Deep Distance Metric Learning for Similarity Preserving Embedding of Point Clouds

Ahmed Abouelazm, Igor Vozniak, Nils Lipp, Pavel Astreika, Christian Müller

Abstract: Point cloud processing and 3D model retrieval methods have received a lot of interest as a result of the recent advancement in deep learning, computing hardware, and a wide range of available 3D sensors. Many state-of-the-art approaches utilize distance metric learning for solving the 3D model retrieval problem. However, the majority of these approaches disregard the variation in shape and properties of instances belonging to the same class known as intra-class variance, and focus on semantic labels as a measure of relevance. In this work, we present two novel loss functions for similarity-preserving point cloud embedding, in which the distance between point clouds in the embedding space is directly proportional to the ground truth distance between them using a similarity or distance measure. The building block of both loss functions is the forward passing of n-pair input point clouds through a Siamese network. We utilize ModelNet 10 dataset in the course of numerical evaluations und