Efficiently Modeling 3D Scenes
from a Single Image
Satoshi Iizuka1

Yoshihiro Kanamori1 Jun Mitani1,2 Yukio Fukui1
1
University of Tsukuba 2JST ERATO
e-mail: iizuka@npal.cs.tsukuba.ac.jp {kanamori, mitani, fukui}@cs.tsukuba.ac.jp
Abstract— This paper presents a system for assisting the user tocreate a 3D model easily and quickly from a single image. Our
scene model is composed of a background and foreground objects
whose coordinates are calculated based on a “boundary” between“ground” and “wall”. Moreover, we introduce a fast method forextracting a foreground object by combining image segmentationand graph cut-based optimization. We show that the proposedsystem enables efficient modeling of foreground objects, easy
creation of their textures, and rapid construction of a 3D scenemodel that is simple but produces sufficient 3D effects.
Keywords: image-based modeling/rendering, single view modeling,
foreground extraction

1 INTRODUCTION
Recently, approaches for creating a photorealistic scene basedon image-based rendering have been of much attention. In
these methods, realistic 3D scenes can be generated withoutmodeling accurate geometry thanks to the use of images as
textures. By moving the viewpoint in the scene, users canexperience a virtual walkthrough in the image. These methods
are often used for a three-dimensional representation of a scene
and have been adopted for general applications such as
Microsoft Photosynth or the street views from Google maps.
However, the typical previous methods often demand a dozen
numbers of photographs or special devices, or requirecomplicated and time-consuming tasks.
In this paper, we present a system for creating a 3D scene withsimple user inputs from a single image. Our scene model iscomposed of a background model and foreground models
made of simple textured planner polygons. In order to calculate
the coordinates of the 3D model, the user interactively specifies
the boundary with a polygonal line between “ground” and“wall”. Based on the boundary, depth is assigned to each model.
We also propose a method for fast extraction of foregroundobjects using image segmentation and graph cut optimization
to model the foreground objects. Although these models are
simple and do not reconstruct accurate 3D geometry of the
input image, the user can obtain convincing 3D effects througha walkthrough animation produced by our system.
Our method requires only a single image as input. Thus, even asingle-view image, e.g., an oversea landscape image from theInternet or a scenery painting, suffices to allow the user to walkaround in the virtual 3D scene. Of course, our system does notalways work well. However, compared to existing methods,
our system can handle various types of landscape images
interactively and can be a useful tool for constructing awalkthrough animation from an image.

2 RELATED WORK
There are many research studies for constructing a 3D modelusing image-based rendering. The recent general applications
such as the street views from Google maps can construct awide range of a scene using a large number of photographs
overlapping with each other. Similarly, there have been several
methods that construct 3D models from multiple photographs
or movies with user interactions for specifying object shapes or
depth [Debevec et al. 1996; Chen et al. 2011]. These methods,
however, need considerable user interaction or are hard to be
adopted in case that only a single or a few images are available.
Compared to these approaches, making a 3D model from onlya single image is more difficult because of the less information
about the scene.
Several methods to create a 3D model from a single image
have been proposed previously. Hoiem et al. [2005] proposed afully automatic method based on machine learning and image
segmentation. Their method classifies regions in the image into
three categories (“ground”, “sky” and “vertical”) and thencreates a 3D model using the regional information. Anothermethod of automatic model construction, Make3D [Saxena et
al. 2009] uses a Markov Random Field to estimate a correct 3Dmodel structure from a single still image. Although abovementioned methods are fully automatic, they often fail toreconstruct the scene structure due to errors of depth estimation
or image segmentation. Moreover, these methods are difficult
to model foreground objects. Unlike these methods, Zhang et al.
[2001] described a modeling method of free-form scenes thatare constructed of curved surfaces using a sparse set of userspecified constraints on the shape of the scene. In this method,
the user should specify many constraints such as surface
positions to make a 3D model, and the interface is less intuitive.
Oh et al. [2001] proposed an interactive system for modeling a
3D scene by representing the scene as a layered collection of
depth images. This system allows users to edit the shape, color,and illumination of the image-based objects, but the wholeprocess imposes significant manual labors (e.g., segmentationof the image into layers, creation of textures using a clone
brushing tool, and the depth assignment) in editing the image.
The Tour Into the Picture (TIP) [Horry et al. 1997] is a simple

