In the last decade, very realistic rendering of plant architectures have been produced in computer graphics applications. However, in the context of biology and agronomy, acquisition of accurate models of real plants is still a tedious task and a major bottleneck for the construction of quantitative models of plant development. Recently, 3D laser scanners made it possible to acquire 3D images on which each pixel has an associate depth corresponding to the distance between the scanner and the pinpointed surface of the object. Standard geometrical reconstructions fail on plants structures as they usually contain a complex set of discontinuous or branching surfaces distributed in space with varying orientations. In this thesis, we present a method for reconstructing virtual models of plants from laser scanning of real-world vegetation. Measuring plants with laser scanners produces data with different levels of precision. Points set are usually dense on the surface of the main branches, but only sparsely cover thin branches. The core of our method is to iteratively create the skeletal structure of the plant according to local density of point set. This is achieved thanks to a method that locally adapts to the levels of precision of the data by combining a contraction phase and a local point tracking algorithm. In addition, we present a quantitative evaluation procedure to compare our reconstructions against expertised structures of real plants. For this, we first explore the use of an edit distance between tree graphs. Alternatively, we formalize the comparison as an assignment problem to find the best matching between the two structures and quantify their differences.
