A new paradigm for automatic non-photorealistic rendering is introduced in this paper. Non-photorealistic rendering (NPR) provides an alternative way to render complex scenes by emphasizing high level or salient perceptual features. Particularly, the pen-and-ink rendering style produces sketchy-like drawings that can effectively communicate shape and geometry. This is achieved by combining drawing primitives that mimic ink patterns used by artists. Existing NPR approaches can be categorized in two groups depending on the type of input they use: image based and object based. Image based NPR techniques use 2D images to produce the renderings. Object based techniques work directly on given 3D models and make use of the full volumetric representation. In this paper we propose to enjoy the best of both worlds developing an hybrid model that simultaneously uses information from the image and object domains. These two sources of information are provided by a calibrated stereoscopic system. Given a pair of stereo images and the calibration data we solve the stereo problem in order to extract the normal and principal direction fields, which are fundamental to guide a texture synthesis algorithm that generates the NPR renderings. In particular, normals guide tonal variations, while principal directions determine the orientation of stroke-like texture patterns. We describe a particular, fully automatic, implementation of these ideas and present a number of examples.