Panoramic 3D Reconstruction Using Rotational Stereo Camera with Simple Epipolar Constraints

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1 Panoramic 3D Reconstruction Using Rotational Stereo Camera with Simple Epipolar Constraints Wei Jiang Japan Science and Technology Agency 4-1-8, Honcho, Kawaguchi-shi, Saitama, Japan Masatoshi Okutomi, Shigeki Sugimoto Tokyo Institute of Technology , O-okayama, Meguro, Tokyo, Japan Abstract In this paper, we propose a novel method for panoramic 3D scene recovery using rotational stereo cameras with simple epipolar constraints. By rotating two parallel stereo cameras about a vertical axis with a constant velocity, we acquire two sampled spacio-temporal volumes which are made of the sequential images captured by a uniform angular interval. The two spacio-temporal volumes can be resampled into a set of multi-perspective panoramas. We analyze the epipolar geometry among images (panoramas and original images) of two spacio-temporal volumes. The result shows that only three types of simple epipolar constraints (epipolar line is row or column of image) exist in the two spacio-temporal volumes. Then we compute a depth map from four image pairs using a multi-baseline algorithm with the three types of epipolar constraints; that is horizontal, vertical and combination of them. Experimental results using both synthetic and real images show that our approach produces high quality panoramic 3D reconstruction. 1. Introduction 3D reconstruction for panoramic (3 degrees) environments is an important issue for various applications such as virtual reality, robot navigation, and environmental simulation. A lot of methods have been developed for panoramic 3D reconstruction of real scenes. We can roughly divide these methods into two approaches: one is stereo reconstruction from perspective panoramas (i.e. single-viewpoint images), and the other is from multi-perspective panoramas. This research described in this paper was conducted while the first author was a PhD student at Tokyo Institute of Technology. He is currently on loan to National Institute of Industrial Safety: 1-4-6, Umezono, Kiyose, Tokyo, JAPAN In the first approach, two (or more) omni-directional cameras are used for estimating a depth map. Various types of omni-directional (or panoramic) imaging sensors have been designed [1, 5], and a geometric analysis for these sensors has been given by Baker and Nayar [1]. An omnidirectional camera can capture a whole scene at a time into a panorama. Therefore this approach is adequate for real-time applications such as robot navigation and video surveillance. However, in general, panoramas captured by omni-directional sensors have low spatial resolution which engenders low measurement accuracy and limited density of depth maps. Instead of using such special sensors, Kang and Szeliski [3] rotate standard cameras and create dense perspective (cylindrical) panoramas by resampling the regular images. Then they estimate a dense depth map from the perspective panoramas. The restricted camera motion is beneficial for producing high quality depth maps. However, for creating desirable panoramas, we have to ensure that each camera is rotating about an axis passing through its optical center. Such careful settings may be required for different environments individually. On the other hand, panoramic stereo reconstruction from multi-perspective panoramas [12] 1 has attracted recent attentions [2, 4, 9, 7, 11]. A set of multi-perspective panoramas is created by resampling regular perspective images captured by a single rotating camera whose optical center is offset from its rotation axis. Compared with the approach using perspective panoramas, this approach has mainly two advantages. First, only a single camera (mounted on a rotational stage) is required. This property enables us to design a portable and inexpensive image acquisition system. Secondly, if we create two multi-perspective panoramas by extracting two symmetrical columns from the regular images, the epipolar geometry consists of simple horizontal lines [4, 7, 9, 11]. The second advantage can be valid in such two symmetrical perspective panoramas. 1 It is actually equal to multiple-center-of projection image [10], manifold mosaic [8], and circular projection [9]. 1