Figure 1: Overview of our 3D scene construction. (a) Given a single image, the user specifies (b) the boundary by a polyline (bluedashed line) and lassoes foreground objects by a brush interface (blue line). Then, (c) the texture images of background andforeground objects and (d) a 3D polygon model is constructed at once. (e) The 3D scene model is generated by mapping the
textures onto the corresponding polygons. (f) The user can experience walkthroughs in the scene.
user-guided method that constructs a simple 3D model withfive polygons (floor, right wall, left wall, rear wall, andceiling) based on a vanishing point. Kang et al. [2001]
improved the TIP by using a vanishing line rather than avanishing point to handle more various types of outdoor
images including horizons. The use of horizons, however,
impedes applications of this method to urban or indoor scenes
where complex structures often appear. Furthermore, preparing
textures is the most time-consuming manual process.
In this paper, we propose an efficient single-view modelingsystem that semi-automatically accomplishes the wholeprocesses for making walkthrough animations, including
accurate foreground extraction, successive image completion(inpainting) for the background texture as well as calculation of
the scene geometry. These processes are performed with quite
simple user inputs and do not require complicated 3D
operations; the user just has to draw a few strokes on the image,i.e., to draw a polygonal line on the boundary between “ground”
and “wall”, and to place a lasso around the object.

3 SYSTEM OVERVIEW
Our goal is to obtain 3D walkthrough animations from a single
image with simple and intuitive user operations. Particularly,
inspired by the pioneering work “Tour Into the Picture” (TIP)[Horry et al. 1997], we focus on construction of scene models
that are simple but provides convincing 3D effects, instead ofmodeling complicated geometries accurately. The main
contribution of this paper is the overall design of an interactive
system that allows construction of such models from simple
and intuitive user inputs, without requiring technical skills on
3D modeling and image editing.
Given a single scenery image including the flat ground, oursystem constructs a simple 3D scene consisting of abackground model and foreground models made of textured
polygons. The foreground models are vertically placed on the
ground region of the background model. To make a scenemodel, the user employs only two simple tasks;
1) drawing of a boundary with a polygonal line, and2) lassoing the foreground object coarsely for extracting them.
These tasks are fully performed on the 2D image and do not
require any complex 3D operations. Figure 1 shows theoverview of our system.

3.1 Definition of the boundary line
The scene model is determined by the boundary that isinteractively specified by the user. In this paper, we define theboundary as a polygonal line that separates the image into theground plane and wall plane. For example, in Figure 1(b), theboundary separates the scene into the ground and buildings.
The buildings and sky in the image are represented as wall
planes in the model. In the case where the boundary becomes acurve as illustrated in Figure 8(e), the boundary can beapproximately specified by adding more vertices to thepolygonal line. Based on the boundary, our system determinesthe 3D structure of the background model and the positions offoreground models (see Figure 1(e)). Moreover, the boundaryis also used as a constraint to improve the completion of
extracted foreground regions for making a background texture.
The more details are described in Section 4.1.

3.2 Extracting and modeling foreground objects
A foreground object mentioned in this paper is an object thatis placed on the ground region, and is modeled as a single
planar polygon whose texture is extracted from thecorresponding region in the image. In general, accurate(a) 2D image(a) completion w/o constraint(b) result w/o constraint(c) completion w/ constraint(d) result w/ constraint(b) 3D model

Figure 2: (a) A 2D image and (b) the corresponding 3D model.

extraction of foreground objects is a time-consuming process.
Therefore, we propose a fast method to extract foregroundregions easily based on image segmentation and graph cutbased optimization. To extract a foreground object, the userroughly lassoes a foreground region and then the system
optimizes the region. In case that the extracted region is
inaccurate at the first attempt, the user marks the miss-labeledparts with a brush tool to label them as foreground or
background. Then, the system refines the extraction according
to this additional user input. As a result of these steps, the user
can obtain a desired labeling of the foreground object.

4 SCENE MODELINGIn this section, we introduce the basic algorithms of our system.
Section 4.1 describes the algorithm to create the backgroundmodel, and the way to generate the background texture. Section
4.2 presents methods for fast foreground extraction and model
construction with a billboard transform and a ground constraint.

