2013 IEEE International Conference on Robotics and Automation (ICRA)
Karlsruhe, Germany, May 6-10, 2013

Colorization of 3D Geometric Model utilizing Laser Reflectivity
Shuji Oishi1 , Ryo Kurazume2 , Yumi Iwashita2 , and Tsutomu Hasegawa2

Abstract— In this paper, we propose a new technique foradding color to a surface of a 3D geometrical model utilizing
laser reflectivity. A time-of-flight laser scanner obtains a range
image from the sensor toward the target by measuring theround-trip time of a laser pulse. At the same time, for most laser
scanners, the reflectance image (which is the strength of thereflected light) is available as a by-product of the range value.
The proposed technique first assigns appearance information
to a 3D model by colorizing a reflectance image based on
the similarity of color and reflectance images. Then the color
information is transfered to the corresponding range image, andthe colorized 3D model is obtained. We carried out experiments
using a laser scanner and showed the performance of the
proposed technique in several conditions.

I. I NTRODUCTION
Three-dimensional geometric modeling of a real object
using a laser scanner has been used in many applications suchas Virtual Reality (VR), digital archives of cultural heritages[1], and remote control of a rescue robot in a hazardous
environment [2]. Moreover, to display a 3D model with highrealism, it is effective to add color and texture information
to the surface and provide not only a geometry but also
appearance information. For instance, it is quite helpful tocontrol a rescue robot in a dark and hazardous environment
appropriately if a colored 3D model around a rescue robot isdisplayed for an operator. Texture mapping [3], which maps acolor image obtained by a camera to a 3D geometric model,
has been widely used for creating photo-realistic models.
However, if a color image and a 3D geometric model arecaptured by different sensors such as a digital camera and alaser scanner, precise calibration between these sensors mustbe performed. Moreover, multiple color images are required
to add surface appearance to an entire or large-scale 3D
model, and small registration errors or changes in lighting
conditions cause an unsatisfactory gap and discontinuity due
to human perception.
In this paper, we propose a new technique for addingcolor to a 3D geometric model utilizing laser reflectivity [4].
A time-of-flight laser scanner obtains a range image froma sensor toward a target by measuring the round-trip timeof a laser pulse. At the same time, laser reflectivity whichindicates the power of the reflected laser is also obtained asa side product of distance value. Therefore, each pixel in
S. Oishi is with Graduate School of Information Science andElectrical Engineering, Kyushu University, Room 928, BuildingWest-2, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japanoishi@irvs.ait.kyushu-u.ac.jp

R. Kurazume, Y. Iwashita, and T. Hasegawa are with Graduate Faculty ofInformation Science and Electrical Engineering, Kyushu University, Room
928, Building West-2, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japankurazume, yumi, hasegawa@ait.kyushu-u.ac.jp
978-1-4673-5642-8/13/$31.00 ©2013 IEEEthe range image has a corresponding reflectance value. In
other words, the range image and the reflectance image are
precisely aligned.
Focusing on the characteristics of laser reflectivity, we
propose a new technique for creating a realistic 3D modelwith appearance information. First, we colorize the reflectance image by a non-calibrated color image based on
the similarity of color and reflectance images. Second, thecolor information is transfered to the corresponding range
image, and the colorized 3D model is obtained. As mentioned
above, range and reflectance images can be considered to becalibrated precisely, so we just need to copy each color valueof the pixel in the reflectance image to the corresponding
pixel in the range image without determining correspondence
between range and reflectance images.
The rest of this paper is organized as follows. In Section 2,
an overview of the previous approaches will be presented. In
Sections 3, we will propose a new colorization technique fora 3D geometric model using a reflectance image. In Section
4, we carry out some experiments using a laser scanner, andverify the performance of the proposed technique.
II. R ELATED R ESEARCH
Texture mapping [3], which is a fundamental techniquefor creating a photo-realistic 3D model, has been widelyused in the field of computer vision. In some applications, itmaps a color image on a range image and creates a colored
3D model. Usually range and color images are captured
from different viewpoints by two independent sensors, suchas a laser scanner and a digital camera. Therefore, it isnecessary to determine the correspondence between range
and color images in order to map color information on the3D geometric model precisely.
For aligning a 3D model and a color image, Yoshida
et al. [5] proposed an alignment technique by assigningseveral matching points between range and color images
manually. Neugebauer et al. [6] proposed a similar techniquethat calculates suitable camera parameters according to theinteractive choice of corresponding points between 3D range
data and a 2D color image.
In contrast, techniques that align range and color images
automatically have been developed by several researchers.
Viola et al. [7] proposed a technique that utilizes statisticalcharacteristics of both images. Stamos et al. [8] also proposeda method that extracts several planes from the range dataand edges in the color image, and calculates the intersectionlines of the planes and the edges. Some approaches thatcompare a 2D image contour with a silhouette image ofthe 3D geometric model have been proposed. Iwakiri et2311

