AR Visualization of Thermal 3D Model by Hand-held Cameras

Kazuki Matsumoto

, Wataru Nakagawa

, Hideo Saito

, Maki Sugimoto

Takashi Shibata

and Shoji Yachida

Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, Japan

NEC Corporation, 1753 Shimonumabe, Nakahara-ku, Kawasaki, Kanagawa, Japan

Keywords:

3D Model, Augmented Reality, Temperature, RGB-D Camera, Thermal Camera, Camera Tracking, Viewpoint

Generative Learning.

Abstract:

In this paper, we propose a system for AR visualization of thermal distribution on the environment. Our

system is based on color 3D model and thermal 3D model of the target scene generated by KinectFusion

using a thermal camera coupled with an RGB-D camera. In off-line phase, Viewpoint Generative Learning

(VGL) is applied to the colored 3D model for collecting its stable keypoints descriptors. Those descriptors

are utilized in camera pose initialization at the start of on-line phase. After that, our proposed camera tracking

which combines frame-to-frame camera tracking with VGL based tacking is performed for accurate estimation

of the camera pose. From estimated camera pose, the thermal 3D model is ﬁnally superimposed to current

mobile camera view. As a result, we can observe the wide area thermal map from any viewpoint. Our system is

applied for a temperature change visualization system with a thermal camera coupled with an RGB-D camera

and it is also enables the smartphone to interactively display thermal distribution of a given scene.

1 INTRODUCTION

Thermal imaging is utilized for various purposes such

as recordingenergyrelated issues of power equipment

and observingbody temperature of animals. Recently,

a lot of work has been done to combine state-of-the-

art computer vision techniques with the thermal imag-

ing. For example, (Szab´o et al., 2013) propose a

new augmented reality(AR) system of the patient’s in-

frared tissue temperature maps for directly visualizing

myocardial ischemia during cardiac surgery. (Kandil

et al., 2014) present a method for automatically gener-

ating 3D spatio-thermal models, and enables owners

and facility managers to quickly get the actual energy

performance data for their existing buildings by lever-

aging recent image-based 3D modeling approaches as

well as thermal cameras.

Recently, smartphone attachable thermal imaging

devices have started to appear. Since the cost of

these devices is considerably less than thermal cam-

eras originally built for military or medical purposes,

thermal imaging is starting to be more widely used in

consumer applications(Yanai, 2014).

However, the resolution and the ﬁeld of view of

the thermal cameras is limited and it is difﬁcult to

monitor large machines and areas. Since the cost of

such a device is also high, it makes it hard to use sev-

eral cameras to cover a large surface. Also, the cali-

bration of these devices is difﬁcult due to the low res-

olution images obtained from single camera.

In this paper, we propose a system for visualizing

wide area temperature map from arbitrary viewpoints.

The goal of our paper is AR visualization of a ther-

mal 3D model with ordinary hand-held cameras in

order to monitor the spatial temperature distribution

of the target scene. Our approach is based on precom-

puted RGB 3D model and thermal 3D model of the

target scene achieved with an RGB-D camera coupled

with the thermal camera shown in Figure 1. These 3D

models are generated using KinectFusion (Izadi et al.,

2011). The colored 3D model is used with the View-

point Generative Learning (VGL) (Thachasongtham

et al., 2013) algorithm to detect feature points robust

to viewpoint changes and to generate a database with

corresponding 3D positions and descriptors of these

features. We then estimate the pose of the camera by

ﬁnding keypoint correspondencesbetween the current

view and the database. We also combine the frame-

to-frame tracking with the VGL based tracking for

accurately estimating the camera pose. By knowing

480

Matsumoto K., Nakagawa W., Saito H., Sugimoto M., Shibata T. and Yachida S..

AR Visualization of Thermal 3D Model by Hand-held Cameras.

DOI: 10.5220/0005290904800487

In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISAPP-2015), pages 480-487

ISBN: 978-989-758-091-8

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

the pose of the camera, we are then able to perform

AR visualization of thermal 3D model from any view-

point with a hand-held camera.