Figure 3: Synthesis of the background texture by compositing
the region behind the object from similar patches (yellow). (a)The statue is labeled as foreground (blue) and (c) the boundary(red) between the ground and the building is specified. The
result of completion (d) using similar patches with theboundary constraint (green) is much more natural than (b) thatwithout the constraint.
where 𝑚 𝑛 is zero ideally since we assume thatis anideal point. However, an ideal point cannot be displayed, thus
we set 𝑚 𝑛 to a fractional value (we use 𝑚 𝑛1). Basedon these two coordinates, each vertex of the ground polygon iscomputed:

4.1 Background model
The input image is separated into a ground polygon andmultiple vertical polygons based on the boundary specified by
the user (see Figure 2). The background model is constructedby assigning an appropriate depth value to each vertex of theground polygon and vertical polygons. The polygon model isthen textured with an image synthesized from the input image
by extracting the foreground objects and by completing holes
caused by extraction.
In our system, we assume that the camera is located at theorigin, the view direction is towards , and the focal length isthat is known. Also, the ground plane intersects the bottom of
the image. The model is computed by transforming the() into homogeneous
coordinates of vertex icoordinates(). Let the screen coordinate of the), and the vertex
bottom-left corner of the image be (with the maximum y value among the boundary vertices() , respectively. Then, their correspondingbehomogeneous coordinates are represented as follows:
(

1)

(

𝑚 𝑛)

(

)

where

𝑚𝑛

1

(1)

The rest of vertices are calculated based on the constraint thatwall polygons are perpendicular to the ground.
Synthesizing the region behind foreground objects
In addition to the calculation of vertex coordinates, we require
the texture image for the background model. The backgroundtexture image is synthesized from the input image by
extracting foreground objects (see Section 4.2) and completingthe holes caused by the extraction. For the image completionor inpainting, we employ a very fast patch-based method
called “PatchMatch” [Barnes et al. 2009] to find appropriate
patches from the input image and fill the holes using them.
Our system instantly inpaints the hole every time a foreground(a) input image(b) segmentation(c) user input(d) region-based selection(e) graph cut optimization(f) modification(g) extracted foreground(h) novel view

Figure 4: Extraction of a foreground object. (a) The input image is (b) segmented in preprocessing by a mean shift-based approach[Comanniciu and Meer 2002]. (c) The user lassoes a foreground object roughly. (d) The segmented regions outside and across thelasso stroke are labeled as background, and the rest becomes the initial foreground region. After (e) the graph cut-based
optimization is performed, (f) further user editing is applied to correct the miss-labeled pixels. Finally, (g) the extracted foregroundimage is texture-mapped onto the quadrangular polygon to construct the foreground model. (h) The 3D scene is generated by
combining the background and foreground object.

4.2 Foreground modelB – user brush
L – labeled superpixels
U – uncertain superpixels

Figure 5: Labeling of superpixels using the user stroke.

object is extracted, and thus the background texture isavailable when the extraction is done.
However, naïve inpainting causes artifacts in the inpainted
region when inadequate patches are selected and used (seeFigure 3(b)). To avoid this, we constrain the search space ofsimilar patches using the boundary line as a guide as follows.
First, the hole region along the boundary is completed with thepatches sampled from the non-hole regions along theboundary. Then, the upper part of the hole is completed withpatches sampled from the upper region of the boundary, andsimilarly for the lower part. Consequently, the boundary plays
two roles in our system; a reference for the calculation of 3Dmodels and a guide for the synthesis of the background texture.
As a metric of patch similarity, we calculate the sum ofsquared distance for patches of size 7×7 pixels in RGB color
space, similarly to [Barnes et al. 2009].

