  Each year thousands of people suffer from various types of cranial injuries
and require personalized implants whose manual design is expensive and
time-consuming. Therefore, an automatic, dedicated system to increase the
availability of personalized cranial reconstruction is highly desirable. The
problem of the automatic cranial defect reconstruction can be formulated as the
shape completion task and solved using dedicated deep networks. Currently, the
most common approach is to use the volumetric representation and apply deep
networks dedicated to image segmentation. However, this approach has several
limitations and does not scale well into high-resolution volumes, nor takes
into account the data sparsity. In our work, we reformulate the problem into a
point cloud completion task. We propose an iterative, transformer-based method
to reconstruct the cranial defect at any resolution while also being fast and
resource-efficient during training and inference. We compare the proposed
methods to the state-of-the-art volumetric approaches and show superior
performance in terms of GPU memory consumption while maintaining high-quality
of the reconstructed defects.
