English: In the last few decades a number of methods have been proposed for the acquisition of 3D models from real objects, including 3D scanning, 3D point- based photography and image-based modeling, among others. These tech- niques provide users with highly-detailed models, especially for simple and convex objects, which can be sampled from all the view directions surround- ing the object. However, the detailed acquisition of very large models of natural environments is a challenging problem for several reasons, including inaccessibility (e.g. forest area), insu cient lighting (shadowed areas) and presence of highly self-similar structures. For example, point-based models of outdoor environments often combine highly-detailed areas with low-detail or even unsampled areas. In this work we propose a reconstruction method for enhancing and enriching the geometry and appearance of point-based models of natural environments. Although our technique is orthogonal to the original acquisition technique, we mostly focus on enhancing models ac- quired through image-based modeling techniques such as multi-view stereo. Our method uses information from highly-detailed areas to ll unsampled or poorly sampled areas. Since most components in natural environments, and in particular the terrain, are fractal by nature, we propose to estimate the fractal dimension of the surface in highly-detailed areas and to use fractal generation techniques to both improve the overall appearance by increasing the amount of detail and ll-in undersampled areas with new geometry hav- ing similar fractal characteristics. Our method starts by computing a triangle mesh approximating the input point-based model. The main purpose of this step is to create a triangle mesh lling the holes corresponding to poorly sampled regions of the input model. Then, we subdivide the mesh by using a fractal perturbation scheme until the model has the desired level of detail. The output of our algorithm is a triangle mesh which approximates the input point-based model but has much more detail and better appearance. Although the resulting model is not a faithful copy of the original environment, it provides a plausible visu- alization with much higher quality and does not su er from undersampled areas.