al. [9] proposed a real-time texturing method that aligns acolor image and an image of a 3D geometric model froma virtual camera. Lensch et al. [10] proposed a silhouettebased algorithm that determines the camera transformation
based on an XOR-operation between a silhouette image ofa 3D model and a color image.
Some registration techniques that exploit reflectance images have been proposed. Reflectance image, obtained asa by-product of a range image for most laser scanners,
has a similar appearance to a color image. Focusing onthat, some approaches make use of reflectance images forthe registration between a 2D image and a 3D geometricmodel for texture mapping. Boughorbal et al. [11] utilized
the similarity between the reflectance image and the intensity image based on the χ2 -metric. Umeda et al. [12]proposed a technique to determine relative relations between
a range sensor and a color sensor based on the gradientconstraint between reflectance and color images. On theother hand, local features in both images are effective toestimate the correspondence between reflectance and color
images. Kurazume et al. [4] proposed a calibration method
for texture mapping that minimizes the error between edges
extracted from reflectance and color images respectively
using the robust M-estimator. Boehm et al. [13] utilized
SIFT(Scale-Invariant Feature Transform) [14] to estimate
extrinsic parameters by matching them in reflectance andcolor images. Inomata et al. [15] proposed a SIFT-based
technique that calculates not only the extrinsic parameters but
also the intrinsic parameter and the distortion of the camera
lens simultaneously. This method uses Soft-matching that retains correct matches while removing false matches between
reflectance and color images according to the similarity of
the appearances based on Bhattacharyya distance.
All the methods mentioned above assume that color
images which correspond to range images are captured.
However, in some cases, it is difficult or almost impossible toprovide color images perfectly, for example, in dark and wide
areas. The proposed technique in this paper gives a solution
for providing a colored 3D model even in these conditions.
III. C OLORIZATION OF 3D G EOMETRIC M ODELUTILIZING L ASER R EFLECTIVITY
In this section, we propose a new colorization techniquefor a 3D geometric model using a reflectance image. The
proposed technique is based on a local similarity between
color and reflectance images. First, both images are divided
into small regions and local features are calculated. Then thecorrespondences of these small regions between color andreflectance images are determined considering a similarity of
local features. Next, the reflectance image is colorized using
color information assigned in the regions using the image
colorization method. Finally, color in the reflectance image
is transfered to a range image and the colorized 3D model
is obtained.
The proposed technique does not require a precise calibration between color and range images which is required for theconventional texture mapping. Thus the gap or discontinuity

2D Laser range finder

Parent robot
Rotating table

Child robot 1

Child robot 2

Fig. 1: Acquisition system of a panoramic range image [1](a) Range image(b) Reflectance image

Fig. 2: Range and reflectance images

in appearance can be avoided even if several images must be
registered on a model. Moreover, the appearance of the entire
surface of a model can be assigned from a partial view of themodel only if the appearance does not change significantlyin the entire model.
A. Reflectance image
A time-of-flight range sensor, such as a laser scanner,obtains range data by measuring the round-trip time of alaser pulse reflected by an object. Fig. 2(a) shows an example
of a range image acquired by a 3D laser scanner (Fig. 1[1]). On the other hand, the most range sensors provide thestrength of the reflected laser pulse (reflectivity). Fig. 2(b)shows a reflectance image that depicts reflectance values as agrayscale image. As mentioned above, an unique reflectancevalue is determined for each pixel in the range image. In
other words, the range image and the reflectance image arefundamentally aligned.
B. Image colorization
Image colorization is a technique for adding color to a
monochrome image and has been used in some specific applications, such as coloring monochrome movies or creatingcolor-coded images for electron photomicrograph or X-ray
imaging. Since adding color values to a monochrome image
has no general solution, the current approaches attempt toestimate the entire colors based on some clues given as seedpoints manually [16] [17] or automatically [18] [19] [20].