Extraction of foreground objects is one of the most timeconsuming tasks in image editing. Previous research has
introduced painting-based approaches that let users directly
paint the regions using a brush or boundary-based approaches
that trace the region boundary. However, these methods
sometimes require accurate and detailed user operations
according to the intended foreground shape. To eliminate suchlabor-intensive operations, we propose a scribble-based
selection approach whose interface is just like a lasso tool.
Extraction is performed by roughly specifying a possible
foreground region based on nearly-uniform regions calledsuperpixels, followed by fine optimization of the region basedon graph cut.
As preprocessing, the input image is segmented into
superpixels when loaded using mean shift [Comaniciu andMeer 2002]. This method handles each pixel as a 5D vector
consisting of the pixel position (x, y) and color (L, u, v), andapplies mean-shift segmentation, yielding high accuracy forclustering similar pixels in various images. We use thesegmentation with the parameters () (1 ) inall results in our paper, where is the kernel bandwidth of thespatial domain, is the kernel bandwidth of the range domainof the feature vector, and M is the minimum pixel counts ofsuperpexels. The detailed description of the parameters isshown in [Comaniciu and Meer 2002]. For example, in Figure
4, the input image is segmented into 568 superpexels.
In the editing session, the user loosely encloses a possible
foreground region using a lasso tool (see Figure 4(c)).
According to the lasso stroke, the labeling of pixels isdetermined for each superpixel as shown in Figure 5; thesuperpixels outside and across the stroke are labeled as

(a) input image(b) Photoshop Quick Selection(c) [Rother et al. 2004](d) our method

Figure 6: Comparison with other methods. The user labels pixels as “background” with red strokes and “foreground” with whitestrokes, and obtains the resultant extracted objects in blue. Compared to (b) Photoshop Quick Selection and (c) GrabCut [Rother et
al. 2004], (d) our method allows quicker foreground extraction with more rough and fewer sketches.
background (see Figure 4(d)). The inside of the stroke islabeled as unknown, and then optimized by graph cut. Notethat we use a Gaussian Mixture Models exactly as GrabCut[Rother et al. 2004]. Whilst the original GrabCut approach
often requires several iterations for graph cut and thus is notwell suited for an interactive use, our method requires almost
no iterations to achieve a sufficient labeling of the foregroundregion thanks to the pre-segmentation.
In preparation for failure cases at the initial extraction, oursystem also supplies users with manual correction tools. In oursystem, the user can correct the miss-labeled regions using
rough brush strokes, as shown in Figure 4(f). Then, the labelingof pixels is updated in unit of superpixels computed in
preprocessing. The graph cut optimization is performed once
after that. These steps are iterated until the foreground region isappropriately extracted. This extraction method can label theforeground region accurately without precise and detailed user
operations.

(a) input image

Figure 6 compares our method with Adobe Photoshop CS5
Quick Selection, which is a powerful image editing tool themost similar to ours, and our implementation of GrabCut
without “border matting”. The input image size is 800×600
pixels. In our system, pre-segmentation was done when theimage was loaded and took only 0.71 seconds. The timerequired for our optimization for a single user stroke was 0.41
seconds. The total time for extracting the foreground regions
including the user operation time is 107 seconds in Photoshop
Quick Selection, 115 seconds in GrabCut, and 18 seconds inour method. Note that the accuracy of our method depends onthe graph cut optimization, and thus it does not improve thequality of results than previous graph cut-based approaches like
GrabCut. However, compared to the other methods, our systemachieves more rough strokes and less user interactions by
combining pre-segmentation and graph cut-based optimization.

(c) result without constraint

Care must be taken for the failure cases of image segmentationin preprocessing; foreground and background pixels can bemixed in a single segmented region, which breaks the border
between the background and foreground regions. This situation
can be avoided by taking advantage of other selection tools
provided in our system. The user can apply graph cut without
image segmentation or directly paint the regions for labeling.
These options make our system applicable to any foregroundobjects.

(b) ground constraint(d) result with constraint

Figure 7: An example of the ground constraint. (b) The wallis labeled as the foreground (blue) and the ground constraint is
specified by a line (orange). (c) Without the constraint, thesame depth is assigned to the vertices of the foregroundpolygon. (d) The constraint line modifies the depth to fit theaccurate geometry.
The foreground object extracted in this way is modeled as aquadrangular polygon textured with the corresponding
foreground image, and is located vertically on the ground. The
3D coordinates of the foreground model are computed basedon the pixel coordinate with the minimum y value in theforeground region using Equation (1).
Billboard transformation. Because a foreground object ismodeled as a planner polygon, it loses the sense of reality ifviewed from the side. To avoid this, our system employs abillboard transformation technique. This method rotates thetarget polygon so that it faces the viewpoint, and it is applied
particularly to a foreground object such as a tree or columnshaped object that commonly appear in landscape images.

