PUB - Publikationen an der Universität Bielefeld

While 3D data is rich in information, it often comes with the drawback of being tedious to handle. Recent work in the Geometric Deep Learning community focused on developing high quality 3D datasets for tasks like mesh segmentation. However, the label quality can never be assured to be perfect. To improve label quality in 3D datasets, we propose an interactive algorithm combining DeepView, a method to visualize the classification function of neural networks, with Intrinsic Mesh CNNs, which generalize the convolution to Riemannian manifolds, to smartly select adequate sets of vertices from triangle mesh data for label correction.