Existing optimization-based methods for non-rigid registration typically
minimize an alignment error metric based on the point-to-point or
point-to-plane distance between corresponding point pairs on the source surface
and target surface. However, these metrics can result in slow convergence or a
loss of detail. In this paper, we propose SPARE, a novel formulation that
utilizes a symmetrized point-to-plane distance for robust non-rigid
registration. The symmetrized point-to-plane distance relies on both the
positions and normals of the corresponding points, resulting in a more accurate
approximation of the underlying geometry and can achieve higher accuracy than
existing methods. To solve this optimization problem efficiently, we propose an
alternating minimization solver using a majorization-minimization strategy.
Moreover, for effective initialization of the solver, we incorporate a
deformation graph-based coarse alignment that improves registration quality and
efficiency. Extensive experiments show that the proposed method greatly
improves the accuracy of non-rigid registration problems and maintains
relatively high solution efficiency. The code is publicly available at
this https URL

利用对称化的点对平面距离和基于变形图的粗对齐实现非刚性配准的高准确性和解决效率提升。