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Comparison of Camera Motion Estimation Methods for 3D Reconstruction of Infrastructure Abbas Rashidi1, Fei Dai2, Ioannis Brilakis3 and Patricio Vela4 1

PhD student, School of Building Construction, Georgia Institute of Technology, E-mail: [email protected] Post-Doctoral Researcher, Construction Information Technology Group, Georgia Institute of Technology 3 Assistant Professor, School of Civil and Environmental Engineering, Georgia Institute of Technology 4 Assistant Professor, School of Electrical and Computer Engineering, Georgia Institute of Technology 2

Abstract: Camera motion estimation is one of the most significant steps for structure-from-motion (SFM) with a monocular camera. The normalized 8-point, the 7-point, and the 5-point algorithms are normally adopted to perform the estimation, each of which has distinct performance characteristics. Given unique needs and challenges associated to civil infrastructure SFM scenarios, selection of the proper algorithm directly impacts the structure reconstruction results. In this paper, a comparison study of the aforementioned algorithms is conducted to identify the most suitable algorithm, in terms of accuracy and reliability, for reconstructing civil infrastructure. The free variables tested are baseline, depth, and motion. A concrete girder bridge was selected as the “test-bed” to reconstruct using an off-the-shelf camera capturing imagery from all possible positions that maximally the bridge’s features and geometry. The feature points in the images were extracted and matched via the SURF descriptor. Finally, camera motions are estimated based on the corresponding image points by applying the aforementioned algorithms, and the results evaluated. Keywords: Camera motion estimation, Corresponding points, Essential matrix, Infrastructure Introduction The 3D spatial data of infrastructure contain useful information for civil engineering applications including as-built documentation, on-site safety enhancement, progress monitoring, and damage detection. Accurate, automatic, and fast acquisition of the spatial data of infrastructure has been a priority for researchers and practitioners in the field of civil engineering over the years. Advances in computer vision provide a useful path for 3D data acquisition from images and video frames. Vision-based 3D reconstruction has been investigated in the area of computer vision for two decades. Based on the setup, such as the type of sensor (monocular or binocular camera) or the type of captured data (image or video), a number of frameworks have been proposed by researchers Fathi and Brilakis (2010). Each framework, as a pipeline, consists of several stages, and each stage can be

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implemented using different algorithms. Selecting the most appropriate algorithm for each stage is a critical decision that depends not only on the application of the framework but also the user’s requirements. In computer vision, algorithms that are proposed are usually tested and evaluated using synthetic data or data obtained indoors. For 3D reconstruction of infrastructure, such as bridges, the distance between the camera and the bridge is usually more than 10 m.n The scene itself consists of several distinct elements such as trees and sky. Thus, evaluating the performance of such algorithms in real conditions and choosing the best one for specialized applications is of great importance. In this paper, we evaluate and compare different algorithms for the estimation of camera motion. As explained in Section 3, camera motion estimation is an essential part of every monocular 3D reconstruction framework. The performance of commonly used methods is evaluated and compared in terms of specific metrics determined by the requirements of infrastructure systems. The rest of the paper is organized as follows. In Section 2, an overview of the necessary steps for camera motion is presented. Section 3 presents the matrices and experimental setup used to compare the performance of different algorithms, and the obtained results are discussed in Section 4. The conclusions of the investigation are presented in Section 5. Camera Motion Estimation In computer vision, 3D reconstruction means the process of capturing the 3D data of an object using captured images or video. It starts with the capturing of images or the videotaping of the object from different views and ends with a 3D point cloud or a 3D surface generated for that object. Several approaches have been proposed by researchers for obtaining 3D information from visual data, a number of which are very famous: Snavely proposed the approach called “Photo Tourism” for the 3D reconstruction of the world’s well-photographed sites, cities, and landscapes from Internet imagery (Snavely et al., 2007); Pollefeys presented a complete system for building 3D visual models from uncalibrated video frames (Pollefeys et al., 2004). In the construction area, Golparvar-Fard proposed a simulation model based on the daily photographs of construction sites for visualizing construction progress (Golparvar-Fard et al., 2009). He also provided a 4D augmented reality model for automating the construction progress data collection and processing (Golparvar-Fard et al., 2009). Fathi provided a framework to obtain a 3D sparse point cloud of infrastructure scenes using a stereo set of cameras (Fathi & Brilakis, 2010). As one of the most significant steps of every structure-from-motion algorithm, the problem of obtaining the motion of a camera from feature point correspondences is an old one. The first documented attempt to solve the problem dates to more than 150 years ago (Hartley & Zisserman, 2004). There are a variety of solutions given the (minimum) number of correspondences available. The three most common algorithms are the normalized 8-point algorithm and the 7-point algorithm suggested by Hartley et al. (2004 and 1997, respectively), and the 5-point algorithm, which was first solved efficiently by Nistér (2004). The performance of these algorithms was evaluated by Rodehorst et al. (2008) using synthetic data injected with noise. In this paper, the necessary steps to obtain the camera’s motion are briefly reviewed, considering the

