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The investigation of man-made objects lying on or embedded in the sea floor can be carried out with acoustic imaging techniques and subsequent data processing. In this paper, we describe a processing chain that starts with a 3-D acoustic image of the object to be examined and ends with an augmented reality model, which requires minimal user involvement. Essentially, the chain includes blocks devoted to statistical 3-D segmentation, semi-automatic surface fitting, extraction of measurements, and augmented reality modeling. In particular, the 3-D segmentation method presented here is based on a volume-growing approach, which is essentially a 3-D extension of the traditional 2-D region growing. The volume-growing operation is guided by a statistical approach based on the optimal decision theory. The surface-fitting block is based on predefined geometric models, i.e., one of them is tentatively selected by the user after a preliminary study of the segmented object and is automatically or partially manually adapted to the segmented data by exploiting an inertial tensor. The proposed chain was successfully applied to the analysis of some 3-D acoustic images obtained from both simulated and real signals acquired by different sonar systems and containing objects that were completely or partially buried. The segmentation results provided an effective help in the identification of the object's shape, i.e., facilitating the subsequent surface-fitting step and the extraction of related measurements.