As a result, our system has two applications. The

ﬁrst one is temperature change visualization system

using an RGB-D camera and a thermal camera. The

other is interactive AR visualization of thermal 3D

model on smartphone display. The user can reveal

the 3D thermal model by touching relevant image re-

gions on the smartphone. The paper is structured as

Figure 1: Our capture system is composed of the Microsoft

KINECT and Optris PI160 thermal camera.

follows. The related works are discussed in Section

2. After describing the detail of our system in Sec-

tion 3, Section 4 will show the two applications of

our method and discuss the accuracy and the runtime

of our camera tracking. We ﬁnally conclude the paper

and describe our future works in Section 5.

2 RELATED WORKS

The 3D representation of heat distribution has at-

tracted the interest of researchers because of the de-

velopment of 3D modeling techniques, depth cameras

and thermal imaging (Borrmann et al., 2012), (De-

misse et al., 2013). These systems reconstruct a ther-

mal 3D model and exploit it for acquiring not only ac-

curate and wide scope thermal data but also geograph-

ical information of a building so that the observer can

run simulations of thermal distribution and can eas-

ily ﬁnd the areas with abnormal temperatures. They

involve a mobile wheeled robot with a laser scanner

and a thermal camera for simultaneous acquisition of

3D laser scan data and thermal images. However, this

robot is not capable of exploring bumpy ground or

conﬁned spaces.

Our work is inspired from a mobile 3D thermal

system introduced in (Vidas et al., 2013) which used

only two cameras - an RGB-D camera and a ther-

mal camera. This system uses the KinectFusion al-

gorithm (Izadi et al., 2011) for generating dense and

high-ﬁdelity 3D thermal model. Inspired from their

approach, we also use the KinectFusion for recon-

structing 3D thermal model. The major advantages

of such approach is its ease of use in conﬁned spaces

and its relatively low price when compared with 3D

LiDAR and robotics platform.

The problem of 3D pose estimation of rigid ob-

jects has been studied for several decades because es-

timating the pose of a known object is a signiﬁcant

issue in Augmented Reality. (Saito et al., 2014) pro-

pose on-line diminished reality system using View-

point Generative Learning (VGL) based camera pose

estimation. The VGL generates a database of feature

descriptors from the 3D model to make the pose es-

timation robust to viewpoint changes. Therefore, we

apply the VGL for tracking the mobile cameras be-

cause the purpose of our AR system is to enable the

observer to move hand-held camera arbitrarily and to

visualize the thermal map in a given scene.

3 PROPOSED METHOD

The overview of our system is shown in Figure 2. As

it can be seen, our system pipeline consists of an off-

line phase and an on-line phase. In the pre-process

stage of this system, we estimate the intrinsic parame-

ters of the RGB-D camera and the thermal camera by

using an ordinary circle grid pattern (Zhang, 2000).

We also need to calibrate the relative pose of these

distinct devices. For this reason we use our own cal-

ibration board that makes easier to detect the circle

grid pattern with thermal camera. After the prepro-

cessing, we generate two 3D models using Kinect Fu-

sion (Izadi et al., 2011) - one with the RGB infor-

mation another with the corresponding temperature

distribution at the capturing time. The RGB colored

3D model is the source of stable keypoints database

stored by Viewpoint Generative Learning in off-line

phase and the thermal 3D model will be used for aug-

mented reality in on-line phase. The stable keypoint

database will be available for estimating the camera

pose in on-line phase.

During the on-line phase, we ﬁrst initialize the

camera pose reference to the RGB 3D model. The

initial camera pose is calculated by the correspon-

dences between the stable keypoints in VGL database

and h keypoints extracted from the ﬁrst frame. Af-

ter this initialization, the camera pose is computed

using frame-to-frame tracking in combination with

VGL based camera pose estimation. We can then

align the thermal 3D model with the current viewpoint

and superimpose the thermal information on the cur-

rent image.

ARVisualizationofThermal3DModelbyHand-heldCameras

481

(a) (b) (c)

Figure 3: (a)lower plate (b)upper plate (c) captured image from thermal camera.

Figure 2: System Overview.

3.1 Camera Calibration

In order simplify the pose estimation of the thermal

camera in reference to the RGB-D camera, we use

a special calibration board that is visible from both

color and thermal cameras. This calibration board

is constructed of two plastic plates stacked together.