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most efficient algorithm for each step. Then, using a real infrastructure scene, the performances of the three motion estimation algorithms are evaluated. The approach for the estimation of camera motion between two views using an essential matrix consists of three main steps: the calibration of the camera; the computation of correspondence feature points; and the computation of the essential matrix, camera rotation, and translation between two views. As depicted in Figure 1, each step also contains sub-stages, which are briefly described in the next few sections.

Figure 1- common framework to estimate motion of a camera from corresponding points Calibration of camera In computer vision, the process of obtaining the intrinsic parameters of a camera is called calibration. Intrinsic parameters define the pixel coordinates of an image point with respect to the coordinates in the camera reference frame. The parameters that are known as camera intrinsic parameters are: - Focal length; - Image center or principle point; - Skew coefficient (defines the angle between the X and the Y pixel axes); and - Coefficients of lens distortions; In this paper, we used the method proposed by Zhang for calibration (Zhang, 1999). The method only requires the camera to observe a planar pattern shown at a few (at least two) different orientations. Feature points detection and matching One of the more sensitive stages within the 3D reconstruction pipeline is the detection of specific points within each image and the matching of these points in across images. Several approaches have been proposed to detect and match these so called feature points, among which the most popular are SIFT and SURF (Bauer et al., 2007). The SIFT keypoint detector is the most widely used detector in the field of computer vision. Benefits include robustness to changes in scale changes, view, and illumination. However, the high cost of computation makes it infeasible for real-time

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applications. In recent years, another feature point detector and descriptor known as SURF has become more popular. While the SIFT method uses a 128D vector as the descriptor, the SURF descriptor uses a 64D vector. Thus, from the viewpoint of identifying matches, SURF is more computationally efficient than SIFT. According to the research conducted by Leo Juan et al. (Bauer et al., 2007), though SIFT performs slightly better than SURF in terms of accuracy, the performance of these two descriptors are almost the same after applying a RANSAC algorithm to remove outliers. In this paper, we use the SURF method as a feature detector and descriptor. We also used the Euclidian distance between descriptors as the criterion to find corresponding matches. In order to improve matching efficiency, an approximate nearest neighborhood matching strategy, a ratio test described by Lowe (2004), has been applied rather than the classification of false matches by thresholding the distance to the nearest neighbor. Moreover, since camera motion estimation algorithms are so sensitive to false matches, the detected matched features are refined by the calculation of the fundamental matrix between the two views using the RANSAC approach. Further information on such refinement can be obtained from Snavely et al. (2007). Camera Pose Estimation To compute the camera ego motion, the essential matrix should be calculated. In the case of camera pinhole models, the essential matrix is a 3×3 matrix which relates the corresponding points of two view frames if intrinsic parameters of camera are known. Assuming that homogenous normalized image coordinates of corresponding points in two view frames are y  ( x, y,1)T and y  ( x, y,1) respectively, the essential matrix, E , will relate these points by: ( y)T Ey  0 (1)

It has been proven that to solve the problem and compute the essential matrix, at least 5 corresponding pairs of feature points should be known (Nistér, 2004). The solution approach to Equation (1) is what differentiates the various algorithms, such as the (normalized) 8 point, 7 point and 5 point algorithms. A brief description of each of these algorithms is presented in the following sub-sections. After computing the essential matrix, a 3×1 translation matrix and a 3×3 rotation matrix are obtained from the following procedure (Nistér, 2004): If singular value decomposition of essential matrix represented as E  Udiag (1,1, 0)V T , where U and V are chosen such that det(U )  0 and Det (V )  0 , then the translation matrix is equal to: [t ]x  VDdiag (1,1, 0)V T (2) Where[t ]x is cross product matrix of t and rotation matrix is equal to: Ra  UDV T or Rb  UDT V T

