Abstract
Abstract. In the last few years, data fusion has been an active research topic for the expected advantages of exploiting and combining different but complementary techniques for 3D documentation. The data fusion process consists of merging data coming from different sensors and platforms, intrinsically different, to produce complete, coherent, and precise 3D reconstructions. Although extensive research has been dedicated to this task, we still have many gaps in the integration process, and the quality of the results is hardly sufficient in several cases. This is especially evident when the integration occurs in a later stage, e.g., merging the results of separate data processing. New opportunities are emerging, with the possibility offered by some proprietary tools to jointly process heterogeneous data, particularly image and range-based data. The article investigates the benefits of data integration at different processing levels: raw, middle, and high levels. The experiments are targeted to explore, in particular, the results of the integration on large and complex architectures.
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