Both details were made with 3D printer. The lower

plate is made of a planar surface covered with cir-

cular bumps corresponding to the black parts of the

circle grid calibration pattern. The upper plate is de-

signed to plug on top of the ﬁrst one, it is thus made

of a planar surface with holes where the black pattern

of lower calibration board should appear. Combin-

ing both plates creates a ﬂat calibration pattern like

the ones commonly used. Just before calibration, we

heat the lower plate while the upper one remains at

ambient temperature so that the circle grid pattern in

thermal image can be detected as shown in Figure 3.

The intrinsic parameters of RGB-D camera and

thermal camera are calculated using Zhang’s method

(Zhang, 2000). In order to estimate the pose of the

thermal camera in reference to the RGB-D camera,

we obtain a set of 3D-2D correspondences by de-

tecting both 3D positions of black circles captured in

RGB-D camera and its corresponding 2D locations in

the thermal image. However, the depth map captured

with RGB-D camera is contaminated with structural

noise especially in black circle areas. Therefore we

compute the planar surface equation from acquired

3D points on circle grid and project them onto the es-

timated plane for accurately estimating their 3D posi-

tion. We then apply the efﬁcient Perspective-n-Point

algorithm (Lepetit et al., 2009) to estimate the extrin-

sic parameters.

3.2 Colored 3D Model Generation

The KinetFusion algorithm (Izadi et al., 2011) is used

to generate the uncolored 3D model of target scene.

While KinectFusion is running, we save not only the

camera poses of RGB-D camera but also the color im-

age, depth image and thermal image for each frame.

Using the pose estimation of each frame, we then gen-

erate colored 3D model by projecting RGB data onto

the uncolored 3D model.

3.3 Thermal 3D Model Generation

Since the RGB-D camera and the thermal camera are

located at slightly separate positions, we need to ap-

ply rigid transformation calculated in Section 3.1 to

thermal image and deal with occlusions for correctly

mapping the thermal data. In order to remove oc-

cluded thermal data, we ﬁrst project the 3D points

corresponding to the pixels of the depth image onto

the thermal image and generate the depth image from

the thermal camera viewpoint. Then, we remove oc-

cluded depth values by replacing the neighboring ab-

solutely difference values with the average value of

this area. Finally, for each pixel of the RGB-D image,

corresponding thermal values can be found while re-

moving occluded area. The process of thermal data

mapping is illustrated in Figure 4.

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Figure 4: Thermal data mapping from estimated camera

pose.

3.4 Viewpoint Generative Learning

Conventional local features such as SIFT and SURF

are robust to scale and rotation changes but sensi-

tive to large perspective changes. In order to solve

this problem, (Yoshida et al., 2013) proposed a sta-

ble keypoint matching method which is robust even

under strong perspective changes by using Viewpoint

Generative Learning (VGL). However, this method is

only focusing on planar surfaces and can not deal with

3D objects. Therefore, (Thachasongtham et al., 2013)

modify this algorithm so that they can estimate the

pose of a 3D object from stable keypoints stored in

VGL database.

During on-line phase, we need a robust camera

tracking algorithm against strong viewpoint changes

because the user of our system is observing the tar-

get scene with a hand-held camera. Therefore, we

apply the Viewpoint Generative Learning to the RGB

3D model generated in Section 3.2. In the ﬁrst step,

we generate patterns of the model from various view-

points using the OpenGL rendering process as shown

in Figure 5. For each generated viewpoint, we col-

lect not only the patterns and extracted SIFT features

but also the depth and viewpoint of those rendered

images. Then, all detected keypoints are projected

from pixel coordinate system to 3D coordinate sys-

tem and conserve only the ones that can be detected

over multiple views. We deﬁne these features with

high repeatability as stable keypoints and collect the

corresponding descriptors.

After that, k-means++ (Arthur and Vassilvitskii,

2007) is applied to cluster the set of collected descrip-

tors of each stable keypoint and store the barycenter

descriptors and the 3D positions of each stable key-

point in the VGL database.

Figure 5: Viewpoint Generative Leaning.