2312Yatziv et al. [21] proposed a fast image colorization
technique using Dijkstra’s distance [22]. This techniqueestimates the color at each pixel in a grayscale image by
calculating a weighted average of Dijkstra’s distances fromeach seed point which has chromatic information. Dijkstra’s
distance is computed considering changes in luminance inthe monochrome image. If the change in luminance from theseed point is small, the chromatic information at the seedpoint is mainly copied to the pixel. More precisely, if thecolor is described in YCbCr color space, the color of eachmonochrome pixel is estimated using Dijkstra’s distance asfollows:
j∈Ω w(i, j)cj
(1)
ci =  cj∈Ωc w(i, j)

Eij =

n−1

k=1

w(i, j) = rij −α(2)rij = argmin Eij(3)|Ypk+1 − Ypk |pk+1 ∈N (pk ),p1 =i,pn =j(4)

Where, ci is the estimated color value(CbCr) in pixel i,
Ωc is a set of seed points which have color information,rij is Dijkstra’s distance from pixel i to pixel j, α is again parameter that controls the effect of weighting function
w(i, j) based on Dijkstra’s distance, and Ypk and N (pk ) are
the intensity and the neighbor pixels of the pixel pk .
Equation (2) indicates that the seed point, which has asmall Dijkstra’s distance to the target pixel, is preferentiallyselected to colorize the monochrome pixel in the grayscale
image. In contrast, a seed point which has a Dijkstra’s pathwith large luminance change contributes little to the color
estimation of the monochrome pixel.
C. Colorization of range image using reflectance image
Based on the image colorization technique describedabove, we propose a new colorization technique for a 3D
geometric model utilizing a reflectance image and a color image. The basic idea of the proposed technique is as follows;since reflectance and range images are fundamentally andprecisely aligned, we colorize the reflectance image using its
similarity with a color image at first, then transfer the color to
the range image. In the following sections, we introduce ourtechniques, which determines correspondences in reflectanceand color images using HOG features [23].
1) Assignment of seed points in a reflectance image:
We colorize a reflectance image obtained by a laser scannerbased on Yatziv’s method [21] shown in Section III-B.
Firstly, we assign some seed points which have chromatic
information in the reflectance image. We adopt the following
two approaches for assigning seed points.
(i) Manual assignment by human intervention(ii) Automatic assignment by determining the corresponding regions between reflectance and color images
In the manual assignment, a 3D geometric model is colorizedaccording to the human instruction. Several seed points

0

SLIC

160

HOG

Fig. 3: Assignment of seed points by local features with SLIC andHOG

are selected manually in a reflectance image, and color
information in a color image is assigned to these seed points.
On the other hand, the automatic assignment determines
the correspondences of small regions between reflectance andcolor images automatically utilizing SLIC [24] and HOG
features [23], and then transfers chromatic information froma color image to a reflectance image. SLIC (Simple Linear
Iterative Clustering) proposed by Achanta et al. [24] isa technique to divide an image into small segments withsimilar size called “superpixels”. Each segment extracted by
SLIC holds pixels that have similar intensity or color. The
HOG (Histogram of Oriented Gradients) feature is proposedby Dalal et al. [23] for pedestrian detection in camera images.
HOG is able to describe local object appearances robustly
according to the distribution of gradient orientation of theintensity.
First, we divide reflectance and color images into small
segments with SLIC, and then the local features are extracted
by applying HOG to small regions around the segments.
Now we use canny-filtered reflectance and color images
for feature extraction in order to compare the outlines ofobjects in these images. Finally, based on the similarities
of HOG features between the small regions in reflectanceand color images, the correspondences of the regions in both
images are determined (Fig. 3). Seed points in the reflectanceimage are chosen at the center of the segmented regions andthe chromatic information is copied from the center of thecorresponding regions in the color image.
2) Colorization of reflectance image using range image:Once the seed points are determined by the method mentioned above, the Yatziv’s method in Section III-B can beapplied to a reflectance image simply in the same way as fora monochrome image. However, as you can see in Fig. 2(b),jump edges in a reflectance image are obscure in many cases.
Thus, if we apply the Yatziv’s method to the reflectanceimage directly, the color information will be spread beyond
these jump edges and unsatisfactory results will be obtained.
On the other hand, a range image contains jump edges
clearly as shown in Fig. 2(a) and these edges can bedetected easily. Since the range image and the reflectanceimage are fundamentally aligned precisely, we can utilize
the jump edges in the range image as the additional edges
for colorizing the reflectance image.
To take these edges in the range image into account, we
define a new energy function using a quadratic function2313

instead of Eqs. (3), (4) as follows.