2 y right panoramic image P Rotation axis r Optical center Optical axis left slit y x center left panoramic image (a) Single rotating camera. (b) Two panoramic images. Figure 1. Traditional panoramic imaging using single rotating camera. y right slit input images 2ψ slit baseline length slit r O Figure 2. Baseline length. 2ψ α ( < 2ψ ) However, we can indicate the following two weak points in the use of a single camera. The first weak point is that the accuracy of depth estimation is limited by horizontal FOV of the single camera. It is well known that the accuracy of depth estimation is influenced by the stereo baseline length about two images. If we use a single rotating camera, the maximum baseline length about two multiperspective panoramas is limited by not only the rotation radius of the camera but also the horizontal angle of the camera s FOV (field of view). When we require a large baseline length making full use of the rotational radius, in general, we need a camera which has a large FOV or multiple cameras. The second weak point is that depth estimation tends to suffer from noise and repeated patterns. It is also well known that the depth estimation from only two images suffers from image noise and repeated patterns in the scene. For improvement of this problem, Li et al. [4] have proposed a approach to estimate depths from a number of multi-perspective panoramas using a cylinder sweep algorithm or a multi-baseline matching technique on approximated horizontal epipolar geometry. For improving these weak points, we propose a novel method for panoramic 3D reconstruction using rotational stereo cameras. In our method, we use two large collections of images taken by rotating two parallel cameras whose motions are constrained to a planar concentric circle. The two collections of regular perspective images are resampled into four multi-perspective panoramas. Then we estimate a depth map from four image pairs using a multi-baseline stereo algorithm with three types of simple epipolar constraints; that is horizontal, vertical and combination of them. In the case of depth estimation using multi-perspective panoramas from a single camera, the baseline length is limited by the horizontal FOV of the single camera. We can show that our method using two cameras is equivalent to a method using a single virtual camera which has a larger horizontal FOV than that of an actual single camera. This improves the accuracy of the depth estimation as mentioned above. Moreover, a multi-baseline stereo algorithm with four image pairs produces high quality depth maps by averaging out noise and reducing ambiguities caused by repeated patterns. The remainder of the paper is structured as follows. Panorama imaging from two rotating cameras is shown in Section 2. Section 3 describes the epipolar geometries among images of two spacio-temporal volumes. Depth estimation using a multi-baseline algorithm is described in Section 4. We present experimental results for showing the validity of our method in Section 5, and conclude this paper in Section Panorama Imaging In this section, we describe a panorama imaging process using two rotating cameras. In the beginning, let us roughly explain a traditional method which generates multiperspective panoramas using a single off-center rotational camera. Then we extend the single camera case to the use of two cameras Traditional panorama imaging A camera is mounted on a rotational stage so that the optical center of the camera is offset from the rotation center as shown in Figure 1(a). The camera is looking outward from the rotational center. A stereo pair of multi-perspective panoramas is generated by extracting two symmetric slits of each regular image as shown in Figure 1(b). In this case, epipolar lines are the panorama rows [11]. Figure 2 shows that a 3D point P is projected on the two slits of the images captured by the camera with two different positions. Its depth is estimated by finding corresponding points on the two panoramas. The accuracy of depth estimation is effected by a baseline length indicated by a thick line between two cameras in Figure 2. However, the baseline length depends on an angle (indicated by α) between the two positions of the camera. Letting 2ψ be an

