# 图像拼接--Creating full view panoramic image mosaics and environment maps

Creating Full View Panoramic Image Mosaics and Environment Maps In ACM SIGGRAPH, pages 251–258, 1997 Proceeding SIGGRAPH '97 Proceedings of the 24th annual conference on Computer graphics and interactive techniques

2 Cylindrical and spherical panoramas Cylindrical panoramas 圆柱形全景 经常被使用，因为构建它比较简单。为了构建一个圆柱全景图，相机固定在一个 leveled tripod 上面，拍摄得到图像序列。如果相机的焦距或 field of view 已知，那么 每个 perspective 图像可以被 warped 到 圆柱坐标体系内。

Figure 1a shows two overlapping cylindrical images—notice how horizontal lines become curved.

Ideally, to build a cylindrical or spherical panorama from a horizontal panning sequence, only the unknown panning angles need to be recovered

Therefore, both a horizontal translation t x and a vertical translation t y are estimated for each input image. 所以对每个输入图像，我们需要估计一直水平位移和一个垂直位移

To recover the translational motion, we estimate the incremental translation δt = (δt x ,δt y ) by minimizing the intensity error between two images 最优化问题通过泰勒级数展开，通过简单的最小二乘求解 simple least-squares solution 使用 cylindrical or spherical coordinates 创建全景图存在几个问题： 1）相机运动约束较强 it can only handle the simple case of pure panning motion 2） cylindrical or spherical coordinates 在顶部和底部误差较大 even though it is possible to convert an image to 2D spherical or cylindrical coordinates for a known tilting angle, ill-sampling at north pole and south pole causes big registration errors 3）相机焦距的获取有点难度 it requires knowing the focal length (or equivalently, field of view)

3 Perspective (8-parameter) panoramas 针对 cylindrical or spherical coordinates 的问题，有学者提出了 使用 full planar perspective motion models， The planar per- spective transform warps an image into another using 8 parameters

The 8-parameter perspective transformation recovery algorithm works well provided that initial estimates of the correct transformation are close enough.

4 Rotational (3-parameter) panoramas 这里我们提出使用 3个参数的 the 3-parameter rotational model

Figure 2 shows how our method can be used to register four images with arbitrary (non-panning) rotation. Compared to the 8-parameter perspective model, it is much easier and more intuitive to interactively adjust images using the 3-parameter rotational model

5 Estimating the focal length 相机焦距的估计： A convenient way to obtain this estimate to deduce the value from one or more perspective transforms computed using the 8-parameter algorithm

Alternative techniques for estimating the focal length are presented in [8, 16, 13, 10]

Once an initial set of f estimates is available, we can improve these estimates as part of the image registration process, using the same kind of least squares approach as for the rotation [15]

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