Constructing immersive virtual space for HAI with photos Shingo Mori Yoshimasa Ohmoto Toyoaki Nishida Graduate School of Informatics Kyoto University GrC /11/09

Abstract We automatically construct immersive virtual spaces for human agent interaction – Scenes are drawn by external photo images – Depth maps are reconstructed to express occlusion – Rough 3D models are added for agents – Processing time is about 4.7 days to reconstruct a 20m×20m virtual space 2

Introduction We want to observe HHI using HAI in a virtual space For example, for a virtual sightseeing task: – we can select faraway place such as foreign country – we can easily prepare an environment to observe Our Goal: creating a system to construct an environment for such a task 3

Introduction To do the sightseeing task and observe interaction, the environment should look like the real world – virtual spaces should be immersive – scenes recreated by real world photos are needed – spatial relationship between agent and object should be correct – users can walk freely on some level How to construct such a virtual space? 4

Related Work Model Based Rendering (MBR) – can reconstruct 3D models – make arbitrary consistent views easily – weak at trees or texture-less surfaces [1-3] are good methods but, – [1] can’t use outside scenes because they use Manhattan World Assumption – [2,3] need expensive equipment or lots of time and effort [1] Furukawa et al. 2010, Reconstructing build-ing interiors from images [2] Pollefeys et al. 2008, Detailed real-time urban 3d reconstruction from video [3] Ikeuchi et al, 2004, Bayon digital archival project 5

Related Work Image Based Rendering (IBR) – make a new viewpoint image by interpolation – draw clearly complex structures such as natural objects – weak at occlusion [4-5] have good image quality but, – they don’t consider agents – movable space is restricted [4]Google Street View [5] Ibuki, 2009, Reduction of Unnatural Feeling in Free-viewpoint Rendering Using View-Dependent Deformable 3-D Mesh Model (Japanese) 6

Our Method To make the immersive environment, we use IBR – because high image quality is needed to show the scene – use panorama images and omnidirectional display to show environment 7

Our Method To collect photo images – divide a space in into a 1-2m grid – shoot about 18 photos in each grid We use interpolation when moving from one shooting point to another obstacle shooting point shooting direction 1-2 meter 8

Our Method 3D geometry is needed for agents – use Structure from Motion and stereo method in a similar way [1,5] – create depth map for occlusion between objects and agents This information is used for better IBR – camera position & rotation – 3D position of a point cloud 9

System Pipeline depth map segmented image camera parameter Photos Structure from Motion Segmentation Creating Depth Map Show a Immersive Virtual Space interpolated image Interpolation ： Input ： Process ： Output panorama image panorama depth map panorama image panorama depth map Creating Panorama Use previous work Tackle in this research CMVS patches rough 3D model CMVS patches rough 3D model Multi view Stereo System of Constructing Virtual Space

Structure from Motion (SfM) Estimate camera parameters (projection matrix) from multiple photos – we use Bundler[6] camera position photos points clout and camera positioncamera position [6] Snavely et al. 2006, Photo tourism: exploring photo collections in 3D 11

Multi view Stereo Reconstruct 3D geometry – we use CMVS[7] and Poisson Surface Reconstruction[8] – get a point cloud (patches) and rough 3D model photos and translate matrix patches and rough 3D model [7] Furukawa et al. 2010, Towards internet-scale multi-view stereo [8] Kazhdan et al. 2006, Poisson surface reconstruction 12

Create Depth Map Deal with holes and outliers of the point cloud Using an assumption that the real world is constructed by a planar surface – reconstruct surface from projected patches – Vertical surface can be almost reconstruct raw image segmented imagedepth map project patches

Create Panorama Image To show a scene in an omnidirectional display, we create panorama images – we use Microsft ICE[9] – canonicalize direction of panorama image from camera rotation panorama image and depth map [9] MicrosoftCorporation, Microsoft image composite editor us/um/redmond/groups/ ivm/ice.html.

Interpolation project patches to use as feature point To move freely, we create interpolated images between near panorama images correctly move direction and distance about object two raw panorama images about 1-2m away from each other find corresponding point interpolate by morphing (medium point between raw images)

Demo 16

Processing Time We experimented with 3 spaces Most of the processing time is SfM – We can drastically improve if we use [10] Each shooting times are about one hour [10] Agarwal et al.2009, Building rome in a day

Conclusion – create a system to automatically construct virtual spaces for HAI – unify various methods to create the system Future work – expand virtual spaces – research how natural and useful it for HAI – observe HAI and feed back to the real world 18

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