This paper introduces a concept for a self-supervised approach to apply a pre-trained 2D model for processing 3D point clouds. This approach helps to eliminate the need for 3D labeled data, which is extremely labor-intensive to create. The approach is able to work with a large amount of data while keeping the computational overhead low by using culling techniques and camera projection.