Statistical shape models (SSM) are an essential tool in medical image analysis and computational anatomy, facilitating a deeper understanding of anatomical variability across populations. Despite the evident utility of SSMs, their creation often comes with the drawback of depending on some form of human supervision, e.g. in the form of correspondence annotations. Although unsupervised learning-based 3D shape matching methods have made a major leap forward in recent years, the correspondence quality of existing methods does not meet the demanding requirements necessary for the construction of SSMs of complex anatomical structures.