AR Marker Hiding with Real-time Texture Deformation
Norihiko Kawai∗

Tomokazu Sato†

Yuta Nakashima‡

Naokazu Yokoya§

Nara Institute of Science and Technology

A BSTRACT
Augmented Reality (AR) marker hiding is a technique to visually
remove AR markers from a real-time video stream. The conventional approach transforms a background image with a homographymatrix calculated on the basis of a camera pose and overlays thetransformed image on the AR marker region in the real-time frame,
assuming that the AR marker is on a planar surface. However, thisapproach can cause discontinuities in textures around the boundary
between the marker and its surrounding area when the planar surface assumption is not satisfied. This paper proposes a method for
AR marker hiding without discontinuities around texture boundaries under unknown and nonplanar background geometry. The
proposed method estimates dense motion in the marker’s background based on feature point correspondences around the marker,
together with a smooth motion assumption, and deforms the background image according to the dense motion. Experimental results
demonstrate the effectiveness of the proposed method in various
environments with different background geometries and textures.
Index Terms: H.5.1 [INFORMATION INTERFACES AND PRESENTATION]: Multimedia Information Systems—Artificial, augmented, and virtual realities
1

I NTRODUCTION

With the commodification of smartphones, tablets, and portable
game consoles, applications that use augmented reality (AR) to
overlay various objects on a real-time video stream of a real environment, such as furniture arrangement simulation and AR gameswith virtual characters, are becoming increasingly available. Suchapplications must estimate the camera pose in real time, and various
approaches have been employed to accomplish this, e.g., simultaneous localization and mapping (SLAM) and AR marker-based approaches [6, 10, 14]. Although SLAM-based approaches [10, 14]have been developed intensively, the marker-based approach [6] iswidely adopted because of the ease by which virtual objects canbe placed at the user’s desired positions and its robustness against
various textures and shapes in a scene. However, the marker-based
approach requires visible AR markers, which can hinder seamlessfusion of the real environment and the virtual objects.
Some methods that attempt to visually remove the AR markers
from a real-time video stream without incurring user burden havebeen proposed to address this problem. Siltanen et al. [15] synthesized a background image by mixing several pixel values around themarker for each frame and replaced the marker region with this image. This simple method works quite quickly but may cause significant visual artifacts, especially for complex textures. Korkalo et al.
[11] and Kawai et al. [8] have proposed AR marker hiding based ona planar assumption, in which the AR marker and the background∗ e-mail: norihi-k@is.naist.jp† e-mail: tomoka-s@is.naist.jp‡ e-mail: n-yuta@is.naist.jp§ e-mail: yokoya@is.naist.jp