3.5 Camera Pose Initialization

After learning is ﬁnished, we ﬁrst detect SIFT key-

points and extract feature descriptors from the ini-

tial frame. For each detected keypoint, we search

the two most similar descriptors by evaluating Eu-

clidean distance of their high dimensional value us-

ing the Fast Library for Approximate Nearest Neigh-

bors (FLANN) algorithm. Then, we evaluate the ra-

tio of the distance of closest descriptor to that of the

second closest descriptor, and if the ratio is under

a given threshold we validate the established corre-

spondence. As a result, we can identify the 3D/3D

correspondences between the stable keypoints on the

model in 3D and the current RGB-D camera view. Fi-

nally, the pose of RGB-D camera is deduced with a

singular value decomposition associated to RANSAC

for excluding wrong correspondences. The accuracy

of initialization is evaluated by the ratio between the

number of the 3D/3D correspondences and that of ex-

tracted keypoints from current image . If the ratio is

over the threshold, we assume the initialization is suc-

cessfully performed and start frame-to-frame tracking

from the next frame.

(a) current image (b) camera pose

Figure 6: Camera pose estimation.

3.6 Camera Tracking

In the frame-to-frame tracking, we continuously ex-

tract descriptors from RGB-D frames. Under the as-

sumption that the current camera position is close to

the previous one, we search for correspondences with

ARVisualizationofThermal3DModelbyHand-heldCameras

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the features from previous frame in their local neigh-

borhood . The matching pairs are evaluated based on

Euclidean distance, and the closest pair is selected as

matching pair. If the ratio of matched pairs to key-

points extracted in previous frame is over 10 %, the

current camera pose is estimated by singular value de-

composition and RANSAC (as explained in previous

Section), otherwise we apply the VGL based tracking

to re-initialize the camera pose.

However, sometimes the frame-to-frame tracking

fails even in the case where we can ﬁnd keypoint cor-

respondences correctly. In order to solve this prob-

lem, we evaluate the Euclidean distance of camera po-

sition and the rotation of camera poses between cur-

rent frame and previous frame. If the distance is over

5cm or the rotation is over 10 degrees, we consider

the frame-to-frame tracking as wrong estimation and

apply the motion model calculated from previous 3

frames for predicting current camera pose. As a re-

sult, our camera pose tracking is stable as shown in

Figure 6.

3.7 AR Visualization of Thermal Model

During the on-line processing, we superimpose pre-

computed thermal 3D model on current view from es-

timated camera pose as shown in Figure 7. The ren-

dering process is performed in GPU with CUDA and

OpenGL so that the observer can see the temperature

distribution in real-time. As a result, we can visualize

the thermal distribution of the scene from any view-

point.

(a) Off-line generated thermal

3D model

(b) AR visualization of thermal

model

Figure 7: Examples showing AR visualization.

4 EXPERIMENTS

In this section, we introduce two applications for ther-

mal observation system using AR visualization of

thermal 3D model. The ﬁrst one is AR visualiza-

tion for detecting the temperature distribution change

with an RGB-D camera coupled with a thermal cam-

era. The second one is to interactively visualize the

thermal 3D model on smartphone display. For gener-

ating thermal 3D model and RGB 3D model, we used

Microsoft Kinect(640 × 480 pixels resolution, 30fps)

and Optris PI160 with a resolution of 160 × 120 pix-

els and a frame rate at fps. Vertical and horizontal

values of the ﬁeld of view of the PI160 are 21

◦

and

◦

, respectively.

4.1 AR Visualization of Temperature

Changes Distribution

We demonstrate the use of our AR system by visu-

alizing chansing temperatures of electrical equipment

within a scene in real-time. This system can visualize

more widespread thermal distribution than our previ-

ous work (Nakagawa et al., 2014).

In on-line phase, the user moves same camera set

as in the off-line phase and to record current thermal

distribution. During camera tracking, this application

continuously projects the thermal data onto the off-

line generated uncolored model from the estimated

viewpoint as shown in Figure 8. Then, we simulta-

neously superimpose both off-line generated thermal

3D model and on-line rendered thermal map on cur-

rent view as illustrated in Figure 9.

Additionally, this application enables us to visu-

alize the difference of the current thermal state in re-

spect to the recorded thermal 3D model from the same

viewpoint. Figure 9 shows the results by blending the

augmented thermal map with current image RGB data

int regions of considerable temperature change.

4.2 Quantitative Evaluation

4.2.1 Runtime Evaluation

The experiment was carried out on a PC with 16GB

of RAM, an Intel Core i7-4800MQ CPU and a Nvidia

GeForce GTX 780M graphics card. Table 1 shows

the breakdown of the processing times. We believe

that this system can be considered to be running in

real-time frame rates. Also, we compare the runtime

for camera pose estimation with VGL database based

tracking proposed by (Thachasongtham et al., 2013).

