Constructing immersive virtual space for HAI with photos

Shingo MoriYoshimasa Ohmoto

Toyoaki NishidaGraduate School of Informatics Kyoto University

GrC2011 2011/11/09

Abstract

• We automatically construct immersive virtual spaces for human agent interaction– Scenes are drawn by outside photo

images– Depth maps are reconstructed to

express occlusion– Rough 3D models are added for agent– Processing time is about 4.7 days to

reconstruct a 20m×20m virtual space

Introduction

• We want to observe HHI using HAI in a virtual space

• For example, sightseeing task:–we can select faraway place such as

foreign country–we can easily prepare environment to

observe

• Our Goal: creating system to construct environment to use such a task

Introduction

• To do sightseeing task and observe interaction, environment should be looked like the real world– virtual spaces should be immersive– outside scenes created by real world

photos is needed– spatial relationship between agent and

object should be correct– users walk freely on some level

• How to construct such a virtual space?

Related Work

• Model Based Rendering (MBR)– Reconstruct 3D models–Weak at trees or texture-less surfaces

• [1-3] are nice methods but,– [1] can’t use outside because of a

constraint of axis-aligned surface– [2,3] use high expensive equipments or

use a lot 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

Related Work

• Image Based Rendering (IBR)– draw clearly complex structure such as

natural object–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)

Our Method

• To make up immersive environment, we use IBR– because MBR has hole and low

resolution for a task– use panorama images and

omnidirectional display to show environment

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 move from one shooting point to another

obstacle

shooting point

shooting direction

1-2 meter

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

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 imagepanorama depth map

Creating Panorama

Use previous work

Tackle in this research

CMVS PatchesRough 3D model

Multi view Stereo

System of Constructing Virtual Space

Structure from Motion (SfM)

• Estimate camera parameter (translate matrix) from multi photos–we use Bundler[6]– it is robust and accurate camera position

photos points clout and camera positioncamera position[6] Snavely et al. 2006, Photo tourism: exploring photo collections in 3D

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

Create Depth Map• Deal with holes and outliers• Using an assumption that the real

world is constructed by a planar surface– assume two points as same planar

surface if they are segmented to same area

– reconstruct surface from projected patches

raw image segmented image depth mapproject patches

Create Panorama Image

• To show a scene to 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 editorhttp://research.microsoft.com/en-us/um/redmond/groups/ivm/ice.html.

Interpolation

project patchesto use as feature point

• To move freely, we create interpolated image between near panorama images• correct move direction and distance about

objecttwo raw panorama imagesabout 1-2m away from each other

find corresponding point

interpolate by morphing (medium point between raw images)

Demo

Processing time

• We experimented with 3 spaces• Most of 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

• Conclusion– create a system to automatically construct

virtual spaces for HAI– unify various methods to create a system

• Future work– expand virtual spaces– research how natural and useful it for HAI