从前两幅图像的图像特征中估计第3幅图像的特征，在计算机视觉领域中有着广泛的应用，例如，视觉识别，基于模型的视觉动画、视图合成、目标检测和跟踪。Faugeras和Robert指出第3幅图像的特征可以通过前面两个摄像机图像的双线性函数来进行估计，其基本上是通过基础矩阵来计算的，因而他们的方法在实际计算过程中有很大的缺陷。为此提出了一种新的估计方法，即从三焦点张量中来估计第3幅图像的特征。这一方法继承和发展了Faugeras等的方法。此外还给出了一个定理说明了本文的条件与Faugeras给出的条件是等价的，但本方法简单目更加系统。实验结果证明了该方法的可靠性。

This paper considers the problem of estimating image features in an image from image features in two other images. The problem in computer vision has a wide practical appliance, such as Visual Recognition, model based vision Animation, View Synthesis, and object detection and tracking. O.Faugeras and L.Robert have shown that features can be estimated in the third image as a bilinear function of its image in the first two cameras. Since relied on the use of the given fundamental matrix, the method has a serious deficiency that rules it out as a practical approach. In this paper, a new method was provided to estimate image features in the third image based on the trifocal tensor, and, obviously, it is continuous and development of the former. Furthermore, a theorem given in this paper shows that the condition is as weak as the one provided by O.Faugeras, but the method is simpler and more systemic. Finally, the applicability of the method was demonstrated with experiments on synthetic and real data.