(3)

,

where:

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 0 1 0  D  1 0 0  .  0 0 1 

(4)

The 8 point algorithm The 8-point algorithm, which is the most straightforward method for the calculation of the essential matrix, was first introduced by Longuet-Higgins (Hartley, 1997). The great advantage of the 8-point algorithm is that it is linear, and hence, it is fast and easily implementable. If 8-point matches are known, the linear equations are simply solved. For more than 8 points, a linear least-squares minimization problem must be solved. The key to the success of the 8-point algorithm lies in proper normalization of the input data before the construction of the equations to be solved. In this case, a simple transformation (translation and scaling) of the points in the image before formulating the linear equations leads to an enormous improvement in the conditioning of the problem, and hence, in the stability of the result. The complexity added to the algorithm as a result of the normalizing transformations is insignificant. The 7 point algorithm When the essential matrix is vectorized as per  E   E11 E12 E13 E 21 E 22

E 31 E 32 E 33  (5)  Equation 1 gives rise to a set of equations of the form AE  0 , where the number of rows in the matrix A varies based on the number of point matches:  x1 x1   . AE    .   x n x n

T

E 23

x1 y1 .

x1 .

y1 x1 .

y1 y1 .

y1 .

x1 .

y1 .

. x n y n

. x n

. y n x n

. y n y n

. y n

. xn

. yn

1  .   E (6 .  )  1 

If A has rank 8, then it is possible to solve for E up to scale. In the case where the matrix A has rank 7, it is still possible to solve for the essential matrix by making use of the singularity constraint. The most important case is when only 7 point correspondences are known, leading to a 7 × 9 matrix A , which generally has rank 7.  The solution to the equations AE  0 in this case is a 2-dimensional space of the form (Hartley & Zisserman, 2004): aE1  (1  a ) E2 (7) Where a is a scalar variable. The matrices E1 and E2 are obtained as the matrices corresponding to the generators of the right null-space to A. Next, we exploit the constraint det E  0 . Since E1 and E2 are known, this leads to a cubic polynomial equation in a. This polynomial equation may be solved to find the value of a. There will be either one or three real solutions, giving one or three possible solutions for the essential matrix.

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The 5 point algorithm As mentioned before, the minimum number of corresponding points required to compute the essential matrix is 5. Let us rewrite equation 1 in the following form:  T  q E 0 (8)

where:

 q   q1 q1 q 2 q1

q 3 q1

q1 q 2

q 2 q 2

q 3 q 2

q1 q 3

q 2 q 3

q 3 q 3 

T

(9)

Considering X, Y, Z, W as four 3×3 matrixes, essential matrix could be written in the form of:

E  xX  yY  zZ

(10)

th

This form is a 10 degree polynomial whose roots are the values of the essential matrix. The detailed procedure for solving the equation and computing the essential matrix is available at Nistér (2004). Comparison Matrices and Experimental setup To evaluate the performance of different motion algorithms, several motion primitives have been designed. These scenarios are based on the combination of translation in different directions as well as rotation in different planes. A camera trajectory would consist of sequences of such primitives. The motion primitives are listed in Table 1 and shown in Figure 2.

Table 1: Different camera motion primitives 1 2 3 4 5

Translation(T) X X X and Z X and Z Y

Rotation(R)* α α -

6 7 8 9 10

Translation(T) Y Y and Z Y and Z Z Z

Rotation(R)* β β α

*: α and β mean angles of rotations in XZ and YZ planes respectively.

Figure 2: Vector motion depictions of the camera motion primitives. Two parameters are considered for evaluating the performance of these algorithms: the length of the baseline and the depth value. For each motion scenario, three possible

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baseline lengths have been defined: 60,100, and 140 cm. For sideway motion scenarios (items 1 to 8), four different depth values have been selected: 12, 16, 20, and 24 m. Consideration of these parameters implies 102 motion primitives in total. In order to run the test, a concrete girder bridge located on Interstate 75, McDonough, GA, has been chosen as our target infrastructure. The two-span bridge consists of three rows of concrete columns, and each row contains five columns (Figure 2). We used a high-resolution 8-megapixel Nikon camera installed on a tripod as our sensor. The tripod was marked such that it was possible to measure the degree of rotation in different configurations. A tape measure was used to measure the actual translations of the sensor.

