Generally caused by extreme events, landslides cause severe landscape modifications and may endanger local population. It is important to be able to map them in order to better understand landscape evolution. 3D LiDAR point clouds (PCs) are a relevant choice compared to 2D imagery to directly sense ground shape modification under vegetated areas. Most of the studies propose to rely on rasterization of PCs, or multistep semi-automatic process with tedious manual results refinement in the 3D PCs. In this study, we aim at experimenting a deep learning method to directly extract landslide sources and deposits from raw 3D PCs. To this end, we train an Encoder Fusion SiamKPConv network, designed for 3D PCs change detection, for the specific task of landslides identification in PCs acquired before and after Kaikōura earthquake (New-Zealand). The experimental results (93.87% of accuracy) show the relevance of this model.