Their method takes 0.12 seconds for camera pose es-

timation since requesting the database for ﬁnding cor-

responds keypoints is slower than the local area key-

point searching in frame-to-frame tracking.

Table 1: Breakdown of Processing Time.

Process runtime (sec)

Total 0.46

Camera pose estimation 0.10

On-line thermal mapping 0.12

Visualization 0.24

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Figure 8: The process of on-line thermal 3D map rendering.

Figure 9: AR temperature change visualization system.

4.2.2 Accuracy Evaluation

In order to evaluate the accuracy of camera pose esti-

mation, we calculate the RMSE score of re-projection

error from estimated camera pose and compare it with

that of VGL based tracking in 10 frames (Figure 10).

As table 10 shows, our proposed method outperforms

Figure 10: RMSE of re-projection error (mm).

the VGL based tracking in terms of re-projection er-

ror. Since our system applies singular value decom-

position to the the corredponding 3D-3D keypoints

for camera pose estimation, it is more accurate than

the VGL based camera pose estimation deduced from

2D-3D correspondences.

Figure 11 shows the Euclidean distances between

the camera positions of two consecutive frames. Even

when the cameras were moved slowly, the VGL based

tracking sometimes fails to track the camera (as high-

lighted in green circles on the graph). Our method is

much more stable, since we combine frame-to-frame

tracking, VGL based initialization and motion model

for camera pose estimation.

4.3 AR Visualization of Thermal 3D

Model on Smartphone

This system consists of a smartphone and a server PC

and they are connected via wireless network in order

to transfer the video data between those devices. Fig-

ure 13 shows communication ﬂow between a smart-

phone and a server. The server has two threads -

one for network operations and one for AR process-

ing. The network thread continuously waits for new

frames from the smartphone and buffers them to be

used in the AR thread. As the AR thread is rather

slow in processing frames, it always retrieves the lat-

est frame in the buffer and drops older frames. The

processed frames with AR overlay are buffered in the

network thread to be sent back to smartphone. In the

server PC, RGB 3D model and thermal 3D model of

a target scene are generated and its robust keypoint

features are stored in VGL database. In the on-line

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485

Figure 11: The Euclidean distances between the camera positions of two consecutive frames.

Figure 12: The observer can select the thermal visualization

area.

phase, the observer can capture the scene with the

smartphone and select target area by touching display

of the device (Figure 12). The correspondences be-

tween the keypoints on the current image in 2D and

stable keypoints of the model in 3D are searched in

the same way as the temperature change visualiza-

tion system described in Section 4.1. Since smart-

phones can not obtain depth image of current frame,

the Perspective-n-Point method (Lepetit et al., 2009)

with RANSAC instead of singular value decomposi-

tion is applied to the 2D-3D correspondences for de-

ducing the camera pose. We can ﬁnally superimpose

the thermal 3D object on the target area of smartphone

display and visualize the temperature distribution of

the area as shown in Figure 14.

The experimental platform was implemented on

smartphone with 1.0GHz MSM8255 processor and

512MB of RAM and a laptop with 2.5GHz Intel Core

i7-2860QM processor GeForce GTX 560M graphics

card and 16GB RAM. The smartphone camera image

size is 320×240 pixels.

Figure 13: Connection ﬂow between smartphone and

server.

(a) thermal model is superimposed in touched area

(b) The target area is increasing

Figure 14: AR visualization of thermal 3D model in smart-

phone.

5 CONCLUSIONS & FUTURE

WORK

In this paper, two systems for visualizing temperature

distribution of a given scene using an RGB-D cam-

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era and a thermal camera are presented. The ﬁrst one

is temperature change visualization system compar-

ing on-line and off-line thermal 3D models from any

viewpoint. Another is the interactiveAR visualization

of thermal 3D model on smartphone display. Both ap-

plications use AR visualization of off-line generated

thermal 3D model. In the off-line phase, the uncol-

ored 3D model of a given scene is reconstructed and

the poses of the camera with the corresponding color

and thermal images are saved by using KinectFusion.

After mapping color and thermal images on separate

uncolored models, Viewpoint Generative Leaning is

applied to the RGB 3D model in order to store the

stable keypoints and their clustered descriptors in the

VGL database. During the on-line phase, hand-held

camera poses are estimated by combining frame-to-

frame tracking with the camera pose estimation us-

ing correspondences between keypoint descriptors in

the current image and in the VGL database. Finally,

the thermal 3D model is superimposed on the current

hand-held camera view.

