To watch 360 videos on normal 2D displays, we need to project the selected part of the 360 image onto the 2D display plane. In this paper, we propose a fully-automated framework for generating content-aware 2D normal-view perspective videos from 360 videos. Especially, we focus on the projection step preserving important image contents and reducing image distortion. Basically, our projection method is based on Pannini projection model. At first, the salient contents such as linear structures and salient regions in the image are preserved by optimizing the single Panini projection model. Then, the multiple Panini projection models at salient regions are interpolated to suppress image distortion globally. Finally, the temporal consistency for image projection is enforced for producing temporally stable normal-view videos. Our proposed projection method does not require any user-interaction and is much faster than previous content-preserving methods. It can be applied to not only images but also videos taking the temporal consistency of projection into account. Experiments on various 360 videos show the superiority of the proposed projection method quantitatively and qualitatively.
use a geometric model to project spherical panorama images onto an image plane. Rectilinear, Stereographic, and Pannini projection @cite_5 models belong to this category. Rectilinear projection is the perspective projection from the center point of the spherical image onto the image plane. This model preserves all lines, but the contents on the margin of a projected image can be extremely stretched and distorted when the FOV is large. On the other hand, stereographic projection is the perspective projection from the opposite point to the point of tangency as the center of projection. This model preserves a conformality of the contents, but not lines. Pannini projection based on the cylindrical projection keeps the vertical lines straight. Furthermore, the horizontal lines or radial lines are selectively preserved by the vertical compression. The methods mentioned above are very simple and fast, but have a common drawback --- they can not preserve all lines and salient objects simultaneously.
@cite_5
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The widely used rectilinear perspective projection cannot render realistic looking flat views with fields of view much wider than 70°. Yet 18th century artists known as 'view painters' depicted wider architectural scenes without visible perspective distortion.We have found no written records of how they did that, however, quantitative analysis of several works suggests that the key is a system for compressing horizontal angles while preserving certain straight lines important for the perspective illusion. We show that a simple double projection of the sphere to the plane, that we call the Pannini projection, can render images 150° or more wide with a natural appearance, reminiscent of vedutismo perspective. We give the mathematical formulas for realizing it numerically, in a general form that can be adjusted to suit a wide range of subject matter and field widths, and briefly compare it to other proposed alternatives to the rectilinear projection. This paper proposes a new projection model for mapping a hemisphere to a plane. Such a model can be useful for viewing wide-angle images. Our model consists of two steps. In the first step, the hemisphere is projected onto a swung surface constructed by a circular profile and a rounded rectangular trajectory. The second step maps the projected image on the swung surface onto the image plane through the perspective projection. We also propose a method for automatically determining proper parameters for the projection model based on image content. The proposed model has several advantages. It is simple, efficient and easy to control. Most importantly, it makes a better compromise between distortion minimization and line preserving than popular projection models, such as stereographic and Pannini projections. Experiments and analysis demonstrate the effectiveness of our model. A new approach to computing a panoramic (360 degrees) depth map is presented in this paper. Our approach uses a large collection of images taken by a camera whose motion has been constrained to planar concentric circles. We resample regular perspective images to produce a set of multiperspective panoramas and then compute depth maps directly from these resampled panoramas. Our panoramas sample uniformly in three dimensions: rotation angle, inverse radial distance, and vertical elevation. The use of multiperspective panoramas eliminates the limited overlap present in the original input images and, thus, problems as in conventional multibaseline stereo can be avoided. Our approach differs from stereo matching of single-perspective panoramic images taken from different locations, where the epipolar constraints are sine curves. For our multiperspective panoramas, the epipolar geometry, to the first order approximation, consists of horizontal lines. Therefore, any traditional stereo algorithm can be applied to multiperspective panoramas with little modification. In this paper, we describe two reconstruction algorithms. The first is a cylinder sweep algorithm that uses a small number of resampled multiperspective panoramas to obtain dense 3D reconstruction. The second algorithm, in contrast, uses a large number of multiperspective panoramas and takes advantage of the approximate horizontal epipolar geometry inherent in multiperspective panoramas. It comprises a novel and efficient 1D multibaseline matching technique, followed by tensor voting to extract the depth surface. Experiments show that our algorithms are capable of producing comparable high quality depth maps which can be used for applications such as view interpolation. This article proposes a variational multi-view stereo vision method based on meshes for recovering 3D scenes (shape and radiance) from images. Our method is based on generative models and minimizes the reprojection error (difference between the observed images and the images synthesized from the reconstruction). Our contributions are twofold. 1) For the first time, we rigorously compute the gradient of the reprojection error for non smooth surfaces defined by discrete triangular meshes. The gradient correctly takes into account the visibility changes that occur when a surface moves; this forces the contours generated by the reconstructed surface to perfectly match with the apparent contours in the input images. 2) We propose an original modification of the Lambertian model to take into account deviations from the constant brightness assumption without explicitly modelling the reflectance properties of the scene or other photometric phenomena involved by the camera model. Our method is thus able to recover the shape and the diffuse radiance of non Lambertian scenes.