  Non-rigid registration computes an alignment between a source surface with a
target surface in a non-rigid manner. In the past decade, with the advances in
3D sensing technologies that can measure time-varying surfaces, non-rigid
registration has been applied for the acquisition of deformable shapes and has
a wide range of applications. This survey presents a comprehensive review of
non-rigid registration methods for 3D shapes, focusing on techniques related to
dynamic shape acquisition and reconstruction. In particular, we review
different approaches for representing the deformation field, and the methods
for computing the desired deformation. Both optimization-based and
learning-based methods are covered. We also review benchmarks and datasets for
evaluating non-rigid registration methods, and discuss potential future
research directions.
