaming, Automotive, Medical, Defense, E-learning & GPS Applications People use AR 0.1% (2011) 1% (2016) Google Project Glass Revenue $181M (2011) $5,156M (2016)
6 Introduction What is Mixed Reality? A superset concept of Augmented Reality A continuum with two extrema, real and virtual Real Environment Mixed Reality (Milgram et al. 94) Virtual Environment My view AR is a subjective experience and human-oriented Power-gain is the key (How useful the UI is) MR is an objective entity and technology-oriented Consistency is the key (How well V and R are mixed)
7 Introduction Video Analysis for AR / MR Geometric Consistency Where should the information be shown? Photometric Consistency How should the information be rendered? MR-oriented topic Scene Understanding What information should be presented? AR-oriented topic 0.79lb 2.49lb 1.49lb
8 Geometric Consistency Where should the information be shown?
9 Where Geometric Consistency Vision-based Pose Estimation Estimates camera pose from captured images Xc Camera Coordinates Zc Xw Zw p(u,v) P(X,Y,Z) Yc Yw World (Marker) Coordinates But How? éuù é ê ú ê lêvú = ê ëê 1ûú ê ëê fk u k s u 0 0 fk v v Intrinsic parameters é ùê úê úê ú ûú ê ëê R 11 R 12 R 13 T 1 R 21 R 22 R 23 T 2 R 31 R 32 R 33 T Extrinsic parameters ùéxù úê ú úêy ú úêz ú úê ú ûú ë1 û
10 Where Geometric Consistency Vision-based Pose Estimation (cont d) Marker-based Rekimoto s CyberCode ( 96) Kato s ARToolkit ( 99) etc. etc. Model-based Lepetit ( 03) etc. etc. Feature-based Template-based SLAM Klein s Parallel Tracking and Mapping ( 07) etc. etc.
11 Where Geometric Consistency Visual Tracking and Reconstruction Region-based Bastian et al. Umakatsu et al. Pixel-based DTAM Real-time RGB-D camera KinectFusion
12 Photometric Consistency How should the information be rendered?
13 How Photometric Consistency Photorealistic Rendering How to acquire the lighting condition of the real environment How to render photorealistic objects in real-time Reflection Soft shadow Motion blur (Pixar, 1984)
14 How Photometric Consistency Global Illumination Evaluating light reflected from a point x by taking into consideration all illumination that arrives at the point Rendering Equation How to acquire a Bidirectional Reflectance Distribution Function (BRDF) How to render objects with a BRDF Use tons of random rays (Monte Carlo methods) e.g. Ray-tracing / Radiosity L o (x,w) = L e (x,w)+ Outgoing light w Generated light n W òw Total reflected light f r (x,w,w') L i (x,w ')(w 'n)dw' BRDF w ' Incoming light Diffuse Incident angle Mirror x Specular Diffuse + Specular
15 How Photometric Consistency Image-based Lighting with HDRI High Dynamic Range Imagery + Global Illumination (Debevec, 98) 1) Acquire a radiance map by a light probe 2) Build a light-based model of the scene 3) Render virtual objects with global illumination
16 How Photometric Consistency Inverse Lighting with LDRI Inverse lighting and photorealistic rendering for AR (Aittala, 10) Mirror ball (for comparison) Ping pong ball Virtual object Detected lights Basic rendering Photorealistic rendering
17 How Photometric Consistency Advanced Visual Effects for AR / MR Motion blur / Depth of field (Park et al, 09) (Okumura et al, 06) Diminished Reality Color harmonization (Herling et al, 10) (Gruber et al, 11)
18 Scene Understanding What information should be presented?
19 What Scene Understanding Sushi Scenario How can we annotate each sushi piece? Powerful object recognition technology is necessary to handle the real world problems Wavelet / SIFT / SURF / Haar-like / HOG / Shapelet
20 What Scene Understanding Real-time Object Recognition for AR Simultaneous Recognition, Localization and Mapping (Castle, 10) Use a SIFT-based planar object database Recognized object names Key-points 3D view of recovered objects Depth-assisted Real-time 3D Object Detection for AR (Lee et al., 11) Use RGB+D information in template matching
21 What Scene Understanding Eye tracking for Context Awareness Parallax-free, super wide-view eye camera (Mori et al., 10) Image on the Reflective Mirror Image on the Half Mirror User eye Eye tracking OLED display (Fraunhofer IPMS, 11)
22 Conclusion Where : Geometric Consistency Tracking and Reconstruction dramatically improved Practical solutions at hand, though some problems remain How : Photometric Consistency Photometric studies getting more and more attention Many open issues remain What : Scene Understanding Scene understanding / object recognition much studied in CV field, but still rare in AR / MR systems Context-aware AR will be the core topic in near future Video analysis for AR / MR will be even hotter in future
23 Thank you very much! Any questions?
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