surface are on the same plane. These methods generate backgroundimages by applying image inpainting to the marker regions in acertain frame in a real-time video stream and overlay the background images on a newly captured frame by transforming themwith a homography matrix. These methods also adjust image intensities in the generated background to reduce discontinuities aroundthe boundaries between the marker regions and their surroundings,
which are caused by changes in lighting conditions. However, thesemethods can suffer from discontinuities on the boundary, because
the marker is often placed on a nonplanar surface or it is attached
to a thick base, such as cardboard, so that the marker cannot bend.
For AR marker hiding, using diminished reality (DR) technology, which visually removes a variety of real objects in real time, isalso a promising approach. Among diminished reality methods,
methods that use preliminarily captured background images andthose that use image inpainting are possible ones.
The methods in [2] and [12] preliminarily capture multiple background images in which occluding objects are removed or collects
images that captures a target scene from the Internet to obtain background images. These methods then overlay the background image
on target object regions in the real-time frames to visually remove
the target objects. The main challenges in such methods are finding an image that is suitable for the current frame from a number of background images and transforming the background image
to seamlessly overlay it on the frame. Cosco et al. [2] selectedbackground images based on view-dependent texture mapping criterion [3] and rendered a background image for the frame assumingthat the background geometry is available as a mesh model. Li etal. [12] searched the Internet for images captured from a position
that is close to the current frame and transform the image using
a homography matrix. To address the previously mentioned challenges, these methods must satisfy one of the following conditions:(i) the background geometry must be known or should be possible
to be reconstructed with high accuracy; or (ii) the images must be
densely captured from various viewpoints. Unless one of these conditions is satisfied, it is difficult to seamlessly overlay backgroundimages on real-time frames.
Methods using image inpainting can be classified into those thatgenerate a background image for each frame [4, 5] and those thatgenerate a background image in a certain frame and geometricallyand photometrically adjust for other real-time frames [7]. The former approach can generate a plausible background image for eachframe in many cases; however applying image inpainting to eachframe can cause temporal inconsistency in the target region. Assuming that the background geometry is approximated by multiple
local planes, the latter transforms a background image generated fora certain frame using a homography for each plane in each frame.
However, this method can also suffer from texture discontinuities
on the boundary between the target region and its surrounding if
the background geometry assumption is not satisfied.
In this work, considering a scenario in which users wish to hidemarkers in AR applications without burden, we propose a method
for AR marker hiding. In the proposed method, users can visually
remove markers that are possibly fixed to the environment fromthe real-time video stream with unknown background geometry
and marker thickness. The proposed method obtains a backgroundimage by capturing a single image before placing a marker or by

In the case of preliminary capturing(I) Obtainment of background image
Preliminary capturing

Image inpainting(I-1) Capture abackground image(I’-1) Capture an image
with a marker(I-2) Place a marker(I’-2) Rectify the image(I-3) Rectify the image

(I-3)
(I-1)

(I-2)

In the case of image inpainting(I’-3) Inpaint the image(I-4) Detect feature points and calculate confidences(I’-3)

Rectified background image(I’-1)(II) Marker hiding for every frame

Rectifiedbackgroundimage B(I’-2)(II-1) Capture a frame

Figure 2: Obtaining rectified background image B.

(II-2) Estimate a camera pose

(II-4) Deform the background image

Next frame(II-3) Rectify the frame(II-5) Adjust color of the background image(II-6) Overlay the background image(II-7) Display the marker-less frame

Figure 1: Flow diagram of the proposed marker hiding method.

applying image inpainting to an image with a marker. Given a
new frame from the real-time video stream, the proposed method
overlays the background image on the marker region with geometric deformation and photometric adjustment. To relax the planar assumption, our method estimates the pixel-wise motion of themarker’s background to deform the background image according to
the estimated motion and overlays the deformed image on real-timeframes. This approach reduces discontinuities on the boundary between the marker region and its surroundings.
2 OVERVIEW OF AR M ARKER H IDING
Figure 1 shows a flow diagram of the proposed AR marker hiding
method. The proposed method consists of two processes: (I) obtaining a background image and (II) hiding an AR marker in eachframe in a real-time video stream.
In process (I), the proposed method first obtains a backgroundimage without a marker by either preliminarily capturing an actual background or by applying an image inpainting technique tothe AR marker region. As shown in Fig. 2, when capturing thebackground image preliminarily, we first capture a background image (I-1) and subsequently place a marker while the camera pose isfixed (I-2). Next, a homography matrix is determined to transformthe image with the AR marker in (I-2) so that the marker can bea square. The homography matrix then transforms the backgroundimage captured in (I-1) to obtain a rectified background image B
in (I-3). Note that for image inpainting, we follow our previous
marker hiding method [8]. Specifically, we first place a marker andcapture an image with a marker (I’-1). The captured image is rectified so that the marker can be a square (I’-2). An image inpainting
method then generates the rectified background image B in (I’-3).
The proposed method then detects feature points around the marker
region in the rectified background image B and calculates the degrees of confidence of the feature points (I-4), each of which is an

