The paper deals with the fusion of multiobject information over a network of heterogeneous and geographically dispersed nodes with sensing, communication and processing capabilities. To exploit the benefits of sensor networks for multiobject estimation problems, like e.g. multitarget tracking and multirobot SLAM (Simultaneous Localization and Mapping), a key issue to be addressed is how to consistently fuse (average) locally updated multiobject densities. In this paper we discuss the generalization of Kullback-Leibler average, originally conceived for single-object densities (i.e. probability density functions) to (both unlabeled and labeled) multiobject densities. Then, with a view to develop scalable and reliable distributed multiobject esti- mation algorithms, we review approaches to iteratively compute, in each node of the network, the collective multiobject average via scalable and neighborwise computations.