Recently, some devices for converting the smart-

phone camera into thermal camera have appeared in

the market such as Therm-App for Android and FLIR

ONE for iPhone. We plan to use these devices for

getting thermal information from smartphones so that

our AR visualization system can detect temperature

distribution changes on the ﬂy.

REFERENCES

Arthur, D. and Vassilvitskii, S. (2007). k-means++: The

advantages of careful seeding. In Proceedings of the

eighteenth annual ACM-SIAM symposium on Discrete

algorithms, pages 1027–1035. Society for Industrial

and Applied Mathematics.

Borrmann, D., N¨uchter, A., Dakulovic, M., Maurovic, I.,

Petrovic, I., Osmankovic, D., and Velagic, J. (2012).

The project thermalmapper?thermal 3d mapping of in-

door environments for saving energy. In Proceedings

of the 10th International IFAC Symposium on Robot

Control (SYROCO), volume 10, page 1.

Demisse, G. G., Borrmann, D., and Nuchter, A. (2013). In-

terpreting thermal 3d models of indoor environments

for energy efﬁciency. In Advanced Robotics (ICAR),

2013 16th International Conference on, pages 1–8.

IEEE.

Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe,

R., Kohli, P., Shotton, J., Hodges, S., Freeman, D.,

Davison, A., et al. (2011). Kinectfusion: real-time 3d

reconstruction and interaction using a moving depth

camera. In Proceedings of the 24th annual ACM sym-

posium on User interface software and technology,

pages 559–568. ACM.

Kandil, A., Hastak, M., and Dunston, P. S. (2014). Rapid

3d energy performance modeling of existing build-

ings using thermal and digital imagery. Bridges,

10:9780784412329–100.

Lepetit, V., Moreno-Noguer, F., and Fua, P. (2009). Epnp:

An accurate o (n) solution to the pnp problem. Inter-

national journal of computer vision, 81(2):155–166.

Nakagawa, W., Matsumoto, K., de Sorbier, F., Sugimoto,

M., Saito, H., Senda, S., Shibata, T., and Iketani,

A. (2014). Visualization of temperature change us-

ing rgb-d camera and thermal camera. In Com-

puter Vision–ECCV 2014: 13th European Confer-

ence, Zurich, Switzerland, September 6-12, 2014, Pro-

ceedings. Springer.

Saito, H., Honda, T., Nakayama, Y., and de Sorbier, F.

(2014). Camera pose estimation for mixed and di-

minished reality in ftv. In 3DTV-Conference: The

True Vision-Capture, Transmission and Display of 3D

Video (3DTV-CON), 2014, pages 1–4. IEEE.

Szab´o, Z., Berg, S., Sj¨okvist, S., Gustafsson, T., Carleberg,

P., Upps¨all, M., Wren, J., Ahn, H., and Smedby,

(2013). Real-time intraoperative visualization of my-

ocardial circulation using augmented reality tempera-

ture display. The international journal of cardiovas-

cular imaging, 29(2):521–528.

Thachasongtham, D., Yoshida, T., de Sorbier, F., and Saito,

H. (2013). 3d object pose estimation using viewpoint

generative learning. In Image Analysis, pages 512–

521. Springer.

Vidas, S., Moghadam, P., and Bosse, M. (2013). 3d ther-

mal mapping of building interiors using an rgb-d and

thermal camera. In Robotics and Automation (ICRA),

2013 IEEE International Conference on, pages 2311–

2318. IEEE.

Yanai, O. (2014). Thermal imaging as a smartphone ap-

plication: exploring and implementing a new concept.

In SPIE Defense+ Security, pages 90700M–90700M.

International Society for Optics and Photonics.

Yoshida, T., Saito, H., Shimizu, M., and Taguchi, A. (2013).

Stable keypoint recognition using viewpoint genera-

tive learning. In VISAPP (2), pages 310–315.

Zhang, Z. (2000). A ﬂexible new technique for camera cal-

ibration. Pattern Analysis and Machine Intelligence,

IEEE Transactions on, 22(11):1330–1334.

ARVisualizationofThermal3DModelbyHand-heldCameras

487