Vision-based motion estimation and 3D reconstruction, which have numerous applications (e.g., autonomous driving, navigation systems for airborne devices and augmented reality) are receiving significant research attention. To increase the accuracy and robustness, several researchers have recently demonstrated the benefit of using large field-of-view cameras for such applications. In this paper, we provide an extensive review of existing models for large field-of-view cameras. For each model we provide projection and unprojection functions and the subspace of points that result in valid projection. Then, we propose the Double Sphere camera model that well fits with large field-of-view lenses, is computationally inexpensive and has a closed-form inverse. We evaluate the model using a calibration dataset with several different lenses and compare the models using the metrics that are relevant for Visual Odometry, i.e., reprojection error, as well as computation time for projection and unprojection functions and their Jacobians. We also provide qualitative results and discuss the performance of all models.
Several other models for large field-of-view lenses based on high-order polynomials exist. For example, the main differences between @cite_2 and the KB model are as follows: the model calibrates two separate polynomials for projection and unprojection to provide a closed-form solution for both, and for projection it uses the angle between the image plane and the point rather than of the angle between the optical axis and the point. We expect this model to have similar performance and do not explicitly include it in our evaluation.
@cite_2
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We introduce a new rational function (RF) model for radial lens distortion in wide-angle and catadioptric lenses, which allows the simultaneous linear estimation of motion and lens geometry from two uncalibrated views of a 3D scene. In contrast to existing models which admit such linear estimates, the new model is not specialized to any particular lens geometry, but is sufficiently general to model a variety of extreme distortions. The key step is to define the mapping between image (pixel) coordinates and 3D rays in camera coordinates as a linear combination of nonlinear functions of the image coordinates. Like a "kernel trick", this allows a linear algorithm to estimate nonlinear models, and in particular offers a simple solution to the estimation of nonlinear image distortion. The model also yields an explicit form for the epipolar curves, allowing correspondence search to be efficiently guided by the epipolar geometry. We show results of an implementation of the RF model in estimating the geometry of a real camera lens from uncalibrated footage, and compare the estimate to one obtained using a calibration grid. Tomographic image reconstruction, such as the reconstruction of computed tomography projection values, of tomosynthesis data, positron emission tomography or SPECT events, and of magnetic resonance imaging data is computationally very demanding. One of the most time-consuming steps is the backprojection. Recently, a novel general purpose architecture optimized for distributed computing became available: the cell broadband engine (CBE). To maximize image reconstruction speed we modified our parallel-beam backprojection algorithm [two dimensional (2D)] and our perspective backprojection algorithm [three dimensional (3D), cone beam for flat–panel detectors] and optimized the code for the CBE. The algorithms are pixel or voxel driven, run with floating point accuracy and use linear (LI) or nearest neighbor (NN) interpolation between detector elements. For the parallel-beam case, 512 projections per half rotation, 1024 detector channels, and an image of size 512 2 was used. The cone-beam backprojection performance was assessed by backprojecting a full circle scan of 512 projections of size 1024 2 into a volume of size 512 3 voxels. The field of view was chosen to completely lie within the field of measurement and the pixel or voxel size was set to correspond to the detector element size projected to the center of rotation divided by 2 . Both the PC and the CBE were clocked at 3 GHz . For the parallel backprojection of 512 projections into a 512 2 image, a throughput of 11 fps (LI) and 15 fps (NN) was measured on the PC, whereas the CBE achieved 126 fps (LI) and 165 fps (NN), respectively. The cone-beam backprojection of 512 projections into the 512 3 volume took 3.2 min on the PC and is as fast as 13.6 s on the cell. Thereby, the cell greatly outperforms today’s top-notch backprojections based on graphical processing units. Using both CBEs of our dual cell-based blade (Mercury Computer Systems) allows to 2D backproject 330 images s and one can complete the 3D cone-beam backprojection in 6.8 s . This paper proposes a lenslet image compression method scalable from low bitrates to fully lossless. The subaperture images are split into two sets: a set of reference views, encoded by a standard lossy or lossless compressor, and the set of dependent views, which are reconstructed by sparse prediction from the reference set using the geometrical information from the depth map. The set of reference views may contain all views and all views may also be dependent views, in which case the sparse predictive stage does not reconstruct from scratch the views, but it refines in a sequential order all views by combining in an optimal way the information about the same region existing in neighbor views. The encoder transmits to the decoder a segmented version of the scene depthmap, the encoded versions of the reference views, displacements for each region from the central view to each of the dependent views, and finally the sparse predictors for each region and each dependent view. The scheme can be configured to ensure random access to the dependent views, while the reference views are compressed in a backward compatible way, e.g., using JPEG 2000. The experimental results show performance better than that of the baseline standard compressor used, JPEG 2000. This paper presents a flexible approach for calibrating omnidirectional single viewpoint sensors from planar grids. Current approaches in the field are either based on theoretical properties and do not take into account important factors such as misalignment or camera-lens distortion or over-parametrised which leads to minimisation problems that are difficult to solve. Recent techniques based on polynomial approximations lead to impractical calibration methods. Our model is based on an exact theoretical projection function to which we add well identified parameters to model real-world errors. This leads to a full methodology from the initialisation of the intrinsic parameters to the general calibration. We also discuss the validity of the approach for fish-eye and spherical models. An implementation of the method is available as opensource software on the author's Web page. We validate the approach with the calibration of parabolic, hyperbolic, wideangle and spherical sensors.