Fast and accurate pose estimation of additive manufactured objects from few X-ray projections. (1st March 2023) - Presenti, Alice; Liang, Zhihua; Pereira, Luis Filipe Alves; Sijbers, Jan; De Beenhouwer, Jan
Abstract: X-ray Computed Tomography (CT) is a commonly used imaging technique for non-destructive inspection of manufactured objects. However, a full CT scan requires a long acquisition time, making this method unsuitable for inline applications. In contrast to X-ray CT, inspection can be performed directly in the projection space, using simulated X-ray projections of a reference model of the manufactured object. However, to effectively compare simulated and measured projections, an accurate 3D pose estimation of the object and consequent alignment between the measured object and the reference model are crucial. In this paper, we present a fast method to estimate the 3D pose of a measured object based on convolutional neural networks (CNNs). Through experiments on synthetic and measured data, we demonstrate that our method allows estimating the 3D pose of the object with sub-pixel accuracy. Even if very few projections are available, our approach is comparable to CT-based methods for registration, and outperforms state-of-the-art deep learning methods for radiograph-based pose estimation. Highlights: Our framework allows projection-based 3D inspection from very few images. CNN-based 3D pose estimation from one or multiple radiographs. High accuracy and precision demonstrated on simulated and measured data. Comparable to CT-based registration. Outperforms state-of-the-art DL-based 3D pose estimation from radiographs.