(a) input image(b) automatic photo pop-up(c) spidery mesh(d) vanishing line(e) boundary line
[Hoiem et al. 2005][Horry et al. 1996][Kang et al. 2001]
Figure 8: Typical results from our system and existing methods for creating walkthrough animations. The yellow lines show theuser input and the red lines represent the remaining wireframe of each model.

Ground constraint. If a foreground object faces an oblique
direction against the camera, the object gets away from theground because the same depth is assigned to each vertex bydefault (see Figure 7). Our system provides users with a tool
to adjust the depth by specifying the border between theforeground and ground using a polygonal line. This function
can handle foreground objects composed of multiple polygons.

4.3 Discussion
Here we discuss the design principle for our system while
mentioning the differences between our method and previous
methods. Existing automatic methods such as [Hoiem et al.
2005] and [Saxena et al. 2009] often fail to handle foregroundobjects or separation between the ground and vertical regions.
Such tasks are essentially recognition of objects in images
according to their semantic characteristics in the real world,which is still quite hard for computers. On the other hand, inthe case of human, even a child can easily recognize theboundaries between the ground and walls or between
foreground and background. The key idea of our system is toleverage such human recognition capability for model
construction via intuitive operations (i.e., drawing of aboundary line and lassoes), and to let the system perform
precise operations that human is not good at and might require
much labors and technical skills (e.g., accurate foregroundextraction and texture synthesis), in order to exploit theadvantages of human and computer by combining them.
Recall that the goal of our system is that even casual users canaccomplish the whole processes of making 3D walkthroughanimations from a single image. The work of Oh et al. [2001]
seems excellent as an accurate modeling system for various
scenes because it even enables to edit precise geometries ofobjects and lighting effects in the scene. However, theversatility of their system is at the cost of laborsome manual
operations for, e.g., separating the image into layers, assigning

depth to each layer and inpainting the holes behind foregroundobjects. Such versatility and heavy tasks are not appropriate foreasy creation of plausible walkthrough animations, which weseek.
The Tour Into the Picture [Horry et al.1997; Kang et al. 2001],
which is the most similar to our system and shares the samegoal, concentrates the construction of 3D polygonal models
and leaves the remaining tasks untouched; the user must extract
foreground objects and inpaint holes using photo-retouchingsoftware, which ends up laborsome manual operations
requiring technical skills. Additionally, even for the scenes
without foreground objects, the modeling interfaces such as the
spidery mesh or the vanishing line cannot handle winding or
curved boundaries, as shown in Figure 8(c)(d).
To solve the flaws mentioned above, we propose
1. the boundary interface for more accurate modeling of the
background model (see Figure 8(e)), and
2. on-the-fly extraction and inpainting for foregroundobjects with lassoing.
Both of them are accomplished with simple and intuitive
operations, and as a whole our system allows easy creation of
3D walkthrough animations.

5 RESULTS
We implemented our prototype system with C++, OpenGL and
GLUT, and ran the program on a PC with Intel Core i7 620M
2.67GHz CPU and 4.00GB RAM. The sizes of input images
are all in the range of 0.5 to 1 megapixels.
In Figure 1, two streetlights are specified as foreground and theboundary is specified by a polygonal line with five vertices. Asviewed from the side, the streetlights seem tactile due to thebillboard transformation. A standard tree or a cylindrical object
like the streetlight can be represented by the billboard

transformation. In our scene model, each region such as theground or wall is modeled as a planner polygon; the staircase
and the rear building seem unnatural because they are
represented as the same plane.
Figure 8 compares the background modeling of ours and theexisting methods. The figure shows that our boundary interface
can more accurately separate the ground and walls to model
more natural shapes than the automatic pop-up [Hoiem et al.
2005], the spidery mesh interface [Horry et al. 1999] and thevanishing line interface [Kang et al. 2001]. Compared toautomatic methods [Hoiem et al. 2005], we emphasize that
our human-assisted system offers more flexibility to produce
higher quality as discussed in Section 4.3.
Figure 9 shows several types of input images with foregroundobjects and the walkthroughs in the 3D scenes generated by oursystem.
Note that the creation time using our system is within 3
minutes (including manual operations) in all examples
illustrated in this paper. The most time-consuming part in theprocess is the extraction of the foreground objects. For example,
if foreground extraction is not necessary (the bottom of Figure8), the creation time is only around 14 seconds, but the time forthe scene with foreground objects (the top row of Figure 9) is
127 seconds. The time increases according to the number offoreground objects. For this reason, the fast extraction methodin our system plays a very important role in the creation of themodels.

