In this paper, we propose a basic workflow for the automatic extraction of glacier contours from high-resolution multi-temporal 3D point clouds. Based on the hypothesis, that glacier movements cause changes in multi-temporal surface models, glacier contours can be detected even in scenarios where glacier margins are not clearly visible, such as in the case of debris-covered glaciers and rock glaciers. After applying a robust registration algorithm, glacier and non-glacier points are filtered in several steps and spatial resolutions based on dense clusters of significantly changed points. Finally, the glacier margin is mapped using a contour extraction algorithm. The method is applied to various datasets in two scenarios: first, to a slightly debris-covered glacier in Norway; second, to a rock glacier in the Austrian Alps. The results clearly demonstrate the basic functionality of the proposed method. However, morphological changes not caused by glacier movement limit the effectiveness of the filtering, which relies exclusively on density-based elevation changes. Against this background, future work will focus on incorporating additional features, such as velocity fields and prior knowledge of rock glacier dynamics, into the point cloud filtering.