This paper presents a method for real-time wide-baseline feature matching. The approach is based on the work of Lepetit and colleagues [9], where randomized decision trees are trained to establish correspondences between detected features in a training image, and those in input frames. Though extremely promising, their actual results can vary depending on the viewpoint and illumination conditions. We combine two approaches to alleviate its limitations. The first aims to update the trees at run-time, adapting them to the actual viewing conditions. The second consists in spatially distributing the trees, so that each of them models a certain viewing volume more precisely. The result is a more stable matching method that significantly extends detectable range and is much more robust to illumination changes, such as cast shadows or reflections.