Accurate and efficient 3D reconstruction of small-scale objects remains challenging due to intricate geometries, limited imaging volumes, and sensitivity to acquisition conditions. This study presents a quantitative comparison between two close-range photogrammetric acquisition methods: a conventional manual tripod setup and a custom-built, automated turntable platform controlled by an Arduino microcontroller. Four geometrically distinct objects were reconstructed using both approaches and analyzed through a unified Structure-from-Motion (SfM) workflow. Dimensional accuracy was assessed using reference measurements obtained with a digital vernier caliper (±0.01 mm precision), while geometric fidelity was evaluated through Cloud-to-Cloud (C2C) surface deviation analysis. Results consistently favored the automated system. For instance, the used house object achieved a Root Mean Square Error (RMSE) of 0.18 cm with the turntable system versus 0.70 cm manually. The used jug, with complex occlusions, exhibited a C2C mean deviation of 0.411 cm in the manual method. The used jug showed a maximum deviation of 1.1 cm, while the used ceramic swan yielded the lowest mean error of 0.006 cm. In terms of efficiency, the automated platform reduced acquisition time by nearly 50%, improved repeatability, and minimized operator input. These findings underscore the potential of low-cost, semi-automated acquisition systems for improving the accuracy, reliability, and scalability of photogrammetric measurement workflows. The proposed system is especially well suited for technical education, low-budget laboratory environments, and object-scale documentation scenarios requiring consistent measurement standards.