This paper presents a stereo matching method that uses multiple stereo pairs with different baselines to obtain precise depth estimates without ambiguity. In stereo processing, a short baseline leads to less precise depth estimates due to narrow triangulation, while a longer baseline increases the disparity range to search for matches, making matching more difficult and increasing the possibility of false matches. The proposed method uses multiple stereo pairs generated by lateral camera displacement, computes the sum of squared-difference (SSD) values for each pair, and represents these SSD values with respect to inverse depth rather than disparity. The resulting function, called SSSD-in-inverse-depth, exhibits a unique and clear minimum at the correct matching position even when the scene contains ambiguous or repetitive patterns. This method eliminates false matches and increases precision without search or sequential filtering. The paper first defines a stereo algorithm based on SSSD-in-inverse-depth and presents a mathematical analysis to show how the algorithm removes ambiguity and increases precision. It then presents experimental results with real stereo images to demonstrate the algorithm's effectiveness. The method uses multiple baselines to reduce ambiguity by ensuring that the minimum of the SSSD-in-inverse-depth function occurs at the correct depth. The analysis shows that using multiple baselines increases the period of the function, reducing ambiguity and improving precision. The algorithm is also shown to be effective in real-world scenarios, such as the "Town" and "Coal mine" data sets, where it produces more accurate depth maps with fewer errors compared to single-baseline methods. The method is also suitable for parallel hardware implementation, as it can be processed using multiple cameras and SSD calculators. The key idea is to relate SSD values to inverse depth rather than disparity, which allows for a single minimum in the SSD function when summed over multiple baselines. This approach eliminates the need for search or sequential estimation procedures, leading to more accurate and precise depth estimates.This paper presents a stereo matching method that uses multiple stereo pairs with different baselines to obtain precise depth estimates without ambiguity. In stereo processing, a short baseline leads to less precise depth estimates due to narrow triangulation, while a longer baseline increases the disparity range to search for matches, making matching more difficult and increasing the possibility of false matches. The proposed method uses multiple stereo pairs generated by lateral camera displacement, computes the sum of squared-difference (SSD) values for each pair, and represents these SSD values with respect to inverse depth rather than disparity. The resulting function, called SSSD-in-inverse-depth, exhibits a unique and clear minimum at the correct matching position even when the scene contains ambiguous or repetitive patterns. This method eliminates false matches and increases precision without search or sequential filtering. The paper first defines a stereo algorithm based on SSSD-in-inverse-depth and presents a mathematical analysis to show how the algorithm removes ambiguity and increases precision. It then presents experimental results with real stereo images to demonstrate the algorithm's effectiveness. The method uses multiple baselines to reduce ambiguity by ensuring that the minimum of the SSSD-in-inverse-depth function occurs at the correct depth. The analysis shows that using multiple baselines increases the period of the function, reducing ambiguity and improving precision. The algorithm is also shown to be effective in real-world scenarios, such as the "Town" and "Coal mine" data sets, where it produces more accurate depth maps with fewer errors compared to single-baseline methods. The method is also suitable for parallel hardware implementation, as it can be processed using multiple cameras and SSD calculators. The key idea is to relate SSD values to inverse depth rather than disparity, which allows for a single minimum in the SSD function when summed over multiple baselines. This approach eliminates the need for search or sequential estimation procedures, leading to more accurate and precise depth estimates.