autocorrelation of an image patch around a feature point.
In process (II), the proposed method visually removes a marker
from a real-time video stream. It first acquires a frame from thereal-time video stream (II-1) and estimates the camera pose using
the marker in the frame (II-2). The frame is rectified as in process(I) using the homography matrix obtained on the basis of the camera pose. In process (II), the homography matrix is set so that themarker’s size can be the same in the rectified frame and the image
obtained in process (I) (II-3). Next, the proposed method finds the
pixels in the rectified frame that correspond to the feature points detected from B (II-4), and then deforms B based on the feature point
correspondences (II-4). Poisson blending [13] is then used to adjust
the color of the deformed image to compensate for the differencein luminance between the rectified frame and the deformed background image (II-5). The background image is transformed using
the inverse of the homography matrix obtained in (II-3) and is overlaid on the marker in the original frame from the real-time video
stream (II-6). Finally, the resulting frame is displayed to the user
(II-7). Note that processes (I) and (II) can be performed simultaneously for an image inpainting case as in [8].
In the following sections, we describe feature point detection andthe confidence calculation in (I-4) and real-time background image
deformation (II-4).
3

D ETECTION OF F EATURE P OINTSC ALCULATIONANDC ONFIDENCE

In process (I-4) in Fig. 1, the proposed method first determines
marker region Ω, which includes the AR marker but is slightly
larger than the actual marker region in the rectified background image. We also determine the marker’s surrounding region ∂ Ω whichis the relative complement of Ω in Ω’s dilated region by l pixels(Fig. 3). We then detect the feature points in ∂ Ω and calculate theirdegrees of confidence.
To alleviate discontinuities in textures, the proposed feature point
detector must satisfy the following requirements. (i) Feature points
should be distinguishable from their surroundings to determine reliable correspondences. (ii) Feature points should distribute uniformly, and feature points on straight edges are acceptable if nocorners exist around them. (iii) The number of detected featurepoints should be sufficiently large for accurate motion interpolation.
Considering these requirements, the proposed method employs thefollowing algorithm for feature point detection, which also provides
the degree of confidence for each feature point as a measure of distinguishability.
Specifically, as shown in Fig. 3(left), the proposed feature point
detector applies the Laplacian of Gaussian (LoG) filter to the recti-

4∂Ω

Process (II-4) first determines correspondences between the featurepoints selected in the marker’s surrounding region ∂ Ω in the rectified background image B and pixels in the rectified frame. This
process then interpolates the motion in the marker region Ω and its
surrounding region ∂ Ω using the correspondences and deforms therectified background image based on the interpolated motion. In thefollowing sections, we describe the background image deformationof the f -th frame.

ΩFigure 3: Examples of our feature point detection. Left images show
edges extracted with LoG and right images show detected featurepoints in region ∂ Ω.

fied background image B and finds zero crossing points as featurepoint candidates. Then, the degree of confidence is calculated foreach candidate in ∂ Ω. The degree of confidence C(xi ) is calculated
for pixel i at xi based on autocorrelation of the local patch centeredat xi as follows:C(xi ) =|Nxi | − ∑x ∈Nx NCC(B,B) (xi , x )i

|Nxi |,(1)

where Nxi is a set of pixels in the local patch centered at xi excluding the pixel at xi and |Nxi | is the number of pixels in Nxi .
NCC(B,B) (xi , x ) is the normalized cross correlation of pixel values
between local patches Nxi and Nx in rectified background image B
and is defined as follows:
(B,B)



∑c ∑p∈W IcB (xi + p)IcB (x + p)(xi , x ) = ,∑c ∑p∈W IcB (xi + p)2 ∑c ∑p∈W IcB (x + p)2(2)
where IcB (xi ) is the pixel value in channel c (one of red, green, andblue channels of the pixel in the RGB color space) of the pixel at
xi in rectified background image B, and p is a shift vector in local
patch W . With this definition, a pixel whose patch has low correlation with its surrounding patches has a high degree of confidence.
Next, as shown in Fig. 3(right), the feature point detector picks
feature points from candidates using their degrees of confidence
while maintaining their uniform distribution. For this, the featurepoint detector determines candidates as feature points if the candidates satisfy the following conditions in descending order of theirdegrees of confidence.
NCC