Figure 3: Concrete girder bridge used as selected infrastructure to conduct the test(left) and test-bed platform(right)

Experimental Results The average calculated error in computing the translation and rotation for one of the motion primitives (table 1, number 2) with different baseline and depth values is presented in Figure 4: 5

4

5 point algorithm 7 point algorithm 8 point algorithm

4

5 point algorithm 7 point algorithm 8 point algorithm

3

3 2

2 1

1

0

0

1

2

3

4

5

Number of Motion

6

7

8

1

2

3

4

5

Number of Motion

6

7

8

Figure 4: Average translation and rotation errors for 3 different algorithms, motion primitive number 2. By observing the results, we obtain the following: - Experiments demonstrate that 5-point algorithm is more accurate than 7- and 8-point algorithms. The main reason for this is that a 5-point algorithm is less sensitive to outliers. Even then, in a number of scenarios including forward motion (Cases 9 and 10), the other two algorithms also performed well. - The algorithms are sensitive to outliers. In the case where wrong correspondences existed, the algorithms performed poorly.

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- The length of the baseline in the applied range (60 to 140 cm) has no specific effect on the accuracy of the results; however, increasing the depth value usually leads to less accurate results. Summary and Conclusion In this paper, a comparison between three algorithms for the camera motion estimation between different views of the same structure’s scene is presented. Controlled parameters used for performance evaluation were the baseline length, and the depth (distance between the camera and the infrastructure), and different motion primitives. To run the test, a concrete girder bridge was selected, and frames were captured according to the defined motion primitives. Ground truth data was obtained by measuring the real translation and rotation of the camera between different camera poses. The outputs obtained by implementing the three different motion estimation algorithms were also computed, and the average error for each one is calculated. Examination of the results indicates that the 5-point algorithm is better in comparison to the others, in terms of accuracy. A further research extension is the completion of the whole process of 3D reconstruction to obtain a 3D point cloud of the civil infrastructure. The selection of robust strategies to reduce computational load and the evaluation of the performance of these algorithms from the viewpoint of computing efficiency will be the focus areas of our future research. Acknowledgements: This material is based upon work supported by the National Science Foundation under Grant #1031329. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. References Bauer, J., Sunderhauf, N., & Protzel, P. (2007). “Comparing Several Implementations of Two Recently Published Feature Detectors.” In Proc. of the International Conference on Intelligent and Autonomous Systems, IAV, Toulouse, France. Fathi, H., and Brilakis, I. (2010). “Automated sparse 3D point cloud generation of infrastructure using its distinctive visual features.” Journal of Advanced Engineering Informatics, in press. Golparvar-Fard, M., Peña-Mora, F., and Savarese, S. (2009). “D4AR- A 4Dimensional augmented reality model for automating construction progress data collection, processing and communication.” Journal of Information Technology in Construction (ITcon), Special Issue Next Generation Construction IT: Technology Foresight, Future Studies, Road-mapping, and Scenario Planning, 14, 129-153. Golparvar-Fard, M., Peña-Mora, F. Arboleda, C. A., and Lee, S. H. (2009). “Visualization of construction progress monitoring with 4D simulation model overlaid on time-lapsed photographs.” ASCE J. of Computing in Civil Engineering, 23 (6), 391-404. Hartley, R. (1997). “In defense of the eight-point algorithm.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(6), 580–593.

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Hartley, R., and Zisserman, A. (2004). “Multiple view geometry.” Cambridge, UK: Cambridge University Press. Lowe, D. (2004). “Distinctive image features from scale-invariant keypoints.” International Journal of Computer Vision, 60(2), 91-110. Nistér, D. (2004). “An efficient solution to the five-point relative pose problem.” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 26(6), 756-770. Pollefeys, M., Van Gool, L., Vergauwen, M., Verbiest, F., Cornelis, K., Tops, J., and Koch, R. (2004). “Visual modeling with a hand-held camera.” International Journal of Computer Vision, 59(3), 207-232. Rodehorst, V., Heinrichs, M., and Hellwich, O. (2008). “Evaluation of relative pose estimation methods for multi-camera setups.” In proceedings of ISPRS08, B3b: 135 ff. Snavely, N., Seitz, S., and Szeliski, R. (2007). “Modeling the world from internet photo collections.” International Journal of Computer Vision, 80(2), 189-210. Zhang, Z. (1999). “Flexible camera calibration by viewing a plane from unknown orientations.” International Conference on Computer Vision (ICCV99), pages 666— 673.

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