Extracting measures on surfaces is a problem in many areas of research. The archaism of some systems or the costliness of sophisticated devices prevent the fast and robust extraction of these parameters. Yet these measures are essential in many areas, such as roughness parameters involved in many physical phenomena or dendrometric values for the study of biodiversity. In parallel, the use and production of 3D content has grown dramatically this past year in very diverse domains. The purpose of this thesis is to use these innovations in the context of surfaces parameter measurements. It is necessary to create a complete chain of 3D reconstruction, using pictures taken without constraint, in order to be open to as many people. This chain uses robust stereo-vision algorithms in order to produce a point cloud for each pair of images. After the generation of these point cloud in the same geometric frame, a filtering step of 3D points and a deletion step of redundancies are necessary and a smoothing step allows us to obtain the final point cloud. To reveal the good results, a validation step has enabled us to verify and investigate the robustness of the developed chain. The roughness and dendrometric parameters are finally extracted. We will study in both cases, how to extract this information and their uses