rij = argmin Eij

Eij
=

n−1





β(Dpk+1 − Dpk )2 + 1 |Ypk+1 − Ypk |

(5)
(6)

k=1

Where, Dpk is an intensity value at pixel pk in a range
image, and β is a gain parameter to control the effect of
the range image for color estimation. Equation (6) meansthat if no jump edge exists along the path from a seed point
to a target pixel in both reflectance and range images, theDijkstra’s distance becomes small and the seed point affects
the color estimation of the target pixel in the reflectanceimage significantly. In contrast, the seed point with a small
Dijkstra’s distance due to jump edges in a reflectance and/or
a range image slightly influence to the color estimation.
3) Proposed method: Consequently, the proposed colorization techniques for a 3D geometric model are summarized as follows. Note that the YCbCr color space is used inthis paper, however, the proposed technique works in othercolor spaces such as YUV or lαβ.
Method 1 : Manual assignment of seed points(i) Acquire range and reflectance values and a color image
by a laser scanner and a digital camera, respectively.
(ii) Create range and reflectance images in which grayvalues of each pixel are proportional to the measured
range and reflectance values.
(iii) Assign seed points in the reflectance image manually according to the correspondence between thereflectance and the color images.
(iv) Apply the proposed colorization technique in Eqs. (1),(5), and (6) using range and reflectance images, andobtain a colorized reflectance image. Luminance Y
in the colorized reflectance image is determined by areflectance value.
(v) Transfer the color value of each pixel in the colorizedreflectance image to the corresponding range image
and construct a colorized 3D model from the range
image.
Method 2 : Automatic assignment of seed points
To assign seed points automatically, we roughly determine
the correspondence between reflectance and color images
using HOG features. To do so, (iii) in the method 1 is
replaced as follows.
(iii) Divide reflectance and color images in small segments
using SLIC, and determine the correspondence between segments according to HOG features. Then,assign the color information to the center pixel of eachsegment in the reflectance image from the corresponding region in the color image.

(a) Scene1(b) Scene2(c) Scene3
Fig. 4: Experimental setup(a) Range image(Scene1)(b) Reflectance image(Scene1)(c) Range image(Scene2)(d) Reflectance image(Scene2)(e) Range image(Scene3)(f) Reflectance image(Scene3)

Fig. 5: Range and reflectance images

IV. E XPERIMENTS
This section introduces the results of the colorization
experiments. Range and reflectance images are obtained by
the 3D laser measurement robot CPS-V shown in Fig. 1 [1].
This robot captures surrounding range and reflectance databy rotating the laser scanner (SICK, LMS151) on a rotarytable. The image size is 760 × 1135 pixels. Color images
are taken by a digital camera (Fujifilm, FinePix S7000) by

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(a)Seed points assigned manually. (b)Colorization result(a)Original 3D mesh modelFig. 6: Proposed method 1 in scene 1 (manual)(b)Proposed method 1 (manual)(a)The correspondence between Fig. 4(a) and Fig. 5(b)(c)Proposed method 2 (automatic)(b)Seed points assigned automatically. (c)Colorization result

