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Sonar profiling of the seabottom provide 3D data sets that can cover huge survey areas with many gaps. We describe a multiresolution framework or visualization pipeline that is being optimized for dealing with such data, taking into account both the CPU time and the user interactivity. We describe the techniques employed: (a) the construction of a quadtree that allows to eliminate gaps by interpolating available 3D data, (b) a first but coarse visualization at a high tree level in order to rapidly change or adjust the region of interest, and (c) a very efficient triangulation (mesh reduction) that allows for a fast interactivity even at the highest detail level. By using one single octree, all processing can be combined because (1) gaps can be filled by interpolation since they are smaller at higher tree levels, and (2) connected components can be projected down the tree and refined using the data available there. As a result, huge data sets can be visualized in near realtime on normally-sized discrete grids using shading instead of wireframes, and this enables a fast searching for objects in the seabottom. Real CPU times are presented for a real sonar data set which is visualized at a low resolution, showing the overall shape of the seabottom, and at a high resolution, showing a (semi)buried pipeline. In order to detect an object at such a high resolution additional techniques are applied to the data: (a) an interslice interpolation in order to cope with the increased data sparseness and (b) a maximum-homogeneity filtering in order to cope with the decreased signal-to-noise-ratio. After the extraction of the pipeline a thinning technique is applied in order to be able to quantify its length.