Some projective points (2D-points) of the same 3D-point captured by different cameras are called corresponding points. The task of finding the corresponding points is named as point correspondence. It is a essential problem in computer vision, and it paves the way for 3D reconstruction. Epipolar constraint is a widely-used geometric constraint for point correspondence, some methods based on it have been proposed for this task. However, the threshold is set by empirical approach in these methods; this approach is influenced by the parameters of cameras. On the other hand, epipolar constraint is not a sufficient condition for this task; this will result in the mismatch for two 2D-points. According to the rule of Propagation Uncertainty and practical situation of the computer vision system, this paper introduces the enhanced epipolar constraint comprising normalized coplanar constraint, nonparallel constraint and interval constraint. The normalized coplanar constraint provides a simple and rational method to set the threshold of epipolar constraint; the nonparallel constraint and interval constraint can reduce the occasion of mismatch.