Fig. 8: Colorized 3D geometric model in scene 1

Fig. 7: Proposed method 2 in scene 1 (automatic)

hand. In the experiments, the range and reflectance images
are normalized from 0 to 255, and the parameters are set as
α = 6 and β = 1.0.
Fig. 4 shows three experimental conditions: a simple
environment with two road cones of different colors (scene1), a complex environment with a human and other objects(a table and chairs) (scene 2), and a house made of red bricks(scene 3). Fig. 5 shows the range and reflectance images ofthese scenes captured by the measurement robot.
First, we assigned seed points to the reflectance images inFig. 5(b) manually (Fig. 6) and automatically (Fig. 7). Fig.
6(a) and Fig. 7(b) show the seed points in the reflectanceimage in both methods. Colorized reflectance images areshown in Fig. 6(b) and Fig. 7(c), respectively. Fig. 8(a) isthe 3D mesh model constructed from the range image shownin Fig. 5(a). Fig. 8(b) and Fig. 8(c) show the 3D meshmodel colorized by the method 1 (manual) and the method
2 (automatic), respectively. It is clear that the proposed
methods successfully add color information to the surface ofthe 3D geometric models without accurate pose estimation.
Next, we carried out the experiments using a reflectanceimage and a color image taken in scene 2 (Fig. 4(b)).

Similar to the experiments in the scene 1, we assigned seedpoints manually (Fig. 9) or automatically (Fig. 10). Fig. 9(a)and Fig. 10(b) show assigned seed points in both methods.
Colorized reflectance images are shown in Fig. 9(b) and
Fig. 10(c), respectively. The colorized 3D model for scene
2 is shown in Fig. 11. Fig. 11(a), (b), and (c) are the 3D
mesh model constructed from the range image (Fig. 5(c)),
the 3D mesh model colorized by the method 1 (manual) , andthe one colorized by the method 2 (automatic), respectively.
From these results, we verified that the proposed methods
are capable of creating colorized 3D geometric models in
more complex scene.
Finally, we carried out experiments in scene 3 (Fig. 4(c)).
In this experiment, we colorized an entire 3D geometricmodel of a house made of red bricks with a partial viewof the house shown in Fig. 4(c). The entire 3D geometricmodel is created from four range and reflectance images
shown in Fig. 5(e) and Fig. 5(f). Fig. 12 shows colorizedreflectance images using the method 1 manually, and themethod 2 automatically.
The colorization results of the 3D geometric model (Fig.
13(a)) in scene 3 are shown in Fig. 13(b) and (c). In addition,
we show the colored model using three color images by2315

(a) Seed points assigned manually. (b) Colorization result
Fig. 9: Proposed method 1 in scene 2 (manual)(a)Original 3D mesh model(a) The correspondences between Fig. 4(b) and Fig. 5(d)(b)Proposed method 1(b) Seed points given automatically. (c)Colorization result
Fig. 10: Proposed method 2 in scene 2 (automatic)

the proposed method 2 in Fig. 13(d). You can see that thecolored regions increase by using several color images takenfrom different viewpoints. Consequently, we successfullycolorized the entire geometric model using a partial viewof the target house.

(c)Proposed method 2
Fig. 11: Colorized 3D geometric model in scene 2

V. C ONCLUSION
In this paper, we proposed a new colorization technique fora 3D geometric model acquired by a laser scanner utilizing
laser reflectivity. The proposed technique is able to add color
information on the surface of the 3D geometric model bycolorizing the reflectance image manually or automatically.
The automatic colorization is easy but needs enough correspondence between reflectance and color images. On theother hand, the manual colorization gives a colored modeleven if the correspondence is slight between these images.
Precise calibration between color and range images, whichis indispensable for the conventional texture mapping, is not
required. Thus a gap or discontinuity in appearance can be
avoided even if several images must be registered on a model.
Moreover, the appearance of the entire surface of a model

can be assigned from a partial view of the model if theappearance does not change significantly in the entire model.
The reflectance image is not affected by the lighting
conditions and thus can be acquired in day and night.
Therefore, even if a clear color image is not available due tobad light conditions, it is possible to colorize 3D geometricmodels utilizing color images from websites such as Flickr.
In the future, we will perform quantitative evaluation of
the proposed technique for a variety of scenes, and extend
the technique to super-resolution of reflectance images forrealizing more realistic appearance.

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ACKNOWLEDGMENT
This work was supported in part by JSPS KAKENHI
Grant Number 246404 and a Grant-in-Aid for Scientific
Research (B) (23360115).
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(a)Reflectance images(b)Proposed method 1 (manual)(c)Proposed method 2 (automatic)
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(a)Original 3D mesh model(b)Proposed method 1 (manual)(c)Proposed method 2 (automatic)(d)Proposed method 2 using three pictures taken from difference viewpoints
Fig. 13: Colorization of the entire 3D geometric model
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