3 angle between the two slits, α is no more than 2ψ. This means that the accuracy of depth estimation is limited by horizontal FOV of the camera. slit angle left slit ψ right slit virtual slit angle virtual optical axis 2.2. Stereo panorama imaging We use two parallel cameras mounted on a rotational stage as shown in Figure 3. The two cameras rotate about a vertical axis passing through the center of a line which connects two optical centers of the two cameras. We obtain two spacio-temporal volumes which are composed of the regular images acquired by the two cameras as shown in Figure 4, and then produce four panoramas by extracting symmetric y- slits from two spacio-temporal volumes as shown in Figure 4. y x Ll Cl Cr Figure 3. Two parallel rotating cameras. Lo P ll P lo Lr Rl Prr P rl Rr Ro Figure 4. Two spacio-temporal volumes and four multi-perspective panoramas. These panoramas are denoted as Ll, Lr, Rl and Rr respectively, as shown in figure 4. The capital letters denote the positions of cameras, and small letters denote the relative slit positions used for extracting panoramas. Assuming that one of the two cameras is virtually moved by 180 along rotational path, so as to make the two optical centers correspond each other, we can consider our system with two cameras is equivalent to a system which has a single virtual camera with four slits, as shown in Figure 5(b). P rr (a) single camera system right slit left slit ψ left slit ψ (b) two camera system Figure 5. Comparision of slit angle. right slit The four panoramas acquired by the virtual camera can be obtained by rectifying panoramas acquired by our imaging system. For example, when panorama Rl, Rr are shifted by 180 in the direction, the four panoramas are the same as the panoramas generated from the four slits of the virtual camera. Figure 5(a)(b) show that the angle between two slits of the virtual camera (b) is much larger than that of the single camera (a). This means that the accuracy of the depth estimation can be improved by using our imaging system with two cameras. 3. Epipolar Geometry In general, epipolar constraints are used for reducing the search space of matching points in stereo vision. We use three types of simple epipolar constraints for finding corresponding points between the base panorama Rl and each of other reference images. In this section, we explain (but omit proofs) that only three types of simple epipolar constraints exist in the two spacio-temporal volumes. p lr φ Lr Cl p ll C' l ΔLr O P Q l ΔRl C' r Δ r Rr R p rr Figure 6. Geometric relation of two cameras Horizontal epipolar constraints on panoramas Figure 6 shows that a 3-D point P with a depth l is projected on each of four slits of the cameras at four different positions. C r and C r denote the positions of the right φ φ Rl Cr p rl 0

4 camera (optical centers) where P is projected on the left and the right slit, respectively. C l and C l denote the positions of the left camera in the same manner. The projected points P ll,p lr,p rl, and P rr appear on the four panoramas Ll, Lr, Rl and Rr (see Figure 4), respectively. Let ( 0,y 0 ) be the coordinates value of the base point P rl on panorama Rl, and ( lr,y lr ) be the coordinates of the reference point P lr on panorama Lr. We can be easily see that y lr = y 0, since the two rays C r P and C l P are symmetrical about the line OP. Therefore the search for the corresponding point P lr is restricted on a horizontal line at y = y 0 in the reference panorama Lr. Therefore we call it a horizontal epipolar constraint. Horizontal epipolar constraints also exist between Rr and Ll. Let f(l) be the horizontal disparity between P rl and P lr. By using the triangle QOC r in Figure 6, f(l) is represented as follows : f(l) =2Δ Rl π, (1) where Δ Rl = arctan l2 tan ψ + r l 2 (1+tan ψ) r 2 r 2 l 2, tan ψ where r is the camera rotation radius, and 2ψ denotes the angle between two slits of the camera. The depth l can be computed from a horizontal disparity f(l) obtained by matching the points P rl and P lr Vertical epipolar constraints on panoramas In Figure 6, Δ R represents the rotation angle between C r and C r. By using triangle OC r C r, Δ R is represented as follows: Δ R = φ Rl φ Rr = constant, (2) where φ Rl is the angle between the extensions of OC r and C r Q, and φ Rr is the angle between the extensions of OC r and C rq. Eq.