The state-of-the art method of wound assessment is a manual, imprecise and time-consuming procedure. Per- formed by clinicians, it has limited reproducibility and accuracy, large time consumption and high costs. Novel technologies such as laser scanning microscopy, multi-photon microscopy, optical coherence tomography and hyper-spectral imaging, as well as devices relying on the structured light sensors, make accurate wound assessment possible. However, such methods have limitations due to high costs and may lack portability and availability. In this paper, we present a low-cost wound assessment system and architecture for fast and accurate cutaneous wound assessment using inexpensive consumer smartphone devices. Computer vision techniques are applied either on the device or the server to reconstruct wounds in 3D as dense models, which are generated from images taken with a built-in single camera of a smartphone device. The system architecture includes imaging (smartphone), processing (smartphone or PACS) and storage (PACS) devices. It supports tracking over time by alignment of 3D models, color correction using a reference color card placed into the scene and automatic segmentation of wound regions. Using our system, we are able to detect and document quantitative characteristics of chronic wounds, including size, depth, volume, rate of healing, as well as qualitative characteristics as color, presence of necrosis and type of involved tissue.