D EFORMATION OF BACKGROUND I MAGE BASED ON M O TION I NTERPOLATION(I) The distance between the candidate and any feature point obtained so far should be greater than L1 , where L1 is a constant.
(II) The degree of confidence C of the candidate should exceed
P2 % of that of an already selected feature point if the distance
between the candidate pixel and the feature point is less thanL2 , where P2 and L2 are constants.
(III) The degree of confidence C of the candidate should be greaterthan P3 % of that of the first feature point, which has the largest
degree of confidence among the feature points, where P3 is aconstant
Condition (I) prevents the distribution of feature points from becoming overly concentrated. Condition (II) inhibits a candidate
with a relatively greater degree of confidence from being selected
when there is another feature point with a greater degree of confidence near the candidate. Condition (III) selects a candidate with arelatively low degree of confidence when there are no other featurepoints with greater degree of confidence around it to prevent the
distribution of feature points from becoming too sparse. Note thatthe k-th feature point is denoted by xk in the following.

4.1 Correspondence in marker’s surrounding region
Pixel y f ,k is determined in the rectified f -th frame corresponding
to feature point xk in region ∂ Ω in the rectified background image
B, essentially by finding a region in the f -th frame that is similar
to the local patch around xk . Note that naı̈vely scanning the entire
image is inefficient. Considering that the proposed method shouldfind correspondences at a sufficiently high frame rate, the temporal consistency of the pixels in the f -th and the ( f − 1)-th frames
corresponding to xk can be leveraged. Based on this temporal consistency, the proposed method presumes that y f ,k is in the region
G(y f −1,k ) centered at y f −1,k . Region G(y f −1,k ) can be small because, even without the planer assumption, a point sufficiently close
to the marker after rectification in (II-3) remains at nearly the same
position regardless of the camera motion. Based on this, the proposed method finds pixel y f ,k corresponding to xk in descendingorder of xk ’s degree of confidence as follows:
NCC(B, f ) (xk , y ),y ∈G(y f −1,k ) 1 + ∑xl ∈Mxk Ds (t)y f ,k = arg max(3)

where s = xk − xl and t = y − y f ,l . Here NCC(B, f ) (xk , y ) is thenormalized cross correlation between the patch centered at pixel
xk in rectified background image B and the patch centered at pixely in the rectified f -th frame (see Eq. (2)). Mxk is the set of thefeature points whose degrees of confidence are greater than xk in acertain region centered at the feature point xk . This means that Mxk
contains the feature points to which corresponding pixels have beenfound in the rectified f -th frame. Ds (t) is a cost term based on the
difference in shift vectors and the distance between feature points,
which is defined as follows:
0 (d(s) > t),(4)
Ds (t) =κ (otherwise)
where d(s) is a monotonically increasing function that gives
higher value as s increases. For example, we use d(s)) =s/10 in our experiments. Ds (t) is a cost function that encourages a feature point to move in a similar manner to the neighboring
feature points. The cost function allows the difference in the shift
vectors t to become greater as the distance s between featurepoints increases.
4.2 Motion Interpolation
Using y f ,k corresponding to feature point xk , the proposed methodinterpolates the shift vectors from the rectified background image B
to the rectified f -th input frame for all pixels in the marker region
Ω and the marker’s surrounding region ∂ Ω in B, and deform therectified background image B based on the shift vectors. Note thatthe shift vectors of not only pixels in Ω but also pixels in ∂ Ω arerequired, because pixels in ∂ Ω in B may be occluded by the marker
in the f -th frame.
Specifically, based on the assumption that pixels move in a similar manner to feature points with high degrees of confidence aroundthem and the motion of adjacent pixels is highly correlated, the proposed method estimates the motion of each pixel in Ω ∪ ∂ Ω in B byminimizing the following energy function:E=∑ ∑ ωi,k ui − uk 2 + α ∑ ui − u j 2 ,i∈Ω∪∂ Ω k