6 CONCLUSION AND FUTUER WORKof reality. We also plan to improve the quality of the scenetexture that is elongated due to the perspective projection,using super-resolution approaches. We believe that our system
enriches the user understanding of photos by navigating into
the scene.

ACKNOWLEDGMENT
We would like to thank the following Flickr users for Creative
Commons imagery: emilio Labrador, ell brown and Jorge
BRAZIL.

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In this paper, we have proposed an efficient modeling systemfor constructing a 3D scene model from a single image. The
scene model consists of a background model and foregroundmodels, and the 3D coordinates are computed based on theboundary between “ground” and “wall”, which is interactively
specified by users. By the modeling scheme using a boundary,
we can make 3D scene models from various types of landscape
images. Whilst our model does not reconstruct an exact
complex geometry in the input image, a realistic walkthroughanimation can be generated by navigating the scene. We havealso introduced a method for extracting a foreground object
easily to model them. This method optimizes the foregroundregions roughly specified by the user based on the combinationof image segmentation and graph cut-based approach. The
extracted foreground region is inpainted automatically to makea background texture. Our method enables users to model a 3D
scene from a single image more easily and quickly thanprevious methods.

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photo pop-up. ACM Trans. Graph. 24, 3, 577–584.

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Figure 9: Given input images (the left-most column) and user inputs (the second column from the left), the walkthrough images(the third and forth columns from the left) are generated using our system.

Satoshi Iizuka received his B.S degree in computer science from the University of Tsukuba in 2010, Japan. Since April 2010, he
has been a master's degree student in the Department of Computer Science at University of Tsukuba. His research interests centerin computer graphics including interactive simulations, image-based modeling and image editing.
mailing adress : University of Tsukuba, 1-1-1 Tenno-dai, Tsukuba, Ibaraki 305-8573, Japan
phone/fax: 029-853-5388
Email: iizuka@npal.cs.tsukuba.ac.jp

Yoshihiro Kanamori received his B.S., M.S., and Ph.D. degrees in computer science from the University of Tokyo, Japan, in
2003, 2005, and 2009, respectively. Since April 2009, he has been an assistant professor in the Department of Computer Scienceat University of Tsukuba. His research interests center in computer graphics including real-time rendering, point-based graphics,
and interactive simulations.
mailing adress : University of Tsukuba, 1-1-1 Tenno-dai, Tsukuba, Ibaraki 305-8573, Japan
phone/fax: 029-853-5388
Email: kanamori@cs.tsukuba.ac.jp

Jun Mitani In 2004 Mitani received Ph.D. in Engineering from the University of Tokyo for his research on designing 3D
papercraft models with computer. In 2006 he started working at University of Tsukuba. His present post is associate professor at
Graduate School of Systems and Information Engineering in University of Tsukuba. He is studying about designing threedimensional curved origami with computer as part of research activities.
mailing adress : University of Tsukuba, 1-1-1 Tenno-dai, Tsukuba, Ibaraki 305-8573, Japan
phone/fax: 029-853-5388
Email: mitani@cs.tsukuba.ac.jp

Yukio Fukui received Ph.D. in Engineering from the University of Tokyo in 1993. He had been working at National Institute of
Bioscience and Human-Technology, M.I.T.I. and Institute of Information Sciences and Electronics at University of Tsukuba
since 1993 and 1998 respectively. He has been a professor in the Department of Computer Science at University of Tsukuba since2004.
mailing adress : University of Tsukuba, 1-1-1 Tenno-dai, Tsukuba, Ibaraki 305-8573, Japan
phone/fax: 029-853-5388
Email: fukui@cs.tsukuba.ac.jp