Active sensing is the process of exploring the environment using multiple views of a scene captured by sensors from different points in space under different sensor settings. Applications of active sensing are numerous and can be found in the medical field (limb reconstruction), in archeology (bone mapping), in the movie and advertisement industry (computer simulation and graphics), in manufacturing (quality control), as well as in the environmental industry (mapping of nuclear dump sites). In this work, the focus is on the use of a single vision sensor (camera) to perform the volumetric modeling of an unknown object in an entirely autonomous fashion. The camera moves to acquire the necessary information in two ways: (a) viewing closely each local feature of interest using 2D data; and (b) acquiring global information about the environment via 3D sensor locations and orientations. A single object is presented to the camera and an initial arbitrary image is acquired. A 2D optimization process is developed. It brings the object in the field of view of the camera, normalizes it by centering the data in the image plane, aligns the principal axis with one of the camera's axes (arbitrarily chosen), and finally maximizes its resolution for better feature extraction. The enhanced image at each step is projected along the corresponding viewing direction. The new projection is intersected with previously obtained projections for volume reconstruction. During the global exploration of the scene, the current image as well as previous images are used to maximize the information in terms of shape irregularity as well as contrast variations. The scene on the borders of occlusion (contours) is modeled by an entropy-based objective functional. This functional is optimized to determine the best next view, which is recovered by computing the pose of the camera. A criterion based on the minimization of the difference between consecutive volume updates is set for termination of the exploration procedure. These steps are integrated into the design of an off-line Autonomous Model Construction System AMCS, based on data-driven active sensing. The system operates autonomously with no human intervention and with no prior knowledge about the object. The results of this work are illustrated using computer simulation applied to intensity images rendered by ray-tracing software package.