Fast self-supervised 3D mesh object retrieval for geometric similarity

Abstract

Digital 3D models play a pivotal role in engineering, entertainment, education, and various domains. However, the search and retrieval of these models have not received adequate attention compared to other digital assets like documents and images. Traditional supervised methods face challenges in scalability due to the impracticality of creating large, labeled collections of 3D objects. In response, this paper introduces a self-supervised approach to generate efficient embeddings for 3D mesh objects, facilitating ranked retrieval of similar objects. The proposed method employs a straightforward representation of mesh objects and utilizes an encoder–decoder architecture to learn the embedding. Extensive experiments demonstrate the competitiveness of our approach compared to supervised methods, showcasing its scalability across diverse object collections. Notably, the method exhibits transferability across datasets, implying its potential for broader applicability beyond the training dataset. The robustness and generalization capabilities of the proposed method are substantiated through experiments conducted on varied datasets. These findings underscore the efficacy of the approach in capturing underlying patterns and features, independent of dataset-specific nuances. This self-supervised framework offers a promising solution for enhancing the search and retrieval of 3D models, addressing key challenges in scalability and dataset transferability.