Pavement macrotexture contributes greatly to road surface friction, which in turn plays a vital role in reducing road accidents. Conventional methods for macrotexture measurement are either expensive, time-consuming, or of poor repeatability. Based on multi-view smartphone images collected in situ, this paper develops and evaluates an affordable and convenient alternative approach for pavement macrotexture measurement. Photogrammetric computer vision techniques are first applied to create high resolution point clouds of the pavement. Analytics are then developed to determine the macrotexture metric: mean profile depth by using the image-based point clouds. Experiments are carried out with 790 images over 25 spots on three state routes and six spots at an Indiana Department of Transportation test site. We demonstrate multi-view smartphone images can yield results comparable to the ones from the conventional laser texture scanner. It is expected that the developed approach can be adopted for large scale operational uses.