Three-dimensional reconstruction is a crucial technique for mapping and object-search tasks, but it is challenging in sonar imaging because of the nature of acoustics. In underwater sensing, many advanced studies have introduced approaches that have included feature-based methods and multiple imaging at different locations. However, most existing methods are prone to environmental conditions, and they are not adequate for continuous data acquisition on moving autonomous underwater vehicles (AUVs). This paper proposes a sensor fusion method for 3D reconstruction using acoustic sonar data with two sonar devices that provide complementary features. The forward-looking multibeam sonar (FLS) is an imaging sonar capable of short-range scanning with a high horizontal resolution, and the profiling sonar (PS) is capable of middle-range scanning with high reliability in vertical information. Using both sonars, which have different data acquisition planes and times, we propose a probabilistic sensor fusion method. First, we extract the region of interest from the background and develop a sonar measurement model. Thereafter, we utilize the likelihood field generated by the PS and estimate the elevation ambiguity using importance sampling. We also present the evaluation of our method in a ray-tracing-based sonar simulation environment and the generation of the pointclouds. The experimental results indicate that the proposed method can provide a better accuracy than that of the conventional method. Because of the improved accuracy of the generated pointclouds, this method can be expanded for pointcloud-based mapping and classification methods.