(5)(i, j)∈A

where the summation over k is calculated for all indexes of featurepoints, and A is the index set of adjacent pixel pairs in Ω ∪ ∂ Ω.
Here, ui is the shift vector for pixel i. Shift vector uk for featurepoint xk is given by uk = y f ,k − xk . Note that weight ωi,k is calculated based on the distance between feature point xk and pixel
xi as well as the degree of confidence C(xk ) of feature point xk asfollows:xi − xk 2
ωi,k = max C(xk ) exp −
,
(6)
σ2
k

Figure 4: AR marker used in experiments.

Scene C A step background geometry with grid-patterned texture(Fig. 7).

camera was mostly static. The second and third rows show the results when camera motion was in motion. In the following, we
discuss the results obtained for each scene in further detail.
Figure 5 shows the experimental results for scene A. As can beseen, the appearance of the texture changes around the marker inthe rectified image because of camera motion and the curved geometry as shown in (e). Thus, (b) demonstrates large discontinuities in the texture around the boundary. In (c), the edges in thegrid pattern are successfully connected on the boundary; howeverbrightness between the marker and its surrounding regions differs.
Conversely, the proposed method did not yield geometric and photometric discontinuities in the texture. The proposed method deformed the background image (Fig. 5(f)) based on the tracking offeature points in the marker’s surrounding region.
Compared with scene A, the appearance changes in the textureof scene B (Fig. 6) do not seem to be significant (Fig. 6(e)); however displacement of the texture occurs because of the thickness ofthe marker base. Thus, the discontinuous straight lines can be seenin (b) without deformation. Noticeable difference in brightness canalso be observed without color adjustment in (c). The proposed
method yields natural results without significant visual artifacts, as
is shown in (d). For the stripe texture in this scene, the proposed
method does not always yield accurate correspondences between
the feature points detected in the rectified background image andpixels in the input frame because of the aperture problem. Therefore, these inaccurate correspondences deform the background image excessively, as is shown in (f). However, this excessive deformation does not cause visual artifacts if the textures in the marker
region and the surrounding region are the same, because the proposed method can compensate for the displacement in a direction
orthogonal to the stripes.
In scene C shown in Fig. 7, the marker was leaned against a step.
The vertical plane is visible in the rectified background image, as isdemonstrated in the first row of (f); however, it can become invisible in the input frame depending on the camera pose, as is demonstrated in the input frames in (a) and rectified frames (e). Since
the proposed motion interpolation method assumes smooth motion,
the proposed method cannot handle such discontinuity in shift vectors. This results in visual artifacts as shown in the third row ofTable 1 shows the parameter values used in experiments. To obtainbackground images, we used our previous approach [8] for scenes
A and B, which applies an image inpainting method [9] to a rectified
image with a marker. Initially, we captured a single backgroundimage for scene C.
In the experiments, we compared the results obtained by the proposed method ((d) in Figs. 5-7) with those obtained by a conventional approach that uses a homography matrix to transform thebackground image with color adjustment ((b) in Figs. 5-7). To confirm the effectiveness of the color adjustment, we also show theresults obtained by the proposed method without color adjustment((c) in Figs. 5-7). (a) and (e) in Figs. 5-7 show the input frames
and the rectified frames with the tracking results obtained by the
proposed method. (f) in Figs. 5-7 shows the deformed images of
the rectified background images in the marker and its surrounding
regions. The first row in each figure shows the results when the

