Abstract-For distributed smart camera networks to perform vision-based tasks such as subject recognition and tracking, every camera's position and orientation relative to a single 3D coordinate frame must be accurately determined. In this paper, we present a new camera network localization solution that requires successively showing a 3D feature point-rich target to all cameras in the network. Using the known geometry of a 3D target, cameras estimate and decompose projection matrices to compute their position and orientation relative to the coordinatization of the 3D target's feature points. As each 3D target position establishes a distinct coordinate frame, cameras that view more than one 3D target position compute translations and rotations relating different positions' coordinate frames, then share the transform data with neighbors to realign all cameras to a single coordinate frame established by one chosen target position. Compared to previous localization solutions that use opportunistically found visual data, our solution is more suitable to battery-powered, processing-constrained camera networks because it only requires pairwise view overlaps of sufficient size to see the 3D target and detect its feature points, and only requires communication to determine simultaneous target viewings and for the passing of the transform data. Finally, our solution gives camera positions in a 3D coordinate frame with meaningful units. We evaluate our algorithm in both real and simulated smart camera network deployments. In the real deployment, position error is less than 1" when the 3D target's feature points fill only 2.9% of the frame area.