Structure and motion estimation from apparent contours under circular motion

Wong, Kwan-Yee K.; Mendonça, Paulo R. S.; Cipolla, Roberto

doi:10.1016/s0262-8856(02)00015-x

Cited by 13 publications

(10 citation statements)

References 62 publications

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“…This work presents a method for estimating the motion of an object rotating around a fixed axis from information provided by its profiles alone. It makes use of symmetry properties [41], [12], [15] of the surface of revolution swept out by the rotating object to overcome the main difficulties and drawbacks present in other methods which have attempted to estimate motion from profiles, namely, 1) the need for a very good initialization for the epipolar geometry and an unrealistic demand for a large number of epipolar tangencies [6], [2], [1] (here, as few as two epipolar tangencies are needed), 2) restriction to linear motion [31] (whereas circular motion is a more practical situation), or 3) the use of an affine approximation [39] (which may be used only for shallow scenes).…”

Section: Introductionmentioning

confidence: 99%

Epipolar geometry from profiles under circular motion

Mendonça

Wong

Cippolla

2001

IEEE Trans. Pattern Anal. Mach. Intell.

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ÐThis paper addresses the problem of motion estimation from profiles (also known as apparent contours) of an object rotating on a turntable in front of a single camera. Its main contribution is the development of a practical and accurate technique for solving this problem from profiles alone, which is precise enough to allow for the reconstruction of the shape of the object. No correspondences between points or lines are necessary, although the method proposed can be used equally when these features are available without any further adaptation. Symmetry properties of the surface of revolution swept out by the rotating object are exploited to obtain the image of the rotation axis and the homography relating epipolar lines in two views in a robust and elegant way. These, together with geometric constraints for images of rotating objects, are then used to obtain first the image of the horizon, which is the projection of the plane that contains the camera centers, and then the epipoles, thus fully determining the epipolar geometry of the image sequence. The estimation of the epipolar geometry by this sequential approach (image of rotation axisÐhomographyÐimage of the horizonÐepipoles) avoids many of the problems usually found in other algorithms for motion recovery from profiles. In particular, the search for the epipoles, by far the most critical step, is carried out as a simple one-dimensional optimization problem. The initialization of the parameters is trivial and completely automatic for all stages of the algorithm. After the estimation of the epipolar geometry, the Euclidean motion is recovered using the fixed intrinsic parameters of the camera obtained either from a calibration grid or from self-calibration techniques. Finally, the spinning object is reconstructed from its profiles using the motion estimated in the previous stage. Results from real data are presented, demonstrating the efficiency and usefulness of the proposed methods.

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Section: Introductionmentioning

confidence: 99%

Epipolar geometry from profiles under circular motion

Mendonça

Wong

Cippolla

2001

IEEE Trans. Pattern Anal. Mach. Intell.

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“…In [9], we extended the algorithm presented in [8] to cope with incomplete circular motion and more widely space images. However, there are two main drawbacks of using circular motion: 1) new views cannot be added easily at a later time, and 2) part of the structure will always remain invisible under circular motion.…”

mentioning

confidence: 99%

“…The system first employs our previous technique [9] for estimating the circular motion of an object from its profiles alone, and builds an initial 3D model of the object by an octree carving technique [10] using the profiles and the estimated camera positions. The system then allows the 3D model thus obtained to be refined incrementally by adding new arbitrary general views of the object and estimating the corresponding camera positions.…”

mentioning

confidence: 99%

Reconstruction of Sculpture From Its Profiles With Unknown Camera Positions

Wong¹,

Cipolla

2004

IEEE Trans. on Image Process.

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Profiles of a sculpture provide rich information about its geometry, and can be used for shape recovery under known camera motion. By exploiting correspondences induced by epipolar tangents on the profiles, a successful solution to motion estimation from profiles has been developed in the special case of circular motion. The main drawbacks of using circular motion alone, namely the difficulty in adding new views and part of the object always being invisible, can be overcome by incorporating arbitrary general views of the object and registering its new profiles with the set of profiles resulted from the circular motion. In this paper, we describe a complete and practical system for producing a 3D model from uncalibrated images of an arbitrary object using its profiles alone. Experimental results on various objects are presented, demonstrating the quality of the reconstructions using the estimated motion. Index TermsStructure and Motion, Shape from Profiles, 3D Model Acquisition.

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“…As the works showing, both sparse features and dense features are used to establish corresponds between consecutive frames from input image sequence. Some of the most common choices are point [33], lines [34], contour [35], and hybrid [36], which can be tracked in both spatial and temporal domains. Point feature is the most commonly used of all, because of its simplicity.…”

Section: Literature Reviewmentioning

confidence: 99%

Forward and Backward Visual Fusion Approach to Motion Estimation with High Robustness and Low Cost

et al. 2019

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We present a novel low-cost visual odometry method of estimating the ego-motion (self-motion) for ground vehicles by detecting the changes that motion induces on the images. Different from traditional localization methods that use differential global positioning system (GPS), precise inertial measurement unit (IMU) or 3D Lidar, the proposed method only leverage data from inexpensive visual sensors of forward and backward onboard cameras. Starting with the spatial-temporal synchronization, the scale factor of backward monocular visual odometry was estimated based on the MSE optimization method in a sliding window. Then, in trajectory estimation, an improved two-layers Kalman filter was proposed including orientation fusion and position fusion . Where, in the orientation fusion step, we utilized the trajectory error space represented by unit quaternion as the state of the filter. The resulting system enables high-accuracy, low-cost ego-pose estimation, along with providing robustness capability of handing camera module degradation by automatic reduce the confidence of failed sensor in the fusion pipeline. Therefore, it can operate in the presence of complex and highly dynamic motion such as enter-in-and-out tunnel entrance, texture-less, illumination change environments, bumpy road and even one of the cameras fails. The experiments carried out in this paper have proved that our algorithm can achieve the best performance on evaluation indexes of average in distance (AED), average in X direction (AEX), average in Y direction (AEY), and root mean square error (RMSE) compared to other state-of-the-art algorithms, which indicates that the output results of our approach is superior to other methods.

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Structure and motion estimation from apparent contours under circular motion

Cited by 13 publications

References 62 publications

Epipolar geometry from profiles under circular motion

Epipolar geometry from profiles under circular motion

Reconstruction of Sculpture From Its Profiles With Unknown Camera Positions

Forward and Backward Visual Fusion Approach to Motion Estimation with High Robustness and Low Cost

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