TAssembly: Data-driven fractured object assembly using a linear template model

Abstract

Fractured object assembly is a fundamental problem across many disciplines such as digital archaeology, accident recovery, paleontology, cranio-facial and orthopaedic surgical repair, and many more. With advances in 3D acquisition devices and improved fidelity models of fractured pieces, a flurry of semi-automatic and automatic computational assembly solutions have been developed. These methods have the potential to reduce human labor in assembling fractured objects. The challenge is to balance the time efficiency with the fidelity of the reconstructed assembly. This paper introduces TAssembly, a data-driven approach to fractured object assembly. TAssembly leverages learned feature descriptors of the underlying objects. Its assembly pipeline seamlessly integrates multiple objective terms, including feature matching, rigid matching, and regularization for matching and stitching adjacent pieces. Many experimental results show that TAssembly significantly outperforms previous fractured object assembly methods.