Change detection is an important application of laser scanning data. It is also a challenging application as errors that are inevitably present when determining the geometric state of a scene of interest in a certain epoch will somehow add up when comparing the geometric state between epochs. As a consequence it is often difficult to distinguish real changes from differences caused by measurement and/or processing errors. On top of that, data volumes are rapidly increasing. Therefore successful change detection methods should not only be robust against errors but also computational efficient. In this paper a not necessarily complete overview of recent methodology is given that is presented in connection with the applications considered by the original authors.