Manufacturing-Aware Reconstruction

Abstract

Most of the objects that surround us begin their life as designs. The machines and people involved in the manufacturing process must communicate with a high degree of precision, demanding information-rich designs capable of specifying geometry down to individual mathematically-defined surfaces, as well as other relevant details such as functionality, materials, and assembly instructions. Consequently, designs are a concise representation for the built objects in our environment. In this work, we explore various techniques to reconstruct man-made objects from measured data using geometric priors inherent in these design representations to enhance the accuracy and robustness of our methods.Some key questions we address are: Can we reconstruct designs when our observations of the world are partial? And can we achieve this without significantly sacrificing generality? We will explore methods for using prior knowledge about the manufacturing domain to extract complete carpentry designs from partial visual information. We will see that knowledge of the manufacturing process, even in the absence of the full procedure, allows us to more effectively leverage visual data for reconstruction. We will also present work on reconstructing partially-observed objects in terms of a broader class of geometries associated with computer-aided design (CAD) representations, achieving high precision reconstructions of objects in the wild across many different fabrication domains. Looking beyond geometry, we also present work on augmenting purely geometric designs with functionality by inferring the motions of parts using deep learning. In summation, we demonstrate new techniques to reconstruct various types of designs from partial observations, exploring geometry- and functionality-focused methods, by leveraging manufacturing priors.