(2) indicates that the angle Δ R has a constant value independent of the depth l of the point P. Therefore, the search for the corresponding point P rr on the reference panorama Rr is restricted on a column. We call it a vertical epipolar constraint. Vertical epipolar constraints also exist between Lr and Ll. Using trigonometric relation between the triangle C r QP and C rqp, the vertical disparity g(l) between P rl and P rr is represented as follows : g(l) = 2ry 0 sin ψ r sin ψ + l 2 r 2 cos 2 ψ, (3) where y 0 is the vertical coordinate of the base point P rl, and the other notations have the same meaning in Eq.(1). Then depth l can be computed from a vertical disparity g(l) obtained by matching the points P rl and P rr Horizontal epipolar constraints on panorama and original image In our approach, two cameras are setup as normal parallel stereo vision. In this case, horizontal epipolar constraints are also satisfied between two original images (Lo and Ro in Figure 4). We can see the base point P rl on panorama Rl exists original image Ro too. Therefore, a reference point, denoted by P lo in Figure 4, on the original image Lo has the same vertical coordinate with the base point P rl. This means that the search for the corresponding point P lo is restricted on a horizontal line at y = y 0 on the reference image Lo. That is, horizontal epipolar constraints exist between the panorama Rl and the original image Lo. Let d(l) be the disparity between P rl and P lo, we can easily write the relation between disparity d(l) and the depth l as follows: d(l) = 2rf, (4) l where f is the focal length, and the other notations are the same in Eq.(1). Then depth l can be computed from horizontal disparity d(l) obtained by matching the points P rl and P lo. 4. Depth Estimation Using Multi-baseline Algorithm horizontal constraint horizontal constraint Rl 0 Lo 0 Lr y 0 x 0 P lo vertical constraint Rr y 0 base point P rl horizontal and vertical constraint φ Rl - φ Rr P rr g(l) Ll φ Ll horizontal constraint f(l) P lr vertical constraint - φ Lr + f(l) Figure 7. Epipolar constraints among five images. Figure 7 summarizes relations among the five images (four panoramas and an original image) and the three types of epipolar constraints that are described in Section 3. According to Section 3, the depth l can be estimated by using arbitrary one of the four stereo pairs. However, the depth estimation using only one pair often causes matching errors due to image noise and repeated patterns. In this paper, for reducing such errors, we compute the distance of each pixel of base panorama Rl using a multi-baseline algorithm [6] with simple epipolar constraints. P ll g(l)

5 4.1. Definition of SSD for each stereo pair We now define four SSD (Sum of Squared Differences) values. Each of them represents a similarity between a region of the base panorama Rl and that of each reference images. Then we define a SSSD (Sum of SSD) value which sums up the four SSD values. SSD between panorama Rl and Lr Let Rl(, y) and Lr(, y) be the pixel value at (, y) in panorama Rl and Lr, respectively. As mentioned in Section 3.2, the horizontal constraints exist between panorama Rl and Lr. The constraints indicate that the search for a point (, y) on panorama Lr corresponding to ( 0,y 0 ) on panorama Rl is restricted by y = y 0. Additionally, the disparity is determined by = 0 + f(l) when a depth l is decided. As a consequence, we define the SSD value that represents the similarity between Rl( 0,y 0 ) and Lr(, y) as follows: SSD Rl,Lr ( 0,y 0,l)= [Rl( 0 + i, y 0 + j) i,j W Lr( 0 + i + f(l),y 0 + j)] 2, (5) where W is a window for matching, and f(l) is determined by Eq.(1). SSD between panorama Rl and Rr The vertical constraints between panorama Rl and Lr denote that the search for a point (, y) on panorama Rr corresponding to ( 0,y 0 ) on panorama Rl is restricted by = 0 +Δ R. Moreover the disparity is determined by y = y 0 + g(l) when a depth l is decided. Letting Rr(, y) be the pixel value at (, y) in panorama Rr, we define the SSD value that represents the similarity between Rl( 0,y 0 ) and Rr(, y) as follows: SSD Rl,Rr ( 0,y 0,l)= i,j W [Rl( 0 + i, y 0 + j) Rr( 0 +Δ R + i, y 0 + g(l)+j)] 2, (6) where g(l) is determined by Eq.