<p>Mechanical scanning sonar (MSS) plays an important role in high-precision object recognition and detection in underwater environments. However, existing research on MSS has focused on large objects, such as subsea structures and sunken ships, and often relies on unreleased datasets collected in confined water tank environments, limiting the study of small underwater objects and their application to real marine environments. Therefore, in this study, a Small Underwater Objects 3D Point Cloud (SUOP) Dataset was constructed using an MSS (BV5000) in the actual underwater environments at the seafloor. The dataset contains over 1,500 high-quality 3D point clouds for five objects, corresponding initial sonar scan data files, sonar system metadata, and 2D sonar images. The practicality of the proposed dataset was verified by applying it to an object recognition model. The results demonstrate that the SUOP dataset, with its object types, materials, and scanning conditions, enables accurate and robust evaluation of underwater object detection models, hence proving to be a valuable resource for research on marine underwater object detection.</p>