Input image
Marker size in a rectified image
Marker region Ω
Width l of surrounding region ∂ Ω
L 1 , L2P2 , P3
Range for calculating confidence N
Search range G
Size of patch W
Range for interinfluenceof feature pointsM
κ, α, σwhere σ is a constant. Minimization of E in Eq. (5) is equivalent
to solving a symmetric and positive-definite linear system obtainedby setting its partial derivatives with respect to the horizontal andvertical components of uk to zero. The system’s coefficient matrix
is sparse; thus it can be solved by the conjugate gradient method forsparse systems, which works efficiently on GPUs (example implementation is provided in [1]).
Finally, rectified background image B is deformed by forwardprojecting each pixel in image B based on the obtained shift vectors
and linearly interpolating pixel values. Note that our implementation uses texture mapping provided in OpenGL.
5

E XPERIMENTS

To demonstrate the effectiveness of the proposed method, we performed experiments to visually remove an AR marker using a PC
with a Windows 7, Core i7-990X 3.46 GHz CPU, 12 GB memory,and a GeForce GTX Titan GPU. We used a USB camera (Logicool
Qcam Pro 9000) to capture real-time input video streams, each ofwhose frames consists of 640 × 480 pixels. We used ARToolkit[6]for camera pose estimation and a square AR marker with edge
length of 80 mm, which was attached to a relatively thick object
with edge length of 95 mm and thickness of 7 mm, as is shown in
Fig. 4. The proposed method was tested under the following threeenvironments:
Scene A A curved background geometry with grid-patterned texture (Fig. 5).
Scene B A planar background geometry with stripe texture(Fig. 6).

Table 1: Parameters and values used in experiments.

640×480 pixels
80×80 pixels
140×140 pixels
15 pixels
11 pixels, 31 pixels
80%, 1%
3×3 pixels
5×5 pixels
11×11 pixels
50×50 pixels
0.001, 1000, 25

(a) Input frame(b) Result without
deformation(c) Result without
color adjustment(d) Result by the
proposed method(e) Feature tracking
in rectified frame(f) Deformation of rectified
background image

Figure 5: Experimental results for scene A with a curved shape and a grid texture.

(a) Input frame(b) Result without
deformation(c) Result without
color adjustment(d) Result by the
proposed method(e) Feature tracking
in rectified frame(r) Deformation of rectified
background image

Figure 6: Experimental results for scene B with a planar shape and a stripe texture.

Fig. 7. However, the proposed method generated more continuous
texture than the method without deformation on the boundary between the marker and its surroundings excluding the region of thevertical plane.

Note that the numbers of detected feature points for scenes A, B,
and C were 24, 20, and 34, respectively, and the frame rates for thescenes were 4.5, 4.2, and 4.4 fps, respectively.

(a) Input frame(b) Result without
deformation(c) Result without
color adjustment(d) Result by the
proposed method(e) Feature tracking
in rectified frame(f) Deformation of rectified
background image

Figure 7: Experimental results for scene C with a step background geometry with grid-patterned texture.

6 C ONCLUSION
This paper has proposed an AR marker hiding method based onreal-time deformation of a background image. The proposed feature point detection and tracking algorithm provides uniformly distributed feature points, which is desirable for motion interpolation.
The proposed method can achieve real-time pixel-wise deformation
using an energy function that can be efficiently minimized using
GPUs.
In experiments, we confirmed that the proposed texture
deformation-based AR marker hiding method can generate visually natural images even for a thick AR marker and for nonplanarbackground geometries. Note that we have also obtained good results for a scene with a stripe texture, which suffers from the aperture problem when detecting feature points. However, the proposed
method could not handle scenes with relatively complex geometry,
which causes occlusions according to camera motion, because theproposed method assumes that the motion of adjacent pixels is similar. Future work includes AR marker hiding for a variety of background geometries at higher frame rates. In addition, we plan to
apply real-time texture deformation to DR techniques that visually
remove various objects from a real-time video stream.
ACKNOWLEDGEMENTS
This research was supported in part by the Ministry of Internal Affairs and Communications SCOPE No. 152107001 and the Japan
Society for the Promotion of Science KAKENHI No. 15K16039.
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