(3). SSD between panorama Rl and Ll Epipolar constraints between panorama Rl and Ll is neither horizontal nor vertical. But we can describe it as a combination of the vertical and horizontal constraints as shown in Figure 7. Considered with constraints via Rl Lr and Lr Ll, the horizontal constraint between panorama Rl and Lr is described in 3.1, and the vertical constraint between panorama Lr and Ll is actually the same between panorama Rl and Rr described in 3.2. This means that the disparity on panorama Ll is determined by = 0 +Δ R + f(l) and y = y 0 + g(l), when a depth l is decided. Using Ll(, y) in the same manner, we define the SSD value that represents the similarity between Rl( 0,y 0 ) and Ll(, y) as follows: SSD Rl,Ll ( 0,y 0,l)= [Rl( 0 + i, y 0 + j) i,j W Ll( 0 +Δ R + f(l)+i, y 0 + g(l)+j)] 2. (7) SSD between panorama Rl and original image For each column of base panorama Rl, we can extract a corresponding original image from the left spacio-temporal volume. As mentioned above, the horizontal epipolar constraints exist between the column and the extracted original image. By letting Lo(x, y 0 ) be the pixel value on the reference image Lo, we describe the SSD value between panorama Rl and original image Lo as following: SSD Rl,Lo ( 0,y 0,l)= [Rl( 0 + i, y 0 + j) i,j W Lo(x 0 + i + d(l),y 0 + j)] 2, (8) where x 0 is the x-coordinate of the base point P rl in the right spacio-temporal volume Depth estimation from SSSD Normally, the correct depth can minimize each of the SSD values defined above. In addition, we can obtain an estimated depth ˆl that minimizes one SSD. However, such depth estimation often causes errors due to image noise and repeated patterns. Therefore we compute SSSD (Sum of SSD) for averaging out noise and reducing ambiguities [6]. SSSD is defined as follows: SSSD Rl ( 0,y 0,l)= SSD Rl,Lr ( 0,y 0,l) + SSD Rl,Rr ( 0,y 0,l) + SSD Rl,Ll ( 0,y 0,l) + SSD Rl,Lo ( 0,y 0,l). (9) We obtain the estimated depth ˆl that minimizes the SSSD. 5. Experimental Results For evaluating the proposed approach, we estimate depth maps of both synthetic and real scenes. We also show comparison results between a previous method which uses a single camera [7] and our method. In all experiments, the window size W for computing the SSD values is 5 5, the rotational radius r is 20[cm], and the horizontal angle ψ between the left and the right slit of the camera is Experimental results of synthetic scenes We created a room (2.5[m] 2.5[m]) and mapped textures on its walls by computer graphics. Then we obtained

6 (a) Base(left) panorama generated by previous method [7] (b) Depth map estimated by previous method [7] (c) Base panorama Rl generated by the proposed method (d) Depth map estimated by the proposed method Figure 8. Experimental results of synthesized scene the original images ( pixels) with additional Gaussian noise (mean=0 and standard deviation=2[gray level]). Figure 8 shows several results for the synthetic environment using the both methods. Figure 8(a) and (c) indicate the base panoramic images (3 multi-perspective panoramas) generated by the tow methods. Figure 8(b) and (d) show the estimated depth-maps. We easily see the result of the previous method (b) is remarkably influenced by image noise and occlusions. In contrast, the result of our method (d) is greatly improved by reducing errors caused by noises, repeated patterns and occlusions (see regions in the right of a ball and a cylinder which exist in the room). For evaluating the effects of the proposed device (its large baseline length) and the proposed algorithm separately, we also compared estimated cross-sections. Figure 9 shows the estimated cross sections of the same scene. In the case without image noise, as shown in Figure 9(a), the straight lines corresponding to the walls in the synthetic scene seems to be stairs because of quantization errors caused by a small baseline length in the previous method. On the other hand, thanks to a larger baseline length, our methods obtained smooth lines as shown in Figure 9(b). The effects of the proposed algorithm can be observed by comparing Figure 9 (c) and (d), where the errors caused by noises and occlusions are clearly reduced by the multibaseline algorithm Experimental results of real scenes We also apply the both methods to a real scene. Figure 10 shows our image acquisition device composed with two parallel cameras and a rotational stage. Figure 11 shows several results by the two methods. Figure 11 (a)(c) show 3 multi-perspective panoramas, and (b)(d) show estimated dense depth-maps. Comparing results (b) and (d), we can observe that the estimation result by the proposed method is greatly improved. Figure 10. Image acquisition device. Figure 12 shows estimated cross sections. Comparing between Figure 11 (a) and (b) indicates that our method is able to improve the accuracy of depth estimation for real scenes. To demonstrate our high-quality 3D reconstruction

7 4000(mm) (mm) (mm) (mm) (a) (b) (c) (d) Figure 9. Comparison of estimated cross-section of a synthetic scene. (a): by previous method using the 100th row of the base panorama (Figure 8(a)) without noise. (b): by proposed method using the 100th row of the base panorama (Figure 8(c)) without noise. (c): the same as (a) in the case with noise. (d): the same as (b) in the case with noise. for real scenes, we also show a top-down view and a center view of the reconstructed real scene, in Figure 13(a)(b). We can see that the proposed method obtains satisfactory results. 6. Summary and Conclusions In this paper, we have proposed a novel method for panoramic 3-D reconstruction using rotational stereo cameras. In the proposed method, two spacio-temporal volumes are acquired by two parallel cameras. Then we estimate a depth map from four image pairs using a multi-baseline algorithm with the three types of epipolar constraints. The proposed method offers a larger baseline length and has produced high quality depth maps by averaging out noise and reducing ambiguities caused by repeated patterns. In the future work, we study a method which maintains the simplicity of epipolar constraints using more than two cameras. We will also examine approaches using a telephoto lens to outdoor environments. References [1] S. Baker and S. K. Nayar. A theory of single-viewpoint catadioptric image formation. International Journal of Computer Vision, 35(2):1 99, [2] W. Jiang, S. Sugimoto, and M. Okutomi. Panoramic 3D reconstruction using rotating camera with planar mirrors. In Proceedings of Omnivis 05, pages 123 1, October [3] S. B. Kang and R. Szeliski. 3-D scene data recovery using omnidirectional multibaseline stereo. International Journal of Computer Vision, 25(2): , [4] Y. Li, H.-Y. Shum, C. K. Tang, and R. Szeliski. A stereo reconstruction from mutiperspective panoramas. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(1):45 62, January , 2 [5] S. K. Nayar and V. Peri. Folded catadioptric cameras. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages , June [6] M. Okutomi and T. Kanade. A multiple-baseline stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(4): , , 5 [7] P. Peer and F. Solina. Panoramic depth imageing:single standard camera. International Journal of Computer Vision, 47(1-3):149 1, , 5, 6, 8 [8] S. Peleg and M. Ben-Ezra. Stereo panorama with a single camera. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages , June [9] S. Peleg, M. Ben-Ezra, and Y. Pritch. Omnistereo:Panoramic stereo imaging. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(3): , [10] P. Rademacher and G. Bishop. Multiple-center-of-projection images. In Proceedings of ACM SIGGRAPH 98, pages , July [11] S. M. Seitz and J. Kim. The space of all stereo images. IEEE International Journal of Computer Vision, Marr Prize Special Issue, 48(1):21 38, June , 2 [12] D. N. Wood, A. Finkelstein, J. F. Hughes, C. E. Thayer, and D. H. Salesin. Multiperspective panoramas for cel animation. In IEEE Computer Graphics Proceedings(SIGGRAPH 97), pages , August

8 (a) Base(left) panorama generated by previous method [7] (b) Depth map estimated by previous method [7] (c) Base panorama Rl generated by the proposed method (d) Depth map estimated by the proposed method Figure 11. Experimental Results of real scene. 5000(mm) (mm) (a) A top view of the real scene (a) (b) Figure 12. Comparision of estimated cross-section of real scene. (a): estimated result by previous method from the th row of Figure 11(a). (b): estimated result by proposed method from the th row of Figure 11(c). (b) A different view of the real scene Figure 13. Two views of 3D reconstruction result of real scene.

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