Central to many problems in scene understanding based on using a network of tens, hundreds or even thousands of randomly distributed cameras with on-board processing and wireless communication capability is the "efficient" reconstruction of the 3D geometry structure in the scene. What is meant by "efficient" reconstruction ? In this paper we investigate this from different aspects in the context of visual sensor networks and offer a distributed reconstruction algorithm roughly meeting the following goals 1. Close to achievable 3D reconstruction accuracy and robustness ; 2. Minimization of the processing time by adaptive computing-job distribution among all the cameras in the network and asynchronous parallel processing ; 3. Communication Optimization and minimization of the (battery-stored) energy, by reducing and localizing the communications between cameras. A volumetric representation of the scene is reconstructed with a shape from apparent contour algorithm, which is suitable for distributed processing because it is essentially a local operation in terms of the involved cameras, and apparent contours are robust to our-door illumination conditions. Each camera processes its own image and performs the computation for a small subset of voxels, and updates the voxels through collaborating with its neighbor cameras. By exploring the structure of the reconstruction algorithm, we design the minimum-spanning-tree (MST) message passing protocol in order to minimize the communication. Of interest is that the resulting system is an example of "swarm behavior". 3D reconstruction is illustrated using two real image sets, running on a single computer. The iterative computations used in the single processor experiment are exactly the same as are those used in the network computations. Distributed concepts and algorithms for network control and communication performance are theoretical designs and estimates.