Pouring precise liquid quantities remains a challenging task for autonomous robots. Particularly this is due to various factors as: robust container estimation, liquid’s volume estimation, arm’s or device motion uncertainty, and platform instability. This work presents a vision-based approach to estimate the tilt of cylindrical containers, a key geometric variable for inferring poured volume. The method processes frontal images using Canny edge detection and the probabilistic Hough transform to extract container and liquid boundaries. From these, a projective geometry model computes the container’s inclination without additional sensors. The proposed approach avoids the explicit calculation of fluid dynamics by providing a tool for performing a smooth pouring process. Experimental results demonstrate the feasibility of this low-cost, vision-based estimation approach.