This paper presents a stereo matching method that utilizes multiple stereo pairs with varying baselines to achieve precise depth estimates without suffering from ambiguity. The method, called SSSD-in-inverse-depth, represents the sum of squared differences (SSD) values for each stereo pair with respect to the inverse depth rather than the disparity. This approach allows for the elimination of false matches and increased precision without the need for extensive search or sequential filtering. The authors demonstrate that the SSSD-in-inverse-depth function exhibits a unique and clear minimum at the correct matching position, even in scenes with ambiguous or repetitive patterns. The paper includes mathematical analysis to show how the method removes ambiguity and increases precision, followed by experimental results using real stereo images to validate the effectiveness of the algorithm. The method is shown to be particularly useful in scenarios with periodic patterns or repetitive structures, where traditional methods often fail.This paper presents a stereo matching method that utilizes multiple stereo pairs with varying baselines to achieve precise depth estimates without suffering from ambiguity. The method, called SSSD-in-inverse-depth, represents the sum of squared differences (SSD) values for each stereo pair with respect to the inverse depth rather than the disparity. This approach allows for the elimination of false matches and increased precision without the need for extensive search or sequential filtering. The authors demonstrate that the SSSD-in-inverse-depth function exhibits a unique and clear minimum at the correct matching position, even in scenes with ambiguous or repetitive patterns. The paper includes mathematical analysis to show how the method removes ambiguity and increases precision, followed by experimental results using real stereo images to validate the effectiveness of the algorithm. The method is shown to be particularly useful in scenarios with periodic patterns or repetitive structures, where traditional methods often fail.