A structured light system simplifies three-dimensional reconstruction by illuminating a specially designed pattern to the target object, thereby generating a distinct texture on it for imaging and further processing. Success of the system hinges upon what features are to be coded in the projected pattern, extracted in the captured image, and matched between the projector's display panel and the camera's image plane. The codes have to be such that they are largely preserved in the image data upon illumination from the projector, reflection from the target object, and projective distortion in the imaging process. The features also need to be reliably extracted in the image domain. In this article, a two-dimensional pseudorandom pattern consisting of rhombic color elements is proposed, and the grid points between the pattern elements are chosen as the feature points. We describe how a type classification of the grid points plus the pseudorandomness of the projected pattern can equip each grid point with a unique label that is preserved in the captured image. We also present a grid point detector that extracts the grid points without the need of segmenting the pattern elements, and that localizes the grid points in subpixel accuracy. Extensive experiments are presented to illustrate that, with the proposed pattern feature definition and feature detector, more features points in higher accuracy can be reconstructed in comparison with the existing